esa-sceva/satcom-chunk-collection huggingface dataset:
# A Zero-Watermarking Algorithm Based on Scale-Invariant Feature Reconstruction Transform
## 5 Conclusions  
Zero-watermarking technology is a form of digital copyright protection that preserves the original data of the carrier, with a superior performance in terms of invisibility and embedding capacity compared to traditional embedding watermark algorithms. Innovative research in this technology can expand the application scope of digital intellectual property protection laws and provide some degree of technical support for the digital transformation of traditional industries. The zero-watermarking algorithm based on SIFRT constrains the extraction range of image feature data to sub-block regions and annular neighborhoods surrounding key points. This not only reduces the computational scale of the algorithm but also avoids introducing excessive interference. The innovative aspect of this algorithm lies in its core feature matrix, which originates from key point descriptors and neighborhoods, inheriting the spatial stability of traditional SIFT. By utilizing the element quantity from the MrLBP texture feature matrix, it imposes constraints on the overall feature matrix. Under the premise of stability in scale and rotation transformations, the concatenation of these two feature matrices yields a high-performance binary feature matrix.  
One limitation of the algorithm is that when a significant number of key points and their neighboring areas are erased by attack methods, errors at the vector level arise during the construction of the feature matrix. In the face of high-intensity attacks, the computed watermark information may be incomplete. To address this deficiency, we plan to introduce error-correcting code data-encoding techniques in future algorithmic research [20]. This approach aims to recover lost data using known key point information and encoding constraints. The traditional zero-watermark methods heavily rely on manual design and computation. Future research directions could also focus on applying deep learning and neural network frameworks [21] to the feature construction steps to ensure the creation of higher-performing zero-watermark information.  
Writing--original draft preparation, F.L.; supervision, Z.-X.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflict of interest.
# Carbon Balance in Salt Marsh and Mangrove Ecosystems: A Global Synthesis
## 7 Data Refinements and Future Needs  
Both carbon budgets reveal some important shortcomings in the databases. For both ecosystems, there is a need for more or greater  
* Clarity of their global area, as areal estimates vary greatly;
* Estimates of ecosystem GPP, NPP and R, using eddy covariance technology;
* Estimates of belowground root production;
* Quantification and extrapolation of algal mat production and carbon fixation of nitrogen fixers on tree stems, downed wood and plant debris (litter; leaves); \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ecosystem** & **Area** & \(\mathrm{R_{E}}\) & \begin{tabular}{c} **Global** \\ \(\mathrm{R_{E}}\) \\ \end{tabular} & \begin{tabular}{c} **GPP** \\ \end{tabular} & \begin{tabular}{c} **Global** \\ \(\mathrm{GPP}\) \\ \end{tabular} & \
\begin{tabular}{c} **Mean** \\ \(\mathrm{P_{GFP}}\)/\(\mathrm{R_{E}}\) \\ \end{tabular} &
\begin{tabular}{c} **NEP** \\ \end{tabular} \\ \hline Salt marsh & 5.5 & 1727 & 95 & 2109 & 116 & 1.22 & 382 & 21 \\ Mangrove & 8.6 & 3558 & 306 & 4186 & 360 & 1.18 & 628 & 54 \\ Seagrass & 16.0 & 2133 & 342 & 2752 & 441 & 1.29 & 619 & 99 \\ Macroalgae & 354.0 & 1572 & 5565 & 2159 & 7643 & 1.37 & 587 & 2078 \\ Coral reef & 60.0 & 1572 & 943 & 1720 & 1032 & 1.09 & 148 & 84 \\ Global coastal ocean & 2750 & 1034 & 28,435 & 1028 & 28,270 & 0.98 & \(-\)6 & \(-\)165 \\ Salt marsh contribution & 0.20\% & - & 0.33\% & - & 0.47\% & - & - & 1.0\% \\ Mangrove contribution & 0.31\% & - & 1.08\% & - & 1.27\% & - & - & 2.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Contribution of salt marshes and mangroves to carbon balance in the global coastal ocean compared with other marine ecosystems. Global seagrass area from [195], macroalgae area from [196] and area of other ecosystems from [188], except the global coastal ocean [194], which encompasses estuaries, other wetlands, and continental shelves. Seagrass data [188] updated from [197, 198, 199, 210] and macroalgae data from [211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226]. Plankton GPP and R in salt marshes from [237] and in mangroves from [52]. All other data from [188], except global coastal ocean data from [194]. Percentage contributions of salt marsh and mangrove NEP are based on comparison with sum of all ecosystem NEP plus the negative global coastal ocean NEP (= 2217 Tg C a\({}^{-1}\)). Percentage contributions of salt marsh and mangrove R\({}_{\mathrm{C}}\) and GPP are based on comparison only with global coastal ocean \(\mathrm{R_{E}}\) and GPP. Abbreviations and units: \(\mathrm{R_{E}}\) (g C m\({}^{-2}\) a\({}^{-1}\)) = ecosystem respiration (\(\mathrm{R_{C}}+\mathrm{R_{S}}+\mathrm{R_{WATER}}+\mathrm{R_{MICROALGAE}}\)); GPP (g C m\({}^{-2}\) a\({}^{-1}\)) = gross primary production; NEP (g C m\({}^{-2}\) a\({}^{-1}\)) = net ecosystem production.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 6 Quantitative Classification of Ineffective Circulation Channels
### Application of Ineffective Circulation Channels
This approach is highly advantageous as it enables dynamic identification of relevant parameters, providing updated values at each time step, which facilitates real-time monitoring and improves decision-making accuracy in reservoir management. The corresponding technical route has considerable guiding significance for the engineering background involving injection wells and production wells, such as geothermal resources exploitation, surface drilling, and extraction of coalbed methane wells and carbon dioxide underground storage. However, the method also has certain limitations, primarily in terms of data requirements. The method requires input data to be continuous and uninterrupted, which can present challenges in certain special cases in the field, such as well shut-in operations. Under these circumstances, the model may struggle to accurately identify dynamic parameters. Addressing these challenges will be a key focus of our future work.
## 4 Discussion
### Uncertainties  
In this study, the PML model and LUE model were coupled to simulate spatiotemporal variations in ET and GPP at the national scale between 1999 and 2018. Long-time-series,  
Figure 11: Spatial patterns of dominant contribution factors for ET (**a**) and GPP (**b**) variations. Prec is precipitation, Temp is temperature, WS is wind speed, SH is specific humidity, SR is solar radiation, LAIH is LAI influenced by human activities.  
remotely sensed LAI, Albedo, and meteorological factors were employed as inputs. The parameters of the constructed model and the accuracy of the input data can create uncertainties in the simulation results. We optimized the model parameters based on 72 EC observed records from seven flux sites, but the spatial distributions of the sites were sparse when compared to the entire study area, which may have influenced the accuracy of our results. Secondly, the time-series land cover began from 2001; this study applied the data in 2001 to replace the true land cover data in 1999 and 2000. In addition, we resampled the LAI with a spatial resolution of 8 km and meteorological data with a spatial resolution of 10 km to 5 km via bilinear interpolation to match the 5 km spatial resolution of land cover. Mismatching in the data and the selection of the resampling method also may have generated uncertainties [73]. Therefore, more site observation data and higher-precision driving data are needed to minimize uncertainties when modeling ET and GPP in China in future research.
## 4 Conclusions  
This study aimed to investigate the impact of the ozone treatment of film-forming solutions (FFSs) on the quality characteristics of biopolymer films containing whey protein concentrate (WPC). The study's findings demonstrated the significant impact of ozone treatment on various features of the biopolymer films. The treatment of FFSs with ozone resulted in an increase in pH values and average particle diameter. In contrast, the zeta potential remained negative. The proposed modifications may affect film formation and stability, resulting in improved film characteristics. Changes in the colour characteristics and opacity were observed in the ozone-treated films. The films became lighter in colour, slightly greenish, and more opaque as the ozonation time increased. These changes may affect the UV protection and visual appearance of food packaging. The application of ozone treatment resulted in improvements to the thermal stability and mechanical properties of the films. Increased ozone treatment time improved the films' tensile strength, elongation at break, and toughness. This suggested that the films demonstrated enhanced strength, flexibility, and energy absorption capacity prior to fracturing. The potential modification of WPC and gelatine was significant in the improvement of the films.  
To conclude, the findings indicate that the ozone treatment of FFSs which contain WPC can potentially improve the quality characteristics of biopolymer films utilised in food packaging. These films might enhance mechanical performance and thermal stability, and provide UV protection, making them appropriate for various food packaging applications.  
One of the limitations of this study is that it focused only on the effect of ozone treatment on film properties without considering its effects on food preservation or shelf life. Future research could investigate the combined effects of ozone treatment and food packaging on food quality and safety. Furthermore, investigating these ozone-treated biopolymer films' long-term stability and environmental impacts would provide valuable information on their feasibility and sustainability as packaging materials. Furthermore, optimizing ozone treatment process parameters such as concentration and duration could further improve film properties and potential cost savings in production. Therefore, future research efforts should address these limitations and optimize the ozone treatment process to develop more sustainable and environmentally friendly packaging solutions.  
This research received no external funding.  
Not applicable.
## 2 Proposed Array Antenna
The maximum gain of the patch element is obtained by slightly steering toward \(\theta=5^{\circ}\), while the monopole element has the peak radiation gain at \(\theta=-45^{\circ}\). The resulting antenna characteristics demonstrate that the proposed two-element array antenna can maintain independent radiation patterns with high isolation and low correlation between array elements, even with the narrow array spacing. Figure 11 illustrates the measured and simulated results of the array radiation patterns for the proposed array in _zx_- and _zy_-planes. The total radiation pattern is calculated by the summation of the monopole and patch element patterns having the same weights. From these results, the peak gains of the measurement and simulation in _zx_-plane are 4.6 dBi and 5.3 dBi, respectively, and those in _zy_-plane are 2.9 dBi and 2.7 dBi, respectively. Minimum measured cross-polarization levels of \(-31.62\) dBi and \(-19.3\) dBi in _zx_- and _zy_-planes are also observed. Though the total array radiation pattern of the proposed array is different from the conventional regular element array, the proposed array can provide sufficient nulling performances in a practical nulling application, which will be discussed in Section 3.2. Figure 4:1 Simulated and measured mutual coupling (S\({}_{21}\)) of the proposed array: (**a**) magnitude of mutual coupling; (**b**) phase of mutual coupling. Figure 4:2 Simulated and measured reflection coefficients of the proposed array. Figure 4:3 Measured and simulated 2D co- and cross-polarization radiation patterns of the proposed array in the \(zy\)- and \(zx\)-planes at 1.6 GHz: (**a**) monopole element in the \(zy\)-plane (E-plane); (**b**) monopole element in the \(zx\)-plane (H-plane); (**c**) patch element in the \(zy\)-plane (E-plane); (**d**) patch element in the \(zx\)-plane (H-plane). Figure 4:4 Simulated and measured maximum gains in the upper hemisphere of the proposed array.
## 2 LiCSAR System Architecture
### LiCSAR Earthquake InSAR Data Provider

Table 1: Earthquake response threshold criteria for the Earthquake InSAR Data Provider (EIDP), based upon empirical scaling of fault dimensions with magnitude and anticipated wavelength of measurable surface displacement of a buried elastic source. The postseismic interval indicates the length of time the near-real time processing by EIDP is undertaken, prior to return to their regular processing. Figure 6: LiCSAR Earthquake Interferometric Synthetic Aperture Radar (InSAR) Data Provider processing flowchart. The system leads to the generation of co-seismic and post-seismic interferograms initiated by ingesting information about current earthquakes from the United States Geological Survey (USGS) ComCat service. facility for resampling the post-event data with the use of parallel processing and rapid generation of interferometric products. Here, the EIDP system controls the processing stages in 30 min 'ticks'. One tick is a series of operations to ingest new events to the EIDP database tables, identify earliest and expected post-event acquisition times, send frame preprocessing job requests, check and download available post-event data, send processing jobs for generation of co-seismic and post-seismic data, and finally update information about the processing status to EIDP tables. The frames remain in their active status for a pre-defined period to allow for rapid production of post-seismic interferograms (a post-seismic InSAR response). We scale this time period depending on the magnitude of the event and the number of expected Sentinel-1 images in that location, see Table 1. In case of very large events, the post-seismic interferograms are further generated by LiCSAR continuous rolling updates that are being performed on straining regions (see Section 3.1). We also plan to extend the system to volcanic activity rapid response.
## 4 Simulation and Performance Evaluation  
Network Simulator NS-3 version 3.32 with an underwater wireless sensor network module provided by the UWSim project is used. The proposed scenario simulates a network of underwater wireless sensor nodes operating in a two-dimensional space. The nodes were deployed randomly in a 500 m \(\times\) 500 m area with a uniform distribution. The communication range of each sensor node was set to 50 m. The data transmission rate was set to 10 kbps. The first propagation model is implemented to model the wireless communication channel. The proposed framework consists of Ad-hoc On-demand Distance Vector (AODV) routing protocol for maintaining the network topology. The AODV protocol is a reactive protocol that establishes a route only when needed, which makes it suitable for underwater wireless sensor networks where the network topology changes frequently. We used 128-bit symmetric keys for encryption and decryption of data packets. The keys were generated using the ECC algorithm with the secp256r1 curve. The secp256r1 curve is a widely used elliptic curve for crypto-graphic applications and provides a good balance between security and computational efficiency.  
We present the performance evaluation of the proposed secure and lightweight key management framework for underwater wireless sensor networks. We evaluated the performance of the proposed framework based on the following metrics:  
* Key distribution time: This is the time required to distribute keys to all sensor nodes in the network.
* Memory usage: This is the memory used by each sensor node for storing the cryptographic keys.
* Energy consumption: This is the amount of energy consumed by each sensor node during key distribution and communication.
* Scalability: This is the ability of the proposed framework to handle an increasing number of sensor nodes in the network.  
We conducted simulations using the NS-3 simulator to evaluate the performance of the proposed framework. The simulation parameters used in the performance evaluation are shown in Table 3.  
We evaluated the performance of the proposed framework for different key distribution mechanisms, namely pre-distributed keys, pairwise keys, and group keys. The results of the performance evaluation are presented in the following subsections.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Point addition** & **Point multiplication:** \\ \hline \(\text{Pt}+\text{Q}=\text{R}\text{t}\) & \(\text{kP}=\text{R}\) \\ \(\chi\text{R}=\lambda^{2}-\chi\text{P}-\chi\text{Q}\) & \(\text{R}=0\) \\ \(\text{yR}=\lambda\text{(}\chi\text{P}-\chi\text{R})-\text{yP}\) & for i = m
## 2 Materials and Methods
### Machine Learning Methods
#### 2.4.1 k-Nearest Neighbor (kNN)  
The idea behind the k-nearest neighbors (kNN) technique is that data points with similar characteristics typically have similar labels or values [17]. The model keeps the entire dataset as a reference library throughout training. The label is assigned based on the majority class among its \(k\) nearest neighbors after calculating the distance (such as the Euclidean distance) between the query instance and all stored training examples.
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 4 Simulation Setup
### Experimental Setup and Network Topology
#### 4.1.1 Network Topology  
The network topology of six nodes is shown in Figure 5. Sender S1 is attached with n1 and receiver D1 is attached with n2. There are two intermediate routers R1 and R2. Duplex links n1-R1, n3-R1, R1-R2, R2-n4, and R2-n2 have bandwidth of 2, 1, 1.5, 1, and 2 Mbps and delay of 10, 10, 100, 10, and 10 ms, respectively. Each node uses a DropTail queue of size 10. Fast TCP, MFast TCP, EMFast TCP, TCP New Reno, and SCTP are the agents attached at the sender node (S1) one by one to study the behavior of these protocols while executing simulation scenarios. The simulation is run for 100 sec. An error model is attached with the link n1-R1 to drop a packet to monitor the behavior of transport protocol after the fast-recovery and to study its impact on goodput and throughput.  
Figure 5: Network topology.
# Consistency Models of NoSQL Databases
## 5 NoSQL Consistency Implementations
### Cassandra
#### 5.2.3. QUORUM Read Consistency Level and ONE Write Consistency Level  
From Figure 7, we can conclude that the time needed to reach full consistency of a given data object is the shortest of all configurations here (excluding the Figure 5 configuration). Three nodes are approached by the coordinator and the most updated version among them is returned. For each read operation, Cassandra cluster uses its _Read Repair_ feature to propagate to all three nodes inside the Quorum (the three nodes), so that they all have the most updated version of the requested data among them. Because _Read Repair_ is always triggered by a read, the cluster reaches full consistency faster on the given data object.
###### Abstract  
Finger lime is receiving growing attention as an ingredient of gastronomic preparations of haute cuisine for its delicious flavor and fragrance and for its appealing aspect. Volatile compounds play a crucial role in determining the organoleptic characteristics of the fruit and its pleasantness for consumers. The aim of the present study was to investigate the volatile profiles by headspace solid phase micro-extraction (HS-SPME) coupled to gas chromatography-mass spectrometry (GC-MS) in the peel and, for the first time, in the pulp of three Australian finger lime cultivars grown in Sicily (southern Italy): Pink Pearl, Sanguinea, and Faustrime, allowing to overall identify 84 volatile organic compounds (VOCs). The analytical data showed that the three cultivars were characterized by distinct volatile chemotypes: limonene/sabinene/bicyclogermacene in the Pink Pearl, limonene/\(\gamma\)-terpinene/bicyclogermacene in the Sanguinea, and limonene/\(\beta\)-phellandrene/\(\gamma\)-terpinene in the Faustrime. Moreover, some volatiles, found exclusively in one cultivar, could be considered potential markers of the individual cultivar. PCA allowed us to clearly discriminate the samples into three clusters: the first related to the Sanguinea peel, the second to the Faustrime peel, and a third group associated with the Pink Pearl peel along with the pulp of the three cultivars. Accordingly, the VOCs that mostly contributed to the differentiation of the three finger lime cultivars were also identified. Among them, D-limonene, sabinene \(\gamma\)-terpinene, \(\alpha\)-pinene, \(\alpha\)-phellandrene, \(\beta\)-myrcence, p-cymene, linalool, \(\delta\)-elemene, ledene, bicyclogermacene \(\beta\)-citronellol, \(\alpha\)-bergamotene, \(\alpha\)-caryophilene, and \(\beta\)-bisabolene, have been previously reported to exhibit important biological activities, suggesting that these cultivars, in addition to possessing unique volatile profiles, can show promise for several applications in pharmaceutical and food industry, namely for development of functional foods.  
finger lines; volatile composition; HS-SPME/GC-MS; authentication; statistical analysis; discrimination +
Footnote †: journal: _m molecules_