esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Implications for the New Round of the GGP  
To investigate the gap between new plantings and retained vegetation in improving ecological conditions, a paired [PERSON]'s \(t\)-test was conducted to compare the annual average \(\text{FVC}_{\text{max}}\), \(\text{LAI}_{\text{max}}\), \(\text{NPP}_{\text{avg}}\), \(\text{WR}_{\text{pua}}\), \(\text{SWAL}_{\text{pua}}\), and \(\text{SWIL}_{\text{pua}}\) between LULC A and LULC B from 2000 to 2010. The null hypothesis and alternative hypothesis are listed in Table 8. All the indicators except \(\text{SWIL}_{\text{pua}}\) passed the \(t\)-test at a very significant level. The annual average \(\text{FVC}_{\text{max}}\), \(\text{LAI}_{\text{max}}\), \(\text{NPP}_{\text{avg}}\), and \(\text{WR}_{\text{pua}}\) in LULC A were respectively 2.56%, 6.74%, 8.81%, and 22.34% less than those in LULC B, and the annual average \(\text{SWAL}_{\text{pua}}\) and \(\text{SWIL}_{\text{pua}}\) were respectively 7.39% and 5.22% higher than those in LULC B, suggesting that the new planting had not reached its potential maximum in terms of ecosystem condition enhancement. Therefore, as the new round of GGP is initiated, more attention should be paid to the protection and restoration of old-growth original forest, the cultivation of young planted forest, and the management of artificial forests, apart from only afforestation and reforestation. To achieve this goal, besides the institutional arrangements such as legislation, setting up nature reserves and enhancing incentive payments, which the government always does, market tools, the development of new technology, and international programs should be taken into account. Responsible forest management could be promoted by bringing in market-based third-party certification for evaluating forests according to a set of standards [97; 98]. Improving the biological, physiological, and genetic quality of forest planting stock through plant biotechnology, a high level of environmental control in the growth chambers, and transnational co-operation are also practical ways to carry out forest restoration projects [99; 100; 101; 102]. International climate change mitigation programs, which are designed to reduce emissions from deforestation and forest degradation in developing countries (REDD+), should also be taken into consideration as collateral payments for the substantial carbon benefit in maintaining forest [103]. Our studies suggested that not all the ESs increased simultaneously under the scenario of reforestation and afforestation, and significant differences and lags existed between the improvement of each pair of ecological indicators. This implied the existence of trade-offs and synergies between ESs, which complicates the restoration mechanism and must be considered in policy-making [19; 23]. Considering the more important role GGP played in ecological change and the variation of ecological
# Three-Dimensional Real-Scene-Enhanced GNSS/Intelligent Vision Surface Deformation Monitoring System
## 4 Data Processing
### GNSS and Meteorological Data Processing
#### 4.2.3 Local Data Buffering Strategy for Edge Device  
To address network instability in field environments, the edge device layer employs a lightweight local data buffering mechanism for GNSS receivers and weather instruments. The solution maintains data continuity during network outages or transmission delays in complex field environments.  
Specifically, during network anomalies, devices temporarily store real-time monitoring data (GNSS coordinates and meteorological parameters) via built-in SD cards, appending a timestamp and device identifier to each data unit to ensure temporal order and traceability. The buffering strategy combines a capacity threshold with a First-In-First-Out mechanism: when the buffer storage reaches a limit of 500 entries, the oldest data are automatically discarded to free up space and prevent buffer overflow.  
The network status monitoring module determines network availability in real time by periodically probing server heartbeat packets. Once three consecutive heartbeat responses are detected, the system triggers a batch upload of buffered data. Data are packaged in chronological order for reliable transmission via the MQTT protocol at the QoS 1 level. Upon receiving a server confirmation response, uploaded data are deleted from the buffer to avoid redundant transmission and storage. This mechanism effectively ensures data integrity in weak network environments, reduces device dependence on real-time connectivity, and balances low power consumption and storage efficiency requirements for embedded devices while enhancing system robustness.
## 2 Materials and Methods
### Soil pH Mapping Framework  
To explore the effectiveness of incorporating crop rotation in mapping soil pH and compare it with cropland type and Sentinel images, we designed seven scenarios with different combinations of environmental variables. Each scenario was used to predict soil pH combining the soil samples and the random forest (RF) model. Based on the scenario with the highest accuracy, we then verified whether and to what extent grouping soil samples by crop rotation and separate modeling could further improve the digital mapping of soil pH. The flowchart of the method of this study is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spectral Index** & **Expression** & **Properties** & **References** \\ \hline Normalized difference vegetation index (NDVI) & (NIR – R)/(NIR + R) & Vegetation & [47] \\ \hline Soil-adjusted vegetation index (SAVI) & (NIR – R) \(\times\) (1 + 0.5)/(NIR + R + 0.5) & Vegetation & [48] \\ \hline Land surface water index (LSWI) & NIR – SWIR1/NIR + SWIR1 & Leaf water and soil moisture & [49] \\ \hline Soil salinity index1 (SSI1) & (B \(\times\) R)/G & Soil salinity & [46] \\ \hline Soil salinity index2 (SSI2) & (G + R)/2 & Soil salinity & [46] \\ \hline Soil salinity index3 (SSI3) & (B \(\times\) R)\({}^{-1/2}\) & Soil salinity & [50] \\ \hline \hline \end{tabular} Note: B, G, R, NIR, and SWIR1 are the surface reflectance of bands 2, 3, 4, 8, and 11 in the Sentinel-2 multispectral instrument (MSI) sensor.  
\end{table}
Table 2: Spectral indices calculated from seasonal composites of Sentinel-2.
# Optimization of Acoustic Communication Links for a Swarm of AUVs: The COMET and NEMOSENS Examples
## 6 Conclusions  
In this paper we addressed all the AUVs swarm communication process developed for COMET and NEMOSENS projects, with a focus on the proposal of a method for adapting some physical layer parameters. We discussed about the achievable benefits when these vehicles can exchange information among them. The usage of underwater communications improves navigation and localization in an AUV swarm. In addition to that, we showed our designed communication process for an underwater AUVs network. At first, we presented the selected channel access method, and we proposed the usage of an adaptive guard interval for improving the network usage rate. We also addressed all the aspects concerning the physical layer. We explained the modem employed at the transmitter and receiver side, along with its operation modes. Subsequently, we proposed an optimization method for adapting some physical layer parameters: the number of subcarriers (\(N_{\text{sub}}\)) and the cyclic prefix length (\(T_{\text{CP}}\)). This method utilizes only information about the transmission geometry and does not require any kind of feedback information. The idea behind this approach is to utilize the location and speed information that each AUV periodically broadcasts. We showed that this optimization is able to provide similar results as the ones obtained by a genie-aided approach. At the end, we presented results for the case the modem is operating with this optimization method. These results, utilizing the Watermark simulator, show a performance improvement in terms of bit-error rate. We intend to perform sea-experiments for evaluating the benefits of this new optimization feature.  
Conceptualization, F-X.S., B.Z. and T.L.M.; Funding acquisition, R.B.; Project administration, C.L.B.; Supervision, C.L.; Validation, C.L., F-X.S., B.Z., T.L.M. and R.B.; Writing--original draft, C.M.G.G.; Writing--review & editing, C.L. and F-X.S. All authors have read and agreed to the published version of the manuscript. This research was funded by Region Bretagne and BPI France. The authors declare no conflict of interest.  
Figure 15: Modem operating at KAU1 channel, and for distinct maximum overhead values.
## 4 The Experimental Results Discussion  
### Threshold Range \(\Theta\)  
The results of the threshold range experiment are shown in Figure 3. The results showed that there was no suitable threshold range when the crack width was 0.05 mm, 0.1 mm, or 3 mm, and they indicate that the flood-filling algorithm was not applicable. The error range was within 10% when the threshold range \(\Theta\) was confined in [60; 80]. So, it was reasonable and optimal that the mid-value of threshold range \(\Theta\) was selected as 70 while the measured crack width range was 0.2 mm to 2 mm.
# Guard Band Protection Scheme to Facilitate Coexistence of 5G Base Stations and Radar Altimeters
## 2 Modelling for Guard Band Protection and Analysis
### Aclr
This peak-cancellation technique serves to mitigate undesirable out-of-band \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Model & & DL-FRC-FR1-QPSK \\ Channel bandwidth & MHz & 100 \\ Subcarrier spacing & kHz & 30 \\ Duplexing mode & & TDD \\ Cell identity & & 1 \\ Windowing & \% & 0 \\ Sampling rate & Hz & 614,400,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 5G NR-TM waveform parameters. Figure 4: NR spectrum with 100 MHz channel bandwidth: (**a**) before filtering and (**b**) after filtering. interference. Furthermore, complex calculations for ACLR are facilitated using MATLAB helper functions, specifically hACLRParametersNR and hACLRMeasurementNR. In the first adjacent channels, ACLR increases by 23.1785 dB and 22.8709 dB. Ultimately, after waveform filtering, ACLR is optimised to 75.6678 dB.
## 7 Conclusions  
The methodology of this paper is driven by the use-case of operating a (trans)national e-business platform in Europe and deploying an in-the-wild test method using four distributed data replication platforms. The use-case provides the benchmark for system performance and the topology of the network and infrastructure. Two readily available Byzantine fault tolerant blockchain platforms, Quorum and Tendermint, one crash fault tolerant blockchain, Hyperledger Fabric and one crash fault tolerant data streaming platform, Apache Kafka, are tested and compared with identical transaction loads, network and hardware conditions. The overall finding, that the crash fault tolerant data streaming is significantly faster than the Byzantine fault tolerant platforms, is unsurprising. A surprising finding however is that the crash fault tolerant blockchain, Hyperledger Fabric, which uses Apache Kafka as a ordering service, performs very differently compared to a stand-aloneimplementation of Apache Kafka. Moreover, this paper's main contribution is to quantify how much faster crash fault tolerant platforms are compared to Byzantine fault tolerant ones, for the same use-case running on the same hardware and network infrastructure. Measuring the difference is a way of putting a performance cost on decentralised trust as a system feature, and provides incentives to carefully consider whether decentralised trust is an essential system property. With respect to the e-business use-case, it is clear that Byzantine fault tolerant protocols, running on standardised cloud infrastructure, with realistic transaction volumes and sizes, are currently insufficient to work on a EU scale, and only adequate to work on a national scale.  
J.S.S.: Conceptualization, Methodology, Project Administration, supervision, Writing--original draft. P.W.E.: Conceptualization, Methodology, supervision, Writing--review & editing. L.H.: Software, investigation, data curation. J.S.: Conceptualization, Methodology, investigation, Software. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
This paper proposes a path planning framework that combines the experience replay mechanism from deep reinforcement learning (DRL) and rapidly exploring random tree star (RRT*), employing the DRL-RRT* as the path planning method for the manipulator. The iteration of the RRT* is conducted independently in path planning, resulting in a tortuous path and making it challenging to find an optimal path. The setting of reward functions in policy learning based on DRL is very complex and has poor universality, making it difficult to complete the task in complex path planning. Aiming at the insufficient exploration of the current deterministic policy gradient DRL algorithm twin delayed deep deterministic policy gradient (TD3), a stochastic policy was combined with TD3, and the performance was verified on the simulation platform. Furthermore, the improved TD3 was integrated with RRT* for performance analysis in two-dimensional (2D) and three-dimensional (3D) path planning environments. Finally, a six-degree-of-freedom manipulator was used to conduct simulation and experimental research on the manipulator.  
deep reinforcement learning; twin delayed deep deterministic policy gradient; path planning; six-degree-of-freedom manipulator +
Footnote †: journal: Applied
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 6 Conclusions  
In this article, the security threats with respect to their position of attack and to the MPTCP by exploiting the vulnerabilities of MPTCP options are explored, and their impact on security goals have been analyzed. The step-by-step procedure to initiate an ADD_ADDR attack to hijack the connection using the ADD_ADDR option vulnerability is demonstrated in the paper. The probable security solutions for the various vulnerabilities of MPTCP have been analyzed and compared in terms of attack vectors. In order to prevent the off-path active attacks and enhance security of MPTCP from ADD_ADDR vulnerability and eavesdroppers in the initial handshake, the SKEXMTCP using IBE is proposed and tested using the Linux kernel implementation of MPTCP. Using IBE, the session keys exchanged during the initial handshake and used in the future for authentication can be encrypted by using the IP address and port (used as an ID in IBE) as a public key, and the corresponding private keys will be provided by the PKG. The security complexity and overhead of the proposed model on MPTCP is analyzed. The analysis shows that the proposed solution enhancing the security of MPTCP and does not create any overhead on the existing protocol. The main limitation of MPTCP is the available option size for MPTCP, and in TCP, the header is 64 bits, which can be increased to improve the security of MPTCP in future. Moreover, the security of the IBE scheme can be extended by increasing the complexity of BDH and by generating the unique ID for the encryption. In the future, the solution can be extended to prove the security against other categories of attacks. Moreover, the unique ID generation based on IP address and port can be proposed to generate the public key. The lightweight IBE solution can be proposed for resource-constrained devices. The integration of machine learning to identify threats and prevent attacks by data flow management in MPTCP could be a promising area of research in MPTCP security.  
Conceptualization, K.P., V.V.K., M.R.N.M.Q. and A.S.A.; methodology, K.P., V.V.K., M.R.N.M.Q. and A.S.A. software, N.A., A.S.A. and R.E.A.M.; validation, N.A., A.S.A., R.E.A.M. and V.V.K.; formal analysis, K.P., R.E.A.M. and A.S.A.; investigation, A.S.A., R.E.A.M. and N.A.; resources, R.E.A.M.; writing--original draft preparation, K.P., V.V.K., M.R.N.M.Q. and A.S.A. writing--review and editing, M.R.N.M.Q. and N.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship of Scientific Research, King Khalid University, Kingdom of Saudi Arabia, and the grant number is R.G.P.2/178/43.  
Not applicable.  
We would like to express our gratitude to the Deanship of Scientific Research, King Khalid University, Kingdom of Saudi Arabia for funding this work, as well as family, friends, and colleagues for their constant inspiration and encouragement.  
The authors declare no conflict of interest.
## 4 Results
### Robustness Check
#### 4.3.2 Econometric Benchmark: System GMM Estimation  
To complement our machine learning analysis and address potential endogeneity in cloud adoption dynamics, we estimated a system GMM model [53; 54] as a conventional econometric benchmark. This approach accounts for unobserved heterogeneity, autocorrelation, and potential reverse causality, offering a robustness check on our primary results.  
The model includes lagged cloud adoption, ICT specialists, higher education, GDP per capita, broadband access, and unemployment rate as regressors. Lagged cloud adoption and ICT specialists are instrumented using their own lags in levels and differences, while the remaining predictors are treated as exogenous.  
Key results show a strong and statistically significant persistence effect in cloud adoption (lag coefficient = 0.825, \(p<0.001\)), alongside a positive and significant impact of ICT specialists (\(p\) = 0.023). Other variables--including education; income; and broadband access--do not achieve conventional significance levels in this linear specification.  
Figure 3: Variable importance scores from the tuned random forest model with fixed effects on Panel A. Source: Authors’ estimations in R software.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Rank** & **Variable** & **\%IncMSE** & **IncNodePurity** & **Type** \\ \hline
**1** & ICT Specialists & 28.18 & 33.32 & Core Variable \\
**2** & Broadband Access & 20.12 & 29.08 & Core Variable \\ \hline
**3** & Year (Numeric) & 14.27 & 11.26 & Temporal Trend \\
**4** & geo\_LU (Luxembourg) & 13.70 & 3.15 & Country Dummy \\
**5** & GDP per Capita & 13.32 & 6.64 & Core Variable \\
6 & Higher Education & 10.70 & 11.55 & Core Variable \\
**7** & geo\_BG (Bulgaria) & 8.34 & 0.85 & Country Dummy \\
**8** & geo\_DK (Denmark) & 7.98 & 1.92 & Country Dummy \\
**9** & Unemployment Rate & 7.97 & 4.89 & Core Variable \\
**10** & geo\_FI (Finland) & 7.61 & 3.34 & Country Dummy \\ \hline \hline \end{tabular}
\end{table}
Table 5: Variable Importance Scores—Tuned Random Forest Model with Country Fixed Effects (Panel A).  
These findings reinforce the results obtained from the random forest models: ICT workforce availability emerges as the most consistent and influential driver of cloud uptake across estimation strategies. Furthermore, all diagnostic tests (Sargan, AR(2)) indicate the validity of the instrument set and absence of higher-order autocorrelation.  
However, the machine learning framework captures nonlinearities and complex interactions not fully accommodated by linear panel methods [55; 56].  
The system GMM full estimation output is reported in Appendix A.2, which also includes the Sargan and Arellano-Bond tests in detail.
# Smart Hydroponic Cultivation System for Lettuce
## 2 Materials and Methods
### Monitoring and Control System
The T75 treatment proved to be the most effective of the treatments used in the growth chamber, as it showed the fewest significant differences in the proximal analyses compared to T100 and T50, promoting lettuce with acceptable phenotypic characteristics while achieving 25% nutrient savings. The developed system offers benefits that contribute to a sustainability-oriented approach in agriculture. First, by employing the NFT hydroponic technique, it achieves a 90% reduction in water usage compared to traditional farming methods [49]. Second, supplying nutrients in a controlled manner helps minimize waste. Third, as the system operates within a closed and controlled environment, plant exposure to external factors is minimized significantly reducing the need for agrochemicals and promoting cleaner, more environmentally friendly practices. Although the current energy consumption per plant (0.46 kWh/day) is relatively high, this value can be lowered by optimizing space usage through increased plant density (plants per m\({}^{3}\)), integrating renewable energy sources and applying energy optimization algorithms. Therefore, the system shows strong potential for sustainability. According to the studies reviewed in the state of the art, when comparing environmental management IoT technologies with our proposal, similarities were found in the microcontroller used, as reported in [21, 28], as well as in the monitored variables, which align with those reported in [18, 21, 23, 26]. Similarities were also found across all studies regarding the use of mobile or web applications for data visualization. The main differences are highlighted in Table 6.

In comparison with systems described in previous studies, the system proposed in this work presents several advantages. One of its main strengths lies in its ability to simultaneously monitor multiple environmental variables: air temperature, RH,concentration, photoperiod and PPF. In most of the reviewed works, only two or three variables were monitored, as seen in [18; 21], with [20] being the only one comparable to the system described in this article. Similarly, the described system stands out by offering complete microclimate control in a fully enclosed indoor environment, including automatic regulation of air temperature via air conditioning, ultrasonic humidification and photoperiod control using full-spectrum LED lamps. This level of automation was only partially addressed in [18], which focused on aeroponic systems with more limited environmental control. Another significant contribution is the implementation of a responsive web application and a mobile app for visualizing and modifying the setpoints of environmental variables, unlike [21; 25; 28; 29], the proposed system allows remote access to data outside the local
## 2 Materials and Methods  
### Materials  
Commercial polylactide filament was used to print the scaffolds (1.75 mm diameter, Shenzhen Creality 3D Technology Co. Ltd., Shenzhen, China). Polyethylene glycol (PEG, 100.000 g/mol), 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide (MTT), and sodium cacodylate were obtained from Sigma (St. Louis, MO, USA). Dichloromethane (DCM), methanol (MeOH), chloroform (CHCl\({}_{3}\)), ethanol (EtOH), sodium chloride (NaCl), sodium bicarbonate (NaHCO\({}_{3}\)), potassium chloride (KCl), dipotassium hydrogen phosphate (K\({}_{2}\)HPO\({}_{4}\)), magnesium chloride hexahydrate (MgCl\({}_{2}\)\(\times\) 6H\({}_{2}\)O), hydrochloric acid (HCl, 37 wt.%), calcium chloride dihydrate (CaCl\({}_{2}\) x 2H\({}_{2}\)O), sodium sulfate (Na\({}_{2}\)SO\({}_{4}\)), and tris(hydroxymethyl)aminomethane (TRIS) were supplied by Merck (Darmstadt, Germany) and used without further purification. Dulbecco's modified Eagle medium (DMEM, 1.8 mM Ca\({}^{+2}\)) and fetal bovine serum (FBS) were used to cell culture (Gibco). Deionized water was produced in our laboratory. 3T3 fibroblast cells were obtained from primary mouse epidermal cell cultures. Hydroxyapatite (HA) was synthesized following published procedures (see Supplementary Materials) and characterized by TEM (Figure S1) and SEM-EDS (Figure S2) [31; 32].