esa-sceva/satcom-chunk-collection huggingface dataset:
# An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
## 3 Encapsulated Design Plant Growth Devices
### Climate Parameter Regulation
#### 3.1.3 Lighting  
Sunlight, or, more accurately, photosynthetic light, is one of the limiting climatic factors of the closed environment that determines the growth and development of the plant population. It implies light intensity, spectral composition, and luminance duration. Plants absorb most of the visible spectrum (400-700 nm), i.e., the photosynthetically active radiation (PAR) range, and parts of ultraviolet and far-red radiation (FAR), while the green pigment, chlorophyll, reflects green light (the reason for the green color of leaves) [20, 21]. The radiation of the sun travels to the Earth in the form of energy packets (photons), whose energy is inversely proportional to their wavelength. The part of the spectrum used by plants for the photosynthesis process contains low specific energy (compared to, e.g., \(\gamma\) or X-rays) and compose the majority of the sun's energy that reaches the ground due to the large number of photons at approximately 500 nm.  
According to photoperiodism, wheat culture belongs to long-day plant types. One of the goals of the system is, therefore, to emulate the conditions of the daily sunlight required to achieve the conditions for the photosynthesis process. For normal growth, plants require approximately 500-1500 umol/m\({}^{2}\)/s of PPFD, which is the amount of PAR spectrum photons that reach the plant [21]. This correlates with the required 200-500 W per m\({}^{2}\) of LED light power of the PAR spectrum, which is additionally increased to compensate for the distance of the light source to the plant. Rather than having a multi-kilowatt lighting system, incident sunlight is fully exploited by the glass structure of the encapsulated design, and artificial lighting is also used to increase the intensity, permute the outside conditions, and extend the luminance duration, as depicted in Figures 1 and 2. Artificial lighting is controlled by the PI controller of the LED lighting intensity by the PWM and a corresponding photosensor, in each of the four zones individually. Artificial photosynthetic light can not only improve photosynthesis with intensity, but also increase the duration of the cycle, instead of letting the plants turn to their respiration regimes.
## 7 Emerging Trends in Reinforcement Learning for Robotics
### Scaling DRL for Large-Scale Robotic Systems and Multi-Agent Environments  
Deploying deep reinforcement learning (DRL) in large-scale robotic systems, such as urban autonomous fleets or search-and-rescue missions, presents unique challenges related to computational efficiency, coordination, and policy transferability. Recent studieshave explored various methods to improve the scalability of DRL-based approaches for multi-agent environments.  
One approach is the development of hierarchical DRL techniques that enable more efficient decision-making in large agent populations. [PERSON] et al. [59] provided an extensive survey on scalability challenges in multi-agent DRL (MADRL), highlighting the role of hierarchical structures in managing state-action space complexity. Their study emphasized that scalable algorithms must incorporate robustness mechanisms to handle dynamic environments effectively.  
Graph-based learning methods have also proven effective for large-scale coordination. [PERSON] et al. [60] introduced a graph-based DRL technique for multi-robot task allocation, leveraging graph normalization to enhance scalability. Their approach demonstrated improved task efficiency and adaptability to varying team sizes, outperforming conventional heuristic-based task assignment methods.  
Distributed and federated learning have also been explored as viable solutions for scalability. [PERSON] et al. [61] proposed a federated DRL framework that enables multi-robot systems to share learned policies without centralized computation, reducing communication overhead while maintaining performance in large-scale navigation tasks. Similarly, [PERSON] et al. [62] introduced an open-source framework, MultiRoboLearn, designed to bridge the gap between multi-agent DRL simulations and real-world multi-robot applications, ensuring better generalization across different environments.  
Another critical consideration in scalability is decentralized policy learning. [PERSON] et al. [63] presented an Independent Soft Actor-Critic (ISAC)-based method that enhances decentralized training in large-scale multi-agent path planning. Their results showed improved success rates and computation efficiency compared to centralized learning approaches.  
Overall, these advancements illustrate the growing feasibility of DRL in large-scale robotic systems, paving the way for scalable, efficient, and adaptive multi-agent coordination in real-world applications.
# A Power Processing Circuit for Indoor Wi-Fi Energy Harvesting for Ultra-Low Power Wireless Sensors
## 8 Harvesting System Performance Comparison  
The performance of the proposed harvesting system with another previous work is summarized in Table 6. The comparison is made with others RF energy harvesting systems which used input powers approximately \(-50\) dBm to \(0\) dBm. By comparing the RF-to-DC efficiency of the proposed harvesting system to the stated previous research, work in [19] has the highest efficiency. However, the result was obtained using \(-10\) dBm input power. From the comparison table, it is clearly shown that the proposed RFEHS performs better than the dual or multiband frequencies RF energy harvesting system. The proposed RFEHS also produced a similar performance with other listed research. Moreover, the circuit has operated by using power harvested from non-dedicated RF sources.
## A Survey of Patterns for Web Services Security and Reliability Standards  
**[PERSON] *, [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]**  
Florida Atlantic University, 777 Glades, Boca Raton, FL 33431, USA; E-Mails: [PERSON]L_ADDRESS] (O.A.); [PERSON]DDRESS] (I.B.); [PERSON]DDRESS] (N.D.-G.); [PERSON]DDRESS] (K.H.); [PERSON]_ADDRESS] (M.M.L.-P.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +1-561-297-3466; Fax: +1-561-297-2800.  
_Received: 16 January 2012; in revised form: 26 March 2012 / Accepted: 16 April 2012 / Published: 20 April 2012_  
**Abstract:** An important aspect for the acceptance of Service-Oriented Architectures is having convenient ways to help designers build secure applications. Numerous standards define ways to apply security in web services. However, these standards are rather complex and sometimes overlap, which makes them hard to use and may produce inconsistencies. Representing them as patterns makes them easier to understand, to compare to other patterns, to discover inconsistencies, and to use them to build secure web services applications. Security patterns abstract the key aspects of a security mechanism and can thus be applied by non-experts. We survey here our work on security patterns for web services and their standards and we put them in perspective with respect to each other and to more fundamental patterns. We also consider other patterns for web services security. All the patterns described here have been previously published, we only show here one of them in detail as an illustration of our style for writing patterns. Our main purpose here is to enumerate them, show their use, and show how they relate to each other.  
**Keywords:** web services security; web services standards; security patterns; secure distributed systems; secure SOA; misuse patterns
## 6 Test Results
### Dual Frequency Receiver Testing  
Dual-frequency tests, where the RUT is configured to receive GPS/Galileo signals, in both L1/E1 and L5/E5 frequency bands, were also performed. In particular, a dual-frequency professional grade receiver was assessed in the presence of the wide swept frequency with fast repeat rate interfering signal (i.e., PRO01). The interference was generated only at the L1/E1 carrier frequency. The objective of such test was to investigate if the RUT could intelligently exploit the frequency diversity in the presence of interference in one of the frequency bands.  
Since no information on the L5/E5 signals could be retrieved from the NMEA files, the analysis was conducted using a MATLAB-based script that processes the RINEX files from the RUT. For each of the five signals (GPS L1, GPS L5, Galileo E1, Galileo E5b, and Galileo E5a), the number of tracked satellites and the signal strength from the tracked satellites for each signal were analyzed. The impact of the interference on L1/E1 band is clearly visible in Figures 11a and 12a, which show the C/N\({}_{0}\) values for the GPS L1 C/A and the Galileo E1b signals, respectively. As expected, as the interference power increases, the C/N\({}_{0}\) ratio of the signals in the L1/E1 band decreases until, at some point, the RUT stops tracking them and it is no longer offering the corresponding observations. It is interesting  
Figure 10: East-North-Up deviations for test case MM04-STATIC-SENSITIVITY using (**a**) recorded interference and (**b**) synthetic interference.
## 5 Electrical Characterization
### Temperature Testing
#### 5.4.4 Entropy  
To assess the entropy of the PUF design's responses under different temperatures, we calculated the Shannon entropy, as discussed in Section 5.3, at 0 \({}^{\circ}\)C and 80 \({}^{\circ}\)C. In this analysis, we focused solely on the responses obtained from the high-resistance states, as the resistive state did not impact the entropy.  
\begin{table}
\begin{tabular}{c c c c} \hline
**BER for One Million-Bit-Long Key** & **At 0 \({}^{\circ}\)C** & **At 23 \({}^{\circ}\)C** & **At 80 \({}^{\circ}\)C** \\
**in High-Resistance State** & & & \\ \hline Using 0 \({}^{\circ}\)C enrollments & \(<\)10\({}^{-6}\) & \(<\)10\({}^{-6}\) & \(8\times 10^{-5}\) \\ Using 23 \({}^{\circ}\)C enrollments & \(<\)10\({}^{-6}\) & \(<\)10\({}^{-6}\) & \(2\times 10^{-5}\) \\ Using 80 \({}^{\circ}\)C enrollments & \(9\times 10^{-5}\) & \(7\times 10^{-5}\) & \(<\)10\({}^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 4: The BER of a one million-bit-long key at different temperatures. The key size was 2\({}^{20}\). Where 0 errors were found, the BER was \(<\)10\({}^{-6}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(HD_{inter}\) **High-Resistance State** & **0 \({}^{\circ}\)C** & **23 \({}^{\circ}\)C** & **80 \({}^{\circ}\)C** \\ \hline Filtered & 0.4999 & 0.4999 & 0.4997 \\ Unfiltered & 0.4820 & 0.4849 & 0.4753 \\ \hline \end{tabular}
\end{table}
Table 5: The \(HD_{inter}\) of the filtered and unfiltered responses in a high-resistance state at 0 \({}^{\circ}\)C, 23 \({}^{\circ}\)C, and 80 \({}^{\circ}\)C. The filtered \(HD_{inter}\) had responses filtered for X states, while the unfiltered \(HD_{inter}\) did not.  
At 0 \({}^{\circ}\)C and 80 \({}^{\circ}\)C, the Shannon entropy values for unfiltered keys were 0.9982 and 0.9999, respectively. Similarly, for filtered keys, the Shannon entropy values at 0 \({}^{\circ}\)C and 80 \({}^{\circ}\)C were both 0.99999. These results led us to the conclusion that temperature did not have a significant effect on the entropy density of the PUF responses. The entropy remained consistent regardless of the temperature at which the responses were generated, reinforcing the stability and reliability of the PUF design in maintaining its entropy characteristics across varying temperatures.
## 3 Inland Waters  
Worldwide, the number of operational ground stations for lake and river water levels and discharge monitoring has continuously decreased since the 1970s. Meanwhile, satellite capabilities for inland waters studies have emerged. In particular, many published studies have demonstrated that satellite altimetry allows measuring accurately water level along rivers and lakes [18; 19]. For rivers, satellite altimetry has evolved from investigation of water height retrieval to the monitoring and discharge estimations. For lakes, it has evolved to the monitoring of water storage change [20]. Recent research focuses on integrating altimetry with other remote sensing techniques and hydraulic models to deal with key inland water resources issues such as flood (discharge, water spread and volume), water level monitoring (even in highly braided/fragmented river systems), rating curve generation for remote locations, reservoir operations, calibration of river/lake models, etc. Before it moves to a drifting orbit, SARAL/AltiKa contributes to meeting the growing demand of altimetry data for hydrological studies. Three examples of SARAL/Altika applications regarding rivers and lakes are provided in the following.
# Performance Evaluation of Direct-Link Backhaul for UAV-Aided Emergency Networks
## 4 Methodology  
In this section, the emergency network and scenario definitions for the simulation are presented. Then, the UABS path loss model and link budget parameters are defined, following by the description of the types of drones used for the backhaul analysis. Finally, the simulation algorithm for UAV-aided backhaul network planning was proposed.
# Research on the Compactness of Lunar Soil Simulant Based on Static Cone Penetration Test
## 3 Results and Analysis
### Variation in Cone Tip Resistance with Penetration Depth  
Figure 5 shows the variation in cone tip resistance with penetration depth under different overall compactness levels. For the four groups of lunar soil simulant with different compactness levels, the cone tip resistance generally shows an increasing trend as the penetration depth increases.  
For the two categories of low-compactness lunar soil simulant, the cone tip resistance increases slowly with the penetration depth at depths under 20 cm. At 20 cm, the cone tip resistance of the lunar soil simulant with an overall compactness of 58% is 0.34 MPa, and that of the lunar soil simulant with an overall compactness of 68% is 0.44 MPa. When the penetration depth is greater than 20 cm, the cone tip resistance of both categories of lunar soil simulant grows significantly, and the growth rate of the cone tip resistance gradually increases at an increasing penetration depth. At 60 cm, the cone tip resistance of the lunar soil simulant with an overall compactness of 58% is 2.70 MPa, and that of the lunar soil simulant with an overall compactness of 68% is 4.36 MPa.  
For the medium-compactness lunar soil simulant, the cone tip resistance of the lunar soil simulant increases slowly with the penetration depth at depths under 10 cm. At 10 cm, the cone tip resistance is only 0.21 MPa. When the penetration depth is greater than 10 cm, the cone tip resistance of the lunar soil simulant grows significantly. At 60 cm, the cone tip resistance of the lunar soil simulant is 9.26 MPa.  
For the high-compactness lunar soil simulant, the cone tip resistance of the lunar soil simulant increases slowly with the penetration depth at depths under 8 cm. At 8 cm, the cone tip resistance of the lunar soil simulant with an overall compactness of 86% is 0.06 MPa. When the penetration depth is greater than 8 cm, the cone tip resistance of the lunar soil simulant grows rapidly as the penetration depth increases. The cone tip resistance of the lunar soil simulant with an overall compactness of 86% first grows exponentially and then linearly, reaching a maximum value of 19.56 MPa at 60 cm.  
As the penetration depth increases, the cone tip resistance of the lunar soil simulant continuously increases. At the same penetration depth, the cone tip resistance of the lunar soil simulant increases with the increase in compactness.
## 6 Simulation Results
### Energy Consumption
#### 6.2.2 Number of Search Attempts  
Figure 9 depicts the number of search attempts in the search phase. The numbers in TS and TS-R are equivalent with ERS when \(\varepsilon=0\) and they decrease with increasing \(\varepsilon\). This is because the feasible trail is prolonged when \(\varepsilon\) increases. Therefore, the packets have greater possibility to detect the actor's footprints within certain search attempts, and then the number of search attempts is reduced.  
Figure 8: The energy consumption over the waiting time of actor \(\tau_{A}\).  
Figure 7: The energy consumption over the threshold value of trail age \(\varepsilon\).  
The numbers in TS and TS-R are almost the same because the redundancy reduction scheme has no influence on the search phase.  
Figure 10 shows that \(\tau_{A}\) has minor influence on the numbers of search attempts in TS and TS-R. The reason for this is that the value of \(\tau_{A}\) mainly impacts on the chase phase and has minor influence on the search phase. Thus the number of search attempts in TS and TS-R are almost the same, and both of them has better performance compared with ERS with a certain \(\varepsilon\).
## 7 Conclusions  
Since the existing state-of-the-art approaches rely solely on the semantic information of social media text, this research proposes a novel method for classifying sentiment on social media that incorporates the structural information of the sentences themselves. Combining semantic and syntactic information is an effective way to distinguish emotional polarity. However, if a reliable approach to extracting both semantic and syntactic information characteristics is lacking, the model's performance may deteriorate.  
In this study, we propose a new model, DPG-LSTM, based on the BiLSTM and GCN. DPG-LSTM analyzes the syntax of the user-posted text and employs DPG-Attention to strengthen the semantic and syntactic features. Experiments on real-world datasets show that our approach outperforms state-of-the-art methods in sentiment classification, achieving \(R\), \(P\), and \(F1\) scores of 0.845, 0.842, and 0.843.  
Our ultimate aim is to evaluate how useful the newly introduced syntactic information is for sentiment analysis by feeding it into a deep learning model, which should help us build a high-quality sentiment classifier that handles a wider range of emotions. The use of DPG-LSTM improves performance in numerous domains without the need for domain-specific feature engineering.  
Conceptualization, L.W. and Z.Y.; methodology, L.W., Z.Y. and M.J.H.; software, Z.Y. and J.S.; validation, Z.Y., Y.H. and Y.C.; resources, Y.H. and Y.C.; data curation, Z.Y. and J.S.; writing--original draft preparation, L.W. and Z.Y.; writing--review and editing, M.J.H., Z.Y. and X.Z.; visualization, Z.Y., J.S. and M.J.H.; project administration, X.Z. and L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This study was supported by National Science Foundation of China [No. 81873915], Project of Nantong Health Commission [MB2020045], Science and Technology Project of Nantong City [MS22021027].  
Data Availability Statement: The datasets supporting this paper are from previously reported studies and datasets, which have been cited. The processed data are available from the corresponding author upon request.  
We gratefully acknowledge the assistance of [PERSON] and [PERSON] in funding the experiment.  
The authors declare no conflict of interest.
## 2 Methodology
### Application of Optimization Solvers  
As one can see in Equation (3), the robust data reconciliation problem is an equation-constrained optimization problem, where both the target function and the constraints were nonlinear. For such a problem, only a few algorithms (i.e., optimization solvers) are capable of quickly and accurately solve it. These candidates include the following:  
Sequential quadratic program (SQP): It is a classical algorithm for a nonlinear optimization problem, which converts the target function into a sequence of quadratic approximations in iterations. For each of the quadratic problems, a high-accuracy solution can be acquired based on the Lagrange multiplier and Newton method, which linearize the constraints and turn the problem into a non-constrained one. Thanks to its simplicity, SQP has very high efficiency and is suitable under most situations. However, a large step size or simplification of constraints may sometimes cause trouble, especially for S-CO\({}_{2}\) system calculations, as wrong values of physical properties can interrupt the calculation.  
Interior point method (IPM): It is also called the barrier method, whose practical form was proposed by [PERSON] [33] in 1984. It is an algorithm for the constrained convex optimization problem, featured for its high efficiency (polynomial runtime) and strictly satisfying the constraints, as it always searches for a solution in the feasible region, which  
Figure 2: IF curves of the estimators applied for S-CO\({}_{2}\) systems.  
is indicated by its name. This means it can better cope with the rigorous requirements of the thermal system calculation than SQP.  
To compromise between efficiency and convergence, both algorithms were utilized to solve the robust data reconciliation problem. SQP was chosen as the default solver and applied first. When the SQP solver failed or did not converge for constraints, IPM was used instead to acquire a reasonable solution. The optimization solver for robust data reconciliation was implemented through the MATLAB R2024a platform in this work, which had good convergence, compared with other open-source solvers. It should be noted that equivalent or better results may still be possible with those solvers under carefully tuned parameters, which was not the focus of this paper.
## 3 Results and Discussion
### Sensory Evaluation  
The results of the sensory evaluation of mayonnaise samples are presented in the spider plot shown in Figure 1.  
The mayonnaise made from oil enriched in carotenoids from tomato by-products had an orange-yellowish color, the more intense the higher the concentration of extracted by-product. The lack of egg yolk in vegan mayonnaise made from soy milk determines a whitish, pale color of the mayonnaise, and, therefore, the yellow-orange color obtained by the enrichment with carotenoids made it look like the classic mayonnaise from egg yolk and was appreciated by the panelists. Sample MSTB2.5 was best scored by the panelists in terms of color, as the color of samples MDSB5 and MDSB5B were considered to be too dark. The lightness values of the mayonnaise samples (Table 3) supported this result.  
While no statistical difference (\(p<0.05\)) was found in the consistency properties of the enriched mayonnaise samples comparative to the control sample, the aroma and the taste were significantly influenced by the enrichment. The enriched samples scored higher in aroma and taste with respect to the control due to the characteristic tomato flavor that was perceived in these mayonnaises. The tomato flavor is a result of the interaction of taste components and aromatic volatiles [52]. However, the best-rated sample was the one with the addition of basil essential oil, which was to be expected considering that basil aroma notes are widely used in Italian cuisine and are often paired with tomatoes [53].  
The overall acceptability score values showed a preference of panelists for samples made with oil enriched in carotenoids extracted from dried tomato by-products compared to the control sample with significant differences (\(p<0.05\)). In terms of overall acceptability, the panelists did not find significant differences (\(p>0.05\)) between the enriched samples, although the sample supplemented with basil essential oil displayed the highest overall acceptability score from the panel.  
Figure 1: Sensory analysis of the mayonnaise samples. MC—control mayonnaise, MBHT—mayonnaise made with 1% BHT addition, MDTB2.5—mayonnaise made with sunflower oil macerated with 2.5% DTB, MDTB5—mayonnaise made with sunflower oil macerated with 5% DTB, MDSB5—mayonnaise made with sunflower oil macerated with 5% DTB + 0.05% basil essential oil addition.