esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Analysis and Results
### Achieving Fast Encryption and Decryption for Transactional Data Privacy  
Figures 4 and 5 provide the encryption and decryption times for a message observed for each participant in the SMP, respectively. The average encryption time for the proposed approach was 0.0095 s, while it was 0.1322, 0.1376 and 0.028 s for the approaches in [6; 7; 30], respectively. Similarly, the average decryption time of the proposed approach was 0.010 s, while for the approaches in [6; 7; 30], it was 0.029, 0.051 and 0.0176 s, respectively. This shows that the speedup factor of the proposed scheme was approximately 14 for encryption and 3.5 for decryption when compared with the results in [6; 7; 30].
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 2 Earthquake Database
### Earthquake Events  
Fifty earthquake events, whose moment magnitude is higher than 6 and PGA is greater than 0.01 g recorded after the year 2008 in each site, were adopted. As each earthquake was measured in both east-west and north-south directions, they provided 100 datasets of ground motion acceleration histories. A dataset consists of the acceleration history measured at 100 m beneath the surface (bedrock) and the history measured on the ground surface. For the motions at the bedrock, the velocity and displacement histories were generated from the acceleration histories, and they were all assigned to the input data. For the motions on the surface, the acceleration histories were assigned to the output data. Correction of earthquake data was processed with a methodology with a polynomial linear baseline correction and bandpass filter proposed by [PERSON] ([PERSON], 1982) [19] (Figure 3).  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Site Code** & **Site Name** & \(V_{S,30}\) **(m/s)** & **NEHRP Site Classification** & **Description** & \(T_{G}\) **(s)** \\ \hline
1 & FKSH17 & Kawamata & 544.0 & C & Dense soil, Soft rock & 0.22 \\
2 & FKSH18 & Miharu & 307.2 & D & Stiff soil & 0.39 \\
3 & FKSH19 & Miyakoji & 338.1 & D & Stiff soil & 0.35 \\
4 & IBRH13 & Takahashi & 335.4 & D & Stiff soil & 0.36 \\
5 & IWTH02 & Tamayama & 816.3 & B & Rock & 0.15 \\
6 & IWTH05 & Fujisawa & 442.1 & C & Dense soil, Soft rock & 0.27 \\
7 & IWTH12 & Kunohe & 367.9 & C & Dense soil, Soft rock & 0.33 \\
8 & IWTH14 & Taro & 816.3 & B & Rock & 0.15 \\
9 & IWTH21 & Yamada & 521.1 & C & Dense soil, Soft rock & 0.23 \\
10 & IWTH22 & Towa & 532.1 & C & Dense soil, Soft rock & 0.23 \\
11 & IWTH27 & Rikuzentakata & 670.3 & C & Dense soil, Soft rock & 0.18 \\
12 & MYGH04 & Towa & 849.8 & B & Rock & 0.14 \\ \hline \end{tabular}
\end{table}
Table 1: Information of the sites.
## 3 Modeling of Heterogeneous Cluster Deployment Tasks
### Definition and Transformation of Coordinate Systems  
(1)Geocentric inertial coordinate system  
The geocentric inertial coordinate system, denoted as \(S_{I}\) (\(O_{E}-X_{I}Y_{I}Z_{I}\)), has its origin set at the center of mass \(O_{E}\) of a spherical Earth. The \(O_{E}Z_{I}\) axis is perpendicular to the equatorial plane and aligned with the direction of the Earth's rotational angular velocity. The \(O_{E}X_{I}\) axis lies within the equatorial plane and points towards the direction of the J2000.0 vernal equinox. The \(O_{E}Y_{I}\) axis is also located within the equatorial plane, and the three axes form a right-handed coordinate system. The three axes of this coordinate system do not change with time in inertial space, exhibiting constancy. This coordinate system is commonly used to describe the motion of celestial bodies around the Earth (S). The motion state of a satellite within the Earth's gravitational field can be characterized through orbital dynamics equations in this coordinate system, as illustrated in Figure 1.  
(2)Orbital coordinate system  
The orbital coordinate system, denoted as \(S_{b}(O_{b}-XYZ)\), is also referred to as the local orbital coordinate system. It is a coordinate system that changes with the motion of the primary satellite. The origin \(O_{b}\) of the orbital coordinate system is typically set at the position of the primary satellite. The \(O_{b}X\) axis points from the geocenter \(O_{E}\) towards \(O_{b}\). The \(O_{b}Y\) axis is aligned with the orbital motion direction of the reference primary satellite. The \(O_{b}X\) and \(O_{b}Y\) axes together form the orbital plane, and the \(O_{b}Z\) axis, together with the orbital plane formed by the \(X\) and \(Y\) axes, constitutes a right-handed system, as illustrated in Figure 1.  
Figure 1: The geocentric inertial coordinate system and the satellite orbital coordinate system.
# Estimation of Gross Primary Productivity (GPP)
## 4 Discussion
### An Optimal Spectral Proxy for Describing GPP Phenology of SRC Plantations
The chlorophyll red-edge index (ChlRedEdge) is well recognized as a good proxy of chlorophyll content [120] and has been demonstrated to be a valuable descriptor of the seasonal changes in GPP [121]. Also in our study, ChlRedEdge showed good accuracy in predicting SOS and seasonality of GPP, albeit less than MTCI (refer to Tables 4 and 5; Figure 7). Recently, it has been shown that the ChlRedEdge index can be a reliable proxy for leaf senescence as well for deciduous trees (i.e., European beech, pedunculate oak and silver birch) in the temperate zone [122]. However, this was not the case in our study, as ChlRedEdge did not provide a good estimation of EOS. It is important to note, however, that [PERSON] et al. (2019) focused on the onset of autumn senescence as identified by the breakpoint technique, whereas we used low percentiles and percentages, which correspond with the later stages of senescence, which could explain the different outcome between both studies. Coppicing strongly affects the canopy structure (i.e., leaf angle and distribution, orientation, leaf density) of the plantation modifying leaf light interception. These changes in canopy structure can strongly modify the spectral response of SRC plantations compared to natural deciduous forests. In addition, canopy structure can have a strong impact on the estimation of canopy nitrogen content [123]. During senescence, SRC plantations allocate a larger amount of nitrogen in leaves than deciduous forests. It could explain the different spectral responses of the SRC plantation compared to deciduous forests. Sentinel-2 data are now systematically generated and the Level-2A product provides top of canopy reflectance images globally ([[https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/products-types/level-2a](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/products-types/level-2a)]([https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/products-types/level-2a](https://earth.esa.int/web/sentinel/user-guides/sentinel-2-msi/products-types/level-2a))). This permits to test the use of Sentinel-2 vegetation indices, in particular the MTCI, over other SRC plantations and different biomes. The recent ESA Sentinel-3 mission offers a unique set of data at the global scale with an improved spectral resolution of about 10 nm that may help to better understand plant traits and plant physiological status. The Sentinel-3 OLCI sensor covers the chlorophyll absorption region between 600 nm and 677 nm and the red-edge region between 697 nm and 755 nm. Sentinel-3 Level-1 OLCI products provide the so-called OLCI Terrestrial Chlorophyll Index (OTCI) which is a chlorophyll index that has the same formula of MTCI but it is calculated using the OLCI bands. OTCI is available at a spatial resolution of 300 m instead of the 20 m of Sentinel-2. Thus, the use of an approach similar to the one based on MTCI and presented here could contribute to the understanding of SRC phenology and GPP at a global scale.
# Exploring Decentralized Warehouse Management Using Large Language Models: A Proof of Concept
## 5 Real-World Application
### Cost of Implementation  
One concern with the adoption of LLM-based systems is the cost of implementation--especially when using commercial models such as GPT-4. Running Large Language Models (LLMs) can be resource intensive, particularly when they are deployed at scale. To address implementation costs, we are exploring strategies including smaller models (using distilled or lightweight versions of LLMs that retain performance while reducing computational overhead); prompt optimization (minimizing token usage in prompts to lower processing costs); and cloud solutions (leveraging dynamic cloud-based infrastructure that scales resources based on demand, ensuring cost efficiency). These approaches will help make the system more financially viable for real-world use. In practice, however, the use of LLMs in decentralized warehousing need not be prohibitively expensive:  
* The cost of inference can be minimized by using smaller, open-source LLMs or domain-specific models tailored to logistics tasks.
* Stateless prompting, as used in our simulation, provides compatibility with serverless architectures and API-based deployment, ensuring low computational overhead per transaction.
* Edge deployment of lightweight models (e.g., LLaMA or Mistral variants) can localize decision-making and further reduce latency and cloud costs.  
In addition, the return on investment can be justified by better warehouse utilization, less negotiation effort, and less dependency on central IT infrastructure.
## 3 Materials and Experimental Procedures
The variability of reflectivity coefficients was shown by [PERSON] [25] and [PERSON] et al. [26] under different conditions. This indicates that transmission methods, such as TDM-1 and TDM-2, with no reflectivity terms are preferable. The diffraction loss D from Equation (15) depends on the transducer radius (a) through the s-parameter (s = xv\({}_{L}\) /fa\({}^{2}\)), sometimes known as the Seki parameter [70]. For this work, four transducers with an element radius of 6.35 mm were used. These were an Olympus V111 and V103, Panametrics V1030 (Olympus NDT, Waltham, MA, USA), and NDT Systems C16 (from NDT Systems, Huntington Beach, CA, USA). Their nominal center frequencies are 10, 1, 10, and 2.25 MHz, respectively. For most experiments, the V111 and V1030 pair was used, but other pairings of low-frequency transducers were also used when the attenuation was high. Additionally, two Olympus transducers with a 12.7-mm radius--V104 (2.25 MHz) and V107 (5 MHz)--were used to determine the transmission coefficient correction (TCC). Examples of the face-to-face responses (\(V_{1}\) waveforms and \(R_{1}\) spectra) are shown in Figure 8a,b. Careful alignment was essential in maximizing receiver output. In order to achieve good transducer-sample coupling with Vaseline couplant, it was necessary to apply approximately 200 N force for at least 30 min. Longer holding times were needed for setup 2 (and 3) before the output was stabilized. Peak-to-peak receiver voltages for direct contact were 7 to 10 V into a PicoScope 5242D (14 bit, 8 ns intervals, input impedance of 1 M\(\Omega\); Pico Technology, St. Neots, UK), showing useful signal levels to 15 MHz for pairs of 10 MHz transducers. Despite its low resonance frequency, C16 provided adequate transmitter output to 14 MHz, except at anti-resonance dips. FFT was performed with Noesis software (Enviroacoustics, Athens, Greece, ver. 5.8).
## 4 Conclusions  
The study aimed at using remote sensing to estimate changes in maize yield in the Harare metropolitan area between 1984 and 2018. Cropland areas for 1984 and 2018 were retrieved from LULC maps classified with high accuracy using multispectral Landsat 5 and Landsat 8 imageries. Higher overall classification accuracy in 1984 than in 2018 was attributed to misclassifications due to increased complexity as the city grew. Generally high mapping accuracy in both periods was attributed to increased interclass separability due to the use of multispectral data and the high performance of the SVM classification. The growth patterns in Harare were characterized by the replacement of natural and pervious LULC with buildings and impervious surfaces. This resulted in the shrinking of cropland areas between 1984 and 2018. The findings further revealed that Harare's urban sprawl, which reduced cropland area, was mainly characterized by expansion in high-density residential areas more than other built-up LULC categories. On the other hand, indicators of changes in rainfall showed statistically insignificant patterns over the same period. This implies that rainfall patterns had a minimal effect on the changes in crop production between the periods under study. The growth patterns could be compromising food availability and livelihoods, especially for the low-income strata. Overall, the study  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Year** & **Arable Land (ha)** & \multicolumn{3}{c}{**Potential Maize Yield (Tonnes)**} \\ \hline  & & **Best Case** & **Average Case** & **Worst Case** \\ \hline
1984 & 11,120 & 56,712 & 52,264 & 47,816 \\
2018 & 2631 & 13,418 & 12,366 & 11,313 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Estimated maize yield (tons) derived from cropland area and estimated yield (tons/ha).  
Figure 6: Estimated potential maize yield for three scenarios for 1984 and 2018.  
showed that using remote sensing can be a useful and inexpensive technique to understand the implications of urban growth on the food security of residents. Future studies should aim to update the investigation to current years or even develop predictive tools to study likely trends in the future. Subsequent studies should also use dynamic yield estimation for different periods and incorporate other factors that act together with land use changes to affect urban crop yield. Urban and agricultural policies and planning should consider the value of sustainable urban agriculture, as it sustains livelihoods and has the potential to significantly contribute to urban agriculture.  
Conceptualization, T.D.M.; Methodology, T.M. and T.D.M.; Formal analysis, H.M. and T.M.; Investigation, H.M. and T.D.M.; Resources, T.M.; Writing-original draft, H.M. and T.D.M.; Writing-review & editing, T.M.; Supervision, T.M. and T.D.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The datasets used in this study are readily available upon request.  
The authors declare no conflict of interest.
## 4 Result and Discussion
### Recovery of Ancient Gas Content
#### 4.1.3 Gas Yield Rate per Unit Volume  
To elucidate the correlation between gas volume yield and formation age, we conducted hydrocarbon generation thermal simulation experiments to acquire the yield-maturity curve (Figure 5a) and then determined the corresponding formation maturity values based on the burial history (Figure 5b). As the yield value represents an instantaneous yield, we integrated the yield-maturity curve over time to generate the production time relationship of cumulative volume of gas. By further combining time, we calculated the cumulative yield rate of gaseous hydrocarbons (\(q_{\text{g}}\)).  
The period from 168 to 161 million years ago marks the peak of both cumulative gas yield and yield rate (Figure 5c). This phase was characterized by rapid subsidence of the formation, which, in turn, accelerated the evolution rate of \(R_{\text{O}}\) due to an increase in geothermal gradient. Within a period of 7 million years, the \(R_{\text{O}}\) increased from 1.6% to 2.8%, thereby marking the peak period of gas generation. It was within this phase that the cumulative gas production rate and cumulative production rate reached their highest values.
###### Abstract
ECMS allows for penalizing battery use in case of its undesired state of charge (SOC), temperature, and degradation to maintain the desired operating conditions of the battery [17; 18; 19]. However, the penalization of the ICE use in transient operations cannot be found in the literature. To fill this gap, this paper proposes an approach to reduce the fuel consumption of HEVs in transient phases by imposing a transient correction factor (TCF) proposed in [14] in the control strategy. The objective function to be minimized included TCF as a function of a torque changing. The electric machine can deliver torque quickly and with a minor efficiency variation over the operating range. Hence, the best mode to manage the torque transients is to rely more on the EM. An advantage of this approach is the possibility to reduce extra fuel consumption of the ICE in transient phases. Hence, the contributions of the paper comprise the model of the P2 HEV that includes the transient fuel correction and the developed control strategy to reduce fuel consumption by exploiting the e-motor more in transient phases. The paper is organized as follows. First, the backward model of the HEV longitudinal dynamics and fuel consumption model is briefly described. Then, the transient fuel consumption model and its experimental validation based on experimental data available online [20; 21] are reported. The TCF is introduced in the ECMS objective function. The results obtained with and without TCF are compared for the UDDS driving cycle.
## 5 Conclusions  
In this research program, test schemes were designed for the factors that may affect the performance of asphalt mixture on exhaust gas degradation, and the different influence factors were tested. According to the experimental test results and the statistical analysis findings, the following conclusions can be drawn:  
1. The 5-nm particle size of TiO\({}_{2}\) is better than 10-15 nm on exhaust gas degradation, especially for HC and NO.  
2. The experimental data showed that with an increase of the amount of photocatalytic material, the degradation of CO and CO\({}_{2}\) in the exhaust gas did not obviously increase, while the degradation effects of HC and NO were remarkable.  
3. In the 4-h time extended degradation test, the experimental data show that the extended time has little effect on the degradation rate of CO\({}_{2}\) and CO, and the general trend is that the degradation of exhaust became significant with the extension of time.  
4. 2 h NO degradation rate is set as an indicator. In order to make the index more than 50% or 25%, the average illumination of road surface needs to be not less than 60 lx or 40 lx.  
**Author Contributions:** conceptualization, X.W.; methodology, M.H.; validation, M.H.; formal analysis, [PERSON]; resources X.W.; data curation, M.H.; writing--original draft preparation, M.H.; writing--review and editing, M.H.; visualization; supervision, X.W.; project administration, X.W.  
**Funding:** 2015 and 2018 Technology Program of Shanghai Municipal Engineering Design Institute (Group) Co., Ltd. No. K2015K024 and K2018K081.  
**Acknowledgments:** The authors would like to thank [PERSON] (SHMPI), [PERSON] (Tongji University) and Dr. [PERSON] (SMEDI) for helpful and constructive prophase studies.  
Figure 9: Fitting curves of degradation rates at different light intensities.
# Enhanced Joint and Separable Reversible Data Hiding in Encrypted Images with High Payload
## 5 Experimental Results and Discussion
### Joint System
Moreover, the actual value of pixels is considered, rather than an estimated value, and the absolute difference between neighboring pixels is used in preference to prediction error to calculate the smoothness. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \(G\) & \(t\) & **Embedded Bits** & **Embedding Rate (bpp)** & **Bit Error Rate** \\ \hline
10 & 5 & 4096 & 0.0156 & 0.21 \\
4 & 6 & 4096 & 0.0156 & 0.16 \\
15 & 5 & 7225 & 0.0275 & 0.26 \\
4 & 6 & 7225 & 0.0275 & 0.23 \\
4 & 6 & 16,384 & 0.0625 & 0.35 \\
4 & 6 & 24,576 & 0.0938 & 0.41 \\
4 & 6 & 28,900 Figure 11: BER comparison between the proposed system and the referenced systems for the test image Lena. (**a**) Comparison of systems mentioned. (**b**) Comparison of proposed system and Wu and Sun. Figure 12: BER comparison between the proposed system and the referenced systems for the test image Jet. (**a**) Comparison of systems mentioned. (**b**) Comparison of proposed system and Wu and Sun.
## 4 Discussion
### AI Applications in Safety Management Across Industries
#### 4.1.3 Disaster and Public Safety Sector  
AI technologies significantly enhance urbanization and disaster management by improving public safety through advanced analytics and integration of Big Data. Predictive models forecast disasters like floods and fires, while digital twin technologies simulate disaster scenarios, improving preparedness and response strategies. These systems minimize casualties and property damage, demonstrating AI's societal relevance in addressing large-scale safety challenges. AI-driven platforms that incorporate real-time data from intelligent CCTV and social networks further optimize emergency management [78; 79].
## 5 Discussion
### Improving the Rehabilitation System  
The conducted evaluation, both with patients and therapists, allowed for the identification of several aspects that can be improved. Some of these have been already incorporated into the current version of the rehabilitation system, while others are being considered in the context of future lines of research. In this sense, Figure 6 shows the updated version of the user interface that the system offers to patients when carrying out rehabilitation. The main improvements that have been incorporated are summarized below:  
* The removal of visual noise so that patients can focus on performing the exergames and only the relevant information is displayed. This improvement has been added to enhance the patients' concentration when using the system, but maintaining an adequate level of immersion (currently through the virtual gym scenario) to continue motivating them.
* New interaction mechanism based on contextual voice commands (see the upper right part of Figure 6). Thus, the patient can communicate with the rehabilitation system in a more natural and direct way. This functionality is especially important for patients with motor disabilities. This improvement has been integrated when considering the predisposition of the evaluated patients to use the system at home in an autonomous way.
* New visual representation of the 3D path that is associated with the exergame execution, based on the use of a color code and the size of the virtual spheres. The white spheres represent that part of the exercise that has already been done, while the orange ones represent the remaining part of the exercise. On the other hand, the sphere with a radius slightly larger than the rest is associated with the next step considering the current position of the related patient's joint. This improvement has been added as a result of some patients having difficulty interpreting the position of the spheres. However, it is considered to improve the lighting of the scenes, so that the shadows cast by the spheres can also facilitate the exercise interpretation by the patients.
* The integration of visual components to represent which part of the body is worked in each exergame and show an animation with the correct execution of the exergame (central right part of Figure 6). This improvement has been added, because the analysis of results showed that some patients had difficulties in understanding how to perform the exercise.
* Integration of visual components, so that the patient knows what exergame of the routine he/she is performing at every moment (bottom of Figure 6). The number of circles represents the number of exercises that make up the routine. The orange circle represents the position of the current exercise in the list. This enhancement has been added to facilitate the patient's guidance between exercises.  
The improvements integrated in the latest version of the system will be evaluated in the future through further experiments with patients and therapists.