esa-sceva/satcom-chunk-collection huggingface dataset:
# A False Negative Study of the Steganalysis Tool Stegdetect
## 3 Methodology  
The study has selected one of the automated steganalysis tools, Stegdetect [11] developed by [PERSON]. The purpose of the tool is to identify steganographic content by analysing JPEG images. It is able to detect several steganographic methods (F5 (header analysis), JPHide, invisible secret, outguest and camouflage) [29]. In analysing JPEG images it expresses the level of detection accuracy by appending stars (*, **, ***) to whichever steganographic method is detected. One star means the level of confidence in the detection of the specific steganographic method is low, two star means the level of confidence in the identification of steganographic method is quite good, and three star shows a high level of confidence in it. In this paper, we have used Stegdetect Windows version 0.4 which has an easy to use graphical interface. The tool's detection rate was based on the sensitivity value which is between 0.1 and 10.0. However, we have considered sensitivities of (0.1, 0.3, 0.5, 0.7, 10.0). Ref. [10] indicated that the sensitivity values affect the tool's false-negative ratio.  
To achieve the purpose of the paper, we looked for a popular steganographic method that embeds data in JPEG image which is detectable by Stegdetect. JPHide [30] has both Windows and Linux version developed by [PERSON] in 1999. In this paper we have chosen the Window version 0.5 with a user-friendly interface. Jphide uses least significant bit of the discrete cosine transform coefficient to hide data into any image with JPEG format. Meanwhile, according to [30], 5% insertion rate of data into an image will be very difficult to identify in the absence of the original image. Detection of the Jphide method is independent of the size of the message embedded into the image. This below shows the process we used in generating stego images.
## 6 Conclusions  
This study focused on evaluating CNN, LSTM, and Dense models for urban sound recognition using the UrbanSound8K dataset, aiming to develop an IoT-fog computing framework for real-time applications. The findings demonstrate that the CNN model exhibited the highest overall accuracy at 90%, particularly excelling in sound categories with distinct spectral features, such as car horns and sirens, due to its superior ability to capture local patterns in spectrograms. The Dense model followed with an accuracy of 84%, offering balanced performance across categories but slightly lagging behind the CNN model in terms of handling complex acoustic features.  
In contrast, while the LSTM model achieved 81% accuracy, it faced challenges in recognizing transient and overlapping sounds, such as gunshots and children playing, where temporal dependencies were less pronounced. These results highlight that although the LSTM model is effective in processing sequential data, it may require additional fine-tuning or hybrid approaches for improved performance in such scenarios.  
The integration of these models with a fog computing framework aims to reduce latency, minimize bandwidth usage, and enhance real-time data processing efficiency, making it suitable for smart city environments. However, further research is needed to improve performance in overlapping sound categories and validate the system under real-time conditions.  
Future research will focus on developing hybrid models that combine the strengths of the CNN and LSTM models, exploring advanced feature extraction techniques, and deploying the system in real-world environments to evaluate its scalability and robustness.  
This research received no funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The author declares no conflicts of interest.
## 4 Results and Discussion
### Robustness of the Optimal Configuration Under Uncertainty  
Under the uncertainty propagation scenario, the recommended configuration (x\({}_{1}\) = 47 km, x\({}_{2}\) = 1.21 kWh/ton, x\({}_{5}\) = 0.47 kg CO\({}_{2}\)/kWh) consistently remained within the top 5% of all candidate solutions based on the TOPSIS closeness coefficient in 91.2% of the 500 stochastic realizations. This indicates a high degree of robustness and suggests that the identified compromise solution is not merely a by-product of deterministic assumptions.  
Moreover, the spread of the cost and emission predictions remained tightly concentrated around the original deterministic Pareto frontier, with less than \(\pm\)5% deviation observed for both objectives in the majority of scenarios. These findings confirm the stability of the optimized solution under operational uncertainties and reinforce its practical viability.  
Figure 13 depicts the probabilistic Pareto envelope generated through uncertainty-aware simulations, highlighting the limited drift in the optimal front and the persistent dominance of the selected configuration. The deterministic Pareto front is shown in red, while the cloud of gray curves represents possible trade-off solutions under input uncertainty. Notably, the TOPSIS-optimal configuration (indicated by a blue marker) consistently resides within the most favorable region of this envelope, maintaining a position within the top 5% of all realizations based on closeness coefficient rankings. This graphical evidence confirms the robustness of the recommended solution, indicating that it is not a statistical artifact of single-point assumptions. Moreover, the narrow dispersion of the stochastic frontier around the deterministic curve suggests that the optimization framework is stable and resilient to moderate fluctuations in input data, thereby enhancing the real-world applicability of the results.
# Research on Micro-Vibration Analysis of Segmented Telescope Based on Opto-Mechanical Integration
## 3 Effect of Sub-Mirror Position Error on WFE
### Clock Around the Z-Axis  
The Z' axis passing through the origin of the local coordinate system of each sub-mirror is taken as the rotation axis, and the system's WFE RMS changes are obtained by rotating within 2 mrad, as shown in Figure 7.  
Figure 6: Systematic WFE RMS versus sub-mirror Tip & Tilt (**a**) WFE RMS vs. Tip; (**b**) WFE RMS vs. Tilt.  
Figure 7: System WFE RMS vs. sub-mirror Clock.  
The outer, middle and inner circle mirrors are selected, and when the rotational error of Z' of the sub-mirror is 2 mrad, the corresponding WFE RMS are 0.1539\(\lambda\), 0.12\(\lambda\), 0.0497\(\lambda\), respectively, which are consistent with the theory. When the rotation angle is fixed, the influence of Clock on the system WFE increases with the distance of the sub-mirror from the center of the primary mirror. Additionally, the error tolerance for rotation around the Z' axis is 103 times greater than that for tilting around the X' and Y' axes. So more attention should be paid to the Tip & Tilt error during the mounting and tuning process.
# Edible Packaging: A Technological Update for the Sustainable Future of the Food Industry
## 3 Results
### Alginate Films  
Alginate is an edible, natural hetero-polysaccharide extracted from marine brown seaweed (_Phaeophyceae_), where they occur in the form of calcium, sodium, and magnesium salts of alginic acid [27]. Alginate consists of a linear anionic polysaccharide polymer of \(\beta\)-(1-4)-D mannuronic (M-blocks) and \(\alpha\)-L-guluronic acid (G-blocks). Alginate can exhibit gelling properties because of the large number of GM blocks and their interchain interactions. They are biodegradable, biocompatible, biostable, non-toxic, and hydrophilic [47]. Sodiumalginate has been listed as a GRAS substance by the U.S. Food and Drug Administration (FDA) in Title 21 of the Code of Federal Regulations (CFR) [48], and it can form good films with good mechanical strengths, moisture barriers, and cohesiveness [49; 50]. Alginate edible films are uniform, highly water-soluble, and transparent. The use of divalent cations such as Ca\({}^{2+}\) to cross-link alginate during film synthesis results in ionic interaction, which improves water insolubility and strengthens films [51].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
# A Satellite Task Planning Algorithm Based on a Symmetric Recurrent Neural Network
## 3 Problem Description
### Assumptions and Constraints  
#### 3.2.1 Assumptions  
Based on the research of actual and realistic needs, this paper makes reasonable assumptions regarding the problems raised in this paper, which are as follows:  
1. The scheduling range is defined as the interval between two consecutive satellite-ground communication links. In terms of time, geographically, we only plan observation tasks within a given range of observation tasks.
2. Intelligent satellites operate well throughout the entire dispatch range and will not be affected by space radiation effects.  
Figure 2: Schematic diagram of satellite in-orbit task planning.  
Intelligent satellites have certain autonomic capabilities, allowing them to analyze the collected image information. If an event of interest is detected, a new task can be generated in-orbit.
* Smart satellites can only be charged when idle (for example, without performing a task) and in the sun. The iSAT satellite can be charged while performing tasks and in the sun, but the energy obtained is much less than the energy consumed to observe the payload or transmit the payload, so the energy obtained is negligible.
* When a smart satellite processes a task at any time, the task is not replaced by other tasks. That is, the observation task or the transmission task cannot be interrupted or deleted, once it is executed.
* There is no priority constraint between the tasks, but each task has a constraint on the observation time window. All tasks have one and only one corresponding observation time window.
# A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly
## 2 The Emergence of Privacy Impact Assessment  
PIA is a risk management approach which aims at the evaluation of potential effects that systems may have on privacy, due to processing actions on personal data [11]. Via this systematic approach, organisations can anticipate the risks of their initiatives during their life-cycle, starting from the design phase--enabling a \"privacy-by-design\" approach--but also during their operational life-cycle by performing iterative assessment. Data Protection Authorities (DPAs) and standardisation bodies have established legal frameworks and guidelines which mandate the conduction of PIA. In an effort to gain the European citizens' trust for digital services, the General Data Protection Regulation (GDPR) refers to the obligation of the data controller to conduct an impact assessment and to document it prior to starting the intended data processing (Art. 35). The International Organization for Standardization (ISO) released a PIA guidelines standard, namely ISO/IEC 29134:2017, for standardising the PIA per se, and the reporting process and format [14].  
In what follows, we provide an overview of relevant risk assessment standards and PIA tools in order highlight their benefits but also challenges in comparison to APSIA. The intuition is to showcase that besides the NIST privacy framework [10], the aforementioned privacy concerns are highly overlooked in today's standards while a common quantification formula for calculating the privacy risks together with the existing cybersecurity metrics is still an open and challenging task.
###### Abstract  
This study examines the integration of the Internet of Vehicles (IoV) and blockchain as tools to optimize traffic management in Roll-on/Roll-off (Ro-Ro) terminals within the Spanish port system. Faced with increasing operational complexity, these technologies present innovative solutions to enhance efficiency, reduce waiting times, and ensure transparency in data management. The methodology follows five main phases: analysis of the current scenario, establishment of a baseline, calculations to evaluate key outcomes, analysis of blockchain implementation, and discussion of results. Key variables include waiting times, vehicular flow, CO\({}_{2}\) emissions, and operational costs, comparing manual and automated scenarios. The findings reveal that the combined use of IoV and blockchain can increase vehicular flow by up to 70%, reduce waiting times by 2.56 min, and decrease CO\({}_{2}\) emissions by 57.74 kg per hour. Additionally, automation significantly reduces operational costs, yielding average savings of over EUR 500 per hour. This study concludes that adopting these technologies transforms port operations by fostering sustainability, efficiency, and safety. However, challenges remain, including high initial implementation costs and system interoperability issues. This work underscores the need for strategic approaches to overcome these barriers and positions Spanish ports as potential leaders in logistics innovation, aligning with global demands for sustainable, efficient, and transparent port operations.  
Accepted: 20 February 2025  
17 February 2025  
**Citation**: [PERSON]; [PERSON]; [PERSON] A. IoV and Blockchain for Traffic Optimization in Ro-Ro Terminals: A Case Study in the Spanish Port System. _Future Internet_**2025**, 17, 99.  
[[https://doi.org/10.3390/E17030099](https://doi.org/10.3390/E17030099)]([https://doi.org/10.3390/E17030099](https://doi.org/10.3390/E17030099))
# An Improved NSGA-II-Based Method for Cutting Trajectory Planning of Boom-Type Roadheader
## 2 Methodology
### Optimizing Cutting Paths Using Improved NSGA-II
#### 2.3.3 Adjacent Constraints Design for Cutting Space Points
\begin{array}{cc}1,&\left|\theta_{i1}-\theta_{j1}\right|\leq \Delta\theta,\left|\theta_{i2}-\theta_{j2}\right|\leq\Delta\theta,\left|d_{i} -d_{j}\right|\leq\Delta d\\ 0,&\text{otherwise}.\end{array} \right. \tag{16}\]  
By calculating the adjacency values of all spatial points, the adjacency matrix \(\mathbf{A}\) can be obtained. Assuming bidirectional and unordered trajectory points with uniform cost, the creation of adjacency lists and adjacency matrices hinges on the criterion for adjacency determination. This paper employs the joint space adjacency model depicted in Figure 10 for trajectory points as the basis for adjacency judgment. For instance, trajectory points \"1\" and \"4\" are adjacent to trajectory point \"0\", and thus are classified into the adjacency list of point \"0\". Similarly, other trajectory points are classified accordingly. A 16 \(\times\) 16 matrix is constructed, where, based on the established adjacency lists, the corresponding positions in the matrix are set to 1 if adjacent, and to 0 otherwise. Figure 9: Model of adjacency constraints in joint space. Adjacency lists and adjacency matrices are crucial components in the optimization of cutting trajectories. In the context of high-dimensional path optimization, the rational design of adjacency lists and matrices can significantly enhance efficiency. Furthermore, they provide a foundation for subsequent operations such as initialization and mutation using the NSGA-II algorithm.
## 2 Materials and Methods
### Data Analyses  
Empty cycles, where all data were missing, were excluded from further analysis. Irregular cycles, defined when their duration was shorter than 21 days or longer than 35 days [20], anovolulatory cycles (among those measured), and atypical HC use, defined when the duration was not equal to the 28 days of a pill pack, were removed. Only athletes with at least two uninterrupted full cycles with complete wellness data were retained.  
Athletes and their sport were described using number and frequency. Wellness variables were described using mean and standard deviation (SD). Student's \(t\)-test was performed to check differences between each wellness parameter of athletes with MC and athletes using HC.  
Each wellness variable, that is sleep quality, fitness, mood, and number of menstrual symptoms, were transformed from an ordinal scale into continuous data to perform a linear regression. To look at intra-individual variation, each athlete's averages for each well-being indicator were subtracted. Thus, each athlete had a zero mean for each indicator [3]. Then, the assumptions of homogeneity of variances and data normality, linearity, and independence were verified.  
Let Y be the wellness variable:  
\[Y=\alpha+\sum_{i=1}^{3}\kappa_{s[i]}X^{[i]}+\sum_{j=1}^{6}\eta_{t[j]}X^{t[j] }+\sum_{k=1}^{5}\gamma_{p[k]}X^{p[k]}+\epsilon\]  
\(\alpha\) was the intercept that serves as the baseline reference point;  
\(\kappa_{s[i]}\) was the regression coefficient of the season i;  
\(X^{[i]}\) was the indicator of the season i;  
\(\eta_{t[j]}\) was the regression coefficient of the weekday j;  
\(X^{[i]}\) was the indicator of the weekday j;  
\(\gamma_{p[k]}\) was the regression coefficient of the cycle phases k;  
\(X^{p[k]}\) was the indicator of the cycle phase k;\(\varepsilon\) represents the residuals.  
Menstrual cyclicity was analyzed separately for athletes with a natural MC and for athletes using HC. The menstruation phase for MC-athletes and the pill break phase for HC-athletes were used as references. Cycle phases, days of the week, and seasons were all integrated into a single model to study the menstrual cyclicity considering the weekly cyclicity and the seasonal cyclicity concomitantly. Weekly and seasonal cyclicities were analyzed for MC- and HC-athletes together. Then, cycle phases were not included in this second model. Monday and spring were arbitrarily set as the references for the weekly and seasonal analyses.  
Each coefficient represents the average difference in wellness between the coefficient category and the reference category. The threshold of significance was defined as \(\alpha\) = 0.05. R software was used for all analyses (Version 4.3.3, R Foundation for Statistical Computing, Vienna, Austria).
## 3 Results and Discussion
### The Composition and Changes of VOCs in the Ripening Process of _R. roxburghii_ Fruit
#### 3.2.2. Dynamic Changes of Volatile Alcohols during the Ripening Process of _R. rxburghii_  
Alcohols in plants are derived from sugar metabolism, amino acid decarboxylation/dehydrogenation, and fatty acid oxidation, resulting in a diverse array of compounds that are significant aromatic components of fruits [46; 47]. As illustrated in Figure 2A,B, the relative content of alcohols generally exhibited an increasing trend with advancing fruit maturity in _R. roxburghii_, peaking at stage V (156.11 mg/kg), which was approximately 4.5 times higher than the level observed at stage I (34.76 mg/kg). The proportion of alcohols in _R. roxburghii_ fruit varied across maturity stages, with stage IV exhibiting the lowest proportion at around 4%. As _R. roxburghii_ fruit matured, aldehydes decreased during stages II and V, while alcohols increased, indicating an inverse relationship between the two volatiles. As detailed in Table 1, 2-(E)-hexenal was least abundant at stages III and V, whereas (3E)-3-hexen-1-ol, detected exclusively at these two stages, was the most abundant alcohol in the fruit. This compound is produced through the metabolism of 2-(E)-hexenal by isomerase and ethanol dehydrogenase [43]. 1-Hexanol are present throughout all stages  
Figure 2: The relative contents (**A**) and percentage contents (**B**) of different types of volatile organic compounds (VOCs) in _R. roxburghii_ fruit at different ripening stages. The numerals I, II, III, IV, and V represent 45, 65, 75, 90 and 105 days after anthesis, respectively.  
of fruit ripening and might result from the reduction in hexanal by alcohol dehydrogenase (ADH). It has been demonstrated that during fruit ripening, lipoxygenase (LOX) and hydroperoxide lyase (HPL) convert linoleic and linolenic acids into C\({}_{6}\) aldehydes, which are subsequently reduced by ADH to form the corresponding C\({}_{6}\) alcohols [41; 48].
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
###### Abstract  
Topospheric delay is a major error source that affects the performance of the Global Navigation Satellite Systems (GNSS) Real Time Kinematic (RTK) positioning especially for the medium/long-range baseline. Although high precision tropospheric delay can be estimated by GNSS carrier phase measurement, together with position and ambiguity, a relatively long period of convergence time is necessary. In this study, we develop a new GPS/BDS RTK algorithm constrained with a tropospheric delay parameters from Numerical weather prediction (NWP) model for medium/long-range baselines. The accuracy of the tropospheric delays derived from NWP is assessed through comparisons with the results of GAMIT (GNSS at MIT). The positioning performance with standard GPS RTK, standard GPS/BDS RTK, the developed NWP-constrained GPS RTK and NWP-constrained GPS/BDS RTK over medium/long-range baselines are compared in terms of the initialization time and the positioning accuracy. Experiment results show that the mean differences between the NWP and GAMIT zenith tropospheric delay (ZTD) are between \(-5.50\) mm and \(5.60\) mm, and the RMS values of the NWP ZTD residuals are from \(24.02\) mm to \(32.62\) mm. A reduction in the initialization time of over \(41\%\) and \(58\%\) for medium- and long-range baselines can be achieved with the NWP-constrained RTK (both GPS alone and GPS/BDS RTK solutions) compared to the standard RTK solution, respectively. An improvement of over \(30\%\) can be found with the GPS/BDS RTK compared with that of the GPS alone RTK for both standard and the NWP-constrained modes. The positioning precision of NWP-constrained GPS/BDS RTK is better than \(3\) cm in the horizontal direction and better than \(5\) cm in the vertical direction, which satisfies the requirement of the precise positioning service.  
GPS/BDS; RTK; tropospheric delay; NWP model; medium/long-range baseline 2018 10.3390/rs10071113 10.3390/rs10071113
## 4 Numerical Setup
### Physics Modelling and Solver Parameters  
The Eulerian Multiphase model was used to define two fluid phases, with the initial free surface position and fluid velocity determined using the VOF approach. To reduce the impact of wave reflections against the computational domain boundaries, a damping layer approach was employed. The damping length was chosen equal to the length of the ship model. The damping layer was introduced at the inlet and outlet boundaries. The dynamic fluid body interaction (DFBI) model was used to calculate sinkage and trim, with heave and pitch motions enabled within the simulations at an even keel for the validation study. For simulations at different trims, DFBI was disabled. To accommodate potentially larger sinkage and trim values in numerical simulations at an even keel for the validation study, mesh morphing was utilized. The time step was determined using the following equation:  
\[\Delta t=\frac{T}{c} \tag{10}\]  
where \(T\) represents the period, defined as:  
\[T=\frac{L_{PP}}{v} \tag{11}\]  
where \(v\) represents the ship speed in m/s. The beforementioned coefficient \(c\) for the fine, medium, and coarse time steps is equal to 200, 100, and 50, respectively. The fine, medium, and coarse time steps were 0.024 s, 0.048 s, and 0.096 s, respectively. Five inner iterations were set within each time step.