esa-sceva/satcom-chunk-collection huggingface dataset:
# Controlling Traffic Congestion in a Residential Area via GLOSA Development
## 4 Future Work  
Several suggestions can be applied to identify the most effective strategies for reducing traffic on residential roads. Some potential strategies that we intend to test in future work include the following:  
* Implementing traffic fees or congestion fees in residential areas in order to discourage drivers from taking shortcuts.
* Developing a plan to determine the speed of vehicles within residential areas and the possibility of linking the results with this research.
* Improving public transportation options along the highway to encourage commuters and drivers to use public transit instead of driving their own vehicles.
* It is important to note that the decrease in the number of vehicles taking the shortcut does not necessarily translate to a decrease in overall traffic flow. Other factors, such as the number of vehicles using alternative routes or the number of vehicles entering the road from other directions, can also impact traffic flow.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Before Simulation (CTCRA)**} & \multicolumn{3}{c}{**After Simulation (CTCRA)**} \\ \hline
**No.** & **Speed** & **Average Travel Time** & **No.** & **Speed** & **Average Travel Time** \\
**Vehicles** & **(km/h)** & **(s)/Shortcut** & **Vehicles** & **(km/h)** & **(s)/Shortcut** \\ \hline
100 & 40 & 3.21 & 89 & 40 & 3.20 \\ \hline
150 & 40 & 3.25 & 133 & 40 & 3.25 \\ \hline
200 & 40 & 3.45 & 178 & 40 & 3.50 \\ \hline
250 & 40 & 3.60 & 222 & 40 & 3.63 \\ \hline
300 & 40 & 3.55 & 267 & 40 & 3.60 \\ \hline
350 & 40 & 3.80 & 311 & 40 & 4.00 \\ \hline
400 & 40 & 4.29 & 356 & 40 & 4.38 \\ \hline
450 & 40 & 4.58 & 400 & 40 & 4.67 \\ \hline
500 & 40 & 4.62 & 445 & 40 & 4.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Measure of effectiveness simulation (CTCRA).  
**Author Contributions:** Conceptualization, Y.K.J. and M.N.; methodology, Y.K.J. and M.N.; software, Y.K.J. and M.N.; validation, Y.K.J. and M.N.; formal analysis, Y.K.J. and M.N.; investigation, Y.K.J. and M.N.; resources, Y.K.J. and M.N.; data curation, Y.K.J. and M.N.; writing--original draft preparation, Y.K.J. and M.N.; writing--review and editing, Y.K.J. and M.N.; visualization, Y.K.J. and M.N.; supervision, Y.K.J. and M.N.; project administration, Y.K.J. and M.N.; funding acquisition, Y.K.J. and M.N. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon reasonable request from the corresponding author.  
**Acknowledgments:** Data were obtained from the General Traffic Directorate in Baghdad. This data has significantly affected the quality and depth of the research.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Physicochemical Properties and Volatile Profile of Chito: A Traditional Dry-Cured Goat Meat Product
## 3 Results and Discussion
### Odor Activity Value (OAV)  
The OAV represents the contribution of a compound to the overall odor. Thus, a higher OAV indicates a stronger contribution of the compound to the aroma characteristics [52]. Table 6 shows variations of volatile compounds with OAVs \(>1\). Twenty-four key odor compounds with OAVs of \(>\)1 were detected in NP and P, of which the most important were aldehydes (11), ketones (5), alcohols (5), and terpenes (3). [PERSON] et al. [18] and [PERSON] et al. [45] reported that alcohols and aldehydes are highlighted as the primary volatile compounds responsible for the dominant odor in meat. Notably, in P, five compounds with the highest OAV were identified, which were 2-decenal, 2-nonenal, nonanal, 2-undecenal, and octanal, and these are mainly oxidized from oleic and linoleic acid. In this sense, 2-Decenal, 2-Nonenal, and 2-Undecenal produce citrus fruit, sweet, vegetable, and herbaceous notes, among others. Octanal emits green, meaty, fresh, fruity, and grassy notes, whereas nonanal contributes fruity and sweet aroma notes as well as an unpleasant rancid pungent odor [40]. Thus, these compounds might contribute greatly to the overall aroma of chito by imparting green, fatty, fresh, and grassy notes [47]. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & & & \multicolumn{3}{c}{OAV} \\ \hline
**Volatile Compound** & **Odor Threshold (\(\mu\)g/kg)** & **Ref.** & **NP 2021** & **P 2021** & **NP 2022** & **P 2022** & **NP 2023** & **P 2023** \\ \hline
**Aldehydes** & & & & & & & & & \\ Hexanal & 5 & [38; 39] & 0.44 & 0.26 & 529.12 & 195.07 & 165.92 & 86.97 \\ Heptanal & 2.8 & [38; 39] & nd & nd & 281.51 & 299.84 & 185.92 & 190.44 \\ Octanal & 0.59 & [38; 52] & 5.73 & 16.29 & 772.22 & 1330.0 & 943.51 & 1052.8 \\
2-Ocenal & 3 & [53] & nd & nd & 42.90 & 48.81 & 47.00 & 37.46 \\ Nonanal & 1.1 & [38; 53] & 24.13 & 18.96 & 1238.14 & 1879.46 & 1379.32 & 1644.29 \\
2-Nonenal & 0.19 & [38; 53] & nd & nd & 2112.42 & 290.37 & 293.05 & 2503.47 \\ Decanal & 3 & [38; 39] & 1.07 & nd & 55.25 & 72.39 & 51.28 & 41.33 \\
2-Decenal & 1 & [38] & nd & nd & 4207.30 & 5182.57 & 3437.70 & 4283.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Odor activity value (OAV) of the aroma compounds detected in chito (NP and P). On the other hand, the variable importance in projection (VIP) of volatile compounds showed differences in their contributions across two different processes [non-pressed (NP) and pressed (P)] (Figure 2). When the VIP of an odor component was equal to 1.0 or higher, it was used as a marker to distinguish the effect of pressing. Eight volatile compounds were considered as contributing significantly (VIP > 1), and aroma was contributed by 2-nonanone, hexanal, 1-hexanol, tetradecanal, 2-undecanone, D-limonene, 1-heptanol, and octanal. These compounds have shown odors related to fruity, floral, green, sweet, mut
# A Review of Performance, Energy and Privacy of Intrusion Detection Systems for IoT
## 4 State-of-the-Art in IDS for IoT  
In order to analyze the state-of-the-art within intrusion detection for IoT, we have conducted extensive study of existing efforts using prominent databases such as Google Scholar, IEEE Xplore and ACM library to scout relevant articles. In order to filter articles not related to our study, we used keywords such as _Internet of Things AND Intrusion Detection_ and _IoT AND Intrusion Detection_ enabling us to focus on articles within the IoT paradigm. Further, our research identified an overlap in literature between IoT and Wireless Sensor Networks (WSNs) however we have included articles which are focused at addressing intrusion detection challenges for IoT. This has enabled our study to retain focus on challenges and advancements within intrusion detection for IoT.  
Although there have been efforts to study literature for intrusion detection within IoT as presented in Section 2, this study is focused at highlighting the performance overheads and privacy implications of existing approaches. Within this context, this section presents review of contemporary intrusion detection approaches for IoT to achieve this objective.  
Furthermore, intrusion detection research can be categorized into: Anomaly, Signature, Specification, Hybrid, and Game-based models. Therefore, we present review of existing efforts using these categorization. In doing so, we summarize leading efforts for each category of intrusion detection which can indicate the cutting-edge within that domain. A graphical representation of this categorization is also presented in Figure 2.
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 5 Results and Analysis
### Scenario 4: Frequent Fluctuations in Weather  
In scenario 4, the system experienced weather fluctuations more frequently and abruptly than the previous three scenarios. The weather fluctuated between all the three main categories during both testing periods. Most of the night period was fair, meaning clear skies, and that was also the most erroneous the readings were amongst all four scenarios as the system RMSE was 0.21 m. During the day, it fell within the category of one of the highest RMSEs at 0.48 m.
# Seismic Resilience Evolution of Shield Tunnel with Structure Degradation
## 3 Numerical Simulation
### Structure Load
#### 3.2.2 Boundary Condition and Seismic Input  
When conducting dynamic analysis considering the interaction between the structure and the foundation, it is necessary for seismic energy to dissipate to the far-field foundation. This requires that the outgoing scattering waves at the calculated finite-range foundation boundary have no reflection. Artificial viscoelastic boundaries can absorb the energy radiating outward from the calculation area. They not only effectively simulate the damping effect of radiation in semi-infinite space but also model the elastic recovery performance of semi-infinite foundations well [33]. Simulating an infinite boundary is achieved by applying three-way spring dampers to the finite element grid nodes. The schematic diagram of the viscoelastic boundary is shown in Figure 7[34; 35; 36].  
Figure 6: The Acceleration Time–History and Fourier spectrum of Kobe earthquake motion. (**a**) Acceleration time course curve (0.1 g Kobe); (**b**) The seismic wave Fourier spectrum.  
Figure 7: Three-dimensional viscoelastic boundary.  
Spring and damping mechanical parameters calculation formulas are as follows:  
\[\begin{split} K_{BT}=\alpha_{BT}\frac{\text{G}}{R},C_{BT}=\rho C_{S} \\ K_{BN}=\alpha_{BN}\frac{\text{G}}{R},C_{BN}=\rho C_{P}\end{split} \tag{7}\]  
where KBT and KBN are the stiffness coefficients of the tangential and normal springs, respectively; CBT and CBN are the damping coefficients of the tangential and normal dampers, respectively; \(\alpha_{BN}\) and \(\alpha_{BT}\) are correction coefficients for normal and tangential spring stiffness, which are 1.33 and 0.67, respectively; G is the shear modulus; \(\rho\) is the density; R is the distance from the wave source to the artificial boundary node; C\({}_{\text{S}}\) and C\({}_{\text{P}}\) are shear and compressional wave velocities, respectively.  
Due to the inability of spring dampers to constrain boundary displacements, it is necessary to implement seismic loads through concentrated nodal loads, as shown in the following equation. The affected area of nodes is determined by applying unit loads to the model for statistical purposes.  
\[F_{B}=(K_{B}u_{B}^{ff}+C_{B}\dot{u}_{B}^{ff}+\sigma_{B}^{ff}n)A_{B} \tag{8}\]  
where \(K_{B}\) is the stiffness coefficients of springs; \(C_{B}\) is the damping coefficients; \(\sigma_{B}^{ff}\) is the free-field stress tensor; \(u_{B}^{ff}=\begin{bmatrix}u&v&w\end{bmatrix}\) is the free-field displacement vector; \(\dot{u}_{B}^{ff}=\begin{bmatrix}u&\dot{v}&w\end{bmatrix}\) is the free-field velocity vector; \(n\) is the cosine vector of the normal direction outside the boundary; \(A_{B}\) is the influenced area of the boundary node [37].
## 3 Methodology and Theoretical Background
### Fault Assessment in Selected LSHT Machines
#### 3.2.3 Partial Demagnetization in PMSMs  
Demagnetization is defined as the irreversible reduction in the remanent magnetic flux density of one or more magnets. It can be classified into two types: uniform irreversible demagnetization (UD) and partial irreversible demagnetization (PD). UD occurs when the remanent flux density of all the magnets in the rotor is reduced by the same ratio, whereas PD occurs when the remanent flux density is reduced in only some of the magnets [64].  
Since UD is uncommon in real-world cases, only PD was investigated in the machine specimens. Considering a surface-mounted PMSM, each magnet can be divided into \(Q_{\rm P}\) sectors, and the demagnetization of the \(i\)-th pole can be defined as follows:  
\[\Delta B_{\rm r,i}=\frac{1}{Q_{\rm P}}\sum_{j=1}^{Q_{\rm P}}k_{j,i} \tag{5}\]  
where \(k_{j,i}\) is a factor that accounts for the demagnetization of the \(j\)-th sector of the \(i\)-th magnet, ranging from 0 to 1. If a magnet sector is flawless, the value is 0, while if the magnet sector is completely demagnetized, the value is 1. Considering the above, the total demagnetization of a PMSM can be expressed as follows:  
\[\Delta B_{\rm r,total}=\frac{1}{2 pQ_{\rm p}}\sum_{i=1}^{2p}\sum_{j=1}^{Q_{\rm p} }k_{j,i} \tag{6}\]  
where \(p\) is the factor that accounts for the demagnetization of the \(j\)-th sector of the \(i\)-th magnet ranging.  
In this paper, and similarly to related studies on PD [64; 65; 66], four levels of PD were analyzed:  
* **Level 1:** A 50% demagnetization is applied in half of the magnet of one pole (\(\Delta B_{\rm r,1}=0.25\)), representing an initial PD scenario.
* **Level 2:** A 50% demagnetization is applied in the entire magnet of one pole (\(\Delta B_{\rm r,1}=0.5\)), representing an incipient PD scenario.
* **Level 3:** A 50% demagnetization is applied in half of the magnet of several adjacent poles to account for an overall \(\sim\)10% of total demagnetization (\(\Delta B_{\rm r,total}=0.1\)), representing a mild scenario.
* **Level 4:** A 50% demagnetization is applied in half of the magnet of several adjacent poles to account for an overall \(\sim\)20% of total demagnetization (\(\Delta B_{\rm r,total}=0.2\)), representing a moderate scenario.
## Appendix A Trajectory Planning
### Neural Network Based Trajectory Planning  
Since the optimization process can be time-consuming, during the implementation the trajectory planning is performed through a machine learning-based approach. In order to train the machine learning-based model, a lot of data needed. Therefore, several trajectories have been planned using the proposed algorithm with different parameter sets. There are several machine learning algorithms which can deal with the trajectory planning problem [25]. However, in general, the most flexible solution is provided by the neural networks, since they are capable to solve complex and nonlinear problems. Therefore, in this paper a neural network-based solution is applied to solve this problem [11]. The trained neural network consists of one input, one output, and three hidden layers. Since, the number of neurons can be chosen freely, to determine the optimal number of them, the so-called k-cross validation technique is used. The mentioned method divides the training data into subsets. The first subset is used to train the network, and at the same time, another randomly created subsets are used to validate it. The number of neurons in the hidden layer is chosen to be 18-10-16. As it is described, the activation functions have to be chosen. In this paper, the rectified linear unit (ReLU) and the log-sigmoid functions are used in the network, since these functions can be used to solve nonlinear problems. The training process is based on the widely used Levenberg-Marquardt algorithm [26].
# Synthesis of Hydrogel-Based Microgels and Nanogels Toward Therapeutic and Biomedical Applications
## 3 Fabrication of Hydrogels
### Physical Crosslinking
#### 3.2.3 Ionic Crosslinking  
Ionic crosslinking follows the most straightforward mechanism in physical crosslinking, relying on electrostatic interactions between oppositely charged molecules [93; 110]. It is typically achieved by introducing oppositely charged multivalent ions or particles into a polyelectrolyte solution, creating a localized electrostatic assembly between multiple polymeric chains and a divalent cation which serves as a crosslinking point within the hydrogel network. For instance, alginate hydrogels are conventionally formed when divalent cations, such as Ca\({}^{2+}\), Ba\({}^{2+}\), or Mg\({}^{2+}\), interact with the guluronate blocks of alginate chains [93; 96; 97]. These electrostatic interactions result in the formation of polyelectrolyte complexes, creating a stable, reversible hydrogel network.  
Ionic crosslinking offers the advantage of mild reaction conditions, making it highly suitable for encapsulating sensitive biological materials such as cells or proteins. However, the strength and stability of the hydrogel depend on factors like ion concentration, type of crosslinking ion, and environmental ionic strength.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 5 Conclusions  
This study addresses the critical problem of generating realistic synthetic electrical load profiles in scenarios where access to large, high-resolution datasets is constrained due to privacy concerns or data availability. Traditional methods often struggle with limited data, exhibiting training instability, overfitting, and computational inefficiency. These limitations hinder the adoption of data-driven modeling techniques in domains such as smart grid analytics, load forecasting, and energy simulation, particularly when dealing with small-scale or privacy-sensitive datasets.  
To overcome these challenges, a lightweight convolutional GAN architecture--Conv1D-WGAN-GP, which combines the temporal modeling power of 1D convolutional layers with the training stability of the Wasserstein loss and gradient penalty--is proposed. The model is tailored to generate 24 h synthetic load profiles that maintain the statistical and temporal integrity of real-world energy consumption data.  
Through a comprehensive evaluation on four diverse datasets, Conv1D-WGAN-GP consistently outperformed baseline models (Vanilla-GAN, WGAN-GP, and Conv1D-GAN) across multiple quantitative and qualitative metrics. It achieved the lowest Wasserstein distances, closest alignment in mean and standard deviation, and the Real-vs-Synthetic Distinguishability Score, indicating high realism and indistinguishability from actual data. Visual inspections using t-SNE plots and per-hour histograms further confirmed that the model effectively captured both typical patterns and subtle behavioral variations in load profiles.  
In conclusion, Conv1D-WGAN-GP represents a promising and practical solution to the problem of synthetic load profile generation under data scarcity and privacy constraints. It enables the creation of high-fidelity, diverse, and temporally coherent synthetic data.  
Future work will explore the integration of conditional inputs such as day type, weather conditions, or seasonality to allow for more controllable and context-aware data generation. Additionally, differential privacy mechanisms to further enhance data anonymity and security will be incorporated. Another promising direction is adapting the framework for multivariate time series and finer temporal resolutions (e.g., 15 min intervals).  
Conceptualization, T.K., I.V. and B.E.; methodology, T.K.; software, T.K.; validation, T.K., I.V., K.G.-E. and B.E.; formal analysis, T.K. and I.V.; investigation, T.K.; resources, B.E. and K.G.-E.; data curation, T.K. and I.V.; writing--original draft preparation, T.K., B.E. and I.V.; writing--review and editing, B.E. and T.K.; visualization, T.K.; supervision, B.E.; project administration, B.E.; funding acquisition, B.E. All authors have read and agreed to the published version of the manuscript.  
This study is financed by the European Union--NextGenerationEU through the National Recovery and Resilience Plan of the Republic of Bulgaria, project no. BG-RRP-2.013-0001.
## 4 Experimental Validation
### Energy Recovery Rate with Different Braking Speed  
Similar to the simulation process with the different initial braking speed, the initial braking speed ranged from 100 rpm to 300 rpm at the interval of 50 rpm when the braking intensity was kept at 0.4 and initial pressure of the accumulator was set as 19 MPa. Comparisons of the simulation data and experimental data were exhibited in Figures 15-19 corresponding to the initial braking speed from 100 rpm to 300 rpm. From Figures 15a, 16a, 17a, 18a and 19a, it could be found that the actual speed of the flywheel had overshoot relative to that of the simulation data, because control of accumulator had time-delay. From Figures 15b, 16b, 17b, 18b and 19b, it could be observed that pressures of the accumulator in the experimental process had obvious differences relative to the simulation data, and they had distinct fluctuations. The reason for this phenomenon was similar with that in the experimental validation of the energy recovery rate with different braking intensity.  
The actual braking time, change of the kinetic energy, recycled energy amount, and the energy recovery rate with the different initial braking speed were calculated and summarized in Table 8.  
Figure 19: Comparison of the experimental result and simulation result when the initial braking speed was 300 rpm: (**a**) Evolution of speed of flywheel, (**b**) Evolution of pressure of accumulator.  
Figure 17: Comparison of the experimental result and simulation result when the initial braking speed was 200 rpm: (**a**) Evolution of speed of flywheel, (**b**) Evolution of pressure of accumulator.  
Comparing with the simulation data in Table 4, the actual braking time and energy recovery rate were reduced accordingly, and the variation tendencies of them were consistent.  
From comparisons of the experimental data and the simulation data, effects of the influencing factors of the braking intensity, initial pressure of the accumulator, and the initial braking speed, were analyzed and validated respectively. The research results indicated that energy recovery rate of the regenerative braking system would be improved by optimization of the influencing factors.
## 3 Results and Discussion
### Folate
The Arianna and Francesca fruits had a peak of folate content after 3M of storage, with a content of 229.67 \(\upmu\)g/kg FW and 223.59 \(\upmu\)g/kg FW respectively, while the Silvia fruit had a peak after 5M of storage, with a content of 177.65 \(\upmu\)g/kg FW. In the DF, there was also an increasing trend for folate content until the end of storage, with the highest content detected at the end of storage in the Arianna and Francesca fruits (188.04 \(\upmu\)g/kg FW and 166.46 \(\upmu\)g/kg FW, respectively); in the Silvia fruit, the highest folate content was detected after 5M of storage, with a value of 138.88 \(\upmu\)g/kg FW. There was not a significant difference in folate content between temperature and treatments, but the WF presented more folate content than the DF during storage. The strawberry is one of the most important sources of antioxidants and is considered a functional fruit due to the presence of a diverse range of bioactive components and high levels of vitamin C, vitamin E, folate, phenolic compounds, and fiber [42]. In this respect, variations of the amount of vitamin C in strawberries can affect the folate content, as a higher vitamin C content can lead to increased stability of folate [43]. The retention of folate and ascorbic acid was affected by the same factors, and a high content of ascorbic acid could provide possible protection against folate degradation [44]. This assumption was also confirmed by [PERSON] and [PERSON] [45], who performed in vivo studies to simulate food folate digestion and found out that ascorbic acid stabilizes folate, particularly 5-CH3-H4 folate during digestion. The addition of ascorbic acid in physiological amounts improved the stability of some types of folate, depending on the food matrix [45].