esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Active and Passive Remote Sensing Techniques for Road Extraction
## 3 Road Extraction Based on Different Data Sources
### Road Extraction Based on LiDAR Data
#### 3.4.1 Main Methods
Due to the rough appearance of the intensity images created from the SPL data, small features such as narrow roads were often difficult to distinguish in the intensity images. A summary of road extraction using LiDAR data is shown in Table 7. Notably, ML-ALC has become the main approach for road extraction in recent years.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 7 Challenges and Future Perspectives
Concerning polymeric materials, the main challenge lies in the following aspects: selectivity and permeability, aging, thermal, and chemical stability, as well as scalability and cost [69; 231]. It is important that the polymeric membrane can achieve a high selectivity for CO\({}_{2}\) over other gases such as N\({}_{2}\), CO, and CH\({}_{4}\) while maintaining a relative permeability [232]. In terms of aging, the polymeric membrane must be stable to light, moisture, and oxidation as it will decrease its performance over time [233]. Likewise, the thermal and chemical stability of the polymer is a critical parameter as it is required for industrial application, especially under harsh conditions [234]. At last, the most important parameters, the cost of the membrane fabrication and its corresponding production at a large scale, remain a huge barrier [235]  
In spite of these challenges, a lot of efforts are put into the development of polymers. One interesting aspect that was not discussed in this review is the mixed-matrix membranes. They consist of MOFs/ZIFs or other inorganic material incorporated into polymer matrices in order to enhance their overall performance [35; 236; 237; 238; 239]. A second aspect is the recent improvement of new enhanced fabrication techniques (i.e., additive manufacturing, such as 3D printing) that can likely lead to the production of polymeric membranes with more precise and consistent properties [240; 241]. Finally, the use of post-treatment methods such as cross-linking or thermal annealing to modify the membrane (surface) properties is another option to further increase its CO\({}_{2}\) capture capacity [242; 243; 244]. Addressing these challenges will necessitate a concerted effort in research and development to fully realize the potential of MOFs and polymeric materials in industrial applications.
## 6 Challenges and Future Research Directions
### Inertia Estimation  
Research has been conducted in [106] to estimate the total inertia constant of the power system. The research was aimed at determining spinning reserve requirements for the power system. However, virtual inertia emulated using ESSs and RESs is not going to be constant as in the case of traditional synchronous generation. The available inertia in the system will depend upon whether RES units are online or not, and resource availability (wind speed, irradiance, and state of charge in case of ESS) [107]. System inertia estimation is thus going to be critical for planning purposes for system operators in the future power system with high RES penetrations. Furthermore, such estimates can provide helpful insights into the stable real-time operation of a power system. Inertia estimation using frequency transients measured using synchronized phasor measurement units (PMUs) was proposed in [107; 108]. In [109], a method to estimate the inertial response of power system under high wind penetration based on the swing equation is presented. Accurate detection of frequency events and precise ROCOF measurements are critical for proper inertia estimation [110]. In the context of modern power systems with RES units participating in the inertial response, the inertia of the system will also largely depend on the RES resource availability at any given time as well. So, PV and wind forecasts data can be used to complement and further improve the inertia estimation techniques described before. Accurate inertia estimation methods will help setup a framework for system operator's to procure inertial services.
## 4 Discussion
### Future Direction  
There are several areas where the application of AI and generative AI can be prioritized to advance DFU management. Although various AI-based solutions for DFU have been proposed in the literature, researchers must conduct more studies to clinically validate them in real-world applications. Longitudinal studies that track patient outcomes over time can assess the efficacy and safety of these AI-based solutions. Another important aspect is the acceptability and usability of such solutions. Researchers should conduct studies to assess their usability for both patients and clinicians, as well as their impact on the patient experience. One of the most critical aspects is gaining the confidence of healthcare professionals, which hinges on improving the interpretability of AI models. To address this, developing XAI frameworks in the context of DFU is essential. Researchers could apply existing XAI methods, such as SHAP, Grad-CAM, and LIME, to enhance model transparency. Although these methods have been utilized in some DFU models, such as DFU_XAI [82] and FusionNet [65], researchers still need to conduct a quantitative assessment of their efficiency in DFU. By making AI-driven decisions more interpretable, these frameworks allow clinicians to validate and trust AI-generated recommendations. Another critical focus is the need to increase dataset diversity, a key factor in overcoming biases and improving the generalizability of AI models. Addressing this challenge requires collaboration among researchers, healthcare institutions, and AI developers to create more inclusive datasets. Additionally, synthetic data generation using generative AI techniques, such as GANs and diffusion models, can play a pivotal role in augmenting real-world data. Addressing these priorities will not only accelerate the adoption of AI in healthcare but also ensure effective solutions for managing DFUs.
## 4 Feature Description
### Components and Interactions in [PERSON] et al. [27] Architecture  
In this subsection, components and interactions are summarized from [27].  
1. **Sensor data collection:** The ECU receives data from various sensors such as sound, pulse, gyroscope, and accelerometer sensors. These sensors are responsible for gathering different types of data related to vehicle movement, environment, and user well-being.
2. **Threshold comparison:** The received sensor data are compared against predefined thresholds that are set within the ECU. If any of the sensor values cross their respective thresholds, it indicates a potential event of interest, such as a crash or accident.
3. **Sending data to cloud and deep learning platform:** If a sensor value crosses its threshold, the ECU sends the relevant sensor data to the cloud. These data are then forwarded to a deep learning platform for further analysis and decision-making.
4. **Deep learning model analysis:** The deep learning model processes the sensor data to determine whether a crash or accident has occurred. If the model detects that no crash has taken place, no further action is taken and the ECU continues monitoring the sensor data.
5. **Alert generation for user interaction:** If the deep learning model detects a crash or accident, an alert is generated and is displayed on the head unit of the car. The alert likely asks the users if they are alright or not.
6. **User response handling:** If the user responds to the alert and confirms they are okay, the ECU resumes monitoring the sensor data without taking any further action.
7. **No user response (assumed crash):** If the user does not respond to the alert, the ECU assumes a crash has occurred and proceeds with further actions.
8. **Sending data to TCU:** The ECU sends the sensor data, images captured by the camera, and location information fetched from GPS to the TCU.
9. **Data transmission to cloud:** The TCU sends all the collected data (sensor data, images, location) to the cloud.
10. **Emergency services dispatch:** The cloud processes the received data and informs the appropriate emergency services based on the severity and type of accident. This could include paramedics, law enforcement, or fire personnel.
11. **User-generated alarm:** If the user generates an alarm, indicating that they need help, the same sequence of steps are followed as in the case of crash detection.  
In summary, as shown in Figure 1, this architecture involves the ECU continuously monitoring sensor data and making decisions based on the threshold comparison and the deep learning analysis. It ensures that appropriate actions are taken in response to potential crashes or accidents, and it leverages cloud and deep learning technologies to provide an efficient emergency response.  
Figure 1: Sequence diagram of [PERSON] et al. [27] architecture.
## 3 Results
### GC-MS Analysis of CsE and NmE  
A total of 117 compounds have been found in both CsE and _NmE_, 58 for each, and 1 (10-Octadecenoic acid, methyl ester) shared. According to biological activity, 48 compounds were supposed to have biological activity (37 bioactive, 11 reported previously as a constituent for a natural organism), and 69 have no bioactivity yet. _NmE_ has 9 compounds with anticancer activity and 6 compounds with antimicrobial activity; similarly, CsE contains 15 compounds, which have anticancer activity, 10 compounds with antimicrobial (bacteria, virus) activity, and 1 has antifungal activity (Table 1; Figures 2 and 3). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Compound Name** & **Molecular** & **RT** **(min)** & **MF** & **RMF** & **Prob (\%)** & **CAS** & **Lib** & **ID** & **MW** **(g/M)** & **Ext** & **Biological Activity** \\ \hline d-Mannose & C\({}_{6}\)H\({}_{12}\)O\({}_{6}\) & 15.298 & 705 & 777 & 22.3\% & 3458-28-4 & ML & 37454 & 180 & _NmE_ & Anticancer [57] \\ \hline Cyclohexane,1- & & & & & & & & & & & \\ ethyl-1-methyl-2-(1-methyl-enyl)-4-(1-methylethylidene)- & C\({}_{15}\)H\({}_{24}\) & 12.670 & 736 & 891 & 7.65\% & 3242-08-8 & RL & 17769 & 204 & _NmE_ & Anticancer [59] \\ \hline \(\gamma\)-Elemene & C\({}_{15}\)H\({}_{24}\) & 12.670 & 748 & 944 & 11.5\% & 29873-99-2 & ML, & 91836 & 204 & _NmE_ & Anticancer [60] \\ \hline \(\beta\)-Guaiene & C\({}_{15}\)H\({}_{24}\) & 15.956 & 701 & 763 & 6.59\% & 88-84-6 & ML & 133155 & 204 & _NmE_ & Anticancer [61] \\ \hline (-)-Spathulenol & C\({}_{15}\)H\({}_{24}\)O & 18.751 & 776 & 813 & 10.1\% & 77171-55-2 & ML & 5991 & 220 & _NmE_ & Antiproliferative [62] \\ \hline Isoaromadendrene & C\({}_{15}\)H\({}_{24}\)O & 18.751 & 780 & 841 & 11.9\% & N/A & ML & 2203 & 220 & _NmE_ & Anticancer [63] \\ \hline Undecanoic acid, & & & & & & & & & & & \\
11-bromo-, methyl ester & C\({}_{12}\)H\({}_{23}\)BrO\({}_{2}\) & 17.336 & 666 & 715 & 4.16\% & 6287-90-7 & RL & 9817 & 278 & _NmE_ & Anticancer [64] \\ \hline Desulphosinigrin & C\({}_{10}\)H\({}_{17}\)NO\({}_{6}\)S & 15.725 & 704, & 776 & 27.4\% & 5115-81-1 & ML & 28432 & 279 & _NmE_ & Anticancer [65] \\ \hline Androstan-17-one, & & & & & & & & & & & \\
3-ethyl-3-hydroxy-, & (5x)- & C\({}_{21}\)H\({}_{34}\)O\({}_{2}\) & 18.751 & 722 & 746 & 5.12\% & 57344-99-7 & ML & 55206 & 318 & _NmE_ & Anticancer [66] \\ \hline Propanoic acid, 2- & & & & & & & & & & & \\ methyl-(dodecahydro -6a-hydroxy-9a-methyl-3-methyl-2,9-dioxozaiene [4,5-b]furan-6-y)m & C\({}_{24}\)H\({}_{36}\)O\({}_{2}\) & 16.609 & 723 & 804 & 10.5\% & 40013-88-5 & ML & 88071 & 356 & _NmE_ & Anticancer [68] \\ \hline Mannofuranoside & C\({}_{21}\)H\({}_{36}\)O\({}_{6}\) & 15.956 & 693 & 745 & 4.70\% & N/A & ML & 31505 & 384 & _NmE_ & Anticancer [69] \\ \hline Docosahexaenoicenoic acid & C\({}_{25}\)H\({}_{40}\)O\({}_{2}\)Si & 15.500 & 645 & 658 & 8.94\% & N/A & ML & 37920 & 400 & _NmE_ & Anticancer [70] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bioactive compounds of GC–MS analysis of CsE and _NmE_, with the reported biological activities of each compound \({}^{*}\).
## 4 Experimental Results of the Proposed Systems
### Proposed Systems Evaluation Metrics  
The proposed systems in this study were evaluated based on two datasets for tuberculosis using the same evaluation scales for all systems. All the proposed systems generated the confusion matrix, which is the gold standard for evaluating the performance of the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Datasets** & \multicolumn{3}{c}{**First Dataset**} & \multicolumn{3}{c}{**Second Dataset**} \\ \hline \multirow{2}{*}{**Phase**} & \multicolumn{2}{c}{**Training and**} & \multicolumn{3}{c}{**Training and**} \\  & **Validation** & & **Validation** & \\ \hline \multirow{2}{*}{**Classes**} & **Training (80\%)** & **Validation (20\%)** & **Testing 20\%** & **Training (80\%)** & **Validation (20\%)** & **Testing 20\%** \\ \hline Normal & 209 & 52 & 65 & 2240 & 560 & 700 \\ Tuberculosis & 215 & 54 & 67 & 448 & 112 & 140 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Splitting the two X-ray datasets for TB.  
Figure 5: The basic structure of the second proposal system for diagnosing tuberculosis based on the two datasets.  
systems. The confusion matrix contains all of the appropriate dataset samples, denoted as TP and TN, and inappropriately sample, denoted as FP and FN. The performance of the systems was evaluated by means of accuracy, sensitivity, precision, specificity, and AUC, as in Equations (5)-(9). Therefore, the standard equations used to measure the performance of the systems obtain their data through the confusion matrix [48].  
\[\text{Accuracy}=\frac{\text{TN}+\text{TP}}{\text{TN}+\text{TP}+\text{FN}+ \text{FP}}\;*100\% \tag{5}\]  
\[\text{Sensitivity}=\frac{\text{TP}}{\text{TP}+\text{FN}}\;*100\% \tag{6}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\;*100\% \tag{7}\]  
\[\text{Specificity}=\frac{\text{TN}}{\text{TN}+\text{FP}}\;*100 \tag{8}\]  
\[\text{AUC}\;=\frac{\text{True Positive Rate}}{\text{False Positive Rate}}=\frac{\text{Sensitivity}}{\text{Specificity}} \tag{9}\]  
where TP refers to X-ray images perfectly categorized as tuberculosis, TN refers to X-ray images perfectly categorized as normal, FP refers to normal X-ray images categorized as tuberculosis, and FN refers to tuberculosis X-ray images categorized as normal.
## 4 Numerical Results
### Algorithm Performance and Robustness Analysis
#### 4.2.3 Detailed Performance Metrics Comparison
This implies that it can perform task offloading and resource allocation more effectively, thus minimizing the end-to-end task processing time. The delays of MADDPG and MAAC are higher than MAPPO but lower than the Greedy and Random policies. The Greedy and Random policies have the highest total delays. This further confirms the superior performance of MAPPO in optimizing task processing delay. Energy Consumption: Figure 11 compares the energy consumption (mJ) of different strategies as the offloaded computation (Mcycles) changes. The total energy consumption of the system generally increases with the increase in offloaded computation. This is because processing more computation tasks requires more energy. The MAPPO algorithm typically exhibits the lowest energy consumption across different levels of offloaded computation, although it is close to MADDPG at lower offload amounts. This indicates that while optimizing task offloading decisions, MAPPO can also effectively control the computation and flight energy consumption of the UAVs. Lower energy consumption is an important component for improving overall system efficiency, which is also consistent with MAPPO achieving the highest cumulative reward. Figure 10: Total delay (ms) vs. computing power (GHz). Figure 9: Cache hit rate vs. number of vehicles (N).
# Securing Smart Grid Enabled Home Area Networks with Retro-Reflective Visible Light Communication
## 3 Numerical and Experimental Results
### Working Range and Sniffing Range  
We define the working range as the area within which the uplink BER is lower than 1%. We measure the working range in a lab environment, and show the results in Figure 9. The incidence angle and radiance angle are kept at the same value. The working range is delineated by the closed blue line. With an upright orientation of the Retro-VLC tag, the maximum working distance is 3 m. With the VLC AP perpendicular to the Retro-VLC tag plane, the field of view (FOV) is around 50\({}^{\circ}\). The FOV can be improved by adding a diffusing layer on top of the VLC AP.  
One key feature of Retro-VLC uplink when compared with radio-based wireless communication technologies is directional. In order to intercept the uplink Retro-VLC signal, the working locations (sniffing range) of an eavesdropping device are very limited [9]. In our evaluation, we place a VLC AP and a Retro-VLC tag 3 m apart from each other. The Retro-VLC tag is held upright and faces the VLC AP directly. We use the embedded PD as  
Figure 8: Retro-VLC uplink data rate vs key exchange latency showing the experimental results in blue and the curve fit used to extrapolate the expected time for other rates.  
the sniffer and measure the range in which the sniffer can decode the uplink transmission with less than 1% BER. The area is plotted in Figure 10. We can observe that the shape of the possible sniffing area is a narrow region between the AP and the tag, plus a small region around the AP. The confined sniffing range protects the uplink transmission from typical cyber attacks, such as man-in-the-middle attack.
## 1 Introduction  
Generation of short-wavelength (less than the laser wavelength) dense relativistic electron bunches is of high interest for study and diagnostics of superfast physical processes. Such bunches make possible the direct electron microscopy, as well as the generation of short (atto-second range) pulses of soft X-ray and further X-ray microscopy. Short, harder X-ray pulses can be also generated during the scattering of additional laser pulses by the electron bunches. Generation of thin electron layers is possible using different methods, such as in a laser gas target, whereby the oscillations of nonlinear electron density occur leading to the generation of a sequence of thin electron layers [1,2]. Thin electron layers can be formed during the scattering of linear p-polarized intense laser pulse by a solid target [3,4]. Finally, the fast particle bunches can be generated during the interaction of intense laser pulse with nonuniform targets out of the target's rear [5]; however, their width is much thicker and in the order of 100 nm. Compared to such alternatives, the usage of an ultra-thin grafene target [6, 7, 8], irradiated by circular-polarized pulse, has many advantages due to the fact that only one electron bunch is generated; the bunch charge achieves high magnitude (\(>\)1 nC) and the parameters (energy, width and particles number density) can be easily controlled by changing the laser intensity, pulse duration and layer width.  
In the present work, the possibility of generation of thin dense relativistic electron layers is shown using the analytical and numerical modeling of the interaction of laser pulses with ultra-thin solid foils. The first ultrathin foil is semitransparent for laser radiation, but its electrons are removed by laser pulse and propagate together with them. The electric field of target ion core is much weaker than that of the laser one, thus movement of an electron bunch separated from the target can be described by the used self-consistent system of Maxwell equations and the electron equation of motion. The maximum electron energy can be gained by optimal tuning between the target width, intensity and laser pulse duration. The optimal parameters can be determined from our self-consistent system of Maxwell equations and the equation of motion of electron layer. For thin relativistic electron layers, the gaining of maximum electron energies requires an additional overdense plasma layer, which cuts the laser radiation off the plasma screen at the instant of gaining the maximum energy (Double Relativistic Electron Accelerating Mirror (DREAM) schema). The scattering of counter-propagating probe laser pulse by the generated relativistic electrons mirror makes possible the generation of the hard coherent electromagnetic radiation with quant energy of 1 keV and efficiency of 0.1% with respect to the energy of initial laser pulse generating the electron layer.
# Blockchain-Driven Intelligent Scheme for IoT-Based Public Safety System beyond 5G Networks
## 3 Role of Blockchain in 6G Services
### Data Sharing  
The 6G-based smart applications such as UAV communication, aerial base stations, satellite communication, V2V communication, etc., offer real-time data-sharing features over the Internet through a mobile communication network. Moreover, an increasing number of social media applications, such as Twitter, Facebook, Instagram, etc., and web conferencing applications, such as Webex, Google Meet, Zoom, etc., require content delivery in real-time. These applications generate a colossal amount of audio, video, text, and image data. To control and share a large scale of data requires an efficient data-sharing scheme that overcomes data leakage and security threats. Decentralized blockchain technology offers traceability, security, privacy, tamper-resistance, transparency, and immutability to handle data-sharing issues in the 6G network. [PERSON] et al., in [51], aimed to offer a secure and reliable data-exchange scheme using a private blockchain, i.e., Hyperledger Fabric for vehicular networks. Additionally, they developed a deep-learning-based autoencoder segmentation approach that gathers the vehicles based on their similarity. The proposed data-sharing scheme achieved better throughput, CPU utilization, efficiency, applicability, computational overhead, and time complexity.  
Further, [PERSON] et al. [52] combined permissioned blockchain with federated learning (FL) and proposed a channel-based distributed data-sharing scheme for data sharing and preserving the privacy of the user. Moreover, they proposed a data storage scheme using on- and off-chain data storage for authenticating identity, data, and transactions. The adopted scheme enhanced data sharing and security, improved data model quality, and prevented malicious attacks. Then, in [53], the authors proposed a concrete access control mechanism for vehicular communication (vehicular ad hoc network (VANET)). For that, they integrated blockchain with ciphertext-enabled attribute encryption (CP-ABE) for user and data storage identity management. Further, they enhanced the security mechanism and designed the advanced attributed algorithm that minimizes the lightweight device's computational pressure in VANET. The findings show that the proposed scheme provides effective data security with low performance overhead.
## 4 Materials and Methods
### Codon Usage Pattern and Repeat Analysis  
The GC content was determined using Geneious software v2025.0 (Biomatters, Auckland, New Zealand) [85], and codon usage, including codon numbers and RSCU, was analyzed using MEGA11 [86]. Euclidean distances were calculated based on codon usage frequencies within each gene of the six _Phylotaloca_ species: _P. acinosa_, _P. insularis_, _P. polyandra_,P. japonica, P. lattenia, and P. americana. Average linkage clustering was performed to group genes with similar codon usage patterns. The results were visualized using Heatmapper v1.4 [87].  
Potential simple sequence repeats (SSRs) were identified using MISA v2.1 [88] with the following minimum repeat counts: 10 mononucleotides, 6 dinucleotides, 5 tri-nucleotides, 4 tetra-nucleotides, and 3 both penta- and hexa-nucleotides. Tandem repeats were identified using the Tandem Repeats Finder v4.10.0 [89], with the minimum alignment score set to 50 and the maximum period size set to 500. REPuter v2.0 [90] was used for forward, reverse, palindromic, and complementary repeats.