esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Work** & **Main Techniques/Methods** & **Contributions \& Features** & **Deficiencies or Areas for Improvement** \\ \hline Adversarial samples & Theoretical basis & Reveal the vulnerability of machine learning models and provide new ideas for steganography technology & It is highly theoretical, but its direct application to steganography requires further exploration \\ Deepfool method & Adversarial sample generation & Explicitly generate adversarial samples that can deceive DNNs, and provide new adversarial strategies for steganography technology & Counter the inadequacy of sample generation techniques \\ C\&W method & Adversarial sample generation and evaluation & Improve the efficiency of adversarial sample generation, evaluate DNN robustness, and provide a comprehensive tool for steganography technology & Counter the inadequacy of sample generation techniques \\ ASDL-GAN & GAN, Unsupervised learning & Generate photorealistic steganography images to improve imperceptibility The steganography analysis network is introduced to enhance the authenticity and resistance of steganography analysis of densely loaded images & There may be slight differences in statistical characteristics \\ SGAN & GAN Steganography Analysis Network & Highly concealed, low distortion, more visually natural GANs are used to generate realistic steganography images to improve concealment & Deficiencies at the semantic level may cause concern \\ GNN-based model & GNN, multi-level embedding & Highly concealed, low distortion, more visually natural GANs are used to generate realistic steganography images to improve concealment & The complexity is high, and the implementation is difficult \\ The GAN method & GAN, multi-scale generation & Generate steganography images of different scales to enrich steganography application scenarios & General insufficient GAN method. \\ ISGAN & Color channel strategy & Hide secret information in the Y channel to avoid color distortion & Optimization problem for specific scales. \\ Feature embedding based on deep learning & Deep learning, feature extraction & Take advantage of the model’s dependence on features to improve concealment & It has a strong dependence on a specific model, and the generalization ability needs to be verified \\ [PERSON] et al.’s semantic information steganography approach [35] & Deep learning, semantic information detection & Detect the semantic information embedded in the image, which provides a new means for the security evaluation of steganography technology & Limitations of detection methods of steganography technology \\ [PERSON] et al. [36] & Exploits & Design a hiding method for evasion or attack detection mechanism to improve the steganography effect & It is necessary to have a deep understanding and continuous tracking of the development of machine learning models \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evolutionary framework of image steganography strategies and comparative analysis of algorithms.
## 3 Results and Discussions  
A bidirectional multi-wavelength visible light communication link based on micro-projection enabled OTs at a transmission distance over 0.65 m were initially established to evaluate the system performance in our proof of concept experiments. Although the experimental works were conducted based on a half-duplex communication scenario, the proposed communication approach based on micro-projection enabled OTs can be applied to a full-duplex communication scenario through an appropriate design of LCoS-based SLM since each OT has identical physical components. For the proposed communication system architecture to be compatible with the data modulation format of future possible wireless communication system, several baseband modulation schemes were initially used in our experimental research works to evaluate the proposed system in terms of the highest possible data transmission rate at a BER (Bit Error Rate) of less than \(10^{-3}\). Since several multicarrier modulation schemes, i.e., OFDM and DMT which have been widely used to enhance the bandwidth efficiency of optical wireless communications are all based on the employment of the baseband modulation schemes, i.e., M-ary PSK (Phase Shift Keying) and M-ary QAM (Quadrature Amplitude Modulation) [16], in our experimental research works, NRZ_OOK (Non-Return-to-Zero On-Off-Keying), M-ary PSK and M-ary QAM were therefore used to compare the data transmission performance. The measured eye diagrams and constellation diagrams from the oscilloscope and VSA software respectively were used to estimate and calculate the BERs of the system in the performance evaluation of these baseband modulation schemes used in our experiments. The experimental results in terms of crosstalk and data transmission performance are reported and discussed as follows:
# Flash Flood Early Warning System in Colima, Mexico
## Appendix A  
### Embedded RiverCore Software Process  
1.--Setup:  
1.1.--Configure microcontroller's USB serial port baud rate.  
1.2.--Configure USART 1 baud rate matching the SIM5320A connection.  
1.3.--Configure USART 2 baud rate matching the ToughSonic Remote sensor connection.  
1.4.--Configure USART 2 baud rate matching the XBee or LoRa radio connection.  
1.5.--Configure Teros10 Sensor (analogue).  
1.6.--Configure SIM5320A initialization routine and PIN.  
2.--Tasks:  
2.1.--Initiate routing (30 s).  
2.2.--Start the SIM5320A configuration routine, if it has already been started but not configured (70 s).  
2.3.--Begin the raw data gathering routine from the Teros10 sensor (30 s).  
2.4.--Search for drifters (sniffer) routine, it sends a broadcast message on a specific channel and PAN ID. A packet or frame transmit request 16-bit address (RCFX) is sent.  
2.5.--Publish information of both sensors through the MQTT telemetry protocol to the server.  
2.6.--Publish if a specific drifter has been detected.  
2.7.--Restart the routine if a call or an error message has been detected.  
3.--Serial interruptions:  
3.1.--Serial 1--SIM5320A contains the configuration routine (start, setup, restart).  
3.2.--Serial 2--Obtains distance from the water level sensor.  
3.3.--Serial 3--Collects XBee 802.15.4 information. It stores and obtains information and verifies the \"RCFX\" frame response (which means a drifter has responded).  
3.4.--Clear buffer.  
_Appendix A.2 Embedded Drifter Software Process_  
**1.--Setup:**  
1.1.--Configure microcontroller's Baud rate, matching with the XBee radio.  
1.2.--Configure GPS (I2C--protocol).  
1.3.--Configure Micro SD card reader (SPI-protocol).  
**2.--Task (If there is a GPS signal):**  
2.1.--Obtain a GPS location.  
2.2.--Update date and time of the microcontroller.  
2.3.--Store information (date, time, altitude, latitude, longitude and speed) in JSON format into the MicroSD.  
**3.--Serial interruptions:**  
3.1.--Obtain data from XBee 802.15.4 to then store and verify that it is a frame whose origin is RiverCore or RiverDrone.  
3.2.--Respond with a frame transmit request 16-bit address (RCFX) if the frame comes from RiverCore.  
3.3.--Respond with a frame transmit request 16-bit address (RCDN: altitude, latitude, longitude, speed, and RSSI) if the frame comes from RiverDrone.  
3.4.--Clean reception buffer.  
3.5.--Clean transmission buffer.  
_Appendix A.3 Embedded Sniffer Software Process_  
**1.--Setup:**  
1.1.--Configure microcontroller's USB serial port baud rate.  
1.2.--Configure USART 1 baud rate matching the SIM5320A connection.  
1.3.--Configure USART 3 baud rate matching the XBee 802.15.4 radio connection.  
1.4.--Configure SIM5320A initialization routine and PIN.  
**2.--Tasks:**  
2.1.--Initiate routine (30 s).  
2.2.--Start the SIM5320A configuration routine, if it has already been started but not configured (70 s).  
2.3.--End a broadcast on a specific channel and PAN ID. A packet or frame transmit request 16-bit address (RCDN, 20 s) is sent.
## 3 Relation to vf-OS  
The component presented in this article is part of the vf-OS project [41], which aims to provide an open platform for virtual factories (VFs) and a collaborative environment to support the manufacturing processes at various stages. The project is closely related to the concepts of Industry 4.0, including CPS and Internet of Things (IoT), whereas other relevant subjects are covered such as data processing, data visualization, and cloud computing. The key vf-OS components are: system kernel, virtual factory i/o, data and connect, open applications development kit, service provision framework, and platform environment. The solution proposed in this work belongs to the data management component (DMC) (Figure 1), which aims to manage data flows on a large scale. Another goal is to provide analytical operators for data analysis. DMC is composed of four modules: data infrastructure middleware, data harmonization, data storage, and data analytics [42].  
More specifically, this work is focused on data harmonization and partially on the data storage modules. The data harmonization module is responsible for implementing data mapping mechanisms extended with ontology import interfaces and data visualization capabilities. It is divided into design time and run time submodules. The design time submodule maps the data models, whereas run time is responsible for data transformation. The data storage issue is addressed only partially, as vf-OS requires different types of storage for different types of data, thus using the advantages of various storage approaches in the best way [42]. For the storage part of this component, a Not only SQL (NoSQL) type of database, namely a graph DB, has been chosen. The chosen NoSQL DB, which is Neo4j, is able to cope with large amounts of data and provides agile querying mechanisms, allowing its use in smart storage, which not only \"keeps\" or stores data but also provides tools to perform reasoning operations on the stored data.
# The Influence of the Quality of Digital Elevation Data on the Modelling of Terrain Vehicle Movement
## 2 Materials and Methods
### Selected Digital Elevation Models  
The digital terrain model (DTM) approximates the terrain surface or part of it by using a system of points in 2D space with specified height values. These points are predominantly organised into a regular grid or, in more detailed models, a triangular irregular network (TIN) [41]. Both these variants were included in the study. DEMs are the most important basis for evaluating the terrain relief. It varies according to spatial accuracy, where the height accuracy is lower than the position accuracy. In comparison with other types of data (e.g., vegetation, roads), the terrain does not change much in terms of extent and speed of changes, and thus, can be considered the most reliable. An overview of the elevation models considered in this study is in Table 1.
## 2 Related Work
By introducing snapshot graphs to depict the time-varying attributes of resources, along with virtual links and node transformations to manage resource constraints, simulation results have validated the efficiency and effectiveness of the ETEG method. In the realm of energy consumption minimization, [PERSON] et al. [27] proposes a new model for satellite formation control, highlighting the practicality and effectiveness of graph theory in addressing energy optimization problems in space missions. Their study provides valuable insights for future satellite network design. [PERSON] et al. [28] explores the use of time-expanded graph models to tackle the challenge of energy-efficient delay-constrained multicast in satellite networks. By establishing a time-expanded graph theory model, they optimize energy consumption during data transmission while ensuring compliance with transmission delay constraints. This work not only improved the energy efficiency of satellite networks during large-scale data transmission but also offered an innovative perspective on utilizing graph theory methods to solve problems with real-time and energy dual constraints. With the emergence of edge computing, [PERSON] et al. [29] has applied it to multi-satellite task scheduling, enabling better adaptation to specific scenarios and optimizing resource utilization and repeatability. [PERSON] et al. [30] proposes a high dynamic cluster task scheduling algorithm for satellite edge computing, which solves the problems of traditional satellite task processing, such as too long data transmission time and time-sensitive tasks can not respond in time, and improves the utilization efficiency of satellite cluster meter resources and task processing speed. Despite these innovations, challenges persist in addressing the complex dynamics, temporal variations, and multi-resource coordination of satellite information networks. Given the time-varying adaptability of spatial information network environment, a multi-star task scheduling algorithm that can better adapt to this characteristic is needed. Considering the constraints of airborne resource environment, scheduling complexity and computing time, dynamic real-time problems, and scalability of spatial information network environment, the task is optimized under multiple constraints. A strategy based on multi-task aggregate graph model is proposed, which is further explored by combining the principle of minimum energy consumption in graph theory.
# Pilot-Based Adaptive Channel Estimation for Underwater Spatial Modulation Technologies
## 6 Simulation Results and Discussion
### Generalized Spatial Modulation  
Figure 5 illustrates the BER performance of GSM for P-CSI and estimated channel using the RLS estimation method for different numbers of pilots by ignoring the Doppler-scaling factor. The number of pilots is assumed to be 2%, 5%, 10%, and 15% for time-varying UWA-MIMO channel estimation. The BER performance gain using 2% of pilots using RLS channel estimator is too low with a difference of 9 dB at BER \(10^{-2}\). To attain nearly the same performance as P-CSI, a greater number of pilots are used for the RLS channel estimation method. In comparison to the P-CSI at the target of \(10^{-2}\), BER the SNR gain difference is 1.75 dB, 0.28 dB, and 0.15 dB for 5%, 10%, and 15% pilots using RLS channel estimator, respectively. Figure 5 clarifies that the SNR gain between P-CSI and RSL channel estimation using 10% and 15% pilots is almost negligible.  
The BER performance of RLS channel estimation method using pilots compared with a P-CSI for GSM over time-varying UWA-MIMO channel with Doppler-scaling factor is demonstrated in Figure 6.  
Figure 4: BER performance of the 4 \(\times\) 4 underwater SM-MIMO with Doppler-scaling factor.  
Figure 5: BER performance of the 4 \(\times\) 4 underwater GSM-MIMO without Doppler-scaling factor.  
At BER \(10^{-2}\) the SNR difference gain is 1.78 dB for 5% of pilots, 0.31 dB using pilots 10%, and 0.21 dB for 15% as compared to P-CSI. The SNR gain difference becomes very small with P-CSI while pilots are increased from 5% to 10% and 15% in presence of Doppler effect. However, it can be seen from Figure 6 that there is huge performance gap between RLS channel estimator using 2% of pilots and P-CSI.
# Impact of Carpets on Indoor Air Quality
## 3 Effects of Carpets on IAQ
#### 3.1.3 VOC Transformation Reactions with Carpets
For example, nylon showed to be the least effective at removing ozone from indoor air, while the wool carpet showed the highest percentage of ozone removal and ozone deposition velocity because of the reaction between ozone and functional groups of wool fibre carpet. Additionally, wool carpets showed the lowest molar yield in the formation of secondary carbonyls [54]. It was observed that carpets generally can emit substantial levels of VOCs (secondary emissions) in the presence of ozone [54]. Therefore, to consider the relative IAQ merits, the material composition of carpets for flooring can be selected based on the amount of indoor ozone present. In the case of high concentrations of ozone indoors, the air quality showed to increase when applying wool carpet on the floor. In contrast, nylon showed to be a good option as flooring material in indoor environments that do not have high levels of ambient ozone, while the worst option for flooring material was polyester carpet because of the high emissions of formaldehyde in both indoor environments with and without ozone [54]. Furthermore, secondary emissions from a new carpet may diminish as carpet ages, and ozone removal capacity of a carpet decreases with age because of losing its ability to react with ozone [65; 71].
# Data Security-Based Routing in MANETs Using Key Management Mechanism
## 2 Proposed Model  
This section discusses the proposed trust-management system. The following sections outline the trust-management processes. The proposed method initially establishes a network and initializes the source node. The suggested approach then collects data from the subsequent log reports to see the packets' positive or negative rates between the nodes. The AODV protocol is used as a base protocol.
## 5 Conclusions  
The conducted studies demonstrated the significant potential of DSC/TG for the analysis of multi-compartment, lipid-based formulations such as SLM, especially in the form of a fine powder, but also in the form of an aqueous dispersion, under the condition that the water has been previously evaporated at room temperature.  
A recurring feature of DSC studies of lipid microspheres is the lack of thermal transformations characteristic of drug substances incorporated in SLM. The presence of API and its impact on SLM properties in thermal studies can be inferred indirectly, based on a decrease in the peak temperature associated with lipid melting (DSC) or a subtle change in the decomposition temperature (TG).  
The DSC/TG studies confirmed the lack of effect of the spray drying process on the drug-loaded SLM with Compritol. SLM studies with other lipids forming the microspheresmatrix, especially stearic acid, certainly require continuation. In our study, thermal analysis was successfully applied not only to assess the properties but also the stability of the lipids forming the microsphere matrix.  
Based on the obtained results, we concluded that DSC/TG analysis techniques can be a complementary and useful tool for the evaluation of specific properties of SLM, both in the form of powder and lipid microparticle dispersions (prepared for analysis by the evaporation of water), after production, under further processing (e.g., the spray drying of an aqueous dispersion) or during storage.  
The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app132413282/s1](https://www.mdpi.com/article/10.3390/app132413282/s1)]([https://www.mdpi.com/article/10.3390/app132413282/s1](https://www.mdpi.com/article/10.3390/app132413282/s1)). Figure S1. DSC and TG curves of SLM dispersions: (A) without water evaporation, (B) after water evaporation and (C) after centrifugation and water evaporation; before thermal analysis. Figure S2. DSC and TG curves of SLM dispersions: (A) F11-SLM dispersion with 1% of Ind and (B) F11-SLM dispersion with 1% of Ind after shaking with methanol evaporation. Figure S3. DSC and TG curves of F10-SLM dispersion with Compritol and 0.2% of Ind: (A) after preparation and (B) after one year of refrigerated storage. Figure S4. DSC and TG curves of F5-SLM dispersion with Compritol and 2% of CSA: (A) after preparation and (B) after one year of refrigerated storage. Figure S5. DSC and TG curves of F4-SLM dispersion with Compritol and 1% of CSA: (A) in the form of aqueous dispersion after two years of refrigerated storage and (B) in the form of spray-dried powder with PVP after two years storage at room temperature.  
Conceptualization, E.W.; methodology, E.W.; investigation, E.W. and G.R.J.; project administration, E.W.; resources, G.R.J.; writing, E.W.; writing--review and visualization, E.W.; supervision, G.R.J. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available in the article and Supplementary Material.  
The authors declare no conflict of interest.
## 2 Related Work
[27] proposed DarkCovidNet, a deep learning model based on the DarkNet model that works as a binary classifier and multi-class classifier to classify CXR images into COVID or no findings, and COVID, pneumonia, or no findings. [PERSON] et al. [28] proposed a multi-class classification model that uses VGG-16 to classify CXR images into COVID-19, pneumonia, or healthy. To effectively train a deep network for detecting COVID-19 with a limited dataset, [PERSON] et al. [29] introduced a patch-based convolutional neural network algorithm consisting of a segmentation network and a classification network. The model extracts the lung area from the CXR image, and different patches of the extracted area are classified with a classification network that consists of a network of ResNet. [PERSON] et al., in their benchmarking paper [30], stated that the large number of machine learning models proposed for detecting COVID-19 from CXR images complicates the task for health organizations in selecting an appropriate model. Therefore, they proposed a benchmarking methodology using a multi-criteria decision-making (MCDM) model that evaluated 12 machine learning classification algorithms on ten time-consuming and accuracy parameters, e.g., precision and recall. To conduct the experiment, they constructed a dataset of 50 CXR images, including 25 COVID-19 CXR images, and the InceptionV3 model was used as a feature extractor. [PERSON] et al. in [31] evaluated the performance of a custom CNN and eight different pre-trained CNN-based models, e.g., VGG-16 and Inception-V3, in detecting COVID-19 from CXR images. To reduce model complexity, and improve robustness and generalization, they used modality-specific transfer learning, iterative pruning, and ensemble strategies. [PERSON] et al. [6] introduced an ACGAN-based model for generating CXR images of COVID-19. They compared the performance of a VGG16 model trained on the original dataset to another VGG16 model trained on a dataset augmented by the ACGAN model. Based on CT scan images, [PERSON] et al. [32] applied CGANs to differentiate COVID-19 and non-COVID-19 images. In this study, we propose using CGANs to generate COVID-19 CXR images and then using them along with the original images to detect COVID-19 cases. Although [PERSON] et al. [32] applied a CGAN, they used it with CT images, not CXR images. [PERSON] et al. [7] applied a GAN to CXR images to detect respiratory diseases from the images. The original dataset contained 306 CXR images in four classes, 97 of which were COVID-19 CXR images. The best-performing model was GoogleNet, which achieved 100% accuracy in the discrimination of normal and COVID-19 CXR images compared to 52.8% before using the GAN. Based on 16 classes, [PERSON] [8] empirically evaluated the performance of several deep-learning-based models that detect and classify different respiratory diseases, including COVID-19, from CXR images. The results showed that the best-performing model was ResNet, which achieved 85 to 87% validation accuracy.