esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 4 Discussion
Meaningful image encryption algorithms have the advantage of keeping the embedded cipher image secure, even if it was extracted via steganalysis tools. Since the extracted data are already encrypted and are noise-like images, this adds another layer of security by preventing attackers from directly obtaining the secret image through steganalysis alone. However, these algorithms come with the drawback of high computational requirements due to the implementation of two layers of security. The applications of image encryption are versatile, making it challenging to assign a specific technique to a particular use case. Image encryption techniques are relevant wherever image data protection and security are required. They are instrumental in personal communication, healthcare, finance, surveillance, law enforcement, and military applications. Nevertheless, certain image encryption techniques may excel for specific domains. Chaotic-based encryption algorithms are suitable for resource-constrained platforms, like the Internet of Things (IoT) and edge devices [62, 67], due to their fast computational speed and minimal complexity. DNA-based algorithms can be utilized in encrypting and securely archiving data for extended periods, due to their minimal storage requirements. They also hold potential in espionage applications, as DNA sequences can be discretielly embedded in various forms, that is, physical objects, and require specialized equipment for retrieval. Compressive sensing-based algorithms can be particularly useful in scenarios where there are constraints on data transmission bandwidth. For example, compressive sensing is well-suited for applications involving embedded cameras and drones, or any devices with limited data transfer capacity. Meaningful image encryption adds an additional layer of security through steganography, making it particularly suitable for covert communication. Even though image encryption researchers utilize a comprehensive set of security, quality, and efficiency metrics to evaluate image encryption algorithms, there is a pressing need to establish rigorous security analysis and standardized evaluation metrics. Standardizing the evaluation metrics will not only better assess the resilience of image encryption methods, but will also guide researchers in testing their proposed algorithms. Researchers also need to consider the robustness of the image encryption algorithms against novel attacks, such as adversarial machine learning and quantum computing.
# Wave Propagation in Periodic Metallic Structures with Equilateral Triangular Holes
## 4 Conclusions  
In this paper we have applied a mode-matching formulation to analyze the wave propagation in periodic metallic structures with equilateral triangular holes. We have studied the influence of the geometrical parameters of the unit cell in both mirror- and glide-symmetric configurations. The outcomes of this study have confirmed some the advantages of glide symmetry in relation to the mirror-symmetric structure; namely, reduced frequency dispersion, wider bandwidth, higher equivalent refractive index, and wider stopband. Interestingly, we have also noticed unexpected isotropic behavior from both mirror- and glide-symmetric triangular holey configurations. Since this behavior appears in both configurations, we associated the isotropic behavior to the triangular geometry rather than to the symmetry of the unit cell. A physical insight on the matter has shown that the cause of that isotropic behavior is two-fold. First, the reduced electrical size (high cutoff frequency) of the triangular hole compared to other canonical geometries such as the square or circle. Second, the particular distribution of the most significant modes in the modal basis at the lowest frequencies, which makes possible the excitation of the same total field in the hole independently of the propagation direction.  
Conceptualization, A.A.-A., G.V., F.M., O.Q.-T.; methodology and formal analysis, F.G., G.V., A.A.-A.; programming and software, A.A.-A.; resources, J.M.F.-G., P.P., O.Q.-T.; writing-original draft, A.A.-A., G.V.; supervision, P.P., J.M.F.-G., O.Q.-T.; funding acquisition, P.P., J.M.F.-G., O.Q.-T. All the authors review and edit the document. All authors have read and agreed to the published version of the manuscript.  
This work was partially funded by the Spanish Ministerio de Ciencia Innovacion y Universidades under the project TIN2016-75097-P, and with European Union FEDER funds under projects TEC2017-84724-P and TEC2017-85529-C3-1-R, by the French National Research Agency Grant Number ANR-16-CE24-0030, by the Vinnova project High-5 (2018-01522) under the Strategic Programme on Smart Electronic Systems, and by the Stiftelsen Aforsk project H-Materials (18-302). The authors declare no conflict of interest.
## 6 Simulation and Measurement Comparisons
Figure 23: The simulated received-power values corresponding to times where the aircraft is not detected in measurement (blue), and where it is detected in measurement (orange). A high simulated received-power value does not necessarily guarantee detection (although it makes it slightly more probable), but a low simulated received-power value does guarantee that the aircraft will not be detected. Figure 22: Comparisons between the simulated and measured received-power values (Equation (7)) over the measurement period. Since the absolute measured received-power value is not known, an offset has been applied to the measured data to minimize the mean difference between the simulated and measured power values in all cases where measurement data exists. The values of these offsets are shown in the figure legends and are slightly different in the MoM and SBR+ cases.
## 4 Experimental Comparison and Analysis
### Effect of Differential Evolution Based Mutation  
In this paper, mutation strategy DE/best/2 is used to adjust bandwidth to explore the landscape of the corresponding sub-stages. Therefore, \(\left(\left(x_{i}^{7_{1}}-x_{i}^{7_{2}}\right)+\left(x_{i}^{7_{3}}-x_{i}^{7_{4}} \right)\right)\), regarded as an Experience Operator (EO), is used to indicate the possible maximum searching neighborhood with the increase of generations. It can be adjusted adaptively with the change from population diverse to converging. The same analyzing functions, f1, f10, f21, f28, as detailed above, are chosen to illustrate the performance of the aHSDE algorithm with the increase of generations. Their changing trends of Eos are shown in Figure 2.  
Figure 1: Impact of Harmony Memory Size (HMS) with 30 runs on the aHSDE (f1, f10, f21, f28).  
Figure 2: Change of Experience Operator on aHSDE (f1, f10, f21, f28).  
It can be seen from Figure 2 that EO is gradually convergent with the increase of generations. In the early stage of iteration, the search region of the algorithm is relatively large and EO is relatively large accordingly, which strengthens the global exploration ability. However, with the gradual convergence of HMS, the firing search of the algorithm is gradually conducted to improve the local exploitation at the latter generations. It is possible to provide a promising escape mechanism from the landscape valley with the differential mutation strategy to adjust the pitch in the aHSDE. Therefore, the aHSDE precedes over the original HS and several HS variants, which exploits the valley using a small step size.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 4 Experimental Results and Evaluation  
### Experimental Setup  
The Python 3.6 and the TensorFlow framework were employed on a Linux Ubuntu 16.04 operating system to implement the proposed CEM model. A workstation equipped with a 12 GB NVIDIA Graphics card was used to perform the comparison experiments.  
SegNet [48] and RefineNet [51] are state-of-the-art classic pixel-by-pixel semantic image segmentation models for camera images. As the working principles of SegNet and RefineNet are similar to that of the CEM model, we employed these as comparison models to better reflect the superiority of feature extraction and classification of our model.  
Since the main crop in Feicheng County during winter is winter wheat, we selected the winter wheat area as the target for experiments.  
Total 375 sub-images were selected from the 537 images described in Section 2.3 to compose the training data set, 106 as validation data set, and the remaining 56 as test data set. The training data set, validation set, and test set include all land use types, respectively. Every image in the training data set was processed with color adjustment, horizontal flip, and vertical flip amongst others, to increase the number and diversity of the samples. The color adjustment factors included brightness, saturation, hue, and contrast. After this preprocessing, the final training dataset comprised 4125 images. Table 3 shows the number of samples used in the experiment.
## 5 Performance Comparison
### Comparison to Traditional ML Algorithms
Our proposed DL classifier model achieves an average accuracy of 95.44% for the identification of a wireless technology that falls in the range of SNR 10 dB to 20 dB across all the channel and propagation effects. The average accuracy of the wireless identification in a strong noise environment considering the propagation effect scenarios' effect is 82.55% for SNR range between 0 dB and 5 dB. The overall average accuracy is 91%, calculated across all the SNR values and channel scenarios in the five unlicensed radio signals (across all datasets). Figure 9 depicts the results of the accuracy. Based on our results, we conclude that the traditional supervised ML algorithms such as SVM, KNN, and RF did not perform well for protocol identification to classify unlicensed radio signals' protocols such as IEEE 802.11a, IEEE 802.11n, IEEE 802.11 ac, IEEE 802.11 ax, and unlicensed LTE. Similar results are reported for SVM, KNN, and RF in [47], but it was not including the study of the propagation models in harsh environments.
# Enhancing Machine Learning Models Through PCA, SMOTE-ENN, and Stochastic Weighted Averaging
## 2 Materials and Methods
### Datasets  
The dataset used in this study is the well-known Titanic dataset, sourced from Kaggle ([[https://www.kaggle.com/competitions/titanic/data](https://www.kaggle.com/competitions/titanic/data)]([https://www.kaggle.com/competitions/titanic/data](https://www.kaggle.com/competitions/titanic/data)), accessed on 5 September 2024) (Kaggle, 2024) (Kaggle, 2024). The training set contains 891 records, while the test set includes 418 records. Key features include passenger class (Pclass), gender, age, number of siblings or spouses on board (SibSp), number of parents or children on board (Parch), ticket number, fare, cabin number, and port of embarkment (Embarked). The target variable is'survival', which indicates whether the passenger survived the disaster (1) or did not survive (0). The class imbalance in the dataset, with 61.62% non-survivors and 38.38% survivors, was addressed using robust ensemble models and cost-sensitive learning to minimize bias toward the majority class. Additional experiments with artificially increased imbalance confirmed the stability of the model under these conditions. The distribution of passenger classes (Pclass) in the training set shows that 24.24% of passengers were in the first class, 20.65% in the second class, and 55.11% in the third class. Similarly, in the test set, 25.60% of passengers were in the first class, 22.25% in the second class, and 52.15% in the third class. Survival rates within each class in the training data were 62.96% survived in Pclass 1, 47.28% survived in Pclass 2, and 24.24% survived in Pclass 3. This consistency between the training and test sets validates the use of the computed metrics, as the test set closely mirrors the class distribution found in the training data, ensuring that the model's performance is relevant to real-world scenarios. An exploratory data analysis revealed that 'Fare' has the strongest positive correlation with survival (0.257), while 'Pclass' has a notable negative correlation (\(-\)0.338). Other features such as 'Parch' and 'Age' have weaker correlations with survival.
# Teenagers and Automated Vehicles: Are They Ready to Use Them?
## 4 Results and Discussion
### Focus Group Results
#### 4.1.7 Is There Anything That You Are Worried in Relation to AVs?  
A large number of worries collected through this question were related to the vehicle's behaviour, particularly in the case of unexpected obstacles in urban areas that are crowded, in highway contexts where the vehicles' speed is higher, and towards other road users. Some participants pointed out that AVs will be vulnerable to hackers that will cripple the transport system by misusing them for their profit. The teenagers also pointed out the vulnerability of their personal data, believing that sensitive information (e.g., location data, and home address) would be shared with unknown persons/entities. Opposite voices highlighted that such information is already shared with the given user's consent on various mobile applications.  
_\"Everyone will know what you are doing, where you are going, who you are talking with\"_.  
[PERSON], 14 y/o  
Trustworthiness worries were raised regarding the software. Thus, the participants mentioned that even a small malfunction could bring serious damages and hurt many people. At the same time, being in a car without a steering wheel and not being able to take back the control in an emergency seems to considerably increase their worries.  
Finally, the teenagers were worried about an increase in inequality considering the anticipated high prices of AVs.  
The concerns about the safety of AVs emphasised by the teenagers during the discussions are similar and confirm the perceived importance of safety noticed by [PERSON] [27] among teenagers and by [PERSON] et al. [86] among experts working on AVs and the general public representing the future users. In addition, [PERSON] et al. [87] identified safety as a crucial factor influencing public attitudes towards these vehicles. Thus, overcoming the safety concerns expressed by many groups of potential users will be key to facilitating the large-scale deployment and use of AVs.  
Additionally, the worries expressed by the teenagers, which were determined by the lack of control over the vehicle in an emergency, could be alleviated by equipping AVs with features that make the drivers and passengers aware of the actions and operating status of the AV. Such features were observed to significantly reduce anxiety, increase alertness, and give back the sense of control to drivers [88].  
Another interesting aspect is the teenagers' strong belief that AVs could increase inequality in society as these vehicles would prove accessible only to wealthy individuals. This observation is in line with the literature that sees the cost of AV technology as a major barrier for market adoption [89].  
Conversely, the concerns raised in the literature, which were mainly expressed by older adults, such as learning how to use an AV [90, 91] or the commercial use of AVs (e.g., heavy trucks or semi-trailer trucks) [91], were not reported as worrying aspects by the teenagers in our study.
# Detecting Hateful and Offensive Speech in Arabic Social Media Using Transfer Learning
###### Abstract  
The democratization of access to internet and social media has given an opportunity for every individual to openly express his or her ideas and feelings. Unfortunately, this has also created room for extremist, racist, misogynist, and offensive opinions expressed either as articles, posts, or comments. While controlling offensive speech in English-, Spanish-, and French- speaking social media communities and websites has reached a mature level, it is much less the case for their counterparts in Arabic-speaking countries. This paper presents a transfer learning solution to detect hateful and offensive speech on Arabic websites and social media platforms. This paper will compare the performance of different BERT-based models trained to classify comments as either abusive or neutral. The training dataset contains comments in standard Arabic as well as four dialects. We will also use their English translations for comparative purposes. The models were evaluated based on five metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix.  
deep learning; hate speech detection; natural language processing; social media analytics; text mining +
Footnote †: journal: _Applied_  
The last couple of years have seen a serious increase in hate speech on social media platforms worldwide, especially during the COVID-19 lockdown. For instance, the COVID-19 lockdown was behind a worrying 20% rise in online hate speech in the United Kingdom [3].  
This problem has caught the attention of several researchers worldwide and has urged them to seek solutions in order to detect and prohibit such behavior. The recent literature provides different strategies for automatic hate speech detection. However, most of these works were dedicated to the most spoken languages worldwide, such as English, Spanish, and other languages.  
Surprisingly, despite being among these languages, works related to Arabic, as will be covered in Section 2, have been rare, and mostly dialect-focused. This comes from the fact that social media users in Arabic speaking countries tend to use their own dialects instead of the standard Arabic. Another reason is the choice of letters used when writing. In Middle Eastern countries, social media users prefer Arabic letters, while in the North African countries, the Romanized Arabic, also known as \"Arabizi\", is preferred.  
In this work, as will be described in Sections 3 and 4, we focus on the Middle Eastern region as it has a wider variety of dialects, and also because of their use of the Arabic letters on social media platforms. We propose a selection of transfer learning models based on BERT [4] for hateful and offensive speech detection that can cover statements (social media comments) written in the standard Arabic, as well as three of most spoken Arabic dialects in the region (Egyptian, Iraqi, and Gulf). Section 5 will conclude this paper and suggest possible future directions.
# Blockchain-Driven Generalization of Policy Management for Multiproduct Insurance Companies
## 4 Solution Approach
* Blockchain Network Interaction: Users must be able to execute smart contracts through a user application to perform operations such as purchasing policies, making payments, and filing claims. * The solution provides automation of administrative tasks such as policy creation and claims management through smart contracts, reducing manual intervention, minimizing errors, and improving operational efficiency. * Role and Permission Management: Users will interact with the system via smart contracts, performing transactions on the network. Permissions ensure that only authorized users (according to their roles) can interact with smart contracts to perform specific actions, such as purchasing a policy, processing a claim, or making payments. * The solution provides a decentralized permission management system, ensuring that only authorized users can interact with smart contracts and perform specific operations, such as creating policies or processing claims. Non-functional requirements are:
* Preserve System Security: Ensure the integrity, authenticity, and availability of data within the system. * The blockchain solution, together with Merkle trees, ensures the integrity and verifiability of data, reducing the risk of fraud through the immutable record of each transaction and the continuous verification of data authenticity. * Enhance Efficiency and Performance: Improve the overall operational efficiency and performance of the system. * The solution provides automation of key processes through smart contracts, significantly reducing processing times and improving operational efficiency. * Ensure System Scalability: Make certain that the system can scale to handle increased loads and complexity as needed. * The solution provides standardization of policies, and the use of Merkle trees allows the system to be scaled without increasing technical complexity, facilitating the integration of new insurance products without compromising performance. * Eliminate Duplications and Dependencies: Remove redundant processes and reduce reliance on traditional systems. * The solution eliminates redundancies by centralizing data on the blockchain, ensuring that all participants operate with the same information without duplication of processes. * Reduce Implementation and Maintenance Costs: Lower the costs associated with deploying and maintaining this type of solution. * The solution provides a reduction in technical complexity and automation of key processes that allow a significant reduction in operating and maintenance costs, especially in terms of scalability. * Improve User Experience: Provide a smoother, more intuitive user experience to increase user satisfaction and engagement. * The solution brings simplicity by allowing for more intuitive and efficient interaction through the user application. Each of the functional and non-functional requirements is designed to address the specific challenges identified in the traditional insurance architecture discussed above.