esa-sceva/satcom-chunk-collection huggingface dataset:
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 4 The Proposed System
### Key Generation System
The first subsystem uses five chaotic maps: the Arnold map (for pixel position scrambling), the piecewise map (for enhancing key diffusion), the Ikeda map (for generating complex trajectories), the gingerhead map (for maximizing unpredictability), and the standard map (for increasing key space variability). The second subsystem integrates Henon and Zaslavsky maps to diversify the generated  
Figure 4: System 3. Figure 3: System 2. The following are the system outputs: Output 6. Decimals \(X\) and \(Y\), where \(-1<X\), \(Y<1\)). Output 7. Decimals \(X\) and \(Y\), where \(0<X<1\) and \(-21<Y<22\). The output from System 2 is converted to binary with the following condition: def check_value (value, comparison): global ones, zeros, bit_key; if value \(>=\) comparison: bit_key \(+=\) “1”; ones \(+=\) 1; else: bit_key \(+=\) “0”; zeros \(+=1\), where “value” is the input value (the result of a chaotic equation), and “comparison” is a reference (comparison) value used to determine whether a value is larger or smaller. Here, two bytes are acquired in each move. keys further and introduce additional randomness. Finally, the third subsystem uses the tent map as a control mechanism to select between generated keys based on the chaotic output dynamically. This multilevel chaotic design ensures robust randomness, making the encryption highly resistant to statistical and brute-force attacks. Security Evaluation of Key Generation: The generated keys undergo extensive testing by the NIST randomness suite to ensure resistance against statistical attacks. We employ a multitiered chaotic technique to enhance unpredictability in key selection and minimize the likelihood of successful brute-force decryption. Unlike conventional single-layer chaotic encryption, which faces key correlation issues, our approach guarantees an equal distribution of key bits, preserving the ideal balance between randomness and determinism.
# A Throughput and Energy Efficiency Scheme for Unlicensed Massive Machine Type Communications +
## 3 Throughput Optimization  
### Constrained Optimization  
In this section we evaluate the optimal link throughput in the sense of spectral efficiency. We consider a constrained throughput optimization problem where the constraint is a maximum acceptable error rate imposed by the application at hand. This means that the quality requirement of this network is determined by how often a message is eventually dropped after all retransmission attempts. Considering (3), the optimization problem is defined as below.  
\[\begin{split}\max_{(\beta,m)}&\frac{\log(1+\beta)}{1 +\bar{m}}\left(1-P_{\text{out}}^{1+m}\right)\\ \text{subject to}& P_{\text{out}}^{1+m}\leq\epsilon, \end{split} \tag{4}\]  
where \(\epsilon\) represents the aforementioned quality requirement. In this equation, the SIR threshold \(\beta>0\) and the number of allowed retransmissions \(m\in\mathbb{N}\) are the design variables.  
**Lemma 1**.: _The throughput \(T\) in (3) is a function of the variables \(m>0\) and \(\beta>0\), i.e., \(T=f(\beta,m)\). The function \(f\) is then concave with respect to \(\beta\) if \(\frac{\partial^{2}T}{\partial\beta^{2}}<0\). After that, we can calculate \(\beta^{*}\), which is the value of the SIR threshold that maximizes the link throughput._  
\[\beta^{*}=\left(-\frac{1}{k\lambda}\log\left(1-\epsilon^{\frac{1}{m+1}} \right)\right)^{\frac{\beta}{2}}. \tag{5}\]
# FedCO: Communication-Efficient Federated Learning via Clustering Optimization +
## 1 Introduction
* The conducted experiments have demonstrated the efficiency of FedCO over the FedAvg, FedProx, and CMFL algorithms in terms of convergence rate and communication overhead (Section 6). The rest of the paper is structured as follows. Section 2 reviews the previous studies related to our work. The methodology used in our paper is presented in Section 3. Section 4 is devoted to the proposed FedCO and its strategy. The practical applications of those experimental settings are discussed in Section 5. The conducted experiments and the obtained results are analyzed and discussed in Section 6. The conclusions of our study and potential future works are presented in Section 7.
## 1 Introduction  
Human action data are all-encompassing, which is why such data are of significant interest to the computer vision [1; 2] as well as machine learning communities [3; 4]. There are various views through which we can study human action data. One example is a group of dynamic actions captured by different views of the camera, as shown in Figure 1. Classifying this kind of data is quite difficult in a cross-view situation because the raw data are captured at varying locations by different cameras and, thus, could look completely different. A perfect example is represented in Figure 1a, where the action captured from a top view appears different from the one captured from a side view. This implies that features obtained from a single camera view cannot provide enough discriminative aspects to classify actions in another camera view. Many studies concentrate on ways of developing view-invariant pictures for action recognition [5; 6], where all the actions captured on video are treated as frame time series. Many approaches have utilized a self-similarity matrix (SSM) descriptor [5; 7] to replay actions and have proven to be robust in cross-view outlines. Information shared among camera views are each kept and transferred to all the views [7; 8]. It is assumed that the shared features contribute equally with samples from different views. Through our research, we found that this assumption is not true because the discriminative parameters of one of the views could be very far away from the parameters of other views. Therefore, this may result in a misunderstanding by classifiers, as they do not control the sharing of data between action categories, which would result in an incorrect model result.  
As a response to the assumption of the equal contribution of share features, in this paper, we put forward original networks that can learn view-invariant features for cross-view action categorization, and we have introduced a novel sample-affinity matrix (SAM) that can accurately determine similarities of video samples. By encouraging incoherence between shared and private features, we learned discriminative view-invariant information. Our approaches retain two types of features--strong private features as well as shared features across views acquired by a single autoencoder. SAM focuses on the resemblance between samples, but SSM concentrates on the video frames. The vanity between these facets was achieved by strengthening the coherence between the mapping matrices. In addition, in a layer-wise fashion, we piled several layers of features in order to learn them. After a set of experiments was carried out on three multi-view sets of data, we found out that our method performs much better than state-of-the-art methods. The following pieces of information are covered in the next sections of this paper. We first analyze works related to our topic and follow this with an analysis of view-invariant features. Then, we present a detailed description of the structure of our method together with the algorithm used. Finally, through a set of experiments, we show that our method performs much better than state-of-the-art methods.
## 3 Results
### Sensitivity and Specificity of ARFI Measurements
The cut-off value for the prediction of liver cirrhosis (Metavir F4) by mean ARFI is 1.523 m/sec with a sensitivity of 0.708 and a specificity of 0.830 in the non-treatment group (Table 2). Since inflammation has a significant impact on ARFI value, AUROC was performed on 99 patients with an ALT level lower than 5x upper limit normal (ULN). The cut-off values remain almost the same (1.523) with increasing sensitivity (0.696) and specificity (0.829). When only the 79 patients with an ALT level lower than 2x ULN were included, the AUROC showed a similar cut-off value (1.523) without a significant improvement in sensitivity and specificity as compared with those patients with an ALT lower than 5x ULN. The AUROC for the diagnosis of Metavir F4 by mean ARFI is 0.796 [SE 0.080, (CI: 0.641; 0.952) Figure 2] in the treatment group. The cut-off value of mean ARFI for diagnosis of liver cirrhosis was 1.420 m/sec with a sensitivity of 0.786 and a specificity of 0.750 in the treatment group (Table 2).
# IoT-Driven Resilience Monitoring: Case Study of a Cyber-Physical System
## 4 Implemented Statistical and AI Methods
### Moving Average  
The moving average is a statistical technique used to analyze data trends by smoothing out short-term fluctuations, providing a clearer view of the underlying direction in a dataset [70]. It is particularly useful in time series analysis, where data may exhibit high volatility due to random variations. The most basic form, the Simple Moving Average (SMA), calculates the average of a fixed number of consecutive data points, or \"window size\". As new data points become available, the average recalculates by including the most recent observation and excluding the oldest one, effectively \"moving\" along the dataset [71]. For instance, the SMA over a window size \(N\) is calculated using the formula:  
\[\text{SMA}_{t}=\frac{X_{t}+X_{t-1}+\ldots+X_{t-N+1}}{N}\]  
where \(X_{t}\) represents the current data point, and \(N\) is the window size.  
Moving averages have extensive applications across various fields, such as finance, where they are used to identify stock price trends [72], and economics, where they help analyze indicators like GDP and inflation rates [73].  
Sensor data from photovoltaic (PV) panels for DC power often exhibits noise due to environmental fluctuations, electromagnetic interference, and sensor limitations. Variations in sunlight, temperature, and shading cause inconsistencies, while electronic components introduce interference, affecting data stability. Sensor inaccuracies, often due to limited sensitivity issues, further contribute to this variability, making it challenging to obtain clean data directly from sensors [74; 75].  
Implementing a rolling average for smoothing time series data is a widely adopted technique (39,529 documents in SCOPUS used key term rolling average OR moving average) with its own set of pros and cons. Rolling averages are a commonly used method for data smoothing and trend analysis [76], offering distinct advantages. Rolling averages have been introduced as a convenient method for regulating power-time curves in renewable and conventional energy profiles [77; 78; 79; 80], which served as the main motivation for considering rolling averages in the current study. Nevertheless, their simplicity may sometimes limit their ability to capture complex patterns in data, highlighting the need to explore alternative or complementary approaches for more intricate analyses.  
Recent studies have demonstrated that implementing a rolling average is inefficient for resilience curve construction when data are affected by significant noise [47]. However, in this article, moving averages are used to provide essential smoothing for noisy datasets, making it easier to detect underlying patterns and inform forecasting models [81].
# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
###### Abstract  
Automatic fire detection, which can detect and raise the alarm for fire early, is expected to help reduce the loss of life and property as much as possible. Due to its advantages over traditional methods, image processing technology has been applied gradually in fire detection. In this paper, a novel algorithm is proposed to achieve fire image detection, combined with Tchebichef (sometimes referred to as Chebyshev) moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). According to the correlation between geometric moments and Tchebichef moments, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained first. Then, the TMIs of candidate images are calculated to construct feature vectors. To gain the best detection performance, a PSO-SVM model is proposed, where the kernel parameter and penalty factor of support vector machine (SVM) are optimized by particle swarm optimization (PSO). Then, the PSO-SVM model is utilized to identify the fire images. Compared with algorithms based on Hu moment invariants (HMIs) and Zernike moment invariants (ZMIs), the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%. Moreover, it still exhibits the best performance even if the size of the training sample set is small and the images are transformed by TRS.  
f 2018; 11, 79; doi:10.3390/a11060079
# Service Interface Translation. An Interoperability Approach
## 2 Related Work
### Machine Learning and Artificial Intelligence  
During the last decade, a new research stream inspired by human cognition has gained popularity. New approaches based on machine learning and artificial intelligence provide a promising future that can affect different fields, including interoperability challenges. Examples of the application of these technologies to the stated problem can be found in the literature. The work presented in [18] includes predictive machine learning algorithms and unsupervised self-evolving AI algorithms to address interoperability in the IoT environment. [PERSON] et al. [19] proposed the basics of a novelty semantic translator based on machine learning. This approach is considered for many as the future tool to address interoperability problems. Machine learning is used as a main approach for the aforementioned examples but also can be included as a support mechanism to complement other approaches. This last option will be considered for future implementations of the interface translation. Currently, the major limitations of this approach are the low maturity level and the low accuracy, which are difficulties for its adoption in industrial scenarios.
# Theoretical Study on Prestress Loss in Cross-Tensioned Concrete Pavement with BFRP Tendons
## 5 Conclusions  
This paper proposed a detailed theoretical method and calculation process for determining the prestress loss of the BFRP tendon, a corrosion-resistant and lightweight material, in cross-tensioned concrete pavements with the consideration of the unique relaxation properties of BFRP to improve the durability of the pavement structure. The impact of the slab width and distribution angle of the prestressed tendon on the prestress loss in different stages was studied and compared to the results of traditional steel strands. The conclusions are summarized as follows:  
(1) For both BFRP tendons and steel strands, the prestress loss \(\sigma_{11}\) decreased and \(\sigma_{12}\) increased with the increase of the slab width at a given distribution angle of the prestressed tendon, resulting in a lower proportion of \(\sigma_{11}\) in the prestress loss during the prestressing stage. In contrast, given the same slab width, the prestress loss \(\sigma_{11}\) increased and \(\sigma_{12}\) decreased as distribution angle increased, while the prestress loss was a constant value during the in-service stage.  
(2) During the prestressing stage, the prestress loss of BFRP tendons was far lower than that of steel strands, given that the difference of prestress loss \(\sigma_{11}\) increased with the decrease of the slab width and the increase of the distribution angle, peaking at 10.9% when the slab width was 3 m and the distribution angle was 45\({}^{\rm o}\).  
(3) During the in-service stage, both the prestress losses of BFRP tendons and steel strands were constant, and the former (5.2%) was half of the latter (10.4%).  
(4) For a given slab width (3 m, 4.5 m, 9.0 m, and 12.75 m) and distribution angle (20\({}^{\circ}\), 25\({}^{\circ}\), 30\({}^{\circ}\), 35\({}^{\circ}\), 40\({}^{\circ}\), and 45\({}^{\circ}\)), the prestress loss of BFRP tendons was always lower than that of steel strands, with the difference ranging from 6.4% to 16%. The smaller the slab width, the greater the difference, indicating that BFRP tendons can effectively reduce the prestress loss in concrete pavements, especially when the slab width is smaller.  
Conceptualization: Y.Z.; validation: [PERSON] and Y.Z.; investigation: Y.Z.; methodology: Y.Z.; data curation: Y.Z.; supervision: [PERSON]; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON] and Z.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Jiangsu Province (BK20190604); Jiangsu Planned Projects for Postdoctoral Research Funds (20202327); Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University (K202002); and the Fundamental Research Funds for the Central Universities (JUSRP11937).  
The authors would like to acknowledge the support of [PERSON] and [PERSON] from Jiangnan University.  
The authors declare no conflict of interest.
###### Abstract  
One requirement posed by customers is to achieve adequate durability levels as described in technical requirement documents. Modal analysis is one of the design assessments aimed at identifying the risks of high cycle fatigue (HCF). This article presents a novel application of an artificial immune system (AIS) in the optimization of a nozzle guide vane's modal characteristics. The aim is to optimize the system's natural frequencies in the vibration vane and adjacent hardware (turbine casing). The geometrical characteristics accounted for in the optimization process include the shell thicknesses on the turbine casing side and the nozzle outer band features (hook thickness, leaning and position). The optimization process is based on a representative model established from FEM analysis results. The framework is robust because of the applied metamodel and does not require time-consuming FEM analysis in order to evaluate the fitness function. The aim is to minimize the model area (a derivative of the system weight) with constraints imposed on the frequency (a penalty function). The optimum design is given as the solution with an increased shell thickness in the turbine casing and leaning nozzle outer band hooks to obtain the maximum stiffness of the system. The results obtained by means of the artificial immune system (AIS) and a novel variant based on an additional costtimulation procedure (CAIS) are compared with the solution obtained by means of a genetic algorithm implemented in the commercial CAE software (Ansys version 19.2).  
modal analysis; vane; artificial immune system; optimization; system mode
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 4 Time-Modulated Van Atta Array Testing  
### The SPST Switch Test  
The insertion loss of the designed SPST switch under different bias voltages and currents tested by a vector network analyzer is shown in Figure 11. The resistance of the PIN diode decreases with the increase in the bias voltage, and its insertion loss decreases accordingly. When the bias voltage is 1.2 V, the bias current is 82 mA, and the resistance value of the PIN diode is 1.3 \(\Omega\), the insertion loss is \(-1.5\) dB, which is close to the on state. When the bias voltage is 0 \(V\), the resistance of the PIN diode is about 400 \(\Omega\), and the insertion loss is \(-14\) dB, which is close to the cut-off state. In the working frequency band of 3.45-3.55 GHz designed in this article, the SPST switch ratio can reach 10 dB, which can meet the time-modulation requirements of the Van Atta array.
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 3 Materials and Methods
### Algorithms and Evaluation Metrics
#### 3.3.1. Implemented Algorithm  
The implemented algorithm governing the system operates on a sequential modular synergy for decision-making. The first step took the operator-defined destination points; collision-free trajectories were calculated using an RRT (Rapidly Exploring Random Tree) planner. Position estimation was acquired through sensor fusion, combining data from the Inertial Measurement Unit (IMU) and lidar system. Through this stage, the robot navigated through the environment to capture images.  
The localization was based on SLAM, including an EKF (Extended Kalman Filter), which uses Lidar and IMU, as GPS, in such conditions (indoors), cannot accurately estimate positions. On the other hand, the assignment of inspection points (x, y) was carried out through the interface of the informed map generated (described in Figure 3). The navigation to each point was autonomous, with dynamic obstacle avoidance facilitated by the Lidar system. The path planning, considering known starting and destination points and the environment map, employed an RRT planner and the A* search algorithm.  
On the other hand, vision systems undergo a series of stages, from data acquisition to the identification of victims. Raw images are captured and preprocessed using computer vision techniques to eliminate environmental noise and enhance their quality before entering the neural network. For this purpose, techniques such as erosion, dilation, and Gaussian filtering were applied to the images. In the case of multispectral images, they are matrix-combined using an operation defined by the Victim Detection Index (VDIN). Once processed by the CNN, detection results, such as bounding boxes, labels, and precision, are incorporated into each resulting image. These processed data are stored through logs to generate the final mission percentages at the end of the operation.  
Pseudocode Algorithm 1 delineates the functionality of the implemented system, starting from the exploration phase through zones designated by the operator, the acquisition of visual data from the environment, the processing of three types of images, and the evaluation of system performance.