esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous Coded Plane-Wave Imaging Using an Advanced Ultrasound Forward Model
## 2 Materials and Methods
### Experiment Parameters  
For the simulations and the experiments, a numerical model of the probe was used (LA523E; Esaote, Florence, Italy). The probe consists of 64 elements spaced by 245 \(\upmu\)m, resulting in an aperture of 1.565 cm. The central frequency is 8.5 MHz. The IR of the probe corresponds to the estimation given by the manufacturer. The assumption that the IR is the same for all of the elements of the probe in emission and reception was made in the model. The elements were subdivided by using N = 10 in the forward model.  
The proposed method was simulated using Field II [38; 47] on a synthetic sparse medium (Figure 3a), which contains 35 scatterers. The distance between scatterers varies linearly from 1.25 mm to 50 \(\upmu\)m on the vertical axis, and from 1 mm to 120 \(\upmu\)m on the horizontal axis. The proposed method was also evaluated experimentally on a sparse medium composed of five wires in a water tank (Figure 3b). This phantom was made by uniformly stretching the wires inside a custom PVC mold. For the numerical simulations, the Field II sampling rate \(fs\) was set to 200 MHz, and then decimated to 50 MHz. For the experiment, \(fs\) was set to 50 MHz. The images are reconstructed on a grid with a pixel size equal to 25 \(\upmu\)m in the axial direction and 61.25 \(\upmu\)m in the lateral direction.
# Biogas Production Prediction Based on Feature Selection and Ensemble Learning
## 3 Results and Discussion
### Individual Learner Prediction Performance Analysis  
The prediction analysis of each weak learner is performed before building the stacking model, and LightGBM, XGBoost, CatBoost, RF, SVR, and LSTM models are selected to analyze the learning ability of each model and the degree of correlation between models. The pre-processed data combined with the cross-validation method are predicted separately on each base learner. Figure 6 shows the histogram comparison of the error frequency distribution for each algorithm predicted separately, with the horizontal coordinate being the prediction error and the vertical coordinate being the frequency of error occurrence.  
As can be seen from Figure 6, the overall prediction error frequency distribution of CatBoost is concentrated in the interval \([-5,5]\) when each learner model independently predicts the biogas yield. CatBoost is improved under the gradient boosting decision tree (GBDT) [25] algorithm with a symmetric decision tree as the base learner, and the indexes of the leaf nodes on each layer of the decision tree are encoded as binary vectors with a length equal to the tree depth, which improves the prediction speed while being less prone to overfitting. Considering that the biogas production data is a set of time-series data, the biogas generation is not only related to the production state input at the current moment, but also related to the past input features. An LSTM neural network can make full use of the data information from a past time, so the LSTM prediction error is also relatively small, and most of the errors are concentrated in the \([-4,5]\) interval. There are small outliers in the prediction structure of other models, and these outliers reduce the prediction effectiveness.  
In order to screen the best model as the primary learner, the distribution among the errors obtained by each base learner's individual prediction is compared. The diversity among the models is examined while considering the prediction effect, and the Pearson correlation coefficient index is used to analyze the degree of variation among the models. Figure 7 shows the LightGBM, XGBoost, CatBoost, RF, SVR, and LSTM based learners' prediction error correlation metrics on the original dataset.  
From Figure 7, it can be seen that the error correlation is higher for LightGBM, XGBoost, and CatBoost. This is because they are all representative algorithms based on boosting, they all belong to the ensemble algorithms of decision trees, and there is a certain degree of similarity in their way of processing data. The error correlation is lower for the neural network-based LSTM model, SVR, and RF. This is because there are some differences in the mechanism of data training among these methods. In summary, CatBoost, LSTM, SVR, and RF, having lower error correlations, are selected as primary learners.  
Figure 6: Histogram of the frequency distribution of prediction errors for different models.
## 2 Basic Concepts
### The CCR Model  
The founders of the model are [PERSON], [PERSON], and [PERSON] in 1978 [55], with the model, thus, being called the CCR model, as an abbreviation of the authors' surnames. It was one of the first to be developed, being considered a basic model in the scientific literature. Based on this model, the DEA method has been supplemented with many new models and has been further developed.  
The CCR model was based on the Farrell method of measuring the efficiency of the units studied using production functions [34]. The equation with a nonlinear Farrell program displays a simple production ratio with a single input and output parameter [12; 13; 14]. In this model, multiple input and output parameters of each enterprise are combined into various scalar input and output parameters. This model is based on a constant return to scale (CRS).  
It can be described by a formula that represents a solution to the maximization problem:  
\[Ef=\frac{\sum_{j=1}^{s}U_{j}Y_{j_{0}}}{\sum_{i=1}^{s}V_{i}X_{i_{0}}}\to max! \tag{2}\]  
Under the following restrictions:  
\[Ef=\frac{\sum_{r=1}^{s}U_{r}Y_{r_{\mathrm{r}}}}{\sum_{i=1}^{m}V_{i}X_{i_{ \mathrm{m}}}}\leq 1\]  
\(m=1\), 2,..., n, \(U_{r}\geq 0\), \(j=1\), 2,..., s, \(V_{i}\geq 0\), \(i=1\), 2,..., r.  
where  
\(Ef\) is the efficiency value of the enterprise.  
\(n\) is the number of units compared.  
\(r\) is the number of input factors.  
\(s\) is the number of output parameters.  
\(x_{i_{0}}\) is an observed indicator of the i-th input factor of the enterprise.  
\(Y_{i_{0}}\) is an observed indicator of the j-th output parameter of the enterprise.  
\(X_{i_{m}}\) is the observed indicator of the i-th input factor of the m-th enterprise with i = 1,..., r and m = 1,..., n.  
\(Y_{i_{m}}\) is the observed indicator of the j-th output parameter of the m-th enterprise with i = 1,..., r and m = 1,..., n.  
\(U_{r}\) is the weighting of output factor i with I = 1,..., r.  
\(V_{i}\) is the weighting of input parameter j with j = 1,..., s.  
When solving this maximization problem, the problem of having a quotient in the division of two linear aggregated values arises. These maximization problems are also called linear quotient programming. There are also many possibilities for transforming the linear programming of the quotient into a linear programming problem. For this purpose, [PERSON], [PERSON], and [PERSON], in 1978, formed the so-called inverse program of the following function [55]:  
\[Ef=\frac{\sum_{i=1}^{r}V_{i}X_{i_{0}}}{\sum_{j=1}^{s}U_{j}Y_{i_{o}}}\to min! \tag{3}\]  
Subject to the following conditions:  
\[Ef=\frac{\sum_{i=1}^{m}V_{i}X_{i_{m}}}{\sum_{r=1}^{s}U_{r}Y_{r_{m}}}\leq 1\]  
where m = 1, 2,..., n, \(U_{r}\geq 0,j=1,2,..., s, V_{i}\geq 0,i=1,2,..., r.
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 4 Integration of Artificial Intelligence in LPBF Defect Sensing
### Summary of Applicable ML Methods  
Machine learning (ML) methods can analyze LPBF parts by utilizing various forms of data, such as optical, thermal, and acoustic/ultrasonic, to \"train\" the machine algorithms to recognize surface and microstructure characteristics, including defects. A myriad of ML algorithms is used to detect LPBF defects. Supervised learning (SL) utilizes labeled datasets to train algorithms to classify and recognize LPBF defects. Reported ML techniques utilizing SL to recognize LPBF defects include the following: support vector machines (SVMs), multilayer perceptron (MLP), K-nearest neighbors (KNN), random forest (RF), logistic regression (LR), convolutional neural network (CNN), decision trees (DTs), gradient boosting classification (GBC), classification and regression tree (CART), Gaussian process classification (GPC), long short-term memory (LSTM) neural networks, back propagation neural networks (BPNNs), and the Kriging whale optimization algorithm (Kriging-WOA). Unsupervised learning (UL) conversely utilizes unlabeled datasets to train algorithms to classify and recognize LPBF defects. Reported ML techniques utilizing UL to recognize LPBF defects include the following: bag of words (BoW), the deep belief network (DBN), the convolutional auto-encoder (CAE) neural network, the stacked sparse autoencoder (SSAE) neural network, and the conditional variational autoencoder (CVAE) neural network. Semi-supervised learning (SSL) utilizes a small amount of data of the solidified portion and a large amount of undefined data to learn the general shapes and patterns of the entire data distribution, allowing for self-training and co-training. The only SSL technique currently studied in the literature for detecting LPBF defects is the Gaussian mixture model (GMM) [114]. The basics of each mentioned ML algorithm are briefly discussed below. Figure 10 lists a summary of all ML models or algorithms that are applicable for detecting LPBF part defects.  
A brief definition is provided for the algorithms above for those interested in learning more about each model and how it would fit into the defect detection approach.
## 3 System Model
### Problem Formulation  
This section formulates the MLLM task offloading and resource allocation problem as an MINLP problem. The objective is to minimize the weighted sum of the system latency and energy consumption for MLLM task requests from IoTDs within UAV-assisted SEC networks. The optimization simultaneously optimizes the offloading decisions, transmission power allocation of IoTDs, and UAV trajectory while satisfying the accuracy requirements of the IoTDs. The optimization problem is structured as follows:  
\[\mathcal{P}_{1}:\min_{\mathbf{X},\mathbf{P},\mathbf{L}}\lim_{T\to\infty}\frac{1}{T}\sum_{ t=0}^{T-1}\sum_{k=1}^{K}\eta_{l}T_{k}(t)+\eta_{e}E_{k}(t) \tag{31}\]  
\[\text{such that }\quad(1),(2),\]  
\[x_{k,m}(t),x_{k,n}(t)\in\{0,1\},\forall n\in\mathcal{N},m\in\mathcal{M}, \tag{31a}\] \[x_{k,m}(t)A_{m}+x_{k,n}(t)A_{n}\geq a_{k},\] (31b) \[T_{k}(t)\leq T_{k}^{max},\] (31c) \[P_{k}(t)\leq P_{k}^{max}, \tag{31d}\]  
where \(\mathbf{X}=\{x_{k,m},x_{k,n}\}_{k\in\mathcal{K},m\in\mathcal{M}}\) represents the offloading decisions of IoTDs, \(\mathbf{P}=\{P_{k}\}_{k\in\mathcal{K}}\) indicates the transmitted power allocation of IoTDs, and \(\mathbf{L}=\{L_{k}\}_{k\in\mathcal{K}}\) denotes the locations of IoTDs. Constraint (31a) delineates the offloading decision for the MLLM task associated with IoTD \(k\). Constraint (31b) signifies that the offloaded server node fulfills the minimum accuracy threshold specified by IoTD \(k\). Constraint (31c) establishes the upper bound on acceptable latency for each MLLM task. Constraint (31d) enforces that the maximum transmission power of IoTD \(k\).  
In the objective function, the terms \(T_{k}(t)\) and \(E_{k}(t)\) have distinct physical units and cannot be summed directly. To resolve this, we employ weight factors \(\eta_{l}\) and \(\eta_{e}\) to form the weighted sum \(\eta_{l}T_{k}(t)+\eta_{e}E_{k}(t)\). These factors serve dual purposes: (1) they ensure dimensional consistency by making the terms dimensionless or commensurable, and (2) they normalize the contributions of delay and energy, enabling a fair comparison in the optimization process. The values of \(\eta_{l}\) and \(\eta_{e}\) can be adjusted based on system requirements, such as typical latency and energy scales or the relative importance of each objective, allowing the framework to effectively balance real-time performance and energy efficiency.
## 2 Analytical Methods
### Problem Formulation  
We begin with solving a single borehole scenario, as schematically shown in Figure 1b). In this case, a one-dimensional cylindrical heat conduction model can represent the problem with a governing equation as follows:  
\[\frac{\partial^{2}T}{\partial r^{2}}+\frac{1}{r}\frac{\partial T}{\partial r}= \frac{1}{\alpha}\frac{\partial T}{\partial t}, \tag{1}\]  
where \(T\) is the ground temperature (K), \(r\) is the radial coordinate (m), \(t\) is the time (s), and \(\alpha\) is the thermal diffusivity of the ground (m\({}^{2}\)/s). Here, the spatial and temporal domains are \(a<r<b\) and \(t>0\), respectively. \(a\) is the inner radius (m) and \(b\) is the outer radius (m). This heat conduction problem is subjected to the following boundary conditions and initial condition:  
\[\begin{split}-k\frac{\partial T}{\partial r}\bigg{|}_{r=a}& =q_{0}f(t),\\ T(r=b,t)&=T_{0},\\ T(r,t=0)&=T_{0},\end{split} \tag{2}\]  
where \(k\) and \(T_{0}\) are the thermal conductivity (W/(m\(\cdot\)K)) and temperature at the initial and outer boundaries (K), respectively. The initial (and outer) temperature \(T_{0}\) is also seen as the undisturbed temperature of the ground (K). The heat flux boundary is expressed as \(q_{0}f(t)\) that can be any function of time \(t\), where \(q_{0}\) represents a constant heat flux (W); if  
Figure 2: Schematic diagram of different scenarios of borehole systems: (**a**) single borehole; (**b**) double boreholes; (**c**) 3-by-3 boreholes; and (**d**) N-by-N boreholes.  
does not depend on time (i.e., \(f(t)=1\)), then the boundary reduces to a constant heat flux condition, \(-k(\partial T/\partial r)_{r=a}=q_{0}\).
# Fuzzy Logic-Based Performance Enhancement of FSO Systems Under Adverse Weather Conditions
###### Abstract  
In this paper, we propose an application of fuzzy logic control (FLC) to improve the system performance of free-space optics (FSO) networks using the optical code-division multiple-access (OCDMA) technique. The primary objective is to dynamically adjust the bit error rate (BER) threshold at the receiver based on weather conditions (i.e., rain and fog) and the propagation distance (which significantly affects the received power). The FLC module at the receiver integrates and processes these variables to optimize the BER threshold. The FLC module operates through an algorithm comprising eight well-defined steps, ensuring robust and adaptive control of the BER. Simulation results show that the FSO-FLC-based system has significant advantages over traditional approaches. For instance, under heavy rain conditions, the FSO-FLC system supports 12 users compared to a traditional system, which supports 7 users without FLC over a distance of 2.8 km with BER \(10^{-9}\). Similarly, under heavy fog conditions, the FSO-FLC system can support 22 users compared to a traditional system, which supports 18 users without FLC over a distance of 0.5 km with equal BER. These values show that the performance of FSO under weather conditions significantly improves when using the proposed approach. The computational efficiency and real-time feasibility of the FSO-FLC are also analyzed. The complexity of the FLC is O(1), indicating that the execution time remains constant regardless of input size. An Intel Core i7-1165G7 (2.80 GHz) using MATLAB's fuzzy logic toolbox is used for all experiments. Results show that the proposed FLC executes up to 4 ms per decision cycle, which ensures real-time adaptability for practical FSO communication systems.  
Keywords:adaptive thresholding; atmospheric attenuation; BER; computational efficiency; FLC; FSO; OCDMA; propagation distance; real-time processing; weather conditions +
Footnote †: journal: _photonics_
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC Standalone PKE Protocol
#### 5.1.3 Data Encryption and Decryption (PQC)  
The PQC standalone PKE protocol directly encrypts data using a public-key encryption scheme derived from the chosen KEM.  
Security Assumption  
The confidentiality of encrypted data depends on the post-quantum security of the public-key encryption scheme derived from the KEM, including resistance to chosen-ciphertext attacks (IND-CCA2 security).  
Formal Statement  
The confidentiality of transmitted data is guaranteed if the following conditions hold:  
1. The underlying KEM is IND-CCA2 secure.
2. The public-key encryption scheme derived from it inherits this IND-CCA2 security property.  
If an adversary breaks these assumptions, they can recover plaintext data from ciphertexts.
## 4 Numerical Results and Discussion
Clearly, to enhance the system performance and achieve the preferable QoS as clarified in this paper, the best inter-vehicle position should be selected, and the optimum distance between V2V and V2I should be determined based on the system conditions and requirements such as the outage probability, \(P_{out}\), transmission power, \(P_{V}\), and channel quality in terms of \(\alpha\), vehicle mobility, vehicle density and vehicle speed. The findings of this work can assist in deciding with which vehicle the connection should be established between vehicles-to-vehicles, and how the connection between vehicles-to-infrastructure should be employed to have a proper and reliable connection either between V2V or V2I to improve the overall system performance.
# Toward Collaborative Intelligence in IoV Systems: Recent Advances and Open Issues
## 2 Internet of Vehicles and Collaborative Intelligence
### Collaborative Intelligent in IoV  
Collaborative intelligence is capable of merging the data, power, and intelligence of multiple pieces of connected equipment in order to accomplish a shared objective and purpose. In the IoV system, this usually manifests as real-time communication and interaction, organic combination of data, sharing of computing and storage resources, and synchronization and scheduling of network access IoV entities, including intelligent vehicles, roadside units, users and mobile devices, edge computing servers, cloud computing centers, data centers, etc. In this framework, AI computation and inference will be deployed simultaneously on edges and the cloud. One possible structure is that the front end of the AI model is deployed on edge devices and performs the initial processing and feature computation. Then, intermediate features are sent to the cloud, and the AI back end completes the step of inference [22].  
As shown in Figure 4, ideas and methods of CI can be integrated into all aspects of IoV. Through cooperation between communication-related smart devices, agents are connected to form a heterogeneous collaborative network, so as to carry out collaborative sensing, collaborative communication, and collaborative computing on this network, in order to make collaborative decisions.  
Since AI-based CI methods can produce superior decisions with lower communication overhead by aggregating knowledge and achieving efficient coordination among multiple agents, CI is an inevitable development trend of IoV systems. Specifically, it has the following advantages:  
Figure 4: Collaborative Intelligence in the Internet of Vehicles.  
* Collaborative communications over various wireless spectrum types among many transmitters to increase the efficiency of the spectrum utilization.
* Collaborative computing with an end-edge-cloud task processing framework that is resource efficient, meeting a variety of demands on the massive amount of real-time data processing, including exceptionally high throughput and ultra-low latency.
* Collaborative caching among numerous network entities to decrease service latency.  
Despite the many benefits of CI, such as communication and computation benefits, it also introduces new challenges in practical applications, so new scientific and technological theories need to be established to attain the optimal design. These challenges include:  
* Enable coordination between smart devices.
* Privacy protection and data security issues arising from data and information sharing.
* Efficient collaborative learning with low overhead in scenarios with limited bandwidth and strict latency requirements.
# Backdoor Attacks to Deep Neural Network-Based System for COVID-19 Detection from Chest X-ray Images
## 2 Materials and Methods  
### COVID-Net Model and Chest X-ray Images  
We obtained a COVID-Net model and chest X-ray images based on a previous study [31]. In particular, the COVIDNet-CXR4-A model was downloaded from the GitHub repository on the COVID-Net Open Source Initiative ([[https://github.com/lindawangg/COVID-Net](https://github.com/lindawangg/COVID-Net)]([https://github.com/lindawangg/COVID-Net](https://github.com/lindawangg/COVID-Net))) on 20 November 2020. This model was selected since its prediction accuracy was the highest (94.3%) at that time. Moreover, we downloaded the COVIDx5 dataset, which was constructed using several open-source chest radiography datasets, on 19 November 2020, following the description in the COVID-Net repository (see [[https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md](https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md)]([https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md](https://github.com/lindawangg/COVID-Net/blob/master/docs/COVIDx.md)) (accessed on 19 November 2020) for details). In particular, the dataset consisted of COVID-19 image data collection [36], COVID-19 Radiography Database [37; 38], hospital-scale chest X-ray database (ChestX-Ray8) [39], The Radiological Society of North America International COVID-19 Open Radiology Database (RICORD) [40], etc. The images were in grayscale with a pixel resolution of \(480\times 480\) pixels and a pixel intensity ranging between 0 pixels and 255 pixels. The chest X-ray images in the dataset were classified into three classes: normal (no pneumonia), pneumonia (non-COVID-19 pneumonia; e.g., viral and bacterial pneumonia), and COVID-19 (COVID-19 viral pneumonia). The COVIDx5 dataset comprised 13,958 training images (7966 normal, 5475 pneumonia, and 517 COVID-19) and 300 test images (100 images per class).  
The COVIDx5 dataset was classified into two datasets: Datasets 1 and 2. Dataset 1 contained 6978 training images (3983 normal, 2737 pneumonia, and 258 COVID-19) and 150 test images (50 images per class), which were randomly selected from the COVIDx5 dataset. These training and test images were used to establish a backdoor in the COVID-Net model (i.e., to generate a backdoor COVID-Net model) and to evaluate the performance of the backdoor attacks. The remainder of the COVIDx5 dataset corresponded to Dataset 2, which contained 6980 training images (3983 normal, 2738 pneumonia, and 259 COVID-19) and 150 test images (50 images per class). These training and test images were used to obtain a fine-tuned model from the backdoor COVID-Net model and to evaluate the performance of backdoor attacks on the fine-tuned model.