esa-sceva/satcom-chunk-collection huggingface dataset:
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Comparison between DL and ML Approaches
#### 2.3.1 Training and Overfitting  
The first issue is overfitting, which is intrinsic to models with a large number of adjustable parameters. This is further exacerbated when compared to the volume of training data, which is often negligible compared to the model parameters. The U-Net-based models typically have millions of parameters; this was up to 25 million parameters in [31]. Although authors in [33] created a custom model using a combination of CNNs and MLPs, which reduced the number of parameters to 4241, they only had 2392 samples for training. The ML-based models have fewer parameters and are less prone to overfitting. For instance, [PERSON] et al. [32] has 300 parameters, and [26] has only about 30 parameters. In contrast to DL-based models, ML-based models decouple the process of topographical feature extraction from the regression task of propagation estimation. This decoupling simplifies data requirements and training processes. In fact, authors of DeepLoRa [35] have adopted a similar strategy to create a LoRaWAN-specific model, as they recognized that the cost of training a single DL-based model is higher.
# Improved Ship Detection Algorithm from Satellite Images Using YOLOv7 and Graph Neural Network
## 2 Related Work and State of the Art
### Improvement in Deep Learning (DL)  
The amount of data that are now being saved grows daily. As there are more datasets available, researchers are constantly attempting to improve the algorithms that are currently in use. There are typically many layers of deep learning that are used in the processing of complex information. These include the input and output layers of the deep learning classifier. In order to compare the three machine learning algorithms, [PERSON] and colleagues presented a comparison of the SVM, the histogram of oriented gradient, and the latent SVM.  
In order to detect objects with complex backgrounds and scale variation, [PERSON] et al. [14] developed a fully convolutional neural network algorithm. The algorithm uses a combination of box regression and CNN to label the class [15].  
Through a combination of deep learning and CNN, [PERSON] [16] was able to create two models that can detect ships in the Airbus Satellite dataset. These modelscan be used to deal with various maritime-related problems, such as illegal fishing and resource surveillance.  
Researchers have developed a new technique that allows them to recognize ships using satellite images. The method, known as R-CNN, is mainly used for analyzing the images taken by the RADARSAT-2 and Sentinel-1 satellites [17]. The models were able to recall and improve their accuracy by 89.14% and 89.23%, respectively.  
In order to create a generative transfer learning framework that can be used for ship detection, [PERSON] [18] proposed a method that combines knowledge transfer and ship recognition. The output of this module was fed into a detector model. The goal of the experiment was to analyze the characteristics of the ship detection datasets taken from the Air-SAR-Ship-1.0 and SAR Ship Detection datasets.
## 3 Materials and Methods
### Underlying Software Components
#### 3.2.4 Monitoring Parameters  
The performance of edge computing applications followed different runtime variations depending on the running conditions. Conditions could have included the number of arrival requests and the availability of virtual resources. Additionally, the network connection quality between different interoperating application components distributed over the communication network was an additional key to consider.  
A mechanism to allow the aforementioned resources to be intelligently provisioned with little or no effort an application provider was required. The container manager was configured to ensure that the orchestration process of provisioning resources was done. The resources considered for provisioning in edge/cloud computing were: the usage of CPU, memory, storage, and network.
## 4 Joint Optimization Problem Formulation
### Joint Optimization Problem  
The objective of our work is maximizing the system throughput achieved by cooperation. Therefore, we formulate the joint optimization problem by combining backhaul resource allocation with user pairing and subcarrier mapping. We introduce a set of binary variables \(x_{i,j,k}\in\{0,1\}\), which represent the mapping status of all user sets. When \(x_{i,j,k}=~{}1\), it means the \(i\)-th user in cell A is paired with the \(j\)-th user in cell B and they are both mapped on subcarrier \(k\). Otherwise, \(x_{i,j,k}=0\). Recalling Equations (7) and (8), both sum rate and backhaul overhead must satisfy subcarrier additivity. This is guaranteed by the independency of subcarriers. Substituting Equation (9) into Equation (8), and combining variable \(x_{i,j,k}\), we setup an optimization problem aimed at maximizing system throughput with limited backhaul resource constraints:  
\[\text{P1}:~{}\max_{x,\mathbf{r},\mathbf{\Phi}}\sum_{i=1}^{N}\sum_{j=1}^{N}\sum_{k=1}^ {K}x_{i,j,k}f_{i,j,k} \tag{10}\]  
\[s.t.~{}\sum_{j=1}^{N}\sum_{k=1}^{K}x_{i,j,k}=1,~{}\forall i \tag{11}\]  
\[\sum_{i=1}^{N}\sum_{k=1}^{K}x_{i,j,k}=1,~{}\forall j \tag{12}\]  
\[\sum_{i=1}^{N}\sum_{j=1}^{N}x_{i,j,k}\leqslant 1,~{}\forall k \tag{13}\]  
\[\mathbf{r}^{T}1\leqslant C_{BH} \tag{14}\]\(f_{i,j,k}\) is the achievable rate of user pair \((i,j)\) who occupy the subcarrier \(k\). It is a function of user pairing, subcarrier mapping and backhaul resource allocated to that subcarrier. Equations (11)-(13) are assignment constraints. For convenience, we denote the objective function as:  
\[F(\mathbf{x},\mathbf{r},\mathbf{\Phi})=\sum_{i=1}^{N}\sum_{j=1}^{N}\sum_{k=1}^{K}x_{i,j,k}f_ {i,j,k} \tag{15}\]  
It is clear the optimization problem given in Equations (10)-(14) is a complex optimization problem and could not be solved in polynomial time, so in the following part, our proposed 3-step sub-optimal solution is given.
## 2 Novel Diabetic Retinopathy (DR) Detection Framework and Experiment
### Methodology
#### 2.2.3 Optical Image Pre-Processing  
The reliable detection of DR strongly depends on the quality of the fundus images obtained during screening. As with any real-world dataset, noise is encountered in both the images and the labels. The images may contain artifacts or be out of focus, underexposed, or overexposed. Among the pre-processing techniques applied for image enhancement, some have used signal processing techniques [32], either based on wavelets, such as the simple and quasi-optimal wavelet denoising approach [33], or on the concept of compressed sensing, which reduces noise and preserves the image texture [34]. The employed images may be gathered from multiple clinics, using a variety of cameras and over an extended period of time, introducing further variation.  
Examples of two types of fundus images are shown in Figures 5a and 6a. The image in Figure 5a corresponds to an image of a normal eye, and the image shown in Figure 6a corresponds to an image of an eye with DR. Moreover, the corresponding image processing steps are shown in Figure 5b-d and Figure 6b-d, for normal and DR eyes, respectively.  
The images shown in Figures 5a and 6a were resized as shown in Figures 5b and 6b to ensure their alignment with the input size of the network model employed. The pre-processing stage was applied to eliminate insignificant information and enhance the speed and efficiency of the system. Then, the images were converted to binary-scale images, as shown in Figures 5c and 6c, to emphasize regions of interest and isolate significant areas via thresholding. We focused our equalization efforts on these areas, rather than the entire image, which might have included irrelevant regions.  
After the binarization step, each image was converted into grayscale using color space conversion, i.e., by converting the colored images into grayscale images, as shown in Figures 5d and 6d. This was beneficial for reducing the noise and enhancing image quality. Then, local brightness equalization methods, including adaptive histogram equalization and dynamic histogram equalization, were implemented to compensate for non-uniform illumination. Afterwards, the pre-processed and clean images were fed into the DL algorithms. The goal was to detect DR using different pre-trained DNNs and to explore the performance of DR diagnosis using several algorithms.  
Figure 5: Illustration of the steps involved in processing a fundus image of the right eye of a normal patient, i.e., \"No DR\". (**a**) Original image. (**b**) Resized image of the original image of a normal patient. (**c**) Binary information. (**d**) Grayscale information of the image.  
Figure 6: Illustration of the steps involved in processing the fundus image of the right eye of a patient suffering from DR. (**a**) Original image. (**b**) Resized image of the original image of the DR patient. (**c**) Binary information. (**d**) Grayscale information of the image.
# Optimized Design for NB-LDPC-Coded High-Order CPM: Power and Iterative Efficiencies
## 1 Introduction
Finally, an intensive study on proper iteration match between demodulation and decoding by the EXIT technique and mutual information was conducted to enhance systematic iterative efficiency and attain a suitable tradeoff between the communication reliability and iterative decoding delay. The rest of this paper is organized as follows: Section 2 provides the principle block diagram of the proposed system, in which the modulation symbols are transmitted over the additive white Gaussian noise (AWGN) channel. A detailed analysis of the design process of a competitive CPM for particular \(R\) and \(\eta\) is shown in Section 3. Section 4 presents the analysis of the code design and the potential advantages of NB-LDPC-coded high-order CPM. The extrinsic information operation method, which curbs the positive feedback phenomenon in iterative decoding and detection, is outlined in Section 5. The iteration optimization of demodulation and decoding using the EXIT technique and mutual information is discussed in Section 6. Section 7 depicts the simulation results and feasibility. Finally, we conclude this paper in Section 8.
# A Review of Deep Learning Based Methods for Acoustic Scene Classification
## 4 Data Augmentation Techniques  
Training deep learning models usually requires large amounts of training data to capture the natural variability in the data to be modeled. The size of machine listening datasets increased over the last few years, but lagged behind computer vision datasets such as the ImageNet dataset with over 14 million images and over 21 thousand object classes [39]. The only exception to this day is the AudioSet dataset [40] with currently over 2.1 million audio excerpts and 527 sound event classes. This section summarizes techniques for data augmentation to address this lack of data.  
The first group of data augmentation algorithms generates new training data instances from existing ones by applying various signal transformations. Basic audio signal transformation includes time stretching, pitch shifting, dynamic range compression, as well as adding random noise [41; 42; 43], [PERSON] et al. applied spectral rolling by randomly shifting spectrogram excerpts over time [44].  
Several data augmentation methods allow simulating overlap between multiple sound events and the resulting occlusion effects in the spectrogram. Mixup data augmentation creates new training instances by mixing pairs of features and their corresponding targets based on a given mixing ratio [45]. Another approach adopted from the computer vision field is SpecAugment, where features are temporally warped and blocks of the features are randomly masked [46]. Similarly, random erasing involves replacing random boxes in feature representations by random numbers [47]. In the related research task of bird audio detection, [PERSON] combined several data augmentation techniques in the time domain (e.g., mosaicking random segments, time stretching, time interval dropout) and time-frequency domain (e.g., piece-wise time/frequency stretching and shifting) [48].  
A second group of data augmentation techniques synthesizes novel data instances from scratch. The most common synthesis approaches are based on generative adversarial networks (GAN) [49], where class-conditioned synthesis models are trained using an adversarial training strategy by imitating existing data samples. While data synthesis is usually performed in the audio signal domain [15; 50], [PERSON] et al. instead synthesized intermediate embedding vectors [51]. [PERSON] et al.  
generated acoustic scenes using the SampleRNN model architecture [52]. Recently proposed ASC algorithms use either mixup data augmentation or GAN based methods to augment the available amount of training data.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 1 Introduction  
The Internet of Drones (IoD) [1] represents an innovative network architecture facilitating the interaction of uncrewed aerial vehicles (UAVs), widely known as drones, in controlled airspace. In this environment, drones communicate with each other to exchangecritical information and are deployed in flight areas as needed. The IoD offers an architecture for efficient communication between autonomous aerial vehicles and ground network entities [2]. In this framework, drones operate as intelligent objects that undertake tasks including patrolling, environmental data sensing, and collecting real-time data. These data are transmitted to the ground control station (GCS), the central control authority, and are processed or delivered to remote mobile users [3]. The IoD provides advantages, including rapid deployment, high mobility, flexibility, easy redeployability, and real-time monitoring and coordination, making it applicable to numerous fields, such as smart agriculture, search and rescue, and surveillance systems [4].  
Although the IoD provides numerous benefits, it also presents several challenges that must be addressed. Organizations such as the National Institute of Standards and Technology (NIST) and the European Telecommunications Standards Institute (ETSI) provide crucial frameworks and guidelines for ensuring the security and interoperability of critical cyber-physical systems like UAVs. According to their documents, drones can be vulnerable to known attacks, including spoofing and replay, and a security protocol should be designed considering the limited computational capacity of drones. Furthermore, the open wireless communication environment and the battery-powered nature of drones raise significant security and privacy concerns [5]. Owing to their wireless channels and high mobility, drones are unreliable and vulnerable to data corruption, interference, and other security problems [6; 7; 8; 9]. The high sensitivity of data and open characteristics of the wireless communication channel increase the risk of potential security attacks, including identity and location information leaks and secret information extraction via physical capture attacks [10]. Furthermore, drones are lightweight devices with limited computing power and energy consumption; hence, security protocols requiring high computational complexity can be inefficient [11]. Therefore, this work proposes a secure and lightweight key agreement protocol between drones and mobile users using only cryptographic one-way hash functions and XOR operations while considering the characteristics of the IoD environment.
## Materials and Methods
### Data Processing and Analysis  
According to the different construction stages of the Three Gorges project, we selected five research time-points, namely, 1992, 1996, 2002, 2006, and 2012, to represent five stages of the project process, including the evaluation stage, main channel cutoff, formal impoundment, construction completion, and impoundment to 175 m for full operation, respectively. On this basis, the simulation results from the BIOME-BGC model together with the areas of different forest types derived from remote sensing images at research time-points with spatial geography information were used to analyze the dynamic changes in forest productivity and carbon storage in the reservoir area.  
The total forest net primary productivity (NPP) for each respective year was obtained by calculating the NPP values from the Biome-BGC model and the corresponding areas of various forest types for each district (county).  
\[NPP_{total}=\sum_{i=1}^{5}\sum_{j=1}^{20}NPP_{i,j}\times S_{i,j} \tag{1}\]  
\[NPP_{mean}=\frac{NPP_{total}}{\sum_{i=1}^{5}\sum_{j=1}^{20}S_{i,j}} \tag{2}\]  
where \(NPP_{total}\) is the total forest NPP in a certain year (Tg C); \(NPP_{i,j}\) and \(S_{i,j}\) represent the NPP values (t hm\({}^{-2}\) year\({}^{-1}\)) and area (hm\({}^{-2}\)) of the \(i\)-th (\(i\) =1,2,3,...,5) forest type in the \(j\)-th (\(j\) = 1,2,3,...,20) district (county) in this year; \(NPP_{mean}\) is the area-weighted average NPP value (t hm\({}^{-2}\) year\({}^{-1}\)) of the forest in the reservoir area in the respective year.  
The carbon stocks of forest vegetation, litter, and soil can be calculated by the carbon density values of the vegetation layer, the litter layer, and the soil layer obtained by the Biome-BGC and the corresponding area of various forest types for the district (county) in the respective year. Subsequently, the sum of the above three carbon stocks represents the total forest carbon stock.  
\[C_{vgg}=\sum_{i=1}^{5}\sum_{j=1}^{20}D_{vgg,i}\times S_{i,j} \tag{3}\]  
\[C_{ltr}=\sum_{i=1}^{5}\sum_{j=1}^{20}D_{ltrj,j}\times S_{i,j} \tag{4}\]  
\[C_{soil}=\sum_{i=1}^{5}\sum_{j=1}^{20}D_{soil,j}\times S_{i,j} \tag{5}\]  
\[C_{total}=C_{vgg}+C_{ltr}+C_{soil} \tag{6}\]  
where \(C_{vgg}\), \(C_{ltr}\), and \(C_{soil}\) are the carbon stocks of the vegetation layer, the litter layer, and the soil layer in a given year, respectively (Tg C); \(D_{vgg,i}\), \(D_{ltri,j}\), and \(D_{soil,j}\) are the carbon densities (t hm\({}^{-2}\)) of the forest vegetation layer, the litter layer, and the soil layer for the \(i\)-th (\(i\) = 1,2,3,...,5) forest type in the \(j\)-th (\(j\) = 1,2,3,...,20) district (county) in a given year, respectively; \(S_{i,j}\) (hm\({}^{-2}\)) is the current area of the \(i\)-th(_i_ = 1,2,3,...,5) forest type in the _j_-th (_j_ = 1,2,3,...,20) district (county); _Ctotal_ is the total forest carbon stock (Tg C) of the reservoir area in a given year.
# Secure and Scalable Internet of Things Model Using Post-Quantum MACsec
## 2 Problem Analysis  
### Secret Key Exchange in Quantum Computing Environment  
Quantum computing utilizes quantum phenomena such as superposition and entanglement to perform computations. Quantum computers, which operate on these principles, use qubits that are capable of representing multiple states simultaneously, a concept known as superposition [16]. This fundamental difference enables quantum computers to solve certain problems much more efficiently than classical computers. For example, [PERSON]'s algorithm, introduced in 1994, solves the prime factorization problem in quantum settings,posing a significant threat to public-key encryption systems like RSA, which rely on the difficulty of factorizing large prime numbers [10; 17]. In a similar vein, quantum environments might compromise other public key cryptosystems that depend on the solutions to discrete logarithm problems. Current Transport Layer Security (TLS) communications leveraging RSA, Diffie-Hellman (DH), and Elliptic Curve Diffie-Hellman (ECDH) are at a heightened risk of public key exposure with the emergence of quantum computers.  
Given these circumstances, the development of cryptographic systems has become crucial because they safeguard against quantum computing attacks. PQC encompasses such systems. Efforts have been directed toward developing \"multivariate cryptography\", \"code-based cryptography\", \"lattice-based cryptography\", and \"supersingular elliptic curve isogeny-based cryptography\", all of which have been deemed secure in a quantum computing environment [18].  
The advent of quantum computing necessitates the replacement of current cryptosystems across all devices, including those with limited computing power, such as IoT devices. Hence, the exploration of lightweight post-quantum cryptography is essential. NTRUEncrypt, employed in the current study, represents one such lightweight PQC. It is a lattice-based cryptography solution centered around the shortest vector problem in a lattice. NTRU operations, performed on objects within a truncated polynomial ring featuring convolution multiplication, enable significantly faster operations compared to RSA in OpenSSL, by a factor of 20\(\times\) to 200\(\times\)[19].
## 1 Introduction
The method involved the segmentation of the network into various clusters as this process can be used to locate any potential leak points in the water distribution network. [PERSON] et al. [28] investigated the application of the unsupervised clustering burst detection approach to a district metered area with numerous inlets and outputs. The use of the multiclass support vector machine model for the leakage detection in a large scale network has been explored in [24]. This method involves the subdivision of the water networks into leakage zones, and data were generated using the Monte Carlo method together with the hydraulic model. The result shows that the model could identify the leakage zones using the flow and pressure data. The drawback of this method is that of determining the number of clusters and the impact of the randomization of the first cluster on the clustering process. The use of the Bayesian system identification method for leakage detection in a water distribution network was proposed in [12; 29]. [PERSON] et al. [30] proposed a mixed model based on a data-driven approach to leak localization in a water distribution network; EPANET software 2.21 version was adopted to model the water networks after the calibration. The data used in training the models were obtained from the EPANET software for each possible fault, as well as different operating and uncertain conditions. The literature shows that much research has been conducted on the deployment of machine learning models for the detection and localization of leak events in a water distribution network. However, much of the literature is still lacking in comparisons between different machine learning classifiers for leak detection and localization in a water distribution network. The present study, however, proposes to fill this gap in the knowledge by comparing the ability and robustness of various supervised machine learning models to detect and localize leak events in a water distribution system that is based on the water network of the University of Port Harcourt, Nigeria.