esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Detection of Diabetic Hypertensive Retinopathy in Fundus Images Using Transfer Learning
## 3 Proposed Method
### Image Preprocessing
#### 3.1.1 Acquire Dataset  
Five types of fundus images classified as normal, mild diabetic, moderate diabetic, severe diabetic and hypertensive are considered for this work. All images in the category are taken from the publicly available databases, i.e., MESSIDOR and ODIR. Almost all the categorical images present in the dataset are equal.  
The acquired images cannot be used directly for screening and diagnosis as they have an imperfection in terms of noise, illumination, and so on. The variation of inter and intra pixels exists due to various reasons such as poor camera contrast, incorrect positioning of the lens, and so on. These variations in retinal images can affect the performance for the detection of various abnormalities present in DR and HR. Image pre-processing and enhancement eliminate the unwanted variations present in the image without significantly affecting the image data. Figure 1 shows the diverse steps involved in pre-processing.  
The pre-processing technique first acquires the dataset. The implementation of the proposed work is performed on MESSIDOR [17] and ODIR [18] datasets. Then, adaptive CLAHE is applied to increase the contrast of the color channel. Then, a pre-trained DNCNN model is applied to handle Gaussian denoising with unknown levels. For better image smoothing, the Wiener filter is used. In the following section, the explanation of all the methods is performed in detail.  
Figure 1: Pre-processing methodology.
# Reinforcement-Learning-Based Trajectory Learning in Frenet Frame for Autonomous Driving
## 3 Backgrounds
### Path Generation over Distribution  
Since our policy function will be trained to output trajectories, it is essential to model a probability distribution over trajectories. In this regard, Gaussian random paths (GRPs) [38] are mainly utilized to define and sample from the probability distribution at a trajectory level. GRPs utilize Gaussian process regression (GPR) to compute the Gaussian posterior distribution and to model the distribution of paths with a set of \(M\) anchoring points \(D=(\mathbf{s},\mathbf{x})=\{(s_{i},x_{i})|i=1,2,\ldots,M\}\), where \(s_{i}\) and \(x_{i}\) are \(i\)-th time index and two-dimensional position, respectively. Given a time \(T\) to pass through between the anchoring points, a Gaussian random path \(\mathcal{P}\) with a sequence of \(H\) test time indices \(\mathbf{t}_{test}=\{t_{i}=0,t_{1},\cdots,t_{H}=T\}\) defines a mean path \(\mu_{\mathcal{P}}\) and a covariance matrix \(\mathcal{K}_{\mathcal{P}}\), as follows,  
\[\mathcal{P}\sim\mathcal{N}(\mu_{\mathcal{P}},\mathcal{K}_{\mathcal{P}}), \tag{1}\]  
where  
\[\mu_{\mathcal{P}} =\mathbf{k}(\mathbf{t}_{test},\mathbf{s})^{T}(\mathbf{K}_{\mathbf{ s}}+\sigma_{w}^{2}I)^{-1}\mathbf{x}, \tag{2}\] \[\mathcal{K}_{\mathcal{P}} =\mathbf{K}_{test}-\mathbf{k}(\mathbf{t}_{test},\mathbf{s})^{T}( \mathbf{K}_{\mathbf{s}}+\sigma_{w}^{2}I)^{-1}\mathbf{k}(\mathbf{t}_{test}, \mathbf{s}), \tag{3}\]  
\(\mathbf{k}(\mathbf{t}_{test},\mathbf{s})\in\mathbb{R}^{H\times M}\) is a kernel matrix of time indices, where the element is \([\mathbf{k}(\mathbf{t}_{test},\mathbf{s})]_{(ij)}=k(t_{i},s_{j})\), \(\mathbf{K}_{\mathbf{s}}\in\mathbb{R}^{M\times M}\), where the elements are \([\mathbf{K}_{\mathbf{s}}]_{(ij)}=k(s_{i},s_{j})\) and \(\mathbf{K}_{test}\in\mathbb{R}^{H\times H}\), where the element is \([\mathbf{K}_{test}]_{(ij)}=k(t_{i},t_{j})\), which is the kernel matrix of anchoring time indices and test time indices, respectively. The trajectory \(\tau_{grp}\) is generated by sampling from GRPs.
# Kernel Clustering with a Differential Harmony Search Algorithm for Scheme Classification
## 4 Experiments
### Numerical Experiments of DHS
#### 4.1.1 Benchmark Function Tests
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Function** & & **HS** & **HHS** & **GHS** & **CHS** & **DHS** \\ \hline \multirow{3}{*}{Ackley function} & Mean & 8.49 \(\times\) 10\({}^{-3}\) & 6.16 \(\times\) 10\({}^{-3}\) & 3.95 \(\times\) 10\({}^{-3}\) & 1.12 \(\times\) 10\({}^{-2}\) & 1.57 \(\times\) 10\({}^{-13}\) \\  & Min & 3.29 \(\times\) 10\({}^{-3}\) & 2.44 \(\times\) 10\({}^{-3}\) & 1.35 \(\times\) 10\({}^{-5}\) & 3.56 \(\times\) 10\({}^{-3}\) & 4.57 \(\times\) 10\({}^{-14}\) \\  & Max & 1.62 \(\times\) 10\({}^{-2}\) & 1.16 \(\times\) 10\({}^{-2}\) & 2.10 \(\times\) 10\({}^{-2}\) & 2.54 \(\times\) 10\({}^{-2}\) & 4.69 \(\times\) 10\({}^{-13}\) \\  & Stdv & 2.43 \(\times\) 10\({}^{-3}\) & 1.67 \(\times\) 10\({}^{-3}\) & 3.48 \(\times\) 10\({}^{-3}\) & 3.79 \(\times\) 10\({}^{-3}\) & 6.78 \(\times\) 10\({}^{-14}\) \\ \hline \multirow{3}{*}{Griewank function} & Mean & 1.58 \(\times\) 10\({}^{-2}\) & 1.46 \(\times\) 10\({}^{-2}\) & 5.00 \(\times\) 10\({}^{-4}\) & 1.37 \(\times\) 10\({}^{-2}\) & 5.45 \(\times\) 10\({}^{-10}\) \\  & Min & 8.62 \(\times\) 10\({}^{-5}\) & 1.92 \(\times\) 10\({}^{-5}\) & 1.59 \(\times\) 10\({}^{-7}\) & 9.62 \(\times\) 10\({}^{-5}\) & 0 \\  & Max & 8.13 \(\times\) 10\({}^{-2}\) & 7.38 \(\times\) 10\({}^{-2}\) & 1.13 \(\times\) 10\({}^{-2}\) & 9.15 \(\times\) 10\({}^{-2}\) & 1.69 \(\times\) 10\({}^{-8}\) \\  & Stdv & 1.81 \(\times\) 10\({}^{-2}\) & 1.76 \(\times\) 10\({}^{-2}\) & 1.24 \(\times\) 10\({}^{-3}\) & 1.54 \(\times\) 10\({}^{-2}\) & 2.06 \(\times\) 10\({}^{-9}\) \\ \hline \multirow{3}{*}{Rastrigin function} & Mean & 1.49 \(\times\) 10\({}^{-4}\) & 8.20 \(\times\) 10\({}^{-5}\) & 7.44 \(\times\) 10\({}^{-5}\) & 2.45 \(\times\) 10\({}^{-4}\) & 1.02 \(\times\) 10\({}^{-12}\) \\  & Min & 2.66 \(\times\) 10\({}^{-5}\) & 1.35 \(\times\) 10\({}^{-5}\) & 1.82 \(\times\) 10\({}^{-8}\) & 4.33 \(\times\) 10\({}^{-5}\) & 0 \\  & Max & 3.30 \(\times\) 10\({}^{-4}\) & 2.01 \(\times\) 10\({}^{-4}\) & 1.17 \(\times\) 10\({}^{-3}\) & 9.92 \(\times\) 10\({}^{-4}\) & 6.20 \(\times\) 10\({}^{-11}\) \\  & Stdv & 7.13 \(\times\) 10\({}^{-5}\) & 4.22 \(\times\) 10\({}^{-5}\) & 1.64 \(\times\) 10\({}^{-4}\) & 1.42 \(\times\) 10\({}^{-4}\) & 6.17 \(\times\) 10\({}^{-12}\) \\ \hline \multirow{3}{*}{Rosenbrock function} & Mean & 2.10 & 1.98 & 1.77 & 2.32 & 7.16 \(\times\) 10\({}^{-1}\) \\  & Min & 5.65 \(\times\) 10\({}^{-3}\) & 1.00 \(\times\) 10\({}^{-2}\) & 1. We choose one typical unimodal function and one multimodal function to test the convergence speed of the algorithms. Figure 2 shows the convergence of the DHS algorithm compared to the variants of HS. The results clearly show that the DHS algorithm converges faster than other variants of HS.
# Iterative Matrix Techniques Based on Averages
## 8 Solving the Equation \(Kf=\lambda f\)  
Let us consider now the following equation:  
\[Kf(x)=\int_{a}^{b}k(x,y)f(y)dy=\lambda f(x),\]  
where the kernel is separable: \(k(x,y)=\sum_{j=1}^{r}\alpha_{j}(x)\beta_{j}(x)\). It is obvious that this equation has a non trivial solution if \(\lambda\) belongs to the point spectrum of \(K\). In this case the solution is any eigenfunction \(\varphi\) associated with \(\lambda\). For instance, let us solve the following equation:  
\[Kf(x)=\int_{0}^{1}(8 xy-10x^{2}y^{2})f(y)=f(x),\]  
where \(k(x,y)\) is a separable kernel with \(\alpha_{1}(x)=8x\), \(\alpha_{2}(x)=-10x^{2}\), \(\beta_{1}(y)=y\), \(\beta_{2}(y)=y^{2}\). In this case the eigenvalue is \(\lambda=1\). The matrix associated with the operator, \(A_{ij}=\int_{0}^{1}\beta_{i}(y)\alpha_{j}(y)dy\), is as follows:  
\[A=\begin{pmatrix}8/3&-5/2\\ 2&-2\end{pmatrix}\]  
We look for an eigenfunction of the form: \(\varphi=c_{1}\alpha_{1}+c_{2}\alpha_{2}\). The system of equations to find the coefficients is as follows:  
\[A\begin{pmatrix}c_{1}\\ c_{2}\end{pmatrix}=\begin{pmatrix}8/3&-5/2\\ 2&-2\end{pmatrix}\begin{pmatrix}c_{1}\\ c_{2}\end{pmatrix}=\begin{pmatrix}c_{1}\\ c_{2}\end{pmatrix}\]  
The spectral radius of \(A\) is \(\rho(A)=1\). Consequently, the usual iterative methods to solve this system do not converge. We apply the N-iteration to the matrix \(A\) starting at \(C_{0}=(1,1)\) and we obtain a sequence of vectors for the approximate solutions of \(\varphi\) in terms of the maps\(\alpha_{i}\). The results are collected in Table 6. The last outcome is \(c_{1}=0.375075\), \(c_{2}=0.25009\), providing the approximate solution:  
\[\varphi(x)=\sum_{j=1}^{2}c_{j}\alpha_{i}(x)=3.0006x-2.5009x^{2},\]  
with stop criterion \(||C_{n}-C_{n-1}||<10^{-3}\), where \(C_{n}\) is the vector of coefficients computed at the \(n\)-th iteration. An exact solution is \(\varphi(x)=3x-2.5x^{2}\).  
**Remark 5**: _All the computations have been performed using Mathematica software._
# Design of a Heterogeneous-Based Network Intrusion Detection System and Compiler
## 3 Materials and Methods  
### Requirements Analysis  
As described in Section 2 (Related Work), existing heterogeneous intrusion detection systems and rule compilation research still exhibit deficiencies such as insufficient throughput, single-task offloading mechanisms, and poor rule compilation compatibility. To address these limitations, this study proposes corresponding improvements in system design and rule compiler architecture. As illustrated in Figure 1 the overall system architecture is structured as follows:  
The heterogeneous network intrusion detection system comprises two components: a front-end FPGA-based Pre-Filtering module and a back-end CPU-based intrusion detection and rule compilation module. The former performs unified pre-filtering on all incoming traffic to reduce the detection load on the CPU. By designing a reliable and efficient FPGA pre-filtering acceleration framework, it achieves collaborative intrusion detection through five-tuple matching and fast-pattern matching. The latter operates the software-side Snort3 engine to identify anomalous traffic matching rule characteristics and compiles rule sets into FPGA MIF.  
Within the rule compilation module, five-tuple matching compilation is subdivided into port compilation and protocol/IP compilation based on rule features and traffic characteristics. Fast-pattern matching compilation is designed to include hash bitmap construction, shift-or mask computation, and rule mapping. Each component splits the corresponding rule data, then calculates, maps, and stores them into MIF according to the respective algorithms.
## 6 Discussion
### Perceived Physical Safety and Physical Posture  
In terms of PPS, this study provides insights into assessing perceived physical safety in immersive exercise for older adults. Previous models of technology acceptance among older adults lacked a theoretical construct of perceived physical safety as a conceptual factor within the domain of immersive exergames. Although there are studies that focus on the safety of older adults exercising through technologies, they are not self-reported by the users, but rather, the relevant data are obtained through objective techniques [65; 66]. Behavioral acceptance of exercise as a physical activity in older adults in conjunction with virtual reality technology should focus on their perceived physical environment and physical safety. In addition, the exercise posture adopted by the user was proposed as a factor in the theoretical model having a significant effect on PE and PPS. The analyses verified that the measurement items of the PPS were reliable and had acceptable validity.  
Further, older adults were not as concerned about the perceived physical safety of immersive exercise compared to previously studied concerns about safety [67; 68; 69]. Instead, perceived physical safety was negatively related to the acquisition of health performance expected by older adults, and standing posture was expected to be higher for PE compared to sitting posture. Future immersive exergame designs for older adults could, therefore, go beyond upper limb-only activities such as sitting. To a certain extent, immersive exergames with standing postures and a certain amount of movement space can give older adults more stimulation and a better game experience. In addition, users with standing posture expect more health benefits from immersive exergames.
# Classification of Diabetic Retinopathy Based on Efficient Computational Modeling
## 3 Methodology
### Focal Loss Function  
In target detection tasks, there is usually an extreme imbalance in the number of positive and negative samples. Taking an image as an example, the number of candidate frames (positive samples) that can match the target may be only a dozen or tens of frames, while the number of unmatched candidate frames (negative samples) may be as high as tens of thousands or even hundreds of thousands. This imbalance causes the model to easily favor the negative samples during training, thus neglecting the learning of the positive samples. In addition, even among the negative samples, there is an imbalance of hard and easy samples, i.e., most of the negative samples are easy to categorize, while a small number of hard-to- categorize negative samples are crucial for model performance.  
Focal loss is an effective method to deal with the class imbalance problem, by introducing a focal factor and adjusting the sample weights, it makes the model pay more attention to the difficulty-to-classify samples, thus improving the classification performance [51]. It is especially suitable for target detection and other category imbalance tasks. The formula for focal loss is as follows:  
\[FL(p_{t})=-a_{t}(1-p_{t})^{7}log(p_{t}) \tag{1}\]  
Figure 2: Improved VSS module.  
\(p_{t}\) is the model's predicted probability for the target class, \(a_{t}\) is a balancing factor to adjust for the effects between positive and negative samples, \(\gamma\) is the focus factor, which is used to adjust the weights for difficult samples. The modulation factor \((1-p_{t})^{7}\) is used to reduce the loss ratio of easy-to-distinguish samples. Regardless of the foreground or background class, the larger the \(p_{t}\) the easier it is to distinguish the sample, and the smaller the modulation factor.  
Focal loss, as a loss function designed to solve the problem of category imbalance and imbalance between hard and easy samples, has shown great potential in the field of deep learning. By reducing the weight of easy-to-categorize samples and increasing the weight of hard-to-categorize samples, focal loss also enables the model to focus more on samples that are difficult to categorize correctly, thus improving the overall performance. As deep learning technology continues to evolve, focal loss is expected to play a greater role in future applications.
## 3 Results
### XRD and Raman Analysis  
XRD curves of the prepared samples are displayed in Figure 4a. The diffractograms of the materials showed two broad peaks at approximately 22.6\({}^{\circ}\) (002) and 43.6\({}^{\circ}\) (100). The peaks at 22.6\({}^{\circ}\) and 43.6\({}^{\circ}\) correspond to an amorphous carbon portion and graphitic carbon, respectively. We observed a decrease in the intensity of the peaks related to the N-doped carbon aerogel, which might correspond to the entry of heteroatoms into the graphite carbon lattice. We investigated the graphitic structures of the carbon aerogel and N-doped carbon aerogel by Raman spectroscopy. In their Raman spectra, both exhibited two peaks at 1337.5 cm\({}^{-1}\) (D band) and 1594.5 cm\({}^{-1}\) (G band). The D band is related to structural defects of disordered carbon, whereas the G band is associated with the ordered carbon vibration [3,48]. The ratio of \(\mathrm{I_{D}/I_{G}}\) could appraise the graphitic structure of synthesized materials, and the calculated values were 0.94 and 0.98 for the carbon aerogel and N-doped carbon aerogel, respectively, indicating a high degree of graphitization in both samples, in agreement with the XRD results. Graphitization of carbon materials increases their electric conductivity and decreases the equivalent series resistance of supercapacitors; thus, these features will likely affect the impedance behavior [53].
## 2 Error Sources Analysis of Antenna Holographic Measurement  
The aperture field distribution can be obtained by Fourier transform of the far-field data. In turn, we can also reverse the far-field data of the antenna by constructing the aperture field model. In this section, we introduce the theoretical foundation of holographic measurement and establish models of the influence of different error sources on the far-field pattern by using the aperture field.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development  
Since biofilm confers high virulence and strong resistance to mycobacteria, it is important to determine the genes and molecules involved in its development. Illumina RNA-seq technology was used by [PERSON] and colleagues to detect these relative genes, revealing that the expression levels of 437 biofilm cell genes were different from their counterparts in planktonic cells. Specifically, 284 genes were upregulated, while 153 genes were downregulated. These results were also validated by qRT-PCR detections (2018). [PERSON] divided 115 biofilm- and quorum-sensing-associated proteins (BQAPs) into seven functional categories (2018). Here, we will discuss some of them in detail. Their functions and mechanisms in mycobacterial biofilm development are summarized in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factors** & **Gene Function** & **Effect on Biofilm** & **Mechanism** & **Reference** \\ \hline LpqY-Sug-ABC & & & Return trehalose to the & & \\ transporter (\(lpqY,susA\), \(subB\), \(sq\)C) & & & Promote biofilm & bacteria, which assists the & (2018) \\  & \(\qquad\qquad\) & formation & efflux of mycolic acids & (_M. tuberculosis_) \\  & & & & outside the bacteria & \\ Pls1 (\(Rv2946c\)) & & Polykteide synthase & & Promote biofilm & \\  & & involved in lipid & maturation & Not clarified & (2018) \\  & & synthesis & & & (M. tuberculosis) \\  & & & & & \\  & & & & & \\ PpiB (\(Rv2582\)) & & & & & \\  & & accelerating the folding & formation & Not clarified & (2018) \\  & & of proteins & & & \\ GroEL1 (\(Rv3417c\)) & 60 kDa chaperonin & & Promote biofilm & FASII components to & [29; 30] \\  & 1 promoting the & & maturation & modulate the synthesis of & (_M. smegmatis_ \& M. _smegmatis_ \& M. _smegmatis_) \\  & & & mycolate and mycolic acid & & _bovis_) \\  & & & promote biofilm & Sensor of epinephrine & (_M. smegmatis_) \\  & & & formation & Signal of nutritional stress, & [32] \\  & & & formation & regulated by Rel\({}_{\rm Mtb}\) & (_M. tuberculosis_) \\ Poly(P) & - & & Promote biofilm & & \\  & & formation & response, homeostasis & [33] \\  & & & is required & (M. tuberculosis) \\  & & & Promote biofilm & & \\ Cyclic-di-GMP & - & formation; associated & Regulate quorum sensing & (_M. smegmatis_) \\  & & with biofilm dispersal & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Genes and molecules involved in mycobacterial biofilm development.
## 4 Anomaly Detection Method Based on DeepESN
### DeepESN Anomaly Detection Architecture  
The whole architecture proposed here is composed of the DeepESN model and the anomaly detector. The three phases that make up the global architecture are represented in Figure 6. To work in the inference phase, the anomaly detector requires \(\epsilon\) and \(\sigma\) as hyperparameters. To compute them and determine the best hyperparameters for the construction of  
Figure 4: Deep echo state network architecture.  
Figure 5: Anomaly detector architecture.  
the DeepESN model, the model selection and the model assessment phase are performed. For both of them, three steps must be performed (see Figure 6), and these are called, respectively, model training, \(\sigma\) computation, and the evaluation phase. During model selection, the performances of different combinations of hyperparameters are registered together with a related metric result in order to determine the best hyperparameters for the DeepESN model and the best \(\epsilon\) hyperparameter. In this phase, the training set is used to train the model and to compute \(\sigma\), while the evaluation phase is run with the validation set. The true \(\sigma\) used in inference is computed in the model assessment phase. In the model assessment phase, after retraining the model, it is initialized using the best selected hyperparameters, and \(\sigma\) is computed on the training set. The final evaluation phase is performed on the test set to retrieve the final results of the method's performance.  
The model training phase is described in Section 3 concerning preliminaries, while the computation of \(\sigma\) is conducted by taking the trained model and computing the prediction for each time series in the training set. Then, the predictions are compared with the true outputs to get the squared error. In the end, the standard deviation of all the squared errors, called \(\sigma\), has been computed.
# Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study
## 3 Detecting Echo Chambers in Online Social Platforms  
The lack of a formal and actionable echo chamber definition and of a standard strategy to support their identification has, over the years, led to conflicting experimental observations, namely scientific studies whose comparison is rather unfair due to their hard-wiring to platforms' specific characteristics. In this regard, here we propose a general framework to detect echo chambers built on top of features commonly shared by most of online social platforms.  
Before discussing our framework, it is important to fix--in an actionable way--the object of our investigation along with its expected properties.  
**Definition 1** (Echo Chamber): _Given a network describing users' interactions centered on a controversial topic, an echo chamber is a subset of the network nodes (users) who share the same ideology and tend to have dense connections primarily within the same group._  
Following 1, to assess an EC's existence we cannot rely on a single user's digital traces (i.e., following a _micro-scale approach_), nor suppose that all users in the network belong to a polarized community (i.e., assuming a _macro-scale approach_). The proposed EC definition relies on _meso-scale_ topologies. We focus on this particular definition of EC since we are interested in the role that group dynamics play in the increase in polarization. Moreover, we believe that it is quite unrealistic that all the users involved in a controversial debate insulate themselves in an echo chamber. Our framework composes of four steps to identify EC starting from online social platform data, namely (i) controversial issue identification; (ii) users' ideology inference; (iii) debate network construction; (iv) homogeneous meso-scale users' clusters identification.  
In this section, we provide an overview of each of such steps, describing their rationale and supporting our claim of framework generality by providing examples of its applicability to multiple platforms. Subsequently, in Section 4 we propose a specific case study involving the identification and analysis of echo chambers in a well-known online social platform, Reddit. There, we describe a specific instance of the proposed framework and discuss the choices made while implementing its pipeline.