esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
[20] used ML algorithms for the water segmentation in GDLs of PEFCs. For high-temperature PEFCs, [PERSON] et al. utilized an NN to predict performance for a variety of parameters [21], achieving a percentage accuracy compared to numerical methods. [PERSON] et al. [22] investigated the accuracy of gray-box models and applied this technique to the triple-phase boundaries (TPBs) in the micro-structure of a solid oxide fuel cell (SOFC) anode. Meanwhile, [PERSON] et al. [23] presented a data-driven framework for the characterization of porous materials, coupling ML methods with genetic algorithms. The permeability was in the focus of their recent studies. In the field of geoscience, ML and DL methods are well-established. [PERSON] et al. [24] used DL for the prediction of relative permeability in real time. However, there are also pitfalls in the use of AI. As reported by [PERSON] [25], an AI program that declassified world-class Go players was recently defeated by an amateur Go player, underlining the fact that any AI code is bounded by the scope of its training data. Although the training and classical validation was performed on artificial data, real data from image sources can suffer from image errors, which are caused by several factors. In Section 2, methods, both AI-related and physics-based, are briefly summarized. Furthermore, several groups of image data are introduced. The preparation of the data for the training process and the subsequent prediction are described in Section 3. The validation in Section 4 addresses not only the validation of the artificial geometry data but also aspects that occurred in real data obtained from image sources. The results are presented in Section 5. After discussing them in Section 6, the work is summarized in Section 7. Previous work [12] was extended in order to apply its previously developed CNN to real image data. The previously developed CNN [12] is able to predict the TP permeability of artificial GDL structures--uncompressed and compressed (Section 4). Reconstructed images from real GDLs, however, can suffer from image artifacts as discussed in Section 5. This work analyzes the ability to predict the characteristics of real micro-structures using a CNN that was trained on artificial ones, and how to decide about the consistency.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 3 Research Methodology
### Deep Learning Models  
This empirical study built four phishing detection models using four different DL algorithms: DNN, CNN, LSTM, and GRU. The general architecture of a typical DL-based phishing detection model consists of an input layer, one or more middle layers, and one output layer. Inputs to each DL model are website features that have already been converted to feature vectors. A total of 30 features were used in this study and hence, there were 30 neurons in the input layer of the neural network architecture. Different DL algorithms were used in the middle layers to build the phishing detection models, including DNN, CNN, LSTM, and GRU. Finally, only one neuron was used with a sigmoid activation function in the output layer to classify the web page as malicious or benign. DNN. A DNN structure consists of an input layer, an output layer, and one or more hidden layers [18], as shown in Figure 4a. Each node or neuron in one layer is connected to the other nodes in the next layer to form a dense or fully-connected layer [19]. The number of hidden layers and neurons in each hidden layer can vary. The activation functions used in the hidden layers and the output layer are ReLU and sigmoid, respectively. Researchers need to fine-tune these parameters to find the optimal values that provide the highest detection accuracy. CNN. The architecture of a CNN model generally consists of three basic layers: a convolutional layer, a pooling layer, and a fully connected layer [37]. Firstly, a convolutional layer is used for feature extraction and consists of multiple convolutional kernels or filters that divide the input vectors into small blocks. Then, a series of feature maps are generated by performing convolutional operations on the input vectors with the chosen kernels [10]. Secondly, a pooling layer is utilized for dimensional reduction by reducing the dimensionality of the feature maps. The pooling layer has two functions: accelerate the network operation and improve the performance of the entire convolutional network [24]. Thirdly, a fully connected (FC) layer is responsible for classification purposes. FC layer is a
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
## 2 Related Works
developed a proprietary YouTube video dataset containing 5000 streams from 100 categories across different channels, achieving 90% classification accuracy by combining the CNN model with Packets-Per-Second (PPS) features [23][PERSON] [PERSON] et al. evaluated video traffic classification across seven popular mobile applications: Netflix, YouTube, YouTube Live, Twitch, Spotify, WhatsApp, and Skype, using statistical models such as Mixture of Markov Components (MMC), k-Nearest Markov Component (kNMC), and k-Nearest Markov Parameter (kNMP)[PERSON] Their dataset, approximately 6 GB in size, covered four categories: Video on Demand (VoD), Video Live Streaming (VLS), Sharing and Media Streaming (S&MS), and Teleconferencing (TC) [24]. In general, whether using public or proprietary Internet traffic datasets, video traffic is typically treated as a distinct category, though the labeling definitions for this category vary significantly[PERSON] Researchers consider several factors when defining labels[PERSON] First, they consider a priori knowledge about the video, including the web platform, application, content labels, source encoding, resolution, and refresh rate. Second is metadata from the video transmission process, such as transport protocols (e.g., Transmission Control Protocol (TCP), User Datagram Protocol (UDP), and Quick UDP Internet Connections (QUIC)) and streaming technologies (e.g., Dynamic Adaptive Streaming over HTTP (DASH), HTTP Live Streaming (HLS), VoD, and VLS). Additional factors include QoS and QoE metrics, as well as the utilization of Tor, VPNs, and other technologies. These elements impose higher demands for achieving fine-grained network video traffic classification. However, most existing proprietary Internet video traffic datasets still have some limitations, including non-disclosure of the dataset, small sample sizes, weak correlations between samples and the aforementioned video-related factors, and limited interpretability. To address these gaps, we develop three algorithms for generating proprietary Internet video traffic datasets. The main contributions of this paper can be summarized as follows:  
* A novel and explainable proprietary Internet video traffic dataset generation algorithm is proposed, which generates fifteen corresponding datasets. * The resulting datasets demonstrate excellent performance metrics, including completeness, consistency, and transparency. The rest of this paper is organized as follows: Section 3 outlines the research methodology, covering the NFStream preprocessing algorithm, the proprietary Internet video traffic dataset generation algorithm, and the CICFlowmeter preprocessing algorithm. Section 4 provides an analysis of the proprietary Internet video traffic datasets, focusing on imbalance ratios, video traffic histogram density, and feature correlation. Section 5 concludes the paper.
# Robustness and Sensitivity Tuning of the Kalman Filter for Speech Enhancement
## 5 Results and Discussion
### Subjective Evaluation by AB Listening Test  
The mean preference score (%) comparisons for all methods are shown in Figures 13 and 14. The non-stationary (_bubble_) noise experiment in Figure 13 reveals that the proposed method is widely preferred (73%) by the listeners to that of the benchmark methods, apart from the clean speech (100 %) and the KF-oracle method (81%). Amongst the benchmark methods, MDKF-MMSE [17] is most preferred (65%) with AKF-RMBT [22] (60%). Although the AKF-IT [22] produced distorted speech, as confirmed by objective PESQ, SDR, and STOI score comparison as well as spectrogram analysis, the listeners prefer it (47%) over MMSE-STSA [9] (31%). The subjective testing implies that it was considered as an improvement of noise reduction in the speech region than a distortion. The colored (f16) noise experiment (Figure 14) also confirms that the proposed method achieves a significant preference score (75%) compared to the benchmark methods, excepting the  
Figure 11: Comparing the spectrograms of: (**a**) clean speech (utterance sp05), (**b**) noisy speech (corrupt sp05 with 5 dB _bubble_ noise) (PESQ = 2.10), enhanced speech produced by the: (**c**) KF-non-oracle (PESQ = 2.18), (**d**) MMSE-STSA [9] (PESQ = 2.32), (**e**) AKF-IT [13] (PESQ = 2.26), (**f**) AKF-RMBT [22] (PESQ = 2.42), (**g**) MDKF-MMSE (PESQ = 2.48), (**h**) proposed (PESQ = 2.55), and (**i**) KF-nracle (PESQ = 2.61) methods.  
Figure 12: Comparing the spectrograms of: (**a**) clean speech (utterance sp05), (**b**) noisy speech (corrupt sp05 with 5 dB/\(\hat{t}\)6 noise) (PESQ = 2.14), enhanced speech produced by the: (**c**) KF-non-oracle (PESQ = 2.26), (**d**) MMSE-STSA [9] (PESQ = 2.39), (**e**) AKF-IT [13] (PESQ = 2.31), (**f**) AKF-RMBT [22] (PESQ = 2.53), (**g**) MDKF-MMSE (PESQ = 2.58), (**h**) proposed (PESQ = 2.65), and (**i**) KF-oracle (PESQ = 2.70) methods.  
clean speech (100%) and the KF-oracle method (82%). Among the benchmark methods, MDKF-MMSE [17] is found to be the most preferred (67%) with AKF-RMBT [22] (63%). In light of the blind AB listening tests, it is evident that the enhanced speech produced by the proposed method ensures the best perceived quality amongst all tested methods for both male and female utterances corrupted by real-life non-stationary as well as colored noises.
# State-Bust Feedback Control for Fault Recovery of Input/State Asynchronous Sequential Machines
## 3 Diagnoser
To comply with fundamental mode, \(G\) should provide \(C\) with \((x,m)\) only when \(\Sigma\) stays at a stable state. To this end, the end of every stable transition must be identified. In the nominal transitions, it is easily done by referring to \(z\) and \(u\) since the current stable transition will end at \(z_{l}=s(z_{f},u)\). In the case of unauthorized transitions, however, one must determine it only by referring to \(z\), as \(w\) is unobservable. The latter property is termed _fault detectability_[31]. The condition for fault detectability with respect to the state burst is that any state burst generated during the unauthorized transition is not a strict prefix of another one (Theorem 3.7 of [31]). The underlying reason is obvious as elicited in the following. Proposition 2: _For \(x\in X\) with \(W(x)\
eq\emptyset\), \(\Sigma\) is fault detectable at \(x\) if and only if \(\alpha(x,w)\
otin sp(\alpha(x,w^{\prime}))\), \(\forall w,w^{\prime}\in W(x)\) with \(\alpha(x,w)\
eq\alpha(x,w^{\prime})\)._  
Proof: (Only if) Assume that \(\Sigma\) is fault detectable at \(x\) but \(\alpha(x,w_{1})\in sp(\alpha(x,w_{2}))\) for some \(\{w_{1},w_{2}\}\subset W(x)\). Assume further that while \(u\) remains unchanged, \(G\) receives the changed state burst \(\alpha(x,w_{1})\). Then, one cannot determine whether \(w_{1}\) occurs so that \(\Sigma\) reaches the faulty state \(\alpha_{l}(x,w_{1})\in\Gamma(x)\), or \(w_{2}\) occurs so that \(\Sigma\) is on its way to the corresponding faulty state \(\alpha_{l}(x,w_{2})\in\Gamma(x)\), passing through the intermediate transient state \(\alpha_{l}(x,w_{1})\). This contradicts the assumption of fault detectability at \(x\). (If) Suppose that \(G\) receives \(z=\alpha(x,w)\) (\(w\) is unknown) such that \((z_{f},u,z_{l})\
otin s\). Since \(\alpha(x,w)\
otin sp(\alpha(x,w^{\prime}))\) for all \(w^{\prime}\in W(x)\) with \(\alpha(x,w)\
eq\alpha(x,w^{\prime})\), one can identify the end of the unauthorized transition merely by referring to \(z\). Hence \(\Sigma\) is fault detectable at \(x\).
## 2 Medical Background: Retina Anatomy in OCT  
The retina contains several layers with different thicknesses and intensities. Separating and determining the thickness of each layer are taken as vital markers in evaluating the health condition of the retina. The shape and width of the individual layers are considered two of the most important factors that reveal the current status or progress of a disease [5]. The layers may thicken or thin over the course of different diseases. Hence, many retinal diseases can be diagnosed through detecting irregularities in OCT images. Recently, there are many studies, which are conducted to analyze the OCT retinal images and to classify the healthy from diseased patients.  
DME, CNV and drusen diseases are considered in this study. The anatomy of these diseases is portrayed and contrasted to normal anatomy in Figure 1. A brief description of each of the considered diseases in this study is given below.  
Macular edema is the swelling of the central part of the retina [6]. It is considered one of the most common retinal disorders. Increased blood sugar levels, in people with diabetes, can damage the retinal blood vessels or result in tiny bulges protruding from the vessel walls, leaking or oozing fluid and blood into the retina. This leads to the serious eye complication called Diabetic Macular Edema (DME) [7], which may cause vision problems or blindness.  
Another type of retinal disorder is Choroidal Neovascularization (CNV), it is the creation of new blood vessels in the choroid layer of the eye. Neovascular Degenerative maculopathy is commonly accompanied with extreme myopia, malignant myopic degeneration or age-related developments [8]. CNV is a major cause of visual loss.  
Another type of retinal disorder is drusen. It is small yellow deposits of fatty proteins (lipids) that accumulate under the retina. The retina is a thin layer of tissue that lines the back of the inside of the eye, near the optic nerve. The retina contains light-sensing cells that are essential for vision [9]. Drusen are like tiny pebbles of debris that build up over time and can result in central vision loss. Central vision allows us to focus on details straight ahead [9].  
The presence of overt changes in retina layers with various diseases in OCT images, encourage the development of diagnostic systems to differentiate between these diseases automatically. Computer aided diagnosis can support ophthalmologists with accurate timely decisions, thus leading to promoting health care provision.
# Blockchain: Current Challenges and Future Prospects/Applications
## 2 The Unique Value of Blockchain
### Music/Movies  
Starting in the early 1990s, as computers' memory increased manifold and the Internet became faster and widely used, users were able to download any song they wanted without paying or, alternatively, exchange music among themselves. The effect of free downloading/exchanging of music had serious repercussions for the music industry that suffered great financial losses, leaving artists, record labels, and everyone else concerned in an extremely difficult situation, with many firms going bankrupt. Interestingly, the same situation repeated itself with movies 20 years later when high Internet speeds and larger computer storage made downloading movies in short time spans practical. Blockchain is a way to solve the problem of illegal downloading with several innovative ideas being introduced to pay artists and studios for legal downloads. For instance, Mycelia's mission is to \"empower a fair, sustainable and vibrant music industry ecosystem involving all online music interaction services.\" Its blockchain-based platform allows musicians to introduce smart contracts for the sharing of music by ensuring that profits go to the artists and not to the middlemen. Another blockchain-based company, SingularDTV, is setting the foundation for a decentralized entertainment industry by building an Ethereum application empowering artists and allowing them to receive loyalties for their work directly, avoiding the big companies that would receive the lion's share of revenues. The major objective of firms like Mycelia and SingularDTV is to avoid the middlemen, but also bureaucratic obstacles, while reducing the costs when a payment for downloading music or a movie is made.  
Another direction for the music and movie industry is to provide unlimited usage of music or movies with small monthly fees. This is done by firms such as Spotify and Netflix that pay artists a loyalty, often tiny, each time a song is played or a movie is watched. The low monthly fee and the ease of listening/viewing copyrighted content are the disincentives to illegal downloads, creating a win/win situation. At the same time, intermediaries like Spotify and Netflix build great monopolistic power imposing their terms and keeping the largest part of the received revenues, putting the creators of music/movies at a disadvantage. The ultimate purpose of blockchain is to abolish, or at least reduce the power of monopolies, and according to its proponents this will eventually happen using decentralized structures allowing creators to interact directly with users, making intermediaries obsolete. Will this ever become possible? Even if the answer is no, blockchain can substantially reduce the power of monopolies, as interest groups can be created whose members can obtain services directly from artists or other providers. As blockchain technology improves and transaction costs are further reduced, the decrease in the power of monopolies may accelerate, creating the democratization of power between consumers and firms.
## 3 Rotational Feature Enhancement FCOS
### Rotation-Invariant Learning Module
#### 3.1.2 Cross-Gated Channel-Spatial Attention Module  
To adaptively highlight critical rotation-related regions and channels, we propose the cross-gated channel-spatial attention (CG-CSA) module. Given the input feature map \(F_{mix}\in\mathbb{R}^{\mathbf{H}\times\mathbf{W}\times 2\mathbf{56}}\), we concurrently compute the channel attention map \(a_{c}\) and the spatial attention map \(a_{s}\) through separate attention pathways:  
Figure 3: The structure of the RFR module.  
\[a_{c}=\sigma(MLP(GAP(F_{mix}))),\quad a_{c}\in\mathbb{R}^{\mathbf{1}\times\mathbf{1 }\times\mathbf{256}}, \tag{5}\]  
\[a_{s}=\sigma(Conv_{3\times 3}(F_{mix})),\quad a_{s}\in\mathbb{R}^{\mathbf{H}\times \mathbf{W}\times\mathbf{1}}, \tag{6}\]  
where \(\sigma\) denotes sigmoid activation. Furthermore, we introduce the cross-gating operation to enhance the synergy between channel and spatial attention branches. Specifically, calculating the mean of \(a_{s}\) across the spatial dimension produces the gating factor \(b_{c}\), utilized to modify the channel attention; concurrently, calculating the mean of \(a_{c}\) across the channel dimension generates the gating factor \(b_{s}\), employed to adjust the spatial attention. Equations (7) and (8) demonstrate the generation of the gating factors and the modified attention maps.  
\[\beta_{c}=\frac{1}{H\times W}\sum_{h=1}^{H}\sum_{w=1}^{W}a_{s}(h,w,1),\qquad \beta_{s}=\frac{1}{C}\sum_{c=1}^{c}a_{c}(1,1,c), \tag{7}\]  
\[a_{c}^{{}^{\prime}}=a_{c}\cdot\beta_{c},\qquad a_{s}^{{}^{\prime}}=a_{s}\cdot \beta_{s}. \tag{8}\]  
Ultimately, the updated channel attention \(a_{c}^{{}^{\prime}}\) and spatial attention \(a_{s}^{{}^{\prime}}\) are multiplied element-wise to produce an integrated attention representation \(A_{cs}\in\mathbb{R}^{\mathbf{H}\times\mathbf{W}\times\mathbf{256}}\):  
\[A_{cs}(h,w,c)=a_{c}^{{}^{\prime}}(c)\cdot a_{s}^{{}^{\prime}}(h,w),\quad A_{ cs}\in\mathbb{R}^{\mathbf{H}\times\mathbf{W}\times\mathbf{256}}, \tag{9}\]  
where \(A_{cs}\) is directly treated as the output of the CG-CSA module. This module utilizes cross-gating to strengthen sensitive information in each attention branch, efficiently diminishing unnecessary features and highlighting rotation-critical signals, hence improving the discriminability and robustness of the resulting feature. The structure of our CG-CSA module is presented in Figure 4.
# Dynamic Multi-Compartment Vehicle Routing Problem for Smart Waste Collection +
## 4 Mathematical Modelling
### Parameters  
* \(0_{l}^{v}\) : The last station served by the vehicle \(v\) at the time slice \(t_{l-1}\)
* \(\text{CP}_{w}\) : The bin capacity of type w (in kg)
* C: traveling cost per distance unit
* \(d_{ij}\) : Distance between node \(i\) and node \(j\)
* \(q_{wi}\) : The quantity of waste in the bin (in kg) of type \(w\) in station \(i\) (calculated using the information sent by the sensor in \(m^{3}\), which is converted to kg)
* \(Q_{w}\) : The sum of the quantities alarmed at all stations for each waste type \(w\)
* \(Cq_{m}\) : The capacity of compartment \(m\) for each vehicle (in kg)
* \(p_{wi}\) : Penalty for bin overflow for type \(w\) in station \(i\)
* \(\varrho_{w}\): The maximum threshold for the waste amount in the bin \(w\)
* \(\hat{m}\) : Number of compartments for each vehicle (it is assumed that the number of waste types and the number of bins are equal in each station)
* \(h_{w}\) : Number of containers that have alarmed for type \(w\)
## 3 Results and Analysis
### Numerical Analysis of Hybrid EYDF Amplifier
The variation trend in the signal and pump light indicates that more gain and pump absorption can be provided by the multimode fiber. Comparing the signal in the hybrid fiber with and without MMI effect, the attenuation effect of the MMI effect on forward and backward ASE is inferred as another reason for obtaining higher efficiency in the hybrid fiber amplifier. The reduction in backward and forward ASE by the MMI effect can improve the small-signal gain in segment 1 and the energy conversion between Er\({}^{3+}\) and Yb\({}^{3+}\) in segment 3, respectively. The signal power without MMI was 1.52 W at \(z_{1}\) position, while the power with MMI was 1.57 W, which can be further demonstrated by the simulation spectra of the hybrid fiber amplifier at the 1-\(\upmu\)m and 1.5-\(\upmu\)m band. The forward spectra are represented by solid lines in the figure, and the backward spectra are dashed lines. Compared with the uniform fiber amplifier, the forward and backward Yb-ASE spectra of the hybrid fiber amplifier with MMI effect are periodically modulated in Figure 3b. However, the forward and backward Er-ASE at the 1.5 \(\upmu\)m band is hardly modulated, which is plotted in Figure 3c. These results demonstrate that the SMS structure has high transmission loss or are due to the modulation depth at the 1-\(\upmu\)m band. The forward and backward Yb-ASE without considering the MMI effect have the highest intensity because of the additional gain provided by the multimode fiber. This situation also appears in the Er-ASE spectra in Figure 3c. The additional gain increases the output power and ASE intensity at the same  
Figure 3: (**a**) Simulated pump and signal power evolution of amplifier with hybrid fiber and uniform fiber. (**b**) The simulation spectra at the 1-\(\upmu\)m band. (**c**) The simulation spectra at the 1.5-\(\upmu\)m band. time. However, the spectral modulation caused by the MMI effect suppresses the intensity of Yb-ASE. Although there are some spectral peaks in the 1040-1100 nm band, the forward Yb-ASE peak intensity was still suppressed by 0.5-2.9 dB by the MMI effect. The maximum suppression was 8.3 dB at 1073 nm. The backward Yb-ASE had the same trend and the maximum suppression was 8.1 dB at 1073 nm. The forward Er-ASE intensity decreased by only 1-1.6 dB. Although \(\alpha_{SMS}(\lambda)\) in the 1.5-mm band has a loss of 2-3 dB, the Yb-ASE decrement improves the energy conversion between Er\({}^{3+}\) and Yb\({}^{3+}\) and avoids the Er-ASE decrement significantly. The backward Er-ASE intensity decreased by about 5 dB, which is the result of the interaction of additional gain and MMI modulation. In summary, the simulation results demonstrate that the hybrid EYDF with an SMS structure can suppress part of Yb-ASE.