esa-sceva/satcom-chunk-collection huggingface dataset:
# Metro Emergency Passenger Flow Prediction on Transfer Learning and LSTM Model
## 3 Prediction Model for Emergency Event
### Calculation Process  
Under normal conditions, according to the surrounding environment, line distribution, and other factors of the metro, there will be a stationary passenger flow demand \(R_{0}\) per unit time, that is, \(R_{0}=f(X)\). \(X\) is the set of environmental factors, which is only related to the physical space environment of the metro and has nothing to do with the operation behavior.  
Similarly, according to the Metro operator's strategy, such as operation speed, departure interval, number of passengers in the carriage, there will be a stationary maximum transport capacity \(Cap_{max}\) per unit time, that is, \(Cap_{max}=g(Y)\). \(Y\) is the metro operation strategy set, and \(Cap_{max}\) can change with the operator's strategy. Obviously, when  
Figure 1: Impact of an emergency event on passenger flow.  
Figure 2: The proposed transfer learning procedure.  
\(Cap_{max}\) approaches to \(R_{0}\) and is slightly greater than \(R_{0}\), the metro operation efficiency is the highest. Here, we might as well assume that under normal conditions, the transport capacity of the metro can fully meet the demand of transport passenger flow. At this time, the normal transport passenger flow is the demand of normal transport passenger flow and just reaches the limit of transport capacity, that is, \(R_{0}\approx Cap_{max}\).  
As shown in Figure 3, when the metro breaks down suddenly, the transportation capacity of the metro will decrease, but the passenger flow demand will not be affected. Currently, \(R_{0}\) remains unchanged, \(Cap^{\prime}_{max}=g(Y^{\prime})<Cap_{max}\). The difference \(N\) between the two is the passenger flow stranded in the station due to the emergency, that is, \(N=Cap_{max}-Cap^{\prime}_{max}\).  
To simplify the model and improve the universality of the model, we use the LSTM network to forecast the normal passenger flow, taking into account the working day and time period; For the emergency passenger flow, we use the method of expert evaluation to quantify the severity of the fault qualitatively. Taking the severity of the fault as a reference factor, we use the method of transfer learning to predict the emergency passenger flow based on the normal passenger flow prediction network. The input parameters are shown in Table 1.
# System for the Acquisition and Analysis of Maintenance Data of Railway Traffic Control Devices
## 2 The Development of Methods and Systems for the Collection and Processing of Data  
In recent years, there has been considerable development in the methods employed for the collection and processing of information. The process under discussion has been especially influenced by technological progress, digitization, and the development of artificial intelligence. In the span of two decades, methodologies for the automated aggregation of substantial data sets, their processing, and their analysis have undergone significant development.  
Key stages in this process include  
* Between 2000 and 2010: Digitalization and the beginning of Big Data [6; 8; 9].
* Digital transformation of organizations--gradual transfer of data to digital systems and the development of MES and ERP systems.
* Big Data--in relation to data sets so large and complex that they cannot be analyzed by traditional methods (e.g., sensor data and CCTV cameras).
* Analytical tools gained importance.
* From 2010 to the present: Industry 4.0 [5; 10].
* Internet of Things (IoT)--billions of connected devices (smart sensors) that collect data in real time.
* The development of mobile technologies.
* The development of cloud computing--data are stored and analyzed in remote data centers.
* The rapid development of AI and machine learning--systems learning from data are starting to recognize patterns and predict behavior.
* The development of predictive analytics--using data not only to describe the past, but also to predict future events.
* Digital twins are emerging--virtual representations of machines or processes.
* Blockchain--enables decentralized and secure storage and the verification of data.
* Autonomous systems that make decisions without human intervention.  
These processes have led to intelligent systems based on automatic information processing and inference.  
This development has also influenced railway traffic control systems. Modern railway systems collect operating data in the form of Big Data on the state of the railway infrastructure, train movements, and operational events from actuators, i.e., railway light signals, switch drives, track sensors and axle counters, and relay devices. Currently, Maintenance and Diagnostic Centers (MDCs) are responsible for collecting operational data in rail transport in Poland. MDCs are points of collection and presentation of technical and operational data of railway traffic control devices for a selected area of the railway network. The collection of operational data from railway traffic control devices and systems is an important element of modern railway infrastructure management [1,2]. It enables not only rapid response to failures, but also the implementation of advanced maintenance and analytical strategies, supported by technologies, i.e., IoT or AI [10]. Integration of these data with central systems can increase the safety, efficiency, and reliability of rail transport [4,11].
## 3 Traumatic Corneal Injuries
### Corneal Stromal Scarring Risk Assessment  
Corneal stromal scarring, though extremely rare and not yet documented in astronauts, can have significant implications if it were to occur. Even minor visual impairments can hinder mission-critical tasks like navigation, system monitoring, and extravehicular activities. This risk arises from space-specific stressors, including microbial keratitis, herpes simplex virus (HSV) reactivation, and cumulative radiation exposure, which collectively create a high-risk environment for ocular damage during long-duration missions [22].  
Microbial keratitis is a serious threat in microgravity, where immune suppression increases susceptibility to opportunistic infections. Stress-induced immune dysfunction and the unique microbial dynamics of spacecraft enhance the risk of infections progressing to scarring without prompt management [11; 35]. Similarly, HSV reactivation, often triggered by elevated cortisol levels and immune suppression, leads to stromal keratitis characterized by inflammation, edema, and scarring, significantly impacting visual function [11; 22].  
Cumulative radiation exposure further compounds these risks. Galactic cosmic rays (GCR) and solar energetic particles (SEP) induce oxidative stress and apoptosis in corneal cells, as demonstrated in experimental models. Studies reveal that prolonged radiation exposure beyond Earth's magnetosphere increases the likelihood of structural corneal damage, elevating visual risks for astronauts [36; 42].  
Mitigation strategies are critical for reducing these risks. Comprehensive microbial control measures, such as antimicrobial coatings and rigorous decontamination protocols, help prevent keratitis progression [35]. Antiviral therapies targeting HSV reactivation provide another layer of protection against vision-compromising infections [11; 22]. Advanced shielding technologies, such as polyethylene alternatives and hydrogen-rich composites, minimize radiation exposure during deep-space missions [36; 42]. Additionally, psychological interventions and circadian rhythm regulation address stress-induced immune dysfunction, further reducing ocular complications [11; 35].  
Innovative regenerative approaches offer long-term solutions for scarring. Bioengineered corneal replacements, such as those using transparent constructs made with UNIONbioinks, replicate the cornea's optical and biomechanical properties [51; 52]. These constructs, incorporating astronaut-derived stem cells, demonstrate resilience to radiation-induced damage, offering a sustainable and personalized solution to address stromal scarring risks [25; 53].
## 3 Results and Discussion  
### Performance Test of SBS Modified Asphalt with Chinese Evaluation Methods  
#### 3.1.1 Penetration  
The test temperatures were set as 15 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C and 30 \({}^{\circ}\)C in this study. The penetration of matrix asphalt and SBS-modified asphalt for different test temperatures is shown in Figure 1. For SK70# and SK90# matrix asphalt, the SBS modifier could lower the penetration of them, which lead to the asphalt binder being hardened. Take the test temperature of 25\({}^{\circ}\)C and SK90# matrix asphalt as example, the penetration of SBS modified asphalt with SBS dosages of 4.5%, 5%, 5.5%, and 6% decreased by 10.3%, 12.9%, 14.4% and 15.5% respectively, compared to the matrix asphalt.  
For studying the influence of SBS modifier dosage on penetration of asphalt binder, the variance analysis of temperature and SBS modifier dosage on penetration of SK70# and SK90# matrix asphalt were conducted, as shown in Table 3.  
In Table 3, the significance level is 0.05, when the \(p\)-value less than 0.05, that means SBS modifier dosage or temperature has a significant impact on penetration in significance level of 0.05. The smaller the \(p\)-value is, the more significant the effect is. The results showed that, the temperature had a significant effect on penetration of SBS modified asphalt, while the SBS modifier dosage had no considerable influence on it [8; 9]. In addition, the aromatic hydrocarbon and resin in matrix asphalt could be absorbed by SBS modifier, which lead to the swelling behavior. This caused the great variability in the test result of penetration.
## 3 Results and Discussions
[8]) and from theoretical predictions with two different mass energy-absorption coefficients, \((\mu_{a})_{m}\) versus UV-light exposures are shown in Figure 9. The _Net ROD_ values from theoretical predictions were scored over the entire film surface that had been exposed to UV. In the FEM technique, an element consisted of a number of nodes (4 nodes for the tetrahedron used in the present work) each being associated with a shape function. The intensity of the reflected light in an element was determined through summing the intensities over all nodes with consideration of their corresponding shape function. The average reflected intensity was determined as the arithmetic mean of the intensities of elements in the modeled domain [24]. The theoretically obtained _Net ROD_ values were generally in good agreement with the experimental data and, as described above, did not vary noticeably with the chosen mass energy-absorption coefficients (viz., 100 vs. 10 cm\({}^{2}\)/g). The two curves were close to each other, but the numerical values were not exactly the same. When different mass energy-absorption coefficients were employed, the reflected light intensities from the unexposed and exposed films changed by almost the same factor, so the _Net ROD_ values were close. Figure 9 shows that _Net ROD_ increases with the UV exposure. Upon UV exposures, the DA monomer strands gained enough energy to form a dense cross-linked polymerized structure, so the EBT3 film became darker. However, upon excessive UV exposures, polymerization would become saturated and there would be no further film darkening. The deviations between the model and the experimental data in Figure 9 could be due to assumptions and simplifications made in the present model; such as, no other elements than carbon and hydrogen atoms were present in the EBT3 film, and no light leaked from the film. The spatial distribution of _Net ROD_ over the upper and lower surface of an EBT3 film after being exposed to 164.70 J/cm\({}^{2}\) UV radiation is shown in Figure 10. The _Net ROD_ values were larger at the upper surface, which was explained by the loss of intensities of light rays during their traversals of the active layer of  
Figure 8: Reflected intensities (\(log(\phi)\)) from EBT3 films versus UV exposures. the EBT3 film (thickness = 28 \(\upmu\)m). The largest theoretical _Net ROD_ was recorded at the center of the film because a point source was used in the simulations.
## 3 The Proposed FedSAGE Framework for Federated DGA Detection
### Variational Autoencoder Architecture
The VAE is optimized by minimizing the following evidence lower bound (ELBO):  
\[\mathcal{L}_{\text{VAE}}(x)=\mathbb{E}_{q_{\theta}(z|x)}[\log p_{\theta}(x|z)] -D_{\text{KL}}(q_{\phi}(z|x)\|p(z)), \tag{2}\]  
where:  
* The first term encourages accurate reconstruction of the input \(x\) from the latent variable \(z\) in Equation (1). * The second term regularizes the encoder's output to remain close to the prior \(p(z)\). Figure 2: The Variational Autoencoder (VAE) model architecture with encoder, decoder, and latent space with reparameterization. In our implementation, as shown in Figure 2, the VAE model is realized using a character-level BiLSTM encoder and decoder, with the reconstruction loss computed via token-level cross-entropy (rather than MSE). The KL divergence term is scaled by batch size and added to the total loss as in Equation (2). During training, only domain names labeled as benign (i.e., \(y=0\)) are used to construct the latent space. The VAE is trained end-to-end using the Adam optimizer, and the final reconstruction error \(\mathcal{E}_{\text{rec}}(x)\) is computed per domain to support unsupervised anomaly detection. Once trained, the encoder captures meaningful embedding of benign domains, while the decoder effectively reconstructs these inputs. When a new domain name, possibly generated by a DGA, is processed by the VAE, it is projected into the latent space and passed through the decoder. If the input domain differs significantly from the distribution learned during benign training, the _reconstruction error_ (i.e., the difference between the input and the output) increases notably. In practical scenarios, this approach offers several advantages:  
1. The model can detect malicious domains that have not been previously encountered, without requiring explicit DGA labels. 2. The pre-trained encoder can be deployed on lightweight client devices, where it performs simple forward passes to generate domain embeddings, eliminating the need for on-device retraining. 3. It significantly reduces computational and communication overhead in FL environments, particularly at the edge or in IoT settings.
## 2 Background
### Logit-Level Masking  
In typical deep reinforcement learning algorithms, a neural network is commonly used to represent the policy. In the case of a discrete action space, the action probability distribution is produced by implementing the Softmax operation on the output logits of the neural network, which is the framework we will assume in the rest of the paper.  
Invalid action masking is a practical technique in deep reinforcement learning to prevent the agent from taking impossible or unavailable actions. The most popular way for invalid action masking is to replace the logits of invalid actions with negative infinity (or a large negative number in practice) [23], which we call _logit-level masking_. Logit-level masking works by applying a state-dependent masking function \(m(\cdot|s)\) when calculating action probability distribution [23], as shown in Figure 1.  
Specifically, let us define an MDP with the state space \(\mathcal{S}\) and the discrete action space \(\mathcal{A}=\{a_{1},a_{2},\cdots,a_{n}\}\), where \(n\) is the number of discrete actions. The policy \(\pi_{\theta}\) is parameterized by parameters \(\theta\) of the neural network. Taking any state \(s\in\mathcal{S}\) as input, the output logits of the neural network are represented by \(l_{\theta}(\cdot|s)=[l_{\theta}(a_{1}|s),l_{\theta}(a_{2}|s),\cdots,l_{\theta}( a_{n}|s)]\). The logit-level masking function is defined as\[m(l_{\theta}(a_{i}|s))=\begin{cases}l_{\theta}(a_{i}|s)&\text{ if $a_{i}$ is valid in $s$,}\\ -\infty&\text{ if $a_{i}$ is invalid in $s$.}\end{cases} \tag{4}\]  
Then, we can obtain the re-normalized action probability distribution \(\pi_{\theta}^{m}(\cdot\mid s)\) by implementing the Softmax operation on the masked logits  
\[\begin{split}\pi_{\theta}^{m}(\cdot\mid s)&=\text{ Softmax}(m(l_{\theta}(\cdot|s)))\\ &=\left[\frac{\exp(m(l_{\theta}(a_{1}|s)))}{\sum\limits_{a_{i}\in \mathcal{A}}\exp(m(l_{\theta}(a_{i}|s)))},\cdots,\frac{\exp(m(l_{\theta}(a_{n} |s)))}{\sum\limits_{a_{i}\in\mathcal{A}}\exp(m(l_{\theta}(a_{i}|s)))}\right]. \end{split} \tag{5}\]  
Since the values of logits associated with invalid actions are \(-\infty\), the sample probabilities of invalid actions are zero as \(\lim_{l\to-\infty}\exp(l)=0\).
# Impact of Traditional and Embedded Image Denoising on CNN-Based Deep Learning
###### Abstract  
In digital image processing, filtering noise is an important step for reconstructing a high-quality image for further processing such as object segmentation, object detection, and object recognition. Various image-denoising approaches, including median, Gaussian, and bilateral filters, are available in the literature. Since convolutional neural networks (CNN) are able to directly learn complex patterns and features from data, they have become a popular choice for image-denoising tasks. As a result of their ability to learn and adapt to various denoising scenarios, CNNs are powerful tools for image denoising. Some deep learning techniques such as CNN incorporate denoising strategies directly into the CNN model layers. A primary limitation of these methods is their necessity to resize images to a consistent size. This resizing can result in a loss of vital image details, which might compromise CNN's effectiveness. Because of this issue, we utilize a traditional denoising method as a preliminary step for noise reduction before applying CNN. To our knowledge, a comparative performance study of CNN using traditional and embedded denoising against a baseline approach (without denoising) is yet to be performed. To analyze the impact of denoising on the CNN performance, in this paper, firstly, we filter the noise from the images using traditional means of denoising method before their use in the CNN model. Secondly, we embed a denoising layer in the CNN model. To validate the performance of image denoising, we performed extensive experiments for both traffic sign and object recognition datasets. To decide whether denoising will be adopted and to decide on the type of filter to be used, we also present an approach exploiting the peak-signal-to-noise-ratio (PSNRs) distribution of images. Both CNN accuracy and PSNRs distribution are used to evaluate the effectiveness of the denoising approaches. As expected, the results vary with the type of filter, impact, and dataset used in both traditional and embedded denoising approaches. However, traditional denoising shows better accuracy, while embedded denoising shows lower computational time for most of the cases. Overall, this comparative study gives insights into whether denoising will be adopted in various CNN-based image analyses, including autonomous driving, animal detection, and facial recognition.  
denoising deep learning; median filter; Gaussian filter; embedded denoising; traditional denoising +
Footnote †: journal: Medical Imaging
## 3 Results and Discussion
### Second Case Study: Photovoltaic Cogeneration to Activate a Double Source Heat Pump  
The comparison between the different alternatives of Table 2 based on annual energy results allowed to identify the best plant configuration in terms of PVT area, length of the geothermal probes, and tanks' capacities.  
Each alternative also provides 60 m\({}^{2}\) of plain PV. The primary energy saving with respect to a traditional solution (NG fired condensing boiler (100% efficiency on lower heating value), and an air-cooled chiller with monthly mean EER as reported in 20) increases less than proportionally to the PVT area. In fact, the thermal contribution is in excess of the demand already at 40 m\({}^{2}\) (Figure 12). As a matter of fact, the thermal efficiency of the plant decreases when the PVT area increases. Concerning the ground heat exchanger length, an increase from 300 to 500 m does not allow any advantage, as a smaller value is more effective. Instead, it would increase the capital cost of the plant.  
The best alternative considers three ground probes, 100 m each, and 60 m\({}^{2}\) PVT (plus the above considered 60 m\({}^{2}\) of plain PV). This configuration of the plant allows many advantages: a suitable PVT cell cooling to keep the electrical efficiency not far from the nominal value (16% in peak condition) even if the PVT is glazed; a very high PER (around 5) for the combined operation of the heat pump producing simultaneously useful heating and cooling for long during the year. Moreover, the PVT thermal contribution (full DHW demand covered from February till November and heating + DHW satisfied from April till October) allows a high efficiency of the whole plant (PER_plant) (Figure 13).  
Figure 12: Non-renewable primary energy comparison between the five considered different alternatives, each compared with the conventional solution.  
The produced electricity by the plain PV field and the PVT makes the HVAC plant completely energy self-sufficient on a yearly basis. The electricity that exceeds local requirements from March to October is available for other uses of the building, or it is exported towards the grid. The electricity external dependency from the grid can be observed only in January and December, whereas in February and November the self-produced electricity is clearly prevailing.  
On a yearly basis, the traditional plant primary energy consumption is 198.5 GJ, whereas the proposed plant has a surplus of 116.7 GJ.