esa-sceva/satcom-chunk-collection huggingface dataset:
# Security Attack Behavioural Pattern Analysis for Critical Service Providers
## 5 Experiment and Result
### Security Attack Behaviour Analysis of Installed Malware  
**Vulnerability Model (VM) (1) Threat**: Cause a victim to load content into their web browser that bypasses security zone controls and gains access to privileges, **target**: software, **attack pattern:** (cross zone scripting), and **parallel attack:** privilege escalation)  
**Attack-Mechanism Model (AM)**: (1) Cross zone scripting, (2) exploit systems susceptible to the attack, (3) find the insertion point for the payload, and (4) craft and inject the payload.  
**Tasks**: (1) Leverage knowledge of common local zone functionality, (2) find weakness in the functionality used by both privileged and unprivileged users, (3) make the maliciously vulnerable functionality to be used by the victim, (4) leverage cross-site scripting vulnerability to inject payload, and **domain assumption:** insufficient input validation by the system.  
**Behavioural Model (BM)**: The formal behavioural annotation for the sub-attack Cross Zone Scripting can be represented as (Try (G2) U Succeed (G2)); G3; G4); that is, \"Try (Exploit systems susceptible to the attack) U Succeed (Exploit systems susceptible to the attack)\" is followed by \"Find the insertion point for the payloadG:\" and \"Craft and inject the payload\", and the formal behavioural annotation for the sub-attack \"Craft and inject the payload\": can be represented as (T2; DA1; T3); that is, (Make the maliciously vulnerable functionality to be used by the victim) has to be achieved first followed by (insufficient input validation by the system) and (leverage cross-site scripting vulnerability to inject payload).  
**Event Model (EM)**: This model captures the security events E1: Internet and local zone enabled, E2: enable functionality failed, E3: internet and local zone disabled, E4: weakness found, E5: weakness failed, E6: victim-injected payload, and E7: payload injected.
# Migrating from Developing Asynchronous Multi-Threading Programs to Reactive Programs in Java
## 3 Related Work
### Benchmarking Virtual Threads and Reactive WebFlux for Concurrent Web Services  
[PERSON] and [PERSON] [22] analyzed the performance differences between Virtual Threads and Reactive WebFlux in Spring applications, aiming to determine the advantages of each solution under high-demand conditions. The study involved the development of three Spring application prototypes: one using normal threads, one with virtual threads, and another based on the Reactive WebFlux model. The prototypes were tested by simulating interactions between an aggregation endpoint and two underlying services, each configured with fixed delays of 100 ms and 500 ms.  
The results indicate that the virtual thread prototype demonstrated slightly better performance than the reactive prototype in most tests, maintaining low latency and handling a high rate of requests per second even under heavy load. For example, with a 100 ms delay, the virtual thread prototype handled more requests per second compared to the Reactive WebFlux model. However, the study notes that these tests were conducted on Tomcat, a blocking server, which may not fully leverage the non-blocking nature of Reactive WebFlux. This suggests that Reactive WebFlux might yield different results on a non-blocking web server like Netty, potentially enhancing its performance [22].  
Additionally, while virtual threads displayed promising results, their integration into existing systems requires careful consideration of compatibility and potential debugging complexities. The study concludes that virtual threads present a viable alternative for high-concurrency applications in Spring, but further testing in production environments is necessary to confirm their advantages over reactive solutions, underscoring the importance of selecting a concurrency model based on the specific demands of the application and the characteristics of the deployment environment [22].
## 3 Results Analysis
### Operation and Maintenance Stage
#### 3.9.2 Summary  
In summary, during the operation and maintenance stage of an assembled M&E system, it is necessary to regularly inspect and maintain the components, monitor operational data, and troubleshoot. At the same time, maintenance and continuous improvement are necessary to ensure the system's stable operation and efficiency. Its primary research lies in cloud computing and IoT computing, including BIM, algorithms, laser detection, chip processing, and other dimensions. Realize the real-time monitoring and prediction of the operating status of the equipment, discover potential failures, and take measures in advance to reduce the system's maintenance cost and improve the equipment's reliability and stability.  
The assembled electromechanical system has better energy performance in the operation stage compared with traditional construction [120], and modern technology provides a solid technical guarantee for operation and maintenance to realize stable operation and satisfactory maintenance. Now, with the development of the life cycle and the adaptation to the progress of the times and the iteration of technology, we need to enter the next stage, the dismantling and reuse stage. This stage lies in the efficient deconstruction of electromechanical system components and the rational manufacture of economic benefits according to the economic cycle while following the principle of carbon reduction to reduce carbon emissions and waste of resources reasonably.
## 1 Introduction  
The modeling of nanoantennas and nanocircuits depending on the material concept, i.e., a nanosized object as a working element of nano-device or a composite made of nano-inclusions as a macroscopic platform for antennas/circuit realization, could be provided in \"top-down\" and \"bottom-up\" approaches. The first one accesses the device realization by computation of microscopic electromagnetic response of nanoparticle (e.g., carbon nanotube) [1; 2; 3], whereas the second one is expected to describe the collective effects of nanoparticles electromagnetics in the final composition caused by individual nanoparticles antennas' properties through different homogenization and/or averaging techniques [4; 5; 6; 7].  
This communication is one of the second sort. It is motivated by an exceptional interest in nanocarbon based composites close or slightly above percolation concentration as the material support for design light and ultrathin effective electric components, optical and optoelectronic devices, constituent elements for electromagnetic compatibility realization [6; 8]  
Moreover, polymer nanocomposites close to or slightly above the electrical percolation threshold are of tremendous interest as a material-science basis for conformal structurally integrated microwave-to THz antennas [1; 9].  
The percolation threshold for multi-facial irregular media is a problem that was studied for a long time (see [10; 11] and Refs. therein). In modern material science, the main focus is the percolation phenomena in composite with randomly distributed carbon nanoinclusions like graphite nanoplatelets, carbon nanotubes, carbon black, onion-like carbon, etc. [12]. There are many approaches to simulate the nanocarbon composite system. For the case of nanotubes or fibers, the most obvious is to simulate it as a caped [13; 14] or non-caped [15; 16] cylinder. However, this approach is not universal because it does not allow to simulate all possible geometries of nanoinclusions.  
All mentioned objects may be roughly modeled as ellipsoids of revolution with a different axial ratio. Many papers contribute to the modeling of the percolation threshold of overlapping ellipsoids [17; 18; 19]. The method of overlapping inclusions is faster; it describes the transport properties in porous media well [20], as well as the social phenomena [21], _etc._ However, it is not accurate in the case of nanocarbon composites, because these particles are robust and cannot overlap. Very few papers contribute to the modeling of the percolation threshold for the suspension of the non-overlapping ellipsoids [22; 23].  
We report the Monte Carlo model for the calculation of the percolation of the ellipsoids distributed inside a cubic unit cell. The dependence of the percolation threshold on the ellipsoids' distribution, unit cell size and the aspect ratio for the case of oblate ellipsoids will be discussed.
## 4 Security Analysis of LEACH-Based Secure Routing Protocols and ESPDA  
### Security Analysis of LEACH-Based Secure Routing Protocols  
LEACH-based secure routing protocols consider CHs as trusted entities which is undesirable from a data fusion point of view. This fact triggers insight into our security protocol requirements when we analyze the security of each routing protocol.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 2 Related Works
### Open-Source Infrared Datasets  
High-quality datasets are crucial for deep neural network-based super-resolution algorithms. The diversity of images is essential for learning generalized representations, which in turn significantly enhances the model's generalization capabilities. In the realm of visible light image algorithms, a plethora of high-quality open-source datasets are readily accessible. For example, commonly used datasets like DPED [13] and DF2K [11; 14] provide unified training and evaluation benchmarks for algorithms. However, in the field of infrared images, the availability of datasets is limited, with examples including [47; 48; 49] or datasets derived from self-taken images [50]. However, the quality of these images varies significantly, and there is a notable lack of scene diversity. For instance, images extracted from the same video often exhibit considerable similarities, thereby diminishing the diversity of the dataset. Additionally, the majority of infrared images obtained via web crawlers, which typically have very low resolutions, are not conducive to algorithm development.  
Therefore, we constructed a multi-scene infrared super-resolution image dataset [51]. This dataset contains a total of 7224 images across nine distinct scenes, including seasace images, animal images, industrial scenes, urban landscapes, natural landscapes, indoor images, portraits, surveillance perspectives, and vehicle perspectives, as shown in Figure 1. The majority of images have resolutions of (1280 \(\times\) 1024, 1920 \(\times\) 1080), with a minority of (640 \(\times\) 512), providing rich details and texture information with high resolution and quality. This dataset facilitates the development of more powerful and accurate super-resolution algorithms using real-world high-quality infrared image data, thus promoting the application of infrared super-resolution technology across diverse fields.
## 1 Introduction
The latest simulation tools have thus simplified the process of creating simulation models that today are being waived from the use of more straightforward analytical methods [6]. Today, artificial intelligence or virtual reality is the usual supportive technique used in simulations. The importance of simulation grows mainly with the increasing complexity of systems. They are mainly used where an erroneous decision can mean inefficient investment, long-term economic losses, and a weakening of its competitiveness. In the growth of systems complexity and deployment of smart devices that decide on actions in factories of the future, it is, therefore, necessary to determine the outcome of the actions for management needs in a high emergence of processes. [7]. The requirement of frequent changes to the production base requires the rapid commissioning systems of automated manufacturing systems (Ramp Up), which will require new simulation tools. In the case of control, emulating technologies that are tied to the simulation may be used. One of the advantages of the emulation environment is that it can monitor the technical system (such as production, assembly, logistics) in real-time to evaluate the data collected and to update the model in question on a real-system basis and to carry out experiments on the simulation model simultaneously. In Industry 4.0, the introduction of the digital twinning of objects and processes is equally important [8]. The orientation of research into new manufacturing approaches is directed towards the area of intelligent manufacturing systems, using reconfigurable manufacturing systems, adaptive logistics, and the concept of competence islands. New simulation systems must also be adapted to this new requirement. They must possess the ability to simulate agent systems and model large networks. Modelling and simulation will, therefore, be an integral part of the planning and control of the processes of factories of the future. Research on the principles of modelling and simulation and the development of factories of the future have been the long-term areas of research at the Department of Industrial Engineering, University of Zilina. The issue addressed is consistent with the strategy of Industry 4.0. Just by defining the characteristics of the systems used in factories of the future and their properties can be evaluated, as such systems can be modelled and simulated. The article, in its periphery, deals with the description of the manufacturing concepts that are potentially highly applicable for use in factories of the future, and the core descriptions of the use of modelling and simulation, mainly metamodeling, in the processes control of factories of the future. An example of using this approach is described in the processes control of laboratory ZIMS (Zilina Intelligent Manufacturing System).
# MIMO Signal Detection Based on IM-LSTMNet Model
## 1 Introduction
At present, the research on signal detection in a MIMO-OFDM system using neural networks such as LSTM is very scarce. Therefore, this paper proposes a new neural network model, IM-LSTMNet, which combines an LSTM neural network and CNN neural network, and joins Squeeze-and-Excitation Network on this basis. IM-LSTMNet performs end-to-end signal detection, which improves the signal detection performance of the MIMO-OFDM system. The main contributions in this paper are as follows:  
1. We construct a MIMO-OFDM system that uses FrFT to replace the original fast Fourier transform (FFT) in OFDM modulation. This increases the flexibility of the MIMO-OFDM system, allowing it to have more direction choices when carrying out OFDM modulation;
2. We developed an IM-LSTMNet framework and trained it using FrFT-based MIMO-OFDM signal data;
3. The trained IM-LSTMNet model is applied to the online detection terminal to detect the signal of the FrFT-based MIMO-OFDM system. It is compared with other DL-based methods such as LSTM and CNN-LSTM, as well as traditional zero forcing (ZF) methods and minimum mean square error (MMSE) methods, to compare their bit error rate (BER) performance in the MIMO-OFDM system. A summary of the paper's structure is provided in this paragraph. The first section explains the background of MIMO-OFDM signal detection and LSTM neural network research, some work of other scholars, and the content of this paper. The second part describes the MIMO-OFDM system model, in which the OFDM system is the fractional Fourier transform-orthogonal frequency division multiplexing (FrFT-OFDM) system. The IM-LSTMNet framework is constructed and the structure and function of each part are described. This part also explains how to use neural network to detect the signal in the MIMO-OFDM system. The third part describes the data preprocessing and offline training of this experiment, and uses the trained IM-LSTMNet model to detect the signal in the MIMO-OFDM system under different conditions. The fourth part draws the conclusion of the paper and expounds this research's development in the future.
###### Abstract  
In remote-sensing imaging, the modulation transfer function (MTF) for image motion relevant to the mixing of multiple forms of motions is hard to calculate because of the complicated image motion expression. In this paper, a new method for calculating the MTF for complex image motion is proposed. The presented method makes it possible to obtain an analytical MTF expression derived from the mixing of linear motion and sinusoidal motion at an arbitrary frequency. On this basis, we used the summation of infinitely many terms involving the Bessel function to simplify the MTF expression. The truncation error obtained by the use of finite order sum approximations instead of infinite sums is investigated in detail. In order to verify the MTF calculation method, we proposed a simulation method to calculate the variation of MTF in an actual optical system caused by image motion. The mean value of the relative error between the calculation method and the simulation method is less than 5%. The experimental results are consistent with the MTF curve calculated by our method.  
Keywords:remote-sensing imaging; image quality; modulation transfer function; complex image motion +
Footnote †: journal: Applied Physics Letters
## 3 Materials and Methods
### Tabular Autoencoder Pretraining Enhancement (TAPE) Architecture  
We introduce a novel method, TAPE, which leverages unsupervised representation learning through an autoencoder framework to capture intrinsic data structures before fine-tuning for classification:  
Multi-layer encoder network: transforms input features into a compressed latent representation through multiple non-linear transformations:  
\[h_{1}=\text{Dropout}(\delta(\text{BatchNormNorm}(W_{1}x+b_{1}))) \tag{25}\]  
\[h_{2}=\text{Dropout}(\delta(\text{BatchNormNorm}(W_{2}h_{1}+b_{2}))) \tag{26}\]  
\[\vdots\]  
\[h_{L}=\text{Dropout}(\delta(\text{BatchNormNorm}(W_{L}h_{L-1}+b_{L}))) \tag{27}\]  
where \(L\) denotes the number of encoding layers, and \(\delta\) represents the ReLU activation function.  
Symmetric decoder network for reconstruction (used during pretraining):  
\[\widehat{h_{L-1}}=\text{Dropout}\big{(}\delta\big{(}\text{BatchNorm} \big{(}W_{L}^{\prime}h_{L}+b_{L}^{\prime}\big{)}\big{)}\big{)} \tag{28}\]  
\[\vdots\]  
\[\widehat{h_{1}}=\text{Dropout}\Big{(}\delta\Big{(}\text{BatchNorm}\big{(}W_{2}^ {\prime}\widehat{h_{2}}+b_{2}^{\prime}\big{)}\Big{)}\Big{)} \tag{29}\]  
\[\widehat{x}=W_{1}^{\prime}\widehat{h_{1}}+b_{1}^{\prime}. \tag{30}\]  
With reconstruction loss [17] defined as follows: \(\mathcal{L}recon=\frac{1}{N}\sum i=1^{N}|x_{i}-\widehat{x}_{i}|_{2}^{2}\).  
Classification module leveraging learned representations for supervised classification:  
\[h_{c,1}=\delta(W_{c,1}\cdot h_{L}+b_{c,1}) \tag{31}\]  
\[h_{c,2}=\text{Dropout}(h_{c,1}) \tag{32}\]  
\[y=\sigma(W_{c,2}\cdot h_{c,2}+b_{c,2}). \tag{33}\]  
This two-phase training approach [18] enables the model to first learn general data structures from all available samples (including those with missing target values) before specializing in the classification task. While autoencoders are commonly applied to image and text data [7], their use for tabular data has gained attention in recent years. The TAPE architecture's focus on tabular data aligns with emerging research in this area. The TAPE approach offers the advantage of utilizing unlabeled water quality data for representation learning, potentially capturing intrinsic patterns before fine-tuning for potability classification.
# Rib Design for Improving the Local Stiffness of Gearbox Housing for Agricultural Electric Vehicles
## 5 Conclusions  
This study aimed to improve the stiffness of the gearbox housing of agricultural electric vehicles through rib design for vibration reduction. The local stiffness was numerically evaluated based on an FE model to confirm the stiffness improvement according to the rib design.  
The model for the FE analysis was validated to ensure that it reflected the dynamic characteristics of the actual gearbox housing through the EMA and FE model update process. Local stiffness was evaluated in the validated model to identify weakness in the stiffness, and rib design was conducted through elastic strain analysis. The stiffness increase rate was compared according to the width, length, and height of the ribs. Finally, the rib shape which demonstrated the highest stiffness increase rate against the same mass was selected.  
The main conclusions from this study are as follows.  
* EMA and FE model update were conducted to predict the dynamic behavior of the system. In the results, the natural frequency value in the frequency range of interest showed an error within  
Figure 12: Modification model.  
Figure 13: FRF comparison between the original and modification model.  
about 2.84%, and the mode shape was compared with the MAC value, where data had a value close to 1. From these results, the reliability of the FE model was validated.
* Before we designed the rib for stiffness improvement, the local stiffness was numerically evaluated based on the FE model at 65 points on the surface of the gearbox housing in the validated model, through which the stiffness weak point was identified and the stiffness increase rate before and after the rib design were compared.
* The direction of rib design was established through elastic strain analysis, and a parameter study was conducted using the rib width, length, and height as variables. According to the results, increasing the length and height was more effective in terms of stiffness increase than increasing the width in rib design. Finally, ribs with a width of 4 mm, a length of 20 mm, and a height of 25 mm were selected for the modification model, which showed the highest increase in stiffness relative to the same mass.
* When comparing the results of the frequency response analysis of the modified gearbox housing, the maximum acceleration amplitude in the frequency range of interest was reduced by about 1.87 dB compared with the original model, and the stiffness increase rate was about 43.3%.  
Conceptualization, Y.-J.P.; Data curation, G.-H.S. and S.-J.C.; Investigation, S.-J.C.; Methodology, Y.-J.P.; Software, G.-H.S. and S.-J.C.; Supervision, Y.-J.P.; Validation, G.-H.S.; Writing--original draft, G.-H.S.; Writing--review & editing, Y.-J.P.  
This research received no external funding.  
The authors declare no conflict of interest.