esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing an Imbalanced Lung Disease X-ray Image Classification with the CNN-LSTM Model
## 3 Materials and Methods
### Convolutional and Recurrent Networks: CNN and LSTM  
The convolutional neural network (CNN) is a widely used algorithm for processing image data. It employs mathematical operations to learn from a network called a \"convolution\" [35]. The convolution layer, which is a crucial component of CNN, plays a vital role in feature extraction by utilizing local connections and shared characteristic weights [36]. It consists of linear and non-linear operations that work together to extract meaningful patterns from the input data [37]. The convolution layer's ability to extract important features is a key reason why CNN is well-suited for image data. Multiple filters within the convolution layer learn distinct features through a variety of weights [38]. These filters can be represented mathematically as \(n\times n\) matrices, where each element corresponds to a weight. The filtering process is illustrated in Figure 2, where each pair of elements in the filter is multiplied and added to generate a single output value. This process is repeated for each filter, resulting in a \"feature map\" that highlights specific features in the input data. By employing multiple filters with different weights, the convolution layer effectively learns a diverse set of features, enabling it to identify and differentiate various objects or patterns in the input data.  
LSTM, a type of recurrent neural network (RNN), can effectively retain information over long periods and learn from inputs that are widely separated in time [40]. Unlike traditional RNNs, LSTM overcomes the problem of vanishing gradients and captures long-term dependencies by employing memory cells with specialized gating mechanisms [41]. Figure 3 illustrates the architecture of LSTM, where each memory cell consists of three types of gates (forget, input, and output) that control the information flow. The forget gate filters out small values, the input gate determines new information to be added to the memory, and the output gate determines whether to output the stored value. Additionally, each memory cell incorporates three sigmoid activation functions and one tanh activation function [42]. The tanh function maintains values within the range from \(-1\) to \(1\), while the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Normal** & **COVID-19** & **Pneumonia** & **Total** \\ \hline Training & 9173 & 3255 & 1211 & 13,639 \\ Validation & 1019 & 361 & 134 & 1514 \\ Total & 10,912 & 3616 & 1345 & 15,153 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Separating training and test data for each class.  
Figure 2: Illustration of the filtering process on the convolution layer (modified from [39]).  
sigmoid function transforms the range from \(-1\) to \(1\) to \(0\) to \(1\), preventing zero values from entering the memory cells. For a more detailed explanation of LSTM, refer to [43].
## 1 Introduction  
Nowadays, the number of applications in which IoT networks are deployed grows rapidly. These infrastructures operate in different domains such as smart cities, energy, eHealth, and transportation and behave as isolated islands in the global IoT ecosystem [1]. Their secure interconnection is a big challenge that still needs to be resolved. One promising approach towards interoperability of IoT networks across different domains is proposed by the VICINITY project. The project is supported by European Union Horizon 2020 program with the duration of four years (January 2016-December 2019) and the consortium of 15 partners from seven different countries. In this paper, we present a simulation framework to validate the security of a smart energy use case, which is enabled by using homomorphic encryption. The performance of the overall IoT ecosystem is evaluated with and without using the additional homomorphic encryption layer. The real micro-service for the encryption is integrated into the simulation as hardware in the loop with the approach presented in [2].
## 2 Materials and Methods
### Ultrasonography  
Patients were assessed in supine position with the arms extended above the head. The position is the same in which the Chest CT scan was performed. Chest ultrasonography was performed immediately before the Chest CT scan. Ultrasonographic assessment was performed using a convex probe 1-8 MHz. An EsaoteTM (Genoa, Italy) MyLabAlphaR echographic machine was used.  
Six echographic scans were performed, three for each hemithorax:  
1. Basal landmark: Mid-Posterior axillary line, basal-lateral area;
2. Anterior landmark: Anterior axillary line, near nipple line;
3. Apical landmark: Hemiclavear line, area below clavicle.  
These evaluations have been performed in order to obtain 6 echographic scans for each patient. Ten seconds Videos were recorded and stored in each landmark.  
Standardized parameters have been used: an imaging depth of 10 cm (for convex probe), power 40% (MI: 0.9-1), gain 40%, focus on the pleural line, no harmonic imaging, no cosmetic filters enabled. The imaging center-frequency was 5 MHz.  
Metal cutaneous landmarks have been positioned and left during the CT scans, indicating the areas of ultrasonographic assessment. This method supports a more accurate comparison between echographic patterns and CT scans peripheral lung findings.  
Four echographic patterns were described corresponding to worsening fibrotic changes of the peripheral lung: (1) normal or near normal; (2) SIS with predominance of reverberant horizontal artifacts; (3) SIS with vertical predominance and altered pleural line; (4) white lung with altered pleural line (Figure 1).  
Figure 1: LUS patterns. (**A**): pattern 1, near normal; (**B**): pattern 2, SIS with predominance of reverberant artifacts; (**C**): pattern 3, SIS with vertical predominance and altered pleural line; (**D**): pattern 4, white lung with altered pleural line.  
Throughout a review of the evaluation process, the echographic pattern assigned to each video (each ten seconds long) was the worst observed. This video evaluation process has been performed blind to HRCT reports, and contemporaneously by 2 pneumologists (AS and RI) with expertise in lung ultrasonography. A concordant score has been provided.
In [29], CB and OLTC are optimized on the hourly scale and PV-DSTATCOM are optimized on the intra-hourly scale. The main goal is to minimize active and reactive power losses. On the other hand, the authors in [30] provide an OPF-based dynamic model in which only active power is used as a control variable. The proposed optimization model presents a multi-objective problem considering PV active power output maximization voltage deviation and losses minimization, which is turned into a single objective problem and solved by analytical SQP with the trust region solution method. Most of the previously described research proposed convexified and linearized OPF-based models so that the analytical solution method can be applied. In multi-stage optimization, most of the papers propose a combination of centralized and local control; however, this research proposes a fully centralized multi-stage OPF-based model with full AC power flows and two different objective functions of each stage for different time horizons. Therefore, the following contributions are proposed:A proposed time series MINLP OPF-based model that coordinates conventional and cutting-edge control mechanisms in the ADN. The focus is on the existing network components and inverter-based energy resources. The model is developed to minimize two of the most common problems for DSO caused by the impact of high PV integration--voltage violations and power losses. The multi-stage optimization is introduced and performed for different time horizons. * Since the model is MINLP without approximations and represents the most accurate and hardest-to-solve problem, a co-simulation optimization framework is proposed and used to maintain the originality of the problem. The power flow system analyzer is co-simulated with the CI optimization method. The used CI optimization method in this paper is PSO. This approach enables a full unbalanced AC power flow analysis and enhances the efficiency, accuracy and reliability of the model and obtained solutions. * A solar irradiance profile represents real-life data obtained by one of the author's measurements. Ultimately, the paper is structured as follows: Section 2 presents a multi-stage control scheme and mathematical formulation of the OPF-based problem, as well as an optimal solution methodology framework. In Section 3, test network model and case studies are described and explained. Optimization framework settings for the proposed methodology are given in Section 3. Section 4 includes a presentation of the obtained results and a discussion. The conclusion is summarized and presented in Section 5. Section 2 introduces the main idea of this paper. After that, a mathematical model of the optimization problem is formulated. Lastly, the co-simulation optimization framework is briefly described.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 1 Introduction
This study was conducted how to increase the scalability of the fiber-to-the-home (FTTH) network until its maximum capacity without the need for new cable installation. The study was executed by adding some components to the network and measuring the bandwidth to produce not only the highest number of users but also a signal with an acceptable Q factor and bit error rate (BER). Through this study, we found there are numerous techniques that can increase the scalability of the network, maximize the usage of the internet cable, and also increase the efficiency of the existing cable network. Figure 1: FTTH architectural illustration. The technique that we use throughout this study is a spectrum conversion [5], slicing [11] and duplication [12] technique because it is the best technique for scalability as it is very efficient. Furthermore, the output separation ratio can be controlled and can be expanded to the maximum. As we are moving into the world of technologies, the demand for the internet is increasing significantly, and it did so especially during COVID-19. So, this study and research are really important in improving network upscaling and optimizing the efficiency of the existing cable network. Our proposed solution is able to support a huge number of users without deploying new cable installation.
## 7 Robustness
### Security Analysis  
The security framework of the proposed watermarking system incorporates multiple defences against potential threats in forensic evidence distribution. This analysis presents both the theoretical security model and empirical validation through targeted attacks. The security evaluation focusses on three key aspects: the trusted framework and threat model, key management architecture, and quantitative resistance measurements against statistical, collusion, and cryptanalytic attacks.
## 3 Experiment on the Machining Modes with CNC Parameters  
### CNC Parameters' Tuning Criteria for the Machining Modes  
Different workpieces have different requirements in the machining process, such as aerospace or difficult-to-cut materials, which require higher efficiency for material removal; the surface quality and accuracy are not the priority requirements, as defined in the HS mode. Regarding the structure of the wing used in aerospace, the product demand is high geometric accuracy. As surface quality was not the priority requirement for this product, it was defined via the HP mode. The mold of the optical component needs to have good surface quality and the accuracy needs at least to match the requirement, so it defines with the HQ mode. The tuning criteria of the CNC parameters for the three kinds of the machining mode are summarized in Figure 6. The relationship between the CNC parameters' tuning criteria of the three machining modes is defined below:  
* HS mode: The objective is to set the feed motion of machine tools suitable for the larger dynamic errors. It will finish the tool path with the shortest time for motion. In order to reach the target, the CNC parameters' values of jerk, acceleration, corner velocity, and arc velocity must be increased. At the same time, the HS mode will cause larger oscillation after the Acc/Dec motion, and it will mark the vibration texture on the surface.
* HP mode: The dynamic errors should be kept to a minimally tight tolerance. The motion path is close to the ideal path of the NC code. In order to reach the goal, the values of jerk, acceleration, corner velocity, and arc velocity must be reduced for HP mode. Therefore, the smaller values of the CNC parameters result in larger cycle time.
* HQ mode: The goal is to obtain a smoother machining surface. This means that we must maintain minimal oscillation during feed motion. In addition to reducing the values of jerk, acceleration, corner velocity, and arc velocity, smoothing time constant must be provided to restrain the mechanical resonance. The magnitude of the dynamic errors is between the HS mode and HP mode. In the HQ mode, the motion hardly oscillates, and, therefore, contributes to the smooth surface quality on the geometric surface of the product.  
Figure 5: The dynamic errors in corner path and circular paths.
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Real-Time Performance and Efficiency Evaluation
RGB-D-based methods have significantly improved accuracy at the cost of greater computational complexity. By leveraging a lightweight design and efficient multiscale fusion, the proposed method achieves a favorable trade-off between speed, model size, and pose estimation accuracy, demonstrating strong potential for real-world deployment on mobile and embedded platforms.
## 2 Related Work
### Symmetric and Asymmetric Key Management in UWSNs  
Key management is a critical component of security in UWSNs, ensuring secure communication between sensor nodes. In UWSNs, key management frameworks are particularly divided into two categories. symmetric key and asymmetric key. Symmetric key management schemes are simple and efficient, but they are vulnerable to various attacks such as node capture and replay attacks. Asymmetric key management schemes provide a higher level of security but require a higher computational overhead for encryption and decryption, making them unsuitable for resource-constrained underwater sensor nodes [8]. Numerous key management frameworks have been proposed in the literature to address the challenges of UWSNs. One of the most used frameworks is the PKI, which uses an asymmetric key scheme to generate, distribute, and revoke cryptographic keys. However, PKI has a high computational overhead for encryption and decryption, making it unsuitable for resource-constrained underwater sensor nodes [16].  
ECC-based asymmetric key management scheme used in various key management frameworks used for wireless sensor networks. ECC provides a high level of security and is energy efficient. However, the existing state-of-the-art approaches are vulnerable to node capture attacks, where an attacker captures a node and retrieves its cryptographic key, making it unsuitable for UWSNs [17; 18]. Therefore, there is a need for a secure and lightweight key management framework that can overcome the limitations of existing frameworks and ensure the security of the network.
## 5 Conclusions  
In view of the difficulties in the construction of joints between modules, this paper presents an innovative modular connection. The new connection is suitable for the connection of eight modules at the central column. A detailed introduction to the structure and installation method of the new connection is also given. The performance of the new connection is studied through symmetric monotonous static tests. The conclusions are as follows:  
1. The proposed new connection satisfies the requirement of non-welding construction. It is convenient for connecting and is not restricted to the narrow working face between modules. The beam and the column are neither drilled nor weakened. The indoor and outdoor decoration remains undamaged during installation. This provides a design inspiration to achieve the goal of complete decoration in the factory.
2. The load transfer capacity of the connection was studied through a static test. When the diagonal stiffeners were small, a stress concentration would occur in the weld. The quality of the welding line was a key factor in determining the connection capacity. Larger stiffeners can increase the bearing capacity and stiffness of the connection. The diagonal stiffeners can effectively share the force on the welding line. When the welding line was of fine quality, the final failure mode was the buckling of the upper beam. Most failures occurred on the beam or at the joint inside of the module, while the connection between modules remained undamaged, which showed that the new connection has a good bearing capacity under the action of gravity load.
3. In the unbraced frame system, the new connection is similar to the semi-rigid joint. In the braced frame system, the new connection is similar to the rigid joint. Increasing the diagonal stiffeners' size can bring the connection closer to the rigid joint.  
Figure 23: Load–strain curve of S2, areas d, x, y.  
Figure 22: Load–strain curve of S2, area b.  
The stress expanded most obviously where the beam and the diagonal stiffeners joined and within stiffeners. Additionally, stress expands fastest where the upper beam and the stiffeners joined, which corresponded to the failure mode.  
In further study, it is expected that the bolt form can be changed to optimize the design, such as by using self-locking bolts and anti-loosening bolts; additionally, the shear strength and tensile strength of the new connection will be studied, since the building will be subjected to larger lateral forces when the building is tall.  
Conceptualization, Y.W. and J.X.; software, Y.W., R.M. and T.W.; validation, Y.W., R.M. and B.X.; formal analysis, Y.W.; investigation, Y.W., B.X. and T.W.; funding acquisition, J.X.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W. and J.X.  
This research was funded by Fundamental Research Funds for the Central Universities (2018 ZDPY04).  
The authors want to thank the reviewers for constructive comments and suggestions that have helped to improve our manuscript.  
The authors declare no conflict of interest.
# Antivirus Evasion Methods in Modern Operating Systems
## 3 Antivirus Detection Mechanisms
### Sandboxing  
In advanced threat detection methods, such as heuristic-based detection, Sandbox serves as a system that emulates the behavior of a host computer. It functions as a virtual machine where potentially harmful files are executed to monitor their actions. Sandbox offers a secure environment that isolates the executed programs from the client's device, thereby reducing the chances of device infection to negligible levels.
## 3 Results
### AF and AFB Microbial Community at the End of the Start-Up  
To assess the influence of the carrier type (fixed for AF or moving for AFB) on the composition of the microbial community at the end of the experiment (HRT of 2.02 days), samples of biomass on polyurethane carriers from AF and AFB were taken, in which phylogenetic analyses of the 16S rRNA gene sequences at the phylum and genus levels were carried out. The results of high-throughput sequencing are presented in Figure 7.  
In general, it can be noted that the microbial composition in AF and AFB did not significantly differ from each other. Bacteria of two main phyla dominated in the reactors--_Firmicutes_ being the most prolific, and _Actinobacteria_. _Firmicutes_ relative content was 65 and 60% in AF and AFB reactors, respectively. The second-largest representative, phylum _Actinobacteria_, accounted for 26 and 32% of the microbial community in AF and AFB, respectively. Repre  
Figure 6: Dependence of the hydrogen yield on the organic loading rate (OLR) for the anaerobic filter (AF) and anaerobic fluidized bed (AFB).  
Figure 7: Compositions of microbial communities of anaerobic filter (AF) and anaerobic fluidized bed (AFB) at the phylum (**a**) and genus (**b**) levels.  
sentatives of _Proteobacteria_, _Chloroflexi_, and _Bacteroidetes_ constituted a minor part (from 0.9 to 5%) of the AF and AFB microbial communities (Figure 7).  
Representatives of _Lactobacillus_, belonging to _Firmicutes_, made up the largest part of the microbial community at the genus level, 43 and 40% in AF and AFB, respectively.  
Representatives of _Bifidobacterium_ of the phylum _Actinobacteria_ accounted for 24 and 30% of the microbial community in AF and AFB, respectively.  
_Clostridia_, namely _Clostridium sensusticto 1_ and _Clostridium sensusticto 12_, accounted for 13 and 4% of the microbial community in AF, and 6 and 4% of the microbial community in AFB, respectively.  
The remaining bacteria species, including _Lactococcus_, _Haloimpatiens_, _Olsenella_, and others, constituted a minor (each less than 2%) part of the microbial community of AF and AFB.  
The presence of a minor number of methanogenic archaea in the microbial community of the reactors, which amounted to 0.4% and 0.1% in AF and AFB, respectively, should also be noted. _Methanobacterium_ (50% of archaea) dominated in the AF archaeal community, while _Candidatus_ Methanoplasma (25% of archaea) and _Methanothermobacter_ (13% of archaea) were present to a lesser extent. At the same time, _Methanobacterium_ (75% of archaea) prevailed in the AFB archaean community, while _Methanolinea_ and _Methanosaeta_ were present in minor amounts (10% of each archaea).