esa-sceva/satcom-chunk-collection huggingface dataset:
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods
#### 2.2.1 Lagan  
The Lagan algorithm is based on dynamic programming as it uses anchoring to subdivide the alignment into small anchors [28]. The Lagan algorithm also assumes that the two sequences are relatively close. It starts by detecting the anchors and then performs a dynamic programming alignment algorithm on the limited area around the anchors. The Lagan algorithm's complexity is \(O(n^{3})\), where \(n\) is the length of the input sequences.  
1. Generation of Local Alignments: The Lagan algorithm uses the CHAOS method [29] to detect local homologies between the two genomes and chains them into a rough global map. The first step of CHAOS is chaining short exact matches (seeds) which match between the two genomes. The seeds that are close are regrouped to the same anchors. The gaps between the seeds are aligned using a dynamic programming alignment method;
2. Construction of a Rough Global Map: The Lagan algorithm uses local alignments to develop a rough global map. Each local alignment has a score of similarity. The optimal rough global map has the highest-scoring chain, which can be computed using sparse dynamic programming [30].
3. Computation of Global Alignment: To compute the final global alignment, the Lagan method uses the Needleman-Wunsch algorithm to perform an alignment of the limited area between the anchors.  
Multi-Lagan aligns a set of genomes progressively. An alignment of \(n\) sequences is constructed after the \(n-1\) pairwise alignment operation. Multi-Lagan uses a phylogenetic tree of the sequence to choose, at each step, which genome will be used for the alignment. This similarity-based method performs an alignment with high similarity between sequences and can detect orthologous genomic regions between them. Multi-Lagan employs two primary stages to align N genomes with a phylogenetic binary tree connecting them:  
1. Creation of initial global maps: the approximate global map between each sequence pair is determined using the Lagan algorithm;
2. Progressive multiple alignment with anchors: 1. Alignment between the two closest sequences according to the binary phylogenetic tree is performed using the Lagan algorithm; 2. Each alignment is a new multi-sequence. The rough global maps of this multi-sequence and the closest sequence are identified; 3. Steps 2.1 and 2.2 are repeated, and global alignment is carried out between the multi-sequences and the closest sequence; 4. Step 2 is repeated until a multiple alignment of all the set of sequences has been performed (Figure 4).
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 3 The Importance of Quantum Key Distribution (QKD)
When this type of solution is widely utilized, secure communication will revert to what they were before the mass acceptance of asymmetric mechanisms, which is expensive and complex while needing unified secret management and thus employed exclusively by large enterprises. They would, however, be simpler to implement than their QKD-based equivalents because they are suitable with current networks.
## 3 Results
### Discussion of Results  
The parametric analysis of the ANN-PSO hybrid model was conducted to identify the optimal configurations for predicting key performance metrics in the telemedicine queuing system. The analysis evaluated different configurations by varying the number of neurons in the hidden layer, the swarm population size, and the acceleration factors (C\({}_{1}\) and C\({}_{2}\)). The MSE and R\({}^{2}\) values were used to assess the model's accuracy during both training and testing phases. An ideal ANN-PSO model will have a regression scores close to 1 and MSE values near 0 [22].  
The results indicate that parameter selection plays a critical role in the model's ability to generalize well to unseen data.
## 2 Proposed Method
### The Proposed Model  
#### 2.2.1 Network Architecture  
We propose the novel auto-encoder-based GAN architecture illustrated in Figure 1. Our generator takes two latent vectors and consists of several blocks, with each block handling a specific resolution. The latent vectors are fed into each block and transformed by the affine transformation layer. We use an AdaIN [16] layer that stylizes feature maps with information from the affine transformation layer. We divide the generator into front and back modules, with the front module generating feature maps of a relatively low resolution (\(32\times 32\)) and the back module generating the final output image. \(\mathrm{z}_{1}\) is fed into the front module, and \(\mathrm{z}_{2}\) is fed into the back module, meaning \(\mathrm{z}_{1}\) is associated with the overall structure of the image (e.g., the shape or appearance of the face), whereas \(\mathrm{z}_{2}\) is associated with the details of the image (e.g., the microcharacteristics of the face or hair color). The bottom of Figure 1 presents the details of each block. Initially, the input features are upscaled, and there are three sets of Conv-ELU-AdaIN layers. As mentioned above, the AdaIN layer normalizes the features and matches them to new statistics (i.e., the mean and variance from the affine transformation layer). AdaIN is formulated as  
\[\mathrm{AdaIN}(x,y)=\sigma(y)\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\mu(y), \tag{2}\]where \(x\) denotes the feature map, and the new mean and variance (\(\sigma(y)\) and \(\mu(y)\), respectively) are calculated by affine transformation with the input latent vectors. Because of the scale-specific role of each layer, the visual quality of the output images is improved.  
The discriminator of our model has an auto-encoder structure that consists of an encoder and a decoder. The encoder takes a real or fake image as input and encodes it as two latent vectors \(\mathbf{z}_{1}^{\star}\) and \(\mathbf{z}_{2}^{\star}\) of the same size as \(\mathbf{z}_{1}\) and \(\mathbf{z}_{2}\) respectively. The decoder then decodes the image with \(\mathbf{z}_{1}^{\star}\) and \(\mathbf{z}_{2}^{\star}\). Because the decoder has the same structure as the generator, \(\mathbf{z}_{1}^{\star}\) and \(\mathbf{z}_{2}^{\star}\) affect different scale-specific characteristics.
###### Abstract  
The topography of mountainous areas is characterized by large undulations, which lead to a very complex wind field at bridge sites in mountain valleys. The influence of oncoming wind speed on long-span bridges built in mountain valleys is quite pronounced. To investigate the wind characteristics at a bridge site in a mountain valley under different oncoming wind speeds, a wind tunnel test of a terrain model with a scaling ratio of 1:1000, where a long-span bridge would be built in the V-shaped canyon, was conducted. Uniform and atmospheric boundary layer (ABL) inflows were both applied, and the effect of different oncoming wind speeds (basic wind speeds of 6 m/s, 8 m/s, 10 m/s, 12 m/s, and 14 m/s) under three wind directions (0\({}^{\circ}\), 30\({}^{\circ}\), and 180\({}^{\circ}\)) on the wind characteristics at the main beam and two bridge towers were studied. The results indicate that increasing oncoming wind speed leads to decreased wind profiles and wind speed amplification factors and increased wind attack angles, while wind yaw angles remain largely unchanged. In addition, compared to ABL inflow, the variation of fluctuating wind characteristics is more pronounced with the oncoming wind speed under uniform inflow. Under uniform inflow conditions, increasing the oncoming wind speed causes decreased turbulence intensity, reduces the peak frequency of the power spectrum, and slows down the high-frequency decay rate. Under ABL inflow conditions, turbulence intensity and the power spectrum remain unchanged with different oncoming wind speeds. Additionally, the turbulent integral scale derived from fitting with the von Karman wind spectrum is sufficiently accurate, and the variation in the turbulent integral scale is greatly influenced by the terrain. Furthermore, higher wind speeds result in stronger coherence between two points. When two points are at different locations but with the same spacing, the coherence function remains roughly the same. Locations with higher kurtosis and skewness values exhibit steeper probability density functions, with larger kurtosis and skewness coefficients typically found on the leeward side. High wind speeds are more detrimental to bridge safety, and appropriate preventive measures should be implemented in advance to address extreme conditions that may arise at high wind speeds.  
v-shaped mountain valley; long-span bridges; mean wind characteristics; fluctuating wind characteristics; wind tunnel test +
Footnote †: journal: applied sciences  
Article
# Active RIS-Assisted Uplink NOMA with MADDPG for Remote State Estimation in Wireless Sensor Networks
## 3 MADDPG-Based Resource Allocation
### The MADDPG Algorithm  
In this section, we propose the RIS-MADDPG algorithm to address the optimization problem outlined above. The MADDPG algorithm is a widely used multi-agent reinforcement learning (MADRL) method for continuous control problems. For \(N\) agents, the MADDPG algorithm involves \(N\) actor networks and \(N\) critic networks, as shown in Figure 2. Each agent's actor network generates actions based on its policy, denoted as \(\mu=\{\mu_{1},\mu_{2},\dots,\mu_{N}\}\), with each policy implemented by a neural network \(\phi^{\mu}=\{\phi_{1}^{\mu},\phi_{2}^{\mu},\dots,\phi_{N}^{\mu}\}\). The critic networks, parameterized by \(\phi^{\mathbf{Q}}=\{\phi_{1}^{Q},\phi_{2}^{Q},\dots,\phi_{N}^{Q}\}\), evaluate the quality of the actions taken by the agents, considering the system state and the joint actions. Each agent \(n\) has an experience replay memory \(\mathcal{B}_{n}\) that stores transition tuples \((\mathbf{s_{k}},\mathbf{a_{k}},r_{n}^{k},\mathbf{s_{k+1}})\). At each time step, each agent samples a minibatch from its memory to update its policy. The gradient for agent \(n\) is computed as follows [18]:  
\[\
abla_{\phi_{n}^{\mu}}\mathcal{J}(\mu_{n})= \tag{23}\] \[\mathbb{E}_{\mathbf{a_{n}^{k}}\sim\mathcal{B}_{n}}\left[\
abla_{ \phi_{k}^{\mu}}\mu_{n}\left(\mathbf{a_{n}^{k}}|\mathbf{o_{n}^{k}}\right)Q_{n}^ {\mu}\left(\mathbf{s}^{k},\mathbf{a_{k}}\right)\right|_{\mathbf{a_{n}^{k}}= \mu_{n}\left(\mathbf{o_{n}^{k}}\right)}\right].\]  
To stabilize the learning process, MADDPG employs the target network technique. The target networks for the action and evaluation functions, \(\mu_{n}^{\prime}\) and \(Q_{n}^{\prime}\), respectively, are updated using backpropagation. The loss function for updating the evaluation network is  
\[\mathcal{L}\left(\phi_{n}^{Q}\right)=\mathbb{E}_{\mathbf{s^{k}},\mathbf{a_{k} },r_{n}^{k},\mathbf{s^{\prime}_{k+1}}}\bigg{[}\left(Q_{n}^{\mu}\left(\mathbf{s }^{k},\mathbf{a_{k}}\right)-y_{k}\right)^{2}\bigg{]}, \tag{24}\]  
where \(y_{k}=r_{n}^{k}+\psi Q_{n}^{\prime}\left(\left(\mathbf{s}^{k}\right)^{\prime},\mathbf{a^{\prime}_{k}}\right)\big{|}_{(\mathbf{a_{k}})^{\prime}=\mu_{n}^{\prime }\left(\mathbf{o_{n}^{k}}\right)}\). After updating \(\phi_{n}^{\mu}\) and \(\phi_{n}^{Q}\), the parameters of the target networks, \(\phi_{n}^{\mu^{\prime}}\) and \(\phi_{n}^{Q^{\prime}}\), are softly updated as  
\[\phi_{n}^{\mu^{\prime}} =\tau_{a}\phi_{n}^{\mu}+(1-\tau_{a})\phi_{n}^{\mu^{\prime}}, \tag{25}\] \[\phi_{n}^{Q^{\prime}} =\tau_{c}\phi_{n}^{Q}+(1-\tau_{c})\phi_{n}^{\mu^{\prime}}, \tag{26}\]where \(\tau_{a}\) and \(\tau_{c}\) are the update parameters for the target network, and \(\mathcal{C}\) denotes the update interval that determines how frequently the actor and target networks are updated during training. The detailed steps of the RIS-MADDPG algorithm are provided in Algorithm 2.
# Radiator Enablers for Wireless Communication Evolution
## 3 Radiators' Novel Technologies, Design, and Implementation Issues
### Holographic Metasurfaces
#### 3.3.8 Holographic Metasurface Performance and Evaluation  
The evaluation results prove that holographic metasurfaces demonstrate the capability of multiple transformations. An example is SIMs (ref. [70]), which achieves good performance in spectral efficiency and channel capacity. Matrix systems based on HMA (ref. [76]) not only provide robust flexibility to these multiple transmission channels but also offer high efficiency using advanced algorithms taking advantage of near-field and spatial wideband problems. Practical implementations, such as tensor metasurfaces (ref. [77]), demonstrate real-time, user-specific beamforming with high efficiency and gain. In addition, RRS technology (ref. [79]) enhances energy efficiency considerably, hence providing sustainable solutions for high-capacity multi-user communication systems. Near-field channel modeling advancements (ref. [78]) also contribute to refining HMIMO designs for high-capacity 6G applications.  
Ongoing developments in holographic metasurface systems continue to point toward their crucial role in the formation of the future of wireless communication and sensing technologies. Such metasurfaces will enable disruptive applications in a wide variety of fields by ensuring improvements in scalability, energy efficiency, and precision challenges. As research studies further develop their capabilities, holographic metasurfaces will probably form the heart of enabling technologies aimed at the realization of 6G networks and beyond. The breadth of ongoing research and applications in this domain is illustrated well in Table 6, which summarizes key references that highlight the diverse configurations, algorithms, and techniques underpinning holographic metasurface advancements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Near/Far Field** & **Application** & **Configuration** & **Algorithm/Technique** \\ \hline
[71] & Far Field & Holography, imaging & Phase-only holograms & Genetic algorithm \\ \hline
[72] & Far Field & Sensing, imaging, contactless monitoring & DMA & FPGA-based beam steering \\ \hline
[73] & Far Field & Reduced power consumption, spectral efficiency & HMIMOS & - \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of applications, configurations, and techniques from referenced papers on holographic metasurfaces.
# Improved Ensemble Learning for Wind Turbine Main Bearing Fault Diagnosis
## 3 Methodology
### Normal Behavior Model
#### 3.1.2 Train and Test Sets  
The aim of the normality model is that it is capable to cope with the various operational and environmental conditions that the WT will face, see [18]. Thus, the train and test datasets include data from all working conditions: different wind speed regions and their associated regions of operation of the WT (start up, maximize power, and limit wind power to avoid exceeding the safe electrical and mechanical loads), different year seasons, curtailment, etc. Therefore, it is noteworthy that in this work there is no filtering of the data based on specific regions of operation or seasonality. The available SCADA data, for the normality model, are divided as follows: data from 2017 are used to train the model (thus, the training dataset contains a whole year of data), and the remaining two and a half years are used to test predictions. Note that there is no validation set because Bayesian regularization is used to train the ANN.  
Finally, recall that a customized normality model will be built for each wind turbine in the park. This could raise some concerns related to its computational cost. However, note that after the model is trained on one year data, this model is used for two-year predictions ahead, as will be shown in the results section. Thus, the computational cost is low in comparison to the use of a training rolling window of observations preceding the target forecast that must be retrained at each window shift of the data.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Variable** & **Description** & **Range** & **Units** \\ \hline Power & Generated real power & [0, 2000] & kW \\ AmbientTemp & Ambient temperature & [\(-\)5, 40] & \({}^{\circ}\)C \\ BearingCSTemp & Bearing coupling side temperature & [0, 120] & \({}^{\circ}\)C \\ BearingNCSTemp & Bearing non-coupling side temperature & [0, 120] & \({}^{\circ}\)C \\ LowSpeedShaftTemp & Low-speed shaft temperature & [0, 120] & \({}^{\circ}\)C \\ GeneratorTemp & Generator temperature & [0, 175] & \({}^{\circ}\)C \\ GearboxTemp & Gearbox temperature & [0, 120] & \({}^{\circ}\)C \\ RotorSpeed & Rotor speed & [0, 50] & rpm \\ \hline \hline \end{tabular}
\end{table}
Table 3: Selected SCADA variables to develop the normal behavior model, its description, range of possible values, and units. All of them are related to the mean value over a 10-min period.
## 3 Numerical Analysis
### Modeling  
As shown in Figure 1, the ground modeled using numerical analysis was simulated as a single ground object under three rock conditions: hard rock, soft rock, and weathered rock. The mesh for numerical analysis was set as shown in Figure 2. The mesh is divided into small pieces to derive reasonable results. Furthermore, the mesh around the tunnel was divided into smaller meshes than the mesh of the outside ground. In addition, according to the precautions provided by the program, the size of the adjacent mesh was created not to be more than doubled. The boundary conditions in the X-(both sides), Y-(digging direction), and Z-direction (lower edge of the ground model) of the modeled rock were set to the quiet boundary type in the FLAC 3D program to avoid the influence of reflected waves in all directions. Regarding the blasting load, vibration-controlled blasting was assumed based on the fact that the target ground area was located in the center of a city. However, according to the characteristics of the TBM and NATM parallel application method, expansion blasting was modeled only for the contour hole, excluding the center-cut blasting hole. The diameter of the tunnel expanded via NATM expansion blasting was 10.0 m and the diameter of the pilot tunnel excavated by the TBM was 5.0 m. Four types of explosive powder factors (2.0, 1.0, 0.75, and 0.5 kg) were applied to each blasting hole according to the rock type and separation distance, and numerical analysis was conducted for a total of 36 cases.  
The constitutive models applied to the ground and TBM were the Mohr-Coulomb and elastic models, respectively. The physical properties of the ground used in this study were obtained from existing publications, and the physical properties of iron were used for the TBM [17; 18; 19]. An overview of the physical properties is provided in Table 6.  
Figure 1: Numerical analysis modeling of the ground and TBM.  
Figure 2: Setting of mesh for numerical analysis.
###### Abstract  
In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.  
Keywords:dependability; mathematical modelling; availability; wireless sensor networks; visual sensing; coverage failures +
Footnote †: journal: Sensors
Evaluation of the Applicability of the Ramp Metering to a Freeway Using Microsimulation: A Case Study in Riyadh, Saudi Arabia  
[PERSON]  
1 Civil Engineering Department, King Saud University, Riyadh 11421, Saudi Arabia; 435107964@student.  
option due to the limited space and the high cost in urban areas. Another option is to use Traffic System Management (TSM) techniques, which are known to provide a wide variety of possibilities for a government to improve the capacity and efficiency of its transportation systems. One special technique of the TSM is Ramp Metering (RM), which uses a traffic signal usually installed halfway down the ramp on freeway on-ramps. As with a normal traffic light, RM controls the vehicle entrance to the flow of traffic on the freeway by switching between red and green lights. It is considered to reduce overall freeway congestion by controlling merging vehicles onto the freeway, to improve safety, and to provide a better level of service to motorists without any additional lanes on the freeway [6].  
RM has not been thoroughly studied and implemented in Saudi Arabia so far, although police officers are manually breaking up the merging vehicle queues on the on-ramps in freeways every morning and night. Saudi Arabia has not implemented the globally-accepted RM due to lack of research in local conditions. Without actual, concrete research to show how RM may improve local traffic situations, government officials in Saudi Arabia have not yet been convinced of the positive impact RM could have on local freeways. Therefore, this study aims to assess the applicability of RM in improving the current traffic conditions in Riyadh and to find the best RM operation plan among several workable plans using a microscopic traffic simulation model (VISSIM). From the scientific contribution point of view, this case study may appear as non-innovative. However, from the societal impact, this will add value to the RM topic since this is the very first scientific research in Saudi Arabia.  
For this study, the necessary parameters of VISSIM will be calibrated to reflect driving behaviors in a weaving area in Makkah Road, one of the most heavily congested freeways in Riyadh. Several scenarios will be constructed to consider different operational options of RM and will then be analyzed to identify the best scenario for RM application to the study area. Finally, the overall efficiency and effectiveness of RM implemented in the study area during the simulations will be evaluated.  
General explanations on active traffic management and RM will follow with the study results on the effectiveness of RM. After data collection and the preparation process (including parameter calibrations) are discussed, the scenario analysis and results will be summarized. The conclusions and recommendations based on the results of this study will be presented at the end of this paper.