esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 HMBAC Design and Implementation
### Janus Implementation  
The implementation of the system relies on the integration of various technologies. For implementing the Frontend component, an _Electron_[55] application was developed, while blockchains (PBC and DBCs) were developed on the Hyperledger Fabric platform, with _Raft_[56] as the underlying consensus mechanism. The functionality is implemented through Smart Contracts developed in Javascript (the full open-source implementation can be found in [5] as a reproducible artifact).  
Janus orchestration is based on Kubernetes [57]. For security and design modularity, all the components of the multi-blockchain infrastructure were developed in distinct Kubernetes Pods, thus providing software isolation and containerization. In particular, each smart contract, as well as the Frontend, the Inter-BC and the Database APIs are executed as separate Pods. To secure the interaction between Pods, an Ingress API supports TLS termination between the Pods.  
To support the deployment of independent certificate infrastructures, each participating stakeholder establishes and maintains a _Certificate Authority_ (CA), responsible for issuing and revoking certificates for their users who interact with the system. To simplify the deployment, one CA is considered to be a mutually trusted authority and is responsible for issuing certificates used by the system components (e.g., for Pods' TLS connections). Although this is not a strong requirement (e.g., it can be removed by applying cross-certification between the stakeholders), it simplifies the deployment process and it is a reasonable assumption for multi-domain environments. For example, in the healthcare sector, the ministry of health could play the role of mutually trusted authority. In our system, we use _Hyperledger Fabric Certificate Authority_ (provided by the Hyperledger platform) to implement the CAs of the stakeholders. Each CA runs as an instance for each entity in a separate Kubernetes Pod.  
_Hashicorp Vault_[8] is used as an external application to issue, manage and store user and authority credentials and keys. Although it is an off-the-shelf solution, we designed an ABE plugin for Vault, written in Golang, to implement a two-step ABE decryption and support the partial decryption process via the KSSC. Additionally, we run different Vault instances, one for simulating user-side attribute key Vault storage and another for storing domain attribute keys, which is accessible only by the KSSC of each Domain blockchain.  
Finally, the Frontend and the Inter-BC APIs use an instance of _RabbitMQ_[58] software, also executed in a separate pod, to temporarily store requests that remain pending in queues. The Janus implementation design is depicted in Figure 5 and is described in detail in the following subsections.
## 4 Results and Discussion
### Acquisition IR014_1 (275 DE)  
Acquisition IR014_1 (275 DE) was considered because it involves fan-end bearing damage (inner race fault), while the acquisition comes from the drive-end, so the demodulation should be more difficult. Furthermore, as can be observed in Figure 3, this signal involves transients leading to non-stationarities which should negatively affect the spectral kurtosis [26]. The higher difficulty in the demodulation is reflected by the \(dE\) = 0.59. Focusing on the resulting \(iE\) in Table 3, it is easy to see that in this case \(AK\), \(P\), \(MP\). and \(I\) outmatch the spectral kurtosis \(K\), which is better than \(L2\)/\(L1\) and \(SGI\). The corresponding indices as a function of frequency are reported in Figure 6a, where it can be noticed that not one of the indicators reach the optimal solution, but \(AK\) is the nearest.
# A Scalable Spatial-Temporal Correlated Non-Stationary Channel Fading Generation Method
## 1 Introduction
This paper aims to fill the above research gaps and develop a scalable and efficient channel fading generation method. Overall, the main contributions and innovations are as follows. 1. We design and implement a scalable hardware architecture to enhance flexibility and applicability. Based on the improved CORDIC algorithm, the convergence domains of e-exponent, logarithm, and square root functions are expanded to achieve temporally correlated fading in different scenarios. Furthermore, we adopt the SoFM method to ensure the continuity of the Doppler phase in non-stationary scenarios. 2. A lower triangular matrix operation method is proposed, which decomposes its large-scale matrix into low-level matrix operations. This is based on matrix partitioning and time-division multiplexing, which greatly reduces the complexity of hardware implementation for spatially correlated fading. In addition, a selector-based optimization scheme is adopted for complex multiplication, which effectively reduces hardware resource consumption. 3. The proposed hardware generation method is implemented on the XC/VX690T FPGA chip. The hardware simulation results demonstrate that the statistical properties, such as the probability density function (PDF), Doppler power spectrum density (DPSD), temporal correlation function (TCF), and spatial correlation function (SCF), are in good agreement with their theoretical values. Specifically, the generated PDF error with a 16-bit fixed-point is 1.52%, and the average SCF error is 1.47%. In addition, resource utilization is reduced from 9.08% to 3.54%; DSP resources are especially reduced, with a reduction of 11.11%. The remaining sections are organized as follows: Section 2 provides a brief overview of the theoretical generation method. Section 3 presents the FPGA hardware generation scheme and implementation details. In Section 4, the proposed method is implemented and verified on an FPGA platform. Finally, Section 5 presents the conclusions.
# An Enhanced Feature Extraction Network for Medical Image Segmentation
## 3 Proposed Method  
In this section, we will present the details of the EFEN proposed in our work and explain the role of each part. In this paper, two key issues in the tasks of medical image segmentation are considered when designing the network: first, whether the feature extraction capability of the network can be improved, and second, whether the highly extracted features can be compensated by useful information, and whether the compensation information can avoid noise or useless details as much as possible. Since the U-Net performs best in the medical image segmentation tasks, we design the network on the basis of U-Net. Figure 1 shows an overview of the designed network architecture on the basis of U-Net. In Figure 1, U-Net is used as the basic network for the feature extraction of the first stage, and during up sampling the improved skip-connection with a cross-attention mechanism is used to supervise the information from the encoder. After obtaining the outputs of the base U-Net, we merge them with the inputs of the feature re-extraction network to further extract features. In the up-sampling process of the feature re-extraction network, we also use the cross-attention mechanism to supervise the information of the skip-connection. It is worth noting that the information of the skip-connection only comes from the corresponding layer features which are extracted in the down-sampling process of U-Net. In this way, not only can the ability of the network to extract features be improved, but also more accurate boundary features can be obtained. Finally, because we perform medical image segmentation, in order to further enhance the feature extraction ability, we adopt binary cross-entropy and dice loss to jointly optimize the training learning. Section 3.1 introduces the structure of proposed EFEN. Section 3.2 introduces the improved skip-connection. Section 3.3 introduces the loss function.
# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating
### Se-Gan
#### 3.2.3 Feature Reweighting
This padding adds zeros at the bottom of the data, which ensures that the spatial information at the boundaries is maintained. This operation aims to preserve the spatial features of the signal, preventing information loss during subsequent convolution operations. The convolution block is composed of four convolution layers and four Leaky ReLU activation functions. The output of the convolution block is flattened via a view function into a high-dimensional vector, which is then fed into the linear block. These layers help the model learn hierarchical features from the input data at different levels of abstraction. The convolutional layers also progressively reduce the spatial size of the data while increasing the number of feature maps. The linear block includes a fully connected layer that projects the high-dimensional input vector into a one-dimensional output. The input signal first passes through the zero-padding module, then through the convolution block for feature extraction, and finally through the linear block for discrimination, outputting a scalar value. This scalar value indicates the probability of the input signal belonging to either real (if close to 1) or generated channel data (if close to 0).
## 3 Method
### Model Architecture
#### 3.3.2 Vehicle-State Embeddings  
CeT employs FNN and Multi-Head Attention (MHA) to fuse vehicle motion features, lane segment characteristics, and vehicle-type attributes to generate vehicle-state embeddings. Specifically, it decomposes vehicle speed into radial and lateral components based on the angle between the vehicle azimuths and the direction of lane segments, enabling more precise learning of the microscopic behavioral patterns of the vehicle. Subsequently, CeT applies FNN to transform the vehicle's radial and lateral motion components into vehicle motion vectors and uses MHA to fuse them with vehicle-type attributes into vehicle-state embeddings. These embeddings, which combine the vehicle's motion and type characteristics, enable CeT to better learn the evolution patterns of traffic states from traffic data.  
Figure 3b demonstrates the fusion of the vehicle-state vector \(x_{\xi,i}^{t}\) and vehicle-type attribute \(s_{\xi,i}\). When \(x_{\xi,i}^{t}\) enters CeT, it is decomposed and converted into the vehicle motion vector \(x_{\xi,i}^{t}\) as follows:  
\[x_{\xi,i}^{t}=LayerNorm\left(ReLU\left(W_{2}^{vc}\cdot ReLU\left(W_{1}^{vc} \cdot\left[v_{\xi,i}^{t}sin(\phi_{\xi}-\phi_{\xi,i}^{t}),v_{\xi,i}^{t}cos(\phi_ {\xi}-\phi_{\xi,i}^{t})\right]+b_{1}^{vc}\right)+b_{2}^{vc}\right)\right), \tag{5}\]  
where \(ReLU\) represents the activation function and \(LayerNorm\) represents regularization. Meanwhile, \(s_{\xi,i}\) is linearly transformed into \(s_{\xi,i}^{t}\). Subsequently, \(s_{\xi,i}^{t}\) is transformed into the query \(q_{\text{\tiny{New}}}\in\mathbb{R}^{N_{h}\times d_{K}}\), and \(x_{\xi,i}^{t}\) is projected to the key \(k_{\text{\tiny{New}}}\in\mathbb{R}^{N_{h}\times d_{K}}\) and value \(v_{\text{\tiny{New}}}\in\mathbb{R}^{N_{h}\times d_{K}}\), where \(N_{h}\) and \(d_{K}\) denote the number of heads and the head dimension. Finally, an MHA computation is performed on \(q_{\text{\tiny{New}}}\), \(k_{\text{\tiny{New}}}\), and \(v_{\text{\tiny{New}}}\):  
Figure 3: The architecture of CeT and its key components.  
\[MultiHead(q_{\text{true}},k_{\text{true}},v_{\text{true}})=Concat \left(head_{1},head_{2},\ldots,head_{N_{k}}\right)W^{0}, \tag{6}\] \[head_{i}=Softmax\left(\frac{q_{\text{true}}k_{\text{true}}}{\sqrt{d_{ K}}}\right)v_{\text{true}}, \tag{7}\]  
where \(Softmax\) represents the activation function. CeT applies \(Softmax\) as the activation function for calculating attention weights because it can transform input values into a probability distribution, ensuring that the sum of all output values equals 1. Thus, \(Softmax\) can reflect the relative importance among different input elements, thereby effectively allocating weights in attention mechanisms. In contrast, while \(sigmoid\) and \(tanh\) can also map inputs to a range between 0 and 1, they lack normalization properties and cannot directly generate probability distributions. Therefore, \(Softmax\) is particularly suitable for multi-classification tasks and attention mechanisms, as it can better handle weight allocation among multiple elements.
## 6 Conclusions  
In this study, a novel AJODL-DSSEM model was established for the prediction of energy in the smart city environment. The proposed AJODL-DSSEM model mainly accomplished data preprocessing at the initial stage to normalize the data. Further, the AJODL-DSSEM model involved a CNN-ABLSTM model for the prediction of energy. Lastly, the AJO algorithm was applied for the hyperparameter adjustment of the CNN-ABLSTM model. The experimental validation of the proposed AJODL-DSSEM model was tested using two open access datasets, namely the IHEPC and ISO-NE datasets. The comparative study reported the enhanced outcomes of the AJODL-DSSEM model over recent approaches. Thus, the AJODL-DSSEM model can be employed for energy-management-related decision making in the real-time smart city environment. The proposed model can be useful for optimal resource allocation in the smart city environment. It can also assist stakeholders and policymakers in the design of energy solutions for smart cities by providing strategies for the effective modeling and management of energy systems. It is helpful for the stakeholders to understand urban dynamics and evaluate the influence of energy policy alternatives. In the future, feature selection and outlier detection approaches can be integrated into the proposed model to boost the predictive performance. Moreover, the proposed model can be tested on real-time large-scale datasets in the future.  
Conceptualization, A.A.-Q. and H.A.; methodology, J.S.A.; software, H.M.; validation, A.A.-Q., N.N. and L.A.A.; formal analysis, M.A.D.; investigation, F.N.A.-W.; resources, H.A.; data curation, M.A.D.; writing--original draft preparation, F.N.A.-W. and J.S.A.; writing--review and editing, L.A.A.; visualization, M.A.-S.; supervision, M.A.D.; project administration, M.A.D.; funding acquisition, H.A. All authors have read and agreed to the published version of the manuscript. The authors extend their appreciation to the Deanship of scientific research at King Khalid University for funding this work through the Large Groups Project under the grant number (42/43), the Princess Nourah bint Abdulrahman University Researchers supporting project number (PNURSP2022R303), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. The authors would like to thank the Deanship of scientific research at Umm Al-Qura University for supporting this work by grant code: (22 UQU4340237 DSR18). Not applicable. Not applicable. Data sharing is not applicable to this article, as no datasets were generated during the current study. The authors declare no conflict of interest.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### System Evaluation
#### 4.7.4 Comparison of Displacement Change Data  
The mirror component is equipped with two optical cubic mirrors of 13 mm \(\times\) 13 mm \(\times\) 13 mm, which are used to test the positional change of the structural member relative to the optical member before and after the mechanical test. As shown in the Figure 11, one optical cubic mirror is affixed to the edge of the mirror and another is affixed to the edge of the base, which are used to test the angular change between the two reference mirrors before and after the mechanical test.  
After three small-scale feature-level scanning tests, one sinusoidal vibration test, and one random vibration test, the angular changes in the X, Y, and Z directions of the mirror and the base are \(5.678872^{\prime\prime}\), \(-4.180947^{\prime\prime}\), and \(-5.87815^{\prime\prime}\). As shown in the Table 11, the optical cubic mirror test results show that the changes between the mirror and the support structure before and after the mechanical test are less than \(5^{\prime\prime}\), indicating that there is no relative movement between the mirror and the support structure before and after going through the mechanical test, which further proves that the flexible support structure in this paper can withstand the harsh mechanical environment [22].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Variations \(\Delta\) before and after the Mechanical Test (\({}^{\prime\prime}\))**} \\ \hline \multirow{4}{*}{Structure (X, Y, Z)} & \multicolumn{2}{c}{**Mirror (X, Y, Z)**} \\ \cline{2-4}  & \(5.678872\) & \(-7.386751\) & \(-23.4734\) \\ \cline{2-4}  & \(3.457164\) & \(-4.180947\) & \(-6.37103\) \\ \cline{2-4}  & \(23.51495\) & \(6.529278\) & \(-5.87815\) \\ \hline \hline \end{tabular}
\end{table}
Table 11: Variations before and after the mechanical test.  
Figure 11: Schematic of optical cubic mirror position.  
Figure 10: Schematic diagram of sinusoidal vibration test curve of mirror component.
# Multi-Object Detection for Inland Ship Situation Awareness Based on Few-Shot Learning
## 5 Multi-Object Tracking for Inland Ships
Finally, the two features are set a certain weight to obtain our final cost matrix:  
\[c_{i,j}=\lambda d^{(1)}(i,j)+(1-\lambda)d^{(2)}(i,j) \tag{6}\]  
where \(\lambda\) stands for two metric weights. The cost matrix of the determined track and corresponding detection is calculated by the Hungarian matching algorithm. The matching is successful if the cost matrix is smaller than the threshold. The matched track and observation value detection are obtained, and the unmatched track and detection are obtained. Fourthly, combining the failed track with the tentative track and using the IoU matching strategy, IoU matching directly builds all track and detection from IoU as elements into the IoU cost matrix and uses the Hungarian algorithm for matching. The matching method is similar to that of cascade matching. Then, after matching, the system obtains matched, unmatched_track, and unmatched_detection. It modifies the successfully matched track successively, updates the status of the failed track, converts the unmatched detection into a track, and updates the feature set of the successfully matched track. Finally, the Kalman filter gain is used to correct and update the successfully matched track. The Kalman filter gain update formula is shown as follows:  
\[K_{k}=\frac{P_{k}^{-}C^{\top}}{CP_{k}^{-}C^{\top}+R} \tag{7}\]  
\[P_{k}=(I-K_{k}C)P_{k}^{-} \tag{8}\]  
\[\hat{\chi}_{k}=\hat{\chi}_{k}^{-}+K_{k}\big{(}y_{k}-C\hat{\chi}_{k}^{-}\big{)} \tag{9}\]  
where \(C\) is the observation matrix, \(R\) represents the observation noise covariance matrix, and \(K_{k}\) is the Kalman coefficient. The integration of the traditional Kalman filter (KF) into the DeepSORT framework for ship detection and tracking in our maritime context is driven by its core advantages, which include simplicity, effective state estimation, robust noise handling, and cost effectiveness. KF's simplicity and adaptability make it a practical choice for ship tracking in real-world scenarios where achieving reliable results without excessive resource expenditure is essential. After updating the status of each track, deleting the dead track, and updating the feature set of the confirmed track, the process of this frame is finished, and the detection and tracking of the next frame start. In this way, the task of ship tracking with videos is completed.
## 2 Materials and Methods
### Computational Cost  
Likewise, the computational cost is related to the number of samples to be processed, that is, the length of the data window, as well as the number of basic operations, quantified in multiplications and accumulations. Figure 9 shows an estimate of the execution time curve of the entire proposed algorithm, in general, including the noise-reduction process and the fault pattern recognition stage. The execution time increases exponentially as the number of samples to be processed increases. This is mainly due to the multiplication and accumulation processes found in the covariance and convolution operations markedly in the noise-reduction and power spectrum calculation processes.
## 1 Introduction  
Lignin is the second most abundant polymer in nature and can be isolated from wood, annual plants, or agricultural residues by different processes [1]. The global generation of lignin is estimated at 100 million tons/year, attributed to biorefineries and pulp and paper mills [2, 3]. Due to their variety of functional groups (such as hydroxyl, carboxyl, and methoxyl), lignin can be applied as raw material in resins, fibers, fillers, adhesives, and dispersing agents, offering different possibilities for the industry [4]. However, despite their high availability and potential for application, lignins obtained from industrial processes, known as technical lignins, are usually treated as by-products and underutilized [5, 6].  
The main deliginification methods are the Sulfite, Kraft, Soda, and Organosolv processes [7, 8]. These processes generate a waste by-product called black liquor, from which technical lignins are isolated. Lignososulfonates represent the technical lignins obtained from the sulfite process and are widely commercialized as low-cost admixtures for cement-based composites [3]. The dispersing properties of lignosulfonate favor the water consumption reduction in cement mixtures, which results in a more densified and homogeneous matrix with higher mechanical strength and durability [9, 10]. Despite the mentionedproperties, the limited worldwide production and non-standardization of performance and composition restrict the use of commercial lignocellulosic-based admixtures, which motivates the research on new lignin-based admixtures (LBAs) [11; 12]. The production of LBAs from residual lignins contributes to waste management in biorefineries and pulp and paper mills, in addition to being an alternative to reduce the commercialization of petroleum-based admixtures [13; 14]. The construction sector is responsible for pollutant emissions and high consumption of natural resources [15; 16]. In this sense, it is mandatory to develop studies seeking to reduce these environmental impacts, such as LBAs incorporation on cement-based composites.  
The LBAs developed so far were classified as plasticizers [17], superplasticizers [18], set retarders [19], and air-entraining admixtures [20]. These new LBAs can be produced by modifying commercial lignocellulosic-based admixtures [21; 22] or using other technical lignins [23; 24]. Different modification methods can be applied to lignins to improve the efficiency and mechanisms of action of LBAs, which include copolymerization with other compounds and an increase in sulfonic groups, molecular weight, and polarity [25]. The structure and composition of lignins vary according to the botanical species and the delignification process employed [3; 26], which also influences the characteristics and application possibilities of LBAs. In addition, LBAs can be produced from unmodified technical lignins and black liquors [27; 28], avoiding further modification strategies.
# QoS-Aware Resource Management in 5G and 6G Cloud-Based Architectures with Priorities
## 2 Scheduler Priority Queue
### No Buffer Queue for Pre-Empted q-Type Services
The waiting time \(W_{schedule}(i,j)\) can be calculated using \(\Big{(}1-T_{yout(i,j)}\Big{)}\), which can be expressed as \(1-T_{yout(i,j)}=Prob\Big{\{}T_{\mu b}>W(i,j)\Big{\}}\). Since \(T_{\mu b}\) is assumed to be exponentially distributed, \(W_{schedule}(i,j)\) can be obtained by \(W_{schedule}(i,j)=-\frac{1}{\mu_{b}}In\Big{(}1-T_{yout(i,j)}\Big{)}\). Consequently, the average scheduling waiting time of a queued pre-empted packet can be obtained by  
\[ \begin{array}{c}\sum\limits_{j=1}^{n}\sum\limits_{q=0}^{Q-1}W(j,n-j+q)P_{j,n -j+q}\\ \overline{W}_{schedule}=\frac{q\leq j}{\sum\limits_{j=1}^{n}\sum\limits_{q=0} ^{Q-1}P_{j,n-j+q}}.\\ q\leq j\end{array}  \tag{10}\]