esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Study of Black Shale Modified Soil for Road Use Based on Eshelby-Mori-Tanaka Theory
## 4 Study on Modified Soil Mix Proportions with Black Shale  
### Experimental Design  
Combining relevant theories of two-phase composite materials, the modified soil with black shale can be viewed as a two-phase composite material with a base of cement-soil and interspersed black shale fragments. In this context, the base material is considered as the cement-soil with the target moisture content corresponding to the maximum dry density obtained in the compaction tests for various cement mixing ratios (designated as S10, S15, S20, S25, and S30, representing cement mixing ratios of 10%, 15%, 20%, 25%, and 30%, respectively). The black shale is mixed with the base cement-soil in proportions denoted as R = M\({}_{\text{shale}}\):M\({}_{\text{total}}\) = 40%, 55%, 70% (where M\({}_{\text{total}}\) = M\({}_{\text{shale}}\) + M\({}_{\text{clay}}\) + M\({}_{\text{cement}}\)). To prevent the black shale from absorbing water and causing poor adhesion with the surrounding cement-soil, the surface of the black shale is moistened before mixing. Following the requirements of the \"Test Code for Stabilized Materials with Inorganic Binders in Highway Engineering\" [32] (JTG E51-2009), compaction samples were prepared, cured in a standard curing chamber for 7 days (with one day of water immersion before the test), and subjected to unconfined compressive strength tests at a loading rate of 0.5 mm/min, with relevant data recorded (Table 2). The test procedure is illustrated in Figure 7.  
Figure 6: Relationship between unconfined compressive strength and cement content of cement-soil specimen with maximum dry density.  
Building on the results of the cement-soil ratio test and the preliminary ratio test for black shale modified soil, considering the economic feasibility of engineering construction, and analyzing the relationship among strength, cement content, and black shale mixing ratio, the criteria were set to satisfy the strength requirements of highway subgrade specifications. The goal was to maximize the consumption of black shale while minimizing the use of cement, adhering to the principle of reducing construction costs. The optimal mix ratio was selected based on these considerations.  
For the specimens with the optimal mix ratio, further unconfined compressive strength tests were conducted at curing ages of 7 days, 14 days, and 28 days. At the same time, to simulate the actual service environment of the roadbed (dry, long-term immersion, \"drying and soaking\" cycle), the stability of the samples that have been cured for 7 days are further tested under indoor long-term dry conditions, long-term immersion conditions, and \"drying and soaking\" conditions. Among them, a \"drying and soaking\" cycle (1 day) goes through \"11 h drying \(\to\) 1 h cooling \(\to\) 11 h soaking \(\to\) 1 h drying\".
# Denial-of-Service Attacks on Permissioned Blockchains: A Practical Study
## 2 Background
### Permissioned Blockchains  
While public blockchains are open to anyone, permissioned blockchains operate under a different paradigm. They are permissioned networks where only authorized participants can access, interact, and contribute to the blockchain. This characteristic makes permissioned blockchains particularly appealing for enterprises and organizations that require data privacy, controlled access, and efficient transaction processing. Unlike public blockchains, which rely on open consensus mechanisms, permissioned blockchains typically use various consensus protocols [21; 22] to validate transactions between a predefined group of trusted nodes, improving the efficiency and transaction throughput. These blockchains are widely used in enterprise applications, with platforms such as HLF [23], R3 Corda [24], and Quorum [25] facilitating secure transactions in industries like finance, healthcare, and supply chain management [26]. Permissioned blockchains offer advantages such as faster transaction processing, greater scalability, and enhanced data privacy. However, their reliance on centralized governance reduces decentralization and transparency, making them more susceptible to collusion, censorship, or single points of failure [27]. While these trade-offs make permissioned blockchains more efficient for specific use cases, they limit the openness and trustless nature that define public blockchains.
# Formation of ZnO/CuO Heterostructures Based on Quasi-One-Dimensional Nanomaterials
## 5 Discussion of the Presented Results
Two-dimensional metal oxides that can be exfoliated are synthesized by direct oxidation of their elemental metals. The transfer technique can easily be extended to create various 2D heterostructures and devices [158]. Under elastic strain conditions [38; 159; 160; 150; 151; 152] at which the sample is subjected, it is possible to implement regimes of mechanical exfoliation of thin layers of formed layered ZnO/CuO heterostructures from the surface of metallic material, which allows to create a 2D/2D heterostructure. It should be noted that in the area of grain boundaries, a ZnO/CuO layer is formed in which there are fractured ZnO nanowires; this layer is intensively exfoliated and chipped, revealing the unoxidized surface of Cu-Zn alloy grains [153]. As the energy or power density of the laser exposure increases, the formation time of the exfoliated layer decreases and its thickness decreases. For new applications of electronic devices, oxide nanowires and 2D materials can also be integrated, where integration during heterostructure synthesis is the most efficient approach to combine these nanomaterials. The synthesis of an array of one-dimensional ZnO nanostructures is possible in the beginning, then the deposition of Cu nanosheet, which is subsequently oxidized to form 2D and 3D ZnO/CuO hetero-interfaces [144]. The rectifying behaviour of the n-ZnO/p-CuO transition can be useful in photoconductive and photoactive devices, while the unipolar transport of 1D/1D thin p-CuO/n-ZnO films is useful for optical switching. The extension of the field of application of laser irradiation as an advanced method of nanostructure formation makes it necessary to study in detail and comprehensively new possibilities of structure formation with improved physical properties. Specific features for the creation of arrays of quasi-dimensional ZnO nanostructures with different micromorphology on metal substrates are determined. Morphological characteristics are critical for many applications, so controlling these parameters by changing the synthesis conditions is an important task. This task can be solved by adaptation of the laser beam shape and redistribution of energy and power density by using appropriate optical systems. For example, the use of special optical systems based on diffractive optical elements [159; 160] makes it possible to locally process the target areas with a pre-determined intensity [161; 162]. The described approach opens new perspectives for the formation of oxide nanostructures to be used in heterogeneous electronics devices, optoelectronics, sensors, portable energy sources and other practical applications.
# A DFT-Based Running Time Prediction Algorithm for Web Queries
## 5 Experimental Evaluation
### Data Collection and Methodology
The code of the algorithms presented in this paper are available at [[https://github.com/neurovisionhub/dft-running-time-prediction](https://github.com/neurovisionhub/dft-running-time-prediction)]([https://github.com/neurovisionhub/dft-running-time-prediction](https://github.com/neurovisionhub/dft-running-time-prediction)), accessed on 19 June 2021. **Baseline Algorithm:** In the experimentation, we evaluate the performance of our DFT-based algorithm for query running time prediction by using the WAND and the BM-WAND dynamic pruning techniques executed under the BM25 and the TF-IDF document scoring methods. We compare the proposed prediction algorithm against the approach proposed in [10]. We selected this algorithm as our baseline for comparison purposes since it is suitable for predicting the running time of queries solved with the WAND and BM-WAND algorithms for either conjunctive or disjunctive queries. **Evaluation Metrics:** The main performance metrics used in our experiments are (i) the Pearson correlation among the predicted query running times \(y_{j}\) and the actual running times \(x_{j}\) obtained from the real execution of each query solution strategy; (ii) the root-mean-square error (RMSE) \(\sqrt{(}\frac{1}{n}\sum_{j=1}^{n}{(y_{j}-x_{j})^{2}})\), where \(n\) is the number of queries considered in each run; (iii) the variation coefficient relative to the mean value calculated as \(\text{VC}=\frac{\sigma}{\psi}\times 100\); (iv) the average absolute error (AAE) defined as \((sum|x_{i}-y_{i}|)/n\); and (v) the maximum absolute error \(max_{e}\) observed from the differences between the actual and predicted values (\(\max|x_{i}-y_{i}|\)).
# Model Study on the Combination of Operating Parameters of Corn Kernel Harvesters
## 2 Analysis of the Principles
### Harvester Engine Working Condition Analysis  
When the harvester is in operation, the speed of the various working parts, such as the header, threshing cylinder, and cleaning screen, has a fixed relationship with the drive system and engine speed. The harvester can work normally only when the engine speed is within a certain range. Figure 1 shows a diagram of the harvester engine operating conditions, with the shaded area showing the normal operating area of the harvester. In the AB region, before the maximum output torque \(T_{\text{tq,max}}\) is reached, the output power \(P_{\text{e}}\) and the output torque increase with increases in the load. In the BC region, after the output torque reaches the maximum output torque \(T_{\text{tq,max}}\), the output power \(P_{\text{e}}\) continues to increase and the output torque decreases as the load increases. In the CD region, after the output power reaches the maximum output power \(P_{\text{e,max}}\), with the load increasing, the output torque \(T_{\text{tq}}\) and the output power \(P_{\text{e}}\) decrease sharply. At this point, the engine speed is reduced to a certain value by the increasing load; meanwhile, the harvester's operating quality is reduced, which could even lead to faults such as clogging of the threshing cylinder [26].  
The increase in the feed rate, the reduction in the concave clearance, and the increase in the speed of the threshing cylinder will all cause an increase in the harvester's engine load and require an increase in the engine's output power. When the output power reaches the maximum output power, the load of the harvester engine increases, which causes the harvester engine speed and output power to decrease. When the harvester's engine speed decreases above a certain value, the harvester cannot operate normally, its operating efficiency reduces, and it will be prone to blockages and other failures.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbols** & **Meaning** & **Symbols** & **Meaning** \\ \hline \(\omega\) & Angular velocity (rad/s) & r & Equivalent radius (m) \\ \(\frac{\omega}{dt}\) & Angular acceleration (rad/s\({}^{2}\)) & \(J\) & Rotational inertia \\ \(W_{e}\) & Circulation function & \(N\) & Power (kW) \\ \(\gamma\) & Crop grain to grass ratio & \(q\) & Feed rate (kg/s) \\ \(f\) & Rubbing factor & a & Coefficients of friction \\ \(\lambda\) & Ratio of stalk exit velocity to the circumferential & b & Air resistance \\  & velocity of threshing cylinder & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Equation symbol table.  
Figure 1: Harvester engine operating conditions diagram: \(P_{\text{e}}\)—engine output power; \(n\)—engine speed; \(T_{\text{tq}}\)—output torque; \(T_{\text{tq,max}}\)—maximum output torque; \(P_{\text{e,max}}\)—maximum output power; \(\textcircled{1}\)—output torque curve; \(\textcircled{2}\)—output power curve during normal operation; \(\textcircled{3}\)—overload power curve.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Neural Network Structure and Function
#### 3.1.4 Recurrent Neural Network  
The RNN [39] is a type of neural network specialized in handling sequential data. Its core idea is to use the previous output as part of the current input to capture dependencies in time series. The basic structure of an RNN includes a hidden layer and an output layer. In RNNs, each unit in the hidden layer receives a weighted sum of the output from the previous time step and the input at the current time step, undergoes a nonlinear transformation through an activation function, and passes the result to the next time step. This structure enables RNNs to handle variable-length sequence data and perform recursive operations along the sequence evolution direction.  
RNNs have wide-ranging applications in various fields such as text classification, machine translation, speech recognition, image/video captioning, time series prediction, and recommendation systems. In these applications, RNNs can model dependencies between sentences and phrases and temporal dependencies between speech and language, as well as spatial relationships between frames.  
Figure 4: Model of a multilayer feedforward neural network.  
Despite its strong performance in certain domains, RNNs also have some notable drawbacks. During training, RNNs often encounter the problems of exploding or vanishing gradients, leading to unstable training or difficulties in convergence. Additionally, compared with other types of neural networks, RNNs typically require more memory space, limiting their application in large-scale datasets. Moreover, when using certain activation functions, RNNs may struggle to effectively handle excessively long sequences, which can adversely affect their performance.  
To address these issues, researchers actively explore and propose various improvement strategies. Among them, LSTM networks [40] undoubtedly stand out as the most prominent and representative solution. LSTM introduces gate mechanisms and memory units, enabling more effective processing of long sequence data and alleviating the problems of vanishing and exploding gradients. This has led to significant performance improvements for LSTM in many sequence processing tasks.
# Magnetic Force Enhancement Using Air-Gap Magnetic Field Manipulation by Optimized Coil Currents
## 1 Introduction
However, none of these previous works have addressed the active magnetic field manipulation for enhancing the magnetic force in electromechanical devices. Accordingly, this work aims to present the magnetic field manipulation using external coil currents for enhancing the magnetic force in a tangential direction to the air-gap line. An ideally manipulated air-gap magnetic field distribution for maximizing the tangential direction force is first investigated. The ideally manipulated magnetic field distribution might not be realized using the external coil current, but it might be available to find the best possible distribution of coil current. In order to find this best possible distribution, a gradient-based numerical optimization scheme is proposed in this work. As an optimization strategy, the concept of topology optimization [27] is appropriately modified and applied. More specifically, the design domain near the air-gap is divided into small design areas, and design variables are assigned at every small design area. Here, the design variables control not only the strength of the current density (i.e., number of coil turns) but also the magnetic permeability. The current density and permeability are related to the magnetic field distribution, determining the magnetic force. In the optimization, the total sum of external coil currents is constrained in terms of the magnetic field manipulation. By solving the optimization problem, the best possible coil current distribution that maximizes the tangential direction magnetic force by the manipulated magnetic field could be determined. The paper is organized as follows. Section 2 explains the ideal magnetic field distribution for maximizing the tangential direction magnetic force. In Section 3, the optimization strategy to find the optimal coil currents is explained. The optimization problem formulation is then provided in Section 4. In Section 5, the optimization result is provided, and the effectiveness of the magnetic field manipulation using an optimized coil current is validated. Conclusions are provided in Section 6.
# Nonlinear Seismic Response of Multistory Steel Frames with Self-Centering Tension-Only Braces
## 2 Analytical Model of SC-TOBFs
### Modeling of an SC-TOBF  
A typical steel frame equipped with SC-TOBs subjected to lateral loading, \(P\), is illustrated in Figure 2; the solid line indicates a taut HSS cable experiencing positive elongation, while the dotted line indicates a slack, on stand-by for the load reversal. Note that the pulleys mounted on the beam near the beam-column connections are used to guide the HSS cables, which serves a different purpose from  
Figure 1: Self-centering tension-only brace (SC-TOB) system: (**a**) schematic; (**b**) analytical model; and (**c**) hysteretic behavior [27; 28].  
those within the SC-TOBs. The analytical model is constructed using SAP2000 [31]. Key modeling techniques are as follows:  
1. Beam and column modeling. The beams and columns are modeled using frame elements, which concentrate their inelastic behaviors in nonlinear hinges (lumped plasticity).
2. SC-TOB modeling. The multilinear elastic element (MEE) and the multilinear plastic element (MPE) are used to model the nonlinear elastic behavior of PT tendons and the energy dissipation from the frictional device, respectively. For both tendons and frictional device, only the second-stage curves with a softened stiffness of the elements are required; hence, a negligible first-stage deformation has to be specified. For the HSS cable modeling, however, the MEE with a sufficiently long first-stage curve is adopted, because the cable is only permitted to behave in a linear elastic way.
3. Pulley modeling. The pulley is modeled using five hinged frame elements within the dotted circle, in which nodes 1, 2, and 3 are located around the center point O. Because a pulley is almost a rigid body in real structures, the axial stiffness of each frame element has to be assigned a sufficiently large value.
4. Models assembly. A \"body constraint\" is specified between nodes O, A, and B, and their vertical projection O', A', and B', respectively, to ensure that each pair of nodes moves together as a 3D rigid body, except that the translational degree of freedom of node A is released to allow its axial movement.
## 2 Convert Information Mapping Aided GSDM
### The GSDM System Model
Therefore, the transmit signal at Alice can be detailed as  
\[\mathbf{s}_{l,s}=\mathbf{W}\mathbf{\Lambda}\mathbf{x}_{l,s}, \tag{6}\]  
where \(\mathbf{\Lambda}=\text{diag}(\lambda_{1},\lambda_{2},\ldots,\lambda_{N_{t}}) \in\mathbb{C}^{N\times N_{t}}\) is interference matrix and \(\lambda_{i}=e^{j\varphi_{i}},i=1,2,\ldots N_{t}\) denotes interference factors. Every receiver at Bob has a corresponding interference factor, which is relatively big for efficient phase disturbance. Moreover, Bob can set different interference factors to prevent eavesdropping of single legitimate receiver at Bob for further security. Transmitting through the channel in free space, the receive signal at Bob can be represented as  
\[\mathbf{r}_{B} =\mathbf{H}(\Theta_{B})\mathbf{s}_{l,s}+\mathbf{n}_{B} \tag{7}\] \[=\mathbf{H}(\Theta_{B})\mathbf{W}\mathbf{\Lambda}\mathbf{x}_{l,s} +\mathbf{n}_{B}.\]  
With regards to Eve, the receive signal can be expressed as  
\[\mathbf{r}_{E} =\mathbf{H}(\Theta_{E})\mathbf{s}_{l,s}+\mathbf{n}_{E} \tag{8}\] \[\mathbf{H}(\Theta_{B})=\left[\mathbf{h}(\theta_{1}),\mathbf{h}(\theta_{2}), \ldots,\mathbf{h}(\theta_{N_{r}})\right]^{H}, \tag{2}\]0  
where \(\mathbf{H}(\Theta_{E})\in\mathbb{C}^{N_{e}\times N_{t}}\) denotes the channel matrix between [PERSON] and [PERSON], \(\Theta_{E}=\{\theta_{1},\theta_{2},\ldots,\theta_{N_{e}}\}\) is the set of directional angles at Eve. Additionally, \(\mathbf{n}_{B}\) and \(\mathbf{n}_{E}\) are noise vectors to [PERSON] and [PERSON], which obey \(\mathbf{n}_{B}\sim\mathcal{CN}\big{(}\mathbf{0},\sigma_{B}^{2}\mathbf{I}_{N_ {t}\times N_{t}}\big{)}\) and \(\mathbf{n}_{E}\sim\mathcal{CN}\big{(}\mathbf{0},\sigma_{E}^{2}\mathbf{I}_{N_{t }\times N_{t}}\big{)}\), respectively. According to [13], the diagonal elements of matrix \(\mathbf{H}(\Theta_{B})\mathbf{W}\) are all equal to 1 and diagonal elements of matrix \(\mathbf{H}(\Theta_{E})\mathbf{W}\) are all less than or equal to 1, which indicates the amplitude or power of the receive signal at Eve decreases. Hence, it is undeniable that the receive signal at Eve are extremely disturbed while Bob can receive successfully as usual, so the security of transmission gains great enhancement.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 4 Deep Learning-Based Detectors
### Network Architecture Design
#### 4.2.2 Hierarchical MLP with 1 DCNN-LSTM  
This DNN model is built with a hierarchical multilayer perceptron network-based CSS method. It incorporates an end-to-end one-dimensional convolutional neural network long short-term memory (1 DCNN-LSTM) at the SU level. The complete network architecture, which includes the 1 DCNN-LSTM-MLP structure, is illustrated in Figure 5.  
The vectorized sequence of raw samples obtained from antennas is first sequentially used as input in the 1 DCNN module, which comprises two convolutional blocks as in the previous deep neural network model. A max pooling layer is then employed to extract the most noticeable and prominent features from the resulting feature map. The resulting feature map is flattened before it is subjected to the LSTM layer to capture the long-term temporal connections of the input. Therefore, it learns the SU-level PU activity pattern from the features extracted by the convolution operation. Substantially, the end-to-end 1 DCNN-LSTM network extracts local features and global correlations from the input signal. The feature representations corresponding to a particular sensing duration for each SU are then forwarded to the fusion center, which is composed of a cooperative multilayer perceptron. By using an MLP in the fusion center, the architecture can take advantage of the speed and efficiency of MLPs for final decision making, as they are generally faster to compute than LSTMs, thus reducing the processing time at the fusion center, which allows us to mitigate one of the main CSS constraints. The proposed MLP architecture employs ReLU as an activation function for the hidden layer and separates the output with a dropout layer to prevent overfitting and improve the model's generalization. Finally, the output is mapped to a \(2\times 1\) class score vector, which is normalized by the Softmax function to obtain the classification result.