esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Robust LQI Controller Design for Z-Source Inverters
## 3 Robust LQI Controller Design
### Bat Algorithm  
The bat algorithm (BA) is a relatively new meta-heuristic optimization algorithm based on formulating the echolocation behavior of microbats [36]. These bats emit a very loud sound pulse and listen for the echo that bounces back from the surrounding objects. By processing the echoes, they can detect the distance and orientation of the target, the type of prey, and even the moving speed of the prey. Microbats adjust the frequency \(f_{i}\in[f_{min},f_{max}]\), loudness \(A_{i}\in[A_{min},A_{max}]\), and the rate \(r_{i}\in[0,1]\) of their emitted pulses based on the proximity of their target. They increase the frequency of pulses while decreasing their loudness when they fly near their prey until just before contact in which pulses become like a continuous buzz. The constituent steps of BA can be summarized as the schematic pseudo-code shown in Algorithm 1. From the performance comparison of the BA against well-known optimization algorithms like the genetic algorithm (GA) and particle swarm optimization (PSO), it is concluded that the BA is potentially more powerful than the GA and PSO [37].  
Figure 5: Geometric representation of maximum sensitivity.  
```
1 Initialize the bat population \(x_{i}\) and \(v_{i}\) (\(i=1,2,\ldots,n\))
2 Initialize frequencies \(f_{i}\), pulse rates \(r_{i}\), and the loudness \(A_{i}\)
3 while(t < Max number of iterations)do
4 Generate new solutions (weighting matrices) by adjusting frequency as \(f_{i}=f_{min}+(f_{max}-f_{min})\beta\)
5 Update velocities and locations/solutions as \(v_{i}^{t+1}=v_{i}^{t}+(x_{i}^{t}-x^{*})f_{i}\) \(x_{i}^{t+1}=x_{i}^{t}+v_{i}^{t+1}\) for(rand > r_{i})do
6 Select a solution among the best solutions Generate a local solution around the selected best solution as \(x_{new}=x_{old}+\sigma\epsilon_{i}A^{(t)}\)
7 end for
8 Generate a new solution by flying randomly for(rand < \(A_{i}\)\(\xi\)\(f(x_{i})<f(x^{*})\))do
9 Accept the new solutions Increase \(r_{i}\) and reduce \(A_{i}\) as \(A_{i}^{t+1}=\alpha A_{i}^{t}\) \(r_{i}^{t+1}=r_{i}^{0}[1-exp(-\gamma t)]\)
10 end for
11 Rank the bats and find the current best solution \(x^{*}\)
12 end for
```  
**Algorithm 1**Pseudo code of the bat algorithm (BA)
## 3 Proposed Model
### The Effects of the New Parameters on the TYM
#### 3.1.1 Effects of Temperature on Signal Strength  
There is a clear relationship between signal strength and temperature. Generally, when the temperature increases, RSSI drops, and vice versa, which represents a negative relationship between signal strength and temperature [8].
# M-ary Phase Position Shift Keying Demodulation Using Stacked Denoising Sparse Autoencoders
## 5 Simulation and Analysis
### Architecture  
In order to obtain a high detection performance, the number of nodes in the hidden layers may be more important than the learning algorithm chosen or the depth of the model. Considering this, we adopted different architectures corresponding to the different MPPSK parameters and then determine the appropriate numbers of the corresponding hidden units. To simplify the problem and focus on the effect of the generated nodes, the stacked architecture is used without pooling and convolution.  
The stacked DSAE model proposed for the improved 2 PPSK signaling is expressed in forms such as 1050-800-400-200-2, where '1050' indicates 1050 nodes in the input layer; '800' indicates 800 nodes in the first hidden layer; '400' indicates 400 nodes in the second hidden layer; '200' indicates 200 nodes in the third hidden layer; and '2' indicates 2 nodes in the output layer. A 1050-800-400-200-2 network structure means that we train an SAE network of 1050-800-1050 first, then feed forward the input data to the 1050-800 network to obtain the first set of hidden features, and finally train another 800-400-800 network, using 1050-800-400 to obtain the second set of hidden features. After this, we train the 400-200-400 network and use 1050-800-400-200 to obtain the third set of hidden features. After the first SAE network is trained, the activation value of all nodes in the hidden layer is calculated and sent to the second autoencoder as its input. The activation value of each hidden layer is regarded as the extracted features of the original modulation signals. Then, the hierarchical representations of original signals are obtained. Such representations sent to a softmax classifier are effective for detection. Thus, we use softmax to train the 200-2 network, and finally, we cascade all the networks to form the set as 1050-800-400-200-2.
# Optimization Design of Centrifugal Fan Blades Based on Bezier Curve Method
###### Abstract
[14] proposed several stepped leading edge designs for blades with serrated edges that decomposed large vortices into smaller ones and limited the downstream movement of axial vortex structures, thereby reducing the fan's startup noise, albeit with a corresponding decrease in efficiency. [PERSON] et al. [15] defined the blade shape and tip speed ratio using 17 control parameters, and their results indicated that the blade inlet installation angle and installation position were the primary parameters affecting overall fan performance. [PERSON] et al. [16] achieved rapid optimization of two-stage centrifugal fans through an optimization design method that combined one-dimensional and three-dimensional models. Owing to its advantages of fewer control parameters, flexible curvature adjustment, and high-order continuity, the Bezier curve method has been widely applied in the field of rotating machinery. [PERSON] et al. [17] proposed a blade shape generation method based on a six-control-point Bezier curve for Savonius hydraulic turbines, which yielded better performance compared to traditional semi-circular blades or blades generated using the Taguchi method. [PERSON] et al. [18] proposed a parameterization technique based on a global mapping model of Bezier surfaces and integrated this technique with a multi-objective evolutionary algorithm to achieve both global and local optimization of centrifugal compressor blades. [PERSON] et al. [19] used the Bezier curve method to select 10 key structural parameters of the blade for design optimization, resulting in a 7.74% improvement in pump efficiency under design conditions. As can be seen from the above, although the Bezier curve method has been well developed and has demonstrated significant application value in rotating machinery, related research in the field of voluteless centrifugal fans remains insufficient. In this paper, based on parametric modeling, a fourth-order Bezier curve method was used to fit the blade profile, obtaining seven control variables as optimization parameters, with fan static pressure and static pressure efficiency serving as the optimization objectives. The multi-objective optimization design of the voluteless centrifugal fan was achieved by integrating a radial basis function neural network (RBFNN) surrogate model with a multi-objective genetic algorithm (MOGA). By adjusting multiple parameters, the influence of blade geometry on the complex internal flow phenomena within the impeller was elucidated, providing a reference for the broader application of the Bezier curve method in rotating machinery. Our research also provides a new method for optimizing voluteless centrifugal fans.
# Studies on a Piezoelectric Cylindrical Transducer for Borehole Dipole Acoustic Measurements
## 4 Conclusions  
A new piezoelectric cylindrical acoustic transducer for dipole acoustic logging is proposed, in which three pieces of trilaminar bender bars are employed as an actuator. Based on the proposed design, the transducer was fabricated and tested. Both the numerical simulation and test results are in good agreement. From the numerical comparison analyses, it was concluded that the performance of the transducer was improved compared with the conventional transducer, especially in the frequency range lower than 1 kHz. Moreover, its directivity patterns show that this new transducer performs as a pure dipole source in the frequency range of 0.4-6 kHz, which is required in practical acoustic logging. In the present design, only three pieces of bender bars were adopted, which limits the radiation energy and vibration modes. In order to enhance the transmitting voltage response and increase the bandwidth, it is possible to add more bender bars to actuate the transducer in future application.  
Conceptualization, Y.Z.; methodology, Y.Z.; validation, Y.Z. and Y.D.; formal analysis, Y.Z.; investigation, Y.Z.; data curation, Y.D.; writing--original draft preparation, Y.Z.; writing--review and editing, X.W.; project administration, Y.Z. and X.W.; funding acquisition, Y.Z. and X.W. All authors have read and agreed to the published version of the manuscript.  
The work was partially supported by the IACAS Young Elite Researcher Project (ZYTS202004), and Natural Science Foundation of China (No. 11734017).  
Not applicable.  
The authors declare that they have no conflict of interest.  
Figure 11: TVR comparison of simulation result for the proposed transducer (in black color) and the conventional bender-type transducer (in red color).
# Design of a Planar Cable-Driven Parallel Robot for Non-Contact Tasks
## 3 Mechanical Design  
The prototype presented in this section is devoted to non-contact tasks, such as laser engraving, welding, thermal treatment, decoration, and inspection of vertical surfaces, such as building facades. Thus, the main design requirements are the following: (i) it should present a planar vertical workspace, which should be easily scaled upon the redefinition of the installation locations of the guidance systems, (ii) the tool mounted on the platform should be interchangeable.  
The 3D model of the designed prototype is shown in Figure 7. The machine frame (1) is formed by aluminum profiles that define the robot installation (and _WS_) limits. An actuation (2) and a proximal guidance unit (3) are placed at each of the four corners of the frame to coil and uncoil four cables and convey them to the distal guidance unit (4) attached to the mobile platform (5). The platform has \(n=3\)_DoFs_ in the plane, thus, a 4-cable architecture results in an overconstrained manipulator. The constraint redundancy allows firstly to obtain a wider _WS_ and then improve the \(EE\) controllability. Redundant  
Figure 5: Cable knotted at the anchor point on the platform of the _CDPR_ laser scanner [16]. (a) Platform overview. (b) Knot detail.  
Figure 6: Prototypal universal joint at the anchor point on the platform of a _CDPR_. (a) Platform overview. (b) Universal joint detail.  
actuated cables make them pull one against the other to keep the overall system under tension. Reconfigurable anchor points also characterize the prototype: cables can be arranged in a standard (Figure 8a) or crossed (Figure 8b) layout by simply un-mounting and re-mounting the distal guidance unit. As shown in Figure 8, each swivel pulley (1) on the platform, which forms the distal anchor point, can be translated into slots (2) on the support (3); the equivalent operation can be done on the proximal anchor systems on the frame. The crossed layout aims to improve the platform's orientation capability [46], if required by the task, and to withstand external torques induced by gravity (if the tool is not mounted in a barycentric position) or external disturbances.  
The actuation units and guidance systems are the same for each cable, and they are described in the following subsections.  
Figure 7: Planar 4-cable _CDPR_ prototype with two cable arrangements: (1) frame, (2) actuation unit, (3) proximal guidance unit, (4) distal guidance unit, (5) platform. (**a**) Standard layout, front view. (**b**) Crossed layout, front view. (**c**) Standard layout, top view (**d**) Crossed layout, top view.
# Open-Set Signal Recognition Based on Transformer and Wasserstein Distance
## 5 Experiments
### Evaluation Metric  
In order to more comprehensively evaluate the performance of the method, we evaluated all the algorithms on the _AUC_, _Accuracy_ and _PRE_ indicators for both the known and unknown signals.  
_AUC_: The AUC index indicates the probability of the model scores, where the randomly selected positive category is higher than the negative category. The AUC index was obtained by calculating the area under the ROC curve, which is calculated by two parameters (namely, TPR and FPR). The ROC curve is established with FPR as the X-axis and TPR as the Y-axis, which can be expressed as:  
\[\mathit{TPR}:=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}},\quad\mathit{FPR}:= \frac{\mathit{FP}}{\mathit{FP}+\mathit{TN}}. \tag{11}\]  
_Accuracy_: The basic accuracy index refers to the ratio of the number of correctly predicted samples over the total number of predicted samples, which can be expressed as:  
\[\mathit{Accuracy}:=\frac{\sum_{i}(\mathit{TP}_{i}+\mathit{TN}_{i})}{\sum_{i} (\mathit{TP}_{i}+\mathit{TN}_{i}+\mathit{FP}_{i}+\mathit{FN}_{i})}. \tag{12}\]  
_PRE_: The PRE index refers to the ratio of the number of positive samples that are predicted correctly to the number of all positive samples predicted. Different from the accuracy index, the PRE index focuses only on the part predicted as the positive samples, which can be expressed as:  
\[\mathit{PRE}:=\frac{\sum_{i}\mathit{TP}_{i}}{\sum_{i}(\mathit{TP}_{i}+ \mathit{FP}_{i})}. \tag{13}\]  
In order to reflect the difference between the identification performances of the open-set recognition methods, all the experimental indicators were calculated for both the known and unknown categories.
# Wind Forces and Flow Patterns of Three Tandem Prisms with a Small Height-Width Ratio
## 3 Results and Discussions
### Dependency of Wind Force Coefficients on Prism Spacing
As shown in Figure 7a, for two tandem prisms with \(H/W=3\) in Sakamoto and Haniu [2], a similar trend of RMS drag of the downstream prism was observed at \(2<S/W\leq 3.5\), and there was a local peak at \(S/W=3\), but the mean drag of the downstream prism increased monotonically with the increase of \(S/W\), and there was no local peak at \(2<S/W\leq 3.5\). The coefficients of mean and RMS drag of the downstream 2D square prism rapidly increased at \(S/W=2.7\) and always decreased slowly with the increase of the prism spacing at \(S/W>2.7\) (Figure 7a). These results of 2D rectangular prisms arranged in tandem were also reported in [PERSON] et al. [24], and the aspect ratio (\(L/W\)) of the prism only affects magnitudes of wind forces but does not affect their variation trends with \(S/W\). Furthermore, for a large group of prisms with \(H/W=0.5\) in [PERSON] and [PERSON] [13], the mean drag and mean roof uplift of the downstream prism gradually increased with the increase of \(S/W\) without local peaks, as shown in Figure 7a,b. When spacing ratios range from 0.8 to 2.8, mean and RMS coefficients of drag and roof uplift of downstream prisms B and C continuously increase with the increase of \(S/W\). The flow pattern among prisms tends to be the wake interference flow regime [23]. As shown in Figure 7, when \(S/W>2.8\), mean and RMS coefficients of drag and roof uplift of downstream prisms increase slightly with the increase of \(S/W\), and interference effects among prisms become weak. The downwind flow of the upstream prism travels a sufficient distance before encountering the downstream prism, and the flow pattern tends to be the isolated roughness flow [23]. The values of mean and RMS wind forces coefficients of three prisms are shown in Tables A1-A3 in Appendix A.
# Dynamic Cooperative MAC Protocol for Navigation Carrier Ad Hoc Networks: A DiffServ-Based Approach
## 4 DiffServ-Based Dynamic Cooperative MAC Protocol
### Problematic Scenarios in DDC-MAC
#### 4.4.2. Interference and Congestion Problem  
Figure 6c exhibits a problematic scenario for two reasons: (i) the distribution density of pairs is larger than network capacity; and (ii) the number of pairs exceeds the number of available data channels, thus the pairs might interfere with each other and result in network congestion. Figure 6d shows our proposed solutions, which can be expatiated as follows:  
**Case 1:**_The receiver overhears packets from the direction of its sender_. For instance, the receiver \(r_{5}\) of the pair \((s_{5},r_{5})\) overhears the RTS or CTS from the pair \((s_{4},r_{4})\) with a higher priority. Node \(r_{5}\) will reset its NAV according to the overheard packet and copy the NAV value to the duration field of the WARN, which is used to notify sender \(s_{5}\) to wait for the same duration. Then, sender \(s_{5}\) will compete for the access right again after the time duration.  
**Case 2:**_The sender overhears the packets from the direction of its receiver_. For instance, the sender \(s_{6}\) of the pair \((s_{6},r_{6})\) overhears a higher priority packet from the pair \((s_{3},r_{3})\) during waiting for CTS. The sender \(s_{6}\) will switch to another channel according to its ACBS immediately, and then receiver \(r_{6}\) switches to the same channel automatically according to its ACBS after the duration of a timer, and competes for the access rights of the new channel again.  
**Case 3:**_Channel congestion_. If a pair performs Case 1 and Case 2, but is still unable to communicate after switching among all data channels, for example, receiver \(r_{1}\) of the pair \((s_{1},r_{1})\) overhears the higher priority (PP or SP) packets simultaneously from the pairs \((s_{2},r_{2})\), \((s_{3},r_{3})\), and \((s_{4},r_{4})\). In our solution, the pair \((s_{1},r_{1})\) will switch to the first data channel of its ACBS, and wait for channel access rights until a data channel is idle, as shown in Figure 6d.
# Advanced Metamaterial-Integrated Dipole Array Antenna for Enhanced Gain in 5G Millimeter-Wave Bands
## 1 Introduction
At first, an array of non-uniform dipoles is designed to provide a broad operational bandwidth within the millimeter-wave frequency range. The dipole array antenna provides a wider bandwidth and end-fire radiation characteristics with lower gain. To improve the gain of the designed dipole array, a metamaterial array structure is proposed. By utilizing the proposed metamaterial structure, the gain is improved by more than 3 dBi. To validate the proposed work, a prototype of the designed antenna is manufactured and tested. The proposed method is also validated by the measurement results. The rest of the manuscript is organized as follows: The antenna design methods and design procedure are explained in detail in Section 2. The results of the proposed antenna are presented in Section 3 along with the performance analogy of the designed antenna. Finally, the proposed work is concluded in Section 4. Figure 1: Representation of mm wave device connection in 5G-NR environment.
## 4 Conclusions  
In summary, a metallic mesh-based transparent EMI shielding window with high IR transparency and balanced electromagnetic shielding performance across a wide frequency spectrum has been designed and fabricated. The mesh coating was fabricated on a sapphire window using UV photolithography, resulting in both a high IR transmittance of 85.1% and a favorable EMI SE of 23.5 dB. The shielding effectiveness was balanced across a wide frequency range and only weakly dependent on frequency. The optimization of EMI shielding performance through the variation of mesh film thickness was also demonstrated. This design has the added benefit of reducing the time-consuming and energy-intensive process of metal deposition typically required for the preparation of thick meshes, enabling the production of efficient transparent electromagnetic shielding materials with balanced wideband shielding performance. The combination of these characteristics makes the mesh-coated sapphire window an ideal EMI shielding component for optoelectronic systems.  
Conceptualization, X.H.; data curation, Y.L. and W.L.; formal analysis, Y.L. and X.H.; investigation, Y.L.; methodology, J.P.; validation, Z.W., K.W., L.Y. and J.P.; supervision and project administration, X.H. and P.L.; writing--original draft, Y.L.; writing--review and editing, Z.W., K.W., L.Y. and X.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Huxiang Youth Talent Support Program, grant number 2020 RC3028 and the National Natural Science Foundation of Hunan Province, grant number 2022 JJ20045.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Mesh type**} & \multirow{2}{*}{**Mesh Thickness**} & \multicolumn{4}{c}{**Shielding Effectiveness (dB)**} & \multirow{2}{*}{**Ref**} \\ \cline{3-4} \cline{6-7}  & & **(\(\mu\)m)** & & & & & \\ \hline Square mesh & 2 & — & 30 & 19.5 & — & 10.5 & [26] \\ Square Cu mesh & 2 & 30 & 20 & 16 & 10 & 4 & [24] \\ Square Cu mesh & — & — & 13 & 5 & — & 8 & [42] \\ Crackled mesh & 0.2 & — & 21 & 15 & — & 6 & [23] \\ Hexagonal Cu mesh & 11 & 42.5 & 33.2 & 26.3 & 16.2 & 6.9 & [9] \\ Multilayer SiO\({}_{2}\)/ZrO\({}_{2}\) & 0.397 & 34.2 & 25 & — & 8.8 & — & [47] \\ mesh structure & & 30.4 & 30.3 & 29.8 & 0.1 & 0.5 & [49] \\ Hexagonal ring mesh & 3 & 28 & 22 & 16 & 4 & 6 & [50] \\ Square Ti/Au/Ag mesh & 0.14 & — & 23 & 17 & — & 6 & [48] \\ Irregular Cr/Au mesh & 0.32 & 22 & 21.4 & 22.6 & 0.6 & 1.2 & This work \\ \hline \hline \end{tabular}  
* Difference in shielding effectiveness at 4 GHz and 12 GHz. b Difference in shielding effectiveness at 12 GHz and 18 GHz.  
\end{table}
Table 2: Comparison of SE variation at different frequency for various mesh films.
# Deep Feature Fusion via Transfer Learning for Multi-Class Network Intrusion Detection
###### Abstract  
With the rapid advancement of network technologies, cyberthreats have become increasingly sophisticated, posing significant challenges to traditional intrusion detection systems. Conventional machine learning and deep learning approaches frequently experience performance degradation when confronted with imbalanced datasets and novel attack vectors. To address these limitations, this study proposes a deep learning-based intrusion detection framework that employs feature fusion through incremental transfer learning between source and target domains. The proposed architecture integrates convolutional neural networks (CNNs) with an attention mechanism to extract and aggregate salient features, thereby enhancing the model's discriminative capacity between normal traffic and various network attack categories. Experimental results demonstrate that the proposed model achieves a detection accuracy of 94.21% even when trained on only 33% of the available data, outperforming conventional models. These findings underscore the effectiveness of the proposed feature fusion strategy via transfer learning in improving detection capabilities within dynamic and evolving cyberthreat environments.  
Keywords:network intrusion detection; transfer learning; deep learning; feature fusion +
Footnote †: journal: Applied sciences