esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussions
### Results Discussions
#### 3.2.4 Performance and Comparison with Models from Other Studies  
The performance of the proposed model was compared with models from other studies, considered as baseline models. The results are presented in Table 4.  
As shown in Table 4, the proposed model, GenCo, resulted in performances of 98.40%, 89.9%, and 99.26%, better than other models on the Twitter US Airline dataset, Conference Paper dataset, and SMS Spam dataset, respectively. However, on the Twitter US Airline dataset, the ULMFit-SVM model had an accuracy of 99.7%, better than the GenCo model, with an accuracy of 98.4% on the same dataset. The low performance of the proposed model on some datasets can be explained by reference to the mutuality matrices from the different datasets. From these matrices, it can be inferred that the model performs better on datasets on which it can easily maximize the combined mutual value of the features or feature segments but performs less well otherwise.  
Furthermore, the models were statistically compared by calculating their Bayesian information criterion (BIC) [31] on the different datasets. GenCo had the lowest BIC values of 959.18, 782.90, and 431.31 for the Twitter US Airline dataset, Conference Paper dataset, and SMS Spam dataset, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Datasets** & **Models** & **Accuracy (\%)** & **BIC** \\ \hline Twitter US Airline dataset & RoBERTa-GRU [25] & 91.52 & 2455.53 \\  & ULMFit-SVM [26] & 99.78 & 1352.18 \\  & ABCDM [27] & 92.75 & 1178.23 \\  & GenCo (our work) & **98.40** & **959.18** \\ Conference Paper dataset & Linear SVM [28] & 74.63 & 1041.10 \\  & GenCo (our work) & **89.90** & **782.90** \\ SMS Spam dataset & Discrete HMM [29] & 95.90 & 833.61 \\  & Hybrid CNN-LSTM [30] & 98.37 & 2103.36 \\  & GenCo (our work) & **99.26** & **431.31** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance and comparison.  
Figure 5: Results with SMS Spam dataset. (**a**) Confusion matrix. (**b**) Mutuality matrix for the 5 feature segments. (**c**) Combined mutuality of each feature in the segment with the lowest mutuality.
## 8 Applications of UWSN Technology
### Structural Health Monitoring  
* Wireless sensor networks can be used to log data over extended periods of time and monitor the condition of pertinent geophysical processes and civil infrastructure in close to real time by using properly interfaced sensors. The scope for future work in potential applications is listed in Table 5.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Generative Adversarial Networks
#### 2.3.1 Generator  
The generator in a GAN is responsible for creating synthetic data that resemble real data. It starts with random noise as input, typically sampled from a uniform or Gaussian distribution, and transforms this noise into structured data through a series of layers. The generator's goal is to produce outputs that are indistinguishable from real data.  
The process begins by sampling from a latent space, which is a lower-dimensional representation of the input space. The latent space captures the underlying factors that define the data distribution. By feeding noise into the latent space, the generator produces output through a series of transformations, such as fully connected layers, convolutional layers, and transposed convolutions (for images). These transformations allow the generator to map random noise to structured data, like images, audio, or text.  
The generator's objective is to \"trick\" the discriminator into classifying its output as real. This goal is achieved by iteratively updating the weights of the generator through backpropagation, based on feedback from the discriminator. The better the generator becomes at fooling the discriminator, the closer the generated data becomes to the real data distribution.  
Formally, the generator's objective is to minimize the following loss function, as follows:  
\[L_{G}=-\mathrm{E}_{z\sim p_{z}(z)}[\log(D(G(z)))]\]  
where \(G(z)\) is the output of the generator given input noise \(z\), and \(D(G(z))\) is the discriminator's prediction for the generated data.  
The goal is to minimize the discriminator's ability to distinguish between real and fake data.
## 4 Methodology of Research
### Capturing and Processing Aerial Video  
The aerial recording of traffic flows on selected road segments was performed using an unmanned aerial vehicle MAVIC PRO equipped with advanced GPS/GLONASS satellite positioning systems, along with sensor systems enabling intelligent flight control, detection and avoidance of both stationary and moving obstacles during its operation. The drone was additionally equipped with a fully stabilized video camera, capable of capturing  
Figure 2: Flow chart of research methodology.  
12-megapixel photographs and recording aerial video footage in a very high 4K Quad HD resolution of 3840 \(\times\) 2160 pixels. The aerial videos were captured at frame rates of 30 images per second. The recorded aerial video files were extracted from the drone's micro-SD card and transferred to a personal computer. They were then converted from their original MOV format into the MP4 format, ensuring compatibility for subsequent video data processing.
## 4 Complexity Analysis
### CPM Mapper and Decoder  
The complexity due to CPM comes mainly from the Viterbi decoder. For rational values of the modulation index \(h\), the number of trellis states in a Viterbi decoder are equal to \(p\) (\(p\) has been defined in (5) and (6)), which is also the number of operations required for decoding one symbol, i.e., \(O(p)\)[52]. For one OFDM symbol, the total number of operations will be \(O(pN)\).
# LADRC-Based Magnetic Field Measurement Method for a Nuclear Magnetic Resonance Rotation Sensor
## 2 Dual Nuclear Magnetic-Field Measurement Method
### Analysis of Transient Errors in Magnetic Measurement  
In this subsection, two strategies were proposed based on the PLL method. Analysis of the strengths and weaknesses of both strategies were given. As is shown in Figure 3, the two resonant nuclear oscillators with frequencies (\(\omega_{a}\) and \(\omega_{b}\)) are maintained by PLLs. The magnetic field fluctuation changes the Larmor frequencies of the nuclei, which makes the spin oscillators off-resonant. Under the control of PLL, the spin oscillators return to resonant in a new set of frequencies (\(\omega_{a}\)' and \(\omega_{b}\)').  
In the applications of NMRRS, the tasks of the spin control are  
Figure 3: Analysis of transient errors of the magnetic-field measurement.  
* Synchronously tracking the detuned phase of the two isotopes, which makes the differential effect more efficient.
* Observing the Larmor frequency of the two isotopes with a monotone nondecreasing step-response. This will suppress errors of the magnetic field measurement and simplify the magnetic field control loop.  
As they are in Equations (15) and (16), the parameters in the closed-loop transfer function are \(K_{p}\), \(K_{i}\) and \(\Gamma\). In NMRRS, the bandwidth of the controller (determined by \(K_{p}\) and \(T_{i}\)) is ten times greater than \(\Gamma\). Thus, the controller parameters play the dominant role in the synchronous transient processes. Two strategies were proposed here:  
(1) Strategy I  
The controller parameters of the two spins can be set as equal to obtain a better synchronous performance of the phase loop.  
\[K_{p1}=K_{p2}=K_{p},T_{i1}=T_{i2} \tag{17}\]  
\(K_{p1}\),\(K_{p2}\) are the P gains, \(T_{i1}\),\(T_{i1}\) are the integral time constants of the two isotope control loops, respectively.  
Each of the two poles in (16) is greater than the zero \((1/T_{i})\), which is necessary to achieve a fast response of the phase. Based on the theory in [24], an overshot inevitably appears in the step response.  
(2) Strategy II  
The controller parameters of the two spins can be set to equal the lifetime of the nuclear spin to obtain a better observation of the frequency loop.  
\[K_{p1}=K_{p2}=K_{p},T_{i1}=T_{21},T_{i2}=T_{22} \tag{18}\]  
\(T_{i1}\),\(T_{i1}\) are the transverse relaxation times of the two isotopes.  
Then, the system in Equation (16) will be simplified as  
\[\Phi_{U}(s)=\frac{U(s)}{D(s)}=\frac{-1}{s+K_{p}} \tag{19}\]  
It is obvious that monotone nondecreasing step response of the two spin frequency-loops can be achieved. However, the tracking response of the phase loop cannot be synchronous and fast due to the different lifetimes of the two isotopes.  
Overall, based on the PLL method, it is a conflict to improve the phase loop or the frequency-loop performance at the same time. Both of the two strategies given above cannot achieve the goal solely.
## 1 Introduction
The singular values, obtained through singular value decomposition (SVD), capture key structural properties of the signal and act as a highly discriminative, low-dimensional descriptor. These singular value vectors are then used as direct inputs to a compact NN, trained to classify modulation schemes under wireless channel conditions. A distinctive advantage of the proposed method lies in its ability to effectively leverage the informative nature of singular values, which encapsulate modulation-relevant features while significantly reducing input dimensionality. This compactness allows the use of a simple NN architecture without sacrificing classification performance. In contrast to existing methods that depend on deep or complex models to learn from raw or high-dimensional data, our approach achieves competitive accuracy using a minimal NN structure thanks to the expressiveness of the singular value features. While the proposed NN is not an extremely shallow network, it maintains a relatively compact structure compared to many state-of-the-art AMC architectures, i.e., models based on deep convolutional layers, recurrent units, or Transformer blocks. The remainder of this paper is organized as follows: Section 2 describes the proposed method, including the system model, problem formulation, and NN design via Hankelization-based preprocessing. Section 3 reports the simulation results, including the experimental setup, comparative analysis of the latent representations produced by different methods, and modulation recognition performance. Finally, Section 4 concludes the paper with a summary of key findings and discussions.
## 3 Results and Discussion
ZnO and Zn\({}_{1-x}\)Mg\({}_{x}\)O films with wurtzite crystal structure are direct-band materials [73]. \(\beta\)-Ga\({}_{2}\)O\({}_{3}\) is a semiconductor with indirect bandgap of 4.84 eV, while the direct bandgap at the \(\Gamma\) point is only 0.04 eV larger [74]. This insignificant difference, along with a possible influence of impurities upon the bandgap, makes it practically impossible to determine experimentally the value of the indirect bandgap. The same is true for the In\({}_{2}\)O\({}_{3}\). Though its band structure has been extensively discussed, a consensus on the nature of the electronic transition at the edge of the optical absorption has not been reached [75]. Although an indirect band gap was obtained according to theoretical calculations in In\({}_{2}\)O\({}_{3}\), the energy difference between the overall valence band maximum and the highest occupied level at the \(\Gamma\) point was found to be less than 0.05 eV [76]. The power function exponent of 2 is also commonly used to determine the bandgap value of ITO films from optical measurements [77]. Therefore, taking into account these considerations, we used the Tauc plot for direct transitions to determine the bandgap of the prepared samples from the measurements of the optical transmission spectra. These spectra demonstrate that the bandgap values of films change in the range of (4 to 4.85) eV, corresponding to the middle ultraviolet spectral range. Figure 6: Spectral dependence of the absorption coefficient of oxide films.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 3 Deep Learning-Based Target Detection
### Improved Neck Network  
The weighted BiFPN is an efficient neck network structure proposed by [PERSON] et al. in EfficientDet, a single-stage target detection model [26]. The network, which also belongs to convolutional neural networks, introduces learnable weights to learn the importance of different scale features extracted from an efficient backbone network and iteratively uses top-down and bottom-up sampling methods to fuse multi-scale features. Then, the fused features are fed into the class prediction network and box prediction network for detection. Finally, we note that the performance of the network improves with increasing network depth based on the superposition of BiFPN. The structure is as follows.  
As shown in Figure 6, compared with the PANet structure, the BiFPN base block structure contains not only horizontal connections from left to right and vertical connections from top to bottom and bottom to top, but also there are cross-scale connections. In this way, we can extract more features at different levels for fusion so that we can have attention to the whole situation and will not easily miss any information. Moreover, BiFPN blocks can be stacked as many times as needed. BiFPN blocks can be represented as  
\[P_{i}P_{i+1}\dots P_{i+n}=f(C_{i}C_{i+1}\dots C_{i+n}) \tag{6}\]  
where \(n\) is the number of features used for feature fusion, \(C\) is the input features of a layer, \(P\) is the output features of a layer, and \(f\) is a function of the feature fusion process.  
Based on the above, we change the neck network of standard YOLOv5 from the original PANet to a weighted BiFPN. The improvement of the network structure is shown in Figure 7. The neck network structure of YOLOv5 is mainly responsible for feature extraction and fusion and introduces learnable weights by accessing the BiFPN module to learn from the efficient backbone features extracted from the network at different scales. Then, repeatedly employing top-down and bottom-up sampling methods to fuse multi-scale features, thus enhancing the fusion of features. That is, introducing BiFPN can help us focus on the big picture and not miss any information. In the time-frequency images generated by the system modeling in this paper, most of the signals to be detected are  
Figure 6: PANet (**a**) and BiFPN (**b**) base module structure.  
rectangular and strip-shaped. Due to the aliasing of continuous signals and burst signals in the time domain, each target signal to be detected in the figure globally is more relevant, so we think this improvement is helpful for our task.
# How to Prevent Aseptic Loosening in Cementless Arthroplasty: A Review
## 2 Pathophysiology of Aseptic Loosening  
To prevent or treat the aseptic loosening of a cementless total hip replacement, we must first understand the pathophysiology of this process. Aseptic loosening can have different causes, that are (1) improper initial fixation, (2) mechanical loss of fixation over time, and (3) biological loss of fixation due to particle debris [3].  
An improper initial fixation, or osseointegration, can have the following causes: implant properties, bone quality, surgical technique, host's healing process, and loading conditions [4]. The main cause of aseptic loosening is represented by wear debris from the implant's bearing surface, which further initiates an inflammatory response from the host's immune system [5]. This effect increases the bone resorption activity of osteoclasts and decreases the bone-forming process of osteoblasts [4]. The key mechanism which activates the osteoclast resorption process is the receptor activator of the nuclear factor-xB (RANK)/RANK ligand (RANKL) pathway [3]. This pathway is an important target for modern therapies.
# On IRS-Assisted Covert Communication with a Friendly UAV
## 4 Optimization Algorithm
### Optimization Algorithm  
In this subsection, we develop an iterative optimization algorithm based on SCA and Dinkelbach to solve problem (24), which is presented in Algorithm 1.  
```
1:Initialization:Q\({}_{0}\), \(c_{0}\), \(R_{b}\), \(\eta\), and index parameter \(j=1\)
2:obtain optimal \(\Theta\) according to (32)
3:repeat
4: Update \(\left(P_{k},\hat{P}_{u,k}\right)\) based on (42) with obtained \(\left(C_{k-1},\Theta\right)\).
5: Update \(\eta\) based on (40) with \(\left(P_{k},\hat{P}_{u,k}\right)\).
6: Update \(Q_{k}\) based on (65) with \(\Theta\) and \(\left(P_{k},\hat{P}_{u,k}\right)\).
7: Update \(R_{b,k}\) based on (24) with \(\left(C_{k},\Theta,P_{k},\hat{P}_{u,k}\right)\).
8:\(j\gets j+1\)
9:until\(\left|R_{b,j}-R_{b,j-1}\right|\leq J\)
```  
**Output:** maximum average transmission rate received at Bob \(R_{b}\), optimal UAV AN power \(P_{u}\), optimal UAV transmission power \(P\), and optimal trajectory of UAV \(Q\). ```  
**Algorithm 1** Iterative Optimization Algorithm  
As shown in Algorithm 1, the initial trajectory set \(C_{0}\) and coordinate set \(c_{0}\) of the UAV are set up first, then \(R_{0}\) is calculated and the iteration parameter \(k\) is set. The optimal \(\Theta\) is calculated with (32). In lines 3 to 9, an iterative loop design is used. First, \(\left(P_{k},\hat{P}_{u,k}\right)\) is updated by solving problem (42) with the CVX solver. Second, the \(\eta\) factor is updated via (40) and \(Q_{k}\) is updated by solving (65). Finally, \(R_{b,k}\) is updated through (24). The loop terminates when the condition \(\left|R_{b,k}-R_{b,k-1}\right|\leq K\) is satisfied. The algorithm returns the optimal UAV AN power \(P_{u}\), optimal transmission power \(P\), optimal trajectory of the UAV \(Q\), and maximum average covert rate received by Bob \(R_{b}\).  
In Algorithm 1, we first derive the upper bound of a variable, then adopt Dinkelbach method to transform the formulated problem into a convex one. Thus, the complexity of the solution algorithm is determined by the SCA. The complexity of the proposed algorithm is \(\text{O}\left(\text{I}_{\max}(T\times M)^{3}\text{log}\text{x}^{-1}\right)\), where \(T\) is the number of time slots, \(M\) is the number of IRS units, \(I_{max}\) is the iterative function, and \(K\) is the accuracy required for the approximations. Accordingly, the complexity of the proposed algorithm significantly increases with the increase of \(M\) and \(T\), which can become computationally intensive.
## 4 Data and Resources  
This study merged cell-based GPS location data providing human-movement insights, parcel data providing location reference information, and business-level daily economic data. Mobile location data, which are gathered via GPS and other technologies built into smartphones and other mobile devices, have grown in importance as a tool for studying economic activity. These data can provide valuable information on business and other economic entity performances, as well as consumer behavior, such as travel and purchasing trends.
# Optimal Model-Free Mean-Square Consensus for Multi-Agents with Markov Switching Topology
## 2 Preliminaries
### Problem Formulation
Next, when \(\lim_{k\to\infty}E[\|e(k)\|^{2}]=0\), we have \(\lim_{k\to\infty}tr(E[\xi(k)\xi(k)^{T}])=0\). Therefore,  
\[\lim_{k\to\infty}E[\|\xi(k)\|^{2}]=\lim_{k\to\infty}tr(E[\xi(k)\xi(k)^{T}])=0. \tag{14}\]  
For agent \(i\), the infinite-horizon local performance function, also called local cost function, can be defined as  
\[ \begin{split} J_{i}(e_{i}(0),u_{i}(0),u_{-i}(0))&=E [\sum_{l=0}^{\infty}\gamma^{l}r(e_{i}(l),u_{i}(l),u_{-i}(l))]\\ &=E[\sum_{l=0}^{\infty}\gamma^{l}[e_{i}^{T}(l)Q_{ii}e_{i}(l)+u_{i} ^{T}(l)R_{ii}u_{i}(l)+\sum_{j\in\mathcal{N}_{i}}u_{j}^{T}(l)R_{ij}u_{j}(l)]\}, \end{split}  \tag{15}\]  
where \(r(e_{i}(l),u_{i}(l),u_{-i}(l))\triangleq e_{i}^{T}(l)Q_{ii}e_{i}(l)+u_{i}^{T }(l)R_{ii}u_{i}(l)+\sum_{j\in\mathcal{N}_{i}}u_{j}^{T}(l)R_{ij}u_{j}(l)\), \(Q_{ii}\in\mathbb{R}^{n\times n}\), \(R_{ii}\in\mathbb{R}^{m\times m}\), and \(R_{ij}\in\mathbb{R}^{m\times m}\) are positive definite matrices, \(u_{-i}(k)=\{u_{j}(k):j\in\mathcal{N}_{i}\}\) denotes the control inputs of the neighbors of agent \(i\), and \(0<\gamma<1\) is the discount factor. The objective of this paper is to devise a distributed control \(u_{i}(k)\) for the optimal consensus problem, aiming to reach consensus and minimize performance function (15). We would create the PI algorithm to obtain the distributed control protocol. **Assumption 2**. : _[_31_]_ _The pair \((A,B_{i})\) is stabilized, and the pair \((A,Q_{ii}^{1/2})\) is observable._  
**Remark 1**. : _Solving the optimal consensus problem hinges on completing the Riccati equation. The unique and positive definite solution to the Riccati equation, guaranteed by the assumptions of controllability and observability, enables the design of the ideal control law. RL algorithms solve the optimal consensus problem iteratively, differing from traditional methods but still relying on controllability and observability assumptions. According to theoretical analyses in Ref. [32], these assumptions are essential because they ensure the existence of an optimal control policy. This, in turn, allows the algorithm to correctly evaluate and improve policies. If there is an uncontrollable mode with a corresponding eigenvalue that has a positive real part, it is impossible to achieve the consensus objective in an MAS._
## 2 Materials and Methods
### Data Preprocessing Methods
#### 2.2.6 resize_vector Algorithm  
The resize_vector algorithm (Algorithm 7) is designed to resize a one-dimensional array (vector) vector to a new specified length new_size. The algorithm provides two main cases of resizing--length increase and length decrease--and uses different approaches for each case.  
```
1:Input:vector--original one-dimensional array, new_size--new length
2:Output: New array of length new_size
3:old_size \(\leftarrow\) length(vector)
4:if new_size = old_size then
5:return vector
6:elseif new_size > old_size then\(\triangleright\) Interpolation at increasing length
7: Let indices be uniform values from 0 to old_size \(-\) 1 with the number of points new_size
8: Create an output array by interpolating vector at indices points
9:return Resulting array
10:else
11: Let indices be uniform values from 0 to old_size \(-\) 1 with the number of points new_size
12: Initialize resized_vector with zeros of length new_size
13:for\(i\gets 1\) to new_size do
14:\(start\_index\leftarrow\lfloor indices[i]\rfloor\)
15:\(end\_index\gets start\_index+1\)\(\triangleright\) or old_size, if \(i=new\_size\)
16:\(resized\_vector[i]\leftarrow\) mean value of the vector subarray on the interval \([start\_index,end\_index)\)
17:endfor
18:return resized_vector
19:endif
```  
**Algorithm 7** resize_vector