esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Error Estimation for a Reduced Flexible Body
It should be also noted that the error estimation performance highly depends on how close the reduced eigensolution is to the reference eigensolution because the error estimator is derived with the following two assumptions: a small eigenvector error and the use of \(\overline{\chi}_{n}^{i}\). In practice, when the relative eigenvalue error is \(\leq\)10%, the error estimator works well [21; 24; 25]. It is a better way to evaluate the model reliability through the eigenvector error rather than the eigenvalue error. The modal assurance criterion (MAC) is then a typical way to compare the reference and approximated eigenvectors. However, it should be noted that plotting MAC might require the reference eigenvectors. To avoid this problem, the proposed technique indirectly estimates the eigenvalue errors without using the reference eigensolutions. In addition, the comparison of the eigenvalues, which are scalar, is much less expensive than the eigenvector operation. The trend of the accuracy of the eigenvalues follows the one of the eigenvectors since the eigenvalues are computed by using eigenvectors in the typical eigenvalue solvers such as subspace iteration and Lanczos algorithms. However, since eigenvectors are less convergent than eigenvalues, and we here use \(e^{i}\) as 0.1% in the numerical examples for a conservative point of view.
# An Optimized Planning Tool for Microwave Terrestrial and Satellite Link Design
## 3 Tool Description
### Satellite Links
#### 3.5.7 Link Budget  
In this section, using all the elements previously inserted for the user and calculations made in the previous sections, the minimal antenna diameters, radiation efficiency, gain, and emission power to assure a reliable connection, both on ascending and descending links, are presented.  
The user can also change the value of radiation efficiency to analyze the effects of this characteristic on the remaining elements, using the interface depicted in Figure 11.
## 3 Results and Analysis  
### Analysis of the Single-Factor Test Results  
Simulation tests were performed in the EDEM discrete element software with maximum shear force serving as the evaluation index. The outcomes of the single-factor test are shown in Figure 9. The simulation outcomes for varying values of particle contact radius (\(x_{1}\)), critical normal stress (\(x_{4}\)), and critical tangential stress (\(x_{5}\)) exhibited minimal deviation from the experimentally measured maximum shear force, indicating that these parameters have a minor effect on shear performance. In contrast, as the normal stiffness per unit area (\(x_{2}\)) increased, the maximum shear force initially increased and then decreased. When \(x_{2}\) ranged between 0.5 \(\times\) 10\({}^{8}\) and 1.0 \(\times\) 10\({}^{8}\), the simulation results closely matched the measured values. Similarly, the simulated shear force approached the experimental results when the tangential stiffness per unit area (\(x_{3}\)) ranged from 0.5 \(\times\) 10\({}^{8}\) to 1.0 \(\times\) 10\({}^{8}\) and when the bonding radius (\(x_{6}\)) was between 1.4 mm and 1.6 mm. These results indicate that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Levels** & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) \\ \hline
1 & 1.2 & 1.0 \(\times\) 10\({}^{7}\) & 1.0 \(\times\) 10\({}^{7}\) & 1.0 \(\times\) 10\({}^{6}\) & 1.0 \(\times\) 10\({}^{6}\) & 1.2 \\
2 & 1.4 & 5.0 \(\times\) 10\({}^{7}\) & 5.0 \(\times\) 10\({}^{7}\) & 5.0 \(\times\) 10\({}^{6}\) & 5.0 \(\times\) 10\({}^{6}\) & 1.4 \\
3 & 1.6 & 1.0 \(\times\) 10\({}^{8}\) & 1.0 \(\times\) 10\({}^{8}\) & 10.0 \(\times\) 10\({}^{6}\) & 10.0 \(\times\) 10\({}^{6}\) & 1.6 \\
4 & 1.8 & 5.0 \(\times\) 10\({}^{8}\) & 5.0 \(\times\) 10\({}^{8}\) & 50.0 \(\times\) 10\({}^{6}\) & 50.0 \(\times\) 10\({}^{6}\) & 1.8 \\
5 & 2.0 & 9.0 \(\times\) 10\({}^{8}\) & 9.0 \(\times\) 10\({}^{8}\) & 90.0 \(\times\) 10\({}^{6}\) & 90.0 \(\times\) 10\({}^{6}\) & 2.0 \\ \hline \end{tabular} Note: \(x_{1}\) is the particle contact radius, \(x_{2}\) is the normal stiffness per unit area, \(x_{3}\) is the tangential stiffness per unit area, \(x_{4}\) is the critical normal stress, \(x_{5}\) is the critical tangential stress, and \(x_{6}\) is the bonding radius.  
\end{table}
Table 3: Levels of single-factor tests.  
factors \(x_{2}\), \(x_{3}\), and \(x_{6}\) significantly affect the maximum shear force, as variations in their values caused notable changes in the simulation outcomes.
# Adaptive Noise-Powered Diffusion Model for Efficient and Accurate Object Detection
## 3 Method  
### Preliminaries  
The diffusion model starts with an initial data sample \(y_{0}\), which typically represents the ground truth in object detection, specifically defined as  
\[y_{0}:=\text{Set}_{b}=\{\text{box}_{i}\mid\text{box}_{i}=(c_{x_{i}},c_{y_{i}},w_{i},h_{i})\} \tag{1}\]  
where \(\text{box}_{i}\) represents the bounding box of the \(i\)-th object, with \(c_{x_{i}},c_{y_{i}}\) as the center coordinates, \(w_{i}\) as the width, and \(h_{i}\) as the height. The set \(\text{Set}_{b}\) denotes all detected objects in the image.  
In the forward process, Gaussian noise is progressively added over \(T\) steps, generating \(y_{1},y_{2},\ldots,y_{T}\). When \(T\) is sufficiently large, \(y_{T}\) converges to standard Gaussian noise \(\mathcal{N}(0,I)\). This process follows the Markov chain rule, mathematically expressed as  
\[q(y_{t}\mid y_{t-1}):=\mathcal{N}(y_{t};\sqrt{1-\beta_{t}},y_{t-1},\beta_{t}I) \tag{2}\]  
where \(\beta_{t}\) controls the variance of the noise added at each step \(t\), with the noise intensity increasing as \(t\) progresses. \(I\) is the identity matrix, ensuring that the noise components in each dimension remain independent.  
In the reverse process, the model progressively reduces noise to reconstruct the initial bounding box set \(y_{0}\) from the final noisy sample \(y_{T}\). To bridge the gap between noisy data and the final prediction in the reverse process, a neural network decoder is employed, which estimates the original detection result \(y_{0}\), expressed as  
\[\text{Pred}\,y_{0}=\text{Decoder}(y_{t}) \tag{3}\]  
The training process employs the mean squared error (MSE) loss function to measure the difference between the predicted bounding boxes and the ground truth, defined as  
\[L_{\text{train}}=\frac{1}{2}\|y_{0}-\text{Pred}\,y_{0}\|^{2} \tag{4}\]  
However, the standard diffusion model's reliance on random noise introduces challenges in accurately predicting object locations. The random nature of this noise often causes the initial object candidates to deviate significantly from the ground truth, increasing the computational burden during the denoising process. Moreover, the decoder must make substantial adjustments to align these noisy predictions with the actual object positions. Toaddress these challenges, we propose three adaptive mechanisms, which will be detailed in the following sections. As shown in Figure 1, our model adopts a more streamlined structure, offering a distinct contrast to the more complex design of DiffusionDet.
# Low-Frequency Communication Based on Rydberg-Atom Receiver
###### Abstract  
Rydberg-atom receivers have developed rapidly with increasing sensitivity. However, studies on their application in low-frequency electric fields remain limited. In this work, we demonstrate low-frequency communication using an electrode-embedded atom cell and a whip antenna without the need for a low-noise amplifier (LNA). Three modulations--binary phase-shift keying (BPSK), on-off keying (OOK), and two-frequency shift keying (2 FSK)--were employed for communication using a Rydberg-atom receiver operating near 100 kHz. The signal-to-noise ratio (SNR) of the modulated low-frequency signal received by Rydberg atoms was measured at various emission voltages. Additionally, we demonstrated the in-phase and quadrature (IQ) constellation diagram, error vector magnitude (EVM), and eye diagram of the demodulated signal at different symbol rates. The EVM values were measured to be 8.8% at a symbol rate of 2 kbps, 9.4% at 4 kbps, and 13.7% at 8 kbps. The high-fidelity digital color image transmission achieved a peak signal-to-noise ratio (PSNR) of 70 dB. Our results demonstrate the feasibility of a Rydberg-atom receiver for low-frequency communication applications.  
Keywords:Rydberg-atom; low-frequency communication; quantum sensing; atomic receiver; signal modulation +
Footnote †: journal: Electronics
# A CSI-Based Multi-Environment Human Activity Recognition Framework
## 4 Discussion
### Performance Comparison  
Table 16 provides a comparison between the work presented in this paper and other similar papers. Table 16 shows that our proposed approach was able to achieve higher recognition accuracy compared to other existing approaches, which suggests the feasibility of the proposed approach to recognize human activities using CSI signals. We contribute the good performance of our work to the following reasons. First, in this work, we developed a four-staged procedure that achieves the following: removing outliers, converting the signal from non-stationary to stationary, reducing the size of the available data, and smoothing the signals. Second, the four-stage signal denoising procedure was performed twice: before and after segmentation. In addition, finally, a coarse and fine grid search was performed to determine the optimal SVM parameters. Moreover, to the best of our knowledge, our study is one of the first studies that investigates the problem of HAR and fall detection using the LOSO-CV procedure, which is considered more challenging than traditional cross-validation procedures.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Activity** & **A1** & **A2** & **A3** & **A4** & **A5** & **A6** \\ \hline True Positive Rate & 93.75\% & 74.50\% & 100.00\% & 85.00\% & 82.50\% & 62.00\% \\ False Positive Rate & 01.00\% & 03.70\% & 00.36\% & 05.52\% & 03.38\% & 02.27\% \\ Precision & 98.68\% & 77.00\% & 98.04\% & 71.88\% & 80.49\% & 68.13\% \\ F1 Score & 96.15\% & 75.73\% & 99.01\% & 77.89\% & 81.48\% & 64.92\% \\ \hline \hline \end{tabular}
\end{table}
Table 15: The results of our proposed approach obtained for the fifth evaluation scenario.
## 3 Case I: Passive Eavesdropper
### Security Gap Performance  
On the one hand, there is an assumption concerning the relative positions of the various transmitters/receivers. In particular, for the case of user A and user E, it seems coherent and acceptable to consider that the powers of the background noises \(\sigma_{A}^{2}\) and \(\sigma_{E}^{2}\) are identical. On the other hand, under the assumption of channels without loss (unified mean deviations on all the paths) for \(\mathbf{h}_{AA}\) and \(\mathbf{h}_{AE}\), it is possible to simplify the notations and to denote in general the self-jamming power-to-noise ratio as \(\rho_{Sj}\) for both self-interference (\(\rho_{AA}\)) and self-jamming (\(\rho_{AE}\)) channels. Since, in these conditions, we have \(\rho_{Sj}=\rho_{AA}=\rho_{AE}\). The security gap \(S_{g}\) is clearly related to the error rate achieved on the receiver side of user A and user E. In order to adapt to the practical applications, we set up \(BER_{A,max}=10^{-5}\) and \(BER_{E,min}=0.5\) for the maximum and minimum average errors that user A and user E can reach, respectively. Based on the results in Figures 10 and 11, the minimum SNR at the legitimate user A, the \(SNR_{A,min}\) and the maximum SNR at the eavesdropper user E, and \(SNR_{E,max}\) to obtain \(BER_{A,max}=10^{-5}\) and \(BER_{E,min}=0.5\), respectively, can be pointed out. Then, these values are recorded corresponding to different levels of the general self-jamming power-to-noise ratio \(\rho_{Sj}\). Finally, the security gap \(S_{g}\) is calculated and summarized in Table 3.  
Figure 11: \(BER_{E}\) versus the \(SNR_{E}\) in case of a passive eavesdropper.  
Figure 12 shows the security gap versus the various values of the self-jamming power-to-noise ratio (\(\rho_{SI}\)) in the case of blind without feedback and blind feedback at user A. The result shows that the increase in the self-jamming power-to-noise ratio \(\rho_{SI}\) leads to a decrease in the security gap \(S_{g}\). For example, the security gap \(S_{g}\) can be dramatically reduced from 7 to 10 dB when the blind feedback scheme is applied. Therefore, it obtains an important goal of the PLS, which is to maintain the security gap as small as possible. In summary, the use of joint iterative blind estimation and decoding at the legitimate receiver user A significantly reduces the security gap \(S_{g}\) in FD wiretap transmission. Furthermore, when using the blind feedback scheme, the \(SNR_{A}\) of user A is reduced when performing channel estimation or decoding messages, compared with the blind scheme without feedback, which emphasizes that the system not only maintains security but also enhances power consumption by reducing the transmission power.  
In the next section, we will consider the second case, where user E can also send their jamming message to destroy the reception and decoding processes of user A, which we refer to as an active eavesdropper.
## 4 Noise Control
### Experimental Verification on the Improvement of Structure  
The thermal performance and noise characteristics of the improved air compressor were tested. Table 10 shows the comparison of the thermal performance of the air compressor under rated working conditions before and after the improvement. The isentropic efficiency is calculated as follows:  
\[\eta_{\text{is}}=\frac{PR^{\frac{k-1}{k}}-1}{\frac{T_{\text{out}}}{T_{\text{in }}}-1} \tag{5}\]  
where \(k\) is the specific heat ratio of air, \(T_{\text{out}}\) is the temperature of the outlet of the second volute, and \(T_{\text{in}}\) is the temperature of the inlet of the first impeller. After the improvement, the performance of the air compressor changed little, the pressure ratio decreased by 0.40%, the mass flow decreased by 0.19%, and the isentropic efficiency increased by 0.62%. From the comparison results, the thermal performance of the air compressor remains at the same level after the improvement of the air compressor structure for the aerodynamic noise.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Fundamental Frequency**} & \multicolumn{3}{c}{**Second Harmonic**} \\ \cline{2-7}  & **Before** & **After** & & & & \\
**Improvement** & **Difference** & & & & & \\  
The comparison results of near-surface noise of the air compressor under rated working conditions are shown in Table 11. The noise at the first and second stage solutes is reduced by 5.3 dBA and 3.3 dBA, respectively, and the noise at the measuring point of the inter-stage pipeline is reduced by 7.4 dBA.  
Figure 22 shows the noise characteristics at Point 7 before and after improvement. After the improvement, the peak of the sound pressure level at the fundamental frequency and second harmonic is significantly reduced. There is a spill-over effect in the after improvement spectrum at 600 Hz. Reducing the noise at the target frequency may cause a slight increase in the noise at other ranges of frequency. The possible reason for this phenomenon is that, in the process of converting a large-scale eddy to a small-scale eddy, the frequency of the noise induced by eddy will increase.  
\begin{table}
\begin{tabular}{c c c} \hline
**Performances** & **Before Improvement** & **After Improvement** \\ \hline Pressure ratio & 2.51 & 2.50 \\ \hline Mass flow rate [g\(\cdot\)s\({}^{-1}\)] & 125.36 & 125.12 \\ \hline Isentropic efficiency & 77.29\% & 77.77\% \\ \hline \end{tabular}
\end{table}
Table 10: Thermal performance before and after improvement.  
Figure 22: Noise characteristics before and after improvement.
## 2 Preliminaries
### Derivation of the Proposed sSMC-FCM Algorithm
* Step 1: Initialize value for \(V\), let \(l=0\), and set \(\varepsilon>0\). * Step 2: At the \(l-th\) loop, update \(U\) according to Equation (15) for unsupervised elements, or according to Equations (17)-(20) for supervised elements. Step 3: Update \(V\) for the next step \((l+1)\), according to Equation (10), with \(m_{ik}\) calculated using Equation (8). * Step 4: If \(\Big{|}\Big{|}V^{(l)}-V^{(l+1)}\Big{|}\Big{|}<\varepsilon\), then go to Step 5; otherwise, let \(l:=l+1\), and return to Step 2. * Step 5: End. The sSMC-FCM algorithm has similar steps to the FCM and sSFCM algorithms, but it uses Equations (15) and (17)-(20) in Step 2 and Equations (8) and (10) in Step 3.
## 2 Related Work  
### Hash Algorithms  
In our study, we utilize hash algorithms, essential tools in data security, for generating unique, fixed-length representations of input data of any length. These algorithms are widely applied in secure computing to ensure the integrity of data during storage and transmission, offering a reliable means of verifying data authenticity. In practice, hash functions convert input data into a concise \"digest\" or \"hash value\", effectively serving as a digital fingerprint, as shown in Figure 1.  
The process begins by dividing the input data into blocks of fixed length. For example, data can be broken down into multiple blocks, each of 512 bits. Each of these 512-bit blocks is further divided into smaller 32-bit segments, referred to as \"words\". Initially, the block consists of 16 such words. To enhance security and provide a more comprehensive hash, additional words are generated based on the initial 16 words, extending the set to 64 words. This transformation relies on sequential calculations that derive each new word from the preceding ones, forming an expanded data set.  
Ideal hash functions, like those in the SHA series, are designed to be computationally efficient, challenging to reverse-engineer, collision-resistant, and sensitive to even minor changes in input data. In secure computing applications, hash trees, or Merkle trees, utilize these hash algorithms to arrange hash values of data blocks hierarchically. This structure enables efficient data verification, particularly valuable in fields where data integrity is paramount, such as finance, healthcare, and blockchain technology.
Additionally, the skip attention mechanism effectively filters out irrelevant data processing paths, significantly reducing unnecessary computations and improving processing speed. Secondly, we have designed a novel skip loss function aimed at further optimizing model performance. This loss function complements the skip attention mechanism, helping the model accurately identify and focus on critical features related to secure computing during training, thereby enhancing the model's security performance and adaptability in complex data environments. In summary, the proposed model combines the hash tree and skip attention mechanism, addressing the inefficiencies of traditional secure computing methods in processing large-scale data while enhancing the model's performance and adaptability in the field of secure computing. This makes it highly practical and broadly applicable in real-world applications. Finally, the organization of this paper is as follows: Section 2 reviews related work, Section 3 presents the materials and methods used, Section 4 showcases the experimental results and discussions, and Section 5 concludes with a summary of the research findings.
## 4 Experimental Study in Long Delay Regime
### Linewidth Narrowing  
Linewidth narrowing of the mutually injection locked DFB lasers is confirmed by both local oscillator (LO) heterodyne and delayed self-heterodyne. For LO heterodyne in Figure 13a, the recorded beat signal is the convolution between the LO laser (TSL-210V, Santec, Komaki, Japan) and the laser being tested, and details in spectrum can be resolved due to better resolution than optical spectrum analyzer. The heterodyne spectra of free-running (FR) DFB lasers exhibit a Lorentzian shape with megahertz level linewidth. When mutual injection locking is achieved, the LO-heterodyne spectra of both lasers are narrowed significantly.  
The delayed self-heterodyne spectra acquired by electrical spectrum analyzer are plotted in Figure 13b with 2 km (10 \(\upmu\)s) fiber delay line. Compared with the free-running ones, the heterodyne spectra of injection locked lasers transform to the correlated type and exhibit ultra-high coherence features: (1) a delta-function-like RF carrier stands out at the central frequency; (2) notches spaced by 100 kHz emerge on the signal wings, corresponding to the reciprocal of the fiber delay time in the heterodyne measurement.
# Field Trials of SC-FDMA, FBMC and LP-FBMC in Indoor Sub-3.5 GHz Bands
## 4 Experiment Results  
In this section, we analyse and compare the temporal and spectral characteristics of the three waveforms, i.e., SC-FDMA, FBMC, and LP-FBMC, and then we compare the field-tested BER performance of the three waveforms in various real uplink wireless channels. Table 2 shows the major system parameters that are used for the performance measurements.  
Figure 8: Bit error rate (BER) performance measurement set-up for NLoS environment.  
Figure 9: Configuration of host PCs for users, SDR devices for users’ transmitters (TXs), and clock distribution device.
# A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).  
[PERSON]  
1 Department of Computer Applications, Manipal University Jaipur, Jaipur 30307, Rajasthan, India; [EMAIL_ADDRESS]  
[PERSON]  
2 School of Advance Computing, DBS Global University, Dehradun 248001, Uttarakhand, India 2*  
the snoring device is comfortable for users to wear during sleep is also a main challenge. Building a machine learning model for resource-constrained devices, such as wearable devices, is also a major issue. The size and complexity of machine learning models can also directly impact the storage limitation of such wearable devices. Achieving real-time inference on resource-constrained devices is also challenging; audio signals have to be processed for snoring detection. On the other hand, the use of TinyML in building a wearable device for snoring detection offers many benefits, as follows:  
* TinyML models are specifically designed for resource-constrained environments.
* TinyML models are optimized for real-time inference. They enable on-device inference and eliminate the need for continuous data transmission to external servers for analysis.
* TinyML models ensure real-time response because of low latency. In the context of snore detection, this capability is critical for providing timely alerts.
* TinyML models are characterized by their compact size, making them suitable for deployment in wearable devices.