esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Expectation-Maximization-Based Blind Receiver for Low-Complexity Uplink STLC-NOMA Systems
## 5 Simulation Results
Figure 4 depicts the average mean squared error (MSE) of the estimated energies for both STAs, defined as \(\text{MSE}:=\sum_{n=1}^{2}(\sqrt{\gamma_{n}}-\sqrt{\gamma_{n}})^{2}/2\), when the transmit SNRs of the two STAs are 30 dB, i.e., \(\rho_{1}=\rho_{2}=30\) dB. We can observe that the MSE of the BEE algorithm has linearly improved with respect to the frame length in the log-log domain. As earlier mentioned, the more symbols within a frame, the more observations available at the AP, resulting in the MSE improvement. Figures 3 and 4 also show that the designed BEE scheme works well enough for short-length frames. Figure 2: Average BER performance of two STAs in the proposed low-complexity uplink STLC-NOMA and conventional STLC-NOMA systems. (**a**) Numerical BER performance of the proposed STLC-NOMA (Proposed) and conventional STLC-NOMA (Conv.) [23]. (**b**) Analytic and asymptotic BER performance of the proposed STLC-NOMA. Meanwhile, CSI estimation errors may exist in practical wireless networks, even in channel estimation through pilot signals at the STA. Such a CSI estimation error can be statistically modeled as \(\widetilde{h}_{n,m}\triangleq h_{n,m}+\epsilon_{n,m}\), where \(\widetilde{h}_{n,m}\) and \(h_{n,m}\) represent the actual CSI and the estimated CSI, respectively, and \(\epsilon_{n,m}\) indicates the estimation error [23]. In general, the resultant estimation error is assumed to follow an i.i.d. \(\mathcal{CN}(0,\sigma_{e}^{2})\), \(\forall n,m\), where variance \(\sigma_{e}^{2}\) represents the MSE of the estimation such that \(\mathbb{E}[|h_{n,m}-\widetilde{h}_{n,m}|]=\sigma_{e}^{2}\). Figure 5 shows the average BER performance of the two STAs in the uplink STLC-NOMA systems with respect to the MSE of the channel estimation, where \(\rho_{1}=\rho_{2}=\rho\), \(\beta_{1}=\beta_{2}=1\), and \(L=32\cdot M\). In particular, Figure 4(a) shows the results for varying SNRs when the modulation order is 4, and Figure 4(b) presents the results for varying modulation orders when the SNR is 20 dB. The proposed STLC-NOMA employs a blind receiver, whereas the conventional STLC-NOMA assumes that the AP has perfect partial CSI because there is no proposed blind receiver. As expected, we can observe that the BER performance of the STLC-NOMA systems gradually deteriorates as the MSE of estimation increases. It is worth noting that the BER performance is crossed when \(\rho=20\) dB and \(\sigma_{e}^{2}\approx 10^{-2}\) in Figure 4(a), even though the perfect partial CSI is assumed at the AP for the conventional  
Figure 4: Average MSE of the proposed BEE algorithm with respect to the frame length, when the transmit SNRs of both STAs are \(\rho_{1}=\rho_{2}=30\) dB. Figure 3: Average BER performance of two STAs in the proposed low-complexity uplink STLC-NOMA system employing the BEE algorithm at the AP with respect to the frame length. STLC-NOMA. Hence, we can state that the proposed low-complexity STLC-NOMA system is more tolerant of channel estimation errors than the conventional STLC-NOMA system.
## 4 Result and Analysis
### Research Validation
#### 4.4.1 Wilcoxon Test  
\"The Wilcoxon signed ranks test is a nonparametric statistical procedure for comparing two samples that are paired, or related. The parametric equivalent to the Wilcoxon signed ranks test goes by names such as the student's \(t\)-test, \(t\)-test for matched pairs, \(t\)-test for paired samples, or \(t\)-test for dependent samples\".
## 4 Proposed Antenna Codebook and MAB Approach
### Antenna Codebook Design  
To eliminate the need for CSI estimation, discrete PSs are considered for both mmWave BS and RIS, where they constitute the antenna codebook of both. In this context, we will utilize the antenna codebook of WiGig standards for PS design at both BS and RIS [12; 13]. This codebook-based beam switching involves fixed beam patterns and can be realized using a predefined pool of antenna weight vectors maintained at TX and RX. Columns of a codebook matrix specify the beamforming weight vector that corresponds to a unique beam pattern. The TX-RX beam pattern pair that optimizes a certain cost function is searched during beamforming according to an agreed criterion. Codebooks support a variety of antenna array geometries and offer flexibility in terms of the number, size, and spacing between antenna elements. For phased array antennas, the columns of the codebook matrix specify the discrete PSs applied to individual antenna elements. The patterns may be generated without amplitude adjustment to obtain processing power savings. As a guiding principle, the columns of the codebook are made orthogonal to each other so that multiple beams can be generated simultaneously without significant interference. These beams can also be synthesized to create a wider beam. Thus, in this codebook design, the PS vectors for \(K\leq A\), where \(A\) is the total number of antenna elements and \(K\) is the total number of PS vectors (i.e., beam directions), are given by column vectors of the following matrix:  
\[\mathbf{V}(a,k)=j^{\text{floor}\left\{\frac{a\times\text{mod}(k+K/2,K)}{K/4} \right\}}, \tag{7}\]  
\[a=0,\ldots,\ A-1,\ k=0,\ldots,\ K-1\]  
In the case that \(K=M/2\), the PS vector at \(k=0\) becomes  
\[\mathbf{V}(a,0)=(-j)^{\text{mod}(a,K)},\ a=0,\ldots,\ A-1 \tag{8}\]  
Thus, the columns in \(\mathbf{V}\) are the available space for constructing \(\mathbf{f}\) and the diagonal of \(\mathbf{\Phi}\).
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 4 Numerical Simulations
### Performance with Band MMSE Block Equalization  
In this section, the BER performance of UP-OCDM and OFDM with the band MMSE block equalization are compared under the channels with normalized Doppler frequency of 0.1 and 0.3, respectively. We choose a varying approximation \(Q\) of 2, 4, and 6 in our simulations, which means the bandwidths of the approximated channel matrices are 5, 9, and 13, respectively.  
Figure 2: Bit error rate (BER) performance of the UP-OCDM, OCDM, and Orthogonal Frequency Division Multiplexing (OFDM) systems with minimum mean square error (MMSE) equalizers under the 16-ray doubly selective channels with the normalized Doppler spread of 0.1 (**a**) and 0.3 (**b**).  
Figure 3a,b show the BER vs. SNR performance with band MMSE block equalizers for UP-OCDM and OFDM over channels with different Doppler spread. The blue curves represent the performance of OFDM systems, while the red curves represent for UP-OCDM system. From both Figure 3a,b, it can be seen that the proposed block banded equalizers for UP-OCDM outperform its OFDM counterparts. An intuitive explanation is based on the precoded interpretation of UP-OCDM. Recalling Section 3.2, \(\hat{\mathbf{d}}_{MMSE-BLE}=\mathbf{F}^{H}\mathbf{E}^{H}\mathbf{y}\). Note that compared with the OFDM system channel model under the frequency domain, the symbol vector \(\mathbf{d}\) in UP-OCDM can be seemed as precoded by \(\mathbf{F}\), rather than modulated independently on each subcarrier in OFDM. Therefore, the energy of each symbol will be distributed over N subcarriers, and by which diversity gain can be obtained. We can also observe that, with the increase of bandwidth, the performance of both UP-OCDM and OFDM gets better. This is due to the fact that the channel matrix will be approximated more precisely with a bigger bandwidth factor \(Q\). However, we can also note from Figure 3b, as the normalized Doppler spread increases, the BER performance of equalizers will suffer degradation. This is because when we are using a banded approximated channel matrix, we ignore the out-of-band ICI. With the same \(Q\), the larger \(f_{d}T\) will lead to more leakage of the out-of-band ICI, so that the approximation error will increase. Nonetheless, UP-OCDM can still achieve reliable transmissions with \(Q=4\), and the complexity is reduced to \(\mathcal{O}(NQ^{2})\) from \(\mathcal{O}(N^{3})\).
## 4 Method Validation
### Experiment and Results
#### 4.1.2 Results of Meter Classification  
In this paper, we first set the parameters for the convolutional neural network model:  
* **Batch size = 1:** A batch size of 1 is selected, often used in cases where the model handles limited data per iteration. This choice enables the model to update weights more frequently, potentially enhancing convergence and adapting effectively to the specific characteristics of each training sample. This approach can also help in fine-tuning model updates, especially in scenarios with smaller datasets or when system memory is a constraint.
* **Learning rate = 0.0001:** A low learning rate (0.0001) is chosen, ensuring that the model updates weights gradually, preventing sudden fluctuations or potential overshooting of optimal points. Such a small rate is particularly beneficial when training complex networks or when the initial loss is high, as it allows the model to achieve a more stable and smooth convergence.  
Then, input the training set for modeling and begin training. The initial total loss value of the training model is 6.72, and after 21,000 iterations, it is reduced to 1.008, indicating that the model training is beginning to converge. After 51,100 iterations over two hours, the final loss value reached 0.89.  
The 1500 images in the experimental test set are randomly divided into sub-test sets containing 1500, 1000, 500, and 100 images. These four sub-test sets are classified and detected using the trained model, with the test results presented in Table 5.  
In summary, the proposed meter classification algorithm in this paper has an average accuracy of 95.87%.
## 3 Plasmonic Optical Fiber Aptasensors
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Type** & **Analy** & **Optical Detection** & **Sensitivity [nm/RIU]** & **LoD [aM]** & **Reference** \\ \hline \multicolumn{5}{c}{**Silica fiber**} \\ \hline Bragg grating imprinted on & & & & & \\ silica fiber coated with gold & Thrombin & SPR & - & 22 & [PERSON] et al., 2013 [46] \\ (50 nm) & & & & & \\ Silica fiber coated with Au & & & & & \\ (50 nm) & & IgE & SPR & \(\sim\)1500 & \(2\pm 1\) & [PERSON] et al., 2009 [44] \\ Silica fiber coated with Au & & & & & \\ (50 nm) & & Ara h1 & SPR & - & 75 & [PERSON] et al., 2013 [68] \\ Bragg grating imprinted on & & & & & \\ silica fiber coated with gold & Thrombin & SPR & \(400\times 1000\) & 22.6 & [PERSON] et al., 2011 [56] \\ (50 nm) & & & & & \\ Tilted silica fiber Bragg & & & & & \\ gratings coated with gold & & & & & \\ (50 nm) & & & & & \\ Bragg grating imprinted on & & & & & \\ silica fiber coated with gold & & & & & \\ (50 nm) & & & & & \\ \multicolumn{5}{c}{(Circulating tumor} & & 49 cells/mL or 10 cells/mL \\  & & cells) & & & (with amplification) & [PERSON] et al., 2020 [57] \\ Tilted silica fiber Bragg & & Thrombin & SPR & 23.88 & 0.075-0.11 & [PERSON] et al., 2019 [52] \\ Silica fiber with annealed & & Ara h1 & SPR & \(>\)1000 & 1.6 & [PERSON] et al., 2016 [69] \\ gold layer (50 nm) & & HER2 & SPR & 1573.9 & 0.077 & [PERSON] et al., 2021 [73] \\ Glick-coated silica fiber & & Thrombin & SPR & - & 6.1 & [PERSON] et al., 2018 [58] \\ Silica fiber with gold coating & & ATP/Thrombin/DNA & SPR & - & 72,000/36/30 & [PERSON] et al., 2021 [54] \\ Silica fiber in gold & & & & & \\ nanoantennas configuration & & & & & \\ (C/Sc/O), Au 36/24/22 nm & & & & & \\ Gold nanorods on silica fiber & & & & & \\ \hline \end{tabular}
\end{table} 
Table 2: Aptamer-based silica or plastic optical fiber plasmonic sensor systems. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fiber Type** & **Analyte** & \begin{tabular}{c} **Optical** \\ **Detection** \\ \end{tabular}  & **Sensitivity [nm/RIU]** & **LoD [nm]** & **Reference** \\ \hline
**Silica fiber** & & & & & \\ \hline Silica fiber in gold & & & & & \\ nanoannetrans configuration & Bisphenol A & LSPR & \(-7\times 10^{3}\) & \(3.3\pm 0.7\times 10^{-7}\) & [PERSON] et al., 2019 [65] (\(\mathrm{\SIUnitSymbolMicro}\)) \\ (\(\mathrm{\SIUnitSymbolMicro}\)/\(\mathrm{\SIUnitSymbolMicro}\)) A/\(\mathrm{\  
Currently, very few examples have been reported in the literature related to the coupling between aptamers and POF-based biosensors [48; 49; 50; 55; 70; 71]. In contrast, more exhaustive literature exists where different MREs are exploited, as reported in Figure 2.  
With respect to other MREs, aptamers represent a new emerging class for the realization of biosensors and will be increasingly employed due to the establishment of new companies devoted to aptamer selection and optimization [34; 35; 36; 75].
## 5 Results and Discussion
During clustering analysis, the results obtained (as shown in Figure 25) demonstrated that most permissions in the same cluster have similarities in the feature distribution. For instance, READ_CALL_LOG and RECORD_AUDIO have some similarities in their structural distribution as can be seen in RECEIVE_SMS and WRITE_SMS respectively. This also demonstrated that permission in the same cluster have relevant features that are correlated. Figure 24: Android applications threat level classification. We observe that correlated permissions usually appear in locations that are the same. This suggests that Android applications that request CALL_LOG, SMS, or any permission sets that are correlated, similar permission sets will be requested underground and will be operational in the same region. Permissions in the same region execute frequent tasks such as read SMS and access audio and camera, among others. However, not all frequent permissions are dangerous. For example, a permission request to access the internet does not have any permission to the same region. Figure 25: Cluster density plots showing dangerous permission-based clustering. (**a**) Permissions in cluster 1. (**b**) Permissions in cluster 2. (**c**) Permissions in cluster 3. (**d**) Permissions in cluster 4.  
not invoke internet access control obtained by an application. We then inferred that internet permission does not support adequately grained management or control of the device resources. Comparatively, rather than use direct permission features to gain control of a device, malware authors may define some functionalities as access control. This aids in specifying how the malicious program can communicate with pre-defined applications to access the resources in question without necessarily requesting permission related to such resources. Based on the results obtained, we recommend improvement in the permission model of Android OS by differentiating internet permission access and other similar permissions into distinct groups and classes. Let a clear mechanism be provided for specifying Android permissions that are correlated with device resources. When the access control list is specified, this will help permissions definition by developers without using permissions attributes that are self-defined. For Android, numerous permissions have effects on Android end users and even developers. It is then pertinent to configure a system to help understand each permissions' action concerning the device resources rather than over-requesting them by the developers simply because they want an app to function. This could be a good security strategy in preventing a malware infection on mobile devices in general. However, studying actions associated with each permission is beyond the scope of our research.
# An Improved SIFT Underwater Image Stitching Method
## 6 Conclusions  
In this paper, we introduce an enhanced SIFT method designed for the stitching of underwater images characterized by smeared feature contours. Our method stands out  
Figure 7: Stitching results of multiple images in perspective projection on the Ship image pair: (**a–c**) Image inputs to be stitched. (**d**) SIFT. (**e**) APAP, As-Projective-As-Possible warps. (**f**) AANAP, adaptive As-Natural-As-Possible;(**g**) Our method.  
because it does not rely on detector information for feature extraction. Even in regions with blurred texture features, where traditional feature extraction methods falter, our approach significantly augments the number of accurately matched pairs, thereby elevating the precision of feature matching. Our method incorporates two critical phases for the stitching process, each rooted in the SIFT algorithm: coarse feature extraction and precise feature extraction. To enhance the image contrast and quality, we integrate a dynamic threshold white balance algorithm and CLAHE. Additionally, we have refined the fade-in and fade-out technique for generating panoramic images. A series of experiments were conducted to validate the effectiveness of our algorithm. Our results underscore the superiority of our method over the conventional SIFT algorithm. It effectively mitigated ghosting issues in stitched images and enhanced the UIQM measure by an average of 6.04% across ten underwater image stitching experiments. When compared to alternative algorithms such as APAP, SPHP, and AANAP, our method outperformed them, boosting the UIQM index by 4.73%, 9.01%, and 11.06%, respectively. Furthermore, we demonstrated the efficacy of our method in seamlessly stitching multiple images, preserving the integrity of the image content in expansive scenes without significant ghosting artifacts. Our future research endeavors will concentrate on compensating for parallax in cases of substantial movements and addressing the influence of moving objects within the scene on stitching quality.  
Conceptualization, J.M. and W.Z.; methodology, J.M. and W.Z.; software (Pycharm 2020.1.2), R.Z.; validation, R.Z., W.Z. and H.Z.; formal analysis, H.Z.; investigation, H.Z.; resources, H.Z.; data curation, H.Z.; writing--original draft preparation, R.Z.; writing--review and editing, J.S.; visualization, R.Z.; supervision, J.M.; project administration, J.M.; funding acquisition, J.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (42227901 and 52371358), the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (311020011), the Key-Area Research and Development Program of Guang-dong Province (2020B1111010004), and the Special Project for Marine Economy Development of Guangdong Province (GDNRC [2022] 31).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the privacy.  
The authors declare no conflict of interest.
# An Improved GreenOFDM Scheme for PAPR Reduction
## 3 From SLM-OFDM to GreenOFDM
In Proceedings of the 2019 IEEE 2 nd International Conference on Electronics Technology (ICET), Chengdu, China, 10-13 May 2019; pp. 28-33. * [PERSON] and [PERSON] (2019) [PERSON]; [PERSON] Dual symbol optimization-based partial transmit sequence technique for PAPR reduction in WOLA-OFDM waveform. _Int. J. Commun. Syst._**2019**, _32_, e4081. [CrossRef]
* [PERSON] and [PERSON] (2020) [PERSON]; [PERSON] [PERSON] Cumulative symbol optimization-based partial transmit sequence technique for PAPR reduction in low complexity GFDM system. _Trans. Emerg. Telecommun. Technol._**2020**, _31_, e3801. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON] Computational complexity reduction of GreenOFDM. _Ann. Telecommun._**2020**. [CrossRef]
* [PERSON] et al. (2007) [PERSON]; [PERSON]; [PERSON]; [PERSON] A Modified SLM Scheme With Low Complexity for PAPR Reduction of OFDM Systems. _IEEE Trans. Broadcast._**2007**, _53_, 804-808. [CrossRef]
* [PERSON] [PERSON] (2001) [PERSON]; [PERSON] On the Distribution of the Peak-to-Average Power Ratio in OFDM Signals. _IEEE Trans. Commun._**2001**, _49_, 282-289. [CrossRef]
* [PERSON] (2019) [PERSON]. GreenOFDM a New Method for OFDM PAPR Reduction. Ph.D. Thesis, Communaute Universite Grenoble Alpes, Grenoble, France, October 2019. **Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **

(c) 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)]([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))). **
# Integrating Mobile Devices and Wearable Technology for Optimal Sleep Conditions
## 2 Materials and Methods
### HRV Analysis  
When delving into the analysis of HRV concerning sympathetic and parasympathetic nervous activity within the time domain, one crucial metric is the standard deviation of \(NN\) intervals (\(SDNN\)). NN intervals correspond to the intervals between successive \(R\)-waves in the ECG signal. The calculation of \(SDNN\) can be outlined as follows:  
\[SDNN=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}\Bigl{(}RR_{i}-\overline{RR_{i}}^{2} \Bigr{)}} \tag{2}\]  
In this context, where \(RR_{i}\) represents the \(RR\) period in milliseconds, \(N\) denotes the total count of \(RR\) periods, the calculation for \(SDNN\) can be presented as:  
\[RMSSD=\sqrt{\frac{1}{N}\sum_{i=1}^{N}(RR_{i+1}-RR_{i})^{2}} \tag{3}\]  
\[pNN50=\frac{nn50}{TotalNN}*100\% \tag{4}\]  
The frequency domain analysis of HRV involves transforming each change in heartbeat interval into the frequency domain, then enabling the evaluation of sympathetic and parasympathetic nerve activities. This information is then expressed as the power frequency density, allowing for the exploration of physiological states across different time intervals. The calculation procedure is illustrated in Formula (1). By employing specific frequency ranges, the indices for sympathetic nerve activity (0.04-0.15 Hz, considered the low-frequency band) that signify wakefulness and heightened activity; parasympathetic nerve activity (0.15-0.4 Hz, categorized as the high-frequency band), which is representative of relaxation; and the sympathetic-to-parasympathetic activity balance index (LF-to-HF power ratio, denoted as LF/HF), which indicates the equilibrium between hyperactivity and relaxation, are derived.
## 4 Experimental Results
### Implementation Details  
The target detection network training and verification dataset came from the video shot in the substation scene, and the video was extracted frame by frame to obtain 7756 pictures with a resolution of 1920 \(\times\) 1080 dpi, including four kinds of open and closed bouncy locks, for a total of eight datasets, as shown in Figure 6. According to the ratio of 8:1:1, the dataset was divided into a training set, verification set, and test set, used for the training of the algorithm network and the testing of the algorithm's performance.  
In the experiment, the Make Sense annotation tool was used to select the power cabinet bounce lock with a rectangular box and generate the label file in xml format, as shown in Figure 7.  
The model training and testing environment was the Ubuntu 20.04 operating system, and the calculation program adopted Python 3.8 language combined with Python 1.8.1 deep learning framework. During the training process, a NVIDIA GeForce RTX3070 graphics card was used to train the two networks. The super parameter setting in the training stage  
Figure 6: The opening and closing states of four kinds of substation power cabinet spring locks. (**a**) THA0; (**b**) THA1; (**c**) THB0; (**d**) THB1; (**e**) THC0; (**f**) THC1; (**g**) THD0; (**h**) THD1.  
Figure 7: Label fabrication.  
was as follows: the initial learning rate was 0.0031, the attenuation coefficient was 0.12, the momentum was 0.833, the batch size was 16, and the training number was 300.