esa-sceva/satcom-chunk-collection huggingface dataset:
# Software Integration of Power System Measurement Devices with AI Capabilities
## 2 Monitoring Devices
### Phasor Measurement Unit (PMU) and \(\mu\)PMU  
A PMU is a device developed with the purpose of measuring voltage and current phasors, frequency, and the Rate Of Change Of Frequency (ROCOF) synchronized with an absolute time reference through a Global Positioning System (GPS). Precisely, one of the main features of PMUs is their high sampling rate and time accuracy. For a grid frequency of 50 Hz, sample frequencies of 10, 25, or 50 Frames per Second (FPS) are possible. This implies measuring or registering data every 100  ms, 50  ms, or 20  ms, respectively. Time synchronization ensures precise alignment of the data, allowing for an effective comparison of measurements taken at different points on the grid. This opens up the option for more efficient management, enhancing problem detection and increasing the ability to address issues. This is why PMUs are widely used in both the transmission and distribution stages of the power system [11; 12]. Their accuracy is also significant, reaching \(\pm\)1% in magnitude and \(\pm\)1\({}^{\circ}\) in phase accuracy.  
In recent years, there has been a large growth in the use of DERs. These systems introduce considerable complexities and effects in the grid that require thorough study. While PMUs have been effective in monitoring, this increase in complexity calls for a technology that can detect irregularities with even greater accuracy and higher resolution than conventional PMUs.  
That is why the so-called \(\upmu\)PMUs [33] were developed. These devices reach rates of 100 phasors/s for a signal of 50 Hz and 120 phasors/s for 60 Hz, meaning they return instantaneous grid values every half cycle. They also exceed the measurement accuracy given by PMUs, achieving angular accuracy values of up to \(\pm\)0.010\({}^{\circ}\) and amplitude accuracy values of up to \(\pm\)0.05%. They are otherwise similar equipment.
# No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network
## 5 Conclusions  
Inspired by the IGM, this paper proposes a novel NR-IQA method called MT-IRN. MT-IRN is comprised of a multitask image restoration sub-network and a score prediction sub-network. First, a multitask image restoration sub-network is employed to generate a higher-quality pseudo-reference image and extract quality restoration features. Second, a score prediction sub-network is used to extract the high-level features and difference features, and then fuse the multi-scale features with the quality restoration features in the image restoration sub-network. Finally, the high-level features, the fused multi-scale  
Figure 7: Comparison of pseudo-reference image quality. From top to bottom, each row represents the reference image, distorted image, single-task restoration image, and multitask restoration image, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Time (s)** & **Parameters (M)** \\ \hline IQA-CNN & 0.06 & 0.17 \\ MEON & 0.18 & 4.28 \\ DB-CNN & 0.25 & 9.19 \\ MetaIQA & 0.56 & 33.18 \\ Hall-IQA & 0.45 & 23.67 \\ VCRNet & 0.28 & 16.66 \\ MT-IRN & 0.31 & 35.30 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of computation time and the number of parameters.  
features, and the difference features are utilized for quality score prediction. Experimental results on commonly used datasets demonstrate that MT-IRN has achieved performance comparable to the state-of-the-art methods.  
MT-IRN has achieved excellent performance, but there is still room for further improvement. For instance, MT-IRN is pre-trained on the Waterloo Exploration dataset, which only contains four types of distortions. This limitation can result in decreased performance when the model encounters other distortions. Additionally, the use of ResNet-50 as the backbone network enhances performance but also increases computational complexity. Therefore, future research may focus on constructing larger-scale datasets that encompass a broader range of distortion types to support model pre-training. Moreover, considering the utilization of other, more efficient models to reduce computational complexity while achieving similar performance is worth exploring. Furthermore, exploring the simultaneous utilization of both synthetic and authentic distortion datasets for pre-training is a meaningful research direction.  
Conceptualization, F.C.; methodology, F.C.; software, F.C.; validation, F.C.; formal analysis, F.C.; investigation, F.C.; resources, F.C.; data curation, F.C.; writing--original draft preparation, F.C.; writing--review and editing, H.F., H.Y. and Y.C.; visualization, F.C.; supervision, H.F., H.Y. and Y.C.; project administration, Y.C.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Stabilization Support Plan for Shenzhen Higher Education Institutions, grant number 20200812165210001.  
Not applicable.  
The authors declare no conflict of interest.
# Low Complexity Equalization of Orthogonal Chirp Division Multiplexing in Doubly-Selective Channels
## 2 System Model
### Transmission Model over Doubly Selective Channels  
We consider doubly selective channels in this paper. In order to combat the intersymbol interference (ISI), a cyclic prefix will be added at the beginning of each transmission symbol, the length of the cyclic prefix is chosen so large that exceeds the channel's maximum delay. After removing the cyclic prefix at the receiver, the received signal can be written as  
\[r[n]=\sum_{l=0}^{L-1}h_{l}[n]s[n-l]+w[n],\ \ \ n=0,1,\ldots,N-1, \tag{15}\]  
where \(h_{l}[n]\) refers to the complex tap gain of the \(l\)th path at time constant \(n\), \(w[n]\) is the complex additive white gaussian noise (AWGN) with variance \(\sigma_{w}^{2}\), \(L\) is the channel length and the channel's maximum delay equals to \((L-1)T\). Note that \(s[-l]=s[N-l]\) due to the existence of cyclic prefix. We assume \(L_{cp}=L\) for simple notation in this paper. Equivalently, the transmit-receive relationship (15) can be written as  
\[\mathbf{r}=\mathbf{H}\mathbf{s}+\mathbf{w}, \tag{16}\]  
where \(\mathbf{r}=(r[0],r[1],\cdots,r[N-1])^{T}\) is the time-domain receive signal, \(\mathbf{s}=(s[0],s[1],\cdots,s[N-1])^{T}\) is the time-domain transmit signal, \(\mathbf{w}=(w[0],w[1],\cdots,w[N-1])^{T}\) is the additive white gaussian noise vector in time domain, and \(\mathbf{H}\) is the time-domain channel matrix.
# NB-IoT for D2D-Enhanced Content Uploading with Social Trustworthiness in 5G Systems
## 6 Performance Evaluation
Same motivations yield for the differences observed in the uploading time gain, which, as shown in Figure 2a, results to be higher when we have a prevalence of type B malicious nodes. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Cell radius & 500 m \\ Maximum D2D link coverage & 100 m \\ TTI & 1 ms \\ TDD configuration (D2D) & 0 \\ Carrier frequency & 2.1 GHz \\ Tx Cellular power (NB-IoT) & 23 dBm \\ Tx D2D power & \(-19\) dBm \\ COI-MCS mapping for D2D links & “refer to [32]” \\ Noise power & \(-174\) dBm/Hz \\ Cellular link model & Rayleigh fading channel \\ D2D link model & Rician fading channel \\ NB-IoT tones & 288 (i.e., 6 RBs) \\ Content size & 50 MB \\ Weighting factors \(\alpha=\beta\) & 0.5 \\ Simulation time & 100 s \\ \# of Runs & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Main simulation parameters. NB-IoT: narrowband Internet of things; CQI: channel quality indicator; MCS: modulation and coding scheme; TTI: Transmission time interval; TDD: Time division duplex. Figure 2: Impact of type A and type B malicious nodes (feasility threshold, FT = 0.5). (a) Uploading time gain; (b) Average energy gain. toward the eNodeB. In the extreme case, when the feasibility threshold is set to 1 the amount of data loss is about 19 MB compared to 120 MB for the basic solution. In conclusion, the proposed trust-based approach outperforms the basic solutions. Moreover, the highest benefits are obtained by tuning the feasibility threshold to the estimated number of malicious nodes in the system and to the desired performance parameter. In fact, even if a high feasibility threshold increases the chances of correctly forwarding the data towards to eNodeB with lower uploading times and data losses, this can result in a higher energy consumption.
## 3 Review Results
### Extended Value-Based Adoption Model (VAM) and ChatGPT Adoption in Higher Education
**Extrinsic Motivation:**  
Contrary to the expectations, extrinsic motivation, such as external rewards or pressures, did not significantly impact the adoption of ChatGPT in some studies. This finding suggests that intrinsic factors, like perceived usefulness and enjoyment, are more critical in driving adoption. Users are more likely to adopt ChatGPT when they find it genuinely beneficial and enjoyable rather than being motivated by external incentives such as rewards, grades, or mandates. This insight highlights the importance of enhancing ChatGPT's intrinsic value. Developers and educators should prioritize making ChatGPT more useful, efficient, and enjoyable for users. By improving its functionality, ensuring it meets users' needs, and creating an engaging and enjoyable user experience, ChatGPT can become a valuable tool that users are intrinsically motivated to adopt and continue using. This approach aligns with the idea that users are more committed and satisfied when addressing their internal motivations. Therefore, emphasizing intrinsic motivators like perceived usefulness and enjoyment can lead to higher adoption rates and sustained engagement with ChatGPT, ensuring its effective integration into educational and professional settings [50]. The Extended Value-Based Adoption Model (VAM) provides a comprehensive framework for understanding the adoption of ChatGPT in higher education, emphasizing the perceived value derived from using the technology. Confirmed factors such as perceived usefulness, perceived enjoyment, perceived cost, perceived credibility, social influence, self-efficacy, and attitude play significant roles in influencing adoption. However, uncontrolled factors like extrinsic motivation, perceived risk, technology readiness, facilitating conditions, and cost considerations highlight the complexity and variability regarding the technology adoption processes. By incorporating the extended VAM, researchers can capture the multifaceted nature of the value perceptions that facilitate or hinder the adoption of ChatGPT in educational settings. Future research should continue exploring these factors and validating their relevance in diverse educational contexts, ensuring that institutions can effectively leverage ChatGPT to enhance learning outcomes and support educational innovation.
# Advances in Valveless Piezoelectric Pumps
## 2 Configuration of the Valveless Piezoelectric Pump
### Valveless Piezoelectric Pump with Built-in Structures  
Figure 5 shows the working principle of valveless piezoelectric pumps with built-in structures. The working principle is as follows: because \(\alpha\)1 is not equal to \(\alpha\)2, fluid flows into or out of the chamber from the left and right conduits of the pump is different. Therefore, net flow will be generated.  
In 2011, [PERSON] et al. [62] proposed a valveless piezoelectric pump with rotatable unsymmetrical slopes, which can realize the fusion of different fluids. This structure can control the proportion of different fluids flowing into the pump chamber by rotating the rotatable unsymmetrical slopes. [PERSON] et al. [27] proposed a valveless piezoelectric pump with hemisphere-segment bluff-body by the principle of bluff body flow, and the flowrate of 16.4 mL/min was obtained under the voltage and frequency of 110 V and 6 Hz. [PERSON] et al. [63] designed a valveless piezoelectric pump with rotatable unsymmetrical slopes by the principle of Carmen Vortex Street, which could achieve the flowrate of 220.6 mL/min and pressure of 670 Pa. In order to improve the flow rate of the pump, [PERSON] et al. [64] optimized the structure of hemisphere-segment bluff-body; he fabricated a valveless piezoelectric pump with a crescent-shaped structure by 3D printing, and the maximum flow rate of 286 mL/min was achieved when the voltage was 220 V at 82 Hz. Table 4 shows the summary of properties for the valveless piezoelectric pump with built-in structures.  
In summary, the flow resistance difference structure of the valveless piezoelectric pump with built-in structures is located in the pump chamber, and the regular flow tubes are used in the inlet and outlet, which improves the utilization of the pump chamber. This piezoelectric pump has higher integration, and it is not affected by the complex flow channel, which is easier to use in practice. Compared with the valveless piezoelectric pump with external flow tubes, the pump flow rate of this piezoelectric pump is significantly improved.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**First Author** & **Type of** & **Structure** & \(\mathbf{U_{p}}\) & \(\mathbf{f_{p}}\) & \(\mathbf{P_{max}}\) & \(\mathbf{Q_{max}}\) \\
**and Year** & **Valveless** & **Material** & **(V\({}_{\text{P-P}}\))** & **(Hz)** & **(kPa)** & **(mL/min)** \\ \hline
2011 [62] Zhang & Unsymmetrical slopes & N/A & 220 & 50 & N/A & 32.32 \\
2014 [27] Ji & Hemisphere-segment & PMMA & 110/160 & 6 & 0.262 & 30 \\
2019 [63] He & Dome composite structures & N/A & 190 & 130/45 & 0.67 & 220.6 \\
2019 [64] Zhao & Crescent-shaped structure & N/A & 220 & 82 & N/A & 286 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of properties for the valveless piezoelectric pump with built-in structures.  
Figure 5: Working principle of a valveless piezoelectric pump with built-in structures.
## 4 Discussion
Some existing satellite-driven models could make up for the insufficiency of GPP models based on physiologically driven spectral indices, such as MTCI [100; 101]. Additionally, the Fluorescence Explorer satellite, the ESA's eighth Earth Explorer, is expected to be launched by 2022, which can offer important auxiliary data for using Sentinel-3 products to estimate GPP [33; 102].
## 2 Materials and Methods
### ELISA tPA and uPA Activity  
The concentrations of uPA and tPA were determined by Enzyme Linked Immune Sorbent Assay (ELISA) commercial kits (Abcam, Cambridge, MA, USA; tPA [ab198510] and uPA [ab108917]) according to the manufacturer's instructions. RT4 cells were either left untreated (controls) or treated with increasing concentrations of either doxycycline or minocycline (10, 50, 100, 500 and 1000 ng/mL) and tPA and uPA levels assessed 24 h later. Each condition was tested using five different batches of cells (n = 5/group).
## 5 Numerical Results
### Random \(L_{k}\) and \(P_{a}=1\)  
In this subsection, we evaluate the scenario when the number of packets \(L_{k}\) sent by each device is random and follows a discrete uniform distribution as \(L_{k}\sim\mathcal{U}[1,10]\), while we still maintain \(P_{a}=1\) such that \(K=K_{a}\). Figures 8 and 9 show, respectively, the performance  
Figure 6: Average latency \(\times L\), for \(K=600\), and \(M=100\).  
Figure 7: Average network throughput \(\times L\), for \(K=600\), and \(M=100\).  
results of latency and network throughput. One can note that the superiority of the results achieved by the proposed methods over the results achieved by the iQmMTC and baseline methods are preserved. Indeed, the curves in Figures 8 and 9 practically present the same shapes as the ones in Figures 2 and 3, respectively, but with a little performance degradation due to the reduction in the average number of transmitted packets, which limits the learning capability of the QL algorithm in seeking less congested pilots.
# Edge-Guided Dual-Stream U-Net for Secure Image Steganography
## 4 Experiments
### Robustness Analysis  
In the robustness analysis, we comprehensively evaluated the anti-interference capability of our method for hiding 1-bit information on the Div2k dataset. As shown in Table 8, our method demonstrates excellent performance under various common noise conditions. The results show that under three interference conditions--JPEG compression (quality factor of 80), additive Gaussian noise, and multiplicative Gaussian noise--our method maintains a low decoding error rate (\(3\times 10^{-3}\), \(7\times 10^{-4}\), and \(2\times 10^{-4}\), respectively). Additionally, the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) show good values across all three conditions, indicating that our method effectively preserves image quality.  
However, it is worth noting that under JPEG compression, the SSIM and PSNR values are relatively lower (0.883 and 33.57, respectively), suggesting that our method has slightly inferior image quality preservation under JPEG compression. This may be due to the significant loss of high-frequency information caused by JPEG compression, which affects both information hiding and image quality.  
Overall, the proposed method in this study exhibits good anti-interference capabilities for practical applications, efficiently recovering hidden information under various noise conditions while maintaining satisfactory image quality. Future research can further optimize the algorithm to enhance its robustness under conditions such as JPEG compression.
# 1-D Convolutional Neural Network-Based Models for Cooperative Spectrum Sensing
## 3 Overview of Spectrum Sensing and Adopted Deep Learning Algorithms
### Spectrum Sensing Principle  
Spectrum sensing is the task of obtaining awareness about the spectrum usage and existence of primary users in a geographical area. This awareness can be obtained by using geolocation and datasets, by using beacons or by local SS at cognitive radios [6]. Thereafter, the system detects the unoccupied spectrum in a timely manner according to the spectrum utilization characteristics. The reuse of the non-renewable spectrum is achieved by allowing cognitive radios to opportunistically access underutilized frequency bands. In the context of a centralized cooperative CRN comprising multiple multi-antenna SUs, we can formulate a binary hypothesis testing problem to determine the presence or absence of a primary user. We consider two hypotheses: \(H_{1}\) represents the active state of the PU, indicating that its transmission is ongoing, while \(H_{0}\) represents the inactive state of the PU, indicating its non-transmission or idle state. The received signal \(x_{s}^{m}(n)\) at the \(m\)-th antenna of the \(s\)-th SU can be formulated as follows:  
\[\{x_{s}^{m}(n)\}_{n=1}^{N}=\begin{cases}\{w_{s}^{m}(n)\}_{n=1}^{N}&H_{0}\\ \{h_{s}^{m}(n)s(n)+w_{s}^{m}(n)\}_{n=1}^{N}&H_{1}\end{cases} \tag{1}\]  
where \(s(n)\) denotes the PU signal and \(w_{s}^{m}\) denotes the noise signal received at the \(m\)-th antenna of the \(s\)-th SU. \(h_{s}^{m}\) represents the channel gain between the PU and the \(m\)-th antenna of the \(s\)-th SU. When the sensing interval is shorter than the channel coherence time, the channel gain remains constant and stable within this interval. However, across multiple sensing intervals, the channel gain undergoes fading, which refers to variations in the strength or quality of the channel. This fading is modeled using a statistical distribution that characterizes the variations in channel gain over time. To evaluate SS techniques, two criteria are considered: the detection probability (\(P_{d}\)) and the probability of false alarms (\(P_{fa}\)). The detection algorithm's performance is usually indicated by these two metrics:  
\[\begin{split} P_{fa}&=Prob\{T>\lambda\ |\ H_{0}\}\\ P_{d}&=Prob\{T>\lambda\ |\ H_{1}\}\end{split} \tag{2}\]  
The statistical test of the detector, denoted as \(T\), is compared to a threshold value \(\lambda\) to make a decision. \(P_{fa}\) represents the probability that the test falsely declares that the band under consideration is occupied. \(P_{d}\) denotes the probability that the PU is actually transmitting on the band under consideration when the result of the test is positive. The performance of a detector can be evaluated by plotting its receiver operational characteristic (ROC) curve. This is a graphical depiction of detection probability \(P_{d}\) against false alarm probability \(P_{fa}\) for different threshold levels.
## 3 Results
### Two-Way Analysis of Variance (ANOVA) Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Flexibility** & **TES** & **Duncan** & **LES** & **Duncan** & **HMS** & **Duncan** & **LSA (\({}^{\circ}\))** & **Duncan** \\
**Grouping** & **(\%MVC)** & **Group** & **(\%MVC)** & **Group** & **(\%MVC)** & **Group** & **(\%MVC)** & **Group** & **LSA (\({}^{\circ}\))** & **Group** \\ \hline \hline \multirow{3}{*}{TTT} & H & 8.0 (3.4) & A & 7.3 (4.9) & A & 6.4 (3.1) & B & 22.9 (16.5) & A \\  & M & 7.4 (2.9) & AB & 6.5 (3.9) & AB & 7.6 (3.4) & A & 19.0 (17.3) & B \\  & L & 6.6 (3.5) & B & 6.1 (3.5) & B & 7.6 (4.4) & A & 18.4 (12.8) & B \\ \hline \multirow{3}{*}{SRT} & H & 8.0 (3.2) & A & 7.6 (4.8) & A & 6.7 (3.3) & B & 22.7 (16.1) & A \\  & M & 7.3 (3.3) & AB & 6.0 (4.0) & B & 7.0 (3.3) & B & 18.3 (16.2) & B \\  & L & 6.7 (3.4) & A & 6.2 (3.4) & B & 7.9 (4.5) & A & 19.3 (14.4) & B \\ \hline \multirow{3}{*}{MST} & H & 7.6 (3.5) & A & 7.6 (4.8) & A & 7.3 (3.5) & AB & 20.4 (16.7) & A \\  & M & 7.1 (2.7) & A & 6.0 (3.8) & B & 6.5 (3.5) & B & 21.2 (15.4) & A \\ \cline{1-1}  & L & 7.5 (3.6) & A & 6.5 (4.0) & B & 7.8 (3.9) & A & 19.6 (14.7) & A \\ \hline \hline \end{tabular}  
* Notes: TTT—to-touch test, SRT—sit-and-reach test, MST—modified Schooler’s test, TES—thoracic erector spinae, LES—lumbar erector spinae, HMS—hamstring, LSA—lumbosacral angle, H—high, M—middle, L—low, %MVC—percentage of MVC data; Data are presented in mean (standard deviation).  
\end{table} 
Table 5: Muscle activity and LSA of Duncan groups with varying trunk angles. Figure 4: Alterations of the TES, LES and HMS muscles under varying trunk flexions (%MVC, percentage of MVC data).
# Phosphorus Mobilizing Enzymes of _Alnus_-Associated Etomycorrhizal Fungi in an Alaskan
## 2 Materials and Methods
### Molecular Analyses  
To match the enzyme activity with a specific fungal taxon, DNA was extracted from individual root tips using the DNeasy Plant Mini Kit (QIAGEN Inc., Valencia, CA, USA). Prior to extraction, root tips were hydrated in lysis buffer and disrupted using a Kontes pellet pestle attached to a drill. Samples were incubated at 65 \({}^{\circ}\)C for 10 min and then for 16 h at 20 \({}^{\circ}\)C to improve extraction success. The remainder of the extraction followed the standard protocol for the DNeasy Kit. The EMF species on individual roots tips were identified using Sanger sequencing of the fungal nuclear ribosomal internal transcribed spacer (ITS). This region was amplified using the fungal specific primers ITS1-F and ITS4 [36]. After amplification, PCR products were sent to Functional Biosciences (Madison, WI) for sequencing in both directions (5\({}^{\prime}\) and 3\({}^{\prime}\)) with the same primers. We then assembled sequences, removed ambiguous and poor-quality sequences, and grouped sequences into phylobins that were then assigned a fungal identity [37] (Appendix B). Representative sequences from each phylobin and singletons were used to search the GenBank and UNITE databases. Phylobins were identified to species level at higher than 97% similarity; to the genus level at between 93% and 97%, and to the family level at between 83% and 93% [38]. After initial analysis of the fungal community revealed a wide array of fungi not characteristically detected on _Alnus_, we used restriction length fragment polymorphism (RFLP) to identify the plant host of all samples. This analysis revealed that across all stands, root tips belonged to four plant species: _Alnus tenuifolia_ (\(n=151\)), _Populus balsamifera_ (\(n=30\)), _Picea glauc_ (\(n=70\)), and _Salix spp_. (\(n=19\)), and a pool of tips for which host was equivocal (\(n=138\)). The latter pool of unidentified host tips is not included in the analyses here.