esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface-Wave Extraction Based on Morphological Diversity of Seismic Events
## 2 Conventional Method: Extracting Surface Waves in the f-v Domain  
High-resolution LRT is used to image surface-wave dispersive energy [10]. Using this, surface waves and reflections on Z components are present clearly in different locations of the f-v domain when their frequencies and velocities are significantly different. [PERSON] et al. [19] extract surface waves from Z components by a 2D window of the f-v domain.  
The frequency-domain inverse LRT in the matrix-vector form is [18]:  
\[\mathbf{d}(f)=\mathbf{L}(f)\mathbf{m}(f) \tag{1}\]where \(\mathbf{d}(f)\) is a vector of size \(nx\times 1\) representing the Fourier coefficients of the seismic data at the given frequency \(f\), while \(\mathbf{m}(f)\) is a vector of size \(np\times 1\) representing the Fourier coefficients of the Radon panel at the given frequency \(f\). In Equation (1), \(\mathbf{L}(f)\) is a complex matrix of size \(nx\times np\):  
\[\mathbf{L}(f)=\left[\begin{array}{cccc}e^{\frac{-2\pi/\tau_{1}}{\tau_{1}}}&e ^{\frac{-2\pi/\tau_{1}}{\tau_{2}}}&\cdots&e^{\frac{-2\pi/\tau_{1}}{\tau_{3p}}} \\ e^{\frac{-2\pi/\tau_{2}}{\tau_{1}}}&e^{\frac{-2\pi/\tau_{2}}{\tau_{2}}}&\cdots &e^{\frac{-2\pi/\tau_{2}}{\tau_{3p}}}\\ \vdots&\vdots&\ddots&\vdots\\ e^{\frac{-2\pi/\tau_{3p}}{\tau_{1}}}&e^{\frac{-2\pi/\tau_{3p}}{\tau_{2}}}& \cdots&e^{\frac{-2\pi/\tau_{3p}}{\tau_{3p}}}\end{array}\right] \tag{2}\]  
where \(v_{i}(i=1,2,\ldots,np)\) is the apparent velocity and \(x_{j}(j=1,2,\ldots,nx)\) is the offset.  
The frequency-domain high-resolution forward LRT [20; 21] is inverted with a sparse constraint of a priori probability, known as:  
\[\left(\lambda\mathbf{I}+\mathbf{W}_{m}^{-H}\mathbf{L}^{H}\mathbf{W}_{d}^{H} \mathbf{W}_{d}\mathbf{L}\mathbf{W}_{m}^{-1}\right)\widetilde{\mathbf{m}}= \mathbf{W}_{m}^{-H}\mathbf{L}^{H}\mathbf{W}_{d}^{H}\mathbf{W}_{d}\mathbf{d} \tag{3}\]  
where \(\widetilde{\mathbf{m}}=\mathbf{W}_{m}\mathbf{m}\). \(\mathbf{W}_{d}\) is a matrix of data weights, specifically, a diagonal matrix showing the standard deviation, \(\text{diag}(\mathbf{W}_{d})_{i}=\left|\left(\mathbf{d}-\mathbf{L}\mathbf{m} \right)_{i}\right|^{-1/2}\), while \(\mathbf{W}_{m}\) is a diagonal matrix of Radon coefficients indicating how sparse the coefficients are, \(\text{diag}(\mathbf{W}_{m})_{i}=\left|m_{i}\right|^{-1/2}\). \(\mathbf{I}\) is the identity matrix, and the scalar \(\lambda\) is the tradeoff parameter that weights the relative levels of importance of the misfit and the sparsity [22].  
However, it is difficult to extract surface waves correctly from X-component seismic data, because the frequencies and velocities of higher-mode surface waves and PS-waves are close and both of them are generally evident on X components. Disturbed by PS-waves, the dispersive energy is not true for surface waves. So, extracting surface waves in the f-v domain is not a perfect method. We propose a method of surface-wave extraction based on MCA to avoid the overlaps between surface waves and reflections in the f-v domain. High-resolution LRT is one of two transforms and is used to represent surface waves.
## 3 Results and Discussion
### Ferric Reducing Antioxidant Power (FRAP)  
Since the antioxidant activity of a substance is correlated directly to its reducing capacity, the FRAP assay can be applied to study its antioxidant capacity. Thus, the antioxidant activity of GPPP is evaluated via the ability to reduce the Fe\({}^{3+}\)-TPTZ complex to the Fe\({}^{2+}\)-TPTZ complex [48]. The antioxidant capacities of GPPP and ascorbic acid are shown in Figure 4C. In the range of 0.1-0.9 mg/mL for UAE, the FRAP values were increased from 0.25 to 0.34 mmol/L. The antioxidant capacity of GPPP correlated well with increasing concentration. However, compared with ascorbic acid, the antioxidant capacity of GPPP was weak.
## 3 Results
### Sensitivity to the Sampling Frequency Variation
#### 3.2.1 HNR  
Since the values of the algorithm for the HNR vary somewhat with the sampling frequency, a comparative analysis was made between the three sampling frequencies for the 90 control and patient acoustic records. In Figure 7, it is possible to observe the scatter plot for 25 and 12.5 kHz of the 90 HNR measurements over 10 control subjects (left-hand side), and 90 measurements for 10 pathological subjects (right-hand side). Each subject has nine HNR measurements for three vowels, and each vowel has three tones.  
From Figure 7, it is possible to observe some variations in the measurement of HNR. The variations occur mainly in the measurement with Fs of 12.5 kHz in both control and pathological subjects.  
Since this variation exists as a function of the sampling frequency, a second analysis was performed to ensure that the algorithm could be used to extract the HNR for the distinction between pathological and normophonic subjects. Therefore, the average of all 20 subjects (90 sounds for control and 90 for patients) as a function of the sampling frequency is presented in Table 4.  
Table 4 shows that even though there is a variation of HNR with the sampling frequency, there still is a difference in the average HNR between the pathological and control groups. This critical difference is higher at 50 kHz.  
It is concluded that this algorithm can be used cautiously to determine HNR parameters with lower sampling frequency. When the sampling frequency is lowered, the Nyquist frequency of the signal is reduced, losing noise components, which can influence this measure. The minor difference between pathological and control subjects is 2.26 dB,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sampling Frequency (kHz)** & **Patients (dB)** & **Control (dB)** \\ \hline
50 & 21.91 (\(\sigma\) = 2.49) & 25.60 (\(\sigma\) = 2.63) \\
25 & 21.41 (\(\sigma\) = 2.58) & 24.55 (\(\sigma\) = 2.60) \\
12.5 & 20.72 (\(\sigma\) = 2.49) & 22.98 (\(\sigma\) = 2.61) \\ \hline \hline \end{tabular}
\end{table}
Table 4: HNR as a function of the Sampling Frequency for the control and patient groups.  
Figure 7: Comparative analysis of the HNR with 25 and 12.5 kHz over 50 kHz sampling frequency.  
which is indicative of the relevance of the HNR to distinguishing normophonic from pathological speech.
## 3 Results
### Long-Term Communication System Performance Evaluation  
To comprehensively assess the reliability and stability of the integrated communication system, an extended field study was conducted at the Gongliao Aquatic Center from December 24 to January 10. This long-term deployment provided valuable insights into the system's performance under varying environmental conditions and operational scenarios. During the 18-day testing period, the system collected and analyzed a total of 324,800 data transmission events.  
1. Real-time Performance: About 63% of sensor data and control commands reached the cloud server within 1 s, demonstrating the system's capability for near-real-time monitoring and control operations.
2. Overall System Reliability: 95.60% of all data transmissions were successfully completed within 2 s, indicating excellent overall system reliability even under varying 4G network conditions in the coastal aquaculture environment.
3. Sustained Operational Efficiency: The system achieved an average transmission rate of 15 data points per minute, with a success rate of 83.54%. This confirmed the system's suitability for continuous long-term monitoring applications.  
These results demonstrate that the integrated 4G mobile communication system provides stable data transmission capabilities essential for smart aquaculture monitoring. The high success rate indicates that the system can reliably maintain communication links between the underwater ROV, buoy relay station, and cloud server infrastructure. The performance metrics obtained from this extensive field study provide essential validation data for the practical deployment of the multi-platform remote control system, confirming its readiness for operational use in commercial aquaculture settings. To address potential scenarios of telemetry communication loss and ensure operational continuity, the system incorporates robust data protection and recovery mechanisms. The underwater ROV and buoy relay station are equipped with local buffering capabilities for both sensor data and video information. This ensures that critical monitoring data is preserved during communication interruptions and automatically transmitted once connectivity is restored.
# A High-Sensitivity Cesium Atomic Magnetometer Based on A Cesium Spectral Lamp
###### Abstract  
Based on a low-noise cesium spectral lamp, a high-sensitivity self-oscillating cesium atomic magnetometer with a wide operating temperature range has been developed, solving problems with existing sensors such as a limited operating temperature range and difficult startup at low temperatures. The temperature feedback mechanism is used to make adjustments to any fluctuations in the cesium lamp's excitation source in real time, improving the magnetometer's stability and operating temperature range. Herein, the design and optimization of the cesium atomic magnetometer are presented, and a prototype of the magnetometer is described. The quantum limit sensitivity of the cesium atomic magnetometer is estimated by evaluating the intrinsic relaxation rate in the geomagnetic field. A test demonstrates that the cesium atomic magnetometer's sensitivity in the geomagnetic background is around 140 \(\mathrm{fT}/\sqrt{\mathrm{Hz}}\) at 1 Hz at room temperature, and the operating temperature range is from \(-50\)\({}^{\circ}\)C to \(70\)\({}^{\circ}\)C, surpassing most of the commercial products of its kind in terms of sensitivity and operating temperature range.  
atomic magnetometer; magnetic resonance; optically pumped +
Footnote †: journal: Physics Letters A
## 2 Materials and Methods
### Extraction and Analysis of Volatile Components  
The volatile compounds of each processing sample were extracted using headspace-solid phase micro extraction (HS-SPME) adopted from [PERSON] et al. (2014) [6] with some modifications. A 50/30 \(\upmu\)m fiber coated with DVB/CAR/PDMS (Supelco, St. Louis, MO, USA) was used as the hand-held SPME fiber, and was kept in the GC-MS injection port for 30 min at 250 \({}^{\circ}\)C prior to use to prevent the contamination. For the detailed extraction procedures please refer to the supplementary.  
An Agilent gas chromatograph (GC 7890B, Agilent, Santa Clara, CA, USA) equipped with a 5977A mass spectrometer detector system was employed for volatile analysis according to the method from [PERSON] et al. (2014) [6] and [PERSON] et al. (2018) [20] with some modifications. The aroma extracts were injected to DB-wax capillary column (60 m \(\times\) 0.32 mm i.d., film thickness 0.25 \(\upmu\)m, Agilent J&W, Santa Clara, CA, USA) at splitless mode, the flow rate of the carrier gas (99.999% helium) was 1 mL/min, the temperature of GC injector was maintained at 250 \({}^{\circ}\)C, the oven temperature program was started at 40 \({}^{\circ}\)C, held for 1 min, and increased at rate of 3 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C, ending with an isothermal period at 200 \({}^{\circ}\)C for 27 min. For MS detection, the ion source and interface were both programmed at 230 \({}^{\circ}\)C, electron-impact ionization at 70 eV, and acquisition range from 29-450 amu at scan rate of 1.5 scan/s. The volatile compounds were identified by comparison of the linear retention indices (RIS), the volatiles mass spectra fragmentation patterns with the mass data of the corresponding compounds obtained from the National Institute of Standards and Technology MS Library NIST.14. Linear RIs of the volatile components were determined relative to the retention times of a series of n-alkanes (C8-C40). Volatile compounds with mass spectral match factors over 70 and RIs matched the values in NIST.14 library were considered to be identified.  
MSD ChemStation is the program used for integration. The automatic ChemStation parameters were set as follows: initial area reject 0, initial peak width 0.020, shoulder detection off, initial threshold 16.5. To qualitatively estimate the development of the volatile compounds throughout the dehydration process, the peak area of each volatile was compared against the peak area of internal standard (IS: (s)-(+)-2-octanol) using a response factor of 1. Obviously, the use of a single standard does not provide accurate quantification,but it allows the data to be normalized for comparative purposes, and so the changes in the proportion of aroma components that occur during the dehydration process are monitored. All the analyses were performed in triplicate.
# A Review of Multimodal Interaction in Remote Education: Technologies, Applications, and Challenges
## 3 Commonly Utilized Methods
### Data Processing  
Before using multimodal approaches, data must be integrated and analyzed. This is mainly carried out through feature-level fusion and decision-level fusion. Feature-level fusion focuses on tagging and aligning markers. It is well suited for processing experimental datasets [50,51]. Decision-level fusion is more robust and works better for multimodal analyses that involve time-based changes [52,53].  
Multimodal data corpora play an important role in education research. Task-specific educational corpora collect data on interaction signals between teachers and students or among users. These signals include navigational instructions and descriptive language used during tasks [54,55].  
In online learning, multimodal corpora help analyze teachers' use of visuals, gestures, and language. Studies show that gestures and visual symbols, such as arrows, help explain instructional content. This improves students' understanding and memory [56].  
Developing these corpora requires well-designed experimental setups. Studies in human-computer interaction and cross-cultural behavior collect multimodal data, including gestures, intonation, and facial expressions. This expands multimodal resources for education [57,58].
# Listeners' Spectral Reallocation Preferences for Speech in Noise
## 2 Outline of the Experiments  
In two experiments, listeners manipulated one of eight different speech properties, illustrated schematically in Figure 1. The first experiment involved full-bandwidth speech and consisted of the four types of adjustment shown in the left column of the figure, viz.  
increasing or decreasing the level of speech in one of three octave bands, or altering spectral tilt (a preliminary report on spectral tilt adjustments was provided in [PERSON] et al. [68]). In contrast, all conditions of Expt. 2 involved narrowband speech. In three conditions, listeners made changes to speech bandwidth by lowpass or highpass filtering, or by direct manipulation of the bandwidth; in a fourth condition, they selected the centre frequency of an octave band of speech.  
Spectral energy modifications in both experiments were pre-computed at a sufficiently fine range of parameter values to produce the perceptual effect of continuous variation (including smooth mid-utterance transitions) as listeners move a virtual knob or click up/down buttons. Stimulus presentation and response collection were implemented using a software tool (SpeechAdjuster; [69]). In the current study, on each trial, listeners initially adjusted a continuous sequence of sentences, with no time limit, using up/down buttons, then underwent a brief test phase, in which they were asked to identify keywords in sentences presented at the parameter value chosen during the adjustment phase.  
One issue that arises in any attempt to measure listener preference is the possibility of bias due to the form of the instructions given to participants. The current study did not seek to distinguish between factors that could conceivably contribute to a listener's preferred setting. To avoid any bias towards one or other of these potential factors, listeners were asked to adjust the signal to make it understandable, and to consider the task as being analogous to adjusting the volume when watching a film: too high a volume causes discomfort, while too low a volume makes understanding the words difficult.
## 3 Results
### Real-Time Strain-Field Measurement  
Although the system is optimized for strain-controlled testing, it also allows for real-time strain-field measurement with a large number of subsets \(T_{k}\) per image. Figure 9 shows an example measurement with 2500 subsets per image taken at the end of an LCF test after cracks appeared. As the trial was strain-controlled, it was straight forward to compare the image at maximum and minimum strain of \(\varepsilon=\pm 1\) % within the same cycle.  
Due to path-independency, it was possible to compare the minimum and maximum load images directly without need for the intermediate images at a correlation rate of 25 kHz or 10 full-field strain images per second. This rise in the correlation rate from 10 kHz in strain-controlled mode to 25 kHz because CPU and GPU are synchronized less frequently. If local strain is measured with a base length  
Figure 8: Strain-controlled low cycle fatigue (LCF) tests on AlSi piston alloy for automotive applications. Crack propagation can be observed during the experiment.  
Figure 9: Real-time strain-field measurement where the local displacement is superimposed by color to the camera image (**top**). **Bottom**: The green line shows local displacement with \(l_{0}=0.2\) mm with a clear peak at crack 2. For strain-control, mean strain with \(l_{0}=7.5\) mm of the green line is measured between the ends (red line in bottom graph).  
\(l_{0}=0.2\) mm, cracks become clearly visible, as indicated by the green line. The line ends are the center positions of the two ROIs for mean strain measurement in strain control mode. The green line in the upper graph shows the image of the local strain profile given below. Obviously, there is a significant peak where the green line crosses crack 2.  
This shows that the system should be good base for real-time extraction of crack parameters in future implementations. In particular, strain-field measurement can be combined with strain-control to extract strain-field at minimum and maximum load in real-time. This means that the system does not only replace the mechanical extensometer and the optical strain-field measurement, it also adds some synergy by 'load synchronized measurement'.
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 5 Future SON Research Directions for B5G
### NFV and SON (vSON) for 5G Networks--RAN Virtualization/C-RAN Architectures and SON
An example might be the efficient hardware resource allocation assignment through C-SON applications running on the BBU pool which is a well-known issue for operators [91; 101]. Finally, since network slicing allows the creation of multiple logical networks in the NFV domain able to simultaneously run on top of a shared physical network infrastructure, end-to-end virtual networks that include both networking and storage functions can be created. Network resources are partitioned and scenarios with 5G verticals with different latency and throughput demands can be created. In our opinion, this discrimination can be combined with SON functions per scenario.
# Fragile Watermarking for Image Authentication Using the Characteristic of SVD
## 4 Experiments and Performance Analysis
### Performance Comparisons  
In this section, we compare the proposed algorithm with two recently proposed fragile watermarking methods in [10; 12]. To compare the sensory effect of the watermarked images, the peak signal-to-noise ratio (PSNR) is utilized to investigate the proximity degree between the original and watermarked images. For an image \(I\) with size of \(M\times M\), the definition of PSNR is formulated as  
\[\text{PSNR}=10\log_{10}\left[\frac{255^{2}}{\frac{1}{M\times M}\sum\limits_{i =1}^{M}\sum\limits_{j=1}^{M}\left[I(i,j)-I_{w}(i,j)\right]^{2}}\right](\text{ dB}) \tag{5}\]  
Figure 10: Extracted binary watermarks with different key combinations: (**a**) \(k_{1}=75\), \(k_{2}=30\); (**b**) \(k_{1}=70\), \(k_{2}=32\); (**c**) \(k_{1}=32\), \(k_{2}=75\); (**d**) \(k_{1}=75\), \(k_{2}=32\).  
where \(I_{w}\) is the host image after watermark embedding. Table 1 lists the comparison results among these three methods. As listed in Table 1, the proposed method achieves similar PSNR with the other two methods. This is reasonable since the LSB embedding algorithm is adopted in all three watermarking methods. In addition, the PSNR values of the test images are more than 51 dB, which objectively verify the invisibility of the watermark.  
To further test the precision of tamper localization, two evaluation indexes including the false positive rate (FPR) and false negative rate (FNR) are employed in this paper [21]. The FPR refers to the ratio of authentic pixels that are falsely detected as distorted pixels, and the FNR is the ratio of tampered pixels that are incorrectly detected as authentic pixels. Generally, the lower FPR and FNR mean better localization accuracy. Figure 11 illustrates the FPR and FNR comparisons of the above three methods under different cropping sizes. From Figure 11a, we can observe that the FPR values in [PERSON] et al.'s method [10] are almost zeros. However, the FNR values in Figure 11b are all greater than 0.5. In addition, a binary reference image in [10] is needed on the receiving end for image authentication, which is impractical in real life. Compared with [PERSON] et al.'s algorithm [12], the proposed scheme achieves lower FPR except in cropping size 192 \(\times\) 192. Notably, the FNR of the proposed scheme is much lower than that of the methods in [10,12]. From Figure 11, it suggests that the proposed authentication algorithm has lower detection errors and higher detection and localization accuracy.  
Table 2 lists the whole comparisons among the above three algorithms, where the PSNR is the average value of the data in Table 1. From Table 2, it can be observed that, compared with [PERSON] et al.'s method [10], the proposed algorithm achieves blind tamper detection on the receiving