esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Verification of Solutions
### Analysis of New Solutions (Difference of Three Orders)  
The last numerically analyzed results from this work (presented in Figure 6) concern series whose denominators had differences between the zeros of the Bessel function differing by three orders. To avoid subtracting zeros of the Bessel function of negative orders for  
Figure 5: Comparison of selected and newly derived series (serie) with the new polynomial solution (newSol)—difference of two orders.  
comparisons, the lowest \(p\) value was set at \(p=3\) (the next trends to observe were \(p=5\) and \(p=7\)).  
Figure 6: Comparison of selected newly derived series (serie) with the new polynomial solution (newSol)—difference of three orders.  
The obtained test results for Equations (A16)-(A18) were characterized by the fact that no clear (one-way) tendency was visible. In the case of \(l=1\) in Equation (A16) it can be seen that the solution for \(p=3\) had an increasing character as the value of \(n\) increased (with an initial negative value obtained for \(n=1\)), while in the case of \(p=5\) and \(p=7\), it was clear that the initial values obtained for \(n=1\) were positive and then there was a sharp drop to negative values. Thus, the tendency of these series became close to each other from \(n=2\). An analysis of formula Equation (A17) showed that, this time, the initial tendency was similar for the series assuming \(p=3\) and \(p=5\), while the course at \(p=7\) was characterized by a peak value obtained for \(n=2\). Similarly, analyzing the results of \(l=3\) obtained from the formula Equation (A18), there was no single trend visible because the initial value (for \(n=1\)) obtained at \(p=3\) was negative, while for \(p=5\) and \(p=7\) these were positive initial values. Additionally, the absolute initial value was highest when \(p=5\). The above indicates that with a difference of zeros by three orders in the denominators, we started to deal with complicated mathematical formulas that describe this series.  
Interestingly, the graphical analysis of solutions for \(l=1\) Equation (A19), \(l=2\) Equation (A20) and \(l=3\) Equation (A21) showed a similar behavior to the trend for functions in which \(\hat{\eta}_{p,k}^{2}-\hat{\eta}_{p-3,n}^{2}\). Due to the fact that in each of the analyzed cases, Equations (A19)-(A21), the results had positive values, and exceptionally, these results could be presented on a logarithmic-logarithmic scale. This choice of scale was forced by the presence of a peak in the values of the series in these solutions, occurring in all cases for \(n=2\). The value of this peak in each of the tested series, i.e., for \(l=1,2\) and \(3\), is greater than the \(p\) value. The zoom of Figure 6 for \(n\geq 10\) is presented in Appendix D.
# An Intelligent Artificial Neural Network Modeling of a Magnetorheological Elastomer Isolator
## 3 Testing and Results Analysis  
To characterize the MRE isolator, a dynamic compression test is conducted on MTS testing system, as shown in Figure 2. The top and the bottom of MRE isolator are connected to the load cell and actuator. The force and displacement are measured directly by the MTS load cell and a LVDT, respectively. For obtaining the influence of magnetic field on the dynamic performances (e.g., equivalent stiffness and damping properties) of proposed MRE isolator, the variable applied current from 0.0 A to 2.0 A is applied. The applied frequency is 0.5 Hz. The reason for selecting such frequency is that MRE is suitable for applied in low-frequency vibration [13]. A pre-compression deformation as 0.5 mm is set initially, and the balance position is set to be zero. The displacement amplitudes for testing are 0.05 mm and 0.15 mm, respectively. In fabrication process, the thickness of MRE sample is 4 mm. The strain of MRE is respectively 13.75%, 15%, and 16.25%. In some previous work, [PERSON] [17] conducted micro-vibration experiments for setting the strain of MRE as 2.3%, 4.5%, and 6.5%; [PERSON] [15] conducted experiments for setting the strain of MRE as 16%. The strain of MRE may be below 20% for avoiding fracture and failure. Hence, in the manuscript, the displacement amplitude is limited.  
In the future work, the authors will consider other displacement and frequency amplitudes.  
\begin{table}
\begin{tabular}{c c} \hline
**A** & **Magnetic Field Strength (T)** \\ \hline
0.0 & 0.00 \\
0.5 & 0.16 \\
1.0 & 0.32 \\
1.5 & 0.49 \\
2.0 & 0.65 \\ \hline \end{tabular}
\end{table}
Table 1: Corresponding values between applied currents and magnetic field strength.  
Figure 1: Structure schematic of MRE isolator.  
Figure 3 shows the force-displacement results of an MRE isolator for different applied currents and the maximum displacement of 0.05 mm and 0.15 mm. It is seen that, with the increasing input of electric current, the slope of the curves increases. This phenomenon demonstrates that the stiffness of the MRE isolator is altered by the applied currents. Also, the area within the force-displacement curves slightly increases as the applied current increases, which indicates that the damping capacity is also enhanced by the external magnetic field.
## 4 Engineering Example Validation  
In order to verify the effectiveness of the meteorological responsiveness method in the field of risk identification and its application potential in the field of practical engineering, this paper relies on a project for a highway in the southwest of China, hereinafter referred to as (SC2 highway), its position is shown in Figure 4. SC2 highway has a total length of about 131 km, a design speed of 80 km/h, four lanes in both directions, the standard roadbed width of 24.5 m.The climate along the whole route is changeable: there are adverse weather conditions such as heavy rain, ice and snow, and dense fog; the driving environment is complicated, and it is greatly affected by the adverse weather.
# Adaptive and Blind Audio Watermarking Algorithm Based on Chaotic Encryption in Hybrid Domain
## 4 Experimental Results and Analysis  
The detail experimental parameters are shown as follows: (1) the tested original audio consists of 20 songs, sampled at 44,100 Hz and 16 bit quantization; (2) three watermark pictures with different features are shown in Figure 4. The first picture is the logo of Nanjing Metro, and its outline is very clear. The English abbreviation of Nanjing University of Posts and Telecommunications is in the second picture, and its Chinese name is in the third picture; (3) the secret key is \(Ch(0.2,3.9,0.5)\); (4) the length of each segment is 256; (5) the level of DWT is 4; (6) the detailed wavlet coefficient is \(De(4)\); (7) the adaptive embedding depth is determined according to Formula (31), and \(a=0.1\), \(b=0.3\). The experimental environment is described in the following items: (1) the computer system is Microsoft Windows XP Professional; (2) MATLAB 6.5 is used as the programming language to write all programs; (3) Cool Edit Pro V2.1 can be utilized to carry out various attacks on audio media for testing the robustness.
## 5 Validation Results  
### Study Case 1: Load Profiles Model Validation  
Figure 10 shows an example of the output load profile energy consumption in Watthour (Wh) of house lights. This output can be generated for all load profiles under study.  
Figures 11-13 present a comparison between the measured data and the data generated by the simulation load profile model. The simulation and the actual measured data were performed over the same time periods. In these figures the results of the simulated load profile model can be seen on the right side of the graph. On the right side of the graph the red lines indicate the power consumed by the appliance and on the left side of the graph it shows the actual data measured by the smart meters which are expressed as a bar graph. In all the graphs it is observed that they have a similarity in their average power consumption value of each appliance represented by the blue dotted line present in both graphs. The results are not exact to the actual data due to human random behavior.  
Figure 11: Real data vs. Simulation model (Oven).  
Figure 12: Real data vs. Simulation model (Water-Heater).  
Figure 10: Real data vs. Simulation model (Lights), the red line represents the energy consumption in both graphs [Wh].  
Figure 14 shows the total energy consumption per month of a house located in the community under study. This figure is composed of three subplots. One of them is generated for smart meter data (a), the other is generated considered bill data (b), and (c) is the figure obtained from the simulation model, where the red line indicates the power consumed by the whole profile and the blue dotted line represents the average power consumption value of the case proposed. The above-mentioned subplots are presented considering the same period of time. The results show that the smart meter, bill, and simulation model data present similar average energy consumption, approximately to 300 kWh, which corresponds to the house load profile model consumption per mount. The simulated model presents a minimum error in the output load profile.
# Robust Anti-Jamming Algorithm Based on Transmit/Receive Time-Sharing Technology
## 2 Mathematical Model
### Time-Domain Anti-Jamming Model  
Time-domain anti-jamming is a necessary baseband digital signal processing (DSP) module of the navigation receiver, as shown in Figure 3[29; 30]. Among them, the LMS adaptive filter is an important anti-jamming method in the time domain, which can be independently embedded in the receiver with a small size, and its suppression effect on narrowband interference is usually greater than 30 dB [31]. The LMS algorithm uses the steepest gradient descent to minimize the error signal's mean square value, which has the characteristics of small calculation and simple implementation [32].  
Figure 1: Transmit/receive time-sharing model of satellite-ground link.  
Figure 2: Time-domain diagram of the received signal.  
Suppose that the intermediate frequency (IF) signal, noise, and interference received from an antenna and processed after DDC are \(s[n]\), \(n[n]\) and \(j[n]\), respectively, and they are all complex signals [33]. Then, the signal before anti-jamming can be expressed as:  
\[x[n]=s[n]+j[n]+n[n] \tag{2}\]  
The \(M\)-th time-domain filter has \(M+1\) taps, and the filter weight vector is supposed to be [24]:  
\[\mathbf{W}_{M}=[w_{0},w_{1},\ldots,w_{M}] \tag{3}\]  
The output signal after the anti-jamming filter can be expressed in the time domain as [34]:  
\[y[n]=x[n]*\mathbf{W}_{M}=\sum_{k=0}^{M}x[n-k]\mathbf{W}_{M}[k] \tag{4}\]  
The frequency response of the anti-jamming filter can be written as [35]:  
\[H(f)=\text{DTFT}[\mathbf{W}_{M}] \tag{5}\]  
The error signal between the filter output signal and the input signal is defined as \(e(n)\), which can be expressed as [36]:  
\[e[n]=x[n]-y[n] \tag{6}\]  
The adaptive algorithm adjusts the filter tap coefficients according to the error signal and makes the filter weight vector change along the negative gradient direction by iteration [37]:  
\[\mathbf{W}_{M}^{l+1}=\mathbf{W}_{M}^{l}-\mu\
abla_{\mathbf{W}_{M}}E\left\{|e [n]|^{2}\right\} \tag{7}\]  
where \(l\) is the number of adaptive iterations, and the maximum number of iterations is \(L\), \(\mu\) represents the rate at which the gradient is falling.  
In practical applications, the instantaneous mean square error is usually used instead of the mean square error, and the recursive formula of the LMS algorithm is finally obtained by substituting the formula [30]:  
\[\mathbf{W}_{M}^{l+1}=\mathbf{W}_{M}^{l}+2\mu e^{*}[n]x[n] \tag{8}\]  
Figure 3: Navigation receiver structure.
# Low-Cost IoT and LoRaWAN-Based System for Laying Hen Identification in Family Poultry Farms
## 4 Results and Discussion
### Laboratory Testing  
In order to validate the system's proper functioning in a controlled environment, a simulated chicken coop was set up with two nesting boxes and five artificial hens, each equipped with NFC tags. The following components were installed in this environment:  
* Nesting box nodes with NFC readers;
* One environment node with temperature and the humidity sensors;
* A LoRaWAN gateway for data transmission;
* A network server (The Things Stack) to manage communication;
* An application server configured with Node-RED and PostgreSQL;
* A mobile application and Telegram for receiving real-time information.  
During operation, the system successfully detected the hens via NFC tags, and data transmission through the LoRaWAN network proved effective. Furthermore, the integration with the mobile application was successfully validated, as illustrated in Figure 5. The figure shows the application receiving data from Hen 2's visit to Nesting Box 1. The pink color indicates that this box is currently occupied, while Nesting Box 2, marked in green, is available. The application also displays that Nesting Box 1 has received six visits, as reflected in the visit counter, which resets to zero at the beginning of each day.  
Figure 4: IT layer user applications: web application with Grafana (left), Android mobile application (center) where messages like “CONECTADO MQTT” translate as “CONNECTED MQTT” and “DATOS” translate as “DATA”, and Telegram alerts (right), where messages like “Visita de gallina X a ponedero Y” translate as “Visit of laying hen X to nesting box Y”. Date format is YYYY-MM-DD (ISO Standard).  
In addition, the integration with the Telegram bot functioned as expected. As shown in Figure 6, the Telegram interface displays real-time alerts generated during the laboratory tests, specifically indicating visits from Hen 2 to Nesting Box 1 and Hen 4 to Nesting Box 2. While the hens remain inside the nesting boxes, the system continuously sends messages such as \"Visit from Hen 2 to Nesting Box 1\" and \"Visit from Hen 4 to Nesting Box 2\".  
These results confirm the system's ability to operate reliably under controlled conditions, thereby supporting the transition to the next phase of testing.
# Integration of ERT, IP and SP Methods in Hard Rock Engineering
###### Abstract  
Investigation of a hard rock site for the development of engineered structures mainly depends on the delineation of weathered and unweathered rock, and the fractures/faults. Traditionally, borehole tests are used in such investigations. However, such approaches are expensive and time-consuming, require more equipment, cannot be conducted in steep topographic areas, and provide low coverage of the area with point measurements only. Conversely, geophysical methods are non-invasive, economical, and provide large coverage of an area through both vertical and lateral imaging of the subsurface. The geophysical method, electrical resistivity tomography (ERT), can reduce a significant number of expensive drilling tests in geotechnical investigations. However, a geophysical method alone may provide ambiguity in the interpretation of the subsurface, such as electrical resistivity cannot differentiate between water and clay content. Such uncertainty can be improved by the integration of ERT with induced polarization (IP). Similarly, self-potential (SP) can be integrated with other geophysical methods to delineate the groundwater flow. In this contribution, we integrated three geophysical methods (ERT, IP and SP) to delineate the weathered and unweathered rock including the weathered/unweathered transition zone, to detect the fractures/faults, and to map the groundwater flow. Based on ERT, IP and SP results, we develop a geophysical conceptual site model which can be used by site engineers to interpret/implement the findings for build-out. Our approach fills the gaps between the well data and geological model and suggests the most suitable places for the development of engineered structures in the hard rock terrains.  
electrical resistivity tomography (ERT); induced polarization (IP); self-potential (SP); engineered structures; hard-rock Article  
Integration of ERT, IP and SP Methods in Hard Rock Engineering  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], H. Integration of ERT, IP and SP Methods in Hard Rock Engineering. _Appl. Sci._**2021**, _11_, 10752. [[https://doi.org/10.3390/appl12210752](https://doi.org/10.3390/appl12210752)]([https://doi.org/10.3390/appl12210752](https://doi.org/10.3390/appl12210752))  
15 September 2021  
10 November 2021  
15 November 2021  
1 Key Laboratory of Shale Gas and Geogeninering, Institute of Geology and Geophysics, Chinese Academy of Sciences, No. 19, Beitucheng Western Road, Chaoyang District, Beijing 100029, China; [EMAIL_ADDRESS] (P.S.); [EMAIL_ADDRESS] (X.Y.); [EMAIL_ADDRESS] (H.M.)  
2 Innovation Academy for Earth Science, Chinese Academy of Sciences, Beijing 100029, China  
3 College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China  
* Correspondence: [EMAIL_ADDRESS] (Y.S.); [EMAIL_ADDRESS] (M.H.); Tel.: +86-139-100-993-15 (Y.S.); +86-130-513-617-10 (M.H)
# Impact of Audio Delay and Quality in Network Music Performance +
## 4 Subjective Evaluation Design
### Questions Specific to Scenario B  
Evaluate the audio quality. The Perception of Audio Quality (PoAQ) variable measured the perceived quality of the audio stream on a 5-point Likert scale (1 = very poor, 5 = excellent). Although we controlled the audio quality through sampling rate adjustments, subjective feedback was crucial. In earlier work focused solely on delay, participants sometimes misattributed degraded synchronization to poor audio quality [26].
# Individualized Interaural Feature Learning and Personalized Binaural Localization Model +
## 6 Experiments With Simulated Data
### 3-D Space Localization with Mutual Information-Based Feature Selection
#### 6.1.1 Simulation Configuration  
The proposed approach is used to evaluate 950 source locations, ranging from azimuth \(\alpha=-45^{\circ}\) to \(45^{\circ}\) in \(5^{\circ}\) intervals and elevation \(\beta=-45^{\circ}\) to \(230.625^{\circ}\) in \(5.625^{\circ}\) increments, for the first 10 subjects' HRTF measurements in the CIPIC database [24]. The speech samples from the PASCAL 'CHiME' Speech Separation and Recognition Challenge [32] (34 males and females, each with 500 utterances sampled at 16 kHz) are used as inputs; 340 randomly selected utterances are used for the learning process, and a separate 200 utterances are used to evaluate the localization performance. The binaural signals are simulated by convolving the HRTFs of different locations with the uncorrupted speech, and introducing the independent additive white Gaussian noise with three different SNRs of 10, 20 and 30 dB.  
The frequency range for the generic composite feature-based approach is selected empirically, where [0,4] kHz and [3,5] kHz are the phase and magnitude feature regions for the feature-based method, while the full-band signal is used for the correlation approach. During the comparison, the mean angular error is employed as a metric to assess the localization performance. The angular error denotes the angular distance between the estimated and actual source directions in the interaural-polar coordinate system; therefore, the estimation errors of both the azimuth and elevation (\(\alpha\) and \(\beta\)) are implicitly included in the performance assessment.
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
## 4 Discussion
### Limitations of AGLNM  
The defects of the AGLNM are also obvious. As can be seen from the structure of the model in Figure 1, the AGLNM needs to constantly update the graph structure in order to make the graph structure learned by the model more consistent with the real association relationships contained in the data set. The update of the graph structure requires a recalculation of the adjacency matrix and the new adjacency matrix needs to be added to the next update, which obviously consumes more computing resources and more training time. Therefore, this model is not suitable for small data sets.  
As can be seen from Figure 3, AGLNM has a small space to improve the prediction performance of the South China Sea data set, but it requires more computing time and resources. This may be because the South China Sea data set is not subject to seasonal fluctuations and the feature changes are small and stable. For this kind of data set, AGLNM has little effect on graph learning, which leads to a low model efficiency.
## 4 Experimental Result and Analysis
### Seismic Wave Delay and Amplification  
Researchers could obtain the amplification and delay data generated by the maximum peak acceleration when the seismic wave passed through the simulated mountain and reached the top by analyzing the acceleration data collected by the acceleration sensors installed on the tabletop of the shaking table and on the top of the simulated mountain (Table 5). According to the analysis, among the three seismic waves, the maximum delay in the X direction was 0.44 s and the minimum delay was 0.33 s. The maximum value in the Y direction was 0.11 s and the minimum value was 0.06 s. The maximum acceleration  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Phase**} & \multicolumn{4}{c}{**Model Order**} \\ \cline{3-5}  & & **1 st** & **2 nd** & **3 rd** \\ \hline \multirow{2}{*}{Initial} & Frequency (Hz) & 4.83 & 14.24 & 16.46 \\  & Degradation rate & / & / & / \\ \hline \multirow{2}{*}{After frequent 8} & Frequency (Hz) & 4.83 & 14.24 & 16.46 \\  & Degradation rate & / & / & / \\ \hline \multirow{2}{*}{After frequent 8 (SWD and A)} & Frequency (Hz) & 4.81 & 14.23 & 16.46 \\  & Degradation rate & 0.6\% & 0.1\% & / \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model dynamic characteristics.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **Seismic Wave** & **Input Direction** & **Input PGA(g)** \\ \hline White noise & / & X, Y & 0.11, 0.11 \\ \hline \multirow{4}{*}{Frequent 8} & Coalinga & X & 0.20 \\  & & Y & 0.20 \\  & Morgan Hill & X & 0.20 \\  & & Y & 0.20 \\  & Artificial wave & Y & 0.20 \\ \hline White noise & / & X, Y & 0.11, 0.11 \\ \hline \multirow{4}{*}{Frequent (SWD and A)} & Coalinga & X & 0.20 \\  & & Y & 0.20 \\ \cline{1-1}  & Morgan Hill & X & 0.20 \\ \cline{1-1}  & Artificial wave & Y & 0.20 \\ \hline White noise & / & X, Y & 0.11, 0.11 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Seismic Loading Cases.  
amplification coefficient in the X direction was 1.84 and the minimum was 1.55. The maximum value in the Y direction was 1.76 and the minimum value was 1.54. The test results met the requirements of the test plan for providing a seismic wave delay of about 0.4 s in the X direction and amplifying the seismic wave peak acceleration at the top of the simulated mountain by 1.6 times. In general, the expected delay and amplification effect of the three seismic waves input in the test was achieved by installing viscous dampers on the simulated mountain.