esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Matching an MMIC Amplifier Integrated with MIMO Antenna through DNNs for Future Networks
## 2 Proposed Optimization Method
### LSTM-Based DNN for Modeling Antenna (Phase-1)
The output layer also presents the real impedances (\(\text{Z}_{\text{real}}\)) and imaginary impedances (\(\text{Z}_{\text{imaginary}}\)) in each frequency. The training accuracy of the DNN is around 0.085. At the second phase, the amplifier is modeled through DNN based on the predicted optimal impedances from modeled antenna that are directly matched to the drain impedance of transistor. For configuring the amplifier, the SRFT method is employed with the gate and drain impedances achieved from load-pull measurement. Figure 6 presents the configuration of MMIC, which is the class-AB amplifier, and the optimal parameters are achieved through the GA method. Table 3 presents the optimal values of the components included in the design of MMIC. After extracting the MMIC structure with the design parameters, the parametric sweep is performed within the range of \(\mp\)5%, and \(\mp\)10% (like the antenna modeling) and 2600 data in terms of gain, efficiency, and third, fifth, and seventh IMDs at various frequencies are extracted. Afterwards, the multivariate Newton's method (at the output layer) is applied for optimizing linearity specification. The BO is employed to determine the optimal hyperparameters of the second DNN, resulting in 4 LSTM layers with 100 neurons in each layer. The training accuracy for the modeled ampl \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Design Parameters** & **Value (mm)** & **Design Parameters** & **Value (mm)** \\ \hline \(\text{T}_{1}\) & 22.5 & \(\text{W}_{\text{p}}\) & 11 \\ \(\text{L}_{\text{p}}\) & 8.99 & \(\text{L}_{\text{-ps}}\) & 1.4 \\ \(\text{L}_{\text{f}}\) & 18.1 & \(\text{W}_{\text{f}}\) & 3.2 \\ \(\text{R}_{1}\) & 5.2 & \(\text{R}_{2}\) & 3.6 \\ \(\text{R}_{3}\) & 6.2 & \(\text{R}_{4}\) & 1.7 \\ \(\text{W}_{\text{g}}\) & 45 & \(\text{L}_{\text{g}}\) & 38 \\ t & 1.65 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Design parameters of optimized MIMO antenna in Figure 5.  
Figure 5: MIMO antenna structure.
## 2 Materials
### Field Data  
The field experiments (Figure 1 and Table 1) were conducted at two different regions. Experiment 1 (exp1) was performed in the Yucheng Comprehensive Experimental Station of the Chinese Academy of Sciences (36\({}^{\circ}\)57\({}^{\prime}\)N, 116\({}^{\circ}\)36\({}^{\prime}\)E), located at Southwest of Yucheng Town in Shandong Province, during the winter wheat growth period of 2011, whereas experiment 2 (exp2) was performed in the agricultural areas of Hengshui City (37\({}^{\circ}\)03\({}^{\prime}\)N-38\({}^{\circ}\)23\({}^{\prime}\)N, 115\({}^{\circ}\)10\({}^{\prime}\)E-116\({}^{\circ}\)34\({}^{\prime}\)E) in 2017 (Figure 1a). The climate condition and soil texture of exp1 were similar to those of exp2. The following seven types of in situ data were measured: soil moisture (\(\theta\)), leaf water content, spectral reflectance, chlorophyll content (C\({}_{\rm ab}\)), total nitrogen content (TN), dry aboveground biomass (DAM), and yield. In exp1, ten treatments were conducted with five N levels (0, 70, 140, 210 and 280 kg N/ha, which referred to N0, N1, N2, N3 and N4, respectively) and two irrigation water levels (60% and 80% of the field water capacity) from 27 March to 25 May 2011. Ten treatments were conducted in the same field of 0.33 ha. Every treatment had three replications with random arrangements. There were a total 30 plots in exp1 and every plot area was 5 m \(\times\) 20 m. The measurements were performed in every plot about once a week in exp1.  
For each experimental sampling plot of exp2, five smaller sub-plots (Figure 1c) were deployed along diagonal lines, and measurements were performed thrice in 2017, corresponding to the tillering, anti-hesis, and senescence stages of the winter wheat. A total of 22 plots with 110 sub-plots were measured in 11 counties of Hengshui City (Figure 1b,c). The \(\theta\) was determined by the gravimetric method. The leaf water content and DAM were measured via the oven drying method, and the C\({}_{\text{ab}}\) and TN in exp1 were determined by spectrophotometry and the micro-Kjeldahl method, and the C\({}_{\text{ab}}\) in exp2 was determined by SPAD-502 Chlorophyll meter (Konlca Minolta Optics, Inc., Williams Drive Ramsey, NJ, USA). The canopy spectral reflectance was measured by an ASD FieldSpec Pro spectrometer (Analytical Spectral Devices, Boulder, CO, USA). Based on the spectral response function of the MODIS, the measured field spectral reflectance was then converted to four multispectral bands corresponding to the blue, green, red, and near-infrared bands (NIR) of the MODIS. The measured \(\theta\), C\({}_{\text{ab}}\), and DAM data in the exp2 plots were averaged by five measurements from the sub-plots. Moreover, the yield in exp2 was averaged by five productions, which were manually collected at a 1 m \(\times\) 1 m area of the sub-plots at the harvest.
# Monitoring of [PERSON]'s Disease Based on Wireless Sensing Technology
## 7 Conclusions  
As far as we know, this article first proposed a method for monitoring HD patients using wireless sensing technology and studied the feasibility of the proposed technical scheme in depth. We used the self-developed MSP to collect CSI data, then removed the outliers, and filtered the CSI data with the wavelet transform. After that, we extracted eight time domain features from each action data set and trained the model with SVM and RF machine learning algorithms. The experimental results show that the prediction accuracy of the SVM algorithm can reach 98.0%, and the prediction accuracy of the RF algorithm can reach 96.7%. Both algorithms can effectively distinguish the normal actions and the actions of patients with HD, which proves that the technical scheme described in this paper is feasible. This will provide a basis for doctors to objectively diagnose patients' conditions. At the same time, the technology can help doctors to follow up on the patient's condition development and improve the treatment plan in time. At present, the MSP described in this paper is not fully automated but also needs some manual operations. Next, we will continue to improve the experimental platform to make it fully automated to achieve data collection, data processing, and data analysis in a one-click operation. At the same time, we will also further explore the application of wireless sensing technology in medical care.  
Conceptualization, Q.Z.; methodology, L.G.; resources, X.Y.; writing--original draft preparation, Q.Z.; writing--review and editing, M.B.K.; supervision, X.Y.; project administration, X.Y.; funding acquisition, X.Y. 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 grant number 61301175.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this study, we analyzed the characteristics and influencing factors of algal blooms at each weir point in the main stream of the Nakdong river. To analyze the occurrence of blue-green algae, we examined the occurrence status according to SPI and temperature for each weir point. Seasonally, blooms of blue-green algae were most prone to occur during summer. In addition, the average occurrence of cyanobacteria was higher in the lower reaches of the Nakdong river, and the downstream section was judged to have favorable conditions for the occurrence of cyanobacteria, which showed the characteristics of stagnant water due to the increase in hydraulic residence time caused by a decrease in the flow rate and an increase in the water depth. The RF results showed that the HC points in the downstream section, which were found to be one of the major influencing factors, were vulnerable to the occurrence of blue-green algae. A machine-learning model was developed for algal bloom and water quality environmental management. Decision tree and RF were constructed for the weir points to develop a classification model for the occurrence class based on an algae warning system. The criteria for issuance based on the algae warning system were set as the categorical target variables, and the conditions for the occurrence of blue-green algae were analyzed according to the major influencing factors. The probability of blue-green algae occurrence at each weir point was presented based on the numerical standards of the input variables and alert levels. Thus, this study can be used for decision making to customize the management of blue-green algae by branches during heat waves and droughts. A data-based model should eventually accumulate good quality data [24]. Machine learning to build a sophisticated model through high-quality big data can be used as a countermeasure against algal blooms.  
Conceptualization, W.S.J.; methodology, W.S.J. and Y.D.K.; software, W.S.J.; validation, Y.D.K.; formal analysis, W.S.J., B.G.J. and Y.D.K.; investigation, W.S.J. and B.G.J.; data curation, W.S.J.; writing--original draft preparation, W.S.J.; writing--review and editing, B.G.J. and Y.D.K.; visualization, W.S.J.; supervision, Y.D.K.; project administration, Y.D.K.; funding acquisition, Y.D.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was carried out with a research grant from the National Research Foundation of Korea (NRF-2020R1F1A1076887).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
Distortion-product otoacoustic emissions (DPOAEs) elicited with high-frequency (HF; up to 16 kHz) stimuli are measurable and repeatable in normal-hearing adults and children, adult patients, and are sensitive to ototoxic insults in adults. However, objective tests for monitoring basal cochlear function in those too young to respond subjectively need to be developed. DPOAE levels recorded at frequencies <10 kHz are well characterized, but DPOAE levels measured up to 16 kHz do not exist for newborns. The goal of the current study is to determine if HF DPOAEs are measurable and repeatable in newborns. DPOAEs were measured from 2-16 kHz (f\({}_{2}\)/f\({}_{1}\) of 1.22; L\({}_{1}\)/L\({}_{2}\) = 65/55 dB SPL) using two different calibration methods (forward pressure level--FPL and in-the-ear--SPL) in 26 newborns. To assess repeatability, the probe was removed then re-inserted for a second round of testing. Results indicate that HF DPOAEs can be evoked and are repeatable in newborns and the use of FPL calibration shows promise for measuring HF responses and maximizing repeatability. To be implemented in monitoring programs where the highest frequencies with responses are continuously tested, stimulus parameters used to evoke newborn HF DPOAEs and calibration methods need further exploration.  
Keywords:distortion-product otoacoustic emissions; repeatability; newborns; spontaneous otoacoustic emissions; high-frequency stimuli; calibration +
Footnote †: journal: Applied Soft. 2019, 9, 2947; doi:10.3390/app9152947
## 2 Methodology
### Bidirectional Cross Fusion
#### 2.2.2 Scale Direction  
In the feature fusion along the scale direction, the coarse features of the predicted time are combined with the high-frequency features that the reference time image can provide, enhancing the detailed information across different scales. The specific expression is as follows:  
\[F_{s}^{(i)}=L_{0}^{(i)}+f_{scale}\Big{(}H_{0}^{(i)},L_{1}^{(i)},L_{0}^{(i)} \Big{)} \tag{12}\]  
Here, \(F_{s}^{(i)}\) is the spatiotemporal feature after fusion along the scale direction, and \(f_{scale}(\cdot)\) is the fusion operation along the scale direction, which utilizes an attention mechanism similar to that used in the time direction.  
Similar to the design in the time direction, the attention mechanism in the scale direction is also based on the calculation of queries, keys, and values to establish the relationship of spatiotemporal features. The difference is that in the scale direction, the  
Figure 3: The pixel grouping methods in (**a**) SDA and (**b**) LDA.  
computation of queries, keys, and values is adjusted to accommodate the relationship between images at the same resolution from different time points. Specifically, we compute the following queries, keys, and values:  
\[Q_{s}^{(i)}=L_{0}^{(i)}W_{Q_{s}}^{(i)},K_{s}^{(i)}=L_{1}^{(i)}W_{K_{s}}^{(i)},V_{ s}^{(i)}=H_{0}^{(i)}W_{V_{s}}^{(i)} \tag{13}\]  
\[Attention(Q_{s}^{(i)},K_{s}^{(i)},V_{s}^{(i)})=SoftMax\left(\frac{{Q_{s}^{(i)}{ K_{s}^{(i)}}^{T}}}{\sqrt{d}}+B\right)V_{s}^{(i)} \tag{14}\]  
where \(W_{Q_{s}}^{(i)}\), \(W_{K_{s}}^{(i)}\), and \(W_{K_{s}}^{(i)}\) are the learnable weight matrices for the \(i\)-th stage of scale direction fusion. By computing \({Q_{s}^{(i)}{K_{s}^{(i)}}^{T}}\), the potential relationship matrix between \(L_{0}^{(i)}\) and \(L_{1}^{(i)}\) is obtained. This matrix represents the potential differences between images of the same resolution at different time points. This difference is intended to be used to correct the high-frequency detail information obtained from \(H_{0}^{(i)}\), which aids in guiding the detail enhancement of the coarse-resolution image.  
Finally, the time-direction fused features \(F_{t}^{(i)}\) and the scale-direction fused features \(F_{s}^{(i)}\) are combined. The two feature sets are merged using a concatenation operation and processed through a linear transformation to obtain the final spatiotemporal fusion features \(F_{st}^{(i)}\):  
\[F_{st}^{(i)}=linear\Big{(}cat(F_{t}^{(i)},F_{s}^{(i)})\Big{)} \tag{15}\]  
where \(cat(\cdot)\) refers to the concatenation operation along the feature dimension and \(linear(\cdot)\) represents a linear transformation along the feature dimension. This approach ensures that both time and scale direction information is preserved during the fusion process, resulting in spatiotemporal features with richer contextual information.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## 6 Numerical Solution of the Markov Chains
### Markov Chain with Initially Infected Nodes  
For the Markov chain with infected initial nodes, the parameters of the simple chain plus the parameters explained in Table 3 were taken. The basic model corresponds to the normal and conventional operation of the P2P system. However, when an active attack is present, the system would be degraded according to the intensity of the attack. For the _malware_ attack, the intensity of the attack is reflected in the initial number of malicious nodes, \(N_{i}\), while for the DoS attack, the intensity of the attack is reflected in the time of the attack and number of nodes affected by it, given by \(1/\delta\), \(\delta^{l}\), and \(\delta^{s}\), correspondingly.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Description** & **Units** \\ \hline c & 0.02 & Download file rate & files/s \\ \hline \(\lambda\) & 1 & Peer arrival rate & users/s \\ \hline \(\mu\) & 0.00125 & Upload file rate & files/s \\ \hline \(\theta\) & 0.01 & Leecher connection time & s \\ \hline \(\gamma\) & 0.01 & Seed connection time & s \\ \hline \(\eta\) & 0.85 & Download efficiency coefficient & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters used in the simple chain.  
Figure 7: Markov chain of the P2P network under a DoS attack.  
Algorithm A2 represents the numerical solution of the Markov chain that was presented in Figure 5. For this string, we also varied three different parameters over a certain range. These parameters are \(\gamma\), which represents the seed connection time; \(\mu\), which indicates the download rate; and \(\lambda\), which is the arrival rate of new users connecting to the network per second. Three different experiments were performed to vary each of the above parameters against the number of initially infected nodes (\(N_{i}\)), which represents the intensity of the attack, to study the performance of the system in terms of the number of healthy or infected peers and determine which value or range of values favored or harmed the growth of the infected nodes. The value of \(K\) was set to 0.1, although this value can change depending on the specific conditions of the system.
## 3 The Adaptive Attitude Control Strategy of the MAV with Actuator Dynamics  
As the MAV is axis-symmetric, the pitch and the roll dynamics have a similar property. For this reason, this study is focused on the adaptive attitude control strategy of the pitch axis. Without loss of generality, the strategy can also apply to other axes.
## 5 Experimental Verification  
To verify the proposed method, an experimental prototype is built according to Figure 3 with the parameters shown in Table 1. For the primary side push-pull inverter, an IRF3610 MOSFET is used as the switch device. For the secondary side rectifier, a SS36 Schottky diode is selected. The detailed power and data transfer circuit is shown in Figure 11. For the secondary side power transfer circuit, LM311 chip is used as the voltage comparator, the comparison signal is fed to the field programmable gate array (FPGA) chip (Altera Cyclone II EP2C5T144C8). Afterward, the FPGA chip determines the on-off state of the switches \(S\) and \(S_{d}\). For the data transfer circuit, the modulation is produced by a CD4051 chip; a ceramic filter chip is used as the bandpass filter while a LT1816 chip is used as the operational amplifier. The demodulation circuit consists of an envelope detector to get the envelop of the carrier and a comparator (LM311 chip) to discriminate the data.  
Figure 11: _Cont._  
Figure 10: Bode plots comparison between the proposed and traditional data transfer channel.
## 3 Physical Layer Improvement Proposals for PRIME and G3-PLC
### G3-PLC: Tone Map
#### 3.3.1 Standard Tone Map  
A tone map is defined as a six-bit field where each bit represents the content of a group of six subcarriers. If a bit is set to '1', its associated group of subcarriers are active for the payload and include data. If it is set to '0', they are inactive and a pseudo-random sequence is introduced instead  
Whereas the standard definition limits tone map usage to non-robust modes, this study applies this technique to all modulation schemes.  
Using a tone map means that fewer payload subcarriers are used for data exchange in the payload, requiring more OFDM symbols, resulting in lower system throughput for a certain modulation scheme.  
The tone map used for this research work is obtained with the automatic tone map selection algorithm integrated in the G3-PLC modems. For this tone map autodetection algorithm security thresholds are defined for the average SNR of each modulation scheme, and represented in Table 1. For each modulation scheme, this minimum SNR is checked for each of the subcarrier groups. If the average SNR of the group is higher than the defined threshold, the bit that represents that group of subcarriers will be selected as '1', otherwise it will be '0'.  
This process for calculating the tone map is repeated for each available modulation scheme. The combination of the tone map and modulation scheme that has better throughput is selected. In a complete G3-PLC communication, this process would be invoked during the neighbor discovery process of the medium access control (MAC) layer.  
The threshold values presented in Table 1 are between 4 and 6 dB higher than the minimum SNR required for the receiver to decode the frames in presence of AWGN noise [10]. This security margin is included in order to handle possible worse channel conditions not directly measurable by SNR, and to handle variations of the channel in time after its negotiation.
# Optimized Classification of Intelligent Reflecting Surface (IRS)-Enabled GEO Satellite Signals
## 4 Simulation Results
### Case V: Comparison with Existing Techniques  
The proposed classifier was compared with existing state-of-the-art techniques, as shown in Table 5, and it is found that the proposed algorithm correctly classifies the GEO-satellite-enabled IRS signals. Table 5 presents the average classification accuracy, i.e., approximately 100% for the proposed classifier structure, while comparing it with the other techniques.  
As stated earlier in the Contributions section, the proposed method is, to the best of our knowledge, the first of its kind; it utilizes the cascaded channel for the modulation classification, and from Table 5, it is evident that the performance of the proposed classifier is better than that of existing classifiers. The existing classifiers utilize the AWGN and fading channels, such as Rayleigh and Rician, but in our case, the considered channel is the cascaded channel.  
However, the ACA comparisons presented in Table 5 were obtained directly from references and were not evaluated on the same dataset.
# Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
## 2 Literature Review
### Orbital Scale
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Reference** & **Application** & **Fusion Technique** & **Fused Data** & **Mean Accuracy** \\ \hline
[100] & Root zone soil moisture estimation & NN (M), DRF (M), GBM (M), GLM (M) & 2, 11 & 0.90–0.95 \\ \hline
[101] & Gramineae weed detection in rice fields & Haar wavelet transformation (L) & 1, 2 & 0.70–0.85 \\ \hline
[65]\({}^{a}\) & Monitoring agricultural terraces & Coregistering and information extraction (L) & 5 & N/A \\ \hline
[66]\({}^{b}\) & Spectral-temporal response surfaces & Bayesian data imputation (L) & 2, 3 & 0.77–0.83 \\ \hline
[102] & Phenotyping of soybean & PLSR (L), SVR (L), ELR (L) & 1, 2, 4 & 0.83–0.90 \\ \hline
[39] & Soybean yield prediction & PLSR (M), RF (M), SVR (M), 2 types of DNN (M) & 1, 2, 4 & 0.72 \\ \hline
[52]\({}^{b}\) & Crop monitoring & PLSR (M), RF (M), SVR (M), ELR (M) & 1, 2 & 0.60–0.93 \\ \hline
[40]\({}^{b}\) & Evapotranspiration estimation & MSDF-ET (L) & 1, 2, 4 & 0.68–0.77 \\ \hline
[10]\({}^{ab}\) & Review: IoT and data fusion for crop disease & N/A & N/A \\ \hline
[103] & Arid and semi-arid land vegetation monitoring & Decision tree (L/M) & 3, 5 & 0.84–0.89 \\ \hline
[41] & Biomass and leaf nitrogen content in sugarcane & PCA and linear regression (L) & 2, 5 & 0.57 \\ \hline
[70]\({}^{ab}\) & Review: data fusion in agricultural systems & N/A & N/A \\ \hline
[104] & Navigation system for UAV & EKF (L) & 11, 12 & 0.98 \\ \hline
[38]\({}^{a}\) & Detection of cotton plants & Discriminant analysis (M) & 2 & 0.97 \\ \hline
[71]\({}^{b}\) & Vineyard monitoring & PLSR (M), SVR (M), RFR (M), ELR (M) & 2 & 0.98 \\ \hline \hline \end{tabular} \({}^{a}\) Also explores proximal data. \({}^{b}\) Also explores satellite data.  
\end{table} 
Table 4: References considered in this study–aerial scale. L, M, and H mean low-, mid-, and high-level data fusion, respectively. The numbers in the fourth column are those adopted in Table 2 for each “fused data” class.