esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Instrumentation and Acquisition System
#### 3.2.6 Core Module Back End  
The core module back end is mainly responsible for separating the mixed signals from each TIAs output into four independent raw signals containing information at specified wavelengths. As mentioned earlier, the port-D of the microcontroller is used to generate four clocks for multiplexing the LEDs. However, port-B of the MCU is programmed to output another three clocks used for synchronizing the demultiplexer, which separates the mixed signals into independent wavelength components. Clocks A and B control the internal switches of the demultiplexer to separate the mixed signal into four outputs. As the clocks of both ports D and B are synchronized, it is possible to sample a specific wavelength at each demux output. However, when an LED is switched from one state to the other (ON/OFF), there is a slight delay between the transitions, called the transient response of a diode. Therefore, by sampling the entire LED activation period, the switching noise is also sampled. To overcome this problem, the demultiplexer inhibits control and is activated during these transitions, limiting the sampling period only to the middle of the signal, where it is expected that the signal has enough time to settle before sampling (Figure 4).  
Moreover, it is necessary to 'hold' the sampled voltage until the next clock pulse triggers a new acquisition. Accordingly, a hold capacitor is placed at the output of the demux ports, followed by a buffer circuit that offers a high impedance to the hold capacitor to keep the held voltage from discharging prematurely. Previous studies have tested different hold capacitors for PPG signal sampling, establishing that a 220-nF capacitor provides an optimal performance [18].  
After the hold buffer, individual antialiasing filters with a cut-off frequency of 80 Hz were implemented. These passive RC filters limit the bandwidth of the signals and stop the high-frequency switching noise from corrupting them. Every low-pass filter output is the input of an op-amp buffer before the signals are passed onto the NI DAQ card for digitization and further analysis. The raw signals are sampled at 2 kHz and contain both AC and DC components, which are later separated digitally using LabView digital filters.  
Finally, the core module back end also allows the digitalization of the battery's state and LED's current monitoring, which are displayed on the LabVIEW interface to give  
Figure 4: LEDs multiplexing and photodetectors’ signals demultiplexing. Each color box represents a wavelength.  
the user an indication of the battery's state of charge and to adjust the LED's intensity on demand.
## 3 Networks and Methods
### Genetic Algorithm Time-Serialization Convolutional Neural Network (GATSCNN)
#### 3.3.2 Check Function  
In GA, the use of binary encoding for the population may result in data anomalies after the crossover and mutation operations. In such cases, it is necessary to convert the anomalous data into the desired normal data.  
Error 1: After the crossover or mutation operations in GA, the data become 0, as shown in Figure 8.  
Error 2: According to binary encoding rules, if the parameter range to be optimized is within the range of 1-100, seven bits are required to represent the parameter. However, after the crossover or mutation operations, the optimized parameter may exceed the upper limit, as shown in Figure 9.  
Figure 8: The 0 exception.  
Obviously, for a neural network model, regardless of whether the optimized parameter represents the number of layers or number of neurons, a parameter of 0 is incorrect. If the optimized parameter exceeds the upper limit or falls below the lower limit, the result is undesirable. For example, if the optimized parameter represents the length of a time series and if the length is too long, the user may need to wait for a long time to obtain the result. Therefore, the parameters to be optimized must be set to within a reasonable range.  
To address this problem, we propose a check function. The check function consists of three steps. Step 1: Convert the binary population into a decimal population. Step 2: Determine whether each optimized parameter is 0 or exceeds the limits. Step 3: Use the check function to convert anomalous data and replace original population data using the following equation:  
\[\text{Pk}=\text{Pk}\%\left(\text{Max\_K}-\text{Min\_K}\right)+\text{Min\_K} \tag{2}\]  
where Pk represents the data to be inspected, and Max_k and Min_K represent the upper and lower limits of Pk, respectively.
# Effect of a Hypoxia-Controlled Atmosphere Box on Egg Respiration Intensity and Quality
## 3 Results
### Changes in the Quality Indicators of Eggs under Different O\({}_{2}\) Concentrations
#### 3.2.3 Changes in the pH of Eggs Subjected to Different O\({}_{2}\) Concentrations during Storage  
The pH of egg white is related to the freshness of the egg [31]. The pH of egg white on Day 0 was about 8.6 and there were no significant differences among the five groups of eggs (\(p>0.05\); Table 3). During the 35-day storage period, the pH of egg whites showed an increasing trend. Eggs stored in an anaerobic environment (0% O\({}_{2}\)) had the most rapid  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Storage Days** & **A (0\% O\({}_{2}\))** & **B (5\% O\({}_{2}\))** & **C (10\% O\({}_{2}\))** & **D (15\% O\({}_{2}\))** & **E (20\% O\({}_{2}\))** \\ \hline
5 & 0.43 \(\pm\) 0.05 b & 0.39 \(\pm\) 0.03 b & 0.35 \(\pm\) 0.08 b & 0.28 \(\pm\) 0.04 b & 0.76 \(\pm\) 0.29 a \\
10 & 0.71 \(\pm\) 0.08 b & 0.61 \(\pm\) 0.04 b & 0.67 \(\pm\) 0.13 b & 0.79 \(\pm\) 0.09 c & 1.06 \(\pm\) 0.06 a \\
15 & 0.84 \(\pm\) 0.09 b & 0.81 \(\pm\) 0.17 b & 0.83 \(\pm\) 0.14 b & 0.93 \(\pm\) 0.09 b & 1.36 \(\pm\) 0.16 a \\
20 & 1.01 \(\pm\) 0.11 bc & 0.87 \(\pm\) 0.08 c & 0.82 \(\pm\) 0.03 c & 1.28 \(\pm\) 0.14 b & 1.62 \(\pm\) 0.31 a \\
25 & 1.18 \(\pm\) 0.14 c & 1.14 \(\pm\) 0.20 c & 1.10 \(\pm\) 0.14 c & 1.50 \(\pm\) 0.18 b & 1.77 \(\pm\) 0.30 a \\
30 & 1.51 \(\pm\) 0.20 bc & 1.49 \(\pm\) 0.23 c & 1.34 \(\pm\) 0.15 c & 1.87 \(\pm\) 0.10 b & 2.06 \(\pm\) 0.24 a \\
35 & 1.79 \(\pm\) 0.16 c & 1.67 \(\pm\) 0.10 c & 1.68 \(\pm\) 0.33 c & 2.08 \(\pm\) 0.23 b & 2.35 \(\pm\) 0.28 a \\ \hline \hline \end{tabular} Note: different letters in the same row indicate significant differences between treatments (\(p<0.05\)).  
\end{table}
Table 1: Changes in the weight loss rate of eggs under different oxygen (O\({}_{2}\)) concentrations during storage.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Storage Days** & **A (0\% O\({}_{2}\))** & **B (5\% O\({}_{2}\))** & **C (10\% O\({}_{2}\))** & **D (15\% O\({}_{2}\))** & **E (20\% O\({}_{2}\))** \\ \hline
0 & 84.31 \(\pm\) 3.65 a & 84.50 \(\pm\) 2.98 a & 83.29 \(\pm\) 2.45 a & 84.41 \(\pm\) 3.17 a & 83.49 \(\pm\) 2.54 a \\
5 & 74.30 \(\pm\) 4.45 a & 75.40 \(\pm\) 5.75 a & 73.22 \(\pm\) 4.75 a & 73.18 \(\pm\) 6.25 a & 72.02 \(\pm\) 2.06 a \\
10 & 68.00 \(\pm\) 4.89 a & 69.61 \(\pm\) 4.87 a & 70.67 \(\pm\) 8.54 a & 66.49 \(\pm\) 6.77 a & 67.06 \(\pm\) 5.87 a \\
15 & 68.03 \(\pm\) 3.82 a & 66.27 \(\pm\) 4.76 b & 65.30 \(\pm\) 5.45 b & 64.27 \(\pm\) 4.23 b & 65.90 \(\pm\) 3.32 b \\
20 & 6 increase in respiration rate between 0 and 15 days, and the rise in pH slowed down during the middle of the storage period. The eggs in Groups B and C exhibited a slower rise in respiration rate from 0-15 days, which then increased after 15 days. There were no significant differences in pH between Groups D and E in the first 15 days (\(p>0.05\)). After 15 days, the pH of eggs in Group E increased rapidly. There were no significant differences among the three groups with O2 concentration <15% on the 35 th day (\(p>0.05\)).
## 2 System Model and Problem Formulation
### Problem Modeling  
In this study, we model the deep reinforcement learning problem of the agent using a Markov Decision Process (MDP), which can be represented as \(<S,A,F,R>\), where \(S\) denotes the set of environmental states, \(A\) represents the set of actions taken by the agent, \(F\) is the state transition probability function, indicating the probability distribution of the next state given a specific state and action, and \(R\) is the reward function for the agent.  
The base learner uses the classic PPO (Proximal Policy Optimization) deep reinforcement learning algorithm.  
The main parameters include a state space \(S\), an action space \(A\), a state transition probability \(P\), and a reward function \(R\).  
The above parameters are defined as follows:  
* State space \(S\):  
The system state at time slot \(T\) is defined as \(\left(f_{n,T},f^{\prime}_{k,T},p_{n,T},p^{\prime}_{k,T}\right)\), where \(f_{n,T}\) represents the communication channel chosen by the communication system, \(f^{\prime}_{k,T}\) represents the channel number chosen to be jammed by the jammer, \(p_{n,T}\) is the transmission power chosen by the communication system, and \(p^{\prime}_{k,T}\) is the jamming power chosen by the jammer, all in time slot \(T\).  
Equation (2) shows the received power of the communication system \(P_{r,T}\) in time slot \(T\):  
\[P_{r,T}=p_{n,T}\cdot g_{n} \tag{2}\]  
* Action Space \(A\):  
The action space regarding the time slot \(T\) is defined as \(\left(f_{n,T+1},p_{n,T}\right)\), where \(f_{n,T+1}\) represents the transmission channel chosen by the communication system in time slot \(T+1\) and \(p_{n,T}\) represents the power chosen by the communication system in time slot \(T+1\).  
* Reward Function \(R\):  
When the communication system successfully transmits its data once, it will receive a reward; otherwise, if it fails in data transmission, it will receive a penalty.  
The communication system needs to consider the channel switching cost \(C_{f}\) when making decisions. The reward function of the communication system represents the  
Figure 1: The wireless communication system model used in our research.  
immediate reward that can be obtained by executing action \(a_{T}\) under the environmental state \(S_{T}\).  
\[R_{T}(S_{T},a_{T})=c_{T}-\frac{\eta P_{r,T}}{p_{maxf}} \tag{3}\]  
where \(c_{T}\) is the normalized throughput of the communication system and the power discount factor \(\eta\) has a constant value within it [0,1]. The higher the transmission power \(P_{r,T}\) is, the greater the reward discount \(\eta P_{r,T}/p_{max}\) will be.
## 3 Results and Discussion
### The Radar Response According to Soil Moisture Variations  
Figure 6 shows the temporal variations in the radar signal for HH and HV polarizations according to the soil moisture measurements (plot 2e). It shows that the behavior of the radar signal follows the evolution of soil moisture throughout the entire vegetation stage. Similar results were observed for all sampled plots.  
Figure 5: The relationship between the NDVI derived from the optical images and the vegetation measurements ((**a**) LAI, (**b**) HVE, (**c**) BIO and (**d**) VWC).  
Figure 6: An example of the temporal variation in SAR X-band signals in a given training plot (plot 2e) according to soil moisture (**a**), and vegetation biomass and height (**b**). In (**c**) photos with associated fraction vegetation cover (Fr) for plot 2e.  
The first cut occurred in early June (on DOY 150), and the first irrigation that followed was on DOY 158. Before the first irrigation, the soil moisture was approximately 0.18 cm\({}^{3}\)/cm\({}^{3}\) (on DOY 157). After the first irrigation (on DOY 158) two rainfall events occurred (on DOY 159 and DOY 160, with 3.2 and 9.2 mm precipitation, respectively). Following these events, the soil moisture considerably increased (to approximately 0.40 cm\({}^{3}\)/cm\({}^{3}\) on DOY 161). As a result, the radar signal increased between DOY 157 and DOY 161 in both the HH and HV polarizations of approximately 2.6 dB and 1.9 dB, respectively (the Mv increased to approximately 0.22 cm\({}^{3}\)/cm\({}^{3}\); the HVE was approximately 25 cm). On the SAR image acquired on DOY 162 (4 days after irrigation), HH and HV decreased approximately 2.1 dB due to a decrease in moisture from 0.40 to 0.26 cm\({}^{3}\)/cm\({}^{3}\). On the SAR image acquired on DOY 165, the radar signal continued to decrease due to a decrease in soil moisture (approximately 0.22 cm\({}^{3}\)/cm\({}^{3}\)).  
Following irrigation on DOY 189, the _in situ_ soil moisture was approximately 0.47 cm\({}^{3}\)/cm\({}^{3}\) on DOY 189; the radar signal was high (\(\sigma^{\circ}\)HH = -8.0 dB and \(\sigma^{\circ}\)HV = -16.5 dB) and the HVE was approximately 47 cm. Following a large rainfall on DOY 210 (33 mm) and irrigation on DOY 211, the radar signal on DOY 211 showed high HH and HV values (\(\sigma^{\circ}\)HH = -9.7 dB and \(\sigma^{\circ}\)HV = -17.7 dB). After two days (on DOY 213), the soil moisture decreased from 0.37 to 0.27 cm\({}^{3}\)/cm\({}^{3}\); the associated radar signal also decreased.  
On DOY 241, one day after irrigation on DOY 240, the soil contribution was high on the total backscattered signal despite a HVE high value (HVE approximately 90 cm). Indeed, the radar signal increased approximately 1.4 dB in both HH and HV between DOY 238 and DOY 241 (the Mv increased from 0.20 to 0.35 cm\({}^{3}\)/cm\({}^{3}\)).  
In conclusion, the results show that the radar signal could be used to identify three-day-old irrigated plots.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 2 Methods  
In this paper, on the basis of object orientation, change detection is carried out under the framework of a random forest based on the color co-occurrence matrix model. The overall process is shown in Figure 1. It includes four main steps:  
1. **Optimal object construction based on multi-scale segmentation**: In order to ensure the spatial consistency of the two periods of remote sensing images, band combination is carried out. On the basis of over-segmentation based on graph-based superpixel segmentation, the FNEA algorithm is utilized to establish multi-scale objects, and then the optimal segmentation scale is determined according to the maximum criterion of OGF to obtain the optimal object. 2. **Multi-feature extraction based on object:** There are two types of features: spectral features and texture features. Spectral features include the mean and variance of bands, and texture features are extracted by CCM on the basis of a pairwise combination of bands, including 8 directions and 4 statistical variables, i.e., angular second moment (ASM), contrast (Con), correlation (Cor), and inverse different moment (IDM). 3. **Feature filtering based on CBSS:** On the basis of the random forest model, the contribution degree of characteristics of one-time training is estimated. The cumulative value of the contribution degrees obtained from multiple rounds of training is considered as a standard to delete the least important feature in turn until the given minimum number of features is reached. Then, the feature combination with the highest accuracy and the smallest number of features is selected as the optimal feature combination. 4. **Object-oriented change detection based on the random forest**: The differential image based on the optimal feature vector set after screening is incorporated into the random forest model for change detection, and the detection results are evaluated qualitatively and quantitatively.
# NN-Based 8 FSK Demodulator for the Covert Channel
## 5 Simulations Results  
We have conducted a simulation to assess the BER performance in the presence of imperfect SIC operation measured by \(\text{e}_{\text{abs}}\) (distortion expressed in %). The main parameters of the simulation are shown in Table 2. It was assumed that \(\text{e}_{\text{abs}}\) is constant over the transmission of the one symbol 8 FSK. The evaluation of the correlator and NN-based demodulator was done at different stages of the covert signal data reception process:  
\(\bullet\)Hard 8 FSK demodulation;  
\(\bullet\)Hard/soft 8 FSK demodulation with a covert data decoder.
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 5 Conclusions  
In this paper we presented a probabilistic modeling framework for situated design of personalized soundscaping algorithms for hearing aids. In this framework, the hearing aid client gets to design her own sound processing algorithm under situated conditions, based on small recordings of source signal fragments. The framework is very general and allows for plug-in substitution of alternative source models. Since hearing aids are resource constrained devices, we proposed a very compact generative model for acoustic mixtures and execute approximate inference in real-time through efficient message passing-based methods. Furthermore, we have derived novel and more general variational messages for the Algonquin node and the Gaussian scale sum node, and we have described a general procedure for source separation in which mixture models are incorporated. Supported by the experiments, the current approach has shown to be capable of performing source separation. In view of these results, we consider this system an interesting starting point towards user-driven situated design of personalized hearing aid algorithms. Future developments include the automated learning of the model structure and the automated learning of the user preferences for better perceptual performance.  
Source code, experiments and the derivations of the variational messages in Tables 1 and 2 are available at [[https://github.com/biaslab/SituatedSoundscaping](https://github.com/biaslab/SituatedSoundscaping)]([https://github.com/biaslab/SituatedSoundscaping](https://github.com/biaslab/SituatedSoundscaping)).  
Conceptualization, B.v.E.; Formal analysis, B.v.E.; Investigation, B.v.E.; Methodology, B.v.E.; Supervision, A.P., T.I. and B.d.V.; Validation, B.v.E.; Visualization, B.v.E.; Writing--original draft, B.v.E.; Writing--review & editing, A.P., T.I. and B.d.V. All authors have read and agreed to the published version of the manuscript.  
This work was partly financed by GN Advanced Science, which is the research department of GN Hearing A/S, and by research program ZERO with project number P15-06, which is (partly) financed by the Netherlands Organisation for Scientific Research (NWO).  
Not applicable.  
This appendix will describe in detail how exactly the three inference tasks from Section 2.1 are realized under the model specification of Section 2.2. In the upcoming subsection we will discuss the inference tasks: source modeling (2), source separation (3) and soundscaping (5).
## 3 Proposed Protocol: ELEACH-DFL
### Cluster Head Selection  
When each round starts, each node calculates its own chance value through fuzzy operation. To select the optimal CHs to extend the network lifetime, the remaining energy of each sensor node and the local distance, the sum of the distances from the nodes within a certain range, are regarded as input variables. Figure 5 is the first FIS block diagram for CH selection. The inputs and the output of the first FIS are described in terms of variable names and meanings in Table 2. Membership functions of the FIS are configured like Figure 6 to get optimal performances in terms of energy. The selection of CH candidates is fulfilled using Equation (1), _T(n)_ threshold equation of LEACH. Subsequently, after randomly forming a cluster, compare the calculated chance between the CH candidate and the node with the highest chance in a cluster of becoming the CH. The detailed sequence of the CH selection process is shown in below Figure 7.  
Figure 4: A basic FIS (fuzzy inference system) process.  
Figure 5: First FIS for cluster head (CH) selection.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{Input variables} & Energy & Residual energy of the node \\ \cline{2-3}  & LocalDistance & The distance to the surrounding nodes \\  & within a certain range \\ \hline \multirow{2}{*}{Output variable} & \multirow{2}{*}{Chance} & The threshold value (fuzzy crisp output) to be selected \\  & & as the CH \\ \hline \hline \end{tabular}
\end{table}
Table 2: Input and output names and meanings.  
Figure 6: Membership functions of inputs and output in the first FIS.  
Figure 7: Flowchart of the cluster head selection process.
# Satellite Measured Ionospheric Magnetic Field Variations over Natural Hazards Sites
## 5 Results
### Earthquakes
#### 5.2.3 Earthquake M8.2 Ndoi Island, Fiji  
The Ndoi Island, Fiji, EQ (USGS ID us1000 gcii) had a magnitude M8.2 with a focal depth of 600 km, the distribution of the magnetic field fluctuations are shown in Figure 5. CDSM shows a wavelike behaviour along the frequency bands, the upper bands (lower frequencies) are dominated by the pre-earthquake crossings (higher blue bars according to the colour coding). The fluctuations for FGM1 are generally low, the X- and Z-components along the frequency bands are attenuated after the earthquake.  
Figure 5: CSES investigation of the earthquake USGS event us1000 gci with magnitude M8.2, Ndoi Island, Fiji, focal depth 600 km, with a spatial dimension (box size) of \(\pm\)5.5 degrees, but only crossings during night times and east of the event site are considered. **Top**: Distribution of CSES CDSM magnetic field fluctuations grouped in individual frequency bands. The time span around the EQ is \(\pm\)2 segments, i.e., in this case \(\Delta\)t = \(\left\{-6.44,\,-2.43,\,-1.44,\,2.57,\,3.55\right\}\) days shift to the EQ at 2018-08-19700:19:40. **Bottom**: Distribution of the CSES FGM1 magnetic field fluctuations after wavelet analysis. The colour coding is the same as in Figure 2, geomagnetic activity is \(|\)Dist\(|\)\(\leq\) 37 nT for the selected time span.
## 4 Result and Analysis
### BDS-2 and BDS-3 Broadcast Clock Offsets
The STD of all BDS-2 satellites is better than 2.00 ns for three ACs. With the development of BDS, the STD of BDS-3 satellites is approximately better than 0.77 ns for three ACs. The mean STD of all BDS-2 satellites for GFZ, WHU and SHA are 1.22 ns, 1.18 ns and 1.34 ns, and the mean STD of all BDS-3 satellites for three ACs are 0.55 ns, 0.59 ns and 0.47 ns, respectively. The improvement rate of BDS-3 STD of GFZ, WHU and SHA are 54.65%, 60.18% and 56.14% compared to BDS-2, respectively. The mean STD of all BDS-2 satellites of three ACs is 1.22 ns, and BDS-3 is 0.58 ns. The improvement rate of BDS-3 mean STD is 52.49% compared to BDS-2. About the RMS statistics of broadcast clock errors, as mentioned earlier in this paper, the broadcast clock error will absorb the new datum of the reference clock. Hence, the inconsistencies of the broadcast clock errors exist for the same satellite in different ACs. The mean RMS of all BDS-2 satellites for GFZ, WHU and SHA are 2.92 ns, 3.32 ns and 2.45 ns, and that of BDS-3 for three ACs are 1.90 ns, 1.46 ns and 1.84 ns, respectively. RMS of BDS-3 broadcast clock errors for the GFZ, WHU and SHA can improve 34.66%, 55.84% and 24.51% compared to that of BDS-2, respectively. The mean RMS of all BDS-2 and  
Figure 8: The RMS time series of BDS clock errors between broadcast clock offsets and the precise clock offsets obtained from GFZ, WHU and SHA. BDS-3 satellites of three ACs are 2.90 ns and 1.73 ns, respectively, and that of BDS-3 can improve 40.34% compared to BDS-2. From the perspective of comprehensive statistical results, there is a small difference between GFZ, WHU and SHA in STD statistical results and improvement rate. However, the RMS statistical results and improvement rate for GFZ, WHU and SHA are slightly different in some BDS satellites. These findings and conclusions are consistent with Figures 7 and 8. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**PRN**} & \multicolumn{2}{c}{**GFZ**} & \multicolumn{2}{c}{**WHU**} & \multicolumn{2}{c}{**SHA**} & \multicolumn{2}{c}{**Mean**} \\  & **STD** & **RMS** & **STD** & **RMS** & **STD** & **RMS** & **STD** & **RMS** \\ \hline C01 & 0.01 ns & 6.29 ns & 0.73 ns & 0.87 ns & 1.24 ns & 2.76 ns & 0.94 ns & 531 ns \\ C02 & 0.72 ns & 3.17 ns & 0.78 ns & 1.39 ns & 0.94 ns & 2.0