esa-sceva/satcom-chunk-collection huggingface dataset:
# Wheat Ear Detection Algorithm Based on Improved YOLOv4
## 3 Results and Discussion
### Model Training
In Figure 4c, 47 objects were detected, 2 failed, and the accuracy was 95.92%. In Figure 4d, 56 objects were detected, 1 failed, and the accuracy was 98.25%. According to the results, few wheat ears were left out. The main reason is that the wheat ear size is very small, the wheat ear density is relatively large in some areas of the images, and there are mutual occlusions between the wheat ears, and between leaf and wheat ear. From the overall detection results of Figure 4, it can be seen that the algorithm shows high detection accuracy in different datasets. The size and position of the prediction boxes match well with the wheat ears. It demonstrates that the proposed algorithm has high robustness in wheat ear detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **AP(\%)** & \(\mathbf{F_{1}}\) & **Precision(\%)1** & **Recall(\%)1** & **FPS** \\ \hline Proposed & 95.16 & 0.87 & 96.91 & 79.00 & 51.78 \\ YOLOv4 & 92.00 & 0.87 & 95.09 & 79.51 & 54.06 \\ CBAM-YOLOv4 & 96.40 & 0.93 & 89.73 & 96.55 & NA \\ \hline \hline \end{tabular} \({}^{1}\) Score threshold is 0.5.  
\end{table} 
Table 1: Performance evaluation result comparison on HRED dataset. Figure 3: Precision–Recall curves for 2 datasets. (**a**) Precision–Recall curve based on HRED dataset. (**b**) Precision–Recall curve based on GWHD dataset. The numbers of detected wheat ears and true wheat ears by the proposed algorithm and YOLOv4 are fitted and shown in Figure 5. In Figure 5, the \(R^{2}\) value is 0.973 and Root Mean Squared error (RMSE) is 4.81. In Figure 5, the \(R^{2}\) value is 0.954 and RMSE is 6.7. The proposed algorithm works better than YOLOv4. Compared with [28], though RMSE is a little larger, the proposed method has a high \(R^{2}\). In conclusion, the fitted model by the proposed algorithm provides a strong linear relationship between the detected and real numbers. It implies that the model has a robust ability to detect wheat ears. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Detected Number** & **Real Number** & **Accuracy** \\ \hline
170 & 175 & 97.14\% \\
132 & 134 & 98.5\% \\
47 & 49 & 95.92\% \\
56 & 57 & 98.25\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Detected and real numbers of wheat ears. Figure 4: Detection results of some example images. (**a,b**) are from HRED dataset. (**c,d**) are from GWHD dataset.
## 4 Novel Digital Cash Paradigm Description
### Spending an e-Coin  
A customer wishing to acquire some product \(P\) whose price is worth the value of an e-coin asks the vendor to engage in the following procedure:  
1. The customer sends \(\{(X_{S},Y_{S}),(X_{R},Y_{R}),\text{Sign}_{PK_{V}}(Y)\}\) to the vendor together with a digital signature \(\text{Sign}_{Q_{S}}(\mathcal{H}(CurrentTime||Y_{S}||Y_{R}))\) computed with private key \(v_{S}\) (\(\mathcal{H}\) is a hash function).
2. The vendor runs \((Q_{S},r_{S})=\text{OAEP}_{\text{PA}}{}^{-1}(X_{S},Y_{S})\). If the plaintext-awareness checking is met, they check the digital signature received at the previous step under \(Q_{S}\). In case of failure, the e-coin is rejected.
3. The vendor computes \(Y=Y_{S}\oplus Y_{R}\) and checks that \(\{Y,\text{Sign}_{PK_{V}}(Y)\}\) is a valid digest-signature tuple under vendor's public key \(PK_{V}\).
4. The vendor checks that no e-coin with the same \(Y_{S}\) component has been spent before. In such a case, the previously stored digital signature, which includes the time it was spent for the first time, is returned as a proof of double spending and the transaction is rejected. Otherwise, all the data received at step 1 is stored by the vendor.
5. The vendor computes \((Q_{R},r_{R})=\text{OAEP}^{-1}(X_{R},Y_{R})\).
6. The vendor encrypts the product \(P\) under public key \(Q_{R}\) (creating a digital envelope if \(P\) is large) and sends the resulting ciphertext to the customer.
7. If the spent e-coin was valued, the customer decrypts the received ciphertext using private key \(v_{R}\), getting \(P\) as a result. Otherwise, this step is skipped and the customer does not get any product.
## 4 Results
### Models' Comparative Analysis
#### 4.4.5 Giacommini-White Test  
Giacomini-White (GW) test is a test of equal conditional predictive ability.  
From Table 9, GA dominates RNN, meaning that GA has better predictive ability. It is also seen that RNN has a better predictive ability compared to KNN and that GA dominates KNN. From these results, it can be deduced that GA dominates RNN which dominates KNN. Therefore, GA has the best conditional predictive ability compared to the other two models. These results are consistent with those from the Murphy diagrams.  
Figure 10 shows GHI superimposed with GA, RNN, and KNN forecasts during clear sky days (top panels) and during cloudy days (bottom panels) on four selected days during the testing period. For the selected days, the GA tends to over-predict GHI during sunny days compared to the other two models, RNN and KNN. However during cloudy days GA appears to perform better.  
Figure 8: Plots of the RNN and KNN forecasts showing the relationship between their empirical scores (**left panel**) and the differences in empirical scores with 95% confidence intervals (**right panel**).  
Figure 9: Plots of the GA and KNN forecasts showing the relationship between their empirical (**left panel**) and the differences in empirical scores with 95% confidence intervals (**right panel**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Test Statistic** & \(p\)**-Value** & **Result** \\ \hline RNN = GA & 323.925 & \textless{}0.0001 & Sign of mean loss is (\(+\)). GA dominates RNN \\ RNN = KNN & 4.569 & 0.1018 & Sign of mean loss is (\(-\)). RNN dominates KNN \\ GA = KNN & 294.676 & \textless{}0.0001 & Sign of mean loss is (\(-\)). GA dominates KNN \\ \hline \hline \end{tabular}
\end{table}
Table 9: Model comparisons: Giacommini–White test.
# Environmental Remediation of Metribuzin Herbicide by Mesoporous Carbon--Rich from Wheat Straw
## 3 Results and Discussion  
### Biochar Properties  
The remediation efficiency of mesoporous carbon materials is hardly affected by its morphological, composition and structural properties [54]. Table 1 states the characteristics of the WS and WSB. The elemental composition of biochar particularly covers carbon, hydrogen, oxygen, nitrogen and sulfur [55; 56]. The molar ratios of these elements, expressed as O/C and H/C or (O + N)/C, reveal the aromaticity and polarity of the biochar [57]. During the thermal analysis, three events were noticed [58]. The first process at 120 \({}^{\circ}\)C was linked to the release of moisture and adsorbed water from the material surface, while the succeeding event at 550 \({}^{\circ}\)C, was related with cellulose and hemicellulose volatilization. At higher temperatures (800 \({}^{\circ}\)C) which is the final stage, the lignin decomposition took place. In our study after pyrolysis, the carbon content increased from 47.15% to 73.25%, while the oxygen and hydrogen content decreased from 43.93% to 19.67% and 6.56% to 2.61% respectively.  
The O/C and H/C WSB ratios were lower as the WS was raw due to the high carbonization with the development of the extra aromatic and minor hydrophilic WSB structure [59]. The low H/C ratio indicates that the obtained biochar has a high stability, which is a measure of its resistance to microbial and chemical degradation [60; 61]. Similarly, a low O/C ratio shows the aromatic ring's structural arrangement. [62]. Because of the reduced polarity index (O + N)/C, the biochar surface active groups were eliminated, which may enhance metribuzin adsorption [63]. These observations also confirm the results reported by [64] and [65], which sustain that a low O/C ratio denotes a high level of stability of biochar with a half-life higher than 1000 years.  
Biochar's basic components include ash, volatile matter, fixed carbon and moisture percentage [66]. The amount of solid residues left after the sample is completely burned is referred to as the ash content. A high quantity of ash is unsuitable because of the presence of additional minerals, which may obstruct biochar pores and minimize the number of active sites [65]. Based on the low ash content of 5.68% and relatively higher carbon content, the WSB might be suitable for pollutant attraction, Biochar derived from animal manures, for example, contains more ash and less carbon content, making it unsuitable for soil amendment and the removal of toxins and pollutants from the soil and aqueous environment [64].
## 3 Decomposition and Assignment of Observation Missions
### Marine Vehicles Operating Modes for Ocean Observations  
The observation processes also have their own focus and requirements with respect to different ocean phenomena. For example, using shipboard CTD cross-section observations, temperature and salinity at different levels are collected. Moored subsurface buoys are mainly proposed to establish _in situ_ and long-term observing systems for ocean mixed layers and air-sea interface observations. Drifting buoys can provide ocean temperature data covering a large range of ocean, therefore drifting buoys are chosen to establish the global observation array for ocean forecasting and climate studies. Here, the observing requirements and operating mode for marine vehicles are defined as follows:  
* Collaborative observations in a hierarchical plane: We constrain marine vehicles' path on a parameterized geometric curve, and maintain vehicles' positions and velocities with certain relationships, then execute coverage sampling observations. In this mode, the three dimensional observations are simplified to two dimensional observations in a horizontal plane at certain depths. This flexible observation mode makes possible quick adjustment of geometric parameters and collaborative relationships, so the sample regional area can be zoomed to maximize the validity of the sample data. This operating mode is suitable for AUVs, USVs,  
Figure 2: Motion characteristic of marine vehicles.  
and AUGs. We can get real-time connection or NRT connections from AUVs and USVs, and then design the collaborative control methods. For AUGs, when they float to the sea surface, the shore-based control center gets their position and then a control law is designed to pre-plan the gliders' distribution during the next diving period.
2. Cross section (or profile) continuous observations: AUGs can execute repeat and continuous observations on the selected cross section. Compared to the shipboard CTD observations and ARGO floats, AUGs provide sample data with long duration and high sampling density.
3. Virtual mooring array observations: The marine vehicles are controlled in a confined region as an array to execute repeated sampling. This kind of observation is similar to the continuous vertical profile observations achievable with a fixed-point mooring subsurface buoy. A mobile virtual mooring array is made up of multiple marine vehicles. Then the surface observation and cross section observation can be combined together. In addition, through the vehicles' position control, the suitable anchor point is selected as the virtual anchor array center. This mode is suitable for AUGs.
## 5 Conclusions  
In order to blindly demodulate the CDS-SS signals under the colored or non-Gaussian noises condition, a PF-based algorithm is proposed in this paper. To implement this algorithm, the intruder uses a different chaotic system equation to fit the transmitter's chaotic system equation. Moreover, the colored or non-Gaussian noises are formulated by ARMA models. In addition, the range-differentiating factor is imported into the intruder's chaotic system equation. Therefore, two modified PFs are obtained, which are implemented in reciprocal interaction to estimate the message and the chaotic state. Since the range-differentiating factor is able to make the inevitable chaos fitting error advantageous, the CDS-SS signals can be blindly demodulated according to the range of the estimated message signals. Simulations show that the proposed algorithm is robust to both colored or non-Gaussian noises, and the original binary message can be retrieved from the CDS-SS signals with satisfactory BER performance without any knowledge of the transmitter's chaotic map, parameters, or initial value.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 3 Simulation and Signal Processing Tools
### Iridium Signal Processing Tool
Such an averaging reduced the frequency error spread by a factor of \(\sqrt{Ta/Ti}\sim\)7. Figure 4: Processing results of an Iridium burst. (**a**) Baseband signal showing the structure of the burst consisting of a tone over \(\sim\)2.5 ms, followed by a BPSK sequence, and completed by a QPSK sequence; (**b**) the PLL frequency estimate and its filtered value; (**c**) CNR estimate. In Figure 6, we compare the Doppler shift measurements of real bursts to the expected values computed based on the satellite orbits (TLE file of the day of the collect [28]), the position of the observer (static), and the signal recording timestamp. The results show that the measurements and the expectations are consistent. The residual difference can be explained by:  
* the approximate satellites orbits based on TLE-SPG4 used to compute the expected Doppler shift,
* the timestamp error of the records, which is better than 1 s (a time error leads to satellite position and velocity errors, which convert to an error in the expected Doppler shift),
* the clock drifts of the satellites and the signal recorder, which are not taken into account in the expected Doppler shift calculation. We also noticed the periods where we do not have measurements (namely at the beginning when a satellite appears or at the end when it disappears). This is because of the weak signal strength at these periods.
# Tunable non-Hermiticity in Coupled Photonic Crystal Cavities with Asymmetric Optical Gain
###### Abstract  
We report a rationally designed coupled photonic crystal (PhC) cavity system that comprises two identical linear defect nanocavities, and we numerically investigate the controllable non-Hermitian optical properties of the eigenmodes of the nanocavities. Three different coupling schemes, namely, the tuning of the sizes of shared airholes, vertical shifting of one of the nanocavities, and lateral shifting of one of the nanocavities, are proposed. We examined the ability of these schemes to control the coupling strength between component cavities, which is a key factor that determines the non-Hermiticity of the system. Moreover, we introduce controlled levels of spatially asymmetric optical gain to the coupled PhC cavity by employing the vertical shifting scheme and independently tuning the gain and loss of individual nanocavities. Consequently, we successfully achieve the correspondingly tuned non-Hermitian behaviors of complex eigenfrequencies, such as the controlled emergence of phase transitions at exceptional points and the asymmetric development of amplified and decayed eigenmodes.  
photonic crystal cavity; asymmetric optical gain; non-Hermitian optical system; exceptional points +
Footnote †: journal: Applied Sciences
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
## 4 Experimental Results and Settings
### Entropy  
The objective of entropy is to present the average content information of an image. A larger value of entropy is assumed to be good image quality. To compute image entropy, Equation (7) is as follows:  
\[ENT=\ -\sum_{l=0}^{L-1}I(l)logI(l). \tag{7}\]  
Here, \(ENT\) is entropy, \(L\) denotes gray levels, \(l\) means intensity level, and \(I(l)\) is a probability density function.  
It can be analyzed from Table 2 that the entropy measures of the proposed art using four tested images were acceptable because it contained a higher rate of contrast and image information than other approaches. HE, AHE, and CLAHE results in the entropy domain were less acceptable because of a loss of image information that further led to the false detection of DR. Meanwhile, ESIHE results were nearly observed with the proposed method, but it suffered from the over-enhancement of noise in the entropy domain. The obtained entropy by the proposed art was noticed to be acceptable with original tested image entropies. The proposed art enhanced the contrast without an amplification of the noise component. Thus, it can be concluded that the performance of the proposed art using an entropy parameter was highly superior to an existing method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Statistical Parameter** & **Method** & **Image01** & **Image02** & **Image03** & **Image04** \\ \hline \multirow{8}{*}{**Entropy**} & Original & 4.56 & 4.55 & 4.57 & 4.61 \\  & HE & 4.13 & 4.11 & 4.31 & 4.11 \\ \cline{1-1}  & AHE & 4.21 & 4.23 & 4.00 & 4.21 \\ \cline{1-1}  & CLAHE & 5.12 & 4.99 & 4.63 & 4.80 \\ \cline{1-1}  & ESIHE & 4.42 & 5.00 & 4.60 & 4.70 \\ \cline{1-1}  & **PROPOSED** & **4.53** & **4.52** & **4.55** & **4.60** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Entropy measurement of the proposed method with other histogram approaches.  
Figure 8: A histogram variance performance comparison: (**a**) Input image histogram, (**b**) proposed histogram, (**c**) HE histogram, (**d**) AHE histogram, (**e**) CLAHE histogram, and (**f**) ESIHE histogram.
# Assessing the Added Value of Sentinel-1 PolSAR Data for Crop Classification
## 4 Results
### Crop Classification Results  
In this section, we present the classification results obtained by using the initial dataset, containing Sentinel-1 and cloud-free Sentinel-2 imagery, as described in Section 2.3. Table 3 shortly presents the overall performance of the SVM models trained with different inputs. S1 and S2 refer to the SVM models trained with the time-series data derived from the Sentinel-1 and Sentinel-2 satellites, respectively, whereas S1/S2 describes the SVM model trained with all available features. On the other hand, GA refers to the model trained with a 80-feature set derived from one single GA run. Finally, the last row of the table presents the results obtained by exploiting 100 GA runs, which is explained in detail in Section 4.1.4.
###### Abstract  
This paper performs a techno-economic assessment in deploying solar photovoltaics to provide energy to a refrigeration machine for a remote underground mine. As shallow deposits are rapidly depleting, underground mines are growing deeper to reach resources situated at greater depths. This creates an immense challenge in air-conditioning as the heat emissions to mine ambient increases substantially as mines reach to deeper levels. A system-level design analysis is performed to couple PV with a refrigeration plant capable of generating 200 tonne of ice per day to help to mitigate this issue. Generated ice can directly be used in cooling deep underground mines via different types of direct heat exchangers. State-of-the-art technology is used in developing the model which aims to decrease the size and cost of a conventional refrigeration system run on a diesel generator. Costs associated with deploying a solar system are computed as per the recent market value. Energy savings, carbon emissions reduction, and net annual savings in employing the system are quantified and compared to a diesel-only scenario. In addition, two different energy storage strategies: an ice storage system and a battery storage system, are compared. A detailed economic analysis is performed over the life of the project to obtain the net cash flow diagram, payback period, and cumulative savings for both systems. Moreover, a sensitivity analysis is proposed to highlight the effect of solar intensity on solar system size and the area required for installment. The study suggests that the use of solar PV in mine refrigeration applications is technically feasible and economically viable depending on the sun-peak hours of the mine location. Additionally, the economics of deploying an ice storage system compared to the battery storage system has a better payback period and more cumulative savings.  
deep mine cooling; solar electric refrigeration; ice thermal energy storage; techno-economic assessment; solar PV design; renewable energy in mining; solar-powered ice maker +
Footnote †: journal: Applied Sciences
## 3 Error Estimation for the Acoustic Maps
### Cluster Compostion Error on Time Intervals Smaller than the Hour: \(\sigma_{comp}^{2}\)  
Our clustering calculations are based on hourly data of equivalent traffic noise. In order to have a guidance to define the updating time of the noise map, we investigated two time intervals shorter than the 1 h reference; for practical applications, we will deal with time intervals of 5 min, 15 min, and 1 h. Therefore, the question arises as to whether the composition (set of road stretches) belonging to each cluster remains the same, and, if it does not, how the differences are to be quantified. We have therefore repeated the clustering process for the 5 min and 15 min data and compared them with the hourly results in the following way. For each time interval \(\tau\) = (5, 15) min, we obtained the mean values \(\delta_{meas,C1,2(\tau)}(\tau)\) every \(\tau\) mins, averaged over the roads belonging to Clusters 1 and 2 as given by the clustering analysis for time interval \(\tau\). Then, we calculated the same mean quantities using the cluster composition obtained from the hourly clusters, \(\delta_{meas,C1,2(t)}(\tau)\). Finally, the mean-square differences were obtained:  
\[\sigma_{comp,C1,2}^{2}(\tau)=\frac{1}{N(\tau)}\sum_{t=1}^{N(\tau)}[\delta_{meas,C1,2(\tau)}(t)-\delta_{meas,C1,2(t)}(t)]^{2} \tag{8}\]  
where \(N(\tau)\)= 24