esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Better crop yield depends on early growth and developmental phases, namely germination, seedling vigor, and plant development. These phases of plant growth are critically related with soil properties, such as porosity, moisture, and nutrient supply. In this way, germination and seedling quality have vital role on crop growth and yield throughout the cropping season [1]. Poor germination of seeds could be enhanced through scarification (mechanical method to reduce seed dormancy) and temperature treatment to seeds [2, 3],and the decontaminating or screening of seeds using treatment based on magnetic fields and irradiation with light of various wavelength [4; 5]. Germination rate could also be improved by soaking seeds in algal or _Cyanobacteria_ aqueous culture prior to sowing [6]. Soil properties perform a key role in seed germination and growth because early crop stand is directly influenced by soil characteristics such as organic matter (OM), soil bulk density (BD), soil porosity, and soil moisture content [7]. In addition to this, improvement in soil properties also increases nutrients and moisture availability to plants, resulting in high germination percentage and improved seedling vigor of crop. Along with this, improving soil organic contents positively manipulates soil physical parameters that ultimately leads to better crop growth and enhanced productivity [8].  
Biochar is a highly carbon-containing organic material produced through the process of pyrolysis of several kinds of biomasses [9; 10; 11]. Due to its complex aromatic structure, biochar is recalcitrant in nature that makes it more difficult to degrade it in soil [12]. This property of biochar is also an excellent approach to fix organic carbon in soil for longer durations by using crop residues which not only improves soil organic carbon, moisture holding capacity, and physical properties, but also reduces environmental carbon footprints [13; 14]. Biochar mixing into the soil directly or indirectly affects different soil indicators such as soil structure, soil porosity [15], cation retention capacity [12], moisture retention [16], soil fertility improvement [17], retention of contaminants and xenobiotics in soil [18], and bacterial diversity [19; 20]. It also improves soil physico-chemical characteristics by enhancing plant growth and productivity [21]. Although biochar is widely used in agriculture, its effect varies depending on plant variety and soil type [22], as soil type may also affect the function of biochar [23]. Several other researchers also proposed that biochar application into the soil is an attractive approach for better crop growth [24; 25; 26].  
Previous research experiments performed by various scientists were more inclined to assess biochar role in soil health and crop yield when applied to soil [27; 28]. Whereas the effect of biochar on early growth stages of plants, viz. seedling emergence and growth is seldom studied. Therefore, the present study was conducted with the special aim to understand the effect of biochar application as organic amendment on the germination of maize seeds and early growth of its seedlings.
# A Cooperative Communication Protocol for QoS Provisioning in IEEE 802.11p/Wave Vehicular Networks
## 4 Proposed Scheme
### Relay Node Selection
\[PMET_{i}=\frac{R-D_{i}}{2\times|\mathbf{V}_{i}-\mathbf{V}_{o}|}, \tag{1}\]  
where \(R\) is the transmission distance of OBU, and \(D_{i}\) is the distance between itself and the \(i\)-th OBU. \(V_{i}\) is the moving speed vector of the \(i\)-th OBU, and \(V_{o}\) is the moving speed vector of itself. After calculating the \(PMET\), the OBU selects three adjacent OBUs with the longest \(PMET\) and transmits a Coop request message. Figure 2 shows the flowchart of the OBU that does not receive the WSA, and Figure 3 shows the structure of the proposed Coop Request message. WSMP header extension field defines the channel that is used for communication. The PMET field indicates the calculated PMET value. The Data Rate field is set to the data rate that the recipient device recommends to the source device. OBUs receiving the Coop Request message transmit a Coop Relay message to the RSU, and Figure 4 shows the format of the proposed Coop Relay message. The Type field indicates whether the transmitted message is a request or a relay. The PMETSR field indicates the PMET between the OBU requesting the cooperative communication and the relay candidate OBU, and the PMETRD field indicates the PMET between the relay candidate OBU and the RSU. The DRSR field indicates the data rate between the OBU requesting the cooperative communication and the relay candidate OBU, and the DRRD field indicates the data rate between the relay candidate OBU and the RSU. RSU receiving Coop Relay message has to calculate the total transmission time to transmit WSA message from source to destination. Considering the Rayleigh fading model, the received SNR has exponential distribution given by [49]:  
\[f(\gamma)=\frac{1}{\overline{\gamma}}\exp(-\gamma/\overline{\gamma}),\quad \gamma\geq 0 \tag{2}\]  
where \(\overline{\gamma}\) is the average SNR. The probability that the data frame is correctly received at a distance d is given by  
\[P[\gamma(d)\geq\psi]=\exp(-\psi/\overline{\gamma})  
RSU receiving Coop Relay message has to calculate the total transmission time to transmit WSA message from source to destination. Their transmission times are calculated as follows:  
\[T_{\text{CT}}=P(r=R_{SR})\cdot\frac{8L_{W}}{R_{SR}}+P(r=R_{RD})\cdot\frac{8L_{W}} {R_{RD}}+2\cdot T_{DISF}, \tag{6}\]  
where \(L_{W}\) is the size of WSA message. \(R_{SR}\) and \(R_{RD}\) are the data rate from source to relay, and from relay and destination. The \(T_{DIFS}\) is the interframe space interval defined by the IEEE 802.11p specification. The \(RSU\) calculates the relay decision parameter (\(RDP\)) of the relay candidates and selects the OBU with the lowest \(RDP\) as the relay node. The \(RDP\) is calculated as follows. \[RDP=\frac{T_{CT}}{PMET_{SR}+PMET_{RD}} \tag{7}\]
## 2 Results and Discussion
### Catalytic Oxidation of Ethylbenzene
#### 2.1.2 Stability of CeNR in the Liquid-Phase Oxidation Reaction
\begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline
**Catalyst** & **Temperature** (\({}^{\circ}\)C) & **Conversion** **6/120 h** & **PhEtOOT** **Selectivity** **6/120 h** & **PhEt=O Selectivity** **6/120 h** \\ \hline CeNR & 55 & 6/51 & 100/100 & 0/0 \\ \hline CeNR (run 1) & 70 & 15/64 \({}^{\text{a}}\) & 100/82 \({}^{\text{a}}\) & 0/18 \({}^{\text{a}}\) \\ \hline CeNR (run 2) & 70 & 11/64 & 100/82 \({}^{\text{a}}\) & 0/18 \\ \hline CeNR-BuOOH & 70 & 16/97 \({}^{\text{a}}\) & 100/34 \({}^{\text{a}}\) & 0/64 \({}^{\text{a}}\) \\ \hline CeNR-H\({}_{2}\)O\({}_{2}\) & 70 & 10/78 & 100/71 & 0/29 \\ \hline CeNR & 90 & 31/96 & 100/43 & 0/57 \\ \hline CeNR & 105 & 22/88 & 100/35 & 0/65 \\ \hline Ce(SO\({}_{4}\))\({}_{2}.9\)H\({}_{2}\)O & 70 & 0/15 & -/66 & -/34 \\ \hline \end{tabular} \({}^{\text{a}}\) Reaction time:150 h.  
\end{table} 
Table 2: Catalytic oxidation of PhEt with _t_-BuOOH, in the presence of the cerium-based catalysts. However, CeNR600 is inactive in the reaction of PhEt with _t_-BuOOH, at 70 \({}^{\circ}\)C. The calcination process induces marked differences in the colour (CeNR is yellow and CeNT600 is light maroon) and in the emission spectra of these materials, leading changes in the distribution of Ce\({}^{3+}\)/Ce\({}^{4+}\) ions (as discussed ahead in section 2.3). The solid recovered (washed and dried) from the reaction of PhEt with _t_-BuOOH in the presence of CeNR, during 150 h, at 70 \({}^{\circ}\)C, was heated at 280 \({}^{\circ}\)C, during 3 h, under static air, giving a solid denoted CeNT280. The reaction of PhEt with _t_-BuOOH in the presence of CeNR280, at 70 \({}^{\circ}\)C, is slower than that observed for CeNR and CeNR-PhEt-BuOOH, and PhEtOOT is the only product until 54% conversion, reached after 120 h. Thermal treatments seem to gradually destroy the active sites, which may be defect sites in the original material. It has been reported in the literature that defect sites of the type Ce\({}^{3+}\)-O-Ce\({}^{4+}\) are responsible for activating PhEt using N\({}_{2}\)O as oxidant, at 325 \({}^{\circ}\)C, and increasing temperature of H\({}_{2}\) treatment from 300 \({}^{\circ}\)C to 350 \({}^{\circ}\)C led to a drastic decrease in concentration of these defect sites [19]. UV-vis and photoluminescence studies revealed the presence of both Ce\({}^{3+}\) and Ce\({}^{4+}\) in CeNR before and after it exposure to _t_-BuOOH, as discussed ahead.
###### Abstract  
This paper introduced a new ensemble learning approach, based on evolutionary fuzzy cognitive maps (FCMs), artificial neural networks (ANNs), and their hybrid structure (FCM-ANN), for time series prediction. The main aim of time series forecasting is to obtain reasonably accurate forecasts of future data from analyzing records of data. In the paper, we proposed an ensemble-based forecast combination methodology as an alternative approach to forecasting methods for time series prediction. The ensemble learning technique combines various learning algorithms, including SOGA (structure optimization genetic algorithm)-based FCMs, RCGA (real coded genetic algorithm)-based FCMs, efficient and adaptive ANNs architectures, and a hybrid structure of FCM-ANN, recently proposed for time series forecasting. All ensemble algorithms execute according to the one-step prediction regime. The particular forecast combination approach was specifically selected due to the advanced features of each ensemble component, where the findings of this work evinced the effectiveness of this approach, in terms of prediction accuracy, when compared against other well-known, independent forecasting approaches, such as ANNs or FCMs, and the long short-term memory (LSTM) algorithm as well. The suggested ensemble learning approach was applied to three distribution points that compose the natural gas grid of a Greek region. For the evaluation of the proposed approach, a real-time series dataset for natural gas prediction was used. We also provided a detailed discussion on the performance of the individual predictors, the ensemble predictors, and their combination through two well-known ensemble methods (the average and the error-based) that are characterized in the literature as particularly accurate and effective. The prediction results showed the efficacy of the proposed ensemble learning approach, and the comparative analysis demonstrated enough evidence that the approach could be used effectively to conduct forecasting based on multivariate time series.  
Keywords:fuzzy cognitive maps; neural networks; time series forecasting; ensemble learning; prediction; machine learning; natural gas +
Footnote †: journal:
## 3 Testing Results
### AE Characteristics
#### 3.2.4 AE Kaiser Effect  
Many studies have shown that rock AE activities have the Kaiser and Felicity effects under cyclic loading and unloading. When the Felicity ratio is less than 1, there exists the Felicity effect; in contrast, if the Kaiser effect occurs, then the Felicity ratio is greater than 1. The Felicity ratio is calculated as follows:  
\[F_{r}=\frac{P_{\text{AE}}}{P_{\text{max}}} \tag{1}\]  
where \(P_{\text{AE}}\) is the initial stress at which the specimen exhibits active AE activities during repeated loading and unloading; \(P_{\text{max}}\) is the maximum stress applied to the specimen before a certain loading-unloading cycle.  
In this paper, the cumulative AE hit count of the prior loading-unloading cycle is taken as the criterion of \"significant increase\" in the [PERSON] effect of this cycle. Table 3 and Figure 9 list the Kaiser effect points and the corresponding Felicity ratio.  
From Figure 9, in the whole cyclic loading-unloading process, the Felicity ratios are all greater than 1, indicating that the coal specimen has the obvious [PERSON] effect. However, the Felicity ratio fluctuates irregularly.
# Research on a Mixed Gas Classification Algorithm Based on Extreme Random Tree
## 3 Analysis of Experiment Results and Discussion
### Feature Construction and PCA Algorithm Validation  
We use feature construction to increase the data dimension and select the best features for training. The results are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Parameter** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **Accuracy** \\ \hline num=0 & 66.37\% & 86.38\% & 84.26\% & 77.36\% & 44.20\% & 69.75\% & 85.36\% & 85.52\% & 83.88\% & 39.98\% & 72.30\% \\ num=1 & 45.01\% & 72.14\% & 86.81\% & 94.20\% & 57.07\% & 74.64\% & 80.79\% & 84.19\% & 78.93\% & 71.53\% & 74.53\% \\ num=2 & 86.71\% & 91.66\% & 95.33\% & 96.25\% & 78.80\% & 78.00\% & 94.71\% & 99.30\% & 93.27\% & 77.67\% & 89.17\% \\ num=3 & 99.29\% & 98.66\% & 99.51\% & 99.83\% & 98.46\% & 99.88\% & 99.41\% & 98.03\% & 98.83\% & 99.79\% & 99.17\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: 10-fold cross-validation accuracy.  
Figure 8: 10-fold cross-validation accuracy of DTW.  
Figure 7: Dynamic time warping (DTW) implementation flow chart.  
The analysis shows that, if the feature dimension is kept unchanged, the recognition accuracy is only 73.37%, and if the dimension is increased by A-B, it becomes a 28-dimensional data feature, and the recognition accuracy increases to 87.50%. It can be seen that a purposeful elevated dimension has a good effect on the classification accuracy. Therefore, after the feature is upgraded to 56-dimensions, the recognition rate is 18.97% higher than that of the original dimension, and the final recognition rate is 99.17% after the response is reduced by the PCA algorithm.
# Passive Bistatic Ground-Based Synthetic Aperture Radar: Concept, System, and Experiment Results
## 2 System Overview
### LNB Synchronization
Figure 10: (**left**) Time delay measurement setup and (**right**) estimation result. The coherent integration time of each measurement is 10 \(\upmu\)s, while, due to the delay caused by the data transmission from the oscilloscope to the PC, the measurement period is \(\sim\)0.18 s. Therefore, for 5000 measurements, \(\sim\)15 min is needed. Figure 9: Diagram of the clock distribution circuit. Note that only one of “LO” or “Pierce oscillator” is needed. When an external LO is available, it can be used to provide more stable and precise results. In this study, the Pierce oscillator is used. The phase and amplitude stability measurement results are shown in Figure 11. It can be learned that the phase difference (the first measurement is acted as the reference) over 15 min is within the interval of [-10, +10] degrees, the mean value is approximately \(-\)0.05 degrees and the standard deviation is \(\sim\)2.52 degrees. Considering a wavelength of 24 mm, these two phases correspond to displacements of \(-\)1.67 \(\upmu\)m and +84 \(\upmu\)m, which can be ignored for displacement estimation with submillimeter accuracy. The normalized amplitude (similarly, the first measurement is acted as the reference) has a mean value of \(-\)0.9986 and a standard deviation of \(\sim\)0.0077. These results indicate that the proposed method can help to synchronize the frequency and phase of different LNBs, providing two coherent channels for PB-GB-SAR imaging. In addition, the phase stability also shows that the phase difference caused by the COTS boosters can be assumed to be constant. The amplitude stability also indicates that the transmitting power of the TV signal is stable over the measurement time.
## 5 Simulation Results and Analysis
### Analysis and Interpretation of Simulation Results
#### 5.2.2 Mudslide Deposition Depth Analysis  
(1) Deposition depth and velocity: Mudslide deposition depth and velocity are critical factors in hazard assessment. Simulation results indicated that the deposition depths increased significantly with increasing rainfall intensity, particularly at the confluence of the tributary and the main gully, as well as at sharp bends within the gully. The maximum deposition depth was observed at the accumulation zone near the gully outlet.  
(2) Influence of rainfall intensity on sediment transport: Under low rainfall intensities, limited clear water flow resulted in weak erosive power and reduced sediment transport capacity. As rainfall intensity increased, the clear water flow became more substantial, enhancing erosion and leading to larger mudslide volumes. At the maximum rainfall intensity, the sediment transport capacity reached a critical threshold, resulting in a dramatic increase in sediment transport and deposition further downstream towards the gully outlet.  
(3) Absence of deposition fan: Despite varying rainfall frequencies, no distinct deposition fan was formed at the Dashing Gully outlet. This is attributed to the narrow channel  
Figure 6: Spatial distribution of mudslide velocity and deposition depth under different rainfall frequencies.  
geometry, which restricts rapid flow, and the presence of two near right-angle bends that impede mudslide movement and significantly reduce flow velocities. Additionally, the gentle slopes downstream of the gully and at the outlet, combined with the limited space at the gully mouth, further contribute to the absence of a well-defined deposition fan.
# Residual-Attention UNet++: A Nested Residual-Attention U-Net for Medical Image Segmentation
## 4 Experiments and Results
### Results
#### 4.3.2 Cell Nuclei Segmentation  
In this part, [PERSON] was selected as the optimizer, and the initial learning rate was set to \(3\times 10^{-4}\). In addition, epoch and batch size were set to 4 and 150 respectively. The training time of the proposed model was about 5 h.  
Table 3 shows the summary of how well the proposed model performed against UNet++ and other existing method. As observed, Residual-Attention UNet++ improved some metrics that are critical for the semantic segmentation task, including IoU and DC, by 3.9% and 16.87% compared with the UNet++, respectively.  
It is well known that the correct definition of class boundary in the segmentation task of medical images is crucial for subsequent treatment, but it is not easy. Figure 13 shows the qualitative results for the UNet++ and Residual-Attention UNet++ models, respectively. It demonstrates that the output of the proposed approaches showed better segmentation with accurate contour. In addition, the segmented cell nuclei boundary of the proposed model was smoother and clearer than that of UNet++, which is a better match with ground truth. Moreover, Figure 14 shows the comparison between Residual-Attention UNet++ and other methods on F1-score after multiple experiments, which further illustrates the robustness of the proposed model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **F1-Score** & **SE** & **IoU (\%)** & **DC (\%)** \\ \hline FCN & 0.6260 & 0.7008 & 50.80 & 62.65 \\ SegNet & 0.8407 & 0.9051 & 77.59 & 81.26 \\ U-Net & 0.8773 & 0.9381 & 83.84 & 82.17 \\ Attention UNet & 0.8719 & 0.9362 & 83.51 & 76.49 \\ UNet++ & 0.8609 & 0.9367 & 81.54 & 73.51 \\ Attention UNet++ & 0.8771 & 0.9344 & 84.40 & 81.17 \\ Residual Attention UNet++ & 0.8831 & 0.9493 & 87.74 & 85.91 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental performance of Residual-Attention UNet++ and other methods on cell nuclei datasets.  
Figure 13: Qualitative assessment of the performance of UNet++ and Residual-Attention UNet++ on cell nuclei.
# Negotiation-Free Encryption for Securing Vehicular Unicasting Communication
## Modeling
### Threat Model  
With the development of the existing vehicle ad hoc network, the wireless network transmission process with regard to security also encounters increasingly more security problems and challenges. The notable characteristic of VANET is related to the driver's life. With these threats and attacks in the VANET, how to avoid data eavesdropping, tampering and other operations becomes a great challenge. The transmission method will face attacks, which are summarized in the following types.  
1. Message stealing: In the message transmission process, an attacker can steal others' messages by monitoring. Once the message has high confidentiality, it will cause great losses for senders and receivers.
2. Privacy attacks: According to the stolen sensitive information, attackers can analyze drivers, such as driving habits, and this can cause security risks for the drivers.
3. Fake news attack: This passes fake messages to other vehicles in order to achieve illegal purposes.
4. Fake identity attack: This forges fake identities to conceal their real ones to pass messages.
5. Non-repudiation attack: In the vehicle transmission process, if someone maliciously sends some false information to others when tracing the sources for the sender, it has non-repudiation.
6. Replay Attack: A message is resent to the vehicle cloud in order to achieve the role of deception.
7. Dos attack: It sends a large amount of data to plug the communication channel in order to cause a channel jam or prevent vehicles from accessing the network service.
## 5 Results and Discussion
### Impact of Augmentation on Average Accuracy  
Table 3 indicates that PVT consistently exhibits the highest performance, with initial (unaugmented) values of AP50 = 70.05 and AP50:95 = 38.47, surpassing ViT (68.42 and 37.25) and Swin Transformer (69.35 and 38.16). Augmentation techniques generally improve results for all three architectures, although their impact varies. Brightness and scale emerge as notably effective single augmentations, delivering clear gains over the baseline--for instance, brightness increases PVT's AP50 from 70.05 to 73.12 and AP50:95 from 38.47 to 40.82--while translation offers more modest benefits, yielding values close to the original baseline. When combining all listed techniques, each model attains its highest accuracy, with PVT again leading (AP50 = 72.55 and AP50:95 = 40.92), followed by Swin Transformer and ViT. These observations suggest that image augmentation substantially enhances object detection performance, with both brightness and scale demonstrating particularly pronounced effects, while PVT retains an overall performance advantage.  
From an architectural perspective, each backbone's design influences how it interacts with these augmentations. ViT employs a patch-based global attention mechanism that can be more sensitive to local distortions (e.g., translation), whereas Swin Transformer's hierarchical windowed attention structure handles local shifts more gracefully. Meanwhile, PVT's progressive shrinking pyramid design effectively captures both global context and local detail, enabling stronger robustness to a range of augmentations.