esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 The Simulation Validation and Results Analysis
### Effect of Varying Number of UAVs on Estimation Performance  
In Scenario 5, we test the performance of the global estimation of the target state on the number of deployed UAVs. Using Monte Carlo simulations, we tested cases where the number of UAVs ranged from \(N=2\)-\(10\). For each case \(100\) independent experiments were performed. For each experiment, the initial positions and the initial heading angles of the UAVs, and the initial positions of targets are randomly generated in a \(2\) km \(\times\)\(2\) km bound region. The ground speed of the UAVs is \(v_{i}=50\) m/s, the maximum turning rate of the UAVs is \(\omega_{\text{max}}=30\) deg/s. For the target, its initial position is \((-1000,1000)\) m, and its initial movement direction is \(-20\) deg. The position of the base station \(B\) is \((0,-1000)\) m. The safety distance for collision avoidance is \(d_{\text{safe}}=200\) m. The other simulation conditions are the same as Scenario 1.  
The average estimate errors of target position, speed and direction are shown in Figures 37-39, respectively. The average trace of FIM is shown in Figure 40. From Figures 37-39, we can summarize that, the larger number of UAVs, the smaller average estimation errors. It implies the global estimation results are more accurate with increasing the number of the UAVs. From Figure 40, we can conclude that, the larger number of UAVs, the higher average trace of FIM. It implies the global estimate results are more effective with increasing the number of the UAVs. Therefore, the performance of the global estimate of target state will be improved as the number of the UAVs increases. This is for two reasons. On one hand, more UAVs implies more information obtained at each time step, which is beneficial for improving the global estimation performance. On the other hand, with increasing the number of UAVs, the operational range of the UAV swarm can be extended, due to more UAVs being used as relays to provide measurement packets to the remote station. In this case, several UAVs can get close to the target for better observations while their measurement packets can be sent back to base station successfully. It is also beneficial for improving the global estimation performance.
## 4 Security and Performance Analysis  
### Security Analysis  
#### 4.1.1 Security against Malicious Guessing Attacks  
When a sensor network has been deployed for a certain period, the key database of the base station will be updated after a transaction so that an attacker cannot obtain the correct key to use in the next transmission. Each node includes the records of not more than three keys, which consist of two old keys and one newly generated key. When the new key is generated, the oldest key will be updated. This can improve the security of the network and reduce the memory load of the nodes.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 4 Results and Discussions
### Feature Extraction Results  
Feature extraction was applied to the test images. Table 3 shows the number of feature points extracted from each test area. The numbers of feature points extracted for changed reference objects and unchanged reference objects are also shown. Please note that features were not extracted from all reference objects. Some small objects without internal texture patterns did not produce features. Among the datasets tested, features were not extracted from 4.1% of the changed reference objects (26 out of 630) with AKAZE, and from 2.3% of the objects (15 out of 630) with SIFT. Changed objects without features decrease the change detection accuracy of our feature-based change detector, and prevent normalized recall from converging to 1. One can improve the feature extractor to reduce  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Incheon** & **Chilgok** & **Seoul** \\ \hline Number of changed objects & 400 & 82 & 149 \\ \hline Number of unchanged objects & 350 & 35 & 152 \\ \hline Average size of changed objects (pixels/m\({}^{2}\)) & 660/165 & 196/49 & 90/23 \\ \hline Average size of unchanged objects (pixels/m\({}^{2}\)) & 398/100 & 1114/278 & 266/66 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The quantity and sizes of reference objects.  
Figure 6: Examples of changed reference objects (marked in orange) and unchanged objects (marked in green) from (**a**) Incheon, (**b**) Chilgok, and (**c**) Seoul.  
the number of featureless objects, and hence, to increase change detection accuracy. In this paper; however, feature-based change detection was tested without additional handling of featureless reference objects.
## Part A On-Line Energy Optimization
### Theoretical Analysis
The above assumptions may not be very realistic in practice, however, they can be relaxed and in any case allow to perform a first effort towards providing some concrete analytical results. **Lemma 1** ([8]): _The expected \"hops efficiency\" of the local target protocol in the \(a\)-uniform case is_  
\[E(C_{h})\,\simeq\,\frac{\alpha}{\sin\alpha}\]  
_for large \(h_{opt}\). Also_  
\[1\,\leq\,E(C_{h})\,\leq\,\frac{\pi}{2}\simeq 1.57\]  
_for \(0\,\leq\,\alpha\,\leq\,\frac{\pi}{2}\)._  
**Proof:** Due to the protocol, a sequence of points is generated, \(p_{0}=p,\,p_{1},\,p_{2},\ldots,\,p_{h-1},\,p_{h}\) where \(p_{h-1}\) is a particle within \(\mathcal{W}\)'s range and \(p_{h}\) is part of the wall. Let \(\alpha_{i}\) be the (positive or negative) angle of \(p_{i}\) with respect to \(p_{i-1}\)'s vertical line to \(\mathcal{W}\). It is:  
\[\sum_{i=1}^{h-1}d(p_{i-1},\,p_{i})\;\leq\;d(p,\,\mathcal{W})\;\leq\;\sum_{i=1} ^{h}d(p_{i-1},\,p_{i})\]  
Since the (vertical) progress towards \(\mathcal{W}\) is then \(\Delta_{i}\,=\,d(p_{i-1},p_{i})\,=\,\mathcal{R}\cos\alpha_{i}\), we get:  
\[\sum_{i=1}^{h-1}\cos\alpha_{i}\;\leq\;h_{opt}\;\leq\;\sum_{i=1}^{h}\cos\alpha _{i}\]  
From [PERSON]'s equation for the expectation of a sum of a random number of independent random variables [42], then  
\[E(h-1)\,\cdot\,E(\cos\alpha_{i})\;\leq\;E(h_{opt})\;=\;h_{opt}\;\leq\;E(h)\, \cdot\,E(\cos\alpha_{i})\]  
Now, \(\forall i\), \(E(\cos\alpha_{i})\;=\;\int_{-\alpha}^{\alpha}\cos x\,\frac{1}{2\alpha}dx\;=\; \frac{\sin\alpha}{\alpha}\). Thus  
\[\frac{\alpha}{\sin\alpha}\;\leq\;\frac{E(h)}{h_{opt}}\;=\;E(C_{h})\;\leq\; \frac{\alpha}{\sin\alpha}\,+\,\frac{1}{h_{opt}}\]  
Assuming large values for \(h_{opt}\) (i.e. events happening far away from the wall, which is the most interesting case in practice since the detection and propagation difficulty increases with distance) we have (since for \(0\,\leq\,\alpha\,\leq\,\frac{\pi}{2}\) it is \(1\,\leq\,\frac{\alpha}{\sin\alpha}\,\leq\,\frac{\pi}{2}\)) and the result follows. \(\diamond\)
# A Bird Vocalization Classification Method Based on Bidirectional FBank with Enhanced Robustness
## 4 Research Methods
### Improved Wavelet Adaptive Denoising
#### 4.1.2 Optimized Threshold Selection Rule  
In 1992, [PERSON] and [PERSON] introduced the VisuShrink threshold selection rule [19]:  
\[\lambda=\sigma\sqrt{2\ln N}, \tag{4}\]  
where \(\lambda\) denotes the threshold, and \(N\) denotes the length of the corresponding detail coefficient. The formula for calculating the standard deviation \(\sigma\) of the AWGN is expressed as follows:  
\[\sigma=\frac{\text{MAD}}{0.6745}, \tag{5}\]  
where MAD represents the median amplitude of the detail coefficient. VisuShrink is widely used due to its strong performance and good interpretability, and many optimization methods rely on it [34; 37; 38; 39; 40], also was the case in this study.  
Considering the adaptive determination of the optimal decomposition level, we observed noise reduction as the decomposition level increased, suggesting thresholds should be similarly decreased. Furthermore, our research indicated that the detail coefficients were not all noise but included valuable information. Thus, this paper employs the concept of \"peak-to-sum ratio (PSR)\" to assist in the selection of threshold values [41]:  
\[\text{PSR}=\frac{\text{Max}(|d(i)|)}{\sum_{j=1}^{N}|d(i,j)|}. \tag{6}\]  
where \(d(i)\) represents the detail coefficient at level \(i\), \(N\) represents the length of the detail coefficient, and the denominator represents the sum of all the detail coefficients at level \(i\). Our improved threshold rule, building on VisuShrink, is expressed as follows:  
\[\lambda_{i}=\frac{\sigma\sqrt{2\ln N}}{N^{\text{PSR}}\log_{10}(i^{2}+1)}. \tag{7}\]  
In Equation (7), \(\lambda_{i}\) denotes the threshold for each decomposition level, and the useful information is concentrated on the coefficients with larger values in the wavelet transform domain. Therefore, if the detail coefficient contains a large value, the PSR increases, indicating that there is more valuable information at that level, and the threshold is lowered to retain a more significant number of coefficients. Conversely, it means that the level contains more noise, the threshold increases, and more noise is filtered out. At the same time, the threshold decreases as the decomposition scale increases.
## 2 Materials and Methods
### Analytical Methods  
The leave-one-out cross-validation approach was used to test the performance of the multiple linear regression models for estimating VPP. Both R\({}^{2}\) and RMSE were used to evaluate the accuracy of VPP estimates from the statistical models relative to VPP observations from FLUXNET measurements. The LSP metrics derived from MODIS datasets at a 500 m spatial resolution were aggregated to a 0.05\({}^{\circ}\) spatial resolution to enable a comparison with VPP estimates from this study. All phenology metrics values at a finer scale (500 m) within the pixel at the coarser scale (0.05\({}^{\circ}\)) were averaged [2; 49]. When the proportion of available values from pixels at a finer scale within a given pixel at a coarser scale was less than 50%, the pixel was not used. Due to different definitions and detection methods between VPP estimates from this study and LSP estimates from the aforementioned data sources, the mean difference (bias) indicating systematic variations, root-mean-square difference (RMSD), and the RMSD after removing systematic bias (RMSDb) were used to evaluate the agreement between VPP from this study and LSP from other data sources. The R\({}^{2}\) value was used to compare the linkages between VPP, LSP, and GPP.
## 3 Data and Methods
### The Measure of the Direct and Indirect Effects
#### 3.2.2 The Measure of Indirect Effects  
In this paper, we explore the indirect effect of crude oil price shocks on Chinese price level based on the TNOP. Since the sample data are monthly data, this paper assumes that  
Figure 1: The transmission networks of four oil price shocks to Chinese price indices (the red nodes represent crude oil, and the back nodes represent price indices. (**a–d**) represents the transmission network of Brent, WTI, Dubai, and Daqing crude oil price shocks to Chinese price indices, respectively).  
the transmission of price shocks between two variables needs one month. Figure 2 is a simple transmission network. There is an edge from A to B and from A to C; thus, the price fluctuations of A could be directly transmitted to B and C in the first month after A is shocked. Due to the transmission relationships from B to C and from B to D, the affected B could further impact C and D; thus, there is an indirect impact from A to C and from A to D in the second month after A is shocked. Similarly, the influence of the price fluctuations of A could indirectly impact E in the third month. Thus, the indirect effects arise from the second month after a onetime positive shock to A. Furthermore, there is no transmission path from A to F; thus, the price fluctuations of A could not affect F.  
Since \(GR^{(q)}\) is regarded as a direct impact matrix at time \(t\), \(GR^{(0)}\) and \(GR^{(1)}\) are the direct impact matrices for the current month and the first month after the shock, respectively. \(GR^{(0)}\times GR^{(1)}\) is the indirect impact matrix in the first month, and \(GR^{(0)}\times GR^{(1)}\times GR^{(2)}\) is the indirect impact matrix in the second month. Generally, the indirect impact matrix is defined as Equation (10).  
\[IR^{(q)}=GR^{(0)}\times GR^{(1)}\times\cdots\times GR^{(q)}=\begin{bmatrix}ir_{11 }^{(q)}&\cdots&\cdots&ir_{1j}^{(q)}&\cdots&ir_{1n}^{(q)}\\ \vdots&\ddots&\ddots&\vdots&\ddots&\vdots\\ ir_{11}^{(q)}&\cdots&\cdots&ir_{ij}^{(q)}&\cdots&ir_{in}^{(q)}\\ \vdots&\ddots&\ddots&\vdots&\ddots&\vdots\\ ir_{n1}^{(q)}&\cdots&\cdots&ir_{nj}^{(q)}&\cdots&ir_{nn}^{(q)}\\ \end{bmatrix},q=1,2,3,4,\cdots \tag{10}\]  
where \(IR^{(q)}\) represents the indirect impact matrix in the \(q\)th month; \(ir_{ij}^{(q)}\) is the indirect impact of \(i\) on \(j\) in time \(q\). Thus, the indirect influence of crude oil shocks on each price index in the \(q\)th month is \(ir_{1j}^{(q)},\ j=2,\ 3,\ \cdots,\ 9\).
## 4 Model Implementation and Evaluation
### A Comparison of the Model's Results with the Literature Review  
Our research introduced the ILRC algorithm, which enhances the training process of DL models, distinguishing it from prior studies. The ILRC algorithm adjusts the LR dynamically based on the training progress, resulting in faster convergence and a reduced risk of overfitting. We evaluated this mechanism across multiple DL architectures, including EfficientNet-B0, ResNet101v2, InceptionV3, InceptionResNetV2, and Xception. Additionally, we improved these models through transfer learning by freezing layers, applying fine-tuning techniques, implementing residual learning, and utilizing regularization strategies. The models were fine-tuned using the Kvasir-Capsule and KVASIR v2 datasets, which feature images obtained from wireless capsule endoscopy (WCE).  
The models EfficientNet-B0, ResNet101v2, InceptionV3, InceptionResNetV2, and Xception, when utilizing the ILRC, demonstrated exceptional performance compared to existing state-of-the-art methods, achieving accuracies of 99.906%, 98.765%, 98.312%, 99.344%, and 99.719%, respectively, on the Kvasir-Capsule dataset. On the Kvasir-v2 dataset, they attained accuracies of 97.770%, 97.375%, 98%, 98.062%, and 98.062%, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model/Complexity**} & **\# Parameters in Original Model** & **\# Parameters in Proposed Model** & **Number of FLOPS (G-FLOP)** & **Training Time (seconds)** & **Inference Time** \\ \hline EfficientNet-B0 & 5.3 M & 4.39 M & 1,856,573,707 & 1161.97 & 66 s 94 ms/step \\ ResNet101V2 & 44.6 M & 43.16 M & 32,808,287,188 & 2085.89 & 100 s 143 ms/step \\ InceptionV3 & 23.8 M & 22.34 M & 14,373,641,268 & 1212.79 & 59 s 84 ms/step \\ InceptionResNetV2 & 55.8 M & 54.74 M & 33,009,236,916 & 1797.28 & 122 s 175 ms/step \\ \hline \hline \end{tabular}
\end{table}
Table 12: The four DL models’ complexity values (# means number of).  
Thus, the ILRC algorithm combined with the EfficientNet-B0, ResNet101v2, InceptionV3, InceptionResNetV2, and Xception models outperformed the most recent methods listed in Table 13 regarding multi-classification accuracy, as shown by the results. Notably, the algorithm achieved an impressive accuracy rate of 99.906%. Moreover, from the patient's perspective, our methodology improves the diagnostic process by reducing costs, expediting diagnosis, and delaying disease progression. By facilitating the multi-classification of GI diseases, the proposed models with the ILRC will automatically identify the disease. As a result, the proposed models utilizing the ILRC will help clinicians detect GI diseases and understand their primary causes. The evaluation of these models showed that, with appropriate tuning, they produce exceptional results compared to the current use of other frameworks.
# Application of the Segmented Correlation Technology in Seismic Communication with Morse Code
## 2 Principles and Methods
### Experimental Facilities  
When a disaster occurs, it is better for victims to send out a distress signal with a hammer or small vibroseis source [24, 25, 26]. Hammer sources, whose signals are actually shock signals, send out seismic signals by hitting the ground with gravity [27, 28]. However, the components of vibration signals in urban environments are very complex, such as a lot of footstep signals (a shock signal) in crowded places, and it is unclear whether the hammer sources are suitable for urban communication. For instance, the amplitudes of the hammering signals obtained at the acquisition may be close to that of the footstep signals after hammering signals pass through the earth, as shown in Figure 2a. The footstep signals and the hammer signals are all weakened when the collected data are filtered by a 5-100 Hz band-pass filter accepted in the experiment of [PERSON], as shown in Figure 2b. The phenomenon occurs because the bandwidths of their frequencies are overlapped, which may increase the bit error rate of the system, as shown in Figure 2c,d.  
Figure 2: The comparison of hammering signals and footstep signals; (**a**) Hammer signals and footstep signals in raw data; (**b**) Hammering signals and footstep signals filtered by a 5–100 Hz band-pass filter; (**c**) The spectrum of footstep signals in raw data; (**d**) The spectrum of hammer signals in raw data.  
Unlike hammer sources, the vibroseis source is a continuous vibration source whose duration and frequency can be controlled [29; 30; 31]. To test the effect of vibroseis signals on the results, footstep signals are synthetically generated, as shown in Figure 3a. After filtering 200 Hz single-frequency signals generated by vibroseis sources with a 180-220 Hz band-pass filter, the footstep signals are significantly weakened while the vibroseis signals are hardly affected, as shown in Figure 3b. The vibroseis sources accepted in this paper have great advantages in signal modulation compared with hammer sources.  
The vibroseis source sends seismic signals to the underground in the form of radiant energy, which takes several seconds to release energy. Therefore, the relative durations can be defined: marks--dot = 4 s and dash = 8 s, spaces--symbol space = 4 s, word space = 8 s and sentence space = 12 s.
## 5 Conclusions  
1. Improvements in concrete performance were observed when CA was replaced by volume with RT seed shells at a rate of 4%. The strength values at this RSS dosage were CS--51.7 MPa, axial CS--38.9 MPa, axial TS--3.6 MPa, TS in twisting--6.3 MPa, and the values of deformations during compression and tension were respectively equal to 1.99 mm/m \(\times\) 10\({}^{-3}\) and 1.16 mm/m \(\times\) 10\({}^{-4}\). The modulus of elasticity was 33.9 GPa.
2. The increase in the strength of concrete, as well as changes in deformability, according to the results of our examination of the \"stress-strain\" figures were: for CS 6%, for axial CS 8%, for axial TS 6% and for TS in twisting 8%
3. Changes in deformability included an increase in the elastic modulus of 7%, and a reduction in strains under compression of 6% and strains under tension of 5%.
4. The microstructure of hardened concrete samples with partial replacement of CA with RT seed shells in the amount of 2%, 4% and 6% is the most dense, with the fewest pores and microcracks in comparison with the structure of the sample of the control composition, as well as samples with the replacement of CA with RT seed shells in the amount of more than 6%.
5. According to our preliminary estimates, the expedient and effective replacement of CA with RT seed shells leads to a reduction in the consumption of mineral CA by up to 8%.
6. The achieved results established good compatibility of data and the opportunity of analysis of the technology in manufacturing situations. Future research is planned to study the influence of replacing CA with RT seed shells on the physical features of concrete, such as thermal insulation and resistance to cyclic influences, as well as its cost and the development of a cost-effective production technology.  
Conceptualization, S.A.S., E.M.S., A.N.B. and A.A.S.; methodology, S.A.S., E.M.S., Y.O.O., C.A. and V.V.; software, S.A.S., E.M.S., A.N.B., Y.O.O. and C.A.; validation, A.A.S., S.A.S., E.M.S. and A.N.B.; formal analysis, A.E., S.A.S. and E.M.S.; investigation, S.A.S., E.M.S., A.N.B., Y.O.O., C.A. and B.M.; resources, B.M.; data curation, S.A.S., E.M.S. and C.A.; writing--original draft preparation, S.A.S., Y.O.O., C.A., M.K., E.M.S. and A.N.B.; writing--review and editing, M.K., S.A.S., E.M.S. and A.N.B.; visualization, V.V., S.A.S., E.M.S. and A.N.B.; supervision, B.M.; project administration, B.M.; funding acquisition, A.N.B. and B.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The study did not report any data.  
The authors would like to acknowledge the administration of Don State Technical University for their resources and financial support.  
The authors declare no conflict of interest.
## 3 Results  
Recovery efficiencies ranged from 69%-87% for the initial extraction, Figure 1. The first rinse significantly increased the recovery for all treatments with the exception of RT96 (that treatment had high variability). However, each subsequent rinse (rinse two and three) resulted in a smaller recovery of nitrate which did not significantly increase nitrate extraction efficiency. Comparing treatments, temperature was the primary driver for extraction efficiency as the increased temperature from 20 to 50 \({}^{\circ}\)C resulted in a significant increase in nitrate extraction efficiency, Figure 1. This is likely due to temperature influencing the ability of extractants to penetrate the pore space in biochar. Increased extraction temperatures result in weaker water biochar surface interactions [17], allowing extractants to penetrate smaller biochar pores to extract nitrate [15].  
Treatment duration did not significantly impact nitrate extraction efficiency following the first rinse, Figure 1. It was expected that allowing longer exposure time to KCl would result in higher extraction efficiency, similar to what was observed when duration was increased from one to 24 h in previous studies [15]. However, increasing the time to 96 h did not significantly impact extraction efficiency, thus, 24 h is likely suitable for extraction. However, it may be possible to achieve an appropriate extraction efficiency at shorter durations, and future studies should evaluate shaking times between one and 24 h to identify if procedure time could be reduced.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Treatment** & **Temperature (\({}^{\circ}\)C)** & **Duration (h)** \\ \hline RT24 & 20 & 24 \\ RT96 & 20 & 96 \\ HT24 & 50 & 24 \\ HT96 & 50 & 96 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Biochar extraction methods.