esa-sceva/satcom-chunk-collection huggingface dataset:
# Multipath Mitigation for BOC Signals Based on Prompt-Assisted-Offset Correlator
## Appendix A Thermal Noise Performance Analysis for the S-OC and C-OC Discriminators  
Assuming that the code delay \(\tau\) is accurately estimated, the local USB and LSB signals used in the offset correlator can be written as:  
\[\hat{r}_{u-\infty}(t)= c(t-\tau+\tau_{\infty})\exp(j(\hat{\theta}+\hat{\phi}))) \tag{1}\] \[\hat{r}_{l-\infty}(t)= c(t-\tau+\tau_{\infty})\exp(j(\hat{\theta}-\hat{\phi}))) \tag{2}\]  
where \(\tau_{\infty}\) is the forward offset of the offset correlator, \(\hat{\theta}\) is the local carrier phase, and \(\hat{\phi}\) is the local subcarrier phase. Ignoring the noise, the received baseband signal that is fed into the offset correlator for the USB signal can be written as:  
\[s_{u}(t)=\frac{A}{2}c(t-\tau)(\exp(j(\theta+\phi))+\exp(j(-4\pi f_{sc}t+\theta -\phi)) \tag{3}\]  
The offset correlation value for the USB signal without noise can be calculated as:  
\[S_{u-\infty}=\frac{1}{T}\int_{(k-1)T}^{kT}\hat{r}_{u-\infty}^{*}(t)s_{u}(t) dt=\frac{A}{2}\exp(j(\Delta\theta+\Delta\phi))\int_{-\beta_{r}/2}^{+\beta_{r}/2}G_{s}(f) \cos(2\pi f\tau_{\infty})df \tag{4}\]  
where \(T\) is the integration time, \(\Delta\theta=\theta-\hat{\theta}\) is the carrier phase error, \(\Delta\phi=\phi-\hat{\phi}\) is the subcarrier phase error, \(\beta_{r}\) is receiver bandwidth, and \(G_{s}(f)\) is the normalized power spectral density (PSD) of either sideband of the BOC signal. Considering the noise, the offset correlator output for the USB signal is written as:  
\[R_{u-\infty}=S_{u-\infty}+N_{u-\infty} \tag{5}\]  
where \(N_{u-\infty}\) is the noise component and is calculated as:  
\[N_{u-\infty}=\frac{1}{T}\int_{(k-1)T}^{kT}\hat{r}_{u-\infty}^{*}(t)n(t)dt \tag{6}\]  
where \(n(t)\) is the zero-mean Gaussian white noise with a PSD of \(N_{0}\). The mean value of \(N_{u-oc}\) is 0, and the variance of \(N_{u-oc}\) is calculated as:  
\[\text{var}(N_{u-oc})=E\Big{\{}|N_{u-oc}|^{2}\Big{\}}=\frac{N_{0}}{T}\int_{-\hat{ p}_{\tau}/2}^{+\hat{p}_{\tau}/2}G_{\text{s}}(f)df \tag{10}\]  
Ignoring the noise, the received baseband signal that is fed into the offset correlator for the LSB signal can be written as:  
\[s_{l}(t)=\frac{A}{2}c(t-\tau)(\exp(j(\theta-\phi))+\exp(j(4\pi f_{sc}t+\theta+ \phi)) \tag{11}\]  
The offset correlation value for the LSB signal without noise can be calculated as:  
\[S_{l-oc}=\frac{1}{T}\int_{(k-1)T}^{kT}\hat{r}_{l-oc}^{*}(t)s_{l}(t)dt=\frac{A}{ 2}\exp(j(\Delta\theta-\Delta\phi))\int_{-\hat{p}_{\tau}/2}^{+\hat{p}_{\tau}/2 }G_{\text{s}}(f)\cos(2\pi f\tau_{oc})df \tag{12}\]  
Considering the noise, the offset correlator output for the LSB signal is written as:  
\[R_{l-oc}=S_{l-oc}+N_{l-oc} \tag{13}\]  
where the mean value of \(N_{l-oc}\) is 0, and the variance of \(N_{l-oc}\) is equal to \(\text{var}(N_{u-oc})\).
## 4 Results and Discussion
### Optimization Results for One Degree of Freedom
Figure 5 presents the Dunn test for all versions of DE, given a broader view about the comparison of all algorithms studied. Results of Dunn test indicate the sensitivity of the analyzed versions to the \(F\) parameter. The predominant parameter of \(F\) used in algorithms of group A is \(F=1.0\), which indicates this is a good magnitude for this parameter. However,  
Figure 4: Dunn test of some DE versions for achievement of \((\theta_{max})_{m}\). Figure 5: Dunn test of all DE versions studied for achievement of \((\theta_{max})_{m}\). \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Shapiro–Wilk: Normality Test**} \\ \hline
**DE Versions** & _p-Value_ & **Distribution** \\ \hline DE12; DE14 & \(2.785\times 10^{-6}\) & Non-normal \\ DE22; DE24 & \(3.275\times 10^{-7}\) & Non-normal \\ DE31; DE33 & \(1.489\times 10^{-9}\) & Non-normal \\ DE41; DE43 & \(1.870\times 10^{-4}\) & N on-normal \\ \hline \multicolumn{3}{c}{**Kruskal–Wallis: Rank Sum Test**} \\ \hline
**DE Versions** & _p-Value_ & \(\mathbf{\alpha_{s}}\) & **Differences** \\ \hline DE [12; 14; 22; 24; 31; 33; 41; 43] & \(1.932\times 10^{-6}\) & \(0.05\) & Significant \\ All versions & \(1.109\times 10^{-7}\) & \(0.05\) & Significant \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical hypotheses test of DE versions for \((\theta_{max})_{m}\). the algorithms of type DE4\(y\) are an exception. For that, the parameters \(F\) and \(CR\) do not have a significant effect on their performance. From Figure 5 it is possible to observe that the best algorithms to achieve the minimal values of \((\theta_{max})_{m}\) with the highest frequency of achievement of the optimal solution are those classified in group A, followed by the intermediate group AB and group B with the worst results. For the latter group, the variance of \((\theta_{max})_{m}\) in comparison with the best configuration is high and the number of times of achievement of the best performance is low. It is worth mentioning that group AB has no statistical differences in comparison with groups A and B in isolated form. Results from [PERSON]'s test corroborate the hypothesis that the most influential parameters are the \(F\) and mutation operator. For the present investigated conditions, the crossover rate magnitudes studied here (\(CR\) = 0.7 and 0.9) do not affect the algorithm results. In general, even the results for the first level of optimization demonstrate that the statistical analysis of the results reached with the optimization method is important to improve adequate comprehension of the influence of design over the performance of the system. On the contrary, the use of algorithms that leads to several local points of optimum can deviate from the physical analysis of the influence of design over the system performance, which is critical in the investigation of constructal design.
## 3 Results and Discussion
### Interference Limited Communication Range Regions
Finally, we compare the calculated values of the mean interference limited communication region and the results from our simulations, for path loss exponent equal to 2 and 4, Figure 10 plots (a) and (b) respectively, when we relax our assumption on the receiver's position and assume a specific offset \(x\) from the center of the networking area, where \(x\in[0,\)\(r_{max}]\), as we have done previously for the interference study. We can see that when the receiving node moves to the border of the networking area the value of the \(d_{ILR}\) is increasing (since the interference is decreasing), but it remains very close to its value at the center for a 60% or 70% (for \(a=2\)) and for a 80% or 90% (for \(a=4\)) offset from the center of the circle. The calculated values (black line) are always smaller than the simulation results; that is, they correspond to a worst-case scenario. Figure 10: Mean \(d_{ILR}\) values calculated (**black** line) _vs._ simulation results (**light blue** lines) for path loss exponent a equal to 2, plot (**a**), and equal to 4, plot (**b**), for different positions of the receiving node (\(r_{max}=100\) m, and \(r_{min}\)=ranges from 0.4 to 2 m). Figure 9: \(d_{ILR}\) for different \(r_{min}\) and different path loss exponent values.
## 3 Results and Discussion
### Analysis of the Hole Wall Recast Layer
In addition, from the contour curve of the 400 kHz sidewall, it can be observed that there are obvious recasting protrusions on the local area. These abnormal high points on the local sidewall have the most serious damage to surface integrity. They affect the internal airflow state of the gas film hole, cause turbulence, and thus affect the cooling effect. It is necessary  
Figure 12: Cross-section morphology of air film holes processed with different laser repetition frequency. to avoid the protrusion of recasting defects as much as possible. Although the surface of the gas film hole can be observed to be very rough from the three-dimensional morphology, the actual roughness value is similar to that of the mechanical grinding surface, with most 3D roughness values less than 0.8 \(\upmu\)m. This also indicates that the gas film hole drilled by the ultra-fast laser can be applied in actual blade products. Figure 14: Surface roughness near the outlet with different laser repetition frequencies. (**a**) Sidewall surface with repetition frequency 100 KHz; (**b**) sidewall surface with repetition frequency 400 KHz. Figure 13: Thickness of recast layer near the outlet with different laser repetition frequency. In general, the instantaneous power density is the key factor affecting the quality of the orifice. In order to improve the roundness of the orifice and reduce the material defects of the orifice, a higher instantaneous power density should be selected, so it is necessary to choose a smaller rotation speed of the Dove prism, moderate deflection angles of wedge prism, larger power and smaller repetition frequency.
## 6 Discussion
### Effects of Different Algorithms on Identification Results
#### 6.2.1 Discussion on the Effectiveness of XGBoost  
The first aspect of this section is the algorithmic principle of the model [42]. The DT model fits the training data perfectly, especially when the tree has a significant depth and many nodes. However, this makes the model very sensitive to noise in the training data, leading to poor performances with unseen data. Second, in multiclassification problems, the DT model requires more divisions to correctly classify all of the classes, which can cause the tree to become very complex and extensive. This increases the computational cost and may increase the risk of overfitting. RFs consist of multiple decision trees with voting or averaging to improve predictive accuracy and stability. However, some aspects could be improved when dealing with multicategory problems. For example, in the case of category imbalance [43], random forests tend to give higher importance to the majority category, resulting in poor classification performance for the minority category. This issue is further illustrated in the subsequent presentation of limitations and in the discussion. In addition, the random forest model integrates the results of multiple decision trees, which makes the model interpretation worse. GBDT builds the decision trees step by step, and each tree learns the residuals of the previous tree to reduce the model error. It performs well when dealing with regression and binary classification problems. However, errors generated in the upper layers may propagate to the lower layers in decision trees with multiple classes, leading to less satisfactory classification results [44]. Like the RF model, it is also affected by the imbalance in the number of samples in each category and the difficultyin interpreting the model. The XGBoost model learns the nonlinear relationships of the data by constructing multiple decision trees and shows remarkable results when dealing with complex data feature structures. In addition, XGBoost has excellent robustness. It can handle missing values and category imbalance datasets with built-in regularization terms, effectively preventing overfitting and improving generalization ability. The results of this study are the same as those reported by [PERSON] and [PERSON]. [PERSON] combined gradient tree boosting with differential evolution to identify lithology [45]. [PERSON] et al. used five machine learning methods to identify lithology. They concluded that XGBoost performed the best in the ensemble learning method for identification [46]. In addition, in some related studies in the fields of fault diagnosis and classification, XGBoost has shown greater advantages than other models, and the study in this paper is consistent with the results in [47; 48; 49; 50].
# Evaluation Model of Parking Equipment Planning and Design Based on Object-Oriented Technology
## 2 Model Map Extraction  
The evaluation model of parking equipment planning and design proposed in this paper is mainly aimed at the above-ground parking space around the building. Firstly, the high-resolution remote sensing image of the study area is obtained by satellite, and then the model plane map is obtained by multi-scale segmentation, high-resolution image extraction and information geometric processing.
## 2 Related Work
The findings show that the security method suited for mobile WSNs is not suitable for mobile UWSNs due to the performance differences between mobile WSNs and UWSNs. One potential direction for future research is to create secure UWSNs utilizing smart nodes and self-localization features to defend against denial of service (DoS) attacks in mobile UWSNs. Research conducted in [36] proposed two algorithms such as multilayer sink (MuLSi) and MuLSi-Co. MuLSi-Co uses the cooperation technique, and it is the reliable version of MuLSi. MuLSi proposes a network structure that is multilayered. The placement of the sink is optimum in order to avoid multi-hop communication. The nodes closest to the sink are considered as the best forwarder nodes. The scheme proposed in this research does not need node's location information. The performance of network is better using MuLSi, but due to the single link, the operation is not reliable in MuLSi. Therefore, MuLSi-Co utilizes cooperation techniques in which the receiver has multiple data copies. Multiple copies of the same data are combined for the correct reception of data. The scheme proposedis better in terms of reliable data exchange and energy cost and has a smaller number of dead nodes. Research conducted in [37] presented better localization for UWSNs. The authors first presented algorithms of general localization, and then, they deployed beacon nodes for finding accuracy and error of the sensor localization. Then two more algorithms were presented, such as angle-based as well as distance-based localization algorithms. In this research, the realistic case is considered such as when the speed of sound in water is not known, and the sensor nodes are not synchronized. The simulation results reveal that the proposed algorithms are able to achieve better accuracy of localization. The literature is summarized in Table 1.
# A Blockchain Solution for Remote Sensing Data Management Model
## 4 Experiment
### Data Upload Efficiency  
In the prototype system designed in this paper, the remote sensing data are successfully uploaded to IPFS and the blockchain. The data upload time to IPFS storage is shown in the Figure 4.  
The main chain in multiple blockchains is Ethereum, which provides a content-based public search. The secondary chain is built for a specific remote sensing data production application system. Firstly, a large number of remote sensing data are transmitted to IPFS. The IPFS network stores the entire set of data received, while redirecting the data to the consortium chain: RSOC. Secondly, the RSOC chain stores hashes and metadata in blocks and generates RSOC block hashes. Finally, the block hash that can be made public is uploaded to the Ethereum main chain and stored in the block.
# MLKD-Net: Lightweight Single Image Dehazing via Multi-Head Large Kernel Attention
## 5 Ablation Study
### Evaluation on the Number of Heads in MLKA
#### 5.1.2 Evaluation on Channel Expansion Strategy  
To compare with the channel splitting strategy, we conducted a channel expansion experiment. In this setup, the input feature map was expanded along the channel dimension using a \(1\times 1\) convolution, as defined in Equation (2), so that each head received a feature map with the same channel size as the original input. The Expand-H1 configuration represents the baseline in this strategy and is structurally equivalent to Split-H1, as both use a single head without any channel splitting or expansion. Expand-H2 and Expand-H4 indicate models with two and four expanded heads, respectively.  
As illustrated in Figure 6, in the Tiny model, the two-head configuration (Expand-H2) achieved a 1.6% improvement in the PSNR over the baseline (Expand-H1), increasing from 35.71 to 36.29. However, when the number of heads was increased to four (Expand-H4), the performance dropped slightly to 36.14. In contrast to the Tiny model, the Small model showed a decrease in performance at the two-head setting (Expand-H2), with the PSNR dropping from 37.06 to 36.98, but the performance increased again at the four-head setting (Expand-H4), reaching 37.23. These results suggest that channel expansion does not consistently lead to performance gains despite the increase in parameters.  
However, the channel splitting strategy achieves performance improvement by dividing the input channels among heads while introducing almost no change in the number of parameters. Notably, the two-head configuration in the splitting strategy (Split-H2) not only outperformed the expanded four-head setting (Expand-H4) but also did so with approximately 43% fewer parameters, demonstrating superior parameter efficiency. This indicates that, compared to the expansion strategy, the splitting strategy provides a more lightweight and effective solution. Therefore, the two-head configuration in the channel splitting strategy (Split-H2) offers the best balance between model expressiveness and computational efficiency.  
Figure 5: Performance comparison of MLKD-Net with different numbers of split heads. The configurations Split-H1, Split-H2, and Split-H4 correspond to models with 1, 2, and 4 heads, respectively, using a channel-splitting strategy.
## 4 Experimental Results and Analysis
### Recognition Performance Comparison
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Recognition Rate** & **InceptionResNetV2-TA** & **InceptionResNetV2** & **Inceptio-nV3** & **ResNet50** & **QHIRT-UB** \\ \hline \multirow{9}{*}{**BPSK**} & 1 dB & 0.8933 & 0.8633 & 0.82 & 0.8333 & 2060 \\  & 2 dB & 0.9633 & 0.9433 & 0.9033 & 0.9233 & 0.3667 \\  & 3 dB & 0.9966 & 0.9666 & 0.9433 & 0.9633 & 0.4997 \\  & 4 dB & 1 & 1 & 0.9933 & 0.9966 & 0.6027 \\  & 5 dB & 1 & 1 & 1 & 1 & 0.6838 \\  & 6 dB & 1 & 1 & 1 & 1 & 0.748 \\  & 7 dB & 1 & 1 & 1 & 1 & 0.8012 \\  & 8 dB & 1 & 1 & 1 & 1 & 0.8425 \\  & 9 dB & 1 & 1 & 1 & 1 & 0.8735 \\  & 10 dB & 1 & 1 & 1 & 1 & 0.9003 \\  & 11 dB & 1 & 1 & 1 & 1 & 0.9212 \\  & 12 dB & 1 & 1 & 1 & 1 & 0.9369 \\ \hline \multirow{9}{*}{**QPSK**} & 1 dB & 0.7533 & 0.7333 & 0.6933 & 0.7133 & 0.2055 \\  & 2 dB & 0.8733 & 0.8433 & 0.8133 & 0.8333 & 0.3658 \\  & 3 dB & 0.9533 & 0.9333 & 0.8766 & 0.9066 & 4989 \\  & 4 dB & 0.9966 & 0.9666 & 0.9333 & 0.9633 & 0.6033 \\  & 5 dB & 1 & 0.9933 & 0.9666 & 0.9966 & 0.6834 \\  & 6 dB & 1 & 1 & 1 & 1 & 0.7506 \\  & 7 dB & 1 & 1 & 1 & 1 & 0.8011 \\  & 8 dB & 1 & 1 & 1 & 1 & 0.8396 \\  & 9 dB & 1 & 1 & 1 & 1 & 0.8736 \\  & 10 dB & 1 & 1 & 1 & 1 & 0.9 \\  & 11 dB & 1 & 1 & 1 & 1 & 0.9206 \\  & 12 dB & 1 & 1 & 1 & 1 & 0.9367 \\ \hline \multirow{9}{*}{**SPSK**} & 1 dB & 0.6333 & 0.6033 & 0.57 & 0.5966 & 0.2055 \\  & 2 dB & 0.7033 & 0.6933 & 0.6533 & 0.67 & 0.3691 \\  & 3 dB & 0.8433 & 0.81 & 0.7666 & 0.7933 & 0.4971 \\  & 4 dB & 0.9633 & 0.9433 & 0.8933 & 0.9233 & 0.6019 \\  & 5 dB & 0.9966 & 0.9633 & 0.9433 & 0.9633 & 0.6864 \\  & 6 dB & 1 & 0.9966 & 0.9666 & 0.9933 & 0.7503 \\  & 7 dB & 1 & 1 & 1 & 1 & 0.799 \\  & 8 dB & 1 & 1 & 1 & 1 & 0.8416 \\  & 9 dB & 1 & 1 & 1 & 1 & 0.8743 \\  & 10 dB & 1 & 1 & 1 & 1 & 0.8988 \\  & 11 dB & 1 & 1 & 1 & 1 & 0.9203 \\  & 12 dB & 1 & 1 & 1 & 1 & 0.937 \\ \hline \end{tabular}
\end{table} 
Table 1: Recognition rate comparison of BPSK, QPSK and 8 PSK at different SNRs.
## 2 Study Site and Methods
### Complementary Ecosystem Service Assessment Methods  
Our comparative ecosystem service approach only provides qualitative results. Therefore, two other methods were applied that potentially deliver more quantitative data. Our aim was to test whether the alternative approaches can provide complementary information. [PERSON] et al. [33] presented a matrix approach based on land cover data. For 31 ecosystem services and 44 land uses/land covers, they provided ecosystem potential scores ranging from 0 (no potential) to 5 (very high potential). The scores represented a hypothetical central European landscape before the harvest period. This approach was applied to the Hutelmoor scenarios for the four existing land uses/land cover types: sea and ocean, beaches, dunes and sand plains, and moors and heathlands. Using a geo-information system, the location and size of every land use/land cover type was calculated for each scenario: 1980, 2000, 2020 and 2040. Altogether, 31 ecosystem services were used that largely reflect the ecosystem services used in the comparative expert approach. For every ecosystem service, an area-weighted average score (representing the entire Hutelmoor, including the coastal zone) was calculated. These scores were averaged and multipliedwith the total area of 570 ha to obtain an indicator for the total ecosystem service potential and changes between the scenarios.  
Additionally, a quantitative ecosystem service approach was applied. InVEST, Integrated Valuation of Ecosystem Services and Tradeoffs [34], is a system of models for mapping and assessing ecosystem services. It was developed to explore how changes in ecosystems cause changes in ecosystem service flows. These models are freely accessible and well documented. The InVEST models cover only selected ecosystem services and address specific topics. For our purpose, only the InVEST Carbon Storage and Sequestration model was applicable. We used it to estimate the amount of carbon stored in the Httelmoor at different times. It takes into account four carbon pools (aboveground living biomass, belowground living biomass, soil and dead organic matter) based on land use maps. For methodological details see [[https://naturalcapitalproject.stanford.edu/software/invest](https://naturalcapitalproject.stanford.edu/software/invest)]([https://naturalcapitalproject.stanford.edu/software/invest](https://naturalcapitalproject.stanford.edu/software/invest)) (accessed on 15 December 2022).
# Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges
## 8 Research Challenges and Future Directions
### Classification of Research Challenges in Telemedicine  
Leading research challenges in wireless telemedicine implementations include: data security, scheduling, and video compression [3]; Quality of Service (QoS) and Quality of Experience (QoE) [37]; and privacy preservation and security [134]; energy efficiency, fault tolerance, and high bandwidth requirements for video streaming have been highlighted as well [101]. Figure 13 presents the research challenges mentioned above and discussed in the following subsections.  
\begin{table}
\begin{tabular}{l l l} \hline
**Standard/Parameter (Characteristic)** & **ZigBee/IEEE 802.15.4** & **Bluetooth/IEEE802.15.1** \\ \hline Wireless/Operating frequency & 2.4 GHz/868 MHz/915 MHz & 2.4 GHz ISM \\ Data rate & 20 kbps up to 250 kbps & 100 kbps \\ Typical current consumption (Transmission mode) & 30 mA & 50 mA \\ Battery life & 100–1000 days & 1–7 days \\ Network size/Devices per network & Up to 65,536 nodes & Up to 8 nodes \\ Range & 1 to 100 m & 10 to 100 m \\ Stack size & 4 to 32 KB & 250 KB \\ Application & Monitoring \& Control & Cable Replacement \\ Range & 10 to 100 m & 100 m \\ Data rate & 20–250 kbps & 1 Mbps \\ Power profile & Years & Days \\ Complexity & Simple & Complex \\ Network topology & Adhoc, star, mesh hybrid & Adhoc piconet \\ \hline \end{tabular}
\end{table}
Table 6: Key Features of Bluetooth and ZigBee Wireless Technologies [60,107].