esa-sceva/satcom-chunk-collection huggingface dataset:
# Aerial Surveillance Leveraging Delaunay Triangulation and Multiple-UAV Imaging Systems
## 4 Discussion  
The results obtained from the survey mission using both a single drone and a swarm of five drones present intriguing insights into the effectiveness of drone deployment and mission planning. Notably, the single DJI Mini 3 Pro drone, orchestrated through the Dronelink application, demonstrated efficiency in capturing the target at a close range (27.65 m). The subsequent image processing using the 'Recognize' app revealed noteworthy findings, successfully identifying objects within the surveyed area. In contrast, the deployment of five drones, strategically positioned using Delaunay triangulation facilitated by the DT Drones application, yielded enhanced results in terms of detected objects. The distributed arrangement of drones, spanning distances from 32 m to 63 m, provided comprehensive coverage of the target area. The Delaunay triangulation approach optimized the spatial distribution of drones, ensuring efficient coverage and minimizing potential gaps in the surveyed region. These findings align with the working hypotheses, suggesting that the collaborative utilization of multiple drones, guided by geometric algorithms such as Delaunay triangulation, can significantly improve the efficacy of survey missions. The comparative advantage of the five drone approach is evident in the broader coverage achieved, ultimately enhancing the detection capabilities of the 'Recognize' app.  
From a broader perspective, these results contribute to the growing body of literature on optimal drone deployment for geographical surveys. The successful integration of automated mission planning, as exemplified by Dronelink, and geometric algorithms for drone distribution, exemplified by Delaunay triangulation, holds promise for future research and applications. The discussion underscores the potential of such integrated methodologies in various domains, including environmental monitoring, infrastructure inspection, and disaster response.  
Future research directions may explore further refinements to optimize the balance between the number of drones, their spatial distribution, and the specific algorithms employed for mission planning. Additionally, investigations into the scalability of these approaches for larger geographical areas and more complex terrains could offer valuable insights. Overall, this study lays a foundation for advancing the capabilities of drone-based surveys, contributing to the evolving landscape of remote sensing and spatial data collection technologies.
## 4 Research Methodology
The three statistical measures were calculated using the following equations:  
\[R^{2}=\frac{\left(n\sum_{i}y_{i}y_{i}^{\prime}-\sum_{i}y_{i}^{\prime}\sum_{i}y_ {i}\right)^{2}}{\left(n\sum_{i}y_{i}^{\prime 2}-\left(\sum_{i}y_{i}^{\prime} \right)^{2}\left(n\sum_{i}y_{i}^{2}-\left(\sum_{i}y_{i}\right)^{2}\right) \right)}, \tag{1}\]  
\[MAPE=\frac{1}{n}\sum\left|\frac{y_{j}-y_{j}^{\prime}}{y_{j}}\right|\times 100, \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}\left(y_{j}-y_{j}^{\prime}\right)^{2}}, \tag{3}\]  
Figure 5: Schematic flowchart presenting the three machine learning approaches used to estimate FAGP concrete compressive strength. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{6}{c}{**Architectures**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Network & ANN & ANN & DNN & DNN & ResNet & ResNet \\ \hline Layer Normalisation & Yes & No & Yes & No & Yes & No \\ \hline Activation Function & & ReLU & ReLU & ReLU & ReLU & ReLU & ReLU \\ Rectified linear unit (ReLU) & Yes & Yes & Yes & Yes & Yes & Yes \\ \hline Drop-out & Yes & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Details of the setting of six investigated architectures from artificial neural network (ANN), deep neural network (DNN) and deep residual network (ResNet). where \(y_{j}\) and \(y^{\prime}_{j}\) are the compressive strength obtained from experiments and predictions respectively; \(n\) is the number of datasets. The K-fold cross validation method divides data into \(K\) equal folds and then does \(K\) independent training iterations on the prediction model with \((K-1)\) folds while leaving the remaining fold for validation purposes. In this experiment, the common value \(K\) = 10 was used. The performance of the prediction model was judged by averaging the metric measurement (R\({}^{2}\), MAPE, and RMSE) measured in \(K\) training and evaluating the iterations as follows:  
\[M_{K-fold}=\frac{1}{K}\sum_{k=1}^{K}m_{k}, \tag{4}\]  
where \(M_{K-fold}\) denotes a general metric measurement when K-fold cross validation is applied, and \(m_{k}\) is the metric measurement in the fold \(k\) of the procedure. An important note is that the same training and validation sets in each fold were used to train and validate each model. A hypothesis test (e.g., paired \(t\)-test) with a significance level \(\alpha\) = 0.05 was then applied to the accurate measurements of each model on validation sets in 10 divided folds to confirm the statistical significance of the results. The null hypothesis was that these measurements are all in the same population (or belong to the same model), suggesting there is no difference between the performance of two evaluated models. From the \(t\)-test, a \(p\)-value less than the chosen significance level (\(\alpha\) = 0.05) can statistically confirm the advance of a prediction model over the others (rejecting the null hypothesis), while the \(p\)-value greater than this significance level may suggest that the event, or the numerical conclusion, happens by chance (not rejecting the null hypothesis).
## 4 Discussion
Along with bronchial mucosal thickening, an increase in the number of PAS-positive mucus-producing cells in the bronchial mucosa is histopathologically considered as an indicator of an expectorant effect [16; 106; 107]. Consistent with the findings of previous drug efficacy experiments using a PM\({}_{2.5}\)-induced lung injury model [2; 3; 13; 16], the present study also demonstrated that intranasal injection of PM\({}_{2.5}\) (1 mg/kg) results in histopathologically significant lesions, including alveolar septum thickening due to inflammatory cell infiltration, and secondary bronchial mucosal thickening. Additionally, there was an increase in PAS-positive mucus-producing cells. Compared to the vehicle control group, the PM\({}_{2.5}\)-induced lung injury mouse model exhibited significant increases in average alveolar septum and secondary bronchial mucosal thickness, as well as in the number of infiltrating inflammatory cells around the alveoli and amount of PAS-positive mucus-producing cells in the secondary bronchial mucosa. Concurrently, there were associated reductions in ASA in lung tissues. In contrast to the PM\({}_{2.5}\) control group, all IAP-extract-administered groups demonstrated notable increases in ASA and average alveolar septum thickness, along with a decrease in the number of infiltrating inflammatory cells surrounding the alveoli. The observed changes were in a dose-dependent manner. In particular, in the IAP400 group, the inhibition of PM\({}_{2.5}\)-induced alveolar septum thickening and inflammatory cell infiltration and related ASA reduction were comparable to those in the DEXA group. Also, in a part of expectorant activity [16; 107; 106], significant increases in the amount of PAS-positive mucus-producing cells and the mean thickness of secondary bronchial mucosa were found in a dose-dependent manner in all IAP-extract-administered groups. However, in the DEXA group, compared to the PM\({}_{2.5}\) control group, significant changes in the amount of PAS-positive mucus-producing cells and average thickness of secondary bronchial mucosa were not observed. These findings show that the administration of IAP extract significantly activates not only the anti-inflammatory activity but also the mucolytic expectorant effect through the stimulation of gland fluid production in PM\({}_{2.5}\)-induced sub-acute lung injury in a dose-dependent manner. In addition, IAP extract showed expectorant effects on PM\({}_{2.5}\)-induced sub-acute pulmonary injury in mice [108] and an alleviation effect on ovalbumin-induced asthma [109]. Based on the findings of this study, IAP extract shows promise as a potential natural drug or health-functional food for enhancing respiratory function and mitigating the impact of respiratory disorders caused by PM\({}_{2.5}\)-induced lung injury. In this study, during HPLC analysis of IAP extract, substances corresponding to the remaining peaks, excluding arbutin, were not chemically identified. While the protective effect of IAP extract against lung injury was confirmed in this study, the protective effect of each HPLC peak could be studied in the future.
# Analysis of the Impact of Multipath on Galileo System Measurements
###### Abstract  
Multipath is one of the major source of errors in precise Global Navigation Satellite System positioning. With the emergence of new navigation systems, such as Galileo, upgraded signals are progressively being used and are expected to provide greater resistance to the effects of multipath compared to legacy Global Positioning System (GPS) signals. The high quality of Galileo observations along with recent development of the Galileo space segment can therefore offer significant advantages to Galileo users in terms of the accuracy and reliability of positioning. The aim of this paper is to verify this hypothesis. The multipath impact was determined both for code and phase measurements as well as for positioning results. The code multipath error was determined using the Code-Minus-Carrier combination. The influence of multipath on phase observations and positioning error was determined using measurements on a very short baseline. In addition, the multipath was classified into two different types: specular and diffuse, using wavelet transform. The results confirm that the Galileo code observations are more resistant to the multipath effect than GPS observations. Among all of the observations examined, the lowest values of code multipath errors were recorded for the Galileo E5 signal. However, no advantage of Galileo over GPS was observed for phase observations and for the analysis of positioning results.  
GNSS; multipath; Galileo system; Code-Minus-Carrier combination; time-frequency analysis 2021
# Self-Organizing Networks for 5G and Beyond: A View from the Top
## 4 Machine Learning Algorithms for SON
### Docitive Learning (DL)  
Docitive learning algorithms (from the word docere = to teach or transfer information) are mature ML algorithms based on the rationale that a decentralized network of nodes can exhibit a better performance in case nodes exchange information among themselves. Studies have shown that docitive learning can be effectively applied in cognitive radio (CR) networks as well [89].
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 4 Discussion
### Analysis of Historical Land Use/Land Cover Change
The massive conversion of forest into cropland and grassland can also be attributed to the lack of sustainable and effective land management measures. Although the first land policy (i.e., the National Land Policy) of the new century was promulgated in 2004, it focused on land law reform, land use rights, and land fees and taxation management; it did not provide clear provisions for sustainable land development [45]. The lack ofmanagement policies for sustainable land development has led to several land problems, such as the excessive fragmentation of agricultural land by families and land degradation [12]. In 2004, the average area of cropland possessed by a Rwandan rural household was about 0.6 ha, and this value could be as low as 0.25 ha. The very high number of families has caused severe fragmentation, which has accelerated the degradation of the land and intensified soil erosion [46]. This kind of unsustainable development of cropland means that forest land is continuously being converted to other LULC types.
## 5 Results and Discussion
### Maximum Credible Ground-Motion Parameters  
Because all three faults may contribute to the maximum credible ground-motion parameters of the dam, the multi-SFFSM was used to calculate multiple schemes for all three faults. Then, different percentile PSAs were compared with the results of the Longpan-Qiaohou fault (Figure 12). The results showed that the maximum PSA of the Longpan-Qiaohou fault coincided with that of all three faults. Furthermore, the mean PSA for the Longpan-Qiaohou fault was relatively close to its 50 th-percentile results, while the mean PSA for all three faults was higher than the 50 th-percentile results. This was attributed to the low acceleration values for the Daju-Lijiang and Xiaozhongdian-Daju faults.  
The 84 th percentile, which indicates the average value plus one standard deviation, as calculated using multi-SFFSM, accounts for the uncertainty in multi-scheme simulations. The 84 th-percentile PSA of the Longpan-Qiaohou fault can represent the maximum credible ground-motion parameters for the Longpan dam, which was supported by more than twelve thousand ground-motion simulations. The results can provide stable and reasonable ground-motion parameters for seismic analysis assessments of different engineering sites. Certainly, different structural characteristics and design purposes need to be considered for comprehensively evaluating ground-motion parameters [59].  
Although the result showed that a single fault controls the seismic hazard of the dam, it is necessary to calculate all faults that may have an impact on ground-motion parameters. For some dams, the acceleration values of a fault may be higher at high frequencies and lower than those of other faults during long periods. Researchers need to perform ground-motion simulations of different faults to obtain PGA and PSA values that play a dominant role at different frequencies rather than calculating the results of only one fault based on its maximum credible earthquake magnitude and its distance from the dam.  
Figure 12: Comparison between the acceleration response spectra for the Longpan–Qiaohou fault (LQF) and for all three faults.
## 3 Simulation Results
\begin{table}
\begin{tabular}{l l l l l} \hline \hline  & **Controller** & \(\mathbf{e}_{\max}\) & \(\mathbf{e}_{\textit{rms}}\) & \(\|\mathbf{e}\|\) \\ \hline \multirow{2}{*}{Case I. External External Disturbance} & PID & \multirow{2}{*}{\(K_{p}=1501,K_{D}=80,K_{I}=603\)} & \multirow{2}{*}{\(4.7691\times 10^{-4}\)} & \multirow{2}{*}{\(2.1244\times 10^{-4}\)} & \multirow{2}{*}{\(0.0601\)} \\  & \(K_{p}=1501,K_{D}=80,K_{I}=603\) & & & \\ \cline{1-1} \cline{2-5}  & EBSMC & & & \\ \cline{1-1} \cline{2-5}  & EBSMC & & & \\ \cline{1-1} \cline{2-5}  & \(k_{1}=30,k_{2}=50,w=603\) & & & \\ \hline \multirow{2}{*}{Case II. Nonlinear Friction} & PID & \multirow{2}{*}{\(7.6552\times 10^{-4}\)} & \multirow{2}{*}{\(6.7840\times 10^{-4}\)} & \multirow{2}{*}{\(0.1919\)} \\  & \(K_{p}=1501,K_{D}=80,K_{I}=603\) & & & \\ \cline{1-1} \cline{2-5}  & EBSMC & & & \\ \cline{1-1} \cline{2-5}  & \(k_{1}=30,k_{2}=50,w=603\) & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Numerical control performance comparison. Figure 6: Performance comparisons between PID control and extended backstepping sliding mode control (EBSMC). (**a**) system subject to time varying external disturbance; (**b**) system subject to nonlinear friction.
## 3 Results and Discussion
### Microbial Activity  
Figure 2a presents the results of the Revivable Aerobic Mesophilic Flora Test (RAMF) conducted at 30 \({}^{\circ}\)C. This test serves as an indicator of mesophilic flora, allowing for the enumeration of microorganisms present in the hemp shiv samples. The results are expressed in Colony Forming Units (CFUs), which provide a quantitative measure of the viable microorganisms capable of growing and forming colonies under the specified conditions.  
HS1 exhibits a lower microbial load compared to HS2, with log(CFU/g) values of 3.4 for HS1 against 4.3 for HS2 (Figure 2a). The results of the enumeration on selective Sabouraud and chloramphenicol medium (Figure 2b), which quantifies fungi such as yeasts and molds, reflect a similar trend. Specifically, HS1 shows a log(CFU/g) of 1.6, while HS2  
Figure 2: Microbiological tests of HS1 and HS2. FMAR test at 30 \({}^{\circ}\)C (**a**) and SABChl at 72 h (**b**).  
registers a higher value of 2.2 (Figure 2b). The quantitative results indicate that HS1 actually has fewer CFUs than HS2, and by extension, a lower overall microbial load (Figure 2).  
The SABChl values are below the threshold value of log(CFU/g) = 2.85 [34], suggesting a relatively low fungal presence. However, when comparing these values with the FMAR results, it appears that some fungi may still be included within the broader mesophilic microorganism count. This suggests that among the mesophilic microorganisms, fungi are likely present but in numbers that remain below the established threshold [34].  
These low levels of microorganisms could be attributed to the pre-treatment of the hemp shiv, which involved heating the material at 105 \({}^{\circ}\)C for 16 h upon receipt. This treatment was likely intended to stabilize the material, potentially inducing a lifting of dormancy in some microorganisms. As a result, the detected microorganisms might primarily represent those originating from the soil microbiome.  
Recent studies have highlighted the existence of bacterial-fungal interactions (BFIs) [49] where microorganisms coexist, which sometimes benefit from or antagonize one another in their environment [50]. These interactions enable both physical and metabolic interdependence [49; 50; 51].  
The results from the microbiological tests (SabChl and FMAR) point to a potential cohabitation of microorganisms. Consequently, Scanning Electron Microscopy analysis was conducted to visualize bacterial or fungal spores, providing further insight into this possible coexistence.
## 5 Conclusions  
Network slicing and edge computing are key enabling technologies of 5G and beyond networks considering their ability to create scalable and flexible networks while meeting the QoS and SLA requirements of different applications. Recent studies also showed that slicing the edge resources reveals the network capabilities further by satisfying the stringent QoS and QoE requirements of emerging applications. In this respect, this review paper presented existing edge-enabled network slicing solutions, frameworks, and use cases as well as potential benefits. Moreover, we also provided existing studies on how to combine ML methods with these concepts. By doing that, we aim to guide the research with a comprehensive analysis on edge enabled network slicing mechanisms combined with ML methods. In addition, considering the fact that synchronization between the slicing controllers improves the slicing decisions, we also proposed an RL-based controller synchronization solution. In particular, we believe that our method will be useful in terms of optimal routing path detection, resource balancing, and data offloading decisions between both the edge servers and their slices towards more effective edge-enabled network slicing.  
This research was partially funded by ECSEL-JU BRAINE grant number 876967 and NGF Quantum Delta CAT2.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** There are no relevant datasets presented in this article.  
Figure 3: An example system architecture representing proposed research idea.  
**Acknowledgments:** Authors would like to thank all BRAINE and CAT2 partners involved in this work.  
**Conflicts of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Theory
#### 2.1.2 The Polyphase Decomposition  
A potential alternative to resolve this issue is to decompose the slow-time signal into its \(N_{T}\) polyphase components [14]. In the field of multirate signal processing, an \(N\)th order polyphase decomposition of a discrete signal \(s[n]\) is a set of \(N\) periodically interleaved subsequences sampled at \(1/N\) times the framerate. Mathematically, the \(k\)th polyphase component signal \(e_{k}[n]\) is given by  
\[e_{k}[n] =s[Nn+k] \tag{1}\] \[k \in\{0:N-1\}. \tag{2}\]  
If we preserve all the polyphase components, the original signal can be reconstructed exactly by interleaving, such that  
\[s[n]=e_{\text{mod}(n,N)}\left[\left\lfloor\frac{n}{N}\right\rfloor\right], \tag{3}\]  
where \(\text{mod}(n,N)\) is the remainder after division of \(n\) by \(N\) and \(\lfloor\cdot\rfloor\) is the floor function.  
Noting that if we choose the polyphase decomposition order \(N\) such that \(N=N_{T}\), each polyphase component in the slow-time signal corresponds to a particular angle in the transmit sequence. Direct comparisons can be made between slow-time signals of the same transmit steering angle, and thus would be insensitive to sidelobe motion. However, if we choose to do so with a conventional linear sequence, there is no performance improvement relative to just using the HRIs, as each polyphase channel operates at the HRI frame rate. For this reason, we introduce the double transmit sequence as a workaround, as examined in the next subsection.