esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of Molecularly Imprinted Polymers (MIPs) and Coatings  
#### 3.1.1 Scanning Electron Microscopy  
Figure 1 shows SEM images of BPA-MIPs and BPA-NIPs at a magnification of 2000 times. It can be observed that both BPA-MIPs and BPA-NIPs prepared in the experiment have irregular elliptical shapes with an average particle size of 20.0 \(\upmu\)m. Moreover, the surfaces of the elliptical particles are cross-linked and contain numerous cavities. Among them, Figure 1a represents BPA-MIPs, while Figure 1b represents BPA-NIPs. A comparison between the two images reveals that BPA-MIPs have significantly more cavities than BPA-NIPs. It is the presence of these cavities that contributes to the higher selective binding of BPA-MIPs towards BPA compared to BPA-NIPs. Please note that SEM images provide information about the morphology and structure of the materials but do not provide precise details about their chemical composition. To obtain a more comprehensive understanding of the materials' characteristics, additional characterization techniques such as FT-IR analysis, thermal analysis, pore size, and surface area analysis should be employed. The combined application of these techniques can provide a more comprehensive assessment of the performance and properties of BPA-MIPs and BPA-NIPs.  
Figure 2 shows SEM images of BPA-MIPs-SBSE coating (a) and BPA-NIPs-SBSE coating (b) at a magnification of 40,000 times. It can be observed that the molecularly imprinted stir bar sorptive extraction (MIPs-SBSE) coatings prepared using the sol-gel method exhibit noticeable cavity distribution on their surfaces. These cavities are capable of effectively binding the target analyte, BPA, enabling efficient adsorption and extraction of BPA. Among them, Figure 2a represents the BPA-MIPs-SBSE coating, while Figure 2b represents the BPA-NIPs-SBSE coating. A comparison between the two images clearly shows that the cavity distribution on the surface of BPA-MIPs-SBSE is more uniform than that of BPA-NIPs-SBSE, ensuring better adsorption and extraction of the target analyte.  
SEM images provide a visual representation of the uniform distribution of cavities with specific selectivity for the target analyte, bisphenol A (BPA), on the surface of the molecularly imprinted stir bar sorptive extraction (MIPs-SBSE) coating. These cavities enable the effective binding of the target analyte, facilitating the adsorption and extraction of BPA.  
Figure 1: SEM images of BPA-MIPs (**a**) and BPA-NIPs (**b**).
## 4 Discussion
### Impact of Different Remote Sensing FPAR on GPP
Thus, when using a better accuracy GLASS-FPAR to estimate GPP, \(R^{2}\) increased from 0.80 to 0.85. For grasslands (Figure 4f-h), MODIS-FPAR and flux derived GPP has a higher \(R^{2}\) and shows a consistent variation in the vigorous growth period. Through these analyses at site scale, it shows that improving the accuracy of FPAR is essential for GPP estimations. FPAR shows a very high correlation with flux derived GPP, which can be explained biophysically with observations that larger canopy and longer growing season allow vegetation to absorb more PAR and assimilate more carbon [31]. In turn, as more carbon transformed to GPP, more carbon will be assigned to leaf production, which caused larger FPAR and it can maintain the balance by absorbing more PAR. If other factors, such as light, temperature and water, are not restricted, change of FPAR should explain more variations of GPP. Furthermore, it is also observed that FPAR can well capture the dynamics of interannual GPP, but the deviation is larger in the short term which may be caused by short-term stress effect due to light, temperature, water, _etc._ By analyzing the correlations between MODIS-NDVI, -EVI, -LAI and -FPAR products with short-term and annual flux measured GPP, [PERSON] _et al._[31] pointed out that EVI was useful for the analysis of short-term variation in site-estimated GPP. The relationship between EVI and GPP has also been widely used in classic remote sensing GPP models [2, 7, 8]. EVI may display better short-term variation of GPP than FPAR and thus could potentially improve the accuracy of MODIS-GPP. MODIS-FPAR is derived from MODIS-LAI/FPAR algorithm (a complex three-dimensional radiative transfer model with LOOKUP table inversions per biome type [32]), differing form the early method of empirical relationships between values of LAI/FPAR and NDVI, but MODIS-FPAR is linearly proportional to NDVI [32]. Like NDVI, MODIS-FPAR is sensitive to canopy background variations and saturates in areas with dense tree canopy (_i.e._, Figure 4), while EVI is developed to optimize the canopy background information with improved sensitivity in high biomass areas and improved vegetation monitoring through a decoupling of the canopy background signal and a reduction in atmosphere influences [2, 33, 34, 35].
## 2 Materials and Methods
### Comparative Planners  
In order to assess the performance of the proposed GPP algorithm, a comparative analysis with two other IPP strategies has been carried out. In particular, the two planners used as benchmark are as follows.  
1. **Sampling-Based planner (S-B)** This planner corresponds to the implementation of the planning method proposed in [14], which is considered, to the extent of the authors' knowledge, as one of the main state-of-the-art IPP approaches for IG operations. The planner is composed of two phases: The first one is called _Station Searching_ and applies a RRT algorithm to find the most informative point within the target area, named as _Station_. In the second phase, called _Informative Path Planner_, the planner searches for the optimal path according to a trade-off between the DE of path and its length. With this aim, a RRT* algorithm is utilised to find the best path which connects the start point to the _Station_, if any, respecting a budget constraint on the travelled distance. In case the RRT* algorithm fails to find a feasible solution, the path obtained during the _Station Searching_ phase is used.
2. **Random Planner** A random planner has been evaluated to assess whether the genetic operations in the iterative part of the GPP algorithm provide an improvement on the performance of the solutions. Otherwise, they would result to be just time consuming operations which can be avoided. Therefore, this planning strategy consists in an extended global initialisation phase, identical to the one implemented in the GPP algorithm, which generates random chromosomes for all the available planning time. Afterwards, using the fitness and ranking operators, the best path among all the generated ones is selected. Although a similar effect on the chromosomes generation could be achieved using high mutation probabilities \(z\) and \(q\) in the GPP approach, the authors decided to implement the random planner to not perform any kind of optimisation phase in the algorithm. This allows to evaluate the contribution of the optimisation performed by the genetic operations in the GPP algorithm.
## 4 Solution Problem
### UAV-BSs Clustering and Positioning Algorithm (UCPA)
#### 4.2.2 Optimized  
The main goal of the optimized strategy is to efficiently schedule the resources used to support the network, balancing the number of UAV-BS with the quality of service. The strategy is based on the number of users counted in the new coverage area calculated in Equation (29), generating a new dataset. The strategy follows a different methodology than the naive strategy, in that the number of centroids is defined by the number of users in the set \(Loc_{(i,j)}\). Equation (31) shows the calculation used to determine the number of UAV-BSs in the optimized strategy.  
\[Vo_{i,j}=\begin{cases}o,&\tau<=50\\ s,&50<\tau<=100\\ z,&\tau>100\end{cases} \tag{31}\]  
To determine the total number of UAV-BS, the sum of the values of the overloaded macrocell was calculated. Equation (32) simplifies the total number of UAV-BS for the optimized strategy.  
\[Q_{v}=\sum Vo_{i,j} \tag{32}\]  
Once we have all the necessary information to determine which macrocells are overloaded, we can plan the necessary number of UAV-BS to support the overloaded macrocells and, consequently, temporarily reduce the service congestion. Algorithm 2 shows the step-by-step operation of UCPA, starting with the calculation of the user set that determines the number of UAV-BS, based on the naive and optimized strategies, to the optimal location for the UAV-BS.  
This subsection has shown how the UCPA algorithm was presented to calculate the number of UAV-BS needed for each macrocell based on the chosen strategy. We also present the methodology for determining the optimal positioning of UAV-BS based on the number of users in the coverage area of the macrocell. Overall, we present a methodology for solving congestion problems in mobile networks in regions where there is a strong temporal and spatial influence that enables the generation of nonlinear data and complicates the planning of temporary support resources. In the next section, we present the case study to validate the solution to the problem.  
```
1:Input:\(M_{s}\)\(\triangleright\) overloaded macrocell set
2:for i <= \(M_{s}\)do
3:procedure(calculating the number of UAV-BSs)
4:\(Initialize\) with user sets, \(Loc(i,j)\) in (29)
5:if\(strategy=naive\)then
6:\(Vn_{ij}\)\(\triangleright\) number fixed of UAV-BSs
7:else
8:\(Vo_{i,j}\leftarrow(31)\)\(\triangleright\) based on user set Loc(i, j)
9:endif
10:endprocedure
11:\(Q_{v}\gets Vu_{i,j}\)
12:\(Q_{v}\gets Vo_{i,j}\)
13:Return\(Q_{v}\)\(\triangleright\) number of UAV-BSs calculated per macrocell
14:procedure(calculating the optimal placement of UAV-BSs)
15:\(Initialize\) with the variable \(Q_{v}\)
16:\(Initialize\) with the sets \(Loc(i,j)\)
17:\(Kmeans\gets KMeans(Q_{v},init,Ninit,MAXiter)\)
18:\(result\gets Kmeans(Loc(i,j))\)
19:endprocedure
20:Return\(C_{i,j}\leftarrow\) (set of cluster centroids)
21:Output:\(P_{nav}\gets C_{i,j}\)\(\triangleright\) ideal position set
```  
**Algorithm 2**_UAV-BSs Clustering and Positioning Algorithm_
# A Survey on Satellite Communication System Security
## 4 Attacks in Satellite Communication Systems
### Summary  
SCSs face an array of sophisticated attacks that threaten their confidentiality, availability, and integrity, showcasing the critical vulnerabilities inherent in these networks. Eavesdropping, both passive and active, poses a significant risk by enabling unauthorized interception of communications, potentially exposing sensitive personal and national security information. Availability threats, including jamming and DoS/DDoS attacks, disrupt satellite services, affecting everything from military operations to civilian GPS navigation. Spoofing attacks, particularly sophisticated in their execution, compromise the integrity of satellite signals, leading to misleading data being transmitted to receivers. These threats highlight the pressing need for comprehensive security countermeasures that encompass advanced technological defense methods and research to safeguard the vital infrastructure of global communication networks.
## 5 Results
### Speaker Diarization Evaluation
#### 5.2.1 Open-Set Condition Results  
Five teams participated in the open-set condition evaluation. Four teams submitted results on time, but G1-GTM-UVIGO made a late submission. This late submission was not taken into account for the challenge ranking, but we included them in this review, as their results were very impressive. Table 10 presents results for each submitted system by team. G1-GTM-UVIGO obtained a DER of 11.4%, which is almost half of the next system, the contrastive C1 system from the G11-ODESSA team with a DER of 20.3%. The most significant difference between the G1-GTM-UVIGO systems and the rest of the systems was the low speaker error, as G1-GTM-UVIGO obtained 6.6% and the next obtained 16.8%. Figure 2 presents the average per show of the estimated number of speakers of the best systems of each team. Considering that the correct average per show of the number of speakers in the database was 27, G1-GTM-VIGO made a close estimate of 28 speakers per show on average. The rest of the systems underestimated substantially the number of speakers present in each show. This estimation of the number of speakers for each show was a key point that allowed us to explain the good results of the G1-GTM-UVIGO in this evaluation.  
On the other hand, Table 11 presents the DER obtained by each participant for each different show. The lower DER was obtained in \"La Tarde en 24H Tertulia\", which is a talk show about politics and the economy with 20 speakers on average, good quality audio, and no overlapping speech. The rest of the shows include set and outdoor sections with a higher number of speakers: 65 for LM (La Manana), 34 for LA24H (Latinoamerica en 24H) and 29 for EC (Espana en Comunidad).  
Figure 2: Average per show of the estimated number of speakers of the primary system of each team. #SPK is the real average number of speakers. Team and system descriptions in Section 4.2.1.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Team** & **G6** & **G7** & **G14** \\ \hline
**Overall WER** & 22.22 & 21.98 & **19.57** \\
**Overall TNWER** & 20.71 & 19.75 & **17.90** \\ \hline \hline \end{tabular}
\end{table}
Table 9: Speech to text closed-set condition. WER (%) and TNWER (%) for the primary systems per team. Team and system descriptions are in Section 4.1.2. Best results by TV show are in bold.
## 4 Analysis and Discussion
### Finite Element Simulation Results
#### 4.2.2 Bending Moments of Diaphragm Wall  
For the design of the foundation pit support structure, the bending moment of the support structure is an important parameter that engineers concern and which determines the reinforcement and economy of the support structure. Figure 7 shows the variation of bending moments during stepwise excavation of four sub-pits 3A10-1, 3A10-2, 3A5, and 3A8. As the foundation pit excavation proceeded, the bending moment of the diaphragm wall gradually increased, and the bending moments were mainly located in the excavation surface or in the area above the excavation surface. It was worth noting that in sub-pit  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pit Number** & **Monitoring Point** & \begin{tabular}{c} **Monitoring Maximum** \\ **mm** \\ \end{tabular} & \
\begin{tabular}{c} **Numerical Maximum** \\ **mm** \\ \end{tabular} &
\begin{tabular}{c} **Percentage Error** \\ **\%** \\ \end{tabular} \\ \hline \multirow{7}{*}{3A10-1} & ZQT5 & 24.7 & 14.6 & 69.2 \\  & ZQT7 & 40.9 & 38.5 & 6.2 \\  & ZQT9 & 51.8 & 51.7 & 0.2 \\  & ZQT11 & 22.5 & 24.3 & 8.0 \\  & ZQT21 & 17.7 & 20.9 & 18.1 \\  & ZQT22 & 22.2 & 22.3 & 0.5 \\  & ZQT24 & 22.5 & 19.9 & 13.0 \\  & ZQT25 & 21.8 & 18.0 & 21.1 \\ \hline \multirow{7}{*}{3A10-2} & ZQT31 & 12.9 & 11.4 & 13.2 \\  & ZQT33 & 11.7 & 9.0 & 30.0 \\  & ZQT35 & 16.7 & 10.7 & 56.1 \\  & ZQT38 & 14.9 & 10.7 & 39.3 \\  & ZQT39 & 12.1 & 13.4 & 10.7 \\  & ZQT41 & 10.8 & 10.3 & 4.9 \\  & ZQT43 & 13.2 & 10.9 & 21.1 \\ \hline \multirow{7}{*}{3A5} & ZQT27 & 10.2 & 9.0 & 13.3 \\  & ZQT28 & 18.5 & 17.7 & 4.5 \\ \cline{1-1}  & ZQT29 & 10.2 & 7.4 & 37.8 \\ \cline{1-1}  & ZQT53 & 11.2 & 13.8 & 23.2 \\ \hline \multirow{7}{*}{3A8} & ZQT3 & 20.0 & 15.8 & 26.5 \\ \cline{1-1}  & ZQT19 & 11.9 & 10.6 & 12.2 \\ \cline{1-1}  & ZQT52 & 17.7 & 18.7 & 5.6 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Error table of maximum deformation for monitoring and numerical simulation.  
3A10-1, the bending moment of the diaphragm wall on this side was significantly larger than the other two walls on either side due to the influence of the Yuantai Building. As a result of the unloading of the loads imposed by the Yuantai Building and the excavation of the foundation pit in the original active area, the wall on this side showed a negative moment below the excavated surface of the foundation pit.
## 4 Experiment and Results
### Accurate Re-Identification of Surviving Human Targets Using Bio-Radar  
Based on the multispectral technology above, we can only detect the suspected human targets in _Scenario 2_ but cannot make an accurate identification of surviving human targets. Therefore, when suspected human subjects were found by aerial multispectral imaging, the bio-radar will be released around the suspected target (within 8 m) to non-contact detect respiratory signal (the detection direction of radar can be adjusted by the included control components).  
Just as shown in Figure 16a, since the human subjects are distributed in the outdoor environment, we can find that the radar echo contains a lot of interference and noise, which drowns out the breathing signal severely. Consequently, although the respiratory frequency of the subject can be detected as 0.24 Hz in the normalized frequency spectrum, it still contains a large amount of noise and interference components. When the environmental interference is more intense, respiration detection would be more difficult. The noise cancellation result of this signal using our proposed NLMS-ALE method is shown in Figure 16b. Obviously, noise and interference are effectively removed from radar echo, and significant respiration frequency can be acquired in the normalized frequency spectrum. This means that even when the outdoor environment interference is strong, we are still able to detect the breathing characteristics of the target using bio-radar. Then, this respiration information would be transmitted wirelessly. Consequently, we can make accurate identification of surviving human target (T\({}_{12}\)) as shown in Figure 16c and eliminate fake targets.  
Figure 15: Suspected target recognition result in _Scenario 2_ (_green for vegetation, yellow for soil, red for suspected target, blue for noise spot_).
## 3 Data and Methods
### Establishment of the Basic Database
#### 3.3.3 Land Use Type Data  
The land use type data were derived from the 2020 remote sensing monitoring data of land use status, which has a 1 km precision, published by the Environmental Science and Data Center of the Chinese Academy of Sciences. The LUCC classification system categorizes land use types in the study area into the following six categories: cultivated land, forest land, grassland, water area, urban and rural industrial and residential land,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name Abbreviation** & **Proportion\%** & **Agrotype** & **Name Abbreviation** & **Proportion\%** & **Agrotype** \\ \hline LVh & 71.97 & Haplic Luivisols & LPe & 0.46 & Eutric Leptosols, \\ PHh & 15.00 & Haplic Phaeozems & ANh & 0.78 & Haplic Andosols \\ GLm & 8.18 & Mollic Gleysols & HSs & 0.73 & Terric Histools \\ ATc & 2.07 & Cumulative Anthrosols & CME & 0.61 & Eutric Cambiols \\ \hline \hline \end{tabular}
\end{table}
Table 1: The basic soil types within the study area.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Type** & **Coefficient** & **SOL\_BD1** & **SOL\_AWC1** & **SOL\_K1** & **SOL\_CBN1** & **SOL\_BD2** & **SOL\_AWC2** & **SOL\_K2** & **SOL\_CBN2** & **Hierarchical** \\  & ANh & 1.41 & 0.15 & 12.38 & 6.74 & 1.41 & 0.14 & 10.86 & 1.99 & L\_L \\  & LPe & 1.45 & 0.10 & 11.23 & 1.13 & 0 & 0 & 0 & 0 & L\_L \\  & PHh & 1.43 & 0.14 & 11.58 & 1.95 & 1.43 & 0.13 & 11.41 & 0.67 & L\_L \\  & GLm & 1.43 & 0.14 & 12.29 & 1.65 & 1.42 & 0.13 & 7.42 & 0.69 & L\_CL \\  & HSs & 1.41 & 0.10 & 3.42 & 39.4 & 1.47 & 0.11 & 10.05 & 38.46 & CL\_SaCL \\  & ATc & 1.40 & 0.15 & 10.92 & 1.12 & 1.41 & 0.14 & 11.52 & 0.82 & SIL\_L \\  & LYh & 1.44 & 0.13 & 12.51 & 0.74 & 1.42 & 0.13 & 6.78 & 0.36 & L\_CL \\  & CME & 1.44 & 0.12 & 11.68 & 1.00 & 1.44 & 0.12 & 8.52 & 0.37 & L\_L \\  & WR & 1.72 & 0 & 260 & 0 & 0 & 0 & 0 & 0 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: SPAW calculated soil types and levels.  
and unused land, in Table 3, we give a specific description of land classification. These fundamental categories were reclassified into the aforementioned six land use types during the modeling process [29].
## 4 Applications of ML-Based Routing Algorithms in WSNs
### Industrial Applications  
Industrial WSN (IWSN) has the advantages of flexibility, mobility, scalability, and low maintenance, which helps to collect environmental parameters such as temperature and humidity in the factory, or be used to monitor and maintain the operating status of plant equipment. While saving manpower and financial resources, it is also convenient to provide accurate monitoring and correction remotely and improve the output efficiency and quality of products.  
To provide the reliable, low-latency, and low-energy communication services in IWSN, a Q-learning reliable route with a weighted proxy (QLRR-WA) algorithm was proposed [67]. The algorithm combines the characteristics of RL that adjusts the weights in real timeaccording to the current operating conditions and the advantages of the strong reliability of the graph routing algorithm. Compared with the inconvenience of manually adjusting static weights and parameters in traditional graph routing, this method based on RL only needs low computing resources and workload to flexibly adjust the optimal routing according to the status. The effectiveness of the algorithm was validated in terms of reliability and the power consumption.
## 3 Influence Metrics
### Quality of Service Metrics Analysis
#### 4.3.6 Route Overhead  
The execution of Algorithm 3 within the proposed routing protocol determines the quality of the connection between the source device and destination node. This leads to a more restricted packet transmission approach compared to S&W, Epidemic, and Prophet v1 and v2 protocols, resulting in a great reduction in overhead (more than 32% less), as shown in Figure 12. Although the proposed algorithm retains the same number of message copies, they are no longer forwarded to all nodes but only to those that satisfy the conditions specified in Algorithm 3.  
Figure 11: Hop count of delivered packets for different routing protocols, depending on the buffer size, normalized with respect to the results of the S&W routing protocol: (**a**) comparison among all the analyzed routing protocols; (**b**) comparison of the proposed metrics and S&W.  
Figure 10: Average latency for different routing protocols, depending on the buffer size, normalized with respect to the results of the S&W routing protocol: (**a**) comparison among all the analyzed routing protocols; (**b**) comparison of the proposed metrics and S&W.