esa-sceva/satcom-chunk-collection huggingface dataset:
# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
## Abbreviations
(2017)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Remote Sensing Image Classification Based on the Optimal Support Vector Machine and Modified Binary Coded Ant Colony Optimization Algorithm. _Inf. Sci._**2017**, _402_, 50-68. [CrossRef]
* _[PERSON] et al. (2003)_ [PERSON]; [PERSON]; [PERSON] A Practical Guide to Support Vector Classification. 2003. Available online: [[https://www.csie.ntu.edu.tw/~glin/papers/guide/guide.pdf](https://www.csie.ntu.edu.tw/~glin/papers/guide/guide.pdf)]([https://www.csie.ntu.edu.tw/~glin/papers/guide/guide.pdf](https://www.csie.ntu.edu.tw/~glin/papers/guide/guide.pdf)) (accessed on 10 June 2025). * _[PERSON] et al. (2004)_ [PERSON]; [PERSON]; [PERSON] Probability Estimates for Multi-Class Classification by Pairwise Coupling. _[PERSON]. Mach. Learn. Res._**2004**, \(5\), 975-1005. * _[PERSON] et al. (2011)_ [PERSON]; [PERSON] LIBSVM: A Library for Support Vector Machines. _ACM Trans. Intell. Syst. Technol._**2011**, \(2\), 27. [CrossRef]
* [PERSON] (2013) [PERSON] A Comparative Study on the Predictive Ability of the Decision Tree, Support Vector Machine and Neuro-Fuzzy Models in Landslide Susceptibility Mapping Using GIS. _Comput. Geosci._**2013**, _51_, 350-365. [CrossRef]
* _[PERSON] et al. (2010)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON] _ImageSVM Regression, Application Manual: ImageSVM Version 2.1_; Humboldt-Universitat Berlin, Germany, 2010. * _[PERSON] (1985)_ [PERSON]; [PERSON] [PERSON] Standardized Principal Components. _Int. J. Remote Sens._**1985**, \(6\), 883-896. [CrossRef]
* _[PERSON] (1989)_ [PERSON]; [PERSON] Enhancement of Landsat Thematic Mapper Imagery for Residual Soil Mapping in SW Minas Gerais State, Brazil: A Prospecting Case History in Greenstone Belt Terrain. In Proceedings of the 7 th Thematic Conference on Remote Sensing for Exploration Geology, Calgary, AB, Canada, 2-6 October 1989; pp. 1173-1187. * _[PERSON] et al. (2023)_ [PERSON]; [PERSON] Insar-Based Investigation of Surface Displacement Over the Area of the Collapsed Buildings in Istanbul. _Int. Arch. Photogramm. Remote Sens. Sput. Inf. Sci.--ISPRS Arch._**2023**, _48_, 81-85. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Coastal Subsequence Detection and Characterization Caused by Brine Mining over the Yellow River Delta Using Time Series InSAR and PCA. _Int. J. Appl. Earth Obs. Geoinf._**2022**, _114_, 103077. [CrossRef]  
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## 3 Results and Discussion
### Simulation Analysis of the Skywave OTH Channel Characteristics at 00:00 AM
Figure 7: Ray-tracing results of vehicle in summer at 00:00 a.m.: (**a**) range: 1081.239 km, time: 393.4 s; (**b**) range: 1222.943 km, time: 413.6 s; (**c**) range: 1264.368 km, time: 420.4 s.  
From the perspective of the dynamic propagation of rays and the overall characteristics of multipath channel parameters during re-entry, as the re-entry time progresses, the flight distance gradually increases, the flight altitude decreases, and the delay caused by OTH propagation gradually increases. For the Doppler frequency, most paths exhibit a decreasing trend, while a few paths show a dynamic process of first increasing and then decreasing. Secondly, the skywave OTH channel of hypersonic vehicles is affected by multipath propagation, where the number of multipath components may suddenly increase. Figure 8: Characteristic parameters of the multipath channel during re-entry in summer at 00:00 a.m.: (a) loss; (b) delay; (c) Doppler. Figure 10: Characteristic parameters of the multipath channel during re-entry in autumn at 00:00 a.m.: (a) loss; (b) delay; (c) Doppler. Figure 9: Ray-tracing results of vehicle in autumn at 00:00 a.m.: (a) range: 1081.239 km, time: 393.4 s; (b) range: 1170.929 km, time: 406.1 s; (c) range: 1253.401 km, time: 418.4 s.  
increase or disappear. Phenomena such as the merging of two paths or the splitting of one path into two can occur, and the loss, delay, and Doppler characteristics of each path may dynamically change, resulting in distinct behaviors. Furthermore, the channel exhibits the minimum loss and maximum delay at high-elevation angles, and vice versa. In particular, Figure 11 shows that the propagation trajectory of the wave fails to intersect with the vehicle's re-entry trajectory, creating a hollow area where the vehicle cannot receive OTH signals.
# Simulation of a Narrowband Power Line Communications System over Medium Voltage
## 5 Simulation Results and Discussion
### F-OFDM and W-OFDM Signals  
Figure 5a,b depict the PSD of F-OFDM and W-OFDM signals, respectively, at the transmitter and the receiver. The received signals (i) have propagated via an MV-MTL having the features given in Table 1, and (ii) have undergone the superposition of colored Nakagami noise. In both cases, the higher spectral content of the received signal is increased due to the corresponding behavior of the additive noise. The F-OFDM signal plotted in Figure 5a exhibits a main spectral lobe confined within the NB-PLC frequency range (3-148.5 kHz) and a very small 30 dB transition area. On the other hand, in addition to a main spectral lobe, the W-OFDM signal plotted in Figure 5b exhibits an easily discernible spectral content in the frequency range 220-270 kHz. This secondary lobe is the result of the zero padding that is used as input to the IDWT block at the transmitter, which effectively forces W-OFDM to transmit twice the amount of data compared to F-OFDM. It is evident that the transition from the main spectral lobe to the spectral tail is far from being as steep as in F-OFDM. This spectral advantage of F-OFDM is critical, as it results in less inter-carrier interference. The LPF at the receiver is designed to pass only the NB-PLC frequencies, thus suppressing a large part of the quite broader spectral content of the received W-OFDM signal, leading to an increased BER as readily observed from Figure 6.  
Figure 4: (**a**) CDF of the noise superimposed on NB-PLC signals transmitted via MV power lines; and (**b**) PSD(\(f\)) of simulated colored Nakagami noise.
## 2 Germinal Center Optimization
### Germinal Center  
When the body does not have a specific AB for an infection, the body starts the process of affinity maturation with the germinal center reaction. Germinal centers are micro-anatomical regions in the secondary lymph nodes, that form in the present of antigen [10]. The AGs that survive the other immunity mechanism arrives at the secondary lymph nodes, where are capture for the FDCs. The FDCsactivate near B-cells. The active B-cells end up being enclosed by the inactive ones forming a natural barrier that allow the active cells to proliferate, mutate and be selected.  
The B-cells start to proliferate inside the GC, and compete for the antigen, this competition polarizes the GC in two distinct zones, the dark zone and the light zone. The dark zone is where B-cells proliferate through clonal expansion and somatic hyper-mutation, this process ensures the diversity of ABs. On the other hand, the light zone is where the B-cells are selected in accordance with their affinity.  
On the light zone, the B-cells must find AG and internalize it, with the final purpose of digest the AG and expose their peptides to the Th-cell. The Th-cell gives a life signal allowing B-cells high affinity to live more time and therefore, proliferate and mutate with higher success.  
In Figure 1, we show a schematic summary of the process. The GC reaction ensures the diversity through clonal expansion and somatic hyper-mutation in the dark zone, while in the light zone is a competitive process that reward the more adapted B-cells. The B-cells reentry to dark zone making this process an iterative refinement of the affinity [19; 20].  
Finally, when the GC generates high affinity B-cells for certain AG, some B-cells differentiate into plasmonic cells and release their ABs. A few B-cells become Memory B-cells that could live a long time and keeps information about this particular AG. Then, the GCs are capable of generating specific AB for a specific AG, and keep this information in Memory B-cells for future infections [21].
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 3 Background
### Design Challenges
#### 3.1.2 Sensor Node Mobility  
In many cases, terrestrial sensor networks are immobile. However, sensor nodes in the underwater environment often exhibit passive mobility induced by water currents or proactive mobility made possible by sources such as an internal propulsion system. This mobility further complicates the deployment process, commonly requiring frequent redeployment. Therefore, sensor node mobility must be explicitly considered when deploying the gateways to preserve a desired level of coverage.
## 2 Overview  
### Quantum Cryptography  
The motivation for using cryptography can generally be divided into two key objectives. The first is to ensure the confidentiality, meaning that the content of the communication remains inaccessible to unauthorized parties. The second is to guarantee the authenticity, meaning that the communicating parties are able to verify that the message has been originated from a legitimate source. The simplest way to achieve this is to use a secret key known only to the sender and the receiver. However, with conventional communication methods, it is nearly impossible to guarantee that no third party has accessed any part of the key. Quantum cryptography addresses this problem by providing a more secure way to detect whether data has been eavesdropped [6; 7].  
Quantum cryptography relies on two types of communication channels [8]: a pre-authenticated quantum channel, used for the secure distribution of quantum bits used to build cryptographic keys, and a public channel (which also should be pre-authenticated by the communicating parties), used for error correction and the exchange of additional information. In general, quantum cryptography consists of two main phases: quantum key distribution based on communication in quantum channel and key distillation.
# On Maximizing the Probability of Achieving Deadlines in Communication Networks
## 6 Simulation Implementation
### Data Link Layer (DLL)  
The MAC protocol was implemented as a standard unslotted, non-persistent carrier-sense multiple access (CSMA) protocol with acknowledged connectionless service. The implementation of Timeout-MAC [36] in Castalia served as the basis for implementing this protocol. Retransmissions are carried out after 5 ms in case no acknowledgment is received. Acknowledgments are transmitted immediately after the frame to be acknowledged is received, while all other nodes hold contention. After two transmission attempts without successful reception of an acknowledgment by the sender, the frame (packet) is discarded.  
Link delay estimations were implemented in the DLL. For this, a timestamp is noted when data packets or probing packets are passed from the NET to the DLL and queued at the MAC. Once the sender receives an acknowledgment, it estimates the delay to the receiver by calculating the difference between the acknowledgment's reception time and the initially noted timestamp, subtracting the air time of the acknowledgment frame. The measured delay is then forwarded to the NET for maintaining link delay statistics.  
Acknowledgments also include a flag to indicate if a frame has already been received and acknowledged before. If the flag is set, then prior ACKs got lost, and the actual delay experienced by the packet for reaching the neighbor was shorter. In these cases, the acknowledgment is not used for link delay statistics.
## 4 Results and Discussion
### Building Energy End Use Analysis
#### 4.2.3 Istanbul  
The breakdown of end-use energy consumption of the base model was compared between NWC and HST0.75 + PP0.3. The highest energy consumption was in January, which is the coldest month of the year, while the lowest was in September.  
Figure 8 illustrates the percentage breakdown of energy consumption by the end-use of the base model in Istanbul. Space heat (6%) and space cooling (9%) constituted about 15% of energy consumption. The highest consumption category was ventilation (24%) followed by miscellaneous equipment (20%), hot water (20%), lighting (15%), space heating (6%), heat-pump supply (4%) and external use (1%). Most of the energy consumption in the end use (about 44%) was related to ventilation (24%) and hot water (20%). In Istanbul, space heating and space cooling needs were 43.2% and 21.4% higher, respectively, for NWC in comparison with HST0.75 + PP0.3, while other energy end use needs had no significant variations. The HST0.75 + PP0.3 thermal envelope material exhibited lower space heating and cooling energy needs in contrast to NWC.
# Compression-Aware Aggregation and Energy-Aware Routing in IoT-Fog-Enabled Forest Environment
## 5 Performance Evaluation
### Result Analysis
#### 5.3.3 Overhead  
Overhead depicts the number of packets involved in the transmission. The proposed CAA-ERPL is compared with the E-RPL in terms of overhead. It is found that for both protocols, the overhead increases when the network size increases. As we are involving the aggregator and more data packets sent from sensor nodes are compressed and forwarded towards the root node, the number of packets involved in the transmission is less when compared to E-RPL. From Figure 6, it is seen that the overhead of CAA-ERPL and E-RPL is 205 and 246, respectively, for the network size of 20 nodes.  
Figure 4: Total power consumption vs. scalability of nodes.
# Sustainability Enhancement and Evaluation of a Concrete Dam Using Recycling
## 1 Introduction
They evaluated the influence of two strategies--seismic retrofitting and non-retrofitting--on the dam's life cycle. Special attention was given to the impact of retrofitting on environmental indicators like carbon footprint and human health. Their findings underscore the positive environmental effects of dam retrofitting, showcasing a reduction in impact indicators compared to the scenario of dam disposal. [PERSON] et al. investigated the use of calcined dredged sediments as a sustainable cement substitute [22]. Their findings show enhanced eco-strength efficiency and reduced embodied carbon in mortars with up to 25% sediment replacement. Microscopic analyses indicated improved C-A-S-H and C-S-H gel formation. Their cost assessments reveal significant savings, with sediment treatment up to 86% cheaper than cement. The Bluestone dam, located in West Virginia, serves as a case study for this research. The process of evaluating its life cycle is carried out through the utilization of SimaPro v9.5 software along with the IMPACT 2002+ methodology, encompassing stages ranging from construction to decommissioning. Consequently, the LCA adopts a cradle-to-grave approach encompassing both the construction and decommissioning stages, while a cradle-to-gate approach is applied exclusively to the construction stage. Moreover, the influences of recycling for demolished concrete and steel in the final stage on the CFA and LCC results are evaluated. Notably, this investigation exclusively concentrates on evaluating the dam's construction aspects, excluding assessments pertaining to the hydropower plant or other interconnected elements.
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
## 2 Mathematical Background
### Finding the PDF of Samples Using Moments Estimation Function  
The one-point Pade approximation [24] was proposed for estimating the distribution of continuous functions, by using the MGF. However, it has some drawbacks. This method fails to provide accurate estimates for all the training samples. Specifically, it only estimates the PDF for positive random variables and is not applicable to negative ones, limiting its applicability to many PDFs. Additionally, the final estimation exhibits substantial distortion near zero (on the left side of the approximation). In this paper, our goal is to address these problems.  
In this section, we explain how we can estimate the PDF with the aid of the biased approximation of the moments and the one-point Pade approximation. Indeed, through method refinement, the estimation noise can be reduced, leading to improved performance. To simplify the problem, we initially focus on positive random variables with a finite number of samples. Later, we explore how we can estimate the PDF for datasets comprising both positive and negative values. For the purpose of notation, let \(\{S_{i}\}\) with \(N\) coefficients be a set of samples for which we aim to determine the PDF. Let:  
\[S_{i}=\{s_{1},\dots,s_{N}\} \tag{12}\]  
Let \(M_{n}\) denote the \(n\)th moment of \(S_{i}\). Further, let \(f(x)\) be the continuous PDF of the random variable \(X\), and \(F(u)\) be its Laplace transformation, defined as:  
\[F(u)=\int_{x=0}^{\infty}f(x)e^{-ux}\,dx. \tag{13}\]  
We now expand the exponential function by using its Taylor series. By doing this, and simplifying the equation, we get:  
\[F(u)=\int_{0}^{\infty}f(x)\sum_{n=0}^{\infty}\frac{(-u)^{n}}{n!}x^{n}\,dx=\sum _{n=0}^{\infty}\frac{(-u)^{n}}{n!}\int_{0}^{\infty}f(x)x^{n}\,dx=\sum_{n=0}^{ \infty}M_{n}\frac{(-u)^{n}}{n!}. \tag{14}\]As discussed in Section 2.1, where we briefly introduced the concept of moment estimation, we can use this approach to estimate \(M_{n}\). In the next section, we will demonstrate how we can apply the Pade approximation. The primary reason for using the Pade approximation is that it transforms the infinite series of \(F(u)\) into a rational function, which provides a good approximation which also converges more effectively. The rational function is represented as \(\frac{P_{i}}{Q_{D}}\), where \(L\) and \(D\) denote the degrees of the numerator and denominator, respectively. Additionally, converting to a rational function enables us to apply the inverse Laplace transform to find \(f(u)\).
## 4 Response Mechanisms of Load Damage During Freeze-Thaw Cycles
The point at which the total damage variable shows a sudden increase during the uniaxial loading process is defined as the damage response point. Comparative analyses indicate that this point is closely aligned with the axial strain at the conclusion of the elastic phase of uniaxial loading, signifying the transition to the plastic deformation phase. Furthermore, as the number of freeze-thaw cycles applied to the unloaded sandstone increases, the disparity in axial strain between the damage response point and the compression point also increases, indicating an increase in axial strain during the elastic phase. The slope of the curve during the stable development phase of the total damage variable serves as an indicator of the rate of damage progression throughout the uniaxial loading process leading up to failure. The compaction points and damage response points are detailed in Table 5. In this table, variables \(\varepsilon_{d}\) and \(\sigma_{d}\) denote the strain and stress associated with the compaction points, while \(\varepsilon_{r}\) and \(\sigma_{r}\) represent the strain and stress corresponding to the damage response points. The evolutionary trends of single-axis loading compaction points and damage response points in relation to the number of freeze-thaw cycles are characterized and fitted, as illustrated in Figure 20. The strain difference between the damage response points and the compaction points is defined as damage-triggered strain. The fitting curve for the compaction points is vertically shifted upwards to align with the curve of the damage response points at the same initial point, suggesting that the damage-triggered strain under conditions devoid of freeze-thaw cycles is zero. In Figure 20, the shaded area illustrates that the angle of opening progressively increases, signifying that as the number of freeze-thaw cycles escalates, the strain induced by damage also increases correspondingly, and the damage-induced strain is 0.1635%, with no freeze-thaw cycle occurring. After three, six, and nine freeze-thaw cycles, the damage-induced strains rise by 0.0723%, 0.1156%, and 0.1370%, respectively, in comparison to the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number of Freeze–Thaw Cycles** & \(\varepsilon_{d}\) & \(\sigma_{d}\) & \(\varepsilon_{r}\) & \(\sigma_{r}\) \\ \hline
0 & 0.00368 & 18.966 & 0.00534 & 35.043 \\
3 & 0.00551 & 16.583 & 0.00783 & 35.764 \\
6 & 0.00567 & 16.573 & 0.00848 & 35.869 \\
9 & 0.00729 & 17.077 & 0.01032 & 36.637 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Numerical values for the compaction point and damage response point under uniaxial loading conditions. Figure 20: Evolution of compaction points and damage response points. non-frozen condition. This phenomenon can be attributed to the initial unloading damage process of the sandstone, during which the framework becomes more porous. Subsequent frost heave further expands and interconnects internal micropores, necessitating greater compressive deformation to achieve a compacted state during reloading.
## 3 Review of Previous Work
### LRT Based on Channel Statistics  
In [20], a new LRT fusion rule based on the prior information regarding the channel statistic characteristics instead of the instantaneous CSI \(h_{i}\) is proposed. Assuming a Rayleigh fading channel with unit power, _i.e._, \(f(h_{i})=2h_{i}e^{-h_{i}^{2}},h_{i}\geq 0\), the LRT-CS can be written as:  
\[\Lambda_{\text{CS}}=\sum_{i=1}^{N}\log\frac{1+(P_{d_{i}}-Q(az_{i}))\sqrt{2\pi} az_{i}e^{\frac{(az_{i})^{2}}{2}}}{1+(P_{fa_{i}}-Q(az_{i}))\sqrt{2\pi}az_{i}e^{ \frac{(az_{i})^{2}}{2}}} \tag{5}\]  
where \(Q(\cdot)\) is the complementary distribution function of the standard Gaussian expressed as \(Q(x)=\frac{1}{2\pi}\int_{x}^{\infty}e^{-\frac{y^{2}}{2}}dy\) and \(a=1/(\sigma\sqrt{1+2\sigma^{2}})\). The LRT-CS incorporates the Rayleigh fading characteristics into the fusion statistic to avoid the requirement of the instantaneous CSI.