esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
A schematic diagram of the single-frequency hybrid EYDF amplifier is shown in Figure 1. The seed laser is a homemade single-frequency fiber laser with a central wavelength of 1550.14 nm and output power of 30 mW. The linewidth of this seed fiber laser is 630 Hz [24]. The seed laser is coupled into hybrid gain fiber via an isolator and a \((2+1)\times 1\) combiner in sequence. The isolation coefficient of the isolator is 40 dB and the max insertion loss is 0.3 dB. The output power after the isolator is 28 mW. The hybrid gain fiber consists of SM-EYDF-6/125 (Nufern, SM-EYDF-6/125-HE) and MM-EYDF-12/130 (Nufern, MM-EYDF-12/130-HE). The cladding absorption of these active fibers is 1.8 and 4 dB/m at 976 nm, respectively. Three EYDF segments are spliced in the order of SM-6/125, MM-12/130, and SM-6/125 to form an SMS structure in the core. However, the difference in the inner cladding diameters of these fibers is tiny, so there is no additional transmission loss for the inner cladding pump light passing without fusion loss. To keep the amplifier stable and avoid the influence of fiber temperature on the output spectrum of the SMS structure [25; 26], the whole gain fiber is placed on a water-cooled heat sink for heat dissipation. The 976-nm pump laser and input signal are forward coupled into the gain fiber through the pump combiner, which is shown in the dotted box in Figure 1. The residual pump is stripped out by a homemade cladding light stripper (CLS) at the end of the gain fiber. The output pigtail of the amplifier is angle cleaved to launch the signal. A typical EYDF amplifier with uniform gain fiber was theoretically and experimentally investigated for comparison with a hybrid EYDF amplifier. The basic structure and the gain fiber length of this typical amplifier is the same as those of the hybrid amplifier, except that the gain fiber is uniform SM-EYDF-6/125. For the convenience of the reader, the structural diagram of the typical EYDF amplifier is omitted here.
## 4 Experimental Verification
### Success Rate of the Acquisition Process  
As pointing errors can be predicted, the acquisition process has greater potential to improve its success rate. To verify this improvement, a single step of the proposed acquisition process was compared with the traditional acquisition method using actual telemetry data, as described in Figure 12. The results show that the novel acquisition process achieves a success rate of over 90% within an uncertainty cone of 100 \(\upmu\)rad--an improvement that is eight times higher than that of the traditional acquisition process.  
Considering that the acquisition process took 5 min, the success rates of both methods are shown in Figure 13. The success rate of the novel acquisition process is higher than that of the traditional acquisition process when the uncertainty cone is below 3 mrad.  
Figure 11: Error distribution of the predicted pointing error.  
Figure 12: Success rate of each scanning step.
## 4 Challenges and Future Research Directions
### Appropriate Channel Estimation Techniques  
In 6G, IRS emerged has emerged as a viable solution through which to address the primary challenges, including high cost, limited coverage, and excessive power consumption, of previous wireless communication generations. The integration of mMIMO, AI-enabled beamforming, and IRS technologies effectively reduces propagation loss and enhances the overall QoS.  
However, implementing efficient channel estimation for the wireless link between the BS and IRS poses several challenges. Among these challenges, dealing with the randomness inherent in the real channel is particularly difficult. To overcome this, a deep learning approach can be introduced to enable adaptive collaboration between the BS and IRS during channel estimation.  
Moreover, channel estimation is essential for the EH-based NOMA-SWIPT technique [133]. Without appropriate channel estimation, it is very challenging to maintain flawless communication, as well as energizing the end devices.  
Therefore, AI can be incorporated with 6G wireless communication to provide accurate channel estimation for uninterrupted communication, as well as energy harvesting in end devices. In addition, appropriate channel estimation algorithms need to be developed and incorporated in 6G-based smart grid communication networks to fulfill these objectives.
# Deriving High Spatial-Resolution Coastal Topography From Sub-meter Satellite Stereo Imagery
## 1 Introduction
The first civil satellite constellation that acquired stereoscopic imagery and applied DEM reconstruction over large areas was the French SPOT mission (Satellite Pour l'Observation de la Terre) in 1986 [12]. Since then, several very high spatial resolution satellites with stereo capabilities were launched in response to an increased demand [13]. Among them, the Pleiades constellation (built by the French Space Agency (CNES), commercialized by AIRBUS Defence & Space), consists of two high spatial resolution optical spacecrafts: Pleiades -1A and -1B. Both satellites fly over the same near-polar sun-synchronous orbits at an altitude of 694 km with a 180\({}^{\circ}\) phase and descending node. The optical sensors of these satellites have the capability to obtain images with sub-meter image resolution (0.7 m pixel size, resampled to 0.5 m) over a maximum area of 350 km \(\times\) 20 km (swath width of 20 km at nadir). An important aspect of Pleiades is the capacity to revisit any location in the world within 1 day, which is of great interest to monitor rapidly changing processes (e.g., coastal erosion due to storm events). Recent studies based on Pleiades-1A stereo-imagery include snow height mapping in mountainous areas [14], large landmass deformations due to earthquakes [15], surface reconstruction after landslides [16], and glacier topography [17; 18]. The aim of the present work is to explore the use of Pleiades satellite stereo-imagery to develop a high resolution DEM of a 40-km-long sandy coastal section. The satellite-derived DEM is comparedto RTK-GPS cross-shore profiles and an airborne LiDAR-derived DEM. The differences between the concurrent methods are quantified and the precision and accuracy of Pleiades-DEM analysed.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 3 Crown Decomposition and Applications
### \((n-k)\)-Coloring  
Given an undirected graph, determining whether its vertices can be properly colored with \(k\) colors is NP-hard even for \(k=3\). So a kernel or an FPT algorithm is not possible unless P = NP. However, a related problem is whether graph \(G\) on \(n\) vertices can be colored properly with \(n-k\) colors, which is the same as whether \(k\) colors can be saved. The formal definition is as follows.  
This problem defined in Box 2 admits an FPT algorithm and a \(3k\) vertex kernel using Lemma 1 by Chor et al. [5] (also see [11] for more details). The idea is to apply Lemma 2 to the complement graph. If it contains a matching of size \(k\), then those \(k\) pairs of non-edges in \(G\) can help us save \(k\) colors. If not, crown \(C\) is a clique in \(G\) needs \(|C|\) colors, and all vertices in \(C\) are adjacent to every vertex of \(V\setminus(H\cup C)\). Furthermore, the matching saturating \(H\) helps to color vertices in \(H\) using colors used in \(C\). Thus, we can remove \(H\cup C\) and recurse on the remaining vertices. This process stops if we discover that we have already used more than \((n-k)\) colors, so the input is a non-instance or that there are only \(3k\) vertices. Recently, Li et al. [24] have improved this result by giving a \((2+\varepsilon)k\) vertex kernel for this problem. They provided a new structure called 'fractal critical crown' of a graph, we use Proposition 2 and the notion of a factor-critical crown (see Section 3.6 for definition) to obtain their results.  
\[(n-k)\text{-Coloring}\]  
**Input:**_An undirected graph \(G=(V,E)\) and an integer \(k\)._  
**Parameter:**_k._  
**Question:**_Is there a coloring \(\Xi:V\to\{1,2,\ldots|V|-k\}\) such that \(\Xi(x)\
eq\Xi(y)\) whenever \(xy\in E\)?_
## 3 Application Cases
### Multidimensional Scaling and Cluster Analysis  
A matrix for discerning the similarity of the representative samples of earthquake science experience facilities was established, utilizing a 7-point Likert scale for measurement. The smaller the value, the smaller the difference in morphology, and the larger the value, the greater the difference. The experiment involved 100 participants, mainly from Xi'an, Shenzhen, Shanghai, Beijing, Chongqing, and other cities, including 50 professionals with design backgrounds and 50 individuals with similar product experience, enabling evaluations of earthquake science experience facilities from different perspectives. First, the discriminant matrix underwent a reliability test using IBM SPSS Statistics 26 software. [PERSON]'s alpha was calculated to be 0.869, greater than 0.7, indicating high internal consistency and reliability of the discriminant matrix. Next, multidimensional scaling analysis (\(MDS\)) was performed on the data. The discriminant matrix data were imported into \(Spss\) software and a Shepard plot was generated, as shown in Figure 3. The normalized original stress value on the Y-axis in the figure was calculated as the ratio of the original stress value to the maximum original stress value. The Shepard plot helps determine the number of dimensions to retain in the \(MDS\) analysis. The line in the plot appears steep before the third dimension, indicating higher eigenvalues, which contribute more to explaining the original variables. After the third dimension, the line starts to flatten, indicating smaller eigenvalues and a lesser contribution to explaining the original variables. Additionally, the stress index and RSQ value for the 2D and 3D cases were calculated according to Equations (1) and (2), respectively, as shown in Table 3. It can be observed that in the 3D model, the stress index (\(Stress\)) was the lowest, with an \(RSQ\) of 0.96097, which exceeds 0.8. This suggests that setting the dimension to 3 was the most appropriate choice for \(MDS\) analysis. In multidimensional scaling analysis, the selection of distances is based on the data, with the measurement interval using squared Euclidean distance. The standardization method sets the maximum order of magnitude to 1. The standard model adopts individual difference Euclidean distance and allows for negative subject weights. The measurement levels are ordered, and bound observations are released. In our experiment, the termination criterion S stress converged to 0.001, with a minimum S stress value of 0.005. The maximum number of iterations was set to 30. The model ultimately converged after five iterations. Figure 2: Representative sample set. Figure 4 and Table 4 show the spatial distribution and coordinate values of 35 samples in the three-dimensional model. Figure 4: Sample three-dimensional spatial distribution. Figure 3: Gravel chart. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Dimension** & **Stress** & **RSQ** \\ \hline
2 & 0.14311 & 0.93037 \\
3 & 0.09730 & 0.96097 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stress pressure index and fitting degree RSQ.
# A Sibelobe Suppressing Beamformer for Coherent Plane Wave Compounding
## 2 Materials and Methods
### The Description of CPWC Acquisition  
Inspired by [17], we represent the signal in a beamformed CPWC acquisition by a variable \(\mathbf{p}(x,z,a)\), where \(x\) represents the lateral dimension, \(z\) represents the depth in the medium in front of the ultrasonic probe, and \(a\) represents the firing angle. It is assumed that this signal can be described as the summation of three contributions: the mainlobe signal \(\mathbf{m}\), the sidelobe signal \(\mathbf{s}\), and the electronic or thermal noise \(\mathbf{n}\):  
\[\mathbf{p}(x,z,a)=\mathbf{m}(x,z,a)+\mathbf{s}(x,z,a)+\mathbf{n}(x,z,a). \tag{2}\]  
Their contributions have different spatial and angular features. \(\mathbf{n}\) can be considered as a zero-mean Gaussian white noise. Sidelobes are closely related to the delays used for transmission, therefore, their spatial distribution becomes different among angles. From the perspective of the spatial coherence, \(\mathbf{s}\) from various \(a\) has a rather weak correlation. On the contrary, the mainlobe energy originates from the on-axis target. Thus \(\mathbf{m}\) is almost identical within all acquisitions. Therefore, a separation method based on the covariance estimation certainly finds the highest covariance values for the mainlobe signal. This discrimination based on the covariance estimation can be performed by using the SVD of the beamformed RF data.  
Figure 2: Plane wave firings with different steering angles for coherent plane wave compounding (CPWC). The cyst target is within the overlapped region of all plane waves.
## 3 Artificial Intelligence Methods for GWL Modelling
### Deep Learning  
Deep Learning, including ensemble-based XGBoost (eXtreme Gradient Boosting) algorithms, self-attention-integrated Long Short-Term Memory (LSTM), and hybrid LSTM methods, are increasingly utilised for GWL prediction. Indeed, [5] employed ensemble-based XGBoost in the Sacramento River Basin, prioritising well-specific features for accuracy. Ref. [57] integrated self-attention mechanisms with LSTM in Iran, outperforming other methods with a self-attention (SA) temporal convolutional network (SATCN)-LSTM when capturing dependencies. [6] utilised LSTM in Northern France, proficiently filling data gaps and representing fluctuations. LSTM, in combination with the Lion Algorithm [58], excelled in GWL forecasting, considering regional complexities. [59] applied LSTM in Jeju Island, Korea, to predict GWL. Figure 9 shows an example of a DL flowchart for predicting the GWL.  
Figure 9: Schematic representation of a DL flowchart for predicting the GWL (more details in the text) (modified by [58]).  
This study analysed 12 articles published between 2018 and 2023, providing an assessment of each article followed by a general conclusion drawn from them (Figure 10).  
Ref. [60] investigation shows that a robust cost function with the least trimmed squares and the Whittaker smoother effectively controls outliers and noise. This model uses GWL data affected by outliers and noise in South Korea. According to a study by [61], the LSTM accurately predicts daily GWL. This model used daily precipitation data on Jeju Island, Korea. Research by [62] recommended the DL for predicting groundwater depth, demonstrating superior accuracy compared to ELM and GPR. This model uses precipitation, river stage, temperature, recharge, and groundwater depth data in the Konan Basin, Kochi Prefecture, Japan. According to a study by [63], LSTM networks outperform RNN in predicting groundwater table responses to storm events. This model uses groundwater table, rainfall, and sea level data in Norfolk, Virginia, USA. Research by [64; 65] shows that Convolutional Neural Network (CNN)-based Deep Learning methods enhance the prediction of terrestrial Total Water Storage anomalies and improve model performance. This model used GRACE Satellite Data in India. According to a study by [66], comprehensive data preprocessing enhances predictive model accuracy in detecting hydrologically stressed aquifer conditions. These include NARX-DNN, LSTM, Gated Recurrent Unit (GRU), Autoregressive and Exogenous (ARX) methods, and water table level time series data in Pohang Gibuk, Korea, which are used. Research by [67] shows that the proposed model outperformed a Feed-Forward Neural Network (FFNN) and Double-LSTM in predicting water table depth. This method used monthly water diversion, evaporation, precipitation, temperature, and time data from the Hetao Irrigation District, China. Table S4 (Supplementary Materials) summarises the evaluated articles that used DL methods and draws a general conclusion.
## 3 System Model
### System Propagation Model
#### 3.3.3 Doppler Shift Model of Satellite Channel  
In the satellite communication system, doppler shift is caused by relative movement between satellites and users. Doppler shift leads to carrier frequency offset, constellation rotation, and inter-carrier interference, resulting in serious signal distortion, high bit error rate, affecting system throughput. As shown in Figure 4b, when the distance between user and satellite is 1500 km in scenario (a) in Figure 3, the doppler shift of both beams  
Figure 4: (**a**) Free space fading model. (**b**) Doppler shift model. (**c**) Transmission delay model.  
is \(\frac{f_{a_{i}^{c}s_{p}b_{j}}}{f_{cb_{j}}}=1.79\times 10^{-5}\). In scenario (b) in Figure 3, when the distance between satellites and users is 1700 km and 1500 km, the doppler shift is \(\frac{f_{a_{i}^{c}s_{p}b_{j}}}{f_{cb_{2}j}}=1.92\times 10^{-5}\), \(\frac{f_{a_{i}^{c}s_{p}b_{j}}}{f_{cb_{2}j}}=1.79\times 10^{-5}\), respectively. Doppler shift in satellite channel can be expressed as:  
\[\frac{f_{a_{i}^{c}s_{p}b_{j}}}{f_{cb_{p}j}}=-\frac{R_{e}r\text{sin}(\theta) \text{cos}[\arccos(\mu\text{cos}\theta_{0})-\theta_{0}]\omega_{n}}{c\sqrt{R_{e} ^{2}+r^{2}-2R_{e}r\text{cos}(\theta)\text{cos}[\arccos(\mu\text{cos}\theta_{0} )-\theta_{0}]}}, \tag{4}\]  
where \(\mu=R_{e}/r\). The value of \(R_{e}\), \(r\), and \(\theta\) are as described above. \(\theta_{0}\) is maximum access angle which is \(55^{\circ}\).
# Reutilization of Recycled CDW Sand in Mortars, Paving Blocks, and Structural Concrete
## 3 Results and Discussion
### Properties of the Developed Paving Blocks
#### 3.3.2 Density and Water Absorption of Paving Blocks  
Similar to the results obtained for the mortars, the density of the paving blocks generally decreased with increasing CDW aggregate content; see Figure 13a. A slightly lower density was observed in the paving blocks made with CEM II/B-M(Q-L) 42.5R compared to those prepared with CEM I 52.5R. However, this difference was insignificant, as all the values ranged between 2.11 g/cm\({}^{3}\) and 2.32 g/cm\({}^{3}\). The high deviation observed in the density of the paving blocks, compared to the variation seen in the mortar results (Figure 10b), is attributed to differences in the preparation process, which was more manual for the paving blocks than for the mortars. Additionally, the dimensional variability of the paving blocks was significantly greater. Specifically, the volume of each paving block was determined as the average of three measurements for each dimension: length, width, and thickness. In contrast, the mortar samples were assumed to have a constant volume of 40 \(\times\) 40 \(\times\) 160 mm\({}^{3}\).  
As expected, the water absorption coefficient was higher for the paving blocks made with CEM II/B-M(Q-L) 42.5R than for those prepared with CEM I 52.5R, which is attributed to the lower linker content. As shown in Figure 13b, the coefficient progressively increased with the CDW RA content, with values ranging from 8.94% to 12.05%. According to the specifications outlined in Table 4.1 of Standard UNE EN 1338:2004 [43], the produced paving blocks should be classified as Type A, for which water absorption measurement is not required. Regardless of the aggregate used, all the water absorption values exceeded 6%, the threshold to be classified as Class 2 with a B marking. Consequently, these paving blocks would not be suitable for use in areas prone to frost.  
Figure 12: Workability of the paving blocks made with the NA and CDW aggregates.
## 4 Characterization of the Evolution of the Landslide Disaster Chain  
### Chain Evolution Stage of the Landslide Disaster  
Both single-disaster and disaster systems exhibit a phased chain evolution, which is often classified as early gestation, mid-submergence, and late-initiation depending on the temporal alterations of the disaster [51]. The initial incubation stage, characterized by the absence of significant or exceedingly weak damaging effects, encompasses a substantial portion, roughly 70%, of the entire catastrophic period. Following this, the median latent stage, in which a considerable quantity of energy is accumulated or stored to generate the potential destructive capability, lasts just approximately 25% of the time. Finally, the late-induced stage, in which potential energy is released, has a short duration but a high intensity, lasting no more than 5% of the total catastrophic chain cycle.  
According to the sliding characteristics of the Baige landslide, the evolution of the landslide chain can be categorized into three distinct stages:  
(1) Early Gestation (-1996)  
The Qinghai-Tibet Plateau, positioned at the confluence of the Eurasian and Indian Ocean plates, experiences continuous elevation due to crustal dynamics. As a result of the geological process, surface and internal cracks emerge within the slopes. At the same time, the Jinsha River erodes the slope's base, increasing the sliding traction force and causing slow, permanent deformation of the surrounding slope. At this moment, the features of the landslide mass are unclear, the landslide mass's border is uncertain, and there has been no substantial change on the macro scale. As a result, determining whether sliding is occurring based on landslide mass deformation is impossible. Instead, judgment is generally based on the historical landslide occurrence and the region's landform.  
(2) Medium-term slip (1996-2015)  
Over time, due to prolonged rainfall, wind erosion, freezing and thawing, and earthquakes, the potential sliding surface of the Baige landslide progressively develops creep deformation. The slope begins to slide initially, with transverse stress fractures tens of meters long forming at the back edge. Subsequently, it enters the stage of constant velocity sliding. The landslide's general contour was macroscopic, and mirror sliding, scratches, and other phenomena were observed on the sliding surface. The development process took a long period, and the moving slope exhibited an increasing uniform displacement trend.  
(3) Late instability (2015-2018)  
During this stage, the rate of deformation on the Baige landslide slope continues to escalate. The deformation curve becomes steeper as the landslide approaches, signifying an accelerated deformation phase. The sliding surface becomes fully connected, the sliding force of the rock and soil mass intensifies, and the central collapse deformation becomes more pronounced. This stage is shorter in duration compared to the previous two phases and is often accompanied by phenomena such as acoustic emissions and abnormalities in groundwater levels.
# GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks
## 6 Performance Evaluation
### Performance Measurement  
The performance is compared using first node depleted (FND) and percentage nodes depleted (PND) performance metrics for network lifetime results. FND is the number of communication rounds needed for the first node's energy level reaching zero. PND is the percentage of nodes having zero energy after no more communication is possible due to network partition. The higher the percentage of nodes depleted, the better the scheme is in balancing communication loads and thus the better at avoiding the hot-spot problem and hence extending network lifetime.  
Energy-efficiency is determined using average energy consumption per round by a given scheme. The detection capacity is measured using the percentage of attacks detection. These performance measures are evaluated for three different network sizes and three attacks ratios as explained earlier.