esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Self-Adaptive Global-Best Harmony Search Algorithm
### SGHS Computational Procedure  
The computational process SGHS algorithm combines all three new changes in the algorithm which is summarized by the pseudocode of Algorithm 4.  
```
1. Define parameters of SGHS algorithm: \(n\), \(HMS\), \(HMCR_{\text{max}}\), \(HMCR_{\text{min}}\), \(PAR_{\text{max}}\), \(PAR_{\text{min}}\), \(SD\), \(LP\), \(NI\).
2. Initialize \(BW_{\text{max}}\), \(BW_{\text{min}}\), \(HMCR_{m}\) and \(PAR_{m}\). Set learning counter \(lp\) to 1.
3. Initialize HM with random harmony vectors using (19) and calculate the cost function of each vector.
4. Self-adapt \(HMCR\) and \(PAR\) according to \(HMCR_{m}\) and \(PAR_{m}\). Calculate \(BW(k)\) from \(BW_{\text{max}}\), \(BW_{\text{min}}\).
5. Improvise a new Harmony vector \(\textbf{X}_{new}\) based on the three rules of Figure 10.
6. Update the HM with \(\textbf{X}_{new}\) if \((f\left(\textbf{X}_{new}\right)<f\left(\textbf{X}_{new}\right))\) as \(\textbf{X}_{w}=\textbf{X}_{new}\) and record \(HMCR\) and \(PAR\).
7. If \((lp=LP)\) then recalculate \(HMCR_{m}\) and \(PAR_{m}\) and reset \(lp\) to 1. Otherwise, increment \(lp\) by 1.
8. If the maximum number of improvisations \(NI\) is reached, then stop the program and return the best harmony vector \(\textbf{X}_{B}\). Otherwise, go back to step 4.
```  
**Algorithm 4: Self-Adaptive Global-Best Harmony Search Algorithm**
## 3 Numerical Simulations
### Case Investigations Using Numerical Simulation
#### 3.2.3 Fault Detection Using the Present Method  
In the real-world practice, certain additive noises, which might possibly be interferences from the environment and the equipment of mechanical system, are inevitable with the running of rotating machinery. To verify the present personalized diagnosis methodology in the real-world applications, the independent realization of artificial white Gaussian noise (AWGN) is added in the simulation using the command awgn in MATLAB (version 2010, MathWorks, Natick, MA, USA, 2010) as:  
\[Data_{\text{Noise}}=awgn(Data_{\text{Noise}-\text{free}},SNR) \tag{5}\]  
where \(Data_{\text{Noise}-\text{free}}\) and \(Data_{\text{Noise}}\) are the noise-free data and noisy data and SNR is the signal-to-noise ratio (dB). Figure 7 shows the results of adding artificial white Gaussian noise using Equation (5) under SNR = 97 dB. In the present, the signals with the four fault types, such as unbalance, misalignment, rub-impact and the combination of rub-impact and unbalance faults, shown in Figure 7a-d, respectively, are decomposed with the WPT using Daubechies wavelet (Db18) into three layers. Therefore, eight frequency bands are obtained, and the corresponding five time domain feature parameters are calculated using the formulas in Table 1; the values are listed in Table 2.  
Figure 6: The exciting force in each direction of the rub-impact: (a) the exciting force in \(x\)-direction; (b) the exciting force in \(y\)-direction. Figure 7: \(Cont\). As shown in Table 2, for each fault type of the shaft, \(5\times 8=40\) features are forming a sub-feature vector. In order to obtain satisfactory classification results, we use 20 sub-data points (8000 data points in total) of the simulation signal, and hence, the length of the feature vector is 800. Therefore, the data used for training samples (including four types of faults) are an 800 \(\times\) 4 matrix. In the simulation, the 'testing sample' is numerically represented by an 800 \(\times\) 1 vector, which is the same simulation signal adding another SNR = 87 from the training samples shown in Figure 8a-d. To distinguish the four faults numerically, unbalance, misalignment, rub-impact and rub-impact based on unbalance faults are labeled from 1 to 4, respectively. In the process of classification, the adjustable parameters of SVM are selected as \(p_{1}=1.05\) and \(C=10\)[36]. We use the SVM toolkit trained by Franc et al. [40], and four loops are necessary to obtain the four damage parameters.
## 3 Results and Analysis
### Data Processing and Analysis
#### 3.2.2. The Kinematic RT PPP with Different Data Processing Methods  
Different from static RT PPP positioning, the station coordinates will be estimated epoch-by-epoch in kinematic positioning. To assess the performance of the new weighting approach for single- and dual-frequency kinematic RT PPP, the datasets at MAT1 and WROC station on DOY 047, 2018 are conducted in the three processing methods.  
The kinematic GPS/GALILEO RT PPP solutions with different methods are illustrated in Figure 6a,d for the dual- and single-frequency cases, respectively. The PDOP and number of satellites for MAT1 and KRGG station are provided in Figure 6b,e. Figure 6c,f shows the weight factors based on the new method and priori variance. We can find that the weight factors calculated by the new method are increased gradually when the positioning filter converges to a stable value, resulting in high-accuracy positioning solutions. Similar to the static RT PPP, we calculated the statistics of positioning accuracy and convergence times in different methods; these are given in Table 4. It is noted that both single- and dual-frequency convergence performance are improved after adding ionospheric constraints. Using our proposed approach, the RT PPP solution can converge within 15 min and 20 min for dual- and single-frequency cases, respectively, and the largest improvement rate can reach 73%. In terms of the RMS statistics of positioning errors, using the new method can achieve nearly the same positioning performance compared to the method without constraints. But using the priori variance method, obvious offsets exist in three directions after filter convergence, especially in the single-frequency case, which is caused by unreasonable weight in the ionospheric constraints.  
Figure 5: Convergence time using the proposed weighting approach with different smoothing window sizes for dual-frequency and single-frequency METG station.  
Figure 6: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Frequency** & **Methods** & **Convergence Time (min)** & **E (m)** & **N (m)** & **U (m)** \\ \hline \multirow{3}{*}{Dul-frequency} & Without constraint & 56 (73\%) & 0.0682 & 0.0821 & 0.1186 \\  & A priori variance & 38 (32\%) & 0.0852 & 0.0798 & 0.1634 \\  & New method & 15 & 0.0568 & 0.0825 & 0.1183 \\ \hline \multirow{3}{*}{Single-frequency} & Without constraint & 61 (67\%) & 0.1152 & 0.1007 & 0.3257 \\  & A priori variance & 31 (35\%) & 0.1655 & 0.1534 & 0.3760 \\ \cline{1-1}  & New method & 20 & 0.1062 & 0.1186 & 0.3009 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Kinematic RT PPP for three processing methods.  
Figure 6: The kinematic positioning errors (a,b), number of satellites, PDOP (c,d), weight factors (e,f) of dual-frequency by station MAT1 (**a,c,e**) and single-frequency by station KRGG (**b,d,f**).
## 5 Predefined Path-Based Techniques
### Optimal Dual Paths (ODP)  
#### 5.1.1 ODP Concepts  
ODP [17] establishes dual paths for each connection pair of terminal nodes, such as DANH nodes, RedBox nodes, in HSR networks. The dual paths are determined based on the following criteria:  
* The dual paths have optimal link metrics.
* The dual paths have no common nodes.  
Each terminal node automatically establishes dual paths with the other terminal nodes in a network. To determine the dual paths with optimal metrics, each terminal node has to know the metric values  
Figure 13: A comparison of the traffic performance of traffic reduction techniques.  
for all of the network links. Therefore, the ODP approach builds a link metric table, which contains metrics of all the network links. Based on that table, the ODP approach searches all the available paths for each connection pair, sorts them in ascending order of path metrics, and determines dual paths that satisfy the above conditions. After selecting the optimal dual paths, each terminal node informs intermediate QuadBox nodes that are located in the dual paths about the selected dual paths that are passing through that QuadBox nodes. The QuadBox nodes save the path information, and they then forward received frames of that connection pair to a proper output port according to the previously learned information. In this case, the QuadBox node does not duplicate and flood the frames, as in the standard HSR protocol.
## 1 Introduction
Our simulation study and experimental validation on aluminum plates with controlled noise levels reveals critical practical limitations, particularly the degradation of DORT-MUSIC resolution due to signal subspace orthogonality loss. Our discussion on frequency-domain computation for compensating dispersion characteristics, improving the accuracy of array imaging methods, and enhancing the resolution of different imaging methods provides practical guidance for method selection in plate-like structure non-destructive testing and structural health monitoring.
## 3 Challenges
### Uncertainties in Remote Sensing-Based ET  
ET is one of the most important components in the water cycle and is also more complex than other processes. The goal towards higher productivity and lower water loss (i.e., high WUE) has always been pursued by agricultural production and even ecosystem development--which meets the requirements of both vegetation and human beings--especially in order to adapt and respond to extreme weather conditions. However, larger uncertainties exist in remote sensing-based ET than that of production, as discussed in  
Figure 5: Taylor diagrams of MODIS WUE compared to flux measurement for 11 plant functional types and climate zones at 8-day and annual time scales [86]. Reprinted with permission. Adapted with permission from ref. [86]. Copyright 2020 Elsevier.  
Section 2.2.2. Crop consumptive use (i.e., ETa) can be estimated as residual in the water balance equation. Water balance approaches provided accurate data for closed basins over long time spans (typically annual or longer) where changes in surface and groundwater storage are negligible compared to inflows and outflows. However, those conditions are not always met, and water balances are problematic over short time periods, even for individual fields. Crop coefficient methods tend to overestimate crop consumptive use because they are typically derived for optimal conditions, e.g., crops grown in lysimeter, whereas actual field crops have uneven stands, and are subjected to nutrient deficiency and periodic water stress between irrigation intervals. Moreover, neither of these approaches are easily adapted for vegetation, for which crop coefficients might be variable or unknown. Keeping these limitations in view, various researchers have developed numerous methods to estimate ETa through satellite remote sensing [111; 112]. These methods fall into two broad categories: a surface energy balance (SEB) method, which uses satellite data from thermal infrared (TIR) bands to estimate sensible heat flux and then calculate latent heat flux (ETa) as residual in the SEB equation; and VI methods, which combine estimation of green foliage density with meteorological data to estimate evaporation from transpiring vegetation. VI-ETo methods [66] substitute a VI for \(K_{\text{c}}\) to calculate ETa based on locally measured ETo, while application in precision agriculture remains a challenge.
## 4 Advancements in Remote Sensing Technologies for Coastal Monitoring
### Applications of Machine Learning and Artificial Intelligence in Coastal Monitoring
[33] stated that AI will play a more competitive role in automated coastal analysis systems. Their research integrated space imagery with machine learning to create a Coastal Analyst, which can automatically process large volumes of remote sensing data with minimum human intervention. While specific architectures were not profoundly explored, the paper underlined the role of AI in enhancing efficiency in data processing by several orders of magnitude and, hence, supporting better decision-making in coastal management by providing quasi-real-time insights into environmental risks such as erosion and urban expansion. [PERSON] et al. [44] applied AI to multi-platform data by integrating satellite, drone, and video camera platforms to monitor coastal dynamics. ML algorithms played a central role in integrating these data sources, allowing for high-resolution and accurate monitoring of shoreline variations. Moreover, the multi-platform methodologies proposed represent higher spatial and temporal resolutions, complementing the gaps left by traditional single-platform monitoring approaches. ML applied AI to multi-platform data by integrating satellite, drone, and video camera platforms to monitor coastal dynamics. Also, the multi-platform methodologies proposed represent higher spatial and temporal resolutions, complementing the gaps left by traditional single-platform monitoring approaches. The machine learning techniques automatically analyzed and fused heterogeneous data, thus contributing to better overall coastal monitoring and management. Finally, [PERSON] et al. [50] applied neural networks to process satellite photography to map changes in the shoreline of southwestern Australia. Deep learning models, trained on millions of satellite images, showed higher accuracy in detecting shoreline change and shedding light on long-term trends in coastal erosion. This application of AI (deep learning specifically) will be very important for timely and accurate environmental monitoring, understanding, and mitigation measures for coastal erosion, which are key to preserving coasts, ecosystems, and infrastructural-related results. These studies epitomize the transformational role of machine learning and deep learning in coastal monitoring. AI techniques, primarily ensemble and deep neural networks, have emerged as powerful tools for analyzing large-scale and complex datasets that provide accurate and timely information on shoreline dynamics, erosion patterns, and environmental changes. Their merger through AI and remote sensing technologies offers scalable and automated solutions for coastal management, enabling proactive responses to environmental risks and ensuring sustainability in managing coastal resources.
# An LSM-Tree Index for Spatial Data
## 4 ER-Tree Index
### ER-Tree Index Query
#### 4.4.2 Rectangular Range Query  
The rectangular range query of spatial data is used to query the data within the rectangular range of coordinate points based on a pair of coordinate points (x\({}_{1}\), y\({}_{1}\)) (x\({}_{2}\), y\({}_{2}\)), where (x\({}_{1}\), y\({}_{1}\)) are the coordinates of the lower-left corner of the rectangle and (x\({}_{2}\), y\({}_{2}\)) are the coordinates of the lower right corner of the rectangle. First, we need to query the data by the coordinate points on the index and then obtain the <key, value> on the corresponding SSTable. The ER-tree index rectangular range query algorithm is shown in Algorithm 3.  
Figure 8: ER-tree index query flow.  
```
1:Input: QP: query point, ERT: LSM ER-Tree index Output: result: <key, value>, whose value= QP
2: level_ptr \(\leftarrow\) ERT. head;
3: while[level_ptrt = null] do
4: ptr \(\leftarrow\) level_ptr. head;
5: while(ptrt = null) do
6: if include(ptrtMBR, QP) then //include: judge whether the point QP is within the MBR range
7: LeafNode \(\leftarrow\) QPFindLeaf (ptr.SERT, QP); //SERT: SER-Tree index on SSTable
8: temp_result. append (LeafNode.db_ptr);
9: end if
10: ptr \(\leftarrow\) ptr.next;
11: end while
12: level_ptr \(\leftarrow\) level_ptr.next_level;
13: end while
14: foreach(<key, value>in temp_result)
15: if equals(value, QP) then
16: result. append(<key, value>); end if
17: end for
18: return result;
19: function QPFindLeaf (T, QP)
20: begin
21: if T is a LeafNode, then
22: if include(T.mbr, QP), then
23: return T; else return \(\emptyset\);
24: else
25: for each child u of T, do
26: if(include (u.mbr, QP)) then
27: QPFindLeaf (u, QP); end if
28: end function
```  
**Algorithm 2** Point query algorithm  
Algorithm 3 describes the process of the ER-tree index rectangular range query. In the first step, the ER-tree is used to judge, layer by layer, whether the QR intersects (or contains) the tMBR of each SER-tree root node of the ER-tree; if it satisfies this, the QRFindLeaf function is executed to obtain the data <key, value> from the data block on the corresponding SSTable and deposits it into the temp collection (lines 1-10). In the second step, iteration through the temp collection is used to determine whether the value of <key, value> is located in the QR. If it is, it is stored in the result_c collection (lines 11-15). Lines 16-26 refer to the QRFindLeaf function, which uses a recursive approach to judge the leaf nodes where the MBR intersects (or contains) the query range QR.
## 4 SSA-ELMAN Neural Network Model
### Construction of Prediction Model Based on SSA-Elman Neural Network
The results show that using the SSA to optimize the Elman neural network model achieves a higher accuracy than the other prediction methods.
## 5 Future Challenges and Discussion
In this case, the ML architecture will utilize 32 or 128 wavelengths, respectively, one per TCAM cell output, similar to the wavelength addressable large-scale photonic integrated photonic memories of 128-bits [30] or the 128-wavelengths of the optically connected RAM architectures for High Performance Computers [57], as well as an AWG with a similarly high channel count [1]. The assignment of different wavelengths at the various T-CAM cells of an ML certainly adds an extra degree of freedom to system-designs for more efficient architectures, especially when considering multiple simultaneous WDM memory schemes with parallel wavelengths written to the T-CAM cells of an ML for fast memory updates. This can be of utter importance with the emergence of SDN and OpenFlow architectures that enforce a dynamic network operation with frequent updates of network topologies and multiple real time changes in the RIB-list [25; 26]. Although the SDN controllers are increasingly optimized for swift policy updates, the T-CAM tables remain yet unoptimized for fast updates [27; 28], which may trigger hundreds to thousands of table entry-moves and write-memory operations [26]. Measurements on the timings of such AL-table updates have revealed a few hundreds of ms-long response times [25; 26; 27; 28], as AL-updates need to be organized in electronic T-CAM tables with sequential time-multiplexed memory accesses through a memory-bus of a limited bandwidth. In this case, the optical nature of the proposed T-CAM may facilitate wavelength multiplexed memory access schemes in order to perform multiple simultaneous Write operations at 10 Gb/s, either when updating the prefix-list of the optical CAM-table or the outgoing ports stored in the optical RAM table, providing manifold improvements in the AL memory throughput. When developing programmable low power optical AL-memory architectures, further system benefits could potentially be obtained by the introduction of the programmability of the optical memory content [31] or the use of a non-volatile photonic integrated memory [32]. However, apart from the optimization of the memory cell, additional developments are still required, as, e.g., the development of a proper input WDM interconnect network, capable delivering the input bit in vertical CAM-column arrangements, known as Searchlines, or the intermediate encoder/decoder interconnect network and peripheral sub-circuits that undertake the communication between the CAM-table and the RAM table, potentially taking into account the case of priority encoding, when multiple prefix matches are formed by the use of ternary bits. These next steps could potentially follow similar concepts as the ones suggested by the all-passive wavelength-based Column Address Selector [45], the peripheral circuitry [46], and/or the optical tag comparator [47] of the more detailed studies in the area of optical RAM architectures.
## 3 Materials and Methods
### Data Processing  
In this study, the PS-InSAR method in StaMPS software was used to process the Sentinel-1 radar images from 2018 to 2021. Due to the strict orbit control technique used in Sentinel-1, the effect of spatial decorrelation is small. Therefore, we selected the image at the middle time in the dataset as the master image. The longest spatial baselines were 194.5 m and 169.2 m in all interferograms generated by ascending and descending images, respectively. The spatial baseline distribution is shown in Figure 3. We used StaMPS-v4.1 software for data processing. The processing flow included PS point selection, phase unwrapping, estimation and removal of phase noise errors [26]. The surface displacements on the north bank of Dianchi Lake were finally extracted.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 4 Catalytic Applications of Mesoporous Silica Materials
### Organic Synthesis
#### 4.2.4 Dehydration  
Mesoporous silica usually demonstrates interesting catalytic activity when it comes to dehydration and dehydrogenation reactions. Recently, [PERSON] et al. reported on a series of mesoporous silica materials that were embedded in zirconium and phosphotungstic acid via a sol-gel process [121]. They studied conversions of D(-)-fructose catalyzed by a synthetic mesoporous silica nanocatalyst that produced 5-(hydroxymethyl)-furfurfural as the main product. They also examined the relationship between the structure and activity of the catalyst as well as its ambient reaction and found that it exhibited good recycling potential (Figure 9). The characterizationresults revealed that the mixed template copolymer and PVP had flake-like silicates and that both zirconium and phosphotungstic acid were operating as recyclable nanocatalysts capable of good performances. The catalytic process revealed that water was the most appropriate green solvent for the transformation of D(-)-fructose into fine chemicals. Moreover, cholesterol doping in a sol-gel preparation resulted in the formation of more active catalysts. Furthermore, the addition of phosphotungstic acid to the sol-gel had a positive effect on the final yield of the reaction in ethanol.  
In addition, xylose dehydration via a functionalized KIT-6 catalyst was reported by [PERSON] et al. (Figure 10). They showed that the selective and efficient dehydration of xylose to furfural in a water/toluene _bi_-phasic system could be achieved by means of a sulphonic acid-functionalized KIT-6 catalyst. This optimal catalyst provided both high furfural selectivity and xylose conversion (more than 94%). Furthermore, this heterogeneous SO3H-KIT-6 catalyst had high efficiency and could be recycled without further treatment for up to three runs [122].  
Figure 8: Synthesis of DMC on the MgO@_meso_-SiO2 catalyst.  
Figure 10: Xylose dehydration in the presence of the SO3H-KIT-6 catalyst.  
Figure 9: The dehydration of D(-)-fructose in the presence of mesoporous catalysts of zirconium and phosphotungstic acid.  
Compared to previously reported catalysts, the heterogeneous SO3H-KIT-6 catalyst converted more xylose and also had the highest furfural selectivity under the applied reaction conditions, as shown in Table 1.