esa-sceva/satcom-chunk-collection huggingface dataset:
## Results
### Temporal Evolution of Forest Productivity and Carbon Sequestration in the TGRA
#### 3.2.2 Forest Carbon Sequestration  
The variations in the carbon sequestration of the forest vegetation layer, the litter layer, and the soil layer as well as the variations in carbon storage and carbon density of the entire area from 1992 to 2012 are shown in Table 6.  
From 1992 to 2012, the carbon stocks of the forest vegetation layer, the litter layer, and the soil layer as well as the entire area have similar variation trends. Specifically speaking, the stocks of each layer and the entire area continuously increased from 1992 to 2012, while the growing rate began to slow down and stabilize after the rapid increase. From 1992 to 1996, the carbon stocks of the forest vegetation layer, the litter layer, and the soil layer, and the whole area increased by 2.04%-2.16% from 54.53 Tg, 6.678 Tg, 139.637 Tg and 200.845 Tg in 1992, respectively, to 55.707 Tg, 6.814 Tg, 142.607 Tg, and 204.128 Tg in 1996. From 1996 to 2002, the growth rate was significantly higher, and the carbon  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**Carbon Storage (Tg)**} \\ \cline{2-5}  & **Vegetation** & **Litter** & **Soil** & **Total** \\ \hline
1992 & 54.53 & 6.678 & 139.637 & 200.845 \\
1996 & 55.707 & 6.814 & 142.607 & 205.128 \\
2002 & 68.872 & 8.417 & 172.795 & 250.084 \\
2006 & 80.899 & 9.884 & 200.916 & 291.699 \\
2012 & 90.117 & 11.016 & 227.288 & 328.421 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Carbon storage of the Forest Ecosystem in TGR area from 1992 to 2012.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{3}{c}{**NPP (Tg C)**} & \multicolumn{3}{c}{**Average NPP**} \\ \cline{2-7}  & **ENF** & **MIX** & **DEF** & **EBF** & **Shrub** & **Total** & **(Mg C \(\mathrm{hm}^{-2}\) year\({}^{-1}\))** \\ \hline
1992 & 3.433 & 1.611 & 1.208 & 0.369 & 1.206 & 7.827 & 3.833 \\
1996 & 5.058 & 1.971 & 1.476 & 0.481 & 2.352 & 11.338 & 5.437 \\
2002 & 6.656 & 2.192 & 1.55 & 0.474 & 2.474 & 13.346 & 5.235 \\
2006 & 3.207 & 1.338 & 1.129 & 0.228 & 1.269 & 7.171 & 2.415 \\
2012 & 7.067 & 2.924 & 1.778 & 0.474 & 2.756 & 14.999 & 4.465 \\ \hline \hline \end{tabular}
\end{table}
Table 5: NPP of Forest Ecosystem in Three Gorges Reservoir Area in 1992–2012.  
stocks for each layer and the total area increased by 23.63%, 23.53%, 21.17%, and 21.92% to 68.872 Tg, 8.419 Tg, 172.795 Tg, and 250.084 Tg, respectively. From 2002 to 2006, the total forest carbon stock kept growing, although the growth rated decreased to 16.27%-17.46%; the carbon stocks of the three layers and the entire area were 80.899 Tg, 9.884 Tg, 200.916 Tg, and 291.699 Tg. The variation trends remained the same from 2006 to 2012, increased by 11.39%-13.13% to 90.117 Tg, 11.016 Tg, 227.288 Tg, and 328.421 Tg, respectively, in 2012.
# Path Loss Prediction Based on Machine Learning: Principle, Method, and Data Expansion
## 2 Machine-Learning-Based Path Loss Prediction
### Model Evaluation and Path Loss Prediction  
In general, the performance of machine-learning-based path loss models is measured by samples in the test dataset, which do not appear in the model training process. The evaluation metrics include prediction accuracy, generalization property, and complexity.  
In terms of evaluating the accuracy, performance indicators like maximum prediction error (MaxPE), mean absolute error (MAE), error standard deviation (ESD), correlation factor (CF), root mean square error (RMSE), and mean absolute percentage error (MAPE) are commonly used [3; 6].  
Generalization property is to describe the model reusability when the deployment concerns new frequency bands or/and new environment types. The model may have better generalization performance with more data collected from diverse scenarios, such as different terrains, frequencies, and vegetative cover conditions.  
The computational complexity is usually evaluated by processing time and memory cost. As an example, the number of iterations and convergence speed during the training phase are the key factors that affect the processing time of ANN.  
Based on evaluated results, we can select the machine learning algorithm, adjust the hyperparameters, and further improve the prediction model. After the optimal model has been built, path loss values can be generated with new inputs.
## 4 Experiments
### Ablation Studies
#### 4.4.3 Effect of KD-Fused Graph-GRU  
To evaluate the fusion methods for kinematic and dynamic features, we designed two fusion methods:  
1. Direct Feature Addition: Kinematic and dynamic features are directly summed along the feature dimension, and a single Graph-GRU is employed to reset and update the hidden states.
2. Weighted Fusion of Separate Graph-GRUs: Kinematic and dynamic features are individually processed using two Graph-GRUs with identical architectures. The outputs of these Graph-GRUs are then combined through a weighted summation.  
As illustrated in Table 6, the second fusion method exhibits superior performance compared to the first. This indicates that the features extracted using the dynamic and kine  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{3}{*}{Models} & \multicolumn{6}{c}{Average MPJPE} \\ \cline{2-7}  & \multicolumn{4}{c}{Short-Term} & \multicolumn{4}{c}{Long-Term} \\ \cline{2-7}  & 80 ms & 160 ms & 320 ms & 400 ms & 560 ms & 1000 ms \\ \hline Kinematics only & 10.0 & 20.6 & 42.8 & 53.5 & 74.6 & 114.1 \\ KD-fused & **9.6** & **19.8** & **41.0** & **51.3** & **71.9** & **112.5** \\ \hline \hline \end{tabular} Note, the best results are highlighted in **bold**.  
\end{table}
Table 4: Experiments on H3.6M with different input types (Kinematics Only: input of only 3D coordinates; KD-fused: inputs of 3D coordinates and joint torques).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{Models} & \multicolumn{6}{c}{Average MPJPE} \\ \cline{2-7}  & \multicolumn{4}{c}{Short-Term} & \multicolumn{4}{c}{Long-Term} \\ \cline{2-7}  & 80 ms & 160 ms & 320 ms & 400 ms & 560 ms & 1000 ms \\ \hline Joint-Scale Only & 11.5 & 22.8 & 46.0 & 57.4 & 78.4 & 117.9 \\ Limb-Scale Only & 10.9 & 22.4 & 45.7 & 56.8 & 77.7 & 119.3 \\ Part-Scale Only & 11.6 & 22.5 & 45.6 & 56.9 & 77.9 & 117.5 \\ Multiscale Fusion & **9.6** & **19.8** & **41.0** & **51.3** & **71.9** & **112.5** \\ \hline \hline \end{tabular} Note, the best results are highlighted in **bold**.  
\end{table}
Table 5: Comparisons of different scale modeling in our model on the H3.6M dataset.  
matic branches possess distinct characteristics and should not be directly added. Instead, applying weighted fusion after separate feature representations enhances the effectiveness of the feature integration.
## 5 Electrochemical Sandwich Assay Combining Aptamers with Other Materials
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Analyte** & **Components of Sandwich Assay** & **Method of Detection** & **Linear Range** & **Limit of Detection** & **Ref.** \\ \hline \multirow{4}{*}{C-reactive protein} & Magnet/carbon & & & & \\ electrode/streptavidin-coated magnetic & & & & & \\ beads/bioinylated & & & & & \\ aptamer/CRP/bioinylated & & & & & \\ antibody/alalginate & & & & & \\ Phosphatase\(\ast\) streptavidin conjugate & & & & & \\ \hline \multirow{4}{*}{\(\alpha\)-1 antitrypsin (AAT)} & Screen-printed electrode/carbon & & & & \\ nanotubes/aptamer/AAT/Ag & & & & & \\ nanoparticles modified by & & & & & \\ antibody–alkaline phosphatase & & & & \\ conjugate & & & & & \\ \hline \multirow{4}{*}{Platelet-derived growth factor BB (PDGF-BB)} & Gold & & & & \\ electrode/antibody/PDGF/aptamer & Square-wave & & & & \\ with circular DNA probe/methylene & & & & & \\ blue & & & & & \\ \hline \multirow{4}{*}{Mucin protein-1 (MUC1)} & Graphite working electrode/conducting & & & & \\ poly- & & & & & \\ \cline{2-5}  & imper/antibody/MUC1/aptamer/methylene & Differential pulse voltammetry & & & \\ \cline{2-5}  & & & & & \\ \hline \multirow{4}{*}{Mucin protein-16 (MUC16)} & Indium tin oxide electrode/gold & & & & \\ \cline{2-5}  & nanoparti- & Differential pulse & & & \\ \cline{2-5}  & cles/antibody/MUC16/aptamer and & & & & \\ \cline{2-5}  & primer tail/DNA/methylene blue & & & & \\ \hline \multirow{4}{*}{Tumor necrosis factor \(\alpha\) (TNF\(\alpha\))} & Screen-printed graphite electrode/cobalt & & & & \\ \cline{2-5}  & hexacyandorferate/Au & & & & \\ \cline{2-5}  & nanoparticles/aptamer/TNFa/antibody & & & & \\ \cline{2-5}  & conjugated to horseradish peroxidase & & & & \\ \hline \multirow{4}{*}{Insulin-like growth factor (IGF1)} & Interdigitated & & & & \\ electrode/streptavidin/bioinylated & & & & & \\ \cline{1-1}  & aptamer/antibody & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Breast cancer biomarker human epidermal growth factor receptor-2 (HER2) was detected by aptamer-based biosensor using phosphate-functionalized MnO\({}_{2}\) nanosheets as the electrochemical probe [92]. MnO\({}_{2}\) nanosheets were synthesized and phosphate ions and the aptamer (5'-GCAGGGGTGTGGGG-3') were absorbed onto the nanosheets. The reaction of phosphate ion with molybdate ion formed redox-active molybophosphate as a precipitate on the electrode surface that was then detected using square wave voltammetry. The peptide specific to HER2 (CKRLREWNR) was immobilized onto the gold surface followed by the different concentrations of HER2 solution. Then, a modified MnO\({}_{2}\) nanosheet was added to the modified electrode with Na\({}_{2}\)MoO\({}_{4}\) solution and current arising from reduction of molybophosphate was measured. This assay achieved a linear range of 0.1-500 pg mL\({}^{-1}\), and the detection limit was as low as 0.05 pg mL\({}^{-1}\). This assay was also used to detect HER2 in spiked human serum samples giving good agreement with ELISA results.
# Record Linkage of Chinese Patent Inventors and Authors of Scientific Articles
## 2 Record Linkage Algorithm  
Our algorithm links patents and journal articles connected with the same scientist. Several issues make this problem challenging. Firstly, the only attributes shared between two databases are the names of scholars and patent inventors. Secondly, names are not unique and are stored and written differently, and they contain misspellings, initials, given names or family names missing, and given names and family names which are are swapped. Finally, different people can share the same name--especially Chinese authors [28]. For that reason, we built an algorithm that uses fuzzy similarities between names, compares abstracts of patents and papers, and compares subject areas (disciplines/domains) of patent inventors and authors of papers.  
An indexing step reduces the number of candidate record pairs compared in detail. Indexing discards pairs that are unlikely to be true matches (i.e., it is unlikely that they refer to the same real-world entities). Without indexing, the linkage of two databases with \(m\) and \(n\) records, respectively, would produce \(m\times n\) candidate pairs that have to be compared in detail.  
In our approach, we use a combination of both standard blocking and an inverted index-based sorted neighborhood applied to English and Chinese names of scientists. Blocking [6] inserts all records that have the same value of selected attributes into the same block. The number of blocks created is equal to the number of unique values that appear in both databases. In sorted neighborhood indexing [29] matched databases are sorted according to one or more attribute values, called sorting key(s). A sliding window of fixed size (greater than one) is moved over the sorted database and candidate record pairs are generated only from the records within a current window.  
All candidate pairs generated in the indexing step are subject to detailed comparisons to determine their similarity. Paired records are compared using several attributes selected from all the attributes available in the databases/tables that are linked. We use attributes depicted in Section 2.1. The results of comparisons, in the form of numerical similarity, are stored in vectors. Such comparison vectors created for each candidate record pair are inputs to classifiers depicted in Section 2.2, which decide whether a given pair is a match or a non-match.
## 4 Results
### Yearly Comparison and Economic Trends  
Analyzing the penetration rates across 2015-2016, 2017-2018, 2019-2020, and 2021-2022, we should observe the following trends and their implications for GDP and telecommunications development.  
2015-2016: This period likely represents a baseline for fixed telecommunications penetration across Mexican states. During this time, the disparity between wealthier and poorer states in terms of internet penetration may still be pronounced. As mobile networks expand, the reliance on fixed telephony could begin to decline, especially in states with strong economic growth. This behavior is described in Figure 8.  
2017-2018: During this period, we would expect fixed internet access penetration to increase significantly, especially in wealthier states, as more businesses and households demand reliable internet for economic and educational purposes. Restricted television services could start showing a split, where wealthier states may adopt it more widely, reflecting disposable income differences. This behavior is described in Figure 9.  
Figure 8: Overview of the telecommunications penetration rates and GDP correlation for Mexican states in 2015–2016.  
2019-2020: The global COVID-19 pandemic in 2020 had profound impacts on both GDP and telecommunications services. The need for internet connectivity surged dramatically as more individuals and businesses relied on digital services for remote work, online education, and healthcare. States with better pre-existing internet infrastructure may have navigated the pandemic more successfully, leading to a divergence in economic recovery and digital service penetration. Fixed telephony may have further declined as mobile networks and internet-based communications became more prevalent. This behavior is described in Figure 10.  
Figure 10: Overview of the telecommunications penetration rates and GDP correlation for Mexican states in 2019–2020.  
Figure 9: Overview of the telecommunications penetration rates and GDP correlation for Mexican states in 2017–2018.  
2021-2022: As economies began to recover from the pandemic, we would expect the correlation between GDP and fixed internet penetration to strengthen. States with higher GDPs that invested in telecommunications infrastructure are likely to demonstrate both stronger economic growth and higher rates of internet and restricted television services. Conversely, states that lagged in both economic recovery and telecommunications penetration may continue to fall behind. This behavior is described in Figure 11.
###### Abstract  
Small-hole components of Inconel 718 are widely used in aerospace engineering, medical devices, and other fields. Limited by material properties, its machining efficiency seriously restricts its wide application. The objective of this study is to investigate a novel machining technique for Inconel 718 that integrates ultrasonic machining (UM) and hydrogen embrittlement (HE) treatment. Accordingly, the technique is designated as hydrogen embrittlement-enhanced ultrasonic machining (HEUM). Prior to machining, a stress layer is formed on the surface of the workpiece. To ascertain the fundamental characteristics of Inconel 718, the influences of ultrasonic amplitude, HE time, and HE voltage on the specific material removal rate and surface roughness in the presence of HE were empirically examined. To investigate the material removal process for HEUM, the nanoscratch test and nanonidenter were also conducted with HE samples. Further, the subsurface for HEUAG samples were obtained with SEM. The specific material removal rate under experimental conditions of 10 min and 5 V HE increased by 27.4%. Finally, HEUM is proposed to be used for a 1 mm through-hole with Ra 0.318 \(\upmu\)m. A precision hole with a diameter as small as 0.5 mm has been manufactured.  
nickel-based alloys; hydrogen embrittlement; ultrasonic machining; material removal Article
# Efficient Top-\(k\) Spatial Dataset Search Processing
## 6 The Optimized Search Scheme
### Grid Pooling-Based Optimization Strategy
The selection of the pooling threshold \(\epsilon\) affects the size of the grid pool, which in turn affects the filtering efficiency and the space cost of the pooling index. The larger the \(\epsilon\), the more grids each grid pool contains, resulting in lower filtering efficiency and smaller storage overhead. In practical applications, we recommend weighing the time and space overhead and setting the parameter value reasonably. ```
Input: The spatial data repository \(\mathcal{S}\) and the grid pooling threshold \(\epsilon\). Output: The \(\epsilon\)-pooling index \(\mathcal{I}^{\epsilon}=(I^{\epsilon}_{u},I^{\epsilon}_{u})\). 1 Generate the grid distribution representation \(\mathcal{D}\) and \(\text{MCG}^{\epsilon}\ \mathcal{M}^{\epsilon}\) for spatial datasets in the spatial data repository \(\mathcal{S}\);
2 Initialize the \(\epsilon\)-pooling index \(\mathcal{I}^{\epsilon}\), where \(I^{\epsilon}_{u}=\varnothing\) and \(I^{\epsilon}_{l}=\varnothing\);
3 foreach\(S_{i}\in\mathcal{S}\)do
4 Get the set of pool grids \(P_{i}\in M\);
5 Add the pair \((i,P_{i})\) into \(I^{\epsilon}_{u}\);
6 Get the set of grid distribution representation \(D_{i}\in\mathcal{D}\);
7 Add the pair \((i,D_{i})\) into \(I^{\epsilon}_{l}\);
8 Add \(I^{\epsilon}_{u}\) and \(I^{\epsilon}_{l}\) into \(\mathcal{I}_{\epsilon}\);
9 return\(\mathcal{I}^{\epsilon}\);
```  
**Algorithm 3**BuildIndex(\(\mathcal{S},\epsilon\))  
We propose a pooling-based optimization strategy based on the pooling index. The upper layer of the index can quickly determine whether a dataset overlaps with the exemplar dataset in terms of \(\text{MCG}^{\epsilon}\) (as shown in Algorithm 4), which helps to assess whether the dataset could be part of the final search results. If the dataset does not share an overlapping \(\text{MCG}^{\epsilon}\) with the exemplar dataset, it can be filtered out directly without the need for further similarity calculations, thereby improving search efficiency. Figure 4: An example of \(\epsilon\)-pooling index (\(\epsilon=2\)). ```
Input: The minimum covered grid pool collection of the spatial dataset \(S_{i}\) and exemplar dataset \(S_{e}\), \(M^{e}_{i}\) and \(M^{e}_{e}\), respectively. Output:\(true/false\) representing whether \(S_{i}\) has a grid pool overlap with \(S_{e}\). 1 foreach\(g^{e}_{x,j}\in M^{e}_{i}\)do
2 foreach\(g^{e}_{x,j}\in M^{e}_{e}\)do
3 if\(g^{e}_{x,y}==g^{e}_{e,j}\)then
4 return\(true\)
5
6 return\(false\);
```  
**Algorithm 4**_CheckPool(\(M^{e}_{i}\), \(M^{e}_{e}\))_
# Business Intelligence through Machine Learning from Satellite Remote Sensing Data
## 5 Experimentation and Evaluation  
Before proceeding with our model experimentation, we would like to validate the correlation of nighttime lights to human activity. Specifically, we generated a time series for the Oikonomaki street in Volos, which contains mostly cafes and nightlife amenities. The data were collected for the 2015 to 2022 period and their value changes in relation to various important events were evaluated (Figure 4). For example, the rise around the end of 2018 is connected with the increasing number of bars in the area, while the drop in 2020 is related to the COVID-19 pandemic and the lockdowns. This is also emphasized by the fact that we can see fluctuations after 2021, around the times that the lockdowns were relaxed. Apart from that, we also created some map visualizations for the whole region of Magnesia for the years from 2016 to 2021. The average radiance values were normalized by subtracting the mean values and dividing the results by the variance to make the relatively low-resolution _viirs_ images more discernible. Although it is difficult to notice in the provided images (Figure 5), we can identify an increase in nighttime lights between these four years, possibly related to urban expansion and increasing human activity. Some lights became invisible in areas such as Trikeri, but that is probably an artifact of the normalization. The ability of our methods to detect abandoned buildings was evaluated through the platform we created and whose user interface is depicted in Figure 3. The experimentation followed the following steps. For each experiment, we selected areas with different characteristics, such as dense urban core, residential areas near city outskirts, and coastal areas within the Volos municipality. These areas were relatively big, containing roughly 2/3 of the whole city. Each region of interest is split into smaller equally sized polygons (grids) using a fishnet method. This method takes as input the area of interest and discretizes it, according to user input, into \(n\) columns and \(k\) rows. As these numbers increase, the size of the polygons decreases in order so that we can fit more inside the initial area. We experimented with using different sizes, varying between \(5\times 5\), \(15\times 15\) and \(25\times 25\). Next, we evaluated the capability of different classifiers to detect a building's vacancy property on the set grids. In some interesting cases, where the models seemed to perform worse due to their placement inside the polygon, we evaluated them in smaller parts of these areas. The results vary greatly depending on the model, grid size, and selected features.  
Figure 4: Average radiance plot for Oikonomaki street.
## 3 System Overview
### System Model  
Figure 1 presents the overall system model. The description and functionalities of every component are detailed below:  
* Resource Management System: The resource management system contains information about the cluster list, PUE, CFR, total utility power, current IT load, and other metadata information related to the data centers.
* Management Node (MN): The Resource Allocation Management (RAM) algorithm is a daemon that is executed in the management node. It is updated with the cluster list, host list, PUE, carbon footprint rate, and other information related to the clusters in the data center. It activates the scheduling algorithm for VM-to-PM mapping and the resource deallocation algorithm to perform resource recovery, and updates target virtual machine queue (_TargetVMQ_) with VM-to-PM mapping information.
* Cluster Manager (CM): A node in the cluster is nominated as the head node to function as the cluster manager. The overall utilization of the cluster, number of machines in on and off states, maximum and minimum utilization, number of VMs operating in the cluster, and power consumption are maintained by the cluster manager and updated by the management node.
* Physical Machine Manager (PMM): The PM details related to available memory and CPU capacity, current operating frequency, power consumption, percentage of CPU utilization, number of active VMs, and other PM-related information are maintained by the PMM and updated by the CM in the head node.
* Virtual Machine Manager (VMM): The VMM is a daemon that is executed in each PM. It is responsible for maintaining the VMs executing in PM. VM resource utilization, percentage of CPU time utilized, submission time, placement time, active and idle states, remaining execution time, power consumption, and other VM details are maintained by the VMM.