esa-sceva/satcom-chunk-collection huggingface dataset:
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## 4 Simulation Results and Analysis  
In order to verify the effectiveness of our algorithm, this paper adopts MATLAB to simulate, and conducts comparative analysis with the FAF-EBRM and FLEOR algorithms. In addition, five performance parameters will be used in the following paper: average energy consumption (AEC), average hop count (AH), packet loss rate (PLR), energy variance (EV), and node survival rate (NSR). Among them, AEC represents the average energy consumption of all nodes in each data transmission round, which measures the utilization efficiency of network energy. AH can reflect the transmission delay of data in the network, and the smaller the average hop count is, the faster data can be transmitted to sink. PLR can reflect the network's ability in congestion avoidance. EV can give expression to the difference of energy residual value of each node, that is, the smaller the value of EV, the smaller the curve fluctuation is, thus the more balanced energy of the network. NSR is used to indicate the network lifetime and the effectiveness of energy utilization. The steeper the slope of the curve from the death of first node to the death of all nodes, the more concentrated time for the nodes to run out of energy, and the more balanced energy in the network. In addition, in order to avoid the contingency of the experimental results, we repeat each experiment many times to get an average.  
The specific simulation parameters are listed in Table 1.
# Green's Function, Earthquakes, and a Fast Ambient Noise Tomography Methodology
## 4 Discussion and Conclusions  
The present methodology shows promising and reasonable results without the need for large amounts of data, with low runtime speeds, and without the assumption of great-circle propagation or the necessity of inversion for group velocity maps. A comparison of our methodology with more commonly applied ANT methods is shown in Figure 11. Initially, the seismic data retrieval is intermingled with their preprocessing by applying multi-threading programming, which reduces runtime speeds compared to synchronous programming. Moreover, splits and stacks of the data are avoided and the cross-correlations are not performed in the time domain; instead, the whole data series of each station is cross-correlated with each other in the frequency domain. In addition, instead of implementing a 2D inversion for each frequency to derive group velocity maps, a process where straight-ray propagation is traditionally assumed [47], a single 1D inversion of the mean group dispersion curve is obtained. Finally, instead of deriving the 3D model as a composite of many 1D inversions, a direct surface wave inversion method is performed where the sensitivity kernels and ray-paths are iteratively updated until convergence. As far as the new ambient noise tomography methodology is concerned, the following conclusions apply: By cross-correlating in the frequency domain a few days of vertical component noise recordings for each pair of stations on the west coast of central Greece, we extracted the EGFs, as if one station were the source and the other the receiver. Subsequently, applying FTAN, we inferred the group dispersion relation for each Rayleigh fundamental mode. The average of these dispersion relations was then inverted for a 1D shear wave velocity model, which was later proposed as the initial solution in the 3D inversion process. The 3D Vs model produced was consistent with previous studies conducted in neighboring areas.  
Figure 11: Flowchart for (**a**) the methodology applied in this study and (**b**) the common methodology applied in ANT.  
**Author Contributions:** Conceptualization, P.K.V.; methodology, P.K.V. and N.V.S.; software, P.K.V.; validation, P.K.V. and N.V.S.; formal analysis, P.K.V.; investigation, P.K.V. and N.V.S.; resources, P.K.V.; data curation, P.K.V.; writing--original draft preparation, P.K.V. and N.V.S.; writing--review and editing, P.K.V. and N.V.S.; visualization, P.K.V.; supervision, P.K.V. and N.V.S.; project administration, P.K.V. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data used in this study are publicly available at [[https://www.fdsn.org/webservices/datacenters/](https://www.fdsn.org/webservices/datacenters/)]([https://www.fdsn.org/webservices/datacenters/](https://www.fdsn.org/webservices/datacenters/)), accessed on 28 August 2023.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Identification of the MS969 Strain  
The MS969 strain was cultured on Zobell medium at 28 \({}^{\circ}\)C over 24 h under 170 rpm agitation. A total of 500 \(\upmu\)L of culture was centrifuged for 10 min at 7500 rpm. The pellet was washed with 300 \(\upmu\)L phosphate buffer saline (PBS) solution and centrifuged again. Then, 200 \(\upmu\)L of 10 mM Tris with 1 mM EDTA (ethylenediaminetetraacetic acid) were added to the pellet; bacteria were lysed by heating at 95 \({}^{\circ}\)C for 10 min and cleared using centrifugation. The 165 rDNA gene was amplified directly from the supernatant (1 \(\upmu\)L) using polymerase chain reaction (PCR) with 8F and 1489R primers as previously described [42]. PCR amplification was performed using a DreamTaq Green polymerase Master mix (Thermo Fisher Scientific, Illkirch, France). The sequencing of amplified products was carried out by Genoscreen (Lille, France). A consensus sequence was built using forward and reverse sequences with BioEdit software (Figure S1) [43]. Strain identification was performed using nucleotide Blast on the NCBI nr/nt Nucleotide Collection (National Center for Biotechnology Information). Phylogeny was inferred from the alignment with reference 165 rDNA sequences retrieved from Genbank using MEGA7 software (version 7.0) [44] (Table S1). The _Escherichia coli_ type strain sequence (ATCC 11775\({}^{\mathrm{T}}\); X80725) was used as the outgroup. The optimal tree was built using the neighbor-joining method (NJ) [45]. The bootstrap support of nodes was assessed with 1000 replicates [46]. The evolutionary distances were computed using the Jukes-Cantor method [47].
## 4 Secrecy Outage Probability Analysis  
The Secrecy Outage Probability (SOP) is a benchmark for secrecy, indicating the probability of the occurrence of an event where the instantaneous secrecy capacity falls below the target secrecy rate \(R_{S}\). The mixed system's SOP lower bound formula is defined as follows [30]:  
\[P_{\text{out}}^{L}(R_{S})=\int_{0}^{\infty}F_{S_{G}RD}(\theta\gamma)f_{S_{G}S_{ j}E}(\gamma)d\gamma \tag{21}\]  
where \(\theta=\exp(R_{s})\). Substituting (8) and (20) into (21), and then combining the relevant expressions given in [31] and the generalized Laguerre polynomial [32], after some simplifying mathematical operations, the SOP can be obtained as follows:  
\[P_{out}^{L}(R_{S})=\sum_{j=1}^{n}H_{j}Y_{j}^{0.5}\exp\bigl{(}Y_{ j}\bigr{)}\frac{M}{\Omega_{S_{j}E}}\sum_{h=0}^{M-1}C_{M-1}^{h}(-1)^{h} \tag{22}\] \[\times\Biggl{[}\frac{1}{\Omega_{S_{G}E}}\exp\Bigl{(}\frac{-Y_{j} }{\Omega_{S_{G}E}}\Bigr{)}\left(\frac{1}{\frac{Y_{j}}{\Omega_{S_{G}E}}+\frac{ h+1}{\Omega_{S_{j}E}}}+\frac{1}{\left(\frac{Y_{j}}{\Omega_{S_{G}E}}+\frac{h+1}{ \Omega_{S_{j}E}}\right)^{2}}\right)\Biggr{]}\] \[\times\Biggl{\{}1-\exp\Bigl{(}\frac{-\theta Y_{j}}{\Omega_{S_{G}R }}\Bigr{)}\frac{\xi_{\text{mod}}^{2}}{r(a)\Gamma(b)}H_{3,3}^{3,1}\Biggl{[} \begin{array}{c}a\phi\theta\gamma_{j}^{\frac{1}{b}}\\ (b-1)\mu_{r}^{\frac{1}{b}}\end{array}\Biggr{]}\begin{array}{c}(1-b,1),\bigl{(} \xi_{\text{mod}}^{2}+1,1\bigr{)},\bigl{(}1,\frac{1}{r}\bigr{)}\\ \Bigl{(}0,\frac{1}{r}\bigr{)},\bigl{(}a,1\bigr{)},\bigl{(}\xi_{\text{mod}}^{2},1\bigr{)}\end{array}\Biggr{]}\Biggr{\}}\] \[H_{\text{j}}=\frac{\Gamma(n+1/2)Y_{j}}{n!(n+1)^{2}\bigl{[}L_{n}^ {-(1/2)}(Y_{j})\bigr{]}^{2}},Y_{j}\text{ means the jth root of the generalized Laguerre polynomial}\] \[L_{n}^{(-\frac{1}{2})}(Y).\]
# Smart Product Design Process through the Implementation of a Fuzzy Kano-AHP-DEMATEL-QFD Approach
## Literature Review
### Analytic Hierarchy Process (AHP)  
QFD matrix assumes that the importance given by the customer for each one of its needs is known. Nevertheless, the process used to identify those customer preferences is crucial for the design factor prioritization. Therefore, to properly estimate the degree of importance of each customer's need, many authors have used AHP to determine this customer-need importance factor (weight).  
AHP was developed by [PERSON], aiming to solve complex multiple criteria decision problems by ranking decision alternatives and selecting the best one when the decision-maker has multiple objectives or criteria [36]. This methodology allows users to assess the relative weight of multiple criteria or multiple options against given criteria in an intuitive manner [37; 38]. In case quantitative ratings are not available, decision-makers or assessors can still recognize whether one criterion is more important than another. Therefore, pairwise comparisons are done by users. [PERSON] established a consistent way of converting such pairwise comparisons (X is more important than Y) into a set of numbers, representing the relative priority of each of the criteria [39].  
AHP applications are wide and include almost all fields in decision-making problems. For example, AHP has been used as a prioritization tool in the process of selecting the best ICT service in a school in Chile [40], adoption of ICT in different industries [41; 42], and evaluation of software [43; 44], among others. AHP has also been used in healthcare to select equipment [45], to analyze the performance of healthcare services [26], to select the most suitable prosthesis [46], among others.  
The integration of AHP with QFD has been evidenced in several studies. This integration has been made in an effort to address the disadvantages of single methods. The applications are focused on weighting the customer needs. This weighting process tries to determine the impact each customer/decision-maker needs will have on customer decision/maker satisfaction. Nevertheless, though the person conducting the decision-making process can give weights to each need/criteria AHP, the person is not truly aware of the impact this need will have on his/her satisfaction. This happens because the absence of a particular feature in a product does not necessarily drive to customer dissatisfaction, nor its presence necessarily implies customer satisfaction [26]. Therefore, for the adequate impact determination of customer needs, it is important also to consider its impact on customer satisfaction [26]. Another important consideration that makes relevant the integration of AHP to QFD is the need for identifying customer needs and, consequently, propose actions and/or features to fulfill these requirements. This guarantees that each customer's need can be paired with at least one design feature or action, which also should have a positive impact on customer satisfaction [36].
## 3 Particle Swarm Optimization
### Recent Advancements in PSO for a Better Convergence Rate
#### 3.6.4 Dynamic Neighborhood in Particle Swarm Optimization  
The dynamic neighborhood in PSO is an adaptive mechanism that allows particles to adjust their communication network or neighborhood structure during the optimization process [113]. Unlike the traditional fixed neighborhood approach, where each particle interacts with a fixed set of neighbors, the dynamic neighborhood enables particles to dynamically form and update their local neighborhoods based on the problem dynamics or specific optimization requirements [114]. The neighborhood structure is not predetermined, but evolves over time. Initially, particles are assigned to random neighborhoods or a predefined initial configuration [115]. As optimization progresses, particles continuously evaluate their performance and exchange information with their neighbors. Based on this information, particles may reconfigure their neighborhoods by adding or removing neighboring particles, thereby dynamically adjusting the communication network [116].
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 5 Discussion
### Limitations and Future Work  
Despite its effectiveness, the proposed method has some limitations. First, the model was trained and validated solely within the Liuchong River Basin. Although this region exhibits diverse forest types and elevation gradients, geographic transferability to other regions remains untested. Domain adaptation or pretraining with large-scale regional data could help address this limitation [75]. Second, the use of single-date Sentinel-2 imagery may lead to temporal biases due to seasonal phenology or recent land-cover changes [76]. Future work should consider multi-temporal data fusion and the integration of complementary modalities such as LiDAR, radar, or high-resolution topographic information [77].  
Third, Sentinel-2 multispectral bands are known to saturate in high-biomass conditions, which may reduce the model's sensitivity and accuracy when estimating carbon stocks in dense or mature forest stands. This spectral saturation effect limits the representation of subtle structural variation beyond certain thresholds. Future studies may address this limitation by integrating active remote sensing data such as LiDAR or SAR, which provide direct measurements of canopy height and structure, and are less affected by saturation.  
Lastly, incorporating uncertainty quantification and explainability tools (e.g., SHAP, Grad-CAM) would enhance the interpretability and decision support capabilities of forest carbon models.  
In summary, while our study demonstrates promising results within a regional context, future efforts should aim to build more generalized and transferable frameworks that can scale across biomes and temporal settings. In addition, integrating biophysical priors and leveraging explainable learning mechanisms will be critical to bridging the gap between predictive performance and ecological insight.
# CNN-Based Ti-6242 Impeller Forging Process Design for Uniform Strain Distribution
## 3 Improved Forging Process Design Method
### CNN Training Method  
Finite Element Method (FEM) simulation was performed to construct training data for the CNN models in Figure 4. For the simulation, the commercial FEM software, DEFORM 2D version 11.0 from SFTC (Scientific Forming Technologies Corporation, Columbus, OH, USA), was utilized.  
The workpiece initial temperature was set to 900 \({}^{\circ}\)C for the Ti-6242 material, and the material properties were based on the DEFORM database. The two dies were considered as rigid bodies, with the top die speed maintained at 250 mm/s and a temperature of 250 \({}^{\circ}\)C. The initial temperature of the workpiece was set to 900 \({}^{\circ}\)C, and a constant Coulomb friction coefficient of 0.35 [45] was implemented for the friction between the dies. The learning influence due to friction and temperature is described in Section 4.4. Von [PERSON] yield criterion and isotropic hardening were applied to the material during deformation. A heat transfer coefficient of 3000 \(\mathrm{W}/\left(\mathrm{m}^{2}\cdot\mathrm{K}\right)\)[46] was applied for heat transfer between the workpiece and dies during forging simulation. Three axisymmetric impeller shapes, as shown in Figure 5, were designed for CNN training to provide sufficient training data. All fillet radii were fixed at 5 mm, and the inclination angle was set to 3\({}^{\circ}\).  
To construct Model I and Model II, training data for the preform shape should be prepared first. To this end, we conducted upsetting on billets with a volume error of 5% or less, as summarized in Table 1 for each impeller design in Figure 5, with a 5 mm interval. Then, using the impeller design as an input image and the upset billet shape as an output image, we trained the model shown in Figure 3. Subsequently, we obtained five preform shapes for each impeller design in Figure 5 by inputting impeller designs as test inputs, as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Radius [mm]** & **Height [mm]** & **Upsetting Stroke [mm]** \\ \hline Impeller design I & 98.55 & 295.65 & 0–115 \\ Impeller design II & 83.70 & 251.10 & 0–100 \\ Impeller design III & 106.30 & 318.90 & 0–110 \\ \hline \end{tabular}
\end{table}
Table 1: Billet size and upsetting stroke of each impeller design.  
Figure 5: Impeller design used for CNN model training: (**a**) Impeller design I; (**b**) Impeller design II; (**c**) Impeller design III. We used 3 different designs for constructing training data.  
In order to design the forging process in Figure 4, the training data were organized as shown in Table 2. There are three types of training data: upset billets, preforms, and impellers, which are organized for each impeller design in Figure 5. In this study, we defined the three sets of training data created for each impeller design as a single training set, and we organized Training set I, Training set II, and Training set III for each of the three impeller designs, respectively.
## 1 Introduction
### Related Works
#### 1.1.2 HVS-Based Methods  
Building on the robust characteristics of the human visual system (HVS) and Derived Kernel [13; 14], [PERSON] et al. [15] introduced a method based on Laplacian scale-space theory. Subsequently, [PERSON] et al. [16] proposed the local contrast measure (LCM), which also draws on the HVS principles. To better characterize the features of the target's neighborhood, [PERSON] et al. [17] and [PERSON] et al. [18] introduced a three-layer sliding window structure. Building on this concept, [PERSON] et al. and [PERSON] et al. further incorporated weighting strategies and proposed the Weighted Three-Layer Local Contrast (WTLLCM) [19] and the Weighted Tri-Layer Sliding Window Local Contrast (NTLLCM) [20], respectively, both achieving promising results. However, these single-frame algorithms rely solely on spatial information, limiting their effectiveness under low signal-to-clutter ratio (SCR) conditions.
# Feature-Based Approach to Change Detection of Small Objects from High-Resolution Satellite Images
## 4 Results and Discussions
### Analysis of Change Detection Performance
ROC curves were generated based on the number of successfully detected objects. Figure 12 shows the ROC curves. As before, the figure shows that our feature-based approach produced improved recall at the given false alarm rate. Table 7 shows the performance of pixel-based and feature-based change detection in the object domain using the same threshold value for pixel change magnitude. As expected, the false alarm rates decreased in feature-based approaches and precision and accuracy increased. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \
\begin{tabular}{c} **Study** \\ **Area** \\ \end{tabular} & **Method** & **Precision** & **Accuracy** &
\begin{tabular}{c} **False** \\ **Alarms** \\ \end{tabular} & **Recall** & **F1** \\ \hline \multirow{3}{*}{Incheon} & Pixel & 0.9488 & 0.7972 & 0.0746 & 0.7295 & 0.8248 \\  & AKAZE & 0.9659 & 0.7994 & 0.0480 & 0.7189 & 0.8243 \\  & SIFT & 0.9584 & 0.7970 & 0.0592 & 0.7211 & 0.8230 \\ \hline \multirow{3}{*}{Chilgok} & Pixel & 0.7059 & 0.8283 & 0.1210 & 0.7053 & 0.7056 \\  & AKAZE & 0.8358 & 0.8693 & 0.0556 & 0.6870 & 0.7541 \\  & SIFT & 0.7756 & 0.8520 & 0.0826 & 0.6933 & 0.7321 \\ \hline \multirow{3}{*}{Seoul} & Pixel & 0.5677 & 0.7955 & 0.1948 & 0.7664 & 0.6522 \\  & AKAZE & 0.6430 & 0.8300 & 0.1337 & 0.7215 & 0.6800 \\  & SIFT & 0.6074 & 0.8131 & 0.1544 & 0.7156 & 0.6571 \\ \hline \multirow{3}{*}{Mean} & Pixel & 0.7408 & 0.8070 & 0.1301 & 0.7337 & 0.7275 \\  & AKAZE & 0.8149 & 0.8329 & 0.0791 & 0.7091 & 0.7528 \\ \cline{1-1}  & SIFT & 0.7805 & 0.8207 & 0.0987 & 0.7100 & 0.7374 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance of change detection results in the pixel domain.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
## 4 Model Validation Results and Analysis
### Delayed Surface Subsidence Predicted by Improved Model  
The monthly average maximum subsidence rate, maximum subsidence, and subsidence rate at WC1-WC9 for each half-year period from February 2018 to July 2021 are illustrated in Figures 9-11.  
As indicated in Figures 9-11, between August 2018 and July 2021, the maximum subsidence rate for the area decreases from 1.12 mm/month to 0.51 mm/month, and the average subsidence rate of WC1-WC9 decreases from 0.11 mm/month to 0.02 mm/month, showing a decreasing trend. The maximum surface subsidence for the area is reduced from 6.36 mm to 2.8 mm, and the average subsidence of the WC1-WC9 is reduced from 0.63 mm to 0.1 mm. The maximum cumulative surface subsidence for the area is reduced to 25.51 mm, and the average cumulative surface subsidence at the WC1-WC9 is reduced to 2.13 mm. The maximum subsidence rate and the amount of surface subsidence decreased continuously every six months and finally stabilized.  
To further verify the accuracy of the improved model, continuous monitoring was conducted on WC1-WC9 and the maximum subsidence monitoring point from August 2018 to July 2021. A comparison of the monitored results with the predicted results is displayed in Figure 12. The root-mean-square error between the predicted and measured values at the maximum subsidence monitoring point is 1.12, and the root-mean-square error between the predicted and measured values at WC1-WC9 is 0.37.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Model Calculation Results (mm)** & **Subsidence Rate (mm/month)** & **Monitoring of Subsidence Rate (mm/month)** & **Subsidence Rate (mm/month)** & **Error Ratio** \\ \hline July 2015–July 2018 & 18.09 & 0.47 & 17.5 & 0.49 & 3.2\% \\ February 2018–July 2018 & 0.79 & 0.13 & 0.84 & 0.14 & 5.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison table for WC1–WC9 average settlement.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date** & **Model Calculation Results (mm)** & **Subsidence Rate (mm/month)** & **Numerical Simulation Results (mm)** & **Subsidence Rate (mm/month)** & **Error Ratio** \\ \hline July 2015–July 2018 & 75.5 & 2.12 & 78.2 & 2.17 & 3.4\% \\ February 2018–July 2018 & 8.56 & 1.4 & 8.8 & 1.5 & 2.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Maximum Settlement Comparison Table.  
Figure 11: Cumulative predicted surface subsidence at WC1–WC9 from August 2018 to July 2021.  
Figure 12: Predicted vs. measured cumulative surface subsidence at monitoring points from August 2018 to July 2021.  
Figure 10: Variation of predicted surface subsidence at WC1–WC9 from August 2018 to July 2021.  
Figure 9: Variation of predicted subsidence rate at WC1–WC9 from August 2018 to July 2021.