esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance Evaluation and Noise Mitigation of the FY-3E Microwave Humidity Sounder
## 3 RTM Simulation
The high-latitude region is dominated by negative biases, which may be due to an excessively high surface emissivity setting in snow and ice areas and therefore an overestimation of the background field brightness temperature. In contrast, the absolute values of the \"O\(-\)B\" within 50\({}^{\circ}\) S/N are significantly smaller than those at high latitudes. In order to avoid the interference of land surface emissivity error and insufficient accuracy of sea ice information in the background, in the subsequent study, we only selected the data over the ocean. In addition, considering that the Pacific region can provide enough observation data for the study, and also to avoid the impact of including more data along the coastline, this study only focuses on the Pacific region within 50\({}^{\circ}\)S/N for the evaluation research.
## 4 Discussion
Data from the literature explained that the brand of the post-curing oven is not the most important issue to consider regarding the cytotoxicity of 3D print resins [23]. One may achieve excellent biocompatibility outcomes by associating high temperatures, such as 80 \({}^{\circ}\)C, a long post-curing period, such as 30 min, and high UV-light intensity [22]. These combined factors must be present in the post-curing process to promote a higher degree of conversion of the 3D-printed device, which is associated with better outcomes of cytotoxicity [27]. Different commercially available resins did not present statistical differences regarding cytotoxicity for human periodontal ligament fibroblasts when following the manufacturer's instructions [11]. When compared to conventional materials, 3D-printed materials did not show significant differences to acrylic resins on human gingival fibroblast [16; 19]. However, samples milled from resin blocks are less cytotoxic than printed ones [7], while conventional light-cured resin-based composites presented similar cytotoxic behavior to 3D printable resins [7; 15]. After printing, washing, and post-curing a resin-based material, one may increase its biocompatibility by immersing the printed resin in 100 \({}^{\circ}\)C water for 1 to 5 min without affecting its mechanical or optical properties [24]. A limitation of this review is that it does not perform a statistical analysis, such as a systematic review with meta-analysis. Therefore, we cannot ensure what factors, overall, in the literature, significantly impact the outcomes. Future research could organize the extracted data to evaluate the statistical significance of different protocols on the cells' viability.
## 8 Implementation and Results
### Scenario Implementation
\begin{table}
\begin{tabular}{c c} \hline Network topology & AT\&T \\ Total nodes & 625 \\ Backbones & 221 \\ Attacked edge router & 108 routers \\ Target quantity & \(\approx\)55\% of total consumer nodes \\ Adversary quantity & \(\approx\)45\% of total consumer nodes \\ Streaming producer & /ndntube/videos/ \\ Consumers & /ndntube/videos/... \\ Targets & /ndntube/videos/... \\ Attack repetition & 4 for each target \\ CS policies & LRU \\ LFU & LFU \\  & FIFO \\ CS size & 1000 packets \\ CRT decisions & cached by edge node \\  & cached by neighbor node \\  & cached by away node \\ \hline \end{tabular}
\end{table} 
Table 2: Configurations for NDNtube attack scenario. Figure 11: NDNtube attack design. AT&T network topology bandwidth and delays measurements (for various link types) are shown in Table 3. In this scenario, the best-route forwarding strategy is selected under different CS policies: \(i\). LRU removes the least recently used streamed content segment when the CS is full. _ii_. LFU, in which the least frequently used cache block is removed whenever CS is overflowed. _iii_. FIFO, where streamed contents are evicted in the same order as they come into CS.
## 8 Conclusions  
The risk related to extreme marine events is significantly high along the coastal area of the Gulf of Naples, Italy. On 28 December 2020, the area has been hit by an extreme sea storm, with severe consequences along most areas of Naples' coastline. In the waterfront area of Via Partenope, Naples, the waves overrun the roadway, causing extensive damage on the coastal seawall, roads, and touristic structures.  
In this study, we analyze the meteorological evolution of the sea storm and its effects on the waterfront, suggesting that Castel dell'Ovo western bastion and tuff cliffs have enhanced the local-scale wave magnitude and associate coastal damage, due to induced reflective processes on the waves approaching the coastline. Satellite meteorological data were integrated by accurate estimates of meteorological parameters obtained from weather sensor networks and UAS digital photogrammetry data processed with the SfM technique.  
The analysis of the event of 28 December 2020, suggests that our ability in predicting the effects on the coast of a sea storm may be improved by considering all local scale factors, including the duration and intensity of wind events, the tidal component, and wave diffraction and reflection phenomena. For this scope, the application of remote sensing integrated systems, such as UAS surveys and digital photogrammetry, represents an effective support in the assessment of damages, resilience, and vulnerability and the evaluation of adequate risk mitigation measures to be adopted for coastal zone management. The outcomes of the present study also provide a contribute for specialists in construction/design engineering towards the planning of protection structures along the coasts.  
Conceptualization, A.F. ([PERSON]) and F.M.; Methodology, A.F. ([PERSON]), A.F. ([PERSON]) and F.M.; Software, A.F. ([PERSON]); Validation, A.F. ([PERSON]), F.M. and R.S.; Formal Analysis, A.F. ([PERSON]); Investigation, A.F. ([PERSON]) and A.F. ([PERSON]); Data Curation, A.F. ([PERSON]); Writing--Original Draft Preparation, A.F. ([PERSON]), A.F. ([PERSON]), F.M. and R.S.; Writing--Review & Editing, F.M. and M.S.; Visualization, A.F. ([PERSON]) and A.F. ([PERSON]); Supervision, C.T., G.D.N., F.M. and M.S., Project Administration, A.F. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received funding from PRO-DAM project, PO FEAMP 2014-2020 Reg.(UE) n.508/2014-Misura: 2.51 project, and PON-S4E-SCN-00393 project.  
**Data Availability Statement:** The data presented in this study are available in the article.  
**Acknowledgments:** The Massalubrense ondametric buoy data of were made available thank to the courtesy of [PERSON] di Scienze e Tecnologie-Parthenope University-Naples, Italy.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Nature- and Bio-Inspired Metaheuristic Algorithm Optimization for Scheduled Routing Mechanism
BPSO is a variant of the Particle Swarm Optimization (PSO) algorithm that is specifically designed to handle binary variables. In the context of SRM, BPSO can be used to find the optimal schedule that minimizes energy consumption while satisfying the constraints of the network and the hop-by-hop addressing based routing mechanism [39]. In a hybrid approach, the strengths of multiple optimization algorithms are combined to improve the performance of the SRM. For example, GA can be used to provide a diverse population of solutions, while BFA can be used for its ability to handle binary variables. Similarly, FA and LFA can be used for their ability to handle multi-modal optimization problems and EHO and BPSO can be used for their ability to handle optimization problems with discrete and binary variables, respectively. It is important to note that hybridization is not a magic solution, and the performance of hybrid algorithms depends on the problem at hand and the way the algorithms are combined. It is also important to note that an appropriate selection method chooses the best algorithm for the current iteration. The simulation is conducted based on inline scheduled algorithms for underwater nodes. The SRM evaluated the output of the five hybrid algorithms and selected the best one for underwater node broadcasting for forwarding the data packets. It is assumed that all hybrid algorithms underwater node time intervals are equivalent. The simulation was conducted for 700 nodes by using different parameters.
## 2 Results
### Codon Usage Bias and Repeat Sequence Distribution  
A total of 26,803 codons were identified in _P. acinosa_. Analysis of relative synonymous codon usage (RSCU) revealed that arginine, leucine, and serine exhibited the highest RSCU values among the 20 amino acids and stop codons, indicating the preferential usage of specific codons for these amino acids (Figure S2). Comparative analysis of codon usage patterns in six species--_P. acinosa_, _P. insularis_, _P. polyandra_, _P. japonica_, _P. latbenia_, and _P. americana_--showed that codon usage bias was generally conserved across these species, suggesting similar selective pressures on codon selection. As illustrated in Figure S3, codons with strong bias (RSCU > 1) are represented in green, indicating a higher-than-expected frequency, whereas those with weaker bias (RSCU < 1) are shown in red, representing a lower-than-expected frequency.  
In _Phylotaloca_ cp genomes, the number and characteristics of simple sequence repeats (SSRs) and tandem repeats as well as their distribution patterns across the genome were investigated (Figure 3). Most SSRs were identified in the LSC region, followed by the SSC region, and the fewest SSRs were found in the IR region. The number of SSRs in _P. acinosa_, _P. insularis_, _P. polyandra_, _P. japonica_, and _P. latbenia_ showed similar patterns; however, _P. americana_ had 11-15 fewer SSRs than the other five _Phylotaloca_ species. Additionally, the majority of SSRs were detected in the intergenic spacer (IGS) region, with more SSRs found in the introns of _P. acinosa_, _P. insularis_, and _P. polyandra_, whereas _P. latbenia_ exhibited a higher number of exons. In contrast, _P. japonica_ and _P. americana_ exhibited an equal distribution of SSRs between exons and introns. Mononucleotide repeats were the most abundant SSR type, followed by tetranucleotide repeats, and dinucleotide repeats were the third most common. The highest frequency of tandem repeats was observed in the IGS region of LSC, whereas the SSC region contained the fewest tandem repeats. In the six _Phylotaloca_ species, over half of the tandem repeats were 20-40 bp long, with the 61-81 bp range being the least frequent. The numbers of forward and palindromic repeats in _P. acinosa_, _P. insularis_, and _P. polyandra_ were 21 and 29, respectively. Similarly, in _P. japonica_ and _P. latbenia_, the number of forward and palindromic repeats was found to be 20 and 30, respectively. However, _P. americana_ displayed a distinct pattern, with 17 forward repeats, 32 palindromic repeats, and 1 reverse repeat.
## 3 The Proposed Cascade Control Scheme
### Design of the Feedforward Term  
One of the main contributions of this study is the use of a feedforward term in each control loop in the case of a cascade control scheme for a drill string system. The major benefit of the feedforward term is the provision of a fast dynamic response when there are changes in the angular velocity reference and the WOB. To estimate the feedforward term, the system model should be studied in the steady state without stick-slip vibrations as follows [40]:  
\[\dot{x}_{1}=\dot{x}_{2}=\dot{x}_{3}=\dot{x}_{4}=\dot{x}_{5}=\dot{x}_{6}=\dot{x} _{7}=0 \tag{21}\]  
\[x_{1}=x_{3}=x_{5}=x_{7}=\Omega^{*} \tag{22}\]  
In Equation (22), the feedforward term used in the first control loop is the angular velocity reference.  
\[x_{F}=\Omega^{*} \tag{23}\]  
Then, by substituting Equations (21) and (22) into (4), the feedforward term used in the second control loop is defined as follows:  
\[u_{FF}=(c_{t}+c_{r})\Omega^{*}+k_{t}x_{2}-c_{t}\Omega^{*} \tag{24}\]  
where \(x_{2}\) can be written according to Equations (21), (22), and (4) as follows:  
\[x_{2}=\frac{1}{k_{t}}[-c_{t}\Omega^{*}+(c_{t}+c_{tt})\Omega^{*}+k_{t}x_{4}-c_ {tt}\Omega^{*}] \tag{25}\]  
Equation (16) can simplified as follows:  
\[x_{2}=\frac{k_{t}x_{4}}{k_{t}} \tag{26}\]  
According to Equation (4), \(x_{4}\) in the steady state can be defined as  
\[x_{4}=\frac{1}{k_{t}l}[-c_{tt}\Omega^{*}+(c_{tt}+c_{tt})\Omega^{*}+k_{tt}x_{6} -c_{tt}\Omega^{*}] \tag{27}\]  
The simplified version of Equation (27) is described by Equation (28):  
Figure 5: The proposed hybrid neuro-fuzzy PID controller: (**a**) ANFIS1; (**b**) ANFIS2.  
\[x_{4}=\frac{k_{tb}x_{4}}{k_{tl}} \tag{28}\]  
In addition, \(x_{6}\) should be defined in the steady state as follows:  
\[x_{6}=\frac{1}{k_{tb}}[-c_{tb}\Omega^{*}+(c_{tb}+c_{b})\Omega^{*}+T_{fb}(X)] \tag{29}\]  
where the nonlinear torque on the bit \(T_{fb}\) can be estimated in the case without stick-slip vibrations and in the steady state as follows:  
\[T_{fb}(X)=W_{ob}R_{b}u_{b}(\Omega^{*}) \tag{30}\]  
Finally, the torque feedforward term for the second control loop is given using the following simplified equation:  
\[u_{F}=c_{r}\Omega^{*}+c_{b}\Omega^{*}+W_{ob}R_{b}u_{b}(\Omega^{*}) \tag{31}\]  
Regarding Equation (31), the feedforward term for the second control loop is estimated according to some system parameters, the angular velocity reference, and the WOB. The WOB can be adjusted by the operator.
## 1 Introduction
This makes it possible to undertake comprehensive assessments of thermal damage effects during forest fires, providing a valuable reference for impact zone delineation, evacuation planning, and firefighting decision support.
## 3 Materials and Methods
### Climatic Reconstruction Models Based on Changes in the ELA
#### 3.3.2 Temperature Lapse Rate Model (LR Model)  
The simplest and most widely used method for modeling the degree of paleotemperature change during a period of glacier development based on changes in the glacial ELA is to estimate the vertical rate of decrease in \(T\) in the glaciated area [57; 58; 59]. The temperature lapse rate (\(LR\)) model applies the conversion coefficient \(f\) between the glacier accumulation and \(T\) at the ELA to convert the amount of change in \(P\) due to the rise and fall of the ELA into the corresponding amount of change in air \(T\)[59]. The specific equation [39; 60] is given as:  
\[T_{2}-T_{1}=\frac{\partial T}{\partial z}(\text{ELA}_{1}-\text{ELA}_{2})-f\frac {\partial P}{\partial z}(\text{ELA}_{1}-\text{ELA}_{2})-f(\text{c}_{1}-\text{ c}_{2}) \tag{9}\]  
ELA\({}_{1}\) represents the modern glacier ELA (m) and ELA\({}_{2}\) represents the ELA during the LGM (m). \(T_{1}\) represents the modern temperature (\({}^{\circ}\)C) at ELA\({}_{2}\) and T\({}_{2}\) represents the temperature (\({}^{\circ}\)C) at ELA\({}_{2}\) during the LGM (m). \(\text{c}_{1}\) represents the modern accumulation at the ELA\({}_{2}\)(mm) and \(\text{c}_{2}\) is the accumulation at the ELA\({}_{2}\) during the LGM (mm). \(\frac{\partial P}{\partial z}\) is the precipitation gradient vs. P as it changes with altitude (mm 100 m\({}^{-1}\)) and \(\frac{\partial T}{\partial z}\) is the temperature gradient vs. T as it changes with altitude (\({}^{\circ}\)C 100 m\({}^{-1}\)). \(f\) is the conversion coefficient between the glacier accumulation and T at the ELA (\({}^{\circ}\)C mm\({}^{-1}\)).  
The above two methods are usually combined when conducting climatic reconstructions based on changes in the ELA in order to check one against the other.
## 7 Conclusions  
This study investigated the influence of muscle fatigue on the sEMG-based gait phase classification and found a solution to the performance deterioration of the classifier. We train the classifiers by the non-fatigue data and test them by the fatigue data. Our result shows that muscle fatigue has significant influences on gait phase classifiers, and reveals how muscle fatigue affects the classifiers using different features. By testing on the collected data, our training strategy that adds some fatigue data into the training set is effective. In order to explain the results of the classification, we perform the MANOVA on the features, which suggests that there are significant correlations between the features and muscle fatigue. The study contributes to our understanding of muscle fatigue in the domain of sEMG signals. Our findings can reference other studies in which muscle fatigue makes an impact, and our training strategy can be applied in sEMG-controlled devices to improve their performance under fatigue. However, this study is limited by the absence of an accurate scale of muscle fatigue, and we roughly divide the data into the fatigue set and the non-fatigue set. There still are several questions, such as the influence of muscle fatigue on the multi-feature classifiers, the validation of our training strategy on real devices, and the investigation of fatigue induced in different manners. These questions need to be answered by future work.  
Conceptualization, [PERSON] and C.Y. ([PERSON]); methodology, C.Y. ([PERSON]); software, B.W.; validation, C.Y. ([PERSON]), Z.D. and F.J.; formal analysis, J.Z.; investigation, C.Y. ([PERSON]); resources, Z.D.; data curation, B.W.; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON] ([PERSON]); visualization, [PERSON]; supervision, F.J. All authors have read and agreed to the published version of the manuscript.  
The work of this paper is funded by the project of National Key Research and Development Program of China (No. 2018 YFC0806802).  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Ethics Committee of Chinese Ethics Committee of Registering Clinical Trials (ChiECRCT20200319).  
The authors declare no conflict of interest.
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 1 Introduction
The results show that different delay times are needed to reduce the blasting vibration in different explosion distances. Extensive research has been conducted on blast wave superposition under various conditions. However, there are few studies on the superposition effect of blasting seismic waves at the ground surface. This study is based on published research on the superposition effect of blasting seismic waves, and explores the engineering problems of blasting in tunnels crossing under existing railways. A solution has been proposed to reduce the blasting vibration effect. This solution involves designing a millisecond delay time to ensure that the main phases (especially peak values) of seismic waves generated by different boreholes superpose and interfere with each other when propagating to the protected object. Ultimately, the overall amplitude of the synthesized vibration is significantly reduced. Meanwhile, the propagation and superposition laws of blasting seismic waves at the ground surface were analyzed. The organizational structure of this paper is as follows. Section 2 presents the tunnel project background and details the numerical simulation and monitoring setup, with a validation of the simulation model. Section 3 proposes an interference-based vibration reduction method utilizing blasting seismic wave superposition, investigates the quantitative relationship between optimal millisecond delay time and waveform parameters, and simulates the vibration reduction effects of different superposition approaches. Section 4 analyzes the stress evolution law of surrounding rocks from Class III to Class V. At the same time, optimal delay times for different classes of surrounding rocks are explored.
## 2 Sample Expansion Methodology Based on CVAE-Skegan  
### Architecture of CVAE-Skegan Network  
In actual working conditions, machines operate in normal operations for most of the time, and fault samples are very scarce, resulting in sample imbalance problems. Time-frequency analysis methods have important application values in extracting transient features, conducting fault diagnosis, monitoring equipment status, etc., in mechanical systems. Time-frequency images can reveal rich information on bearing health status. Therefore, time-frequency images of bearing vibration signals are adopted as inputs to the network proposed for sample expansion operations, and pseudo time-frequency image samples are generated to expand fault samples. The structure of CVAE-SkEGAN network is shown in Figure 1, which is divided into three parts: encoding network, generator, and discriminator.
# Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features
## 3 Experimental Results  
In this section, results of the computer experiment are presented. The experiment compared the ML algorithms LR, OLR, ANN\({}_{10}\), ANN\({}_{100}\), and SVM and it was performed using \(CAV\), \(R_{CAV}\), and \(\Delta_{CAV}\) as features to determine the ideal features and ML algorithm. Two different training and test data sets were used where the first training set (TR-1) was a randomly sampled 85% of Set-1 data as the training set and the remaining 15% as the first test set (TE-1), i.e., both training and test were performed with data from the same distribution, which is the usual practice in ML. The second training set (TR-2) was the entire Set-1 while the second test set (TE-2) was Set-2, to test the robustness of the features using data from a different distribution than that of the training data.  
As the performance parameter, the test accuracy was computed as follows,  
\[\text{Accuracy}=\frac{TP+TN}{TP+FP+TN+FN}\times 100\%, \tag{18}\]  
where \(TP\) is True Positive, \(TN\) is True Negative, \(FP\) is False Positive, and \(FN\) is False Negative. In these definitions, Positive and Negative imply damaging and undamaged events, respectively. Tables 1 and 2 report the accuracy achieved by each model for TE-1 and TE-2, respectively. Table 1 shows that the highest accuracy of 91.05% was achieved by the SVM model with \(CAV\) and \(R_{CAV}\) as features. ANN\({}_{100}\) and OLR achieved accuracy values of 91% and 90%, respectively, with the same features. As expected, when the dataset was of a big size and the data were representative of the population (i.e., training and test sets came from the same target unknown distribution), the results were not significantly affected by the choice of the ML algorithm. On the other hand, when the ML algorithms were tested over a different set of data, their predictive capabilities were significantly reduced (Table 2). For this latter case, the accuracy reduced for each model, where OLR achieved the highest accuracy of 74.14% with \(CAV\) and \(R_{CAV}\) features. This was due to the fact that ANN and SVM over-fitted the data of the training set and had poor generalization capabilities over the response of different ground motions. Moreover, the simpler models of LR and OLR had better generalization capabilities.  
From Table 2, one observes that the OLR was the most robust algorithm for making predictions about events not available in the training set. Moreover, in both test sets, satisfactory performances were obtained using \(R_{CAV}\), while the best results were achieved using both \(CAV\) and \(R_{CAV}\) as features. For this reason, along with the fact that the computational demand of OLR was significantly lower than that of the ANN or SVM, the ML algorithm OLR with features \(CAV\) and \(R_{CAV}\) was concluded to be suitable for vibration based damage detection tasks of instrumented buildings.