esa-sceva/satcom-chunk-collection huggingface dataset:
# Surface Reconstruction Planning with High-Quality Satellite Stereo Pairs Searching
## 7 Conclusions and Future Work  
This paper addressed the challenge of satellite stereo image acquisition for high-quality 3D surface reconstruction, whose planning is not well addressed. By proposing a quality-driven planning method, this paper has demonstrated that the selection of stereo pairs can be significantly improved through a combination of regression-based quality estimation and multi-objective optimization. The proposed method leverages a CatBoost regression model with feature enhancement and selection to predict DSM quality based on various factors including intersection angle, sun elevation angle, sun azimuth angle, time intervals, etc., to enable accurate quality estimation before image acquisition. Furthermore, the quality-driven planning is modeled and solved using the Grey Wolf Optimizer and can effectively balance different requirements like task quantity, reconstruction quality, and task timeliness, ensuring efficient use of satellite resources. Experimental results on the public 3D mapping dataset and simulations validate the effectiveness of the proposed approach. Future work can be to extend the method to more flexible collaborative stereo sensing in larger constellations.  
Methodology, J.L., G.R., Y.P., J.S. and P.W.; Formal analysis, J.L., G.R., Y.P., J.S., P.W., F.X. and Z.L.; Investigation, J.L., G.R., Y.P., J.S. and P.W.; Resources, F.X. and Z.L.; Data curation, J.L., G.R., Y.P., J.S. and P.W.; Writing--original draft, J.L., G.R., Y.P., J.S. and P.W.; Writing--review & editing, J.L., G.R., Y.P., J.S. and P.W.; Supervision, F.X. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61973313) and Key Project of Basic Research in Institute of Software (ISCAS-JCZD-202309).  
The data presented in this study are available upon request.  
The authors declare no conflicts of interest.
## 6 Conclusions  
The objective of this paper was to use and analyze the accuracy of individual learners (MLPNN, DT) with ensemble learners, using techniques such as bagging, boosting and Python Anaconda. In this approach, the data was trained and tested and a high correlation was obtained between predictions and targets. The following conclusions are drawn from our analysis:  
1. The result of individual learners, DT and ANN, showed a strong correlation between predictions and targets with R\({}^{2}\) = 0.80 and R\({}^{2}\) = 0.77, respectively. However, ensemble learner with bagging and boosting and mostly boosting with Adaboost for DT outburst from the individual learner produced a stronger correlation R\({}^{2}\) = 0.899, and ANN with bagging also produced a stronger correlation R\({}^{2}\) = 0.833. 2. Optimization of ensemble models was conducted with 20 models ranging from 10 to 200 estimators (sub-models). A decision tree with boosting (ensemble = 130) and random forest (ensemble = 130) provided a robust strong correlation with R\({}^{2}\) = 0.89. 3. It is evident that using ensemble learner with a weak learner showed less average error compared to the individual learner. Furthermore, K-fold cross-validation was used to validate models with coefficients of correlation, mean square error, and root mean square error. All the models had low MAE and RMSE errors and a high correlation R\({}^{2}\). Fluctuations in validation were noticed, using K-fold validation to acquire data in steps and then performing validation on unknown data. 4. Statistical analysis was also performed by means of MAE, MSE, RMSE, and MSLE. All ensemble learners produced less error compared to the individual learner, with random forest bagging giving a lesser error compared with MAE, MSE, RMSE, and MSLE. RF and AdaBoost are supervised learning algorithms that yielded strong relationships between prediction and targets. Y.S. : conceptualization, writing--original draft and validation; J.Z. : data curation, and review; K.A.O. : writing--revised draft and supervision; M.F.J. : software, writing and review; A.A.: resources and writing--revised draft; M.I.K. : formal analysis and investigation; F.A. : visualization and editing; R.K.: writing--revised draft. All authors have read and agreed to the published version of the manuscript. This research did not receive external funding. Not applicable. All data generated or analyzed in this research are included in this published article. The authors declare no conflict of interest.
# Double Trapezoidal Wave Transmitting System with Controllable Turn-Off Edge
## 3 Double Trapezoidal Wave Transmitting System
### Absorption Circuit  
When the passive clamping process reaches T2, the tail of the turning-off edge will display obvious current oscillation. The analysis showed that it is caused by the characteristics of the transmitting coil. In this process, resistive (\(Rc\)), inductive (\(Lc\)), and stray capacitance (\(Cs\)) of the transmitting coil constitute the RLC loop. Due to the small resistance value of \(Rc\), under a damped RLC, oscillation is inevitable in the loop. This will seriously affect the early signals of the secondary magnetic field [18; 19]. The primary field caused by the current oscillation will also make the curve received by the sensor show a negative sign, resulting in a false polarization phenomenon. It is necessary to adopt reasonable optimization technology to reduce or eliminate the current oscillation.  
Usually, the method of matching impedance R in parallel at both ends of the transmitting coil is used to suppress the oscillation phenomenon in the late turn-off period of the emission current [20]. The absorption process is:  
\[\left\{\begin{array}{c}i(t)=C_{\mathrm{s}}\frac{d\mathrm{L}_{\mathrm{C}}(t)}{ dt}+\frac{U_{\mathrm{C}}(t)}{R}\\ R_{\mathrm{c}}i(t)+L_{\mathrm{c}}\frac{d\mathrm{i}(t)}{dt}+U_{\mathrm{C}}(t)=0 \end{array}\right. \tag{8}\]  
If the circuit is to be in a non-underdamped state and the oscillation is effectively absorbed, the matching resistance R is required:  
\[\mathrm{R}\geq\frac{R_{\mathrm{c}}L_{\mathrm{c}}C_{\mathrm{s}}+2L_{\mathrm{c} }\sqrt{L_{\mathrm{c}}C_{\mathrm{s}}}}{\left(R_{\mathrm{c}}C_{\mathrm{s}} \right)^{2}-2L_{\mathrm{c}}C_{\mathrm{s}}} \tag{9}\]  
However, in the current turn-off process, due to the existence of matching impedance dissipating part of the energy in the circuit, the TVS diode cannot be kept in a constant voltage clamp state for a long time. The current presents an approximately exponential decay state, which will significantly reduce the decline linearity. To avoid this situation, an absorption circuit is designed based on the characteristics of passive clamp current control. The absorption circuit consists of MOSFETs (S9/S10) and an absorption resistance R, as shown in Figure 6.  
After the current is off, during the constant voltage clamp, S9 and S10 are controlled to open accurately by the timer of the main control circuit, and the matching impedance R is cut into both ends of the transmitting coil in parallel. Because the TVS diode is in short-c  
Figure 6: The absorption circuit.  
matching impedance does not affect the clamping process. Until the current is about to drop to zero, the TVS diode turns to open. The matching impedance, the coil, and the stray capacitance constitute the non-underdamped circuit, and the current begins to decline exponentially until it is zero.
## 2 Background and Motivation
### Typical SFC Environments
#### 2.2.1 The Gi-LAN Mobile Core Network  
A typical environment in which service function chaining is deployed is the Gi-LAN network, which is a component of mobile networks used by operators to provide fine-grained user-specific services such as traffic optimisation, DPI, and firewalls [39]. Gi-LAN implementation by mobile network operators is an emerging use case for SFC architecture [40]. These services are often chained together and are provided by multiple vendors, and the traffic is steered to the right service functions, which is aimed at meeting service level agreements and policy enforcement [41].  
Figure 3: IETF SFC Architecture Components.  
The ability to add or remove a service becomes easier along the processing pipeline, as per-user services can be created for mobile data monetisation [42]. In terms of implementation requirements, implementing a service chain that contains a network function such as NAT, for example, requires that the function is placed on the edge sites, which is closer to the users requesting the service [43]. Another useful requirement when creating a service chain in a mobile core environment is the consideration of the SLA agreement between multiple vendors. For packet classification requirements, the classifier should be located at the packet gateway. Figure 4 depicts a high-level description of the SFC in mobile networks, with service functions deployed in the Gi-LAN segment of the network.
## 3 The Proposed Pattern Recognition Method  
In this section, a novel pattern recognition method, based on LMD and FCM, is presented. Firstly, the LMD method is used to preprocess the measured vibration signals, producing a set of PF components. Then, the optimal PF is selected according to the Kullback-Leibler divergence values between each PF and original signal. Following this, the features of the original signal and the optimal PF are extracted, using the time-frequency statistical parameters. Finally, an improved Laplacian score algorithm is proposed, in order to rank the extracted features, and appropriate features are selected. The obtained new feature vectors are the inputs of FCM, which cluster the different cutting statuses of the shearer. The flowchart of the proposed method is shown in Figure 1.  
Figure 1: Flowchart of the proposed method.
# Distance-Based Resource Allocation for Vehicle-to-Pedestrian Safety Communication
## 5 Reinforcement Learning Based Power Allocation
### Proposed Methodology  
In this section, we will solve the optimization problem presented in Section 3 using the distance-based classification in Section 4 to find the optimum power. A decentralized approach, where each user maintains its own Q-values table has been used since a centralized approach would cause an excessive overhead [23]. Firstly, we need to define the states, agents, actions, and reward function in our scenario.  
_Agent_: The V2P users will act as agents. We have \(v\) numbers of users engaged in V2P communication. Therefore, \(1\leq v\leq V\) number of agents in the network.  
_State_: User \(v\) in a state \(S\), on resource block \(x\), at time \(t\) with an interference level of \(I_{t}^{x}\) is given by:  
\[S_{t}^{v,x}=\left\{I_{t}^{x}\right\} \tag{11}\]The interference level is given by:  
\[I_{t}^{x}=\left\{\begin{array}{cc}1&\gamma_{c}\geq\gamma_{0}\\ 0&\text{otherwise}\end{array}\right. \tag{12}\]  
The equation above guarantees the minimum required SINR for the cellular users.  
_Action_: The actions consists of the uplink power of the V2P users.  
\[P=\left(P_{1}^{v},P_{2}^{v},.........P_{n}^{v}\right) \tag{13}\]  
where the subscript \(n\) denotes that we have '\(n\)' number of powers.  
_Reward Function_: The reward function consists of our objective function, that was mentioned in Section 3.  
\[R^{v2p}=\left\{\begin{array}{cc}\frac{1}{c}\log_{2}(1+\gamma_{v}),&\gamma_{c }\geq\gamma_{0}\\ 0,&\text{otherwise}\end{array}\right. \tag{14}\]  
_Learning Rate and Discount Factors_: We have used a constant learning rate \(\alpha=0.5\). While we used three different discount factors (\(\eta=0.8\), \(0.5\), \(0.3\)) based on the vulnerability of the users.
## 2 Theory
### Theory of Directional Point Source Equivalence
\begin{cases}0&k\
eq l\\ \frac{2}{2l+1}\cdot\frac{(l+n)!}{(l-n)!}&k=l\end{cases} . \tag{6}\]  
Both sides of Equation (5) are multiplied by \(\cos(m\varphi)\) and \(\sin(m\varphi)\), and the points of \(\varphi\). The value of \(A_{nl},B_{nl}\) can be obtained through the above calculation process. \[ \begin{array}{c}A_{nl}=\left\{\begin{array}{cc}\int\limits_{-\pi}^{\pi} \int\limits_{0}^{\pi}\psi(r_{0},\theta,\varphi)\cdot P_{k}^{n}(\cos\theta) \cdot\sin\theta\cdot\cos m\varphi d\theta d\varphi\cdot\frac{2l+1}{2}\cdot \frac{(l-n)!}{(l+n)!}/\left(\pi\cdot h_{l}^{(2)}(kr_{0})\right)&m=n\
eq 0\\ \int\limits_{-\pi}^{\pi}\int\limits_{0}^{\pi}\psi(r_{0},\theta,\varphi)\cdot P _{k}^{n}(\cos\theta)\cdot\sin\theta\cdot\cos m\varphi d\theta d\varphi\cdot \frac{2l+1}{2}\cdot\frac{(l-n)!}{(l+n)!}/\left(2\pi\cdot h_{l}^{(2)}(kr_{0}) \right)&m=n=0\\ 0&m\
eq n\end{array}\right.\\ B_{nl}=\left\{\begin{array}{cc}\int\limits_{-\pi}^{\pi}\int\limits_{0}^{\pi} \psi(r_{0},\theta,\varphi)\cdot P_{k}^{n}(\cos\theta)\cdot\sin\theta\cdot\sin m \varphi\cdot\frac{2l+1}{2}\cdot\frac{(l-n)!}{(l+n)!}/\left(\pi\cdot h_{l}^{(2 )}(kr_{0})\right)&m=n\
eq 0\\ 0&m=n=0,m\
eq n\end{array}\right.\\ \end{array}  \tag{7}\]  
where the orthogonality of the trigonometric function is expressed as  
\[\int_{-\pi}^{\pi}\cos n\varphi\cdot\cos m\varphi d\varphi= \begin{cases}0&m\
eq n \\ \pi&m=n\
eq 0\\ 2\pi&m=n=0\end{cases} ,\]  
\[\int_{-\pi}^{\pi}\sin n\varphi\cdot\sin m\varphi d\varphi= \begin{cases}0&m\
eq n,m=n=0\\ \pi&m=n\end{cases} .\]  
By substituting coefficients \(A_{nl}\) and \(B_{nl}\) into Equation (2), the function expression of sound scattering pressure \(\psi\) and the sound scattering pressure of any field point can be calculated. The sound pressure field of a directional point source can be expressed as \(P_{0}(\theta,\varphi)/\tau\cdot e^{-jkR}\), as shown in Figure 1b. In far field \(r=R\), let \(\psi(R,\theta,\varphi)=P_{0}(\theta,\varphi)/R\cdot e^{-jkR}\) to obtain \(P_{0}(\theta,\varphi)\). That is, the sound scattering field of the elastic structure is equivalent to that of a directional point source.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 8 Future Research  
While numerous factors have been identified as contributing to _H. pylori_ biofilm formation, the precise mechanisms and key targets for eradication remain unclear. Future research should integrate \"the bench and the bedside \"approach, integrating laboratory-based studies with clinical trials to bridge the gap between theoretical knowledge and practical applications. While numerous factors have been identified as contributing to _H. pylori_ biofilm formation, the precise mechanisms and critical targets for eradication remain unclear. Advances in omics sequencing, bioinformatics, and machine learning can accelerate the identification of potential drug targets by analyzing the interactions between antibiotics or antib biofilm drugs and bacterial proteins. These predicted targets can then be validated through wet lab experiments.  
Furthermore, the role of dispersal systems in biofilm removal and the involvement of outer membrane vesicles (OMVs) in mutation, horizontal gene transfer, and biofilm induction require further exploration. The efflux pump, which commonly occurs in biofilms and mainly contributes to antibiotic resistance, still has limited causal proof [120]. Further research is necessary to explore the potential of modifying the extracellular polymeric substance (EPS) matrix as a novel approach to disrupt established biofilms and enhance bacterial susceptibility to antibiotics [82].  
In clinical settings, the unstable nature of _H. pylori_ biofilms and their location within the gastric pit can pose challenges for visualization and studying their impact on patient outcomes. However, evaluating biofilm presence in patients with multidrug-resistant or highly resistant strains is crucial to establish a direct link between biofilms and clinical outcomes. A study involving clinical subjects could facilitate the investigation of multispecies biofilms from the gastric microbiome. Clinical trials for alternative drugs and delivery systems targeting _H. pylori_ biofilms are essential for translating laboratory findings into effective treatments.
## 8 Conclusions and Future Work  
Creating a common semantic representation for multilingual languages is an essential goal of the NLP community. To facilitate multilingual sentence representation and semantic interoperability, this research presented an MParser for parsing local language sentences and providing a common understanding across the heterogeneous sentence. MParser converts complex concepts into a computer-readable and -understandable universal sentence for any simple multilingual sentence. This approach has provided a universal grammatical feature such that any sentence can be processed as a bag of concepts and refer to any term of a natural language. Additionally, it has laid a theoretical foundation for enabling humans and computers to understand sentences semantically through unique _iid_ and _eid_.  
In the future, we plan to apply the approach to more real-world applications. For example, we will conduct research on how to achieve content persistence during construction of the Metaverse [44] by proposing a content-level persistence maintenance model since the ambiguity of the language, the use of synonyms to express a single idea, creates problems. In the blockchain, we will explore the question of how to achieve semantic interoperability between IoT devices and users [45]. In the field of smart contracts, we will study the cross-context issues of smart contracts between unknown business partners such as developers or anybody who even comes from different backgrounds or languages. Since language barriers prevent cross-language searches, most users do not have easy access to most of this [46]. Moreover, it also will be necessary to extend the research, including semantic inference on extracted meaning. We hope that our novel method will inspire the community to integrate various functions into our work.  
Conceptualization, P.Q. and J.G.; methodology, P.Q., J.G., W.T. and B.S.; software, P.Q.; writing--original draft preparation, P.Q.; writing--review and editing, W.T., B.S. and Q.T.; supervision, J.G.; project administration, J.G.; funding acquisition, J.G., Q.T. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by the University of Macau Research Grant No. MYRG2017-00091-FST, MYRG2019-00024-FST and FDCT-NSFC Grant No. 0004/2019/AFJ.  
Not applicable.  
Figure 12: An illustration on semantic consistency.  
**Data Availability Statement**: Not applicable.  
**Acknowledgments**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 4 Results
### A Single-Case Analysis
#### 4.1.2. Matching \(RF\_SI\) to MWTS2 and MWTS3 Pixels
This may be because, despite being covered by convective clouds, the scattering signals derived from the hydrometeors within these areas do not exceed the _CLWP_ threshold. In contrast, the _RF_SI_ method is highly sensitive to convective clouds, often classifying most areas with convective cloud coverage as precipitation. This heightened sensitivity is attributed to the incorporation of the VISSR cloud product as an input feature during the machine learning training process of the _RF_SI_ method. The sensitivity analysis results indicate that convective clouds are most importance to the _RF_SI_ method. To quantitatively assess the precipitation detection capabilities of the _RF_SI_ method, this study employs the _CLWP_ detection outcomes as the 'ground truths'. Consequently, the _ACC_ of the _RF_SI_ method is calculated to be 0.94, the _POD_ is 0.95, and the _FAR_ is 0.04. In summary, compared with the _CLWP_ method for MWTS3, the _RF_SI_ method demonstrates better performance in terms of detecting precipitation from convective typhoon cloud systems, and it may be suitable for applications involving precipitation detection endeavors for MWTS2. Furthermore, the _RF_SI_ method is capable of excluding more data from convective cloud areas than the _CLWP_ method is, and the impact of these data on the resulting assimilation and forecasting outcomes warrants further investigation in future studies.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 3 State-of-the-Art
### Challenges in Industrial Environments
#### 3.3.4 NB-IoT in Industries  
Areas that are difficult to cover with NB-IoT devices experience bad channel conditions and to mitigate this, rigorous re-transmission or repetition is required. This process requires more energy, which, in turn, reduces the battery life of the devices. More so, there is a restriction regarding large numbers of devices being deployed, which is a result of the unclear performance metrics, such as coverage range, throughput, etc., required in an NB-IoT-supported deployment (guard-band, in-band and standalone) [17]. In [106], the authors analyzed the security issues faced by NB-IoT in a layered manner, which provides substantially different security attack proposals on these layers. NB-IoT technology is applicable in most aspects of an industrial operation; however, to some extent, with wireless deployment, the expectations of factory enhancement fail, especially in the areas of latency, reliability, and resilience. Reference [107] provides problem category classification where the deployment of network technologies is achievable. Generally, intensive interference, high noise levels, and shadowing are some of the factors that make the industrial environment harsh to radio or wireless communications. To overcome these, Reference [108] presented measurements for path-loss and shadowing at 900, 1600, and 2450 MHz to enable proper wireless channel design for industrial setups. The future applications of NB-IoT in health industries as well as the challenges faced are discussed in [109]. To harvest the full potential of next-generation cloud computing, Reference [110] studied the possible challenges that need to be overcome, infusing cloud computing infrastructure, connecting people and devices through IoT.
## 2 Experimental Design
### Experimental Setup  
The experimental platform for fatigue hole-edge crack damage detection contained three major parts: an optical sensing and data acquisition system, a fatigue crack measurement system, and a fatigue load-cycling system, which are shown in Figure 2. FBG sensors (FSSR5025) manufactured by the Changcheng Institute of Metrology and Measurement (Beijing, China), were used to monitor the crack propagation behavior. The optical demodulator system (SM125, Micro Optics Inc., Danbury, CT, USA) was adapted to record the reflection spectrum at various crack lengths from 1 to 30 mm, and the measurement accuracy was 1 \(\mu\)\(\varepsilon\). Regions vulnerable to fatigue crack damage were monitored with a traveling optical microscope and a charge coupled device (CCD) camera during loading, and the discovered crack lengths were defined as the true crack lengths. The fatigue cycling load testing was conducted using a hydraulic Mechanical Testing & Simulation (MTS) machine at room temperature, and the constant amplitude tensile loading was applied to the bottom of the plate, with a fixed top boundary. The amplitude was 80 MPa, the stress ratio was 0.1, and the load frequency was 5 Hz. In addition, the fatigue testing experiment was paused for data acquisition, and the processing was repeated twice to eliminate the operation error during each of the pauses. The overall experimental setup is shown in Figure 2. The experiment signal data were investigated to evaluate the effect of the de-noising algorithm and to monitor the size and location of the crack damage.