esa-sceva/satcom-chunk-collection huggingface dataset:
## 14 Towards Next-Generation Composite Structural Systems: Research Perspectives
### Machine Learning-Based Models  
Recent advancements in machine learning have demonstrated significant potential for predicting the seismic performance of steel-concrete composite structures [1; 2; 3; 4; 5; 6]. [PERSON] et al. [79] utilized a random forest with firefly algorithm optimization to estimate the seismic performance of recycled aggregate concrete-filled steel tube columns, achieving high correlation coefficients. [PERSON] et al. [150] developed ensemble machine learning models for bond stress estimation, employing Bayesian optimization and SHAP interpretability techniques to enhance predictive accuracy. [PERSON] et al. [15] introduced a multi-indicator percussion method integrated with machine learning, achieving an exceptional 99.6% accuracy in interfacial debonding detection using key damage indices. [PERSON] et al. [149] applied an extremely randomized trees algorithm to analyze damage sensitivity in steel-concrete composite beam bridges, identifying microcracks in steel beams as the most critical performance factor. [PERSON] et al. [15] developed a backpropagation neural network for predicting high-strength bolt connector strengths, achieving an over 93% goodness of fit. [PERSON] et al. [6] proposed a novel blending fusion model with generative adversarial network augmentation for identifying failure modes of steel tube-reinforced concrete shear walls, improving prediction accuracy by an average of 13-81% across various metrics.  
In conclusion, machine learning-based approaches represent a transformative paradigm in seismic performance prediction, offering unprecedented accuracy, interpretability, and insights into the complex behavior of steel-concrete composite structures under extreme loading conditions.
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 1 Introduction
Among these, P4 is a protocol-independent, high-level programming language that enables programmers to modify how SDN switches process data packets. By maintaining the status of forwarding packets with P4, 5G NS can both support a variety of communication protocols and data transmission mechanisms and achieve independent monitoring, control, and management. Moreover, P4 implements a top-down design concept; the programmer or user determines the function of the underlying switch and how it should process packets, and the underlying communication protocol does not limit the services that the network can support. In order to provide the heterogeneous service quality requirements in a 5G network and to achieve easy management, scalability, and high responsiveness to variations in network conditions, this paper proposes a framework of software-defined networking with P4 switches (SDNPS) to support both mMTC and URLLC in 5G network slices. The main contributions of the proposed SDNPS are the design of an SDN framework with P4 switches and defining a packet format containing in-band network telemetry (INT) [23] data that simultaneously support massive IoT and URLLC traffic in the core of 5G network slices. Thus, the extremely different service requirements of mMTC and URLLC can be satisfied, and the control load on the SDN controller can be alleviated. We assume that all of the switches in the data plane would support P4 and divide the forwarding rules of the P4 switches into two parts: one for mMTC and one for URLLC services. In the SDNPS, INT enables data packets to carry network status information such that P4 switches on the forwarding path can update the included status information before forwarding packets, thereby reducing the control load on the SDN controller. To the best of our knowledge, this paper is the first to implement 5G NS in SDN with P4 switches to support both mMTC and URLLC. Finally, we perform simulations of 5G NS on SDNs with traditional or P4 switches to demonstrate the effectiveness and superiority of the proposed SDNPS. The performance measures include the average throughput, packet loss ratio, and packet delay for the mMTC and URLLC network slices. The remainder of this paper is organized as follows. Section 2 explores the related literature. Section 3 describes the framework and operating procedure of our proposed SDNPS. Section 4 analyzes and compares the simulation results. Finally, concluding remarks and future works are given in Section 5.
## Results and Discussion
### Vibration Spectroscopy
#### 3.2.3 FT Raman  
The average normalized FT Raman spectra of purified basidiocarps are represented in Figure 1c. Bands at 1455-1461 and 1367-1374 cm\({}^{-1}\) were assigned to bending vibrations of CH\({}_{2}\) and CH\({}_{3}\) groups in proteins and polysaccharides [34; 48]. Intense bands at 1106-1120 cm\({}^{-1}\) arose mainly from the C-O and C-C stretching vibrations of polysaccharides. The sharp band at 894 cm\({}^{-1}\) indicated the \(\beta\)-anomeric configuration of monosaccharide units in \(\beta\)-p-glucans and chitin, while bands near 930 and 850 cm\({}^{-1}\) were characteristic for \(\alpha\)-anomeric configuration in \(\alpha\)-p-glucans [14; 26]. However, the tyrosine doublet bands near 830 and 850 cm\({}^{-1}\) overlapped the latter band of \(\alpha\)-glucans [48; 49]. The band at 420-428 cm\({}^{-1}\) was assigned to skeletal vibrations of polysaccharides, so it could be used as non-specific marker of glucans [49]. The bands near 1670 cm\({}^{-1}\) (amide I) and at 1260-1267 cm\({}^{-1}\) (amide III) indicated the  
Figure 1: Average normalized spectra of sixteen _P. ostreatus_ basidiocarp samples: (**a**) Fourier-transformed (FT) mid-infrared (FT MIR), (**b**) FT near-infrared (NIR) and (**c**) FT Raman. The asterisk indicates the peak used to normalize the spectra.  
presence of proteins [48]. In addition, two bands near 1130 and 1004 cm\({}^{-1}\) were assigned to vibrations of phenylalanine moieties; the latter band is often used as a reference for proteins. The broad band centered near 1604 cm\({}^{-1}\) (C=C stretching) indicated unsaturated and aromatic compounds, which were still present in some samples.
## 2 Design of Molten Salt Swing Check Valve  
### Structural Design of Molten Salt Swing Check Valve  
The combination of tower technology and molten salt heat absorption and storage technology gives the tower-type molten salt solar thermal power plant the comprehensive advantages of high solar thermal efficiency, 24 h continuous power generation, and commercial operation on a large scale [27]. Figure 1 illustrates the tower-type collector solar thermal power generation process, which consists of three parts: the heliostat field, the molten salt system, and the power generation system.  
In the molten salt system, the molten salt pump transports the high-temperature molten salt to the steam generation system. The high-temperature molten salt check valve is used at the outlet end of the molten salt pump to prevent the backflow of molten salt, protect the molten salt pump, piping, and related supporting facilities, and ensure the safe and reliable operation of the system.  
Molten salt has properties such as high operating temperature, high freezing point, large operating temperature variation and high corrosiveness. These characteristics place high demands on the design, manufacturing, and production of molten salt valves [28].  
The molten salt valve is designed according to the molten salt system circuit parameters, in particular the DN100 PN100 high-temperature molten salt swing check valve structure, as shown in the schematic diagram in Figure 2, mainly in reference to the valve body, valve seat, valve motion components, sealing components and other components. According to the special needs of high-temperature molten salt media, the valve design in the valve body material uses wear-resistant, high-temperature austenitic stainless steel ASTM A351-CF8C. The valve seat uses 5\({}^{\circ}\) inclined sealing surface and valve self-weight to increase the closing sealing force.  
The pressure seal structure is designed so that the higher the internal pressure is, the greater the sealing force will be. Easy dismantling is made possible by dropping the Bonnet assembly into the body cavity and driving out the four-segmental thrust rings by means of a push pin. An internal hanging structure and butt welding connection can effectively reduce the leakage point.  
The \"herringbone\" valve body flow channel structure is designed to have a good one-way flow which does not easily cause molten salt accumulation at the bottom groove and inflection point. The integrated design of the valve body avoids thermal deformation and thermal stress concentration caused by uneven heating.
## 4 Result Analysis
### Statistical Test Results  
To solve the problem of selecting the best model, statistical significance tests were used. The Friedman test was used in this study to compare machine learning classifiers. The Friedman test results are shown in Tables 6 and 7. These result indicate that the \(p\)-value is 0.0112. The null hypothesis is rejected because the \(p\)-value for classifier accuracy data is less than the significance level of 0.05, indicating that the classifiers perform differently.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Friedman Test** & **Degrees of Freedom** & **Chi-Square** & \(p\)**-Value** \\ \hline
3 & 11.1 & 0.0112 \\ \hline \hline \end{tabular}
\end{table}
Table 6: [PERSON] test results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Number of** \\ **Selected Features** \\ \end{tabular} & **Feature Names** \\ \hline FPA & 21 & Age, BMI, hypertensive, atrial fibrillation, depression, Renal failure, COPD, heart rate, SP O2, hematocrit, RBC, MCV, MCHC, platelets, blood sodium, creatine kinase, creatinine, urea nitrogen, blood calcium, chloride, and magnesium ion \\ \hline PSO & 17 & Hypertension, atrial fibrillation, depression, COPD, heart rate, systolic blood pressure, temperature, hematocrit, MCH, RDW, lymphocyte, urea nitrogen, anion gap, PH, bicarbonate, lactic acid, and PCO2 \\ \hline GA & 12 & Age, BMI, systolic blood pressure, diastolic blood pressure, respiratory rate, hematocrit, MCH, MCHC, neutrophils, PT, blood sodium, and lactic acid.
# Time-Specified Adaptive Robust Control Framework for Managing Nonlinear System Uncertainties
## 2 Background and Related Work
### Finite-Time Control  
Finite-time control is a control approach aimed at ensuring a system reaches a specific target, such as achieving zero stability, within a fixed period. The objective of finite-time control is to design a control strategy that ensures the system's state converges to the desired state within a predetermined and finite time frame. This method commonly utilizes Lyapunov stability theory in conjunction with appropriate control strategies to guarantee convergence within the set time. The Lyapunov function frequently employed in such approaches is  
\[V(x)=\frac{1}{2}x^{T}Px \tag{3}\]  
where \(P\) represents a positive definite matrix. The control law is developed to ensure that the time derivative of the Lyapunov function is negative, thereby meeting the conditions for finite-time convergence.  
Finite-Time Control offers the advantage of ensuring that the system reaches the desired state within a fixed period, making it especially useful for control tasks that demand quick responses. However, for systems with considerable uncertainties, a more robust control design may be required to achieve finite-time convergence. This approach is extensively used in fields where fast and precise responses are essential, including flight control, mobile robotics, and process control.
###### Abstract
On the other hand, long-term time-series NDVI dataset from AVHRR have also revealed significant greening trends of vegetation (based on seasonal NDVI integrals) in the NH from the early 1980s to the late 1990s [7; 17; 18; 20], which have been interpreted as an increase in photosynthesis and vegetation productivity through terrestrial ecosystem models [20; 27; 28; 29]. Thus, the observed 'northern greening' has been central for an increasing carbon sink in the northern ecosystems over the past 30 years [16; 30; 31; 32; 33; 34]. A linkage between changes of LOS and changes in vegetation productivity would be expected, as changing LOS implies changing the duration available for vegetation photosynthesis. In the context of global warming, this changing ecosystem functioning is observed and is expected to impact biomass accumulation of terrestrial ecosystem, and thereby regional and global carbon balances [35; 36; 37; 38; 39; 40; 41; 42]. It has been suggested that longer LOS may extend the period of net carbon sequestration and eventually enhance vegetation productivity in northern ecosystems [38]. Moreover, [PERSON] et al. [41] found that forest phenology derived from carbon flux measurements was closely correlated to both gross and net ecosystem productivity. However, remotely sensed based studies of changes in vegetation phenology and biomass have primarily been conducted independently; thereby disregarding that synthesizing the two research themes might reveal novel information on changes in plant functional traits, ultimately to be associated with imprints from anthropogenic land management. Whether a given area is characterized by either converging or diverging trends in LOS and productivity may be induced by the interplay between human disturbances (land use and land cover changes) and climatic drivers. Analyzing the combined spatio-temporal dynamics of such trends can therefore improve our current understanding of the expected complex relationship between anthropogenic and climatic drivers on changes in vegetation functioning during recent decades. We focused on northern hemisphere ecosystems (>30\({}^{\circ}\)N) (Figure 1), which are characterized by vegetation with clear seasonality. The response of vegetation to global warming is highly sensitive in this region, and northern ecosystems are expected to continue growing at a faster rate and thus accumulating more carbon in a warmer future [16; 20]. This study investigates the relationships (long-term trends and correlations) between LOS and vegetation productivity in the NH (>30\({}^{\circ}\)N) using third generation GIMMS NDVI dataset for a 32-year period (1982-2013) by analyzing converging/diverging patterns in trends in LOS and the growing season NDVI integral as a function of major land biomes at hemispheric and continental scales.
## 1 Introduction  
Minimally invasive restorations have become popular among patients demanding high esthetics. The preparation of the teeth for all-ceramic restorations is usually restricted to enamel [1]. However, dentin exposure is common. The retention of minimally invasive restorations highly depends on the adhesive properties of luting cement rather than the geometric configuration of the preparation [2]. Thus, optimal adhesion between the tooth and restoration is crucial.  
Two different techniques have been described in the literature regarding the timing of the dentin-bonding agent (DBA) application [3]. The conventional procedure consists of the application of a DBA just before the final cementation of the restoration after the etching of the dentin with phosphoric acid; hence, this is called delayed dentin sealing (DDS). The other technique involves the immediate application of DBA upon exposure of the dentin during the preparation session before provisional restoration application and impression-taking, which is called immediate dentin sealing (IDS) [4]. With this technique, an improvement in bond strength to the dentin can be achieved by preventing the contamination of dentin surfaces and the collapse of the unpolymerized hybrid layer using a total-etch adhesive [1; 5; 6; 7; 8; 9; 10; 11]. To achieve optimal adhesion between the restoration and dentin surface, the infiltration of DBA into the dentinal tubules and the formation of resin tags (hybridization) are mandatory, independent of the preferred bonding technique [10; 12]. The clinical performance of a restoration-tooth complex is strongly dependent on the adhesive procedure [13; 14; 15].  
Try-in pastes are glycerin-based hydrophilic substances that can be used for determining a colour match to the final polymerized cement. Using try-in pastes before the final cementation of all ceramic restorations may prevent further colour mismatches [16]. Manufacturers recommend cleaning the dentin and intaglio surface of the restoration with an oil-free air-water spray or running tap water [8]. However, the application of try-in pastes on dentin may contaminate the surface by infiltrating into the dentinal tubules due to its hydrophilic nature.  
This study aimed to evaluate try-in paste contamination on the shear bond strength (SBS) of lithium disilicate glass-ceramics to the dentin surfaces treated with immediate and delayed dentin sealing techniques. The null hypothesis to be tested was that there would be no difference in terms of SBS between lithium disilicate glass-ceramics and try-in-paste-contaminated dentin treated with IDS or DDS.
## 5 Conclusions and Future Works  
We have proposed an intelligent human-UAV interaction system based on the joint cross-validation over action-gesture recognition and scene-understanding algorithm. The flight strategy of the UAV was obtained from the deep learning-based scene-understanding algorithm so that it could follow the flying conditions more smartly. Joint cross-validation was applied to the results of a multi-feature cascade gesture recognition algorithm and that of the AlphaPose+ST-GCN action recognition algorithm to improve the recognition accuracy of our system. The final flight command sent to the UAV was a combination of the flight action obtained from the cross-validation and the scene-understanding result. The effectiveness and efficiency of the proposed system were demonstrated by means of flight experiments on a real UAV with a total of 3 h of flying, and the overall accuracy of the translated flight commands was over 99.5%.  
At present, the UAV pilot is requested to wear gloves, because these can aid our system in distinguishing the UAV pilot from surrounding persons (without gloves) who happen to perform similar actions to the pilot (for example, in the case illustrated in Figure 4), as well as improving the segmentation results of the hand regions. In the future, we would like to introduce a 3D RGB-D sensor (such as the well-known TOF camera Kinect V2) to replace the visible spectrum camera in our system, because it is more natural for a human to control the UAV without gloves. With the aid of 3D information, it will be easy to distinguish the UAV pilot (who is closest to the camera) from surrounding persons. To address some specific task such as UAV rescue/delivery, more important modules needs to be combined with our system such the onboard human detection module and mechanical delivery system to finish the rescue or or delivery operation. Before applying the proposed system to other intelligent robots such as industrial robots, self-driving vehicles, and aerial manipulators [52], we would like to perform further flight tests with additional users.  
**Author Contributions:** C.H.: methodology ans software; B.C.: software and validation, D.L.: validation, Y.H.: validation; J.H.: validation  
**Funding:** This work is supported by the Program of National Natural Science Foundation of China (NSFC) under Award No. U1609210, No. 61573338, No. U1508208.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Logic-Compatible 3T Memory Cell  
In Figure 1, we show the hybrid 3T gain cell schematic and its layout. Here, MW denotes the high threshold-voltage (V\({}_{\text{TH}}\)) write-transistor, MR is the standard V\({}_{\text{TH}}\) read-transistor, and MS is the standard V\({}_{\text{TH}}\) storage-transistor. In addition, DN means data node, RBL is read-bitline, RWL is read-wordline, WWL is write-wordline, WBL is write-bitline, and so on. The bit cell area is 0.5 \(\times\) 0.515 \(\upmu\)m\({}^{2}\), which is 59.2% and 49.9% smaller than those of RD-8T and 6T SRAM in the same 45 nm generic process.  
The voltage waveforms depicted in Figure 2 explain the operating principle of the 3T cell. During the standby, RWL and WWL are grounded while both WBL and RBL are precharged to V\({}_{\text{DD}}\). This hold condition turns MW and MR off, thus storage in the DN is  
Figure 1: Schematic and layout of hybrid 3T gain cell. (DN: data node, RBL: read-bitline, RWL: read-wordline, WWL: write-wordline, WBL: write-bitline).  
preserved. To read the stored information, RWL is raised to high while WWL is grounded. If DN is low, MS is in the off state; hence, RBL is kept at V\({}_{\text{DD}}\). If DN is high, MS is turned on; thus, a read current from RBL flows to the ground. This lowers the RBL level. Two different RBL levels are detected by a voltage sense amplifier (SA). To write a datum on the cell, WBL is driven to either low or high while RWL is grounded. WWL is then boosted to V\({}_{\text{PP}}\) which is higher than the supply voltage. This allows a new datum from the write-bitline to be fully written to DN through the high V\({}_{\text{TH}}\) write-transistor.
## 3 Modeling Approach for Tray Column  
### Rate-Based Modelling for CO\({}_{2}\) Absorption  
The two-film theory developed by [PERSON] and [PERSON] (1924) [14] explains the mass transfer between gas and liquid. The two-film theory is based on the hypothesis that the gas and liquid phases form a thin layer of fluid on each side of the interface ([PERSON] 1962) [15]. Figure 3 illustrates the situation of the concentration of component \(i\) (in our study, \(i\) represent CO\({}_{2}\)) at gas/liquid interfacial area. It should be noted that the concentration of component \(i\) decreases from \(C_{i,G}\) in gas bulk to \(C_{i,G*}\) in the interface. The difference of concentration creates a driving force for component \(i\) to shift from the gas bulk to gas film and then from the gas film to liquid film. The growth of component \(i\) in the liquid film forms a concentration difference between the liquid film and the liquid bulk; likewise, this concentration difference generates a driving force for component \(i\) to shift it from liquid film to liquid bulk ([PERSON] 1962) [15]. \begin{table}
\begin{tabular}{c c c} \hline
**Number of the Experiment** & **The Total Inlet Gas Flow Rate (Nm\({}^{3}\)/h)** & **Pressure (MPa)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 1: The operation conditions of performed experiments in this study with a CO\({}_{2}\) volume fraction of 0.3, inlet water volumetric flow rate of 0.17 m\({}^{3}\)/h, and inlet temperature of the inlet water of 19 \({}^{\circ}\)C.  
The rate of mass transfer of component \(i\) through the gas boundary layer is calculated as follows ([PERSON] 2013) [16]:  
\[N_{i,G}=k_{G}a^{*}(C_{iG}-C_{iG*}) \tag{1}\]  
The rate of mass transfer of component \(i\) through the liquid boundary layer is calculated as follows ([PERSON] 2013) [16]:  
\[N_{i,L}=k_{L}a^{*}(C_{iL*}-C_{iL}) \tag{2}\]  
where \(k_{G}\), \(k_{L}\) are the mass transfer coefficient of the gas-phase and liquid-phase, respectively, \(a^{*}\) is the interfacial area. At steady state, the flux of component \(i\) from bulk gas to the interface must be equal to the flux of component \(i\) from the interface to the bulk liquid ([PERSON] 2013) [17]:  
\[N_{i,G}=N_{i,L} \tag{3}\]  
For modeling the tray column, the rate-based model developed by [PERSON] (1983) [18] was applied. The rate-based model consists of a set of correlations that calculate the mass and energy transfer across the interfacial area using mass transfer coefficients ([PERSON] and [PERSON] 2013) [19]. By applying the rate-based model, the absorber column is divided into stages. Figure 4 shows a stage of the column, which represents a tray in the column. Figure 3: Illustration for the situation of the concentration of component \(i\) at the gas/liquid interfacial area ([PERSON] 2013) (with permission from [16], Copyright Elsevier, 2013).