esa-sceva/satcom-chunk-collection huggingface dataset:
# Ionic Liquids for Development of Heterogeneous Catalysts Based on Nanomaterials for Biocatalysis
## 4 Contribution of Nanomaterials to Effectiveness of Supported Ionic Liquid Phases  
Nanomaterials are an attractive group of materials for supported ionic liquid phase techniques. Due to their small particle size, nanomaterials offer a larger specific surface area than conventional materials, which can improve the immobilization of the protein's particles on the support. Moreover, nanomaterials have unique mechanical, thermo-physical properties and surface morphology, which are important in immobilization methods [95; 96]. The nanomaterials used in the supported ionic liquid phases, for the biocatalytic reactions described before, are summarized in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Nanomaterial** & **Ionic Liquid** & **Enzyme** & **Reaction** & **Ref.** \\ \hline SILLP & Chitosan–silica hybrid & Imidazolium [BF\({}_{4}\)]\({}^{-}\) & PLL (2482 U/g 1, 132.1 mg/g 2) & Triacetin hydrolysis 35 \({}^{\circ}\)C, 10 cycles & [67] \\ \hline SILLP & Chitosan–Fe\({}_{3}\)O\({}_{4}\) hybrid & Imidazolium [PF\({}_{6}\)]\({}^{-}\) & PPL (2879 U/g, 1188/g) & Triacetin hydrolysis 50 \({}^{\circ}\)C, 10 cycles & [68] \\ \hline SILLP & Fe\({}_{3}\)O\({}_{4}\) & Imidazolium [PF\({}_{6}\)]\({}^{-}\) & CRL (132.3 U/g, 639 mg/g) & Oleic acid esterification 30 \({}^{\circ}\)C, 5 cycles & [69] \\ \hline SILLP & Fe\({}_{3}\)O\({}_{4}\) & Imidazolium [CP\({}_{6}\)]\({}^{-}\) & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ & _Penicillin G_ \\ \hline SILLP & Fe\({}_{3}\)O\({}_{4}\)–silica hybrid & Imidazolium [Cl]\({}^{-}\) & CRL & Palm stearin interestricification 35 \({}^{\circ}\)C, 4 cycles & [71] \\ \hline SILLP & MWCNTs & Imidazolium [PF\({}_{6}\)]\({}^{-}\) & CALB (19,354 U/g, 96 mg/g) & Triacetin hydrolysis 36 \({}^{\circ}\)C, 4 cycles & [75] \\ \hline SILLP & MWCNTs & Imidazolium [PF\({}_{6}\)]\({}^{-}\) & CALB (25,350 U/g, 114 mg/g) & Triacetin hydrolysis 36 \({}^{\circ}\)C, 4 cycles & [76] \\ \hline SILLP & MWCNTs & Imidazolium [PF\({}_{6}\)]\({}^{-}\) & CALB (25,350 U/g, 114 mg/g) & Triacetin hydrolysis 36 \({}^{\circ}\)C, 4 cycles & [75] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Nanomaterials used for supported ionic liquid phases in the biocatalysis.
# Automatic Detection of Quasi-Periodic Emissions from Satellite Observations by Using DETR Method
## 5 Discussions
### Impacts of Other Types of Wave Activity  
The simultaneous occurrence of electromagnetic wave phenomena such as magnetic line radiation (MLR), lightning whistle waves (LWs), and hissing waves on the time-frequency map adds complexity to recognition efforts. For example, MLR emissions consist of parallel spectral lines visible in both the electric and magnetic fields within a frequency band ranging from the local proton cyclotron frequency to \(\sim\)8 kHz [32]. This phenomenon is very similar to QP emission in terms of location and textural characteristics, but MLR is a parallel structure and rarely occurs simultaneously with QP emissions, thus having less impact on our detection.  
For LW waves that occur in the frequency range of 100 \(\sim\) 6 kHz, a clear down-tuning feature can be observed, with a morphology similar to the letter \(L\)[33]. In the ELF band, only a few energetic vertical line waveforms can be observed, and this phenomenon often occurs in conjunction with QP emission. In our proposed model, the backbone network has stronger feature extraction capabilities, and the improved deformable attention mechanism enables the model to execute a finer sampling strategy, minimizing the interference of LWs in our recognition.  
The hiss waves, which occur mainly at or below \(f_{\text{cp}}\) at geomagnetic latitudes from 20\({}^{\circ}\) (relatively weak) to 65\({}^{\circ}\) (dominant), or at frequencies above \(f_{\text{cp}}\) to 800 Hz over the equatorial region at 20\({}^{\circ}\), exhibit structureless wave spectral characteristics [34]. For hiss waves below \(f_{\text{cp}}\), instantaneous misidentification is ruled out by our constraints. Hiss waves above \(f_{\text{cp}}\) are similar to the non-dispersive texture characteristics of QP emission, which can cause some interference. However, the frequency band of QP emission is from \(f_{\text{cp}}\) to 4 kHz, which is a significant difference, allowing us to add finer frequency constraints during the prediction stage to rule out the effect of the hiss wave on detection.
# An Experimental Study on the Sealing Mechanism of a Karst Pipeline by Dynamic Water Grouting
## 2 Pilot Study on WEMS
### Experimental Results
#### 2.4.4 Compressive Strength Analysis  
As can be seen in Figure 8, when the amount of Fe\({}_{3}\)O\({}_{4}\) powder tragedy is certain, with the increase in the water-cement ratio, the uniaxial compressive strength of the material shows a decreasing trend. Keeping the water-cement ratio of the material unchanged, when the amount of Fe\({}_{3}\)O\({}_{4}\) powder tragedy is changed, the compressive strength of the material is positively correlated with the amount of Fe\({}_{3}\)O\({}_{4}\) powder tragedy. The main reason is with the increase in the water-cement ratio in the material, the hydration reaction of cement requires too much water, resulting in the existence of hardened microporosity within the material; this microporosity is due to the evaporation of water molecules retained, which will lead to the strength of the lower being larger. As the micron-sized Fe\({}_{3}\)O\({}_{4}\) powder itself has strong strength, the slurry can fill the tiny pores caused by water evaporation due to the hydration reaction but does not participate in the reaction in the middle of the role of a certain skeleton, so it will improve the strength of the material when the water-cement ratio of 0.35, the Fe\({}_{3}\)O\({}_{4}\) powder miserable amount of 40%, and the material compressive strength reach their maximum values. The material basically reaches more than 80% of its strength at the age of 14 days, and the growth rate from 14 to 28 days is less than 10%, so the material basically forms its strength at the age of 14 days.  
Figure 8: Compressive strength results.  
Figure 7: Setting time test results.  
Through the indoor proportion test, we analyzed the influence law of factors, such as the water-cement ratio and Fe\({}_{3}\)O\({}_{4}\) powder tragic amount, on the basic properties of WEMS, such as fluidity, setting time, adsorption range, and compressive strength. Conclusion: With the increase in the water-cement ratio, the fluidity of magnetic self-polymerizing grout increases significantly, and the influence of Fe\({}_{3}\)O\({}_{4}\) powder on the fluidity is negatively correlated with the constant water-cement ratio. The increase in the water-cement ratio inhibited the magnetic self-aggregation ability of the slurry, and the larger the Fe\({}_{3}\)O\({}_{4}\) powder content, the larger the magnetic rod adsorption diameter; that is, the stronger the self-aggregation ability of the slurry. The water-cement ratio will significantly prolong the setting time of the material. The influence of the water-cement ratio on the uniaxial compressive strength of the material is negatively correlated, and the compressive strength of the material is positively correlated with the Fe\({}_{3}\)O\({}_{4}\) powder content. All performance indicators of WEMS meet the requirements and determined the optimal mixing ratio of Fe\({}_{3}\)O\({}_{4}\) powder to be 20-40%, which is an important guiding significance for the treatment of karst pipe water gushing grouting.
# Experimental Validation of a Guaranteed Nonlinear Model Predictive Control
## 2 Background of Dynamic Modeling of the Inverted Pendulum
### Torque, Speed, and Acceleration Computation  
Because our experimental datasets have noises, the recorded signals are consequently processed by applying an eighth-order low-pass Butterworth filter. The input of the system (torque signal) and the joints' velocity and acceleration are required to stabilize the nonlinear inverted pendulum. The speed and acceleration are computed using a non-causal derivative filter. The function _firpm_ of Matlab allows us to synthesize this kind of filter to determine the suitable filter's parameters (impulse response coefficients). The second stage is the actual application of the designed filter to the joints' positions (\(y_{1}\) and \(y_{3}\)), using _filfilt_ command, enabling to process a data in both the forward and reverse directions (zero-phase distortion).  
On the other hand, the torque generated at the base of the rotary arm (i.e., at the load gear) can be expressed as a function of the angular velocity (input variable) denoted \(u\) as below:  
\[\tau=\frac{N_{m}N_{g}\eta_{m}\eta_{g}(u_{0}-u)}{R_{v}} \tag{3}\]  
where \(N_{m}\) and \(N_{g}\) are respectively the motor and gear ratios, \(\eta_{m}\) and \(\eta_{g}\) are the motor and gearbox efficiencies, \(u\) and \(u_{0}\) are the instantaneous and no-load angular velocities, respectively, and \(R_{v}\) is the motor speed-torque constant.
# Frame-Based Phone Classification Using EMG Signals +
## 2 Materials and Methods
### Experiments
#### 2.4.1 Classification Method  
The classifier used for the experimental part of this study is a feed-forward neural network with one hidden layer using a batch size of 256 and 100 training epochs. We chose these parameters based on a hyper-parameter search by tracking the validation accuracy during 250 training epochs for three batch sizes: 64, 128, and 256. We repeated this for Session 101 of all six speakers and averaged the results (see Figure 5). We chose 100 training epochs because at that point the performance reaches a plateau, and we chose the largest batch size because there is no difference between the three batch sizes, and a larger batch size means lower training time. The network has an input dimension equal to the number of features (21 nodes) and a dense layer with twice as many nodes as features (42 nodes in total) using a rectified linear units activation function [45]. The output layer has the same number of nodes as the number of classes (30, which includes 29 phones and a silence) and uses a _softmax_ activation function [46]. Furthermore, a categorical cross-entropy loss function and the _Adam_ optimizer [47] with a learning rate of 0.001 are applied.
# A Quantitative Investigation on the Peripheral Nerve Response within the Small Strain Range
## Materials and Methods
### Stretching Experiments  
A specimen was taken from a sciatic nerve, previously dissected from a posterior limb of a Large White pig (\(\sim\)10 months old, slaughtered in conformity with the Italian National Regulation and frozen until experiments). Before experiments, the nerve specimen (65 mm long with a cross-sectional area of \(\sim\)11.18 mm\({}^{2}\)) was gradually defrosted and re-hydrated for about one hour at room temperature in a bath of aqueous saline solution isotonic to the blood (0.9% sodium chloride) to minimize the time dependence of the tissue hydration. Experiments were then performed at room temperature (\(\sim\)25 \(\pm\) 1 \({}^{\circ}\)C) through an Instron R4464 testing machine (Instron Corporation, Canton, MA, USA) and with a standard load cell (Instron load cell, Instron Corporation, cell type 2525-808, max force 10 N, accuracy 0.25% Full Scale Output). The length of the specimen between clamps was 55 mm, and its physiological characteristics were kept by regularly spraying saline moisture on its external surface, in order to maintain the initial level of hydration. More specifically, the nerve was stretched (velocity \(v=10\) mm/min [27]; maximum strain 5%) after preconditioning to minimize viscoelastic effects [28]. The axial force was digitally recorded for five extensions of the nerve.
## 2 Internal Force Calculation of Lining
### Influence of Pressureless Water Conveyance Level on Lining Internal Force
#### 3.2.1 Influence of Water Conveyance Level on the Maximum Internal Force of Lining  
To reveal the influence of water level on the maximum internal force of lining, four working conditions are selected of tunnel inner radii 2, 3, 4 and 5 m. The maximum internal force is calculated by taking an analysis point at an interval of 1 m from 1 m water level to full water level height. The positive and negative value of internal force only indicates the direction of force, and the selection of the maximum value of each internal force is the same as that in Section 3.1.1. The curve of the maximum internal force with water conveyance level under different lining sizes is shown in Figure 9.  
It can be seen from Figure 9a that, when the water level exceeds half the height of the tunnel, the maximum bending moment will decrease in the local water level section. Then the maximum bending moment continues to increase and the increase rate gradually increases. It can be seen from Figure 9b that the maximum axial force of the lining will decrease abnormally under the condition of full water level. Before that, the maximum axial force increased with the increase in water level. It can be seen from Figure 9c that the maximum value of lining shear force increases with the increase of water level height and the increase rate gradually increases. In addition, at the same water level, the maximum internal force of the lining is affected little by the inner radius. The maximum internal force of the lining of a pressureless water conveyance tunnel is much smaller than that of  
Figure 9: Influence of pressureless water conveyance level on the maximum internal force of lining. (**a**) Maximum bending moment. (**b**) Maximum axial force. (**c**) Maximum shear force.  
a pressurized water conveyance tunnel. Therefore, designing the tunnels with the same water conveyance capacity can comprehensively compare two design schemes: pressurized water conveyance and increased-size pressureless water conveyance.
## Materials and Methods
### Development of Three-Axis Accelerometer Sensing System for Safety Helmet Wearing Management
#### 2.2.3 Mobile App for Data Collection and Transmission  
Figure 7 shows screenshots of the data transmission app for the sensing safety helmet, which can be used on an Android mobile device. The app is designed so that when the mobile device is connected to the sensing safety helmet via Bluetooth, the data from the three-axis accelerometer sensor are automatically stored in the app database. If the data transmission tab is selected in the app, the data that were first saved in the app are sent to the web page of the on-site PC via 3G/LTE. As shown in Figure 7, the X, Y, and Z vector values generated by the three-axis accelerometer sensor according to the real-time movements of the worker can be viewed in the app.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Type** & **Function** \\ \hline Micro Controller Unit (MCU) & JARDUINO-UNO\_BT mini & Transmits data through Arduino I2C communication. \\ Three-Axis-Accelerometer & MMA8452 & Measures raw data of acceleration in the three axes. \\ Real Time Clock (RTC) & DS3231 & Measures real time. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Technical Specification of the Sensing Helmet.  
Figure 6: Prototype of three-axis accelerometer sensing safety helmet: (**a**) Front side of the Safety Helmet; (**b**) Back side of the Safety Helmet.
# A Tube Linear Model Predictive Control Approach for Autonomous Vehicles Subjected to Disturbances
## 2 Tube Linear MPC Methodology
### Tube MPC Theory  
In this section, the framework of the tube MPC for the autonomous system is introduced.  
It is assumed that the autonomous system can be expressed as follows:  
\[\begin{array}{l}\xi_{k+1}=A\xi_{k}+Bu_{k}+w_{k}\\ s.t\;\xi_{k}\in\Xi\\ u_{k}\in U\\ \omega_{k}\in W\end{array}. \tag{7}\]  
The framework of the tube MPC can be divided into two parts. The first part is the nominal system control, which computes the feedforward input of system (7) without disturbance:  
\[\overline{\xi}_{k+i+1}=A\overline{\xi}_{k+i}+B\overline{u}_{k+i},\forall i=0, 1,\cdots,N-1. \tag{8}\]  
The second part is a state feedback controller that attenuates the state errors between the actual and nominal systems. The state errors between the actual and nominal systems and the feedback input can be presented as follows:  
\[e_{k}=\xi_{k}-\overline{\xi}_{k}, \tag{9}\]  
\[\hat{u}=Ke_{k}, \tag{10}\]  
where \(K\) is a stabilizing linear feedback gain.  
The control input of the tube MPC equals the sum of the feedforward and feedback inputs.  
\[u_{k}=\overline{u}_{k}+\hat{u}=\overline{u}_{k}+Ke_{k}. \tag{11}\]  
By putting Equations (9) and (11) into System (7), we can express the actual system with \(e_{k}\) as follows:  
\[e_{k+1}=A_{r}e_{k}+w_{k}=(A+BK)e_{k}+w_{k}, \tag{12}\]  
where \(A_{r}=A+BK\), and there exists a gain \(K\) that makes it that \(A_{r}\) is a Hurwitz function [27].  
If the state \(\xi_{0}\) is close to the nominal state \(\overline{\xi}_{0}\), Control Law (12) will keep the state within the robust positively invariant set \(\varepsilon\) for the bounded disturbance \(w\). Section 2.4 includes a proposal for an algorithm regarding the computation of the invariant set.
## 3 Background of SDN
### OpenFlow  
OpenFlow provides an interface-based communication among the infrastructure and control layer based on open networking Foundation (ONF) [25; 26]. Moreover, it provisions a way for controlling switches regardless of source code disclosure by the vendors. In summary, Openflow provides a way to directly access and manipulate the forward plan of switches and routers [27]. Furthermore, it grants access towards the flowable and provides instructions to the switches on managing and direct traffic of the network. It allows the network managers to alter the network flow in a short time span [28]. According to studies, there are two main categories of OpenFlow-based switches: OpenFlow-only and OpenFlow-hybrid. OpenFlow support is limited to OpenFlow operations, while the hybrid version supports operations and normal Ethernet switches [29].  
The OpenFlow controllers are responsible for managing the OpenFlow switches based on a secure channel protocol called OpenFlow protocol. One or more flow tables are contained within a switch for performing a forwarding operation and packet tracing. A flow table includes a flow entry; each entry possesses header fields, specific counters, and actions. The purpose of a header field is to match up against packets with information related to VLAN, ID, source and destination ports and IP address, etc. There are counters for keeping information about the number of packets their sizes. Action provides information associated to processing and matching packets; Their forwarding action is also specified, such as being sent to the controller or a port or sometimes dropped [30; 31; 32]. OpenFlow channel provides an interface for connecting the switches and controllers. Using this interface, the switches are being managed and configured by the controller; additionally, the events are received, and packets are sent through the switches. Various messages are sent using this channel, including asynchronous messages that involve messages for updating the controller regarding the network event and state change. The second type is controller-to-switch messages; these messages are for managing and inspection of switch states. Lastly, symmetric messages are initiated by the switch or the controller and are sent unsolicited [33; 34; 35]. OpenFlow controller is responsible for managing, allocating, and updating instructions and policies to the networking devices. It can decide how to handle packets with invalid flow entries and control the switch flow table. OpenFlow switch can establish communication with one or more controllers. A multiple-controller architecture can increase network reliability if a switch fails. OpenFlow starts operations when the switch is connected to all its configured controllers simultaneously, whereas related messages are only sent to the next switch [36; 37; 38; 39].
## 2 Results and Discussion
It is also interesting to note the difference in volatility compared to the widely studied Australian systems, where \"traditional\" volatiles are used as long-range attractants, while the European systems utilise less volatile cuticular hydrocarbons, such as the C\({}_{27}\)-C\({}_{29}\) alkenes in _O. sphegodes_, which have been proven sufficiently volatile to lure pollinators from a distance as attractants [15]. Furthermore, it is relevant to note that in the case of _O. insectifera_ and _A. fargeii_, the orchid and pollinator share the exact same semiochemicals, which is in agreement with other investigated _Ophrys_ systems, including _O. sphegodes_[39] and _O. speculum_[17], as well as with most Australian systems [4] (but see [40]). Figure 2: Proposed biosynthesis of bioactive alkenes in _Ophrys sphegodes_ (from [32]) and _O. insectifera_. In conclusion, we have identified (\(Z\))-8-heptadecene (**1**) and pentadecane (**2**) as shared semiochemicals from _O. insecifera_ and _A. fargeii_. Access to denser populations of _A. fargeii_ or _A. mystaceus_ would be required to undertake bioassays testing the field activity of these compounds as pollinator attractants. Nevertheless, this study provides an important first step in the identification of key compounds that, once pollinator populations have been located, are available to be tested in field behavioural bioassays. Furthermore, the identification of these semiochemicals and comparison with related species within the genus shows strong commonalities in structures and suggests a conserved biosynthetic pathway for semiochemical production within _Ophrys_.
## 2 Research Area and Data  
### Study Area Overview  
Yunnan Province is located on the southwest border of China, between 97\({}^{\circ}\)31'106\({}^{\circ}\)11' E and 21\({}^{\circ}\)8'29\({}^{\circ}\)15' N. The total area of Yunnan Province is 394,100 km\({}^{2}\), with mountains, hills, and plateaus accounting for 94% of the province's total area, and basins and valleys accounting for approximately 6% [24]. Yunnan Province is a typical mountainous province with significant mountain features [25] and many lakes and rivers (Figure 1). There are 908 rivers in the Yunnan Province with a runoff area of more than 100 km\({}^{2}\). Most rivers are characterized by large drops, rapid flow, and significant variations in water flow. Regarding climate, the Qinghai-Tibet Plateau and the East Asian and South Asian monsoons have a significant influence, and precipitation is primarily concentrated from May to October.  
Since the 1990s, global climate change has shown a warming trend. As a result, the frequency of heavy rain in Yunnan Province has increased [26]. Because of its unique geographical and climatic characteristics, Yunnan is a region where floods occur frequently. According to incomplete statistical data from 1990 to 2020, a total of 3648 counties in the province experienced flood disasters, with an average of approximately 118 counties (cities and districts) affected every year, with a total of 6439 deaths, and an average of 208 deaths per year. These floods caused the collapse of 875,500 houses and an average of 28,243 houses were damaged by floods every year. The direct economic losses caused by flood disasters are as high as 113,042 billion yuan (at current prices), with an average annual loss of 3.646 billion yuan, accounting for approximately 60% of the direct economic losses from natural disasters in the province [25].