esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
## 3 Modeling of the Active Suspension System  
### Dynamic Model of Quarter Car Active Suspension System  
As shown in Figure 1, the quarter car model is selected as the model of the suspension system, which is simple and can capture important characteristics of full model [3; 28]. A quarter car active suspension model considered in this work is taken from Reference [29], as shown in Figure 2. From Figure 2 and [PERSON]'s second law, the dynamic equations of the quarter car model are presented in Equation (1):  
\[\begin{split}& m_{u}\tilde{z}_{u}+c_{s}(\dot{z}_{u}-\dot{z}_{s})+k_{ t}(z_{u}-z_{q})+k_{s}(z_{u}-z_{s})+F=0\\ & m_{s}\tilde{z}_{s}+c_{s}(\dot{z}_{s}-\dot{z}_{u})+k_{s}(z_{s}-z_{ u})-F=0\end{split} \tag{1}\]  
where \(m_{s}\) and \(m_{u}\) stand for the sprung mass and the unsprung mass, respectively,\(k_{t}\) represents the spring stiffness between tire and road, \(k_{s}\) denotes the spring stiffness between tire and car body, \(c_{s}\) denotes the damping coefficient between tire and car body, \(F\) indicates the control force output from actuator, \(z_{u}\) is the vertical displacement of unsprung mass, \(z_{s}\) denotes the vertical displacement of sprung mass, and \(z_{q}\) represents the vertical displacement caused by road surface irregularity.  
Figure 1: Structure and control principle of the active suspension system.  
The state vector of the quarter car suspension system is selected as \(X\):  
\[X=\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\\ x_{4}\end{array}\right]=\left[\begin{array}{c}z_{u}\\ z_{s}\\ \dot{z}_{u}\\ \dot{z}_{s}\end{array}\right] \tag{2}\]  
The state equation of the quarter car suspension system is established in Equation (3):  
\[\left\{\begin{array}{l}\dot{X}=AX+BU+KW\\ Y=CX+DU\end{array}\right. \tag{3}\]  
where \(Y\) is the output equation of the suspension system, the matrix \(A,B,K,C,\) and \(D\) are constants and with appropriate dimensions, the matrix \(W\) is the disturbance vector input from the road, and the matrix \(U\) is the control vector input from the controller. The matrices \(A,B,K,C,D,W\) and \(U\) are shown as follows:  
\[\begin{array}{l}A=\left[\begin{array}{cccc}0&0&1&0\\ 0&0&0&1\\ -\frac{k_{1}+k_{2}}{m_{u}}&\frac{k_{s}}{m_{u}}&-\frac{c_{s}}{m_{u}}&\frac{c_{s} }{m_{u}}\\ \frac{k_{s}}{m_{u}}&-\frac{k_{s}}{m_{s}}&\frac{c_{s}}{m_{u}}&-\frac{c_{s}}{m_{u }}\end{array}\right],B=\left[\begin{array}{c}0\\ 0\\ -\frac{1}{m_{u}}\\ \frac{1}{m_{u}}\end{array}\right],K=\left[\begin{array}{c}0\\ 0\\ \frac{k_{s}}{m_{u}}\\ 0\end{array}\right]\\ C=\left[\begin{array}{cccc}1&0&0&0\\ 0&1&0&0\\ 0&0&1&0\\ \frac{k_{u}}{m_{s}}&-\frac{k_{s}}{m_{s}}&\frac{c_{s}}{m_{s}}&-\frac{c_{s}}{m_{ s}}\end{array}\right],D=\left[\begin{array}{cc}0&0\\ 0&0\\ 0&0\\ 0&\frac{1}{m_{u}}\end{array}\right],W=\left[z_{q}\right],U=\left[F\right]\end{array} \tag{4}\]
# Concrete Cracks Detection Based on FCN with Dilated Convolution
## 3 The Proposed Model
### Model Description
#### 3.1.1 The Encoder  
The encoder consisted of a residual network and several dilated convolutions as shown in Figure 3. The blue cube represents the feature maps. If there was no special explanation, the paddings of convolution and pooling were set to 0. As can be seen from Figure 3, the input image passed through a convolution layer with a convolution kernel \(k\) of size \(7\times 7\), a stride \(s\) of 2 and a padding \(p\) of 3, which was followed by BatchNorm, ReLU, and AvgPool. It should be noted that \(k\), \(s\), \(p\) of the AvgPool were 3, 2, and 1 respectively. The feature maps F0 obtained at this moment have 64 channels, the height and width of F0 are 1/4 of the input image. F1 with 64 Channels was obtained by extracting features  
Figure 1: Detection of concrete cracks. CNN: convolution neural network.  
Figure 2: The framework of the model.  
from F0 through 2 residual blocks. It should be noted that F1 was the same size as F0. To obtain F2, F1 was processed by two residual blocks and AvgPool that had a kernel of size \(2\times 2\) and a stride of 2 in turn. Similar operations were performed on F2 to obtain F3. Then F3 was processed by two residual blocks to obtain F4, it should be noted that the process from F3 to F4 did not use AvgPool, which reduced information loss and retained more information. As the final output of the encoder, F5 had a complex manufacturing process, which was composed of the feature maps of the output of five branches: (1) The output was obtained by processing F2 with AvgPool that had a kernel \(k\) of size \(2\times 2\) and a stride \(s\) of 2; (2) The output was obtained by processing F4 with a composition layer composed of a BatchNorm, a ReLU, and a convolution that had a kernel \(k\) of size \(1\times 1\) and a stride \(s\) of 1. (3) The output was obtained by processing F4 with a composition layer composed of a BatchNorm, a ReLU, and a dilated convolution that had a kernel \(k\) of size \(3\times 3\), a stride \(s\) of 1, a padding \(p\) of 6, and a dilation rate \(d\) of 6. (4) The output was obtained by processing F4 with a composition layer composed of a BatchNorm, a ReLU, and a dilated convolution that had a kernel \(k\) of size \(3\times 3\), a stride \(s\) of 1, a padding \(p\) of 12, and a dilation rate \(d\) of 12. (5) The output was obtained by processing F4 with a composition layer composed of a BatchNorm, a ReLU, and a dilated convolution that had a kernel \(k\) of size \(3\times 3\), a stride \(s\) of 1, a padding \(p\) of 18, and a dilation rate \(d\) of 18. The feature maps obtained from these five branches were concatenated, and then a convolution with kernel \(k\) of size \(1\times 1\) and stride \(s\) of 1 was used for fusing the feature maps. After that, the feature maps flowed through BatchNorm and ReLU in turn. The height and width of the fused feature maps F5 are 1/16 of the input image. F5 served as the output of the encoder and the input of the decoder.
## 2 Methodology  
### ORC System  
As shown in Figure 1, the ORC system includes four main components: An evaporator, an expander, a condenser, and a pump; and four main thermodynamic processes: isentropic compression (1-2), isobaric heating in the preheater and evaporator (2-4), isentropic expansion in the expander (4-5), and isobaric cooling in the condenser (5-1). So, we assume that there is no pressure drop in the preheater, evaporator, or condenser.  
At the beginning of the cycle, the liquid organic working fluid is adiabatically compressed in the pump, and then enters the preheater and evaporator to absorb heat from the waste heat at a constant pressure. The liquid organic working fluid is converted into high-temperature and high-pressure steam by the liquid organic working fluid. Then the steam enters the expander. In this process, the output mechanical work is processed by the expander during the isentropic expansion process. Next the expanded organic working fluid vapor flows into the condenser where the steam is cooled and condensed into liquid by isostatic heat transfer. Finally, the liquid organic working fluid is sent to the pump for the next cycle. However, in practical ORC applications, the heat transfer and working processes are not strictly isentropic.  
Figure 1: Schematic diagram of the organic ranking cycle (ORC). (**a**) Cycle configuration; (**b**) T-S diagram.
# A Random Forest-Based Precipitation Detection Algorithm for FY-3C/3D MWTS2 over Oceanic Regions
## 2 Data and Methods
### Satellite Microwave Radiometer
#### 2.1.2 Microwave Temperature Sounder (MWTS)  
The MWTS is divided into two generations: MWTS2, which is mounted on the FY-3C/D satellites, and MWTS3, which is mounted on FY-3E. Table 1 presents the characteristics of the MWTS channels.  
MWTS2 is a cross-track scanner that completes one revolution every 8/3 s, scans 90 Earth FOVs with a 33 km nadir resolution, and measures Earth's radiation across 13 channels from 50 to 60 GHz for temperature profiling from the surface to approximately 6 hPa. Owing to the limitations imposed by early industrial technology, China's MWTS2 has consistently lacked a low-frequency microwave precipitation detection channel, such as one at 23 or 31 GHz, which is necessary for calculating the _SI_ or _CLWP_ to complete precipitation detection tasks.  
In contrast with MWTS2, the MWTS3 system has been enhanced with the inclusion of 23.8-GHz and 31.4-GHz channels, which were specifically designed for the detection of clouds and precipitation. MWTS3 is capable of estimating the _CLWP_ by utilizing measurements acquired from both the 23.8-GHz and 31.4-GHz channels.  
To evaluate the applicability of _RF_SI_ to MWTS2, the _RF_SI_ data introduced in this paper can be matched within the MWTS3 pixels. A comparative analysis of the precipitation detection results produced by the _RF_SI_ and _CLWP_ schemes can subsequently be performed.
## 3 Results
### NEP Simulations under All Sky Conditions during 2013-2016
#### 3.3.1. GPP Simulations Using EMGPP Model  
Firstly, the hourly GPP estimates using the 3-factor and 2-factor EMGPP models are presented briefly in Tables 15 and 16[45]. Generally, the EMGPP model showed reasonable performance and overestimation of GPPs by \(\sim\)30%.
# A Network Coding Based Hybrid ARQ Protocol for Underwater Acoustic Sensor Networks
###### Abstract  
Underwater Acoustic Sensor Networks (UASNs) have attracted increasing interest in recent years due to their extensive commercial and military applications. However, the harsh underwater channel causes many challenges for the design of reliable underwater data transport protocol. In this paper, we propose an energy efficient data transport protocol based on network coding and hybrid automatic repeat request (NCHARQ) to ensure reliability, efficiency and availability in UASNs. Moreover, an adaptive window length estimation algorithm is designed to optimize the throughput and energy consumption tradeoff. The algorithm can adaptively change the code rate and can be insensitive to the environment change. Extensive simulations and analysis show that NCHARQ significantly reduces energy consumption with short end-to-end delay.  
ARQ; fountain codes; network coding; underwater acoustic sensor network Article
# Reliable Internet of Things: Challenges and Future Trends
## 3 Major Use Cases of IoT
### Other Emerging Applications of IoT  
Besides these four applications, there are many emerging use cases of IoT in areas such as process industry, agriculture, food and beverages industry, and supply chain. In the process industry such as petrochemical, continuous monitoring of key parameters of many equipment and machines is required. Examples of the important parameters include temperature, humidity, pressure and flow. By sensing these parameters and using data analytic techniques, predictive maintenance of machines can be realized. An example of such use case is measuring the temperature of cooling and heating fluid in a heat exchanger. Other machines such as centrifugal pumps, food storage containers, and winding machines can also use condition monitoring solutions to predict the faults and reduce the down time.  
In agriculture, IoT can be used to efficiently utilize the available resources. Using the data collected by different sensors to monitor water levels, soil characteristics, rain fall, weather conditions, and crop yield, an intelligent machine learning model can be developed to predict crop health. Moreover, optimal use of disinfect sprays and water can be figured out. In this regard, IoT based drones can also be used to collect data and also for automated water spraying.
## 4 Results and Discussion
### Transformer Condition Monitoring
#### 4.8.2 Temperature Oil and Winding  
Temperatures for both oil and windings are fundamental aspects of transformer monitoring. Temperature influences all other accessories that make up the transformer. Excessively high temperatures might represent a fault, which might ultimately damage multiple parts of the transformer if left unaddressed. Temperatures should be monitored in several areas, namely ambient, windings, top oil, bottom oil, tap changer, etc. Ref. [56] mentioned that winding insulation is extremely sensitive to both moisture and temperature. Monitoring the winding hot spot is critical since it has an immediate effect on the transformer's deteriorating rate. Refs. [17; 22] stated that for approximately every 6-degree rise in winding temperature beyond the specified maximum range of 110 degrees, the transformer decaying rate nearly doubles. The reviewed studies revealed the importance of gathering real-time temperature data of transformers to ensure the reliable and efficient operation of these devices in the power system. There are various IoT temperature sensors available in the market, with the DHT11 sensor and LM335Z sensor predominately used for dry-type transformers, small single-phase transformers, and instrument transformers. A waterproof DS18B20 temperature sensor is also available and is predominantly used for IoT studies involving oil-impregnated transformers.
# Sulfuric Acid Resistance of CNT-Cementitious Composites
## 3 Test Results and Discussion  
### Features of Sulfate Resistance  
Figure 7 demonstrates the weight change ratio of CNT cementitious composites after immersion in a sulfuric acid solution. It was found that the weight change ratio decreased as the age increased. The weight of the plain specimen immersed in 5% sulfuric acid solution decreased rapidly after 7 days, while that of the plain specimen immersed in 10% sulfuric acid solution decreased in the early stage (by 7 days) of immersion. It is ascertained that deterioration occurred due to sulfuric acid erosion and the weight decreased because of the peeling and falling-off of the composites. However, as the amount of CNTs increased, a decrease in weight was more rarely found in the CNT cementitious composites than the plain composite.  
Figure 8 demonstrates the compressive strength change ratio after immersion in a sulfuric acid solution. The compressive strength change ratio showed a similar tendency to that of the weight decrease ratio. This is because deterioration occurred due to the reaction of sulfate ions with the cement hydrate, resulting in relaxation and breakage of the hydration structures.  
Figure 8: Compressive strength change ratio of cementitious composites immersed in sulfuric acid solution: (**a**) 5% and (**b**) 10%.  
Figure 7: Weight change ratio of cementitious composites immersed in sulfuric acid solution: (**a**) 5% and (**b**) 10%.  
The SEM photos of the specimen surface taken before and after the immersion of cement mortar in a sulfuric acid solution are exhibited in Figure 9. In particular, the SEM photos in Figure 9 were taken after immersion in a sulfuric acid solution for 28 days. The images were taken at 25 times magnification, which was enough to observe the degree of deterioration of the composite surface. The cement hydrate is a compound that is very vulnerable to acid, and huge damage occurred in the cement hydrate except for aggregate in the plain specimen after immersion in a sulfuric acid solution. However, in the case of the CNT cementitious composites, less damage occurred in the cement hydrate because the nanoparticles with excellent corrosion resistance to acid prevented contact between the cement hydrate and the sulfuric acid solution.
# Intelligent Prediction of Nitro Oxide Capture in Designable Ionic Liquids
## 3 Datasets and Metrics
### Metrics  
To assess the precision of the models, the mean square error (_MSE_), correlation coefficient (\(R^{2}\)), mean absolute error (_MAE_), and root mean square error (_RMSE_) were introduced. The formulations of metrics are presented as follows:  
\[MSE=\frac{1}{n}{\sum\limits_{i=1}^{n}(e_{i}-p_{i})^{2}} \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}(e_{i}-p_{i})^{2}}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Ionic Liquids** & **Temperature (K)** & **Pressure (bar)** & **Solubility (Mole Fraction)** & **N** \\ \hline
1 & [BMIM][BF\({}_{4}\)] [43; 44] & 292.45–373.55 & 0.26–133.30 & 0.001–0.371 & 87 \\
2 & [BMIM][AC] [45] & 283.10–348.20 & 0.0252–2.0007 & 0.001–0.207 & 50 \\
3 & [BMIM][Tf\({}_{2}\)N] [3; 46] & 283.15–323.15 & 0–13.00 & 0–0.363 & 136 \\
4 & [BMIM][SCN] [3; 43] & 293.05–373.30 & 10.30–184.90 & 0.029–0.294 & 49 \\
5 & [BMIM][MP] [43] & 292.35–373.15 & 21.00–164.50 & 0.087–0.336 & 42 \\
6 & [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N] [3; 43] & 302.05–363.45 & 6.10–240.40 & 0.119–0.781 & 102 \\
7 & [(OH)\({}_{2}\)IM][Tf\({}_{2}\)N] [3; 43] & 302.85–363.15 & 10.20–301.00 & 0.152–0.691 & 55 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of IL–N2O systems.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Ionic Liquids** & \(\omega\) & **Tc (K)** & **Pc (bar)** \\ \hline
1 & [BMIM][BF\({}_{4}\)] [43; 44] & 0.8489 & 632.30 & 20.40 \\
2 & [BMIM][AC] [45] & 0.6681 & 867.68 & 29.42 \\
3 & [BMIM][Tf\({}_{2}\)N] [46] & 1.2890 & 831.39 & 27.60 \\
4 & [BMIM][SCN] [43] & 0.4781 & 1047.40 & 19.40 \\
5 & [BMIM][MP] [43] & 0.4714 & 767.50 & 32.60 \\
6 & [(ETO)\({}_{2}\)IM][Tf\({}_{2}\)N] [43] & 0.2817 & 1310.80 & 28.20 \\
7 & [(OH)\({}_{2}\)IM][Tf\({}_{2}\)N] [43] & 0.7267 & 1314.10 & 45.09 \\ \hline \end{tabular}
\end{table}
Table 2: The acentric factor (\(\omega\)), Tc, and Pc of ionic liquids studied here.  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}|e_{i}-p_{i}| \tag{9}\]  
\[R^{2}=\frac{\sum_{i=1}^{n}\left(e_{i}-\overline{e}\right)^{2}-\sum_{i=1}^{n} \left(e_{i}-p_{i}\right)^{2}}{\sum_{i=1}^{n}\left(e_{i}-\overline{e}\right)^{2}} \tag{10}\]  
where \(n\) signifies the number of data points, \(e_{i}\) denotes the laboratory value of gas solubility, \(p_{i}\) denotes the predicted value of gas solubility using the models, and the average laboratory value of gas solubility is denoted by the letter \(\overline{e}\).
## Materials and Methods
### Non-Volatile Compounds
#### 2.4.2 UPLC-MS Analysis of GSL Extracts  
UPLC-MS was performed on a Shimadzu Nexera X2 series UHPLC, coupled with an 8050 triple quadrupole mass spectrometer system (Shimadzu UK Ltd., Milton Keynes, UK). Separation of standards and samples was achieved using a Waters BEH C\({}_{18}\) Acquity column (100 \(\times\) 2.1 mm, 1.7 \(\upmu\)m; Waters Corp., Wilmslow, UK) with an Acquity in-line filter. Mobile phases consisted of 0.1% formic acid in LC-MS grade H\({}_{2}\)O (A), and 0.1% formic acid in LC-MS grade acetonitrile (B) and GSLs were separated during a five minute run with the following gradient timetable: (i) 0-50 s (A-B, 98:2, _v/v/b_), (ii) 50 s-3 min (A-B, 70:30, _v/v_), (iii) 3-3 min 10 s (A-B, 5:95, _v/v_), (iv) 3 min 10 s-4 min (A-B, 5:95, _v/v_), (v) 4-4 min 10 s (A-B, 98:2, _v/v_), (vi) 4 min 10 s-5 min (A-B, 98:2, _v/v_). The flow rate was 0.4 mL per min and the column oven temperature was 35 \({}^{\circ}\)C.  
Two MS methods were used for the identification and quantification of GSLs. First, a Product Ion Scan (PIS) method was established to identify GSLs based on known primary ion masses ([M-H]-) characteristic fragment ions (357, 258, and 97 _m/z_; Table 1). Then, MS/MS spectra were compared to authentic standards and available literature sources [30; 31; 32; 33; 34; 35; 36; 37; 38]. Pentyl GSL (PEN), isobutyl GSL (ISO), glucoputranjivin (GPJ)), and butyl GSL (BUT) were tentatively identified due to the possible presence of isomers [38], and/or no reliable reference MS spectra could be found in the literature. Total ion chromatograms of glucosinolates identified were included in the Supplementary Data (S2).  
MS/MS settings for the PIS method were as follows: samples were analysed in the negative ion mode with a scan range of 70-820 _m/z_. A collision energy of 25 eV and a scan speed of 30,000 u per s\({}^{-1}\). For the quantification of GSLs, a Multiple Reaction Monitoring (MRM) method was established. Based on the fragmentation observed in the PIS method, confirmation and quantification transitions were established (Table 1). Dwell times for each precursor and product ion were set to 5 s.  
Authentic GSL compounds were run as external standards. Limits of detection (LOD) and limits of quantification (LOQ) were established for each and are presented in Table 1. As standard compounds are not available for all GSLs, SIN was used to semi-quantify glucorucolamine (GRM), glucoputranjivin (GPI), diglucothiobeinin (DGTB), glucobotercoin (GBT), glucocochlearin (GCL), glucosactivin (GSV), dimeric 4-mercaptoethanolGSL (DMB), glucobarbarin (GBB), and tentatively identified GSL compounds. Similarly, GBR was used to semi-quantify the indolic GSLs 4-methoxyglucobrassicin (4 MGB) and neoglucbasicin (NGB).
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 5 Experimental Verification  
### Introduction to Experimental Model  
As shown in Figure 18, the laboratory model bridge is not designed in strict accordance with the similarity ratio of an actual single-pylon cable-stayed bridge; however, it can simulate the basic mechanical characteristics of a single-pylon cable-stayed bridge. The length of the cable-stayed bridge model is 3.6 m, the height of the tower is 1.6 m, and the distance between the cable-stayed cables on the bridge is 0.3 m. There are 20 cable-stayed cables in total, and the thickness of the bridge panel is 4 mm.  
To accurately control the preset degree of damage, the cable was specially designed in this model. The cable is mainly composed of four parts in series including a wire rope segment with a diameter of 3 mm, load cell for cable force, spring segment (consisting of eight springs with the same stiffness in parallel), and small flanges for adjusting cable force (see Figure 19). The test bridge was instrumented with a dense array of sensors, including eighteen displacement sensors with an accuracy of 0.001 mm, and twenty load cell sensors for cable force. The diagram of the sensors are shown in Figure 19, illustrating the locations of the deflection sensors. The measurement point of the west side is W1-W10, and that of the east side is E1-E10 (see Figure 20).
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.3 I-Mac  
Improved MAC (I-MAC) avoids collision in high-density VANET scenarios via proper channel access. It reduces the loss of messages due to efficient channel utilization. It employs a dynamic TDMA-based mechanism with CSMA. An initial broadcast table (IBT) containing the vehicle's MAC address is created before sending data. Due to the possibility of more than one vehicle sending to IBT simultaneously, causing a collision, the nodes sense the channel before sending to IBT. A collision may even occur if nodes start transmitting as soon as it senses an idle channel; therefore, I-MAC uses dynamic inter-frame spacing (DIFS), which means nodes wait for a random time before transmitting through an idle channel to provide fair channel access.  
The protocol avoids merging collision and access collision, thus improving the channel's performance and the reliability of data transmission. There is much communication overheads, and the hidden node problem analysis needs further investigation.