esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Advances in Applications of Ionic Liquids in Miniaturized Microextraction Techniques
## 3 Conclusions  
Over the last decade, many researchers have been attracted from the versatile character of ILs and PILs and their potentials. As discussed above, one of the most challenging applications of ILs is their usage in microextraction techniques as extractants, intermediate solvents, mediators, and desorption solvents. Table 2 summarizes the superior properties and characteristics of ILs in comparison with the common sorbent materials that are used in the miniaturized sample preparation techniques described in the present review [4; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]. As the IL-based microextraction techniques are budding gradually, some limitations should be noted, such as the high cost of their synthesis, their incompatibility with GC due to their low volatility and their potential toxicity. In general terms, the research in the field of ionic liquids will not stop evolving as the need for green analytical procedures is the priority of sample preparation. Taking into consideration their promiscuous properties and advantages, not only will the microextraction processed be improved, but also separation techniques, such as liquid and gas chromatography or electrophoresis, will expand their potentials.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **SPME** & **DLIME** & **SBSK** & & & & **SOME** & & **SCSE** & \\ \hline
**Conventional** & & & & & & & & & & & & & & \\
**Southern** & **fromals** & & & & & & & & & & & & & \\
**DVN CAR, ERG, COW** & & & & & & & & & & & & & & & \\ \hline - & low thermal & - & high thermal & - & low density & - & high density & - & limited extraction & - & thermal & - & low viscosity & - & high viscosity & - & low thermal & - & mechanical \\ stability & - & high velocity & - & low viscosity & allow plane & efficiency & - & effective & - & viscosity & & & & & & & & & \\ - & high boiling & - & low viscosity & separation & & & & & & & & & & & & \\ - & short velocity data & points & results high & - & high/speedstic & - & and loss & - & - & - & - & - & - & - & - & - & - & - \\ - & - & small selectivity & - & low decomposition & instability of & - & high/speedstic & - & polar compounds & stability & - & high/speed & - & - & - & - & - & - \\ - - & traging & with heating & the deep & - & miscible or & - & limited number & - & - & - & - & - & - & - & - & - & - \\ - - & limited & & & & & & & & & & & & & & & \\ operating time & & & & with the & & & & & & & & & & & & \\ - & decropysmic & & & & & & & & & & & & & & \\ during heating & & & & & & - & high viscosity & & & & & & & & & \\  & & & & & and surface & & phase when & & & & & & & \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\  & & & & & & & & & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Limitations of the conventional sorbents and superior properties of ILs when they are used in the most commonly used miniaturized microextraction techniques.
# An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios
## 5 Conclusions  
To enhance the vehicle detection accuracy and traffic congestion detection accuracy in expressway surveillance scenarios, this paper proposes an improved vehicle detection algorithm based on CrowdDet, which is called IBCDet. Then, a tracking algorithm based on IBCDet is designed to calculate the running speed of vehicles, and the average running speed is used to achieve traffic congestion detection based on China expressway LoS criteria. The self-built NJRY dataset and the public dataset UA-DETRAC are used to verify the performance of the proposed algorithm. Experimental results demonstrate that our algorithm outperforms commonly used object detection algorithms in terms of both vehicle detection accuracy and traffic congestion detection accuracy. The proposed algorithm provides an effective solution for detecting vehicles and detecting traffic congestion in expressway surveillance scenarios.  
The proposed method in this paper can effectively detect traffic congestion on a dataset collected under good lighting conditions. However, its performance is not satisfactory under complex weather conditions and low-light conditions. In the future, we plan to expand our research to detect traffic congestion in different scenarios and under various weather conditions in order to further validate and improve the robustness and adaptability of our algorithm.  
Formal analysis, C.W.; proiect administration, C.W.; software, Y.C.; writing--original draft, Y.C.; writing--review, J.W.; investigation, Y.C. and J.Q. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Ministry of Transport's Industry Key Science and Technology Project (Project No. 2020-ZD3-029), and 2021 Nanjing Municipal Industry and Information Technology Development Special Fund Project (Project name: Construction of 5G-based Application Scenarios for Digital Operation and Control of Intelligent Transportation).  
Not applicable.  
The data are not publicly available due to involve a certain degree of privacy.  
The authors declare no conflict of interest.
## 1 Introduction
### Imaging of Hyperechogenic Media
In the case of a complete organ containing hyperechogenic bone obstacles and surrounding soft tissues (muscle, fat, skin), the Born-Kirchhoff \"low-contrast\" approximation is no longer sufficient; the different tissues have widely differed acoustic characteristics. However, the linear framework can be applied under some assumptions if, for example, the obstacle is not too large compared with the wavelength (in the case of the Rayleigh-Mie regime). This is the hypothesis made for the particular case of low frequency (<2 MHz) and low intensity (the number of multiple reflections is small) imaging of small bone structures, such as the long poorly ossified bones of a child or bones of a chicken drumstick, as in this work. The bones are thus analyzed as weakly heterogeneous obstacles giving moderate amplitude echoes. In this context, the propagation can be approximated by straight lines within the organ including bones, and the ray-based inversion algorithms are relevant [6; 19]. All perturbation parameters are defined according to the characteristics of the surrounding soft tissue, in which only compression waves propagate. Under these fluid-like modeling conditions, the reflection-mode USCT provides a morphological reconstruction of the contours of the different tissues only. In reflection, with a single transmitter-receiver transducer, the vector \(\mathbf{n}_{0}\) associated with the angle of incidence \(\phi\), and the vector \(\mathbf{n}\) associated with the angle of observation \(\theta\), are collinear:  
\[\mathbf{n}=-\mathbf{n}_{0}\in S,\ k\in[k_{min},k_{max}] \tag{28}\]Rotating the single transmitter-receiver transducer around the organ and transmitting pulses with a limited bandwidth corresponding to \(\left[k_{min},k_{max}\right]\) at each position results in a slice-by-slice spectral coverage of the object spectrum. In this work, the algorithm of reconstruction by summation of filtered backpropagation (FBP) [6] is used either with the ramp filter for pulse-mode USCT, and with the wavelet-matched filter for WCE-mode USCT.
## 2 Results
### Nootkatone Activates Plant Defense  
Since the inhibitory effects of nootkatone on _H. schachtti_ parasitism were not a result of direct compound-induced nematode mortality, and we found that successful nematode root penetration events were significantly decreased in the presence of the metabolite, we decided to investigate whether nootkatone induces plant defense mechanisms. The reactive oxygen species (ROS) burst is one of the plant's first defense lines. Therefore, we checked for changes in the ROS levels in _A. thaliana_ leaf discs  
Figure 4: Effects of nootkatone presence in the growth medium on nematode behavior. (**a**) Chemotaxis analysis of _Heterodera schachtti_ J2s towards _Arabidopsis thaliana_ root-containing discs of Knop agar supplemented with nootkatone or DMSO. (**b**) Percentage of J2s’ penetrating roots of _A. thaliana_ plants growing on Knop agar supplemented with nootkatone or DMSO. Values are means \(\pm\) SE of 3 biological replicates, \(n\) = 27. The asterisk marks indicate significant differences to the control based on a _t_-test (_p_ < 0.05).  
exposed to nootkatone, to the bacterial peptide flg22 as a positive control and to either 0.5% DMSO or water as the negative controls. As expected, the positive control flg22 induced a typical ROS burst peak. However, incubating leaf discs in nootkatone did not induce the ROS burst, as these samples showed a similar behavior to the negative controls (Figure 5).  
To investigate further the lines of the plant defense, the transcript levels of several, commonly used marker genes involved in different plant defense hormone pathways, including salicylic acid (SA), jasmonic acid (JA) and ethylene (ET), were determined by quantitative real-time (qRT)-PCR (Table S1) [20]. The results showed that growing the plant on a medium mixed with nootkatone activated SA- and JA-related defense mechanisms. As shown in Figure 6, the transcript levels of genes related to SA were elevated up to 70-fold. The SA markers PR1 and PR5 were highly upregulated and reached a 74- and 14-fold change of transcript levels, respectively. The SA signaling gene NPR1 was nine-fold upregulated compared with the control. Similarly, the JA pathway was upregulated, as revealed by a transcript level increase of 10- and 11-fold for the biosynthesis genes JAZ10 and CYP82C2, respectively, and 20-fold for the signaling marker LOX3. The transcript level of the ET biosynthesis-related gene ASC2 showed no significant changes in the presence of nootkatone compared to the control (Figure 6).
## 4 System Model
The SINR of the \(mth\) CU utilizing the \(kth\) sub-channel at time slot \(t\) may be expressed as follows:  
\[Y_{m,k}(t)=\frac{P_{C}(t)G_{mn}}{\sum_{n=1}^{N}p_{n}(t)G_{n}+\sum_{n=1}^{N}p_{ n}(t)G_{RUm}+\sigma}, \tag{6}\]  
The data rate of D2D pair \(n\) utilizing the uplink \(kth\) sub-channel can be determined at time slot t as follows:  
\[R_{n,k}(t)=W\;log_{2}(1+Y_{n,k}(t)), \tag{7}\]  
Furthermore, the data rate of CU \(m\) utilizing the \(kth\) channel can be determined at time slot t as follows:  
\[R_{m,k}(t)=W\;log_{2}(1+Y_{m,k}(t)), \tag{8}\]  
SE shows the effectiveness of using the available spectrum in terms of the data rate obtained regarding a given bandwidth. Thus, the SE for the \(nth\) D2D communication pair can be given as follows:  
\[SE_{n,k}(t)=\frac{\sum_{n=1}^{N}\sum_{k=1}^{K}R_{n,k}(t)}{W}, \tag{9}\]  
Based on the obtained data rate and energy consumption, the EE of the D2D communications scheme at time slot \(t\) is given as follows:  
\[EE_{n,k}(t)=\frac{\sum_{n=1}^{N}\sum_{k=1}^{K}R_{n,k}(t)}{\sum_{n=1}^{N}p_{n} (t)+p_{cir}}, \tag{10}\]  
where \(p_{cir}\) denotes the D2D pair circuit power consumption.
## 2 Materials and Methods
### Base Classifiers
#### 2.2.1 Support Vector Machine  
Support vector machine (SVM) is a supervised learning method, which can be widely used in statistical classification and regression analysis [23]. By mapping data points to high-dimensional space, a segmentation hyperplane is found in high-dimensional space. The segmentation hyperplane is determined by weighing the minimum interval and error rate. The interval refers to the distance from the segmentation hyperplane to the support vector, and the error rate refers to the number of incorrectly classified objects. At the same time, kernel functions are used to map data points to high-dimensional spaces, thereby reducing the difficulty of classification. For the binary classification problem in this study, assuming a training set is \((x_{i},y_{i})(i=1,2,...n,y\in[-1,1])\), a hyperplane can be constructed as \(wx+b=0\), where \(w\) is the normal vector determining the direction of the hyperplane, and \(b\) is the offset term determining the distance between the hyperplane and the origin. The solution to the hyperplane is a constrained optimization problem, and by utilizing the Lagrange multiplier duality, it can be transformed into the following optimization problem:  
\[\begin{split}\max_{\alpha}[\sum_{i=1}^{n}\alpha_{i}-\frac{1}{2} \sum_{i=1}^{n}\sum_{j=1}^{n}(\alpha_{i}\alpha_{j}y_{i}y_{j})]\\ \text{s.t.}\sum_{i=1}^{n}(\alpha_{i}y_{i}=0)(0<\alpha_{i}<C,i=1,2,...,n)\end{split} \tag{1}\]where \(\alpha_{i}\), \(\alpha_{j}\) represents the Lagrange multiplier, and \(C\) is the penalty coefficient. The ultimate optimal classification function is given by:  
\[f(x)=sgn[\sum_{i=1}^{n}(\alpha_{i}^{*}y_{i}x_{i}x_{i}^{T}+b^{*})] \tag{2}\]  
where \(\alpha_{i}\) represents the optimal Lagrange multiplier, and \(b^{*}\) is the optimal coefficient \(b\). Moreover, given the nonlinear nature of the problem in this study, we incorporated a radial basis kernel function, expressed as follows:  
\[k(x_{i},x_{j})=\exp(-\frac{1}{2\gamma^{2}}\left\|x_{i}-x_{j}\right\|^{2}) \tag{3}\]  
where \(\gamma\) represents the bandwidth of the radial basis kernel function.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sensor Calibration
#### 3.2.1 pH Sensor Calibration  
First of all, pH sensors need to be immersed in a 3N KCl solution for at least 8 h to activate the sensor after initial use or prolonged storage. This process helps to restore the electrode's original performance and prepares it for subsequent measurements [22]. To ensure the long-term accuracy of the pH sensor, we calibrate it regularly with pH buffer solutions (e.g., pH 4.00, pH 7.00, and pH 10.00) to ensure accurate calibration at different pH values. During daily use, the sensor is rinsed with distilled water after each measurement and stored in a protective cover filled with a 3.3 mol/L KCl solution to keep it moist, preventing the electrodes from drying out or scaling, thus ensuring the accuracy of its measurements. Additionally, if the sensor's response performance decreases, the electrode can be immersed in a 4% hydrogen fluoride solution for 3-5 s before being rinsed with distilled water and reconditioned in a KCl solution to restore its sensitivity.  
Figure 8: Power spectral density (PSD) results for different sampling intervals.
## 4 Evaluation
### Routing with Fixed-Quota Replication  
The message transmission overhead and resource utilization of PRoPHET without a replication technique justifies the need to control the number of message copies generated in the network. With fixed-quota replication, throughput increases with the number of message copies introduced in  
Figure 18: Average delivery delay (in hours) in the Skudai scenario.  
the network. As shown in Figure 17, increasing the quota from 2 to 4 increases throughput by 60%, while further increasing the quota from 4 to 8 causes an additional 50% improvement. Consequently, transmission overhead increases by 28.1% and 22.6%, respectively, and in terms of resource utilization, increases average energy consumption by 57.4% and 32.3%, respectively. These results signify two things. First, the choice of the message replication quota is a crucial determining factor of network performance. Unfortunately, it is difficult to determine the optimal quota under increasing node population and degrees of spatial locality. Furthermore, the optimal quota may vary, depending on current network conditions. Second, while increasing the message replication quota increases throughput, the overall performance does not seem to be improving at a rate that would catch up with LARep's results anytime soon. By increasing the replication quota to 16 and 32, we observed that throughput rather appears to be converging towards a maximum value. Moreover, each increment incurs an additional transmission overhead and consumes more energy, which gradually approaches that of PRoPHET without a replication technique.
# LoRaWAN Network for Fire Monitoring in Rural Environments
## 5 TTN Server Configuration and Application Server Design
### Web Interface  
The web server will also host a website that implements a script written in the interpreted programming and object-oriented language of JavaScript. The script was made using the Google Maps API that allows working with the maps and satellite images that Google offers. In addition, it also permits the use of paint markers, information windows, polygons, heat maps, etc. Figure 13 shows the web interface developed to collect data from the nodes and visualize the values measured by them. The bottom part of the web site shows the legend with the icons used to represent different events, such as information of the nodes (a blue circle, ), the presence (a red flame, ) or no presence of fire (a green leaf, ), and the heat maps (a map, ). The results are updated every 5 min. In Figure 13, which shows a simulated case for graphical testing of the system, three green leaf icons are shown. This means that the CO\({}_{2}\) concentration is below the threshold. One of these icons is not evolved by the heat map of the zone. The heat areas are only shown when the values determined by the 30-30-30 rule have been exceeded. When the threshold of CO\({}_{2}\) is exceeded, i.e., there is fire, the icon of a flame will be displayed. When clicking on each icon, a new dialog box appears with the values of the measured environmental variables. Both the Presence of Fire and Detection of High Fire Risk are closely related since the presence of fire implies the level of CO\({}_{2}\) has values higher than 700 ppm while the combination of temperature sensor, RH sensor and wind speed with a value of CO\({}_{2}\) lower than 700 ppm will give us an alarm of High Fire Risk.  
Figure 12: Workflow diagram of our system.  
When the threshold is exceeded, a pop-up window (see Figure 14) is displayed on the screen. It indicates the coordinate of the node that detected the fire. By accepting the alert, the map is accessed to see the nodes and the measured values (see Figure 15).  
Figure 14: The Pop-up window when a fire is detected.  
Figure 13: Website showing 4 nodes.  
The map shows the detected heat zones. The colored map identifies fire risk zones using green, yellow, orange, and red to indicate a low fire level, low-medium fire level, medium-high fire level, and high fire level, respectively. These are shown when the 30-30-30 rule values are exceeded. Figure 15 shows a scenario where three nodes detect values that exceed the thresholds of the 30-30-30 rule, where one of them has also detected a fire. There is a fourth node that does not detect dangerous levels.
## 2 Materials and Methods
### Definitions  
* The degree of calcification was visually estimated on fluoroscopy, defined as moderate when one-half of the total CTO segment exhibited visible residues, and as severe when the extension of calcification was >50% of the segment.
* Arterial tortuosity arises from abnormal elongation of the arteries and is characterized by blood vessel abnormalities, particularly abnormal twists and turns. The CTO was defined as straight if the pre-occlusive segment contained a bend of <70; moderate when a segment contained either two bends >70, or one bend >90; and as severe when it contained either two or more pre-occlusive bends of >90, or at least one bend of >120.
* The length of coronary occlusions was visually estimated from angiographic projections with single- or dual-contrast injections.  
* Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory lung disease that causes obstructed airflow from the lungs. It is characterized by long-term respiratory symptoms and airflow limitation.
* The morphology of the vessel stump was characterized as tapered, blunt, or unseen, depending on its appearance on fluoroscopy. The angiographic assessment of collateral connections was made according to the Werner classification.
* Dyslipidemia is present when an abnormally high amount of lipids is present.
* Renal failure is a long-term condition of kidney malfunction.
* Family history of coronary disease is present when one or more close relatives had early coronary artery disease (men <55 years, women <65 years).
* Peripheral disease was defined when abnormal narrowing of arteries was present.
* Segmental regional abnormalities were defined as regional abnormalities in contractile function.
* Previous MI was defined as documented history of previous myocardial infarction.
* In-stent CTO was defined when CTO inside a previous PCI existed.
* CTO location represented the vessel in which the CTO is located.
* Previous CABG corresponded to history of coronary artery bypass surgery.
* Technical success was defined as a residual stenosis of <10% at the end of the procedure with TIMI flow grade three antegrade flow.
# Design and Analysis of Independently Adjustable Large In-Pipe Robot for Long-Distance Pipeline
## 4 Simulation and Experimental test
### Experiment  
To verify the fundamental capability of the large in-pipe robot, we made an experiment in a steel tube with an inside diameter of 990 mm. The robot ran repeatedly in three-meter pipes to verify our theory. Initially, the robot was placed into the pipe at the posture shown in Figure 6a, three motors were given the same power.  
The top crawler was given a set pressure with the pipe wall. As shown in Figure 15, the robot completed the whole walking process in 23 s. Observing the motion of the robot, a slight offset had occurred in clockwise direction. This was not an ideal situation, so we monitored the data returned by the encoders and pressure sensors, as illustrated in Figure 16a,b.  
Red, blue and green curves represented different crawlers, respectively. Blue and green curves represented the lower right and left crawlers, respectively. Since encoders directly measured the rotation of the track wheels, the measurement speed fluctuated greatly [26]. In general, speed of two lower crawlers is consistent, and the speed of the top track is the fastest. However, this was due to slippage, the actual distance is less than the measured distance. The pressure data showed different characteristics. The threshold value of the top red curve is given by us. The curve consisted of waveforms of pressure-putter adjustment process. The pressure on the lower two crawlers was within a uniform range, with no significant changing in speed at all. When the robot was designed, the structure of each crawler had the offset along the center, so there was a certain deviation in the pressure. Moreover, with Figures 6a and 7a, the offset was toward the crawler with higher pressure  
Figure 16: (**a**) Speed of three crawlers moving straight; (**b**) pressure on the three crawlers of moving straight.  
Figure 15: Straight driving experiment (every 3 s).  
average power of the lower two crawlers were 56.5 w and 60.3 w. Meanwhile, infrared lidar scans the inside of the tube. The scan, shown as Figure 18, indicates that infrared lidar worked was stable and the vibration of the track part was not transmitted to the central body.  
Next, validated the theory of the in-pipe actively adjusting strategy. We used the PID algorithm to control the three-crawlers differential speed of the robot, as described in Figure 6b. The differential ratio is set to 3%. From Figure 19, the robot generated a rotation angle and reached its maximum rotation in 20 s and the robot completed the operation at this angle. Through many tests, the angle was about 10.9 degrees.