esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Hfq-CTR Stabilizes RNA II Secondary Structure, but Not That of RNA I  
As Hfq appears to have a stabilizing effect on some RNAs [49], we tested the effect of Hfq-CTR on RNA I and RNA II secondary structures. We observed different effects on the stabilization of RNA I and RNA II SL1 structures. Specifically, we observed that SL1 of both RNAs did not form alone and that Hfq-CTR significantly stabilized the RNA II SL1 structure (Figure 9A) but did not stabilize the RNA I SL1 structure (Figure 9B). This could be observed by a significant increase in peaks at 180 and 270 nm. This effect is nearly instantaneous and occurs in less than 2 min.  
Note that the SRCD increase at 183 nm (and to a minor extent at 262 nm) in Figure 6 could be due to CTR-induced RNA II folding. In this case, the increase would only be \(\sim\)5 mdeg as in Figure 9A; nevertheless, it is more than 10 mdeg for RNAi and RNAi annealing by the CTR. Therefore, we can conclude that the effect observed in Figure 6 is indicative for RNAi:RNAII duplex formation and not only from RNAII folding by the CTR.  
Figure 5: Observation of dsDNA CoEl1 origin alignment by Hfq-CTR using SRLD. Dark green: 0\({}^{\circ}\), dark blue 90\({}^{\circ}\), light blue 180\({}^{\circ}\), light green: 270\({}^{\circ}\). No DNA alignment occurs without the protein. As previously shown, Hfq-CTR and short DNA alone do not align [35].  
Figure 6: (**A**) Observation of RNA I and RNA II annealing by Hfq-CTR using SRCD (from blue t = 0 min to red t = 260 min). (**B**) Kinetics of annealing at 183 (blue) and 262 nm (red). Here an initial increase in A-form helix formation (183 nm) is followed by a slower gradual formation of base pairing (262 nm). Kinetic constants \(\mathrm{k_{cat}}^{\mathrm{app}}\) were determined during initial rate conditions. Note that spontaneous RNA annealing also occurs without the protein but to a lesser extent, and it was subtracted.  
Figure 7: Observation of RNAi-RNAII alignment by CTR using SRLD (CTR signal was subtracted). Inset: RNAi-RNAII without CTR; here, only a slight alignment occurs. Dark green: 0\({}^{\circ}\), dark blue 90\({}^{\circ}\), light blue 180\({}^{\circ}\), light green: 270\({}^{\circ}\).
## 4 Selection and Analysis of Resonant Compensation Network  
### Selection of Resonant Compensation Networks  
In WPT systems, adding a resonant compensation network can bring the following advantages. (1) During power transmission, the voltage and current stress of components can be reduced, thereby improving system safety. (2) Resonant compensation networks can adjust impedance, matching the impedance between the transmitting and receivingsides while reducing system reactive power and minimizing losses, thereby improving system efficiency. (3) By integrating different resonant compensation topologies, the system can achieve diverse output characteristics, including constant-voltage (CV) and constant-current (CC) modes, to meet the different needs of various loads and enhance the practicality of the system. Overall, resonant compensation networks can be classified into two types based on their complexity: low-order and high-order.
## 2 Materials and Methods  
### Subjects  
Our study included 40 participants divided into two groups: the DD group, consisting of youths diagnosed with depressive disorder (DD), and the control group of healthy peers. Table 1 provides a summary of the subject dataset. The dataset was obtained from previous research [24].  
The DD group consisted of 20 subjects, of whom 16 (80%) were female, with a mean age of 14.65 years (SD = 1.63). In total, 90% of participants were right-handed. All individuals  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Characteristics**} & \multicolumn{2}{c}{**DD 1**} & \multicolumn{2}{c}{**Control**} & \multirow{2}{*}{_p-Value_} \\ \cline{2-2} \cline{4-6}  & **Mean** & **SD** & **Mean** & **SD** & \\ \hline Age (years) & 14.6 & 1.6 & 15.9 & 0.4 & 0.002 \\ EHI score 2 & 53.8 & 56.0 & 51.8 & 55.2 & 0.989 \\ SMFQ 3 & 13.0 & 6.8 & 6.4 & 5.1 & 0.002 \\ SCARED 4 & 37.5 & 15.7 & 22.8 & 12.3 & 0.002 \\ Duration of disturbances (months) & 17.7 & 10.6 & / & / & / \\ Antidepressant therapy duration & 15.8 & 17.0 & / & / & / \\ (weeks) & & & & & \\ \hline  & n & \% & n & \% & \(p\)-value \\ \hline Number of subjects & 20 & 100 & 20 & 100 & / \\ \hline Antidepressant therapy & 11 & 55 & / & / & / \\ Right-handed subjects & 18 & 90 & 17 & 85 & 0.012 \\ Male subjects & 4 & 20 & 10 & 50 & 0.140 \\ \hline \hline \end{tabular} \({}^{\dagger}\) DD—subjects with DD. 2 EHI score—Edinburgh Handedness Inventory score. 3 SMFQ—Short Mood and Feelings Questionnaire. 4 SCARED—Screen for Child Anxiety-Related Emotional Disorders.  
\end{table}
Table 1: Subject dataset description.  
in the DD group were diagnosed according to DSM-5 diagnostic criteria for depressive disorder [9]. In total, 55% of the subjects received antidepressant therapy--either selective serotonin reuptake inhibitors (SSRIs) or tricyclic antidepressants (TCAs)--an average of 15.8 weeks (range: 1-48 weeks) prior to the experiment.  
The control group consisted of 20 healthy subjects, of whom 10 (50%) were female, with a mean age of 15.9 years (SD = 0.45). In total, 85% of participants were right-handed. All subjects had no history of current or past neurological or psychiatric disorders.  
The following self-report questionnaires were used: (1) handedness--Edinburgh Handedness Inventory (EHI) score [37; 38]; (2) depression--Short Mood and Feelings Questionnaire [39]; and (3) anxiety--Screen for Child Anxiety-Related Emotional Disorders [40].
## 2 Related Work  
### Characteristics of RSSI and CSI  
The low-dimensional RSS information makes it possible to perform the regional level of indoor positioning. RSSI, as an easy to get a signal feature, is often used in an indoor positioning system. RSSI belongs to the Medium Access Control (MAC) layer and comes from each packet. Unlike RSS information, CSI information [17; 18; 19; 20] considers the signal transmission between different subchannels and different antennas. In addition, its dimension and complexity provide information much richer than the RSS information.  
This unique physical feature meets the following requirements:  
1. It has excellent resistance to interference in the 2.4 GHz band signal and has less fluctuation in a stable environment. It can also reflect the changes in the environment.
2. The use of OFDM technology to distinguish signals of different paths as finely as possible.  
CSI is a fine-grained attribute value of the physical layer that describes the amplitude and phase of the frequency domain corresponding to each subcarrier. The CSI can reflect the attenuation of the wireless signal as it travels between the transmitter and receiver. Table 1 demonstrates the differences between RSSI and CSI.  
In this paper, the RSSI value and CSI value were collected at fixed positions of 1 m, 4 m and 7 m in the same indoor environment, and the results of their multipath effects were compared. Figure 2 compares the stability between the sampled RSSI value and the amplitude of the CSI sampled on channel 2.  
Although the CSI value collected on a certain channel will change, compared with the collected RSSI value, the CSI value collected less varies in time and remains stable.
## 4 Discussion
### Main Findings  
The risk of direct contact with the wheels is highest for the limbs and lowest for the head. Lying on the tracks is not surprisingly generally worse than jumping or standing (Figure 6), although this does not account for any voluntary movement such as intentional positioning in the centre of the track. The models therefore more closely resemble a pedestrian falling onto the tracks and not moving. Third rail contact occurs about 20% of the time; however, this is likely an overestimation due to high penetration into the third rail guard, which is a modelling limitation. There is a weak impact velocity dependency on contact scores for impact velocities above 4 ms\({}^{-1}\) (Figure 7). There is a strong impact velocity dependency on primary contact HIC\({}_{15}\) scores but not on secondary contact HIC\({}_{15}\) (Figure 8), which is similar to findings from vehicle-pedestrian collisions [20]. Regarding head injury risk, about 30% of the Baseline Simulation Test Sample (BSTS) cases are potentially survivable, with a HIC\({}_{15}\) < 2000 for both primary and secondary impacts (Figure 9). The contact scores decrease with drainage trough depth up to 0.5 m, with the largest benefit observed in jumping impact scenarios (Figure 10). Increasing the trough depth beyond 0.5 m shows little benefit. Trough depth shows a significant difference in secondary contact HIC\({}_{15}\) between 0 and 0.5 m trough depth (Figure 11), and this is likely caused by a deeper trough reducing the likelihood of a pedestrian hitting the ground headfirst. This does not account for any potential change in other body region injuries.  
Figure 11: Relationship between drainage trough depth and secondary contact HIC\({}_{15}\) score for jumping and standing impact scenarios.
# Numerical Analysis of the Cooling Performance in a 7.2 kW Integrated Bidirectional OBC/LDC Module
###### Abstract  
To satisfy increasing demands for ecofriendly vehicles, researchers are now studying electric vehicle (EV)-related technologies. In particular, integrated bidirectional onboard battery charger (OBC)/low-voltage DC-DC converter (LDC) modules are being researched to improve the efficiency of onboard chargers for EV charging applications. In this study, a numerical analysis method is proposed that considers the power loss and heat flow characteristics in the design of a 7.2 kW integrated bidirectional OBC/LDC module. The developed module supports four operating modes depending on the service situation: OBC and LDC single operation, OBC/LDC simultaneous operation, and LDC operation. The mode is selected based on the power system flow. The characteristics of the circuit were analyzed in each of the four modes to compute the heat loss from the major heating elements. The results of a numerical analysis of the internal cooling characteristics showed that the internal temperature was higher in the OBC single operating mode than in the OBC and LDC simultaneous operating mode in which the power loss was the highest. The results emphasize the importance of ensuring that cooling designs consider the characteristics of various modes as well as the worst-case power loss.  
7.2 kW integrated bidirectional OBC/LDC module; power loss; cooling performance; numerical analysis; circuit simulation +
Footnote †: journal: Applied Sciences  
_Article_  
Numerical Analysis of the Cooling Performance in a 7.2 kW Integrated Bidirectional OBC/LDC Module
## 3 Results and Discussion  
In order to validate the proposed 2 DOF harvester model in relation to the experimental results, the physical parameters of the device must be quantified. Most of them are easily measurable and are listed in Table 2. The electromechanical coupling \(\alpha\) is measured using a quasi-static test of a single magnet moving through the coil [18]. The damping coefficients are determined by the parameter identification method based on experimental frequency response data (comparing time histories from numerical simulation and experiment; see paper [24]).  
Figure 5: 2 DOF electromagnetic lumped-parameter model and electrical circuit.  
Three differential Equations (3)-(5) were numerically solved using the path-following (continuation) method in the Auto-07p software ([[https://sourceforge.net/projects/auto-07p/](https://sourceforge.net/projects/auto-07p/)]([https://sourceforge.net/projects/auto-07p/](https://sourceforge.net/projects/auto-07p/)), accessed on 16 May 2023) [30] in order to predict the magnets' responses and output current over a range of frequencies for different acceleration levels. The continuation method is used to study bifurcations, such as equilibria, stability, periodic and homoclinic orbits, and boundary-value problems. More information about this method can be found in [31].
## 3 Approximate Record Matching in Similarity Space  
### Problem Formulation  
We assume the given normalized similarity metric, \(s_{n}(\mathcal{R}_{1},\mathcal{R}_{2})\), which measures the similarity between two records, \(\mathcal{R}_{1}\) and \(\mathcal{R}_{2}\), as a real number in the interval \([0,1]\), with 1 representing complete similarity and 0 representing complete dissimilarity. Consider a fixed \(\alpha\), \(0<\alpha<1\). The applications distinguish relevant records by a binary classifier into two classes: the set of matches (\(\mathcal{M}\)) and the set of non-matches (\(\mathcal{N}\)), consisting of ordered pairs of records \((\mathcal{R}_{1},\mathcal{R}_{2})\). More formally,  
\[\begin{split}(\mathcal{R}_{1},\mathcal{R}_{2})\in\mathcal{M}& \text{if }s_{n}(\mathcal{R}_{1},\mathcal{R}_{2})\geq\alpha,\\ (\mathcal{R}_{1},\mathcal{R}_{2})\in\mathcal{N}& \text{if }s_{n}(\mathcal{R}_{1},\mathcal{R}_{2})<\alpha.\end{split} \tag{4}\]  
The decision of where to set the match/non-match threshold, \(\alpha\), is a balancing act. The threshold should be chosen based on an acceptable sensitivity (or recall, which is the proportion of truly matching records that are correctly linked by the algorithm) and positive predictive value (or precision, which is the proportion of records linked by the algorithm that are indeed true matches). The task is to find an algorithm that classifies the sets of matches and non-matches as accurately as possible, corresponding to realityas judged by human observation. In the scientific literature, we also encounter the terms _record linkage_, _data matching_, _fuzzy matching_, _entity resolution_, etc.  
In information retrieval theory and search engines, we can analogically meet with the phrase'retrieving relevant documents'. A record can be transformed into tokens of words. That is, each record is split into a set of tokens, \(\{X_{1},\ldots,\)\(X_{j},\ldots,\)\(X_{n}\}\in\mathcal{R}_{1}\). We denote the size of a record, \(\mathcal{R}_{1}\), by \(|\mathcal{R}_{1}|\), which is the number of tokens in \(\mathcal{R}_{1}\).
## 3 Results and Discussion
### Cone Calorimetric Analysis
#### 3.1.2 Time to Ignition (TTI)  
Ignition time is the time that it takes for the surface of the material is ignited when it is burned. The longer TTI becomes, the more difficult it is for the polymer material to ignite under this condition, and thus the better the fire resistance gets of the material. It is one of the important indicators to evaluate the fire resistance of polymer materials [10; 25].  
As can be seen from Table 2, the TTI of EP composites with CF was delayed compared to that of EP. F15 was extended up to 46 s, which was the latest to ignition. This is because CF inhibits the thermal decomposition of the EP, thus inhibiting the release of combustible gases. It can be seen from Table 2 that the TTI of the three-phase composites is also improved. The ignition time of F5N5 and F5N10 are extended to 4 s and 26 s compared to that of F5; the ignition time of F10N5 is 18 s more than that of F10, while the ignition time of F7N7 was shortened by 4 s. This shows that the three-phase composites have excellent performance in fire resistance, thereby reducing the flammability of EP.
## 3 Geocasting Based on Local Coverage Areas
### Global Updating Mechanism for Building Trees and Local Convex Hulls  
To construct and keep up-to-date routing trees and local convex hulls, a _global updating_ mechanism is executed periodically with a frequency \(f\). The value of frequency \(f\) is important to keep up-to-date routing structure. The effect of frequency \(f\) of global updating on the performance of our geocasting protocol is evaluated in Section 5.4. The periodic _global updates_ consist of two phases: (i) _helloing from sinks_ for tree building and multi-sink network partitioning, and (ii) _convex hull forwarding_ to the parent nodes for completing the structure with local convex hulls of sensors and sinks.  
In the first phase, each sink repeats broadcasting \"hello\" packets every \(f\) (_i.e._, frequency) seconds to determine the paths to every node in the network and constructs a routing tree based on a given metric. In this work, we use the minimum hop count metric for tree construction. At the end of helloing process, each sensor node knows its hop count to each sink in the network. We use the definitions _parent node_ and _child node_ to indicate node positions in a routing tree. A child node selects a parent node as intermediate node in order to get messages towards a sink after helloing process. Minimum hop count metric results in the shortest path routing trees, which are constructed by having each node choose the neighbor with the minimum hop count to a sink as its parent. As a result, each node connects itself to the closest sink in terms of hop counts. Details and simulation results of different routing metrics (e.g., energy level) can be found in [23]. After routing tree construction, every node stores the logical address of its parent node in the tree, the sink it is connected to, and the hop count level from the sink (_i.e._, HCL). Nodes do not need to keep track of their (possibly many) child nodes. The reason for this is to keep the minimal necessary information for routing in order to minimize storage of a sensor node.  
In the second phase each node sends its parent a message called HELLO_TO_PARENT that includes the node's convex hull. Periodically, the local convex hull is transmitted to the parent node. For distributed construction of convex hulls along routing tree, every parent node receiving a convex hull from its child node merges the received convex hull with the local convex hull of itself. This procedure constructs a new composite convex hull, which is forwarded further up in the tree until reaching to the sink. Optionally, the convex hull is reduced using some form of compressing before transmitting (in order to limit memory usage by our protocol and energy consumption by reducing the size of transmitted/received coordinate list) as explained in our previous work [6].
# Intelligent Optimization of Process Parameters in Limit Gauge Hot Strip Rolling
## 1 Introduction
[PERSON] compared various online training methods in computational experiments, and the results show that intelligent algorithms are superior to traditional algorithms in terms of computational efficiency and computational accuracy [13]. [PERSON] studied the application status of artificial neural network in the field of rolling. His research shows that the artificial neural network has great advantages in single process parameter prediction, such as rolling force prediction, yield strength prediction, and coiling temperature prediction. The prediction accuracy of steel rolling related parameters can be significantly improved, and the quality of rolled products can be improved [14]. The artificial intelligence method can predict the rolling process based on field and experimental data, and can avoid the error caused by the assumption being divorced from reality and the simplification being too rough [15]. In the research of strip rolling, the commonly used artificial intelligence methods include artificial neural network (ANN) and support vector regression (SVR) [16]. Among them, the back propagation (BP) neural network is widely used in the research of various rolling models because of its high stability and high combination with other algorithms. The genetic algorithm is based on the widely existing natural selection and genetic mechanism in nature, and simulates the biological evolution mechanism on the computer to achieve the purpose of rapid search and optimization. It is simple and universal, and can maintain high optimization accuracy and efficiency [17]. By combining the two methods, the process optimization results with high reliability can be obtained when the number of test samples is small [18; 19]. In this paper, an intelligent optimization method of rolling process parameters for hot strip rolling based on BP neural network and genetic algorithm is proposed, which intelligently recommends the optimal process parameter combination to guide the actual production and improve the rolling stability. At present, the application of intelligent algorithms in the rolling field mainly focuses on the prediction of single process parameters, the optimization of neural network weight thresholds by genetic algorithm. Few authors discuss the problems that how to construct a sample set related to equipment status and how to use artificial intelligence models to optimize process parameter combinations. The author conducts a field study aimed at filling this research gap and closely integrated with the production site to provide instructive advice for production site technicians.
# Enhanced Adsorption of Methyl Orange by Mongolian Montmorillonite after Aluminum Pillaring
## 2 Materials and Methods
### Adsorption Kinetic and Isotherm Models  
#### 2.5.1 Adsorption Kinetic Models  
Adsorption kinetic models were studied in order to understand the rate of MO adsorption by the modified MMt with respect to the equilibration time. Pseudo-first-order (PFO) and pseudo-second-order (PSO) kinetic models were used to predict the adsorption rate of MO. If the adsorption process is fitted to the PFO kinetic model, it can be assumed to be physisorption [31]. The non-linearized form of the PFO model can be presented by the following equation [32]:  
\[q_{t}=q_{e}\Big{(}1-e^{-k_{1}t}\Big{)} \tag{2}\]  
where \(q_{t}\) is the adsorption capacity at time \(t\), mg/g; \(q_{e}\) is the adsorption capacity at equilibrium, mg/g; \(t\) is the adsorption time, min; and \(k_{1}\) is the rate constant, min\({}^{-1}\).  
If the adsorption process is fitted to the PSO kinetic model, it can be assumed to be chemisorption [33]. According to [PERSON] and [PERSON] [34], the non-linearized form of PSO model can be expressed as \[q_{t}=k_{2}q_{e}^{2}t/(1+k_{2}q_{e}t) \tag{3}\]  
where \(k_{2}\) is the PSO rate constant, (mg/g)/min.
## 4 Engineering Example Analysis  
### Project Overview  
With the aim to test CLNVD's vibration-reduction efficiency and optimization strategy, this section takes a 9-floor concrete frame as an example to analyze. This concrete frame building has a height of 36 m and a self-weight of 9784 tons. The seismic fortification intensity is 8 degrees (0.2 g), and the site's characteristic period is 0.45 s. The structural parameters are revealed in Table 2, and the dynamic properties are shown in Table 3. The structure utilizes Rayleigh damping, with a stiffness ratio coefficient of 2.3 \(\times\) 10\({}^{-3}\) and a mass ratio coefficient of 0.35. The additional CLNVD structural optimization design objective is as follows: under the circumstance of fortification earthquakes (PGA is 0.2 g), the damping ratio provided by CLNVD is 18%, and the peak inter-story displacement angle is smaller than 1/400; under rare earthquakes (PGA is 0.4 g),the peak inter-story displacement angle is smaller than 1/200, and \(f_{d}>3.5\) and \(f_{E}>30\).  
\begin{table}
\begin{tabular}{|c c c c c|} \hline \hline
**Sotry** & **Concrete Material** & **Column Section** & **Main Beam Section** & **Secondary Beam Section** \\ \hline \hline  & & **(mm \(\times\) mm)** & **(mm \(\times\) mm)** & **(mm \(\times\) mm)** \\ \hline
1 & C40 & 700 \(\times\) 700 & 300 \(\times\) 600 & 200 \(\times\) 400 \\
2 & C40 & 600 \(\times\) 600 & 300 \(\times\) 600 & 200 \(\times\) 400 \\
3–4 & C40 & 550 \(\times\) 550 & 300 \(\times\) 600 & 200 \(\times\) 400 \\
5–9 & C35 & 500 \(\times\) 500 & 300 \(\times\) 600 & 200 \(\times\) 400 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of the structure.  
Figure 13: The structure’s damping design flowchart.