esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Dynamics of a Freight Wagon Model with Viscous Damping
## 1 Introduction
In the variable damping force variant, the friction wedge and the bolster are mounted on the separate springs, while in the constant damping force, the wedge is attached to the same springs as the bolster [5]. Suspension friction dampers, regardless of the technical solution, have common advantages, namely: simplicity of design and fabrication, low cost, and easy maintenance. However, at the same time, they have significant disadvantages, such as suboptimal damping in the partially laden condition, the difficulties in controlling friction to the desired design values, and changes in friction coefficient due to contaminations and wear of the friction surfaces [6]. These negative factors, together with the need to reduce the impact of freight wagons on the track and increasing transport of goods by rail [7,8], are prompting the search for other solutions that provide adequate running behavior while keeping the bogie design complexity low [9, 10, 11]. One potential improvement to the running gear of freight wagons could be replacing dry friction damping with viscous damping. As an example of the use of viscous damping in a freight wagon, reference can be made to the historical series of experimental HSFV wagons [12] and the TF 25 bogies (Figure 5) that are currently in service in the UK [13]. Due to the literature gap concerning the comparison of the running behavior of freight wagons with variant damping (dry friction or viscous), this paper presents a comparative analysis of the results of simulation tests carried out according to the proposed scenarios. Figure 4: A three-piece bogie [4]. Figure 3: Y25 bogie with parabolic springs: (**a**) Running gear model; (**b**) Diagram of the friction damper with Lenoir link (adapted from [4]). The remainder of this paper is structured as follows. The multibody simulation model of the wagon is described in Section 2. The implemented mathematical damping models are introduced in Section 3. Simulation study conditions, analysis of the results, and discussion are included in Section 4. Section 5 closes with a conclusion and outlook.
## 3 Results and Discussion
### Anticancer Activity of TAEM and TAEE  
Inhibitory effect on A549 cells of various concentrations of TAEM and TAEE (12.5, 25, 50, 100, 200, 400 mg/mL) was measured. Results showed that both of them inhibited A549 cells in a concentration-dependent manner (Figure 5).  
The inhibition rate of A549 cells reached 20-30%, even with TAEM and TAEE at concentrations of 12.5 \(\upmu\)g/mL, and with the IC\({}_{50}\) of TAEM and TAEE being 188.26 and 356.51 \(\upmu\)g/mL. Moreover, at low concentrations (12.5-100 \(\upmu\)g/mL), TAEM had only a slight advantage over TAEE (3%-5%), however, when the concentration increased to 200-400 \(\upmu\)g/mL, TAEM possessed stronger cytotoxicity than TAEE (>12%). It was reported that ergostane triterpenoids of AC, including Antcin A, B, C, H and K exhibited certain anticancer activity [24; 26], especially Antcin K, which had the strongest cytotoxicity effect on human hepatoma cells [26]. However, [PERSON] et al. found that the cytotoxicity of Antcin H against hepatocellular carcinoma cells was very low, and it probably played a role in anti-metastasis [27]. Therefore, the content of Antcin H in TAEM may not be the cause of its increased anticancer activity. [PERSON] et al. [28] found that compounds with hydroxyl groups at C-3 had lower cytotoxicity to cancer cells, while carbonyl groups at C-3 were very important for their cytotoxicity. Antcin H and Antcin K both have a hydroxyl group at the C-3 position, so we speculated that the increase of their two contents is not related to the increase of TAEM anti-cancer cell activity. Antcin C and Antcin A both contain carbonyl groups at the C-3 position, which is the main factor affecting the cytotoxicity of extracts from AC to cancer cells, so this might explain why the difference of anticancer activity between TAEM and TAEE at the same concentration is not particularly large.  
Figure 5: TAEM and TAEE on the average inhibition rate of A549 cancer cells. Data are expressed as mean \(\pm\) SD (\(n=3\)).
_Algorithms_**2013**, \(6\), 485-493; doi:10.3390/a6030485  
_Algorithms_  
**ISSN 1999-4893**  
www.mdpi.com/journal/algorithms  
_Article_  
**A Simple Algorithm for Solving for the Generalized Longest Common Subsequence (LCS) Problem with a Substring Exclusion Constraint**  
**[PERSON] \({}^{1}\) and [PERSON] \({}^{1,2,\ast}\)**  
\({}^{1}\) Faculty of Mathematics & Computer Science, Quanzhou Normal University, Quanzhou 362000, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Faculty of Mathematics & Computer Science, Fuzhou University, Fuzhou 350108, China  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +86-595-22916878; Fax: +86-595-22796091.  
_Received: 14 June 2013; in revised form: 15 July 2013 / Accepted: 24 July 2013 / Published: 15 August 2013_  
**Abstract:** This paper studies the string-excluding (STR-EC)-constrained longest common subsequence (LCS) problem, a generalized LCS problem. For the two input sequences, \(X\) and \(Y\), of lengths \(n\) and \(m\) and a constraint string, \(P\), of length \(r\), the goal is to find the longest common subsequence, \(Z\), of \(X\) and \(Y\) that excludes \(P\) as a substring. The problem and its solution were first proposed by [PERSON] and [PERSON], but we found that their algorithm cannot solve the problem correctly. A new dynamic programming solution for the STR-EC-LCS problem is then presented in this paper, and the correctness of the new algorithm is proven. The time complexity of the new algorithm is \(O(nmr)\).  
**Keywords:** constrained LCS; string-excluding; dynamic programming
# Experimental Analysis and Simulation of a Porous Absorbing Layer for Noise Barriers
## 3 Results and Discussion
### Sound Absorption Coefficient under Diffuse Incidence--Experimental vs. London's Equations  
In Figures 12 and 13, the experimental and theoretical sound-absorption coefficient curves in diffuse field conditions are presented for the mixture AE 5.18. The theoretical simulation and the experimental results were obtained for all scenarios using a thickness of 8 cm.  
Results for normal incident waves and for a diffuse field show considerably distinct behaviors when comparing the sound absorption curves obtained for both scenarios. As anticipated, the diffuse field curves exhibit a broader frequency range where significant sound absorption is obtained, clearly demonstrating the action of refracted sound waves inside the porous concrete material volume.  
When comparing the various methods for estimating diffuse field sound absorption, London's Equation 1 (London 1 exp-data 008 and London 1 HS PMC 008) appears to fit the experimental curve more closely, particularly the one produced using the Horoshenkov-Swift model and the adjusted macroscopic properties of the examined material. Although it predicts greater values at lower frequencies and smaller absorption values in the mid-frequency region, the second London equation also yields findings that are comparable. It  
Figure 11: Cross-validation results of normal incidence sound absorption for 4 cm specimens with the different Aggregate/Cement ratios.  
Figure 12: Experimental and theoretical sound-absorption curves in diffuse field conditions, considering: (**a**) the directly measured normal incidence sound absorption, and (**b**) for Horoshenkov–Swift model with corrected macroscopic parameters.  
should be emphasized that the predicted diffuse field absorption using London's equations is merely a rough estimate and should not be used for anything other than the analysis of locally reacting materials. Since the tested solution, in this scope, is obviously bulk-reacting, considerable variations take place. Additionally, the diffuse field laboratory test was performed on a finite 3 \(\times\) 3 m\({}^{2}\) panel, although all equations apply to infinite panels, thus resulting in different behavior of the whole solution (see [36]).  
Figure 13 shows a final plot comparing the observed sound absorption derived from the reverberant room test together with the two London models, considering bands of one-third of an octave and the entire frequency range between 125 Hz and 4000 Hz. These curves demonstrate that the two simple models provide a good estimation of the trend of the sound absorption curve, albeit with some apparent inconsistencies and oscillations. The predictions based on the Horoshenkov-Swift theoretical model along with the London models appear to relate well with the experimental curve, even in the higher frequency range beyond 2000 Hz (which was not measured in the impedance tube). Nevertheless, the match is clearly not perfect and some deviations can be observed.
## 4 Results and Discussion
### Measurements for Event Dispatching in the Gateway  
Another datum of interest is the time required for an event to be dispatched from the gateway once it has been received from the WSN. This time is measured from the moment an event is notified to the gateway to the time the POST message is delivered to the proper subscribers. Figure 9 shows the results of this after 300 tests.  
Figure 8: Time to generate an event.  
Figure 9: Time to dispatch an event from the gateway.  
In this case the maximum time has been of 79 milliseconds and a minimum of 26. The average mean has been of 32.78 milliseconds and the standard deviation 6.03 milliseconds.
# Forecasting Landslides via Three-Dimensional Discrete Element Modeling: Helong Landslide Case Study
###### Abstract  
Forecasting the occurrence potential of landslides is important but challenging. We aimed to forecast the failure potential of the Helong landslide, which is temporarily stable but has clearly deformed in recent years. To achieve the goal, we used reconnaissance, remote sensing, drilling, laboratory tests, topographical analysis, and electrical resistivity tomography (ERT). The factor of safety (FOS) of the slope was first calculated using a limit equilibrium method. The results show the FOS of the slope was 1.856 under natural conditions, 1.506 under the earthquake conditions, 1.318 under light rainfall, 0.986 under heavy rainfall, 1.075 under light rainfall and earthquake, and 0.832 under simultaneous heavy rainfall and earthquake. When the FOS is less than 1.35, the slope is considered metastable according to the Technical Code for Building Slope Engineering (GB50330-2013) published by the Chinese Ministry of Housing and Urban-Rural Development. Based on the drilling data and digital elevation data, a three-dimensional discrete element method (DEM) model was used to simulate potential landslides. The simulation was used to examine catastrophic slope failure under heavy rainfall conditions within a range of friction coefficients and the corresponding affected areas were determined. Then, we analyzed a typical run-out process. The dynamic information of the run-out behavior, including velocity, run-out distance, and depth, were obtained, which is useful for decision support and future landslide hazard assessment.  
Keywords:landslide; discrete element method; stability analysis; failure potential forecasting +
Footnote †: journal: Applied Sciences
# Scheduling of Industrial Control Traffic for Dynamic RAN Slicing with Distributed Massive MIMO +
## 4 System Model
We then consider a variable frame length, which is a more complex problem but can result in better performance in terms of the optimization objectives. \begin{table}
\begin{tabular}{c l} \hline \hline
**Parameter** & **Meaning** \\ \hline \(\mathcal{K}\) & set of nodes needing to communicate \\  & \((\mathcal{K}=\{1,2,\ldots,K\})\) \\ \hline \(\hat{h}(k)\) & uplink demand rate of node \(k\in\mathcal{K}\) \\ \hline \(\tilde{h}(k)\) & downlink demand rate of node \(k\in\mathcal{K}\) \\ \hline \(d(k)\) & maximum allowable period, in slots, for node \(k\in\mathcal{K}\) \\ \hline \(\mathcal{S}\) & maximal frame (set of slots), \(\mathcal{S}=\{1,2,\ldots,S\}\) \\ \hline \(P\) & maximum allowable number of pilots per slot \\ \hline \(T\) & frame length (\(T\leq S\)), can be a variable as well \\ \hline \(\mathcal{T}\) & feasible frame (\(\mathcal{T}=\{1,2,\ldots,T\}\)) of length \(T\) \\ \hline \(\mathcal{K}(T)\) & set of nodes with \(d(k)<T\) \\  & \((\mathcal{K}(T)=\{k\in\mathcal{K}:\,d(k)<T\})\) \\ \hline \(T\) & frame length (\(T\leq S\)) \\ \hline \(T_{k}\) & last slot of the frame with a pilot allocated to \(k\in\mathcal{K}\) (\(T_{k}\leq T\)) \\ \hline \(t_{k}\) & first slot of the frame with a pilot allocated to \(k\in\mathcal{K}\) (\(t_{k}\leq T\)) \\ \hline \(X^{i}\) & whether or not at least one node is allocated a pilot in slot \(i\in\mathcal{S}\) \\ \hline \(x^{i}_{k}\) & whether or not node \(k\in\mathcal{K}\) is allocated a pilot in slot \(i\in\mathcal{S}\) \\ \hline \(p\) & maximum number of pilots used in any slot \\ \hline \(Z^{i}_{k}\) & binary variables indicating there are remaining slots with pilots allocated to \(k\in\mathcal{K}\) \\  & later in the frame \\ \hline \(z^{i}_{k}\) & binary variables indicating there are no slots with pilots allocated to \(k\in\mathcal{K}\) earlier in the frame \\ \hline \(\lambda_{i}\) & binary variables indicating whether slot \(i\in\mathcal{S}\) is the last slot in the frame \\ \hline \(\mathbb{B}\) & the set \(\{0,1\}\) \\ \hline \(\mathbb{R}_{+}\) & the set of positive real numbers \\ \hline \(\mathbb{Z}_{+}\) & the set of positive integers \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Notation.
## 3 A Short Portrayal of the WSN Network
### Network Model  
Spatially dispersed nodes in wireless sensor networks can convey information gleaned from a monitored or controlled field across a wireless channel. Due to the wide range of potential applications, including those for pressure, light, and other variables, WSNs are a significant area of research. The WSN contains varied SNs signified by \(Z_{S}\), and an SN acts as an active sensor throughout the broadcast of data amid BS and CH. Habitually, the WSN is related to data sensing, radio communique, allotment of the sensor, topology characteristics, and energy utilization. The sensor is distributed arbitrarily in manual mode at every application area. The SNs are linked to form into clusters, wherein the CH is preferred and CH's count is implied by \(Z_{C}\). The SN in the cluster has to encompass a minimal distance from the CH. The whole SNs from the expected area gather correlated data and communicate it to the CH. The linked CH transmits the information to the BS. Each sink node's transmission pattern is based on a uniform distribution and distributes data over the maximum radio range possible within the range. This cluster, which takes the shape of a network, is made up of sensor nodes. The phrase designates the relevant cluster head for every cluster. This method of propagating decrease of from the cluster to the BS is collectively referred to as cluster head-based routing. The network model refers to the distance from other nodes to the cluster head as the distance between the cluster head and the base station. For easy transmission, the cluster head is chosen. In the proposed model, the cluster head is chosen based upon the energy, delay, distance, security, trust, and RSSI. To conserve significantly more energy efficiency, power consumption, and energy load balance during each cycle of the sensor nodes, it is interesting to choose a cluster head.  
The best way to identify the CH is to pick the node that uses the least amount of energy, allowing the CH to send out more data packets. The main factors used to identify the CH are energy or distance. Furthermore, security is regarded as a crucial component because it ensures protected data transmission. The pictorial representation of the BEA-SSA model is shown in Figure 1. Wherein, three cluster groups (Cluster 1, Cluster 2 and Cluster 3) are considered, from each group, CH is selected and connected with the base station. Here, the selection of the CH depends upon the following constraints: Energy, Trust, Distance, Security, RSSI, and Delay.
## 4 Conclusions  
In this study, specimens were produced from a commercial train axle made of manganese steel and subjected to high-cycle fatigue tests and Charpy impact tests over the temperature range of \(-60\) to \(60\)\({}^{\circ}\)C to obtain fatigue strength and impact absorption energy data about the axle material. Some important results were as follows.  
A 20 kHz ultrasonic actuator fatigue tester was successfully developed. It is capable of fatigue tests down to \(-160\)\({}^{\circ}\)C by spraying liquid nitrogen into the chamber.  
The fatigue strength values of the axle material at 2 million cycles at \(+20\), \(-30\), and, \(-60\)\({}^{\circ}\)C were 210.1, 225.2, and 251.9 MPa, respectively.  
The fatigue strength of the axle material at \(-30\) and \(-60\)\({}^{\circ}\)C increased by 7.1 and 19.9%, respectively, compared to the fatigue strength at room temperature.  
Fatigue failure occurred in the \(2\times 106\) to \(1\times 107\) cycle range, above the 2 million cycles traditionally considered to be the carbon steel fatigue limit.  
The shock-absorbing energy of the axle material decreased gradually with decreasing temperature, and the axle material became fully brittle in the range of \(-30\) to \(-40\)\({}^{\circ}\)C. The shock absorption energy at \(-60\)\({}^{\circ}\)C was 81% lower than that at 20 \({}^{\circ}\)C. We inferred from the impact test results that the current axle material is vulnerable to external shocks at temperatures below \(-30\)\({}^{\circ}\)C. These results suggest that it will be necessary to improve the low-temperature properties of the axle material used in this study for use in cold climates.  
Conceptualization and Methodology; Funding Acquisition, H.-S.M.; Analysis, Investigation, Data Curation, Writing--Original Draft and Editing, B.-C.G.; Ultrasonic Fatigue Testing, I.-S.C. All authors have read and agreed to the published version of the manuscript.  
This study was supported by an R&D program of Korea Railroad Research Institute.  
The authors declare no conflict of interest.
## 4 Results and Discussion
### Best BPNN Framework for Each Strategy  
First of all, for the neural network model, they were optimized according to five strategies by the genetic algorithm separately. The optimization process is shown in Figure 2. Through GA, the population was continuously inherited and evolved to find the optimal combination of genes from the gene pool. Each gene represented a hyperparameter, and the combination of hyperparameters was used to construct the final neural network framework, which would avoid repeating invalid attempts and effectively reduce the time spent on model building. After inputting the data into the genetic algorithm model, the hyperparameters of the BPNN were obtained. Then, the best network structure for each strategy was achieved, as shown in Table 4.  
Based on the neural network architecture obtained by genetic algorithm optimization, we used the TensorFlow framework to build a BPNN. Take Strategy A as an example. Figure 8 shows the change in _MSE_ error between the training set and the test set during the training process. At the beginning, the error decreased very quickly. The log coordinates were chosen to make the graph data more uniform. It can be seen that--with the continuous progress of the training epochs--the error of the training set decreased rapidly at first, and then gradually slowed down, while for the test set, after it fell to a certain extent, it began to rise. In order to show the rising trend of loss in the test set more clearly, we drew 50-500 epochs (the shaded part in Figure 8a) as Figure 8b, it can be seen that the model reached the lowest point of loss around the 220 th epoch, and then training the model would only infinitely reduce the training set error, but could not optimize the test set, which mainly resulted from the overfitting of the model. To avoid this phenomenon, the training needed to be terminated when the error of the test set reached the minimum. This method can improve the generalization ability of the model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Strategy/Parameters** & **Network Structure** & **Activation** & **Optimizer** & **Batch Size** \\ \hline
**Strategy A** & [100, 112] & tanh & adam & 32 \\ \hline
**Strategy B** & [112, 128] & tanh & adam & 12 \\ \hline
**Strategy C** & [112, 112] & relu & nadam & 10 \\ \hline
**Strategy D** & [112, 112] & relu & adam & 10 \\ \hline
**Strategy E** & [112, 128] & tanh & nadam & 32 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Neural network structure of each strategy.
# Texture Analysis is a Useful Tool to Assess the Complexity Profile of Microcirculatory Blood Flow
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Complexity Index**} & \multicolumn{2}{c}{**Test Foot**} & \multicolumn{2}{c}{**Control Foot**} & \multirow{2}{*}{\(p\)**-Value (T vs. C)**} \\ \cline{3-3} \cline{5-6}  & & **Mean \(\pm\) SD** & & & **Mean \(\pm\) SD** & & \\ \hline \multirow{3}{*}{raw signal} & Phase I & 34.8 \(\pm\) 6.1 & - & 34.2 \(\pm\) 8.2 & - & 0.843 \\  & Phase II & 15.0 \(\pm\) 11.5 & 0.005 * & 35.9 \(\pm\) 8.7 & 0.182 & \textless{}0.001 * \\  & Phase III & 31.4 \(\pm\) 7.3 & 0.182 & 37.0 \(\pm\) 6.7 & 0.239 & 0.089 \\ \hline \multirow{3}{*}{card} & Phase I & 25.9 \(\pm\) 6.1 & - & 29.3 \(\pm\) 7.2 & - & 0.347 \\  & Phase II & 15.6 \(\pm\) 9.0 & 0.012 * & 28.7 \(\pm\) 6.5 & 0.754 & \textless{}0.001 * \\  & Phase III & 23.8 \(\pm\) 7.3 & 0.224 & 29.7 \(\pm\) 7.8 & 0.875 & 0.089 \\ \hline \multirow{3}{*}{resp} & Phase I & 25.9 \(\pm\) 6.1 & - & 29.3 \(\pm\) 7.2 & - & 0.347 \\  & Phase II & 6.7 \(\pm\) 4.2 & 0.002 * & 14.3 \(\pm\) 4.8 & 0.002 * & 0.001 * \\  & Phase III & 10.1 \(\pm\) 5.5 & 0.002 * & 13.7 \(\pm\) 4.9 & 0.002 * & 0.160 \\ \hline \multirow{3}{*}{myo} & Phase I & 23.2 \(\pm\) 8.7 & - & 25.9 \(\pm\) 7.5 & - & 0.378 \\  & Phase II & 3.9 \(\pm\) 3.1 & 0.003 * & 12.0 \(\pm\) 2.8 & 0.003 * & \textless{}0.001 * \\  & Phase III & 7.9 \(\pm\) 5.4 & 0.008 * & 10.9 \(\pm\) 5.8 & 0.008 * & 0.160 \\ \hline \multirow{3}{*}{sym} & Phase I & 25.9 \(\pm\) 6.1 & - & 29.3 \(\pm\) 7.2 & - & 0.347 \\  & Phase II & 2.6 \(\pm\) 2.4 & 0.002 * & 10.5 \(\pm\) 1.9 & 0.002 * & \textless{}0.001 * \\  & Phase III & 6.8 \(\pm\) 2.2 & 0.002 * & 10.7 \(\pm\) 4.0 & 0.002 * & 0.012 * \\ \hline \multirow{3}{*}{NOd} & Phase I & 25.9 \(\pm\) 6.1 & - & 29.3 \(\pm\) 7.2 & - & 0.347 \\  & Phase II & 2.5 \(\pm\) 3.5 & 0.002 * & 11.5 \(\pm\) 2.6 & 0.002 * & \textless{}0.001 * \\  & Phase III & 5.5 \(\pm\) 2.1 & 0.002 * & 11.0 \(\pm\) 4.8 & 0.002 * & 0.002 * \\ \hline \multirow{3}{*}{NOi} & Phase I & 25.9 \(\pm\) 6.1 & - & 29.3 \(\pm\) 7.2 & - & 0.347 \\  & Phase II & 1.9 \(\pm\) 0.9 & 0.002 * & 10.2 \(\pm\) 3.6 & 0.002 * & \textless{}0.001 * \\ \cline{1-1}  & Phase III & 6.2 \(\pm\) 3.4 & 0.002 * & 13.6 \(\pm\) 5.0 & 0.003 * & \textless{}0.001 * \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mean and standard deviation (SD) of the MSE complexity index for each foot on each phase of the protocol (card—cardiac, resp—respiratory, myo—myogenic, sym—sympathetic, NOd—endothelial NO-dependent, NOi—endothelial NO-independent). Statistical comparison to Phase I is shown (*—\(p\) < 0.05).