esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Viscoelastic Solutions for Surface Settlement under Vertically Distributed Loads
### Viscoelastic Solutions  
Applying the Laplace transform [25] separately to Equations (4) and (5):  
\[L[P]=\overline{P}(s)=G_{1}+G_{2}+\lambda s^{\alpha} \tag{9}\]  
\[L[Q]=\overline{Q}(s)=G_{1}G_{2}+\lambda G_{1}s^{\alpha} \tag{10}\]When the Laplace transform is performed on Equation (8), based on the requirements of the corresponding principle, \(G\) in Equation (8) should be replaced by \(\overline{Q}/\overline{P}\), which can be expressed as follows:  
\[\begin{split}\overline{w}(x,y,s)=\frac{F(x,y)}{4\pi}\Big{[}\frac{ 1}{\zeta_{1}}+\frac{1}{(G_{2}+\lambda s^{\alpha})s}+\frac{3}{(3K+G_{1})s}\\ +\frac{3G_{1}^{2}}{(3K+G_{1})[3K(G_{1}+G_{2}+\lambda s^{\alpha})+G_ {1}(G_{2}+\lambda s^{\alpha})]s}\Big{]}\end{split} \tag{11}\]  
where \(s\) is the parameter involved in the Laplace transform.  
Applying the inverse Laplace transform to Equation (11), the settlement of any point \((x,y)\) on the surface at a time, \(t\), when the vertically distributed load, \(p(\xi,\xi)\), acts on the fractional-generalised Kelvin half-space body is expressed as follows:  
\[\begin{split} w(x,y,t)=\frac{F(x,y)}{4\pi}\Big{\{}\frac{1}{G_{1}} +\frac{1}{G_{2}}\Big{[}1-E_{\alpha}\Big{(}-\frac{t^{\alpha}}{\tau_{1}}\Big{)} \Big{]}+\frac{3}{3K+G_{1}}\\ +\frac{3G_{1}^{2}}{(3K+G_{1})(3 KG_{1}+3 KG_{2}+G_{1}G_{2})}\Big{[} 1-E_{\alpha}\Big{(}-\frac{t^{\alpha}}{\tau_{2}}\Big{)}\Big{]}\Big{\}}\end{split} \tag{12}\]  
where \(\tau_{1}=\lambda/G_{2}\), \(\tau_{2}=[(3K+G_{1})\lambda]/(3 KG_{1}+3 KG_{2}+G_{1}G_{2})\); \(E(\cdot)\) is the Mittag-Leffler function of a single parameter expressed as follows:  
\[E_{\alpha}(z)=\sum_{0}^{\infty}\frac{z^{n}}{\Gamma(1+\alpha n)} \tag{13}\]  
where \(\Gamma(\cdot)\) is the Gamma function defined as \(\Gamma(z)=\int_{0}^{\infty}t^{z-1}e^{-t}dt\) and \(Rez>0\).
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
## 2 The Proposed Method
### Conversion between the Proposed Line Representation and the Plucker Line
#### 2.4.1 Uncertainty Estimation  
Given two observations of a 3D line in different image coordinates, as shown in Figure 1, the proposed compact representation of the line can be solved by triangulation:  
Figure 5: Correspondences of 3D lines and Plücker lines with the same \(\mathbf{d}\) but different \(\mathbf{m}\).  
\[\begin{split}\mathbf{L}(\theta,\phi,m_{l},\alpha)&=\mathbf{L }(\mathbf{m},\mathbf{d})\\ &=f(\mathbf{R}_{acc}^{1},\mathbf{t}_{acc}^{1},\mathbf{p}_{s}^{1}, \mathbf{p}_{e}^{1},\mathbf{R}_{acc}^{2},\mathbf{t}_{acc}^{2},\mathbf{p}_{s}^{ 2},\mathbf{p}_{e}^{2}),\end{split} \tag{16}\]  
where \(f\) is a constructed function for triangulation. In \(f\), we first solve a Plucker line by (5) and then convert it into the proposed representation by Section 2.3.1. The input variables are poses of two cameras and endpoints of the observed line segments in each camera. Assume that all noises in input variables obey Gaussian distribution. The covariance matrix of the solved line \(\mathbf{L}\) can be obtained by error propagation:  
\[\mathbf{\Sigma_{L}}=J_{\mathbf{L}}\mathbf{\Sigma}_{in}J_{\mathbf{L}}^{\text{ T}}, \tag{17}\]  
where \(J_{\mathbf{L}}\) is the Jacobian of \(f\) with respect to all input variables including poses and line segment detections, \(\mathbf{\Sigma}_{in}\) denotes the covariance matrix of all input variables. By constructing the function \(f\), we can compute \(J_{\mathbf{L}}\) numerically. To conveniently apply the proposed method directly to the work utilizing Plucker lines, we give the Jacobian of conversion from a Plucker line to the proposed representation in the Appendix A.  
By analyzing the \(\mathbf{\Sigma_{L}}\) we can evaluate the accuracy of the reconstructed line comprehensively. \(\mathbf{\Sigma_{L}}\) can be blocked as:  
\[\mathbf{\Sigma_{L}}=\begin{pmatrix}\mathbf{\Sigma_{dd}}&\mathbf{\Sigma_{dm}} \\ \mathbf{\Sigma_{md}}&\mathbf{\Sigma_{mm}}\end{pmatrix}. \tag{18}\]  
To evaluate the accuracy of the reconstructed line, we can analyze \(\mathbf{\Sigma_{dd}}\) and \(\mathbf{\Sigma_{mm}}\) individually for orientation and position. The direction is a key attribution of 3D lines and badly reconstructed lines usually present large directional displacements. By separating the direction and position components, we are able to give an independent evaluation for direction and position. In addition, the uncertainty of a 3D line can be visualized by two ellipses by separating the directional and positional components, which is more intuitive and simple.
## 4 Discussion
### Limitations  
We did not perform the measurements in a soundproof room, but in a quiet office room, thus, randomly occurring noises could not be excluded during the measurements. However, we always made sure that no external noise disturbed the measurements. As the intra- and inter-day reliabilities of heart rate variability had previously been demonstrated [36], all the measurements should have been performed in the same time window. Most measurements in our study were performed in the morning. However, a uniform measurement time was not possible because of the scheduling discrepancies of the subjects.  
Furthermore, the measurement durations were longer in other studies. [PERSON] et al. [31] measured for at least 2.5 min for each condition, while for [PERSON] [3] this was >30 min. In our study, the individual measurement periods of 3 \(\times\) 30 s (statistically averaged) were shorter, but we conducted more measurement conditions and all our subjects were blindfolded for the entire measurement time. This represented an enormous challenge for the PD patients whose temporal extension of a measurement condition would have been to the detriment of the standing quality.  
In future studies, it would be desirable to investigate further PD patients with a Yahr grade of disability of III or IV with regard to the present research question, whereby the medication should also be evaluated and its possible influence on the measurement result analyzed.  
In addition, future studies could analyze whether a different starting position (e.g., sitting), a different measurement duration (e.g., 5 min per condition), or medication affects HR.  
Thus, based on these results of this pilot study, it can be concluded that, in general, listening to different music genres or sounds of different tempi barely affects the HR. However, due to the technology used, we do not have time dependent data to calculate heart rate variability in a validated manner. The capture of heart rate variability would be worth setting up future measurement.  
Nevertheless, the between-group comparison and the within-group comparison of the significant measurement conditions 50% of the heart rates were found between the 1 st (25%) and 3 rd quartile (75%) and that, in this range, there was predominantly a range of approximately 20 bpm. Therefore, the medians of the heart rate differences should be considered rather as trends with low clinical relevance.
## 1 Introduction
Even for experienced professionals in the field employing cutting-edge techniques, processing and analyzing large dataset generated from vast internet traffic volumes is challenging. Identification of the type of traffic supports managing the resources and allocating them to the critical application, thus ensuring an improvement in QoS and stability of the system. Due to the high volume of traffic moving across the internet, internet service providers (ISPs) and networking service providers (NSPs) are looking for a number of approaches to properly predicting the type of application flow that is now running through it [7]. Thus, developing a system that dynamically classifies network traffic and reroutes traffic based on the criticality of the data received is the main motivation behind the research. Modern machine learning (ML) advancements have proven the effectiveness of prediction applications, particularly in the field of networking. In this paper, different ML algorithms (for example, logistic regression, Support Vector Machine (SVM), and K-nearest neighbors (K-NN), XGBoost, classification and regression trees (CARTs), and OPTICS) are employed to classify different types of network traffic in order to evade the constraints of traditional approaches and raise QoS. Also, in order to choose the optimal algorithm for implementation, the efficacy of a few algorithms is examined. Load balancing is then performed to give the shortest route higher priority for the crucial traffic type. Additionally, the issue of network congestion is addressed by installing three controllers in the network that can share responsibility for traffic classification and flow table updates. The main contributions of this paper are summarized as follows:  
* A trained model is developed for traffic classification and controller-load-balancing;
* The performance of the models developed are measured with different ML algorithms for effective load balancing in a multi-controller SDN environment. The remainder of the paper is organized as follows: The background and related work are discussed in Section 2, which includes the SDN and its architecture, OpenFlow, the controller placement problem (CPP), traffic classification approaches, load balancing in the SDN, and related works. Section 3 details the methodology, including the system scenario, dataset generation, and the proposed work flow model consisting of a traffic classification model and traffic priority-based load balancing. Results and analysis are presented in Section 4, and, finally, Section 5 concludes with a summary and future work.
## 2 System Model and Problem Formulation
### System Model  
As a major deployment method of the MEC, we consider an edge system consisting of a BS and multiple MECSs from the set \(\mathcal{M}=\{1,\cdots,M\}\), which are physically co-located and share the same power supply in the cell site. Also, the MECSs serve the content requests submitted by \(N\) mobile users (MUs). \(N\) MUs are uniformly distributed within the scope of coverage radius of BS denoted by \(R\). The MECSs store the contents which can be downloaded by the MUs in the coverage areas of the BS. Through separated backhaul links, base stations are connected to the core network, which stores the whole content library. The storage capacity denoted by \(C_{Z}(\text{bits})\) of each MECS is limited to handle a large set of contents in total. This architecture is depicted in Figure 1.  
Figure 1: Architecture of Mobile Edge Computing (MEC) servers’ caching system.
# An Accuracy-Efficiency-Power Consumption Hybrid Optimization Method for CNC Milling Process
## 2 Algorithm and Method  
To determine the optimal machining parameters for better machining efficiency and lower power consumption without losing machining accuracy, it is necessary to understand the relationship between machine power consumption characteristics, machining parameters, and machining quality. Empirical models were first built based on the data (power consumption vs. motor speed or power consumption vs. machining feedrate) collected from the designed experiments. To separate the influence of cutting in motor power consumption, the data collection experiments were divided into two categories: (1) measurements without cutting load and (2) measurements with cutting load. Based on the collected data, a motor power consumption characteristics model with respect to machining parameters was built. Based on the model, the algorithm to search the machining parameter for shorter machining time and lower power consumption was developed. When a milling process and expected improvement of machining efficiency are given, the associated machining feedrate and spindle speed will be calculated based on the theoretical cutting equation, and used as initial values to search based on the motor power consumption characteristics model for the optimal parameters which have a lower motor power consumption.  
To analyze the relationship between electrical power consumption and machining conditions, the investigation and analysis of spindle motor and servo motor were important. The analysis process, first, used various machining parameters to observe the motor power consumption to understand the motor characteristics and power consumption, then established the relationship model between machining parameters and power consumption, the synchronous optimization algorithm of production efficiency and power consumption was established, and a human machine interface (HMI) was designed, to become the optimization manufacturing assisted system. The main function is the optimization of machining parameters to obtain power and production efficiency without decreasing the machining accuracy. Figure 1 shows the flowchart of the research.  
Different materials with different machining conditions will cause different electrical power consumption, and the reasonable chip load of the cutter for different materials will be different too. Although the method and algorithm proposed in this study can be applied to different materials, the performance-power consumption characteristic curves of servo motors (such as power consumption vs. motor speed, or power consumption vs. machining feedrate) need to be investigated and built for different materials as core important data/information for implementation.
# Comparative Accuracy Analysis of Truck Weight Measurement Techniques
## 3 Results
### Measurement tolerances
#### 3.3.1 Gross Vehicle Weight  
All four weighing systems measured Gross Vehicle Weight. Figure 7(a) shows the results of GWW measurements recorded by the certified portable scales and compared to the other measurement techniques: weigh station, WIM, and B-WIM, and Figure 7(b), where the GWW results are compared to truck weigh station. As was expected, the tolerance for GWW measurements for more than 95% results was in the range \(\pm\)10%.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multicolumn{3}{c}{**Portable Scales**} & \multicolumn{3}{c}{**Weigh Station**} \\ \cline{2-7}  & **B-WIM** & **Weigh Station** & **WIM** & **B-WIM** & **Portable Scales** & **WIM** \\ \hline GVW & 10.50 & 7.31 & 13.70 & 9.81 & 7.31 & 11.20 \\ W1 & 6.59 & 4.26 & 5.96 & 6.05 & 4.26 & 6.37 \\ W2 & 6.58 & - & 5.50 & - & - & - \\ W3 & 6.53 & - & 5.91 & - & - & - \\ W4 & 6.89 & - & 7.34 & - & - & - \\ W5 & 7.17 & - & 7.11 & - & - & - \\ AG1 & 10.76 & 5.88 & 7.87 & 7.85 & 5.88 & 7.93 \\ AG2 & 10.50 & 7.31 & 13.70 & 9.81 & 7.31 & 11.20 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Multiple _t_-tests for paired means compared to the portable scales and weigh station measurements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Weighing**} & \multicolumn{2}{c}{**Tandem Axle Weight 2 + 3**} & \multicolumn{2}{c}{**Tandem Axle Weight 4 + 5**} \\ \cline{2-5}  & **Mean [kip]** & **SD [kip]** & **Mean [kip]** & **SD [kip]** \\ \hline Portable Scales & 31.19 & 2.75 & 30.88 & 3.81 \\ Weigh Station & 31.38 & 3.28 & 32.03 & 4.42 \\ WIM & 31.60 & 4.99 & 29.78 & 11.00 \\ B-WIM & 30.89 & 3.43 & 30.62 & 4.48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical parameters of tandem axle weights for all weighing systems.
## 1 Introduction
Besides, through Monte Carlo simulation and the Failure Mode and Effect Analysis (FMEA) method, the paper raised a novel reliability assessment method for the energy supply of multiple energy carriers and sorted reliability assessment indicators. On this basis, a double-layer planning model with optimal configuration, operation, and reliability consideration for the multi-energy microgrid was built. Finally, based on the actual calculations, this study conducted a quantitative analysis on the impact of different configuration schemes on the reliability and economical performances of the microgrid system, and proposed the application of reliability in the maintenance of key equipment. The major contributions of this study are summarized as follows: (1) a novel multi-energy integrated reliability evaluation method was proposed; (2) the reliability factor was integrated into the double-layer planning model of a multi-energy microgrid; (3) the impacts of different equipment configuration schemes on the planning economy and reliability were analyzed quantitatively through practical cases, and the application of reliability in practical maintenance was further analyzed by cases. This study considered the reliability of microgrid system energy supply during the optimization configuration process, and proposed the reliability assessment indicators of the microgrid system energy supply. It combined capacity optimization configuration with operation strategies, and designed the assembly style and capacity in the microgrid system to make the energy supply reliability meet the expectations and minimize the cost in the planning period.
# Anti-Inflammatory Activity of a Demineralized Bone Matrix: An In Vitro Pilot Study
## 2 Material and Methods
### RT-PCR and Immunoassay  
Total RNA was isolated with the ExtractMe total RNA kit (Blirt S.A., Gdansk, Poland). Reverse transcription (RT) was performed with the LabQ FirstStrand cDNA Synthesis Kit (LabQ, Labconsulting, Vienna, Austria). Reverse transcription-polymerase chain reaction (RT-PCR) was conducted (LabQ, Labconsulting, Vienna, Austria) on a CFX Connect(tm) Real-Time PCR Detection System (Bio-Rad Laboratories, Hercules, CA, USA). Primer sequences were mIL6_F GCT ACC AAA CTG GAT ATA ATC AGG A; mIL6_R CCA GGT AGC TAT GGT ACT CCA GAA; mIL1\(\beta\)_F AAG GGC TGC TTC CAA ACC TTT GAC; mIL1\(\beta\)_R ATA CTG CCTG GCC TGA AGC TCT TGT; miNOS_F GGTGAAGGACTGACT; gTT; miNOS_R ACGTTCTCCGTTCTCTTGCAG; mCCL5_F CCTCTGCTTGCCTTACC; mCCL5_R ACACACTTGCCGTTCCTT; mGAPDH_F AAC TTT GGC ATG GAA CG; mGAPDH_R GGA TGC AGG GAT GAT GTT CT; hIL6_F GAA AGG AGA CAT GTA ACA AGA GT; hIL6_R GAT TTT CAC GAG AGT CT; hIL8_F AAC TTC TCC ACA ACC CTC TG; hIL8_R TTGGCAG CTTCCTGATTTC; hGAPDH_F AAG CCA CAT CGC TC; hGAPDH_R AGA CAC CAC CAA TAC TAC GAC CAA ATC C. For primers on proliferation markers, see Supplementary Table S1. The mRNA levels were calculated by normalizing to the housekeeping gene GAPDH using the \(\Delta\Delta\)Ct method. The immunoassay was performed with the mouse IL6 and human IL8 Quantikine ELISA kit (R&D Systems, Minneapolis, MN, USA).
## 2 Materials and Methods
### Raw Materials
#### 2.1.2 Raw-Crushed Wind-Turbine Blade (RCWTB)  
The RCWTB used for SCC development was obtained by shredding panels of wind-turbine blades such as those shown in Figure 3a. These panels were composed of GFRP, balsa wood, and polymers. They were first cut into roughly square pieces with a side length of around 20 cm, and then crushed by a knife mill and screened with a sieve with a mesh size of 10 mm. Those particles that did not pass through the sieve were crushed again until the particles passed. The resulting material can be seen in Figure 3b, which had a real density of 1.63 Mg/m\({}^{3}\) (EN 1097-6 [53]).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Property** & **Standard** & **Siliceous 4/12 mm** & **Siliceous 0/4 mm** & **Limestone 0/2 mm** \\ \hline Fine content (\%) & EN 933-1 [51] & 0.3 & 1.5 & 2.0 \\ Equivalent sand (\%) & EN 933-8 [52] & - & _77_ & 79 \\ Saturated-surface-dry density (Mg/m\({}^{3}\)) & EN 1097-6 [53] & 2.66 & 2.65 & 2.64 \\ Water absorption (\% wt.) & EN 1097-6 & 0.66 & 0.74 & 0.52 \\ Flakiness index (\% wt.) & EN 933-3 [54] & 4.5 & - & - \\ Water soluble chloride content (\% wt.) & EN 1744-1 [55] & 0.00 & 0.00 & 0.01 \\ Sulfur content (\% wt.) & EN 1744-1 & 0.00 & 0.01 & 0.01 \\ Acid soluble sulfates (\% wt.) & EN 1744-1 & 0.01 & 0.01 & 0.02 \\ Light pollutants (\%wt.) & EN 1744-1 & 0.00 & 0.00 & 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical and chemical properties of aggregates.  
Figure 3: Panels of wind-turbine blades (**a**) and RCWTB (**b**).  
Figure 2: Aggregates’ gradation.  
The RCWTB was composed of balsa-wood particles (6.3% wt.), polymer particles (8.3% wt.), GFRP micro-fibers and small non-separable particles (18.6% wt.), which were agglomerated in the form of fluffs, and GFRP fibers (66.8% wt.). All these components can be seen in Figure 4. The particles of polymers and balsa wood could act as lightweight aggregates in the SCC [56], and the GFRP fibers could form a three-dimensional reinforcement in the hardened SCC, but also negatively affect its flowability in the fresh state [28]. The fibers exhibited a 13.1-mm average length, and an average tensile strength of 270 MPa. A much more detailed description of this material is shown in the previous work of the research group [40].
# Augmenting GPS with Geolocated Fiducials to Improve Accuracy for Mobile Robot Applications
## 2 Related Research
Figure 1: Robot performing visual pose estimation on a geolocated fiducial marker with fiducial shown on right. The technique proposed in this paper, FAGPS, performs a similar role to DGPS and RTK-GPS in using a ground based georeferenced landmark to compensate for common mode errors. FAGPS uses close-by fiducials (similar to RTK-GPS) but in contrast to DGPS and RTK-GPS doesn't require any transmitter hardware at the beacon points (only a georeferenced fiducial marker), which means many beacons can be quickly created at very low cost and require no power. Receivers using the FAGPS scheme will need to be coupled with a machine vision platform to allow them to recognise and perform pose estimation on the landmarks. Some operational downtime is experienced as FAGPS receivers will need to physically go to a beacon to receive calibration coordinates at regular intervals.