esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### HPLC-MS/MS Method Validation
#### 3.1.3 Carry-Over  
Fingolimod and Fingolimod-P were injected at the highest concentration (ULOQ, 150 ng/mL) followed by injection of blank matrix: carry-over was negligible (<20% the LLOQ signal, data not shown) using the needle-wash solution H\({}_{2}\)O:MeOH (1:1 \(v/v\)) with 1% HCOOH.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**Fingolimod**} \\ \hline \multirow{2}{*}{**Three Runs**} & \multicolumn{8}{c}{**Nominal Concentrations (ng/mL)**} & \multicolumn{8}{c}{**Calibration Curve**} \\ \cline{2-10}  & **0.3** & **1.5** & **2.5** & **5.0** & **10.0** & **25.0** & **50.0** & **150.0** & \\ \cline{2-10}  & \multicolumn{8}{c}{**Calculated Concentrations (ng/mL)**} & \multicolumn{8}{c}{**Slope**} & \multicolumn{1}{c}{**Intercept**} & \multicolumn{1}{c}{**r\({}^{2}\)**} \\ \hline Mean & 0.302 & 1.47 & 2.37 & 5.30 & 10.24 & 25.40 & 49.50 & 149.00 & 0.1225 & 0.0049 & 0.9954 \\ \(\pm\)SD & 0.003 & 0.09 & 0.25 & 0.35 & 0.50 & 2.27 & 1.76 & 3.46 & 0.0058 & 0.0012 & 0.0002 \\ Accuracy & 100.8\% & 97.8\% & 94.8\% & 106.0\% & 102.4\% & 101.6\% & 99.0\% & 99.3\% & \\ Precision (CV) & 0.8\% & 5.9\% & 10.3\% & 6.5\% & 4.9\% & 8.9\% & 3.6\% & 2.3\% & 4.8\% & \\ \hline \multicolumn{10}{c}{**Fingolimod-P**} \\ \hline \multirow{2}{*}{**Three Runs**} & \multicolumn{8}{c}{**Nominal Concentrations (ng/mL)**} & \multicolumn{8}{c}{**Calibration Curve**} \\ \cline{2-10}  & **1.5** & **2.5** & **5.0** & **10.0** & **25.0** & **50.0** & **150.0** & \\ \cline{2-10}  & \multicolumn{8}{c}{**Calculated Concentrations (ng/mL)**} & \multicolumn{8}{c}{**Slope**} & \multicolumn{1}{c}{**Intercept**} & \multicolumn{1}{c}{**r\({}^{2}\)**} \\ \hline Mean & 1.65 & 2.51 & 4.69 & 9.41 & 26.33 & 50.07 & 150.67 & 0.0963 & \(-\)0.0597 & 0.9951 \\ \(\pm\)SD & 0.08 & 0.12 & 0.17 & 0.22 & 1.30 & 1.22 & 2.08 & 0.0116 & 0.0234 & 0.0007 \\ Accuracy & 109.8\% & 100.5\% & 93.8\% & 94.1\% & 105.3\% & 100.1\% & 100.4\% & \\ Precision (CV) & 4.6\% & 4.7\% & 3.7\% & 2.3\% & 4.9\% & 2.4\% & 1.4\% & 12.0\% & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Linearity of calibration curves and LLOQ for Fingolimod and Fingolimod-P (0.3 ng/mL and 1.5 ng/mL, respectively) in human plasma.  
Figure 1: (i) Representative HPLC-MS/MS chromatograms (extracted for Fingolimod, FTY720, quantitative ion transition) in plasma of (**A**) indicative blank matrix (human plasma source #1), (**B**) IS spiked in the blank matrix (human plasma source #1) at the concentration of use (10 ng/mL) and (**C**) Fingolimod spiked in the blank matrix (human plasma source #1) at the LLOQ (1.5 ng/mL, retention time 4.75 min). (ii) Representative HPLC-MS/MS chromatograms (extracted for Fingolimod-P, FTY20-P, quantitative ion transition) in plasma of (**D**) indicative blank matrix (human plasma source #1), (**E**) IS spiked in the blank matrix (human plasma source #1) at the concentration of use (10 ng/mL) and (**F**) Fingolimod-P spiked in the blank matrix (human plasma source #1) at the LLOQ (3 ng/mL, retention time 5.63 min).
## 4 Model (SIRPM)
### Characteristics and Benefits of the Model (SIRPM)
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Model (SIRPM) Benefits**} \\ \hline \multirow{3}{*}{1.} & \multirow{3}{*}{Increased Operational Efficiency} & \(\bullet\) & **Description** \\ \cline{3-4}  & & & Minimizes unplanned downtime through proactive predictive maintenance. \\ \cline{3-4}  & & \(\bullet\) & Enhances productivity with faster and more precise interventions. \\ \hline \multirow{3}{*}{2.} & \multirow{3}{*}{Cost Reduction} & \(\bullet\) & Prevents catastrophic failures and extends the equipment’s lifespan. \\ \cline{3-4}  & & \(\bullet\) & Optimizes human and material resources based on reliable forecasts. \\ \hline \multirow{3}{*}{3.} & Data-Driven Decision-Making & \(\bullet\) & Generates detailed insights to improve asset management. \\ \cline{3-4}  & & \(\bullet\) & Provides structured data that can be used to create performance metrics and strategic reports. \\ \hline \multirow{3}{*}{4.} & \multirow{3}{*}{Corporate Sustainability} & \(\bullet\) & Reduces energy consumption and minimizes waste, contributing to environmental goals. \\ \cline{3-4}  & & \(\bullet\) & Helps meet regulations related to industrial sustainability. \\ \hline \multirow{3}{*}{5.} & Continuous Improvement and Adaptation & \(\bullet\) & Feedback from alerts allows for an evaluation of their usefulness, adjusting the system as needed. \\ \cline{3-4}  & & \(\bullet\) & Machine learning capability ensures the system stays updated with changes in the operational environment. \\ \hline \multirow{3}{*}{6.} & \multirow{3}{*}{Team Empowerment} & \(\bullet\) & Promotes adaptation of operational teams to modern technologies, increasing their efficiency and technical knowledge. \\ \hline \multirow{3}{*}{7.} & \multirow{3}{*}{Scalability and Flexibility} & \(\bullet\) & Can be expanded to other areas or sectors of the company with similar needs. \\ \cline{3-4}  & & \(\bullet\) & Supports a wide range of industries, from manufacturing to logistics and energy. \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Model (SIRPM) benefits.
## 2 End-to-End QoS in WSNs
### Methods for Reliability  
Unreliability is compensated in the physical layer with channel encoding schemes that reduce packet errors. As channel encoding alone cannot guarantee reliability, network protocols utilize retransmissions. However, retransmissions only in Medium Access Control (MAC) layer are not sufficient, as packet errors and mobility cause link failures, thus causing packet drops.  
The traditional networks use end-to-end transport protocols, such as Transmission Control Protocol (TCP), to guarantee reliability. Those protocols buffer a packet in the source node until it is acknowledged by the destination node. Using an end-to-end transport protocol in multihop WSNs has two drawbacks. First, a packet must be retransmitted again from the source node over several hops even if the previous transmission fails near the destination node. Thus, the communication overhead and energy consumption increase [11]. Second, WSNs often use reduced functionality devices that are dedicated to a specific task and have a very limited memory as source nodes. Therefore, source node buffering will quickly use all available buffers.  
Multipath routing increases reliability by forwarding data via several paths. Then, a packet can be delivered even if routing on a certain path fails. The drawback of the multipath routing is increased messaging that consumes more energy and reduces network capacity. Uncontrolled multipath routing (e.g. flooding) would even have a negative impact on performance as the network congests and packets get dropped due to full buffers.
# Self-Tuning Algorithm for Tuneable Clamping Table for Chatter Suppression in Blade Recontouring
## 7 Conclusions
\begin{tabular}{l l} \((t,r)\) & t: tangential, r: radial \\
**M** & assembly mass matrix \\
**C** & assembly viscous damping matrix \\
**K** & assembly stiffness matrix \\
**H** & assembly receptance matrix \\ \(m_{i}\) & modal (reflected) mass of the \(i\)th substructure \\ \(k_{i}\) & modal (reflected) stiffness of the \(i\)th substructure \\ \(k_{i}\) & flexure stiffness \\ \(k_{\rm s}\) & rotary spring stiffness \\ \(c_{i}\) & modal (reflected) viscous damping of the \(i\)th substructure \\ \(H_{i,j}\) & receptance function between the \(i\)th and \(j\)th substructures \\ \(\theta\) & angular position of the rotary spring \\
**x** & displacement vector \\
**x\({}_{\rm s}\)** & stationary solution vector \\
**p** & perturbed solution vector \\
**F** & force vector \\
**f\({}_{tr}\)** & specific cutting force in tangential-radial (\(t,r\)) coordinates \\
**K\({}_{\rm c}\)** & cutting coefficient vector \\
**K\({}_{\rm e}\)** & edge coefficient vector \\ \(f_{Z}\) & feed per tooth \\ \(\Omega\) & spindle speed \\ \(a\) & axial depth of cut \\ \(h\) & chip thickness \\ \(\varphi_{\rm en}\) & entering immersion angle \\ \(\varphi_{\rm ex}\) & exit immersion angle \\ \(\varphi\) & angular position of the cutter \\ \(\tau\) & regenerative delay \\ \(T_{Z}\) & tooth passing period \\ \(\omega_{Z}\) & tooth passing frequency \\ \(\omega_{\rm c}\) & dominant chatter frequency \\ \(\psi_{i,j}\) & phase of the receptance between the \(i\)th and \(j\)th substructures \\ \(\omega\) & excitation frequency \\ \(g(\varphi)\) & screen function \\ \(g\) & dimensionless excitation frequency \\ \(\mu\) & mass ratio \\ \(\zeta_{i}\) & standalone damping ratio of the \(i\)th substructure \\ \(\omega_{i}\) & standalone natural frequency of the \(i\)th substructure \\ \(f\) & frequency ratio \\ \(h_{i,j}(g)\) & dimensionless receptance between the \(i\)th and \(j\)th substructures \\ \(f_{\rm o,\pm}\) & optimal frequency tuning ratio of the broad-bandwidth strategy \\ \(\zeta_{1,0,\pm}\) & optimal TCT damping ratio of the broad-bandwidth strategy \\ \(f_{\rm fe,\pm 2^{\circ}}\) & zero real part tuning ratio \\ \(f_{\rm fe,\pm 2^{\circ},\pm,i}\) & zero real part derivative tuning ratio \\ \(f_{\rm o}(g)\) & optimal frequency tuning ratio of the excitation frequency dependent strategy \\ \(g_{\rm lim},\pm\) & dimensionless TCT tuning limits \\ \(f_{\rm max}\) & upper tuning ratio limit \\ \(f_{\rm min}\) & lower tuning ratio limit \\ \(g_{\rm a}\) & dimensionless frequency of the stability asymptote \\ \(\
u_{\rm a}\) & dimensionless spindle speed of the stability asymptote \\ \(\
u\) & dimensionless spindle speed \\ \(\sigma\) & dimensionless depth of cut \\ \(B\) & directional milling coefficient \\ \(\beta\) & directional factor \\ \(\Delta F\) & regenerative milling force \\ \(G\) & stationary milling force \\ \(Z\) & number of flutes of the cutter \\ \(D\) & cutter diameter \\ \end{tabular}
# Application of Microbially Induced CaCO\({}_{3}\) on the Reinforcement of Rock Discontinuity
## 3 Results and Discussions
### Shear Displacement-Stress Curve  
The shear characteristics of MICP-cemented rock joints were analyzed through direct shear tests in this section. Direct shear tests were performed on five types of rock samples after 28 days of curing. The shear displacement-stress curves for normal stresses of 0.05 \(\sigma_{c}\), 0.1 \(\sigma_{c}\), and 0.2 \(\sigma_{c}\) are depicted in Figure 10.  
Observation of Figure 10 reveals that all shear stress-strain curves exhibit clear peak points, and the entire shear displacement-stress curve can be segmented into a pre-peak elastic stage, post-peak decline stage, and residual stage. The peak shear stress represents the peak shear strength of the rock joint. The strain obtained by the direct domestication scheme had poor adaptability to the saline-alkaline environment, resulting in a low yield of calcium carbonate, so the reinforcement effect on the cracks was not obvious. Therefore, the peak shear strength of the specimens repaired by the direct domestication strain was only slightly higher than that of the unconsolidated structural plane. The repair ability of the strains obtained by the three-gradient domestication and the five-gradient domestication was significantly enhanced, which was not much different from that of the undomesticated strains in the neutral environment. The repair ability of the strains after the five-gradient domestication was obviously the best, indicating that the strains domesticated by this method could adapt well to the saline-alkaline environment and had a good cementation effect on rock joint specimens. This can increase the peak shear strength of rock joints to a greater extent.  
Figure 11 shows the shear displacement-shear strength curves of specimens with a curing time of 7 days, 14 days, and 28 days, respectively, under the third normal stress (0.05 \(\sigma_{c}\), 0.1 \(\sigma_{c}\), and 0.2 \(\sigma_{c}\)) at normal temperature. It can be seen from Figure 11 that the peak shear strength of cemented joints increased with the increase in maintenance time.  
Figure 10: Shear displacement-stress curves of MICP-cemented sandstone specimens obtained by different domestication schemes after curing for 28 days: (**a**) \(\sigma_{n}=0.05\,\sigma_{c}\), (**b**) \(\sigma_{n}=0.1\,\sigma_{c}\), (**c**) \(\sigma_{n}=0.2\,\sigma_{c}\).
# User Utility Maximization in Narrowband Internet of Things for Prioritized Healthcare Applications
## 6 Conclusions  
This work investigated how healthcare application events are influenced by priority access scheduling and utility maximization in NB-IoT. Our suggested system, PAUM, greatly contributed to resource allocation for prioritized access by allocating a suitable channel, a non-interfering time slot with greater data rate to different data packets by formulating a mixed non integer linear programming solution. Using event-induced prioritization, our system maximizes the utility value of users by allocating resources optimally. Our in-depth examination of the simulation trace file revealed that interference was reduced as a result of prioritized access scheduling to the time slots. Due to event classification, the critical event received the greatest priority to meet its urgency, followed by the other events. The simulation results showed significant performance improvement in terms of utility, fair share of resources, delay, and packet delivery ratio by 65%, 45%, 25%, and 15%, respectively, compared to the \(iRRM\) system.  
In the future, in place of Bayesian learning, other learning approaches including instance-based algorithms, analytical learning, and reinforcement learning can be explored to study scopes for further performance improvement.  
Conceptualization, N.S. and M.A.R.; methodology, N.S.; formal analysis, N.S., F.H. and M.A.R.; writing--original draft preparation, N.S.; writing--review, editing and verification M.A.R.; supervision, M.A.R. and M.M.R. All authors have read and agreed to the published version of the manuscript.  
This research received funding from International Publication Grant of University of Dhaka, Bangladesh.  
Not applicable.  
This work is supported by a student Research Fellowship (2019-2020) of the Information and Communication Technology Division, Ministry of Posts, Telecommunications and Information Technology, Government of Bangladesh and funded by University of Dhaka, Bangladesh.  
The authors declare no conflict of interest.  
Figure 8: Classification Accuracy vs. User Equipment and Simulation Time.
###### Abstract
In addition, it designs a human detection algorithm for the manipulator to increase safety while collaborating with operators. Most recent studies and existing applications in smart manufacturing adopt mobile manipulators with the vision system and designed object detection methods to improve manufacturing production safety, efficiency, and intelligence. In those studies, humans often take great responsibility to control and operate robots through computer programs. Consequently, imitating the humans' behavior, such as the \"eye-brain-hand\" process, is necessary to realize intelligent and autonomous manufacturing production truly and has become a widespread trend [14]. Therefore, this study proposes a deep learning-based intelligent manufacturing approach, aiming to achieve true intelligence and autonomous manufacturing production and machine tending. Compared with other solutions, the benefits of the proposed approach are low costs and high efficiency for small and medium-sized enterprises. In addition, this approach can be applied in lights-out manufacturing to reduce the workload for operators who need to tend machines and tackle the problems on the production floor. In the current study, an autonomous mobile manipulator with a vision system is adopted. The main objectives of this work are to develop an intelligent object detection method for the target CNC machine and HMI display detection in a complex environment and to design a text recognition method for the machine's working status recognition to further assist in autonomous robot decision-making and problems handling. The remainder of this paper is organized as follows: in Section 2, an overview of object detection and text recognition is presented. Section 3 explains the methodology of the proposed intelligent manufacturing approach for automatic machine detection and working status recognition. The case studies and results are demonstrated in Section 4. Section 5 outlines some limitations and potential future work. Finally, conclusions are presented in Section 6.
# Effect of Bentonite Content and Hydration Time on Mechanical Properties of Sand-Bentonite Mixture
## 5 Conclusions  
In this study, the effect of bentonite content and hydration time on the mechanical properties of sand-bentonite mixture was investigated through experimental tests and numerical analysis. The influence of calcium bentonite and sodium bentonite on the stress-strain behavior of the sand-bentonite mixture was analyzed and compared. The mesoscopic mechanism of bentonite influenced on the macroscopic mechanical behavior of the sand-bentonite mixture was revealed. Major conclusions can be summarized as follows:  
1. Under the same bentonite and the same confining pressure, the shear strength of the sand-bentonite mixture specimens was decreased gradually with the increasing of bentonite contents;
2. By comparing the shear strength of mixture specimens mixed with calcium bentonite and sodium bentonite, it can be found that the peak shear stress of calcium bentonite was higher than that of sodium bentonite mixed sand under the same hydration time and bentonite content;
3. The 3D discrete element method results show that the force transfer structure changed with the increase in bentonite contents. The distribution of normal contact force changed obviously with the increase in bentonite content. The contact force distributed more evenly as bentonite was involved in force transfer.  
Figure 11: PDF curves of normalized normal contact force (\(f_{\rm n}/\)\(\zeta_{\rm n}\)\(>\)) for models with various bentonite contents at different confining pressures: (**a**) 200 kPa; (**b**) 300 kPa; (**c**) 400 kPa.  
**Author Contributions:** Conceptualization, Y.Q. and D.X.; methodology, Y.Q.; software, Y.Q.; validation, Y.Q., D.X. and B.L.; formal analysis, Y.Q.; investigation, Y.Q.; resources, Y.Q.; data curation, Y.Q.; writing--original draft preparation, Y.Q.; writing--review and editing, Y.Q., D.X. and B.L.; supervision, D.X.; project administration, Y.Q. and D.X.; funding acquisition, Y.Q. and D.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (project Nos. 41972271, 42177127), the fellow-ship of China Postdoctoral Science Foundation (project No. 2021M692494), Sanya Science and Education Innovation Park of Wuhan University of Technology (project No. 2020 KF0007), Hubei Provincial Natural Science Foundation of China (No.2020 CFB524) and Hubei Key Laboratory of Roadway Bridge and Structure Engineering (Wuhan University of Technology) (Project No.DOJJ201907).  
Not applicable.  
This research was funded by the National Natural Science Foundation of China (project Nos. 41972271, 42177127), the fellowship of China Postdoctoral Science Foundation (project No. 2021M692494) and Sanya Science and Education Innovation Park of Wuhan University of Technology (project No. 2020 KF0007).  
The authors declare no conflict of interest.
## 2 Technical Limitations  
Vehicle-to-everything is a communication system that allows communication from the vehicle to traffic, infrastructure, and the environment [41]. The current network standards for telematic devices using (V2X) communications, are split between dedicated short-range communications (DSRC) and long-term evolution vehicle-to-everything (LTE-V2X) connections [18]. LTE-V2X utilizes a Third Generation Partnership Project (3 GPP) mobile connection for both cellular network communication and direct vehicle-to-vehicle communication. However, there are limitations in the latency and bandwidth of both networking technologies [42]. Two separate studies by [PERSON] et al. and [PERSON] and [PERSON] have found that DSRC suffers significantly with latency issues when introduced to numerous data transmissions in an intersection or traffic jam [43; 44]. [PERSON] et al. further describes DSRC as unsuitable for large networks of connected devices, as low latency is a strict requirement for vehicle safety warnings such as collision warning [43]. The alternative V2X communication, LTE-V2X, is considered unreliable in a paper by [PERSON] and [PERSON]. In this paper, they describe the limitations of V2X due to loss of packets in concurrent transmissions or blocking of signal by objects [45].  
Accurately estimating battery SOH and SOC is challenging. SOC estimations methods which directly monitor the battery such as amber-hour integral requires initial knowledge of the battery SOC to operate. However, determining the SOC of a battery in real-time applications is difficult as this will negatively affect the accuracy of the estimation [37]. Model based methods such as EMs, ECMs and EIMS are criticized by [PERSON] et al. due to their limited ability to accurately estimate the battery SOC [40]. Machine learning based battery SOH measurements are highly dependent on the transmission of high volumes of data. Acquiring real-time training data for these models using a Machine Learning process are time-consuming and costly [39].  
The technical limitations of battery performance monitoring systems reduce the accurate readings of SOC and battery health. Common SOC estimation methods such as EMs, ECMs, and EIMS cannot provide a comprehensive description of battery degeneration and subsequently provide inaccurate readings of SOC [40]. The effects of high SOC levels on battery degeneration and the reduction of the lifetime of the battery has been researched by [PERSON] and [PERSON], further showing the importance of accurate SOC modelling [46].