esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments (Application to [PERSON]'s Airline Passenger Satisfaction Data) and Discussion
8, 9, and 10 from Figure 3 and Table 3 have not been included in Table 5 because--comparing with solution No. 7--they provide very small increases in the test-data-accuracy \begin{table}
\begin{tabular}{l} \hline \hline
**No.** & **Fuzzy Classification Rules** \\ \hline
**Solution No. 1** (\(ACC^{(lrn)}=75.1\%\), \(ACC^{(lst)}=75.2\%\)): \\
1. & **IF** Inflight entertainment is no\_answer or low **THEN** Passenger is neutral or dissatisfied \\
2. & **IF** Inflight entertainment is medium **THEN** Passenger is neutral or dissatisfied \\
3. & **IF** Inflight entertainment is high **THEN** Passenger is satisfied \\ \hline
**Solution No. 2** (\(ACC^{(lrn)}=77.2\%\), \(ACC^{(lst)}=77.2\%\)): \\
1. & _This rule is an extension of rule No. 1 from Solution No. 1_: \\
1. & **IF** Inflight entertainment is no\_answer or low **AND** Seat comfort is low or \\  & medium **THEN** Passenger is neutral or dissatisfied \\
2. & _This rule is an extension of rule No. 2 from Solution No. 1_: \\
2. & **IF** Inflight entertainment is medium **AND** Seat comfort is low or medium **THEN** Passenger is neutral or dissatisfied \\
3. & _This rule is the same as rule No. 3 from Solution No. 1_. \\
4. & **IF** Seat comfort is no\_answer **THEN** Passenger is satisfied \\
5. & **IF** Seat comfort is high **THEN** Passenger is satisfied \\ \hline
**Solution No. 3** (\(ACC^{(lrn)}=80.5\%\), \(ACC^{(lst)}=80.5\%\)): \\
1. & _This rule is the same as rule No. 1 from Solution No. 1_. \\
2. & _This rule is the same as rule No. 2 from Solution No. 2_. \\
3. & _This rule is an extension of rule No. 3 from Solution No. 2_: \\
4. & **IF** Inflight entertainment is high **AND** Type of travel is business travel **THEN** Passenger is satisfied \\
5. & _This rule is the same as rule No. 4 from Solution No. 2_. \\
5. & _This rule is an extension of rule No. 5 from Solution No. 2_: \\
6. & **IF** Seat comfort is high **AND** Inflight WiFi service is high **THEN** Passenger is satisfied \\
7. & **IF** Inflight WiFi service is no\_answer **THEN** Passenger is satisfied \\ \hline
**Solution No. 4** (\(ACC^{(lrn)}=82.8\%\), \(ACC^{(lst)}=82.8\%\)): \\
1. & _These rules are the same as rules Nos. 1 and 2 from Solution No. 2_. \\
3. & _This rule is an extension of rule No. 3 from Solution No. 3_: \\
1. & **IF** Inflight entertainment is high **AND** Type of travel is business travel **AND** Customer type is loyal customer **THEN** Passenger is satisfied \\
4. & **IF** Customer type is disloyal customer **AND** Inflight WiFi service is low (or \\  & medium) **THEN** Passenger is neutral or dissatisfied \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Fuzzy rule bases for SPEA3-based solutions (FRBCs) Nos. 1–5 from Figure 3 and Table 3.  
_Appl. Sci._**2021**, _11_, 5098
# Improved Remote Photoplethysmography Using Machine Learning-Based Filter Bank
## 5 Conclusions  
This study presents a novel denoising algorithm, MLFB, to enhance the quality and improve the accuracy of rPPG signals. The MLFB algorithm proceeds through the following steps: (1) decomposing using 16 sub-band pass filters, (2) feature extraction, (3) computation of the most informative spectral sub-band over time, and (4) generation of the refined pulse signal. Validation on a face video dataset demonstrated that the MLFB algorithm achieved lower MAE in HR estimation and improved SNR compared to the previous methods. The proposed approach offers a robust and reliable method for capturing heart activity. It is suitable for diverse applications such as fitness monitoring, driver alertness assessment, sleep analysis, and various areas requiring non-contact and continuous HR monitoring.  
Conceptualization, [PERSON], [PERSON] and J.W.; methodology, [PERSON] and H.J.; validation, [PERSON] and J.W.; formal analysis, [PERSON] and H.J.; writing and editing, [PERSON] and [PERSON]; visualization, [PERSON] and J.W.; project administration, J.W. All authors have read and agreed to the published version of the manuscript. This study was supported by the 2024 Research Fund of the University of Ulsan.  
The video data used in this study is an open-access dataset by [PERSON] et al. (2020) [22].  
The authors declare no conflicts of interest.
## 3 Results
### Sample Temperature Transitions throughout Mission  
Our first flight experiment was transported to the ISS on SpX CRS-1, so we were eager to obtain as much tracking information as possible, especially since there was no historical data on which to depend. In order to evaluate whether samples were exposed to the expected temperatures and to compare ambient temperatures within the ISS to those of the terrestrial setting of our laboratory, HOBO temperature sensors were included in selected GAPs. Figure 2 provides representative temperature tracings for flight and corresponding terrestrial controls for the duration of the indicated mission. Examples from SpX CRS-1 (Figure 2A), -4 (Figure 2B) and -16 (Figure 2C) are included. SpX CRS-17 utilized FEP bags and thus did not accommodate an accompanying temperature sensor. As illustrated in each panel, the active aspects of the experimental procedures were well aligned between the flight (black lines) and terrestrial controls (green lines) in terms of time and temperature (see activation and termination indicators on tracings, labeled and indicated by red vertical lines). The time offset (usually 2 h) between flight and terrestrial experimental procedures, which is necessary to verify on-orbit procedures by the crew, was removed to improve the temperature comparisons between conditions. Although the mid-mission storagetemperatures varied slightly, with terrestrial controls elevated 1-2 \({}^{\circ}\)C relative to flight in SpX CRS-1 (Figure 2A) and reduced by approximately 7 \({}^{\circ}\)C in SpX CRS-16 (Figure 2C), the sample quality was likely to be minimally affected because the temperature variance was during stasis or after the operational window of the experiment. Of concern though, was the elevated temperatures of the SpX CRS-1 flight samples following undocking (approaching 25 \({}^{\circ}\)C) since the elevated temperature would promote post-microgravity growth of samples being returned live. Other than that specific situation, the temperatures of terrestrial samples were well-aligned with flight samples and the temperature targets were well approximated.
## 2 Experimental Setup and Methods
### Error Estimation
Another major influencing factor would be the roughness of the test rig surface. However, this can be compensated for by establishing an offset determination before each measurement. An overview of all maximum quantified errors is shown in Table 2 for a film thickness of 100 \(\upmu\)m.
# Magnetic Levitation Belt Conveyor Control System Based on Multi-Sensor Fusion
## 4 Results and Discussion
### PID Simulation Analysis  
In industrial control, the most widely used and mature controller is the PID controller, which is a linear controller that directs the controlled object by linearly combining the proportional, integral, and differential deviations of the given and actual values to form the control deviation. The control law of the PID controller is as follows:  
\[u(t)=K_{p}\bigg{[}e(t)+\frac{1}{T_{i}}\int_{0}^{t}e(t)d_{t}+T_{d}\frac{de(t)}{ dt}\bigg{]} \tag{14}\]  
where \(u(t)\) is the control quantity output from the PID controller, \(K_{p}\) is the proportionality coefficient, \(e(t)\) is the difference between the given value of the system and the output value of the system (i.e., the error signal), \(T_{i}\) denotes the integration time constant, and \(T_{d}\) is the differential time constant.  
The step simulation results of the PID control system based on MATLAB/Simulink established for the maglev ball are shown in Figure 9.  
As observed in Figure 9, the time required for the system to reach stability is 0.12 s, and the steady-state error is 0; the steel ball realizes stable suspension, and the system responds quickly and stably. It shows satisfactory dynamic and steady-state performance. Considering that the parameters of the PID control algorithm are not optimal, the performance of  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Numerical** & **Value** \\ \hline Ball mass m/g & 100 \\ Core diameter/mm & 22 \\ Number of electromagnet turns \(N\) & 2450 \\ Amplification factor \(K_{d}\) & 9.776 \\ Sensor coefficient \(K_{s}\) & \(-\)467.4 \\ Balance position \(x_{0}\)/mm & 34 \\ Equilibrium position current \(i_{0}\)/A & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Magnetic levitation ball system parameter description.  
Figure 9: PID simulation result diagram.  
the system can be further enhanced through continuous optimization and modification of the parameters.
## 3 System Models
### Flexible Resource Models
#### 3.1.1 Energy Storage Device  
The energy storage system can realize the transfer of energy in time and effectively boost the flexibility of energy in the system. The connection of electric and gas energy storage devices is involved in this paper. From the perspective of energy conversion, the four types of energy storage devices can be represented by a universal and unified model. When the energy storage device participates in coordinating and optimizing operation, there are constraints from the energy storage capacity of the energy storage device and the upper and lower limits of the energy charging and discharging power of the energy storage device, which are expressed as follows:  
\[E_{t}=E_{t-1}(1-\eta^{loss})+(\eta^{char}P_{char,t}-\frac{P_{disk,t}}{\eta^{dis }})\Delta t \tag{1}\]  
\[E_{\rm min}\leq E_{t}\leq E_{\rm max} \tag{2}\]  
Figure 2: Low-carbon energy sharing framework of multiple regional gas–electric integrated energy systems.  
\[0\leq P_{char,t}\leq\alpha_{char,t}P_{char,\max} \tag{3}\]  
\[0\leq P_{dis,t}\leq\alpha_{dis,t}P_{dis,\max} \tag{4}\]  
\[\alpha_{char,t}+\alpha_{dis,t}\leq 1 \tag{5}\]  
\[E_{1}=E_{T+1} \tag{6}\]  
where \(E_{t}\) represents the storage capacity of the energy storage device during the period \(t\); \(P_{char,t}\) represents the energy storage and charging power of the energy storage device during the period \(t\); \(P_{dis,t}\) represents the energy storage and discharging power of the energy storage device during the period \(t\); \(\eta^{loss}\), \(\eta^{char}\) and \(\eta^{dis}\) represent the energy storage loss rate, charging efficiency, and discharging efficiency of the energy storage device, respectively; \(E_{min}\) and \(E_{max}\) represent the upper and lower limits of the energy storage capacity of the energy storage device, respectively; \(\alpha_{char,t}\) and \(\alpha_{dis,t}\) represent the charging and discharging state, respectively, which is either 0 or 1, with 1 indicating that the device is in the charging or discharging state and 0 indicating that the device stops the charging and discharging state; and \(P_{char,max}\) and \(P_{dis,max}\) represent the maximum charging and discharging power of the energy storage device.
## 2 Materials and Methods
### Statistical Analysis  
Data were analyzed with R software (version 3.5.1; R Foundation for Statistical Computing, Vienna, Austria). We used Shapiro-Wilk for testing normality of data. Considering the pre-activation time (assessed in ms) as primary outcome, the minimum sample size (n = 10) was calculated based on a previous study performed by our group [41], taking into account a potential 10% drop-out rate. Mean and standard deviation were used to describe the data, \(t\)-Student test was used for significance testing, as appropriate for paired samples, and Pearson's r for parametric correlations. Plots were outlined using Graph PRISM (version 4.0 for Windows; GraphPad Software, San Diego, CA, USA). We calculated a post-hoc power analysis for the main outcomes considered.  
Figure 1: sEMG signal of vastus medialis (green line) of one athlete. Pre-activation time is measured as the interval time between the activation of the muscle (dashed line) and the floor contact.
## 3 Results and Discussion
### Adsorption Thermodynamics Studies
The calculated values of \(\Delta H^{\circ}\), \(\Delta S^{\circ}\) and \(\Delta G^{\circ}\) are listed in Table 5. The negative free energy changes (\(\Delta G^{\circ}\)) at all the studied temperatures proposed that the adsorption of X-PR and MGC onto the BS-HVL and BC-HVL adsorbents was feasible and spontaneous thermodynamically. This shows an increased randomness at the solid solution interface throughout the fixation of the dye on the active sites of the adsorbent. The positive value of \(\Delta H^{\circ}\) further reveals that the adsorption is an endothermic process, demonstrating that the adsorption process decreases with an increase in temperature. The positive value of \(\Delta S^{\circ}\) infers that X-PR and MGC in the bulk phase (aqueous solution) were in a much more chaotic distribution in comparison to the relatively ordered state of the solid phase (surface of adsorbent). Additionally, the positive value of \(\Delta S^{\circ}\) represents a result of the affinity of the adsorbent for the two dyes. Figure 18: Representation of \(\ln(\text{K}_{\text{c}})\) as a function of 1/T for determining the thermodynamic parameters of component X-PR and MGC dyes on the BS-HVL and BC-HVL (t = 90 min; m\({}_{\text{ad}}\) = 50 mg; C\({}_{0}\) = 50 mg L\({}^{-1}\), pH = 7 \(\pm\) 0.3, stirring speed 150 rpm).
## 3 Traffic Management Model
### Mobile Agent Unit  
The DNN-IoT-BA traffic management system works as an unmanned traffic assistant system that collects traffic information near the roads and intersections with IoT-BA, as a data transmitter to RSUs. A distributed graph-based model with vehicle set (\(V\)) and edges (\(E\)) is the DNN-IoT-BA traffic management model. In intersections and on roads, the IoT-BA sends the total number of vehicles. The VANETs work with IoT-BA in the proposed method, supporting the infrastructure unit and connecting it to the vehicle for routing operations.  
The IoT-BA in the proposed VANET architecture is a dynamic module moving into VANETs and connecting vehicle units with the infrastructure unit. The IoT-BA is made up of four segments: an identification unit, an execution code unit, a path unit, and a space unit of datum. In order to distribute the data packets from the sources of the source to the destination node through cooperative BAs via the selective routing track, the IoT-BA uploads vehicle information onto the DNN.  
As IoT-BAs contain a wide range of information, each system has a unique identity. The data of current vehicles passing through a road segment are stored in the cloud, and the routing path is defined by DNN, which is a core indicator of packet transmission. Lastly, the data space stores the data from the car units completely. In this case, it is in the RSU Infrastructure Unit that the DNN routing path is found [68].
## 2 Results
### Evaluation of N1 Fluorescently Labeled Cap Analogs as Probes for Microscale Thermophoresis  
First, we performed MST direct binding experiments with fluorescein-labeled probes **6**, **11**, or **12** and murine eIF4E (meIF4E).  
To this end, fluorescent probes (25 nM) were mixed with increasing concentrations of melF4E and microscale thermophoresis was measured for each sample (Figure S5). Baseline corrected normalized fluorescence (\(\Delta F_{n}\)) determined from the MST traces were plotted as a function of protein concentration. \(K_{\text{D}}\) values could then be calculated from the resulting binding curves (Figure 5). Probes **6** and **12** showed similar affinities for melF4E, which were higher than the affinity of probe **11** (Table 2). These findings align with those determined for the parent compounds **1**, **2**, and **3** (Table 1). Additionally, this  
Figure 4: The influence of complete cleavage by hDcpS (30 nM) on the fluorescence properties of probes **6–9** (100 nM). Emission spectra before (0 min) and after (60 min) decapping scavenger (DcpS) cleavage for compounds **6** (**a**) and **9** (**b**); (**c**) The ratios of fluorescence intensity after and before cleavage (I/I\({}_{0}\)) for all studied probes.  
Figure 3: Enzymatic degradation of cap analogs **1–5** (30 uM) by human decapping scavenger enzyme (hDcpS) (30 nM) monitored by RP-HPLC. Representative time-dependent RP-HPLC profiles for compounds **1** (**a**) and **2** (**b**). Percentage of residual substrate as a function of time for all compounds (**c**). Data are average of triplicate experiments \(\pm\) SD.  
suggests that labeling at the N1 position of guanosine does not interfere with already known effects of phosphate bridge modification.  
With these promising results in mind, we decided to use the same approach for studying DcpS enzyme. For this purpose, cleavage-resistant probes **11** and **12** were used. Both probes were high-affinity ligands for DcpS. Notably, probe **12** had 2.5-fold greater affinity for DcpS than probe **11** (\(K_{\text{D}}=14\) and 38 nM, respectively). This is in agreement with previously reported data on the influence of triphosphate chain modifications on cap-DcpS interaction [30; 31; 32].
## 2 Related Works on Bridge Data Schemas
### Bridge Data Schemas Using EXPRESS  
#### 2.1.1. STEP AP 203-Based Bridge Data Schema  
While the Generic AEC Reference Model (GARM) of [PERSON] [23] focuses on buildings, it also contains some aspects related to civil works. However, this is merely a remark that civil works are one of the AEC product types. Actual development of bridge data model based on STEP was mainly implemented during the late 1990s. During that time, the emphasis was on the representation of the geometry of the bridge using the STEP Application Protocol (AP) 203, rather than on the development of data models solely for bridges [24; 25]. [PERSON] et al. [26] developed an entity for a bridge information model by proposing the bridge core model (BCM), and linked this with the CIMsteel integration standards (CIS/2) [27] to allow for the management of the information for structural analysis. Moreover, [PERSON] and [PERSON] [28] developed an integrated framework for a steel bridge information model using STEP AP203 and AP209. However, the BCM added and defined the entities for the elements with low-level representations, without consideration of a classification system. The BCM and the model by [PERSON] and [PERSON] [28] both exclude semantic consideration of the spatial arrangement of the bridge components. As such, spatial semantic information, such as which span the bridge belongs to, must be determined from the assigned location of the physical objects, which is a difficult interpretation task for a computer.
## 3 Results
### Muscle Activation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Muscle** & **Group** & **Baseline** & **Pre-Test** & **Post-Test** & **t** & \(p\) \\ \hline \multirow{2}{*}{Anterior deltoid} & MR group & 65.25 \(\pm\) 1.12 & 53.50 \(\pm\) 1.59 & 54.13 \(\pm\) 2.57 & \(-\)1.718 & 0.106 \\  & Control group & 65.50 \(\pm\) 1.31 & 63.50 \(\pm\) 1.54 & 64.38 \(\pm\) 2.02 & \(-\)2.782 & 0.014 \({}^{*}\) \\ \hline \multirow{2}{*}{Posterior deltoid} & MR group & 73.88 \(\pm\) 1.45 & 62.94 \(\pm\) 1.52 & 63.06 \(\pm\) 1.18 & \(-\)0.368 & 0.718 \\  & Control group & 73.94 \(\pm\) 1.34 & 71.44 \(\pm\) 2.52 & 73.13 \(\pm\) 2.12 & \(-\)1.997 & 0.064 \\ \hline \multirow{2}{*}{Middle deltoid} & MR group & 56.25 \(\pm\) 1.39 & 48.06 \(\pm\) 1.61 & 49.13 \(\pm\) 1.20 & \(-\)2.512 & 0.024 \({}^{*}\) \\  & Control group & 56.81 \(\pm\) 1.42 & 54.88 \(\pm\) 1.54 & 55.69 \(\pm\) 1.49 & \(-\)2.784 & 0.014 \({}^{*}\) \\ \hline \multirow{2}{*}{Upper trapezius} & MR group & 76.56 \(\pm\) 1.36 & 72.94 \(\pm\) 1.18 & 73.69 \(\pm\) 1.19 & \(-\)2.818 & 0.013 \({}^{*}\) \\  & Control group & 76.81 \(\pm\) 1.42 & 74.38 \(\pm\) 1.50 & 74.94 \(\pm\) 1.43 & \(-\)2.522 & 0.023 \({}^{*}\) \\ \hline \multirow{2}{*}{Infraspinatus} & MR group & 53.06 \(\pm\) 1.06 & 49.69 \(\pm\) 1.13 & 49.88 \(\pm\) 1.36 & \(-\)0.716 & 0.485 \\  & Control group & 53.25 \(\pm\) 1.06 & 51.25 \(\pm\) 1.57 & 51.63 \(\pm\) 1.82 & \(-\)1.464 & 0.164 \\ \hline \multirow{2}{*}{Supraspinatus} & MR group & 75.75 \(\pm\) 1.43 & 67.88 \(\pm\) 0.95 & 68.81 \(\pm\) 1.22 & \(-\)2.908 & 0.011 \({}^{*}\) \\  & Control group & 75.69 \(\pm\) 1.53 & 73.31 \(\pm\) 1.74 & 74.19 \(\pm\) 1.47 & \(-\)2.573 & 0.021 \({}^{*}\) \\ \hline \hline \end{tabular} \({}^{*}\)_p < 0.05, mean \(\pm\) standard deviation, electromyographic activation of shoulder muscles reported as a percentage of maximum voluntary isometric contraction (MVIC)._  
\end{table} 
Table 4: Comparison of muscle activation within groups. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Muscle** & **MR Group** & **Control Group** & **t** & \(p\) \\ \hline Anterior deltoid & \(-\)0.62 \(\pm\) 1.45 & \(-\)0.87 \(\pm\) 1.25 & 0.520 & 0.607 \\ Posterior deltoid & \(-\)0.12 \(\pm\) 1.36 & \(-\)1.69 \(\pm\) 3.38 & 1.715 & 0.097 \\ Middle deltoid & \(-\)1.06 \(\pm\) 1.69 & \(-\)0.81 \(\pm\) 1.16 & \(-\)0.487 & 0.630 \\ Upper trapezius & \(-\)0.75 \(\pm\) 1.06 & \(-\)0.56 \(\pm\) 0.89 & \(-\)0.540 & 0.593 \\ Infraspinatus & \(-\)0.19 \(\pm\) 1.04 & \(-\)0.40 \(\pm\) 1.05 & 0.563 & 0.578 \\ Supraspinatus & \(-\)0.94 \(\pm\) 1.28 & \(-\)0.87 \(\pm\) 1.36 & \(-\)0.133 & 0.895 \\ \hline \hline \end{tabular} \({}^{\text{Mean}}\pm\) standard deviation, electromyographic (EMG) activation of shoulder muscles reported as a percentage of maximum voluntary isometric contraction (MVIC).  
\end{table} 
Table 5: Comparison of muscle activation between groups.
## 2 Materials and Methods
### Weighted Gene Co-Expression Network Analysis (WGCNA)  
WGCNA was operated according to the previously published method [18]. The different expression genes (DEGs) filtered between US and TS at each stage were analyzed. The screened FPKM was set as 0.1. The similarity threshold value of module fusion was set as 0.5. The minimum gene number in a module was 30 in order to maintain a high reliability of results.