esa-sceva/satcom-chunk-collection huggingface dataset:
# Combination of Variable Loads in Structural Design
## 5 Implementation  
The proposal can be implemented in the Eurocodes [1,2] by deleting the combination factors in Table 1, i.e., setting the values at unity, \(\psi=1\); in the SLS, it is apparently feasible to set some factors at zero, \(\psi=0\).  
In the ULS, the load combination rule (8.12) is correct and should always be used. The current equation for the combination action calculation is  
\[\sum_{j\geq 1}\gamma_{G,j}G_{k,j}{}^{\omega}+{}^{\omega}\gamma_{P}P^{\omega}+{} ^{\omega}\gamma_{Q,1}Q_{k,1}{}^{\omega}+{}^{\omega}\sum_{i>1}\gamma_{Q,i}\psi_ {i}Q_{k,i}. \tag{13}\]  
It is changed by removing \(\psi\), i.e., it becomes  
\[\sum_{j\geq 1}\gamma_{G,j}G_{k,j}{}^{\omega}+{}^{\omega}\gamma_{P}P^{\omega}+{} ^{\omega}\gamma_{Q,1}Q_{k,1}{}^{\omega}+{}^{\omega}\sum_{i>1}\gamma_{Q,i}Q_{k,i} \tag{14}\]  
Corresponding changes are made in the SLS. The amended equations, other equations, and the design process remain.  
The updated codes are safe regarding codes with combination factors.  
The findings of this study should be verified in actual design practice by conducting comparative designs using both the current Eurocodes and the proposed amended version. The comparison should be made using the combination factors given here. The current factors are based on \(\psi_{0.5}\) values, which exaggerate the reliability gain. In this comparison, a penalty should be applied when the calculation results in an unsafe outcome. This task is relatively straightforward when using an automated design software programme. Key metrics should be evaluated:  
* Safety Margins: Determine if the amended codes maintain or improve the safety margins compared to the current codes.
* Design Efficiency: Evaluate any changes in material usage and overall design efficiency.
* Complexity and Workload: Measure the reduction in complexity and workload achieved by the proposed amendments.  
The study should end with a document of findings to provide a justification for or against the proposed amendments including benefits and drawbacks.  
If the removal of combination factors is executed, the author estimates a significant decrease in complexity and workload, with a slight increase in material use. However, if other proposed amendments [25] are also considered, a significant decrease in material use can be achieved along with decreased complexity and workload.
# An Adaptive UI Based on User-Satisfaction Prediction in Mixed Reality
## 4 Experimental Results and Discussion
### Experiment for Implementation of Proposed Method
\begin{table}
\begin{tabular}{c c} \hline \hline
**Initial Frame** & **MAE** \\ \hline
10 frames & 0.654 \\
20 frames & 0.691 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of user-satisfaction prediction of SVR model using initial frame. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Initial Frame** & **Parameter** & **MAE** \\ \cline{2-3}  & **Epoch** & **Batch** \\ \hline
10 frames & 24 & 4 & 0.531 \\
10 frames & 28 & 16 & 0.538 \\  & 17 & 4 & 0.538 \\ \hline
20 frames & 22 & 2 & 0.540 \\
20 frames & 29 & 8 & 0.543 \\  & 17 & 4 & 0.549 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Top three results of user-satisfaction prediction of MLP model using initial frame. As shown in Figure 11, in most cases, user satisfaction was improved when the object generated on the right side rather than the left side, the bottom side rather than the top side, and the front side rather than the back side, respectively. To confirm whether the difference is significant, the result of the Wilcoxon signed-rank test, which is a non-parametric test, is shown in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **User Satisfaction** & **User Satisfaction** & **User Satisfaction** \\  & **Left-Right** & **Top-Bottom** & **Front-Back** \\ \hline \(Z\) & \(-2.496\)1  & \(-1.999\)1  & \(-2.952\)2  \\ Asymp. Sig. (2-tailed) & 0.013 & 0.046 & 0.003 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Results of Wilcoxon signed-rank test of user satisfaction based on object position. Figure 11: Average user satisfaction obtained from six types of positions (virtual object-generation positions). Figure 10: Classification of positions where virtual objects are generated (six types): (**a**) left side; (**b**) top side; (**c**) front side; (**d**) right side; (**e**) bottom side; (**f**) back side. As shown in Table 4, the significance level of the Wilcoxon signed-rank test was less than 0.05 in all combinations of left-right, top-bottom, and front-back. That is, it was confirmed that user satisfaction was improved when interacting with objects generated in the right, bottom, and front sides compared to the left, top, and back sides. Based on this, the proposed method corrects the object position in the left, bottom, and front directions when the predicted user satisfaction is lower than the threshold. Here, it is judged that the high preference for the right side compared to the left side is because only right-handed subjects were recruited. Since the subsequent experiment process (Section 4.3) was conducted by recruiting only right-handed subjects as before, this result was used as is. However, when targeting left-handed people in the future, it is necessary to reverse the left-right correction direction.
## 3 Results
### Characterization of Sorbent
#### 3.1.3 pH\({}_{\text{PZC}}\)--pH-Drift Titration  
Figure S2 shows the application of the pH-drift method for the determination of the pH\({}_{\text{PZC}}\) of the sorbent. The analysis was performed with two concentrations of background salt (i.e., 0.01 M and 0.1 M NaCl solutions). The pH\({}_{\text{PZC}}\), which corresponds to pH\({}_{0}\) = pH\({}_{\text{eq}}\), varies between 8.07 and 8.76; the value decreases with the concentration of the background salt because of the charge screening effect. It is noteworthy that this variation is more marked than usually (where \(\Delta\)pH\({}_{\text{PZC}}\) rarely exceeds 0.2 pH unit). This means that the sorbent is totally protonated in acid solutions. The higher pH increase is observed at pH\({}_{0}\) 4; logically, the protonation decreases with increasing the pH. The main constituents of G@MChs are crosslinked chitosan and glycine moieties. [PERSON] et al. [49] reported that the pK\({}_{\text{a}}\) of amine groups in chitosan varies with the deacetylation degree of chitosan and the charge neutralization; for most conventional (commercial) chitosan, the pK\({}_{\text{a}}\) is close to 6.5, while the pK\({}_{\text{a}}\) values of glycine are 2.34 and 9.6 for carboxylic group and amine group, respectively [50]. The grafting of glycine onto crosslinked chitosan (magnetite-supported) appears to make the acid-base properties of the composite closer to those of the amine moiety of glycine (compared to carboxylic group end).  
The titration of G@MChs allows the determination of the nitrogen content of the composite: 1.84-1.96 mmol N g\({}^{-1}\) (i.e., 2.57-2.74%, \(w/w\)). In chitosan, the amine groups represent \(\sim\)5-5.5 mmol N g\({}^{-1}\). This means that the fraction of magnetite in the composite is roughly close to 50%. Actually, the theoretical content (based on the amount of reagents used in the synthesis) and the effective titration (for N content) show a large dispersion (meaning that the magnetite content is in the range 50 \(\pm\) 14%).
## 3 Results and Discussion
### Experimental Results
#### 3.1.2 Influence of the Number of Dowels at the Connection  
To study the influence of the number of dowels on the connection, the series with the connection located at mid-span with one and two dowels and beam spans of 450 mm and 950 mm were considered.  
Table 5 summarises the results of the series studied, including the mean value of the splitting failure load, the parameter \(\alpha\), the slenderness of the beam, and the percentage load increase in the connections with two dowels with respect to their counterparts with one dowel.  
As can be seen, the load gain of 2-dowel connections relative to single-dowel connections never reaches twice the value.  
The influence of the parameter \(\alpha\) can be analysed by comparing the series with different values of \(\alpha\) and similar slenderness (4.75 slenderness for \(\alpha=0.32\) and 4.5 slenderness for \(\alpha=0.64\)). The results show that the load gain for \(\alpha=0.32\) is very small, only 9%. However, for the series with \(\alpha=0.64\), the load gain attained is much higher, 51%, although still far from the expected doubling. Note that in this case \(\alpha\) is doubled, but the load gain is more than five times higher.  
Figure 10: Typical splitting failure of series with \(h=100\) mm (group 1 (**a–d**)) and \(h=200\) mm (group 2 (**e–h**)): (**a,e**) 1D with connection at mid-span; (**b,f**) 1D with the connection placed at off-centre of the beam; (**c,g**) 2D with connection at mid-span; (**d,h**) 2D with the connections placed at off-centre of the beam.  
The effect of the span can be analysed by comparing the results of the series with the same depth (\(h\) = 200 mm) but different spans (450 mm and 950 mm). It can be observed that, as the span increases, the load gain for using two dowels instead of one is almost negligible, from 7% for the series with a 450 mm span to 9% for the series with a 950 mm span. These results show that span has hardly any influence, at least for low \(\alpha\) values such as the one studied (\(\alpha\) = 0.32).  
The increase in load obtained as a function of the number of dowels in the connection is consistent with other research [34; 40; 67], where increasing the number of dowels led to an increase in splitting failure load.  
It is worth mentioning that the load carrying capacity of the connection increases slightly with increasing beam span. Figure 11 shows the ultimate splitting loads in relation to the beam span for the series of single- and double-dowel connections at mid-span for \(h\) = 200 mm.  
According to the results, it follows that the larger the span, the higher the splitting loads (13% for 1D and 15% for 2D). In terms of beam slenderness (\(L/h\)), the higher the slenderness, the higher the load carrying capacity, which is in agreement with the results for spruce glulam available in [34; 68]. In the case of the series with \(h\) = 100 mm, this comparison is not possible because the beams with larger spans failed in bending.
## 4 Simulation Results
### Sine Steer  
A sine path wave of 90 deg on the steering wheel is realized by the driver while traveling at 150 km/h to evaluate the responsiveness of the controlling techniques during this test. These maneuvers allow us to investigate the correctness of the implemented dead  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Standard** & **Purpose** \\ \hline _Ramp Steer_ & FMVSS No.126 \& ISO 19364:2016 & Calibration \\ _Step Steer_ & ISO 7401:2003 & Calibration \& Validation \\ _Sine Steer_ & ISO 7401:2003 & Validation \\ _Lane-Change_ & ISO 3888-1:2018 & Validation \\ \hline \hline \end{tabular}
\end{table}
Table 1: Investigated reference maneuvers.  
zone on the states errors since both yaw rate and side-slip consecutively cross 0 values. In the top plot of Figure 9, the steering angle imposed by the driver is visible for the different scenarios. For the SMC-Controlled solutions, the impact of the steering correction control technique is shown.  
Even in this case, the proposed controllers perform quite well with respect to the task of tracking the controlled states. In Non-controlled solutions, the vehicle is unstable, while for LQR and SMC scenarios, both vehicle yaw rate and side-slip angle are controlled by active torque distributions effort of Figures 10 and 11, exhibiting improved stability performances even respect to Commercial-controlled scenario.  
As for the _Step Steer_ test, LQR and SMC control actions show higher control effort values with respect to the reference ESP controller. This leads to improved tracking performances of the controlled states, especially for the side-slip angle, achieving a higher safety margin.  
Figure 6: Evaluation of the states tracking performances in case of Non-controlled (Passive), Commercial-controlled (Active), LQR, and SMC-controlled scenarios, in a _Step Steer_ maneuver at 150 km/h and with 90 deg steering wheel angle.
## 3 Parameter Estimation Method for the Weak PRBC-PAM Signal
### System Output Symbolization
 \tag{10}\]  
where \(i\) and \(j\) represent the \(i\)-th time point and the \(j\)-th frequency point, respectively. \(N\) is the sequence length, and \(g_{\mathrm{Ham}}\) is the Hamming window function which can significantly sidelobe attenuation that can greatly reduce spectrum leakage. The critical threshold E1 between the chaotic state and the critical chaotic state and \(E2\) between the critical chaotic state and the large-scale periodic state can be obtained through experiments. Then, when \(E_{\mathrm{m}}<E1\), the detection system is in a chaotic state; when \(E_{\mathrm{m}}>E2\), it is in a large-scale periodic state; and when \(E1\leq E_{\mathrm{m}}\leq E2\), it is in a critical chaotic state. Therefore, excited by a PRBC-PAM signal, the Duffing oscillator output can be processed to obtain a three-valued signal as follows: obtaining the decision parameter \(E_{\mathrm{m}}\) of the output signal in a certain time interval by the STFT method and then comparing it with \(E1\) and \(E2\) to identify its state; and setting the amplitude value of this period to be \(-1\), \(0\), and \(1\) when it is in a chaotic state, critical state, and large-scale periodic state, respectively. Finally, the three-valued signal of the output signal can be obtained after completing the state discrimination for the whole sequence, and it can be represented by _Sdf(t)_:  
\[Scg(t)=\left\{ \begin{array}{ll}-1,&\text{chaotic state}\\ 0,&\text{critical chaotic state}\\ 1,&\text{large-scale periodic state}\end{array} \right. \tag{11}\]  
Taking the measured weak PRBC-PAM signal frequency \(\omega_{0}\) as equal to the internal driving signal frequency \(\omega\) of the Duffing oscillator as an example, when \(\omega_{0}=\omega=100\) MHz, A = 0.6, and \(T_{\mathrm{c}}\) = 100 ns, the time-domain diagram of the output signal and the time-domain diagram after three-valued processing of the output signal are shown in Figure 9a,b, respectively. Figure 9 shows that the three-valued Duffing oscillator output signal can be effectively measured by the method based on the STFT. Figure 8: Time-domain diagram of the STFT in three states: (**a**) Chaotic state; (**b**) Critical chaotic state; (**c**) Large-scale periodic state.
## 4 Repetition-Based Cooperative Broadcasting Scheme  
In this section, we discuss the detail operations of the RBCB scheme. As shown in Figure 3, the RBCB scheme consists of three phases: initial broadcasting, helper selection, and cooperative rebroadcasting. First, in the initial broadcasting phase, the source node broadcasts the safety message along with the cooperation pattern to its neighboring nodes. Then, in the helper selection phase, the best helpers are selected according to the number of target nodes they can help and the distance to the target nodes. Finally, in the cooperative rebroadcasting phase, the selected helpers and the source node rebroadcast the safety message together in the source node's following slots.  
Figure 1: Frame structure.  
Figure 2: Packet structure.
## 5 Challenges
### Interoperability
Legal frameworks have not kept pace with rapid advancements in AI and IoMT, resulting in ambiguities around liability and accountability in cases of system failures or adverse outcomes. These challenges underscore the need for cohesive standardization, improved data interoperability, and comprehensive governance frameworks to enable integrating AI and IoMT into modern healthcare systems. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Level Name** & **Description** & **IoMT Application Examples** \\ \hline Level 0 (no interoperability) & Independent systems that lack interoperability. & Stand-alone systems. \\ \hline \multirow{6}{*}{Level 1 (also called technical interoperability)} & It employs communication protocols to facilitate data exchange between two systems, emphasizing the physical and network infrastructure for data transmission between devices or systems [224]. & A wearable heart monitor sending data to a smartphone app via a secure Bluetooth connection. \\ \cline{2-3}  & It enables two or more systems to transmit data and share services through a common interoperability protocol (e.g., the High-Level Architecture (HLA) [224]). & A glucose monitoring device and an insulin pump to exchange data using a standardized messaging format like JSON, XML, or HL7 (Health Level Seven). \\ \hline \multirow{6}{*}{Level 3 (semantic interoperability)} & It enables two or more systems to transmit data and share services through a common interoperability protocol (e.g., the High-Level Architecture (HLA) [224]). & A heart rate monitor sends a patient’s data to the hospital’s EHR system. Semantic interoperability ensures that the EHR understands data meaning as well, such as the EHR can trigger an alert to the healthcare providers if the heart rate is over 120 bpm. \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \cline{1-1} \cline{2-3}  & & \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Basic levels of interoperability.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Wavelength** & **Calculated** & **Measured** & & \\
**(nm)** & **Transmission (\%)** & **Transmission (\%)** & **Error (\%)** & **Absorbance** \\ \hline
532 & 61.8 & 60.1 & 2.75 & 0.22 \\
632.8 & 68.7 & 65.8 & 4.22 & 0.18 \\
1550 & 82.1 & 74.9 & 8.77 & 0.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The calculated and measured transmission and absorbance of a 1.5 mm thick polycrystalline PMN-PT slab for three different wavelengths. Figure 17: The calculated transmission spectrum of polycrystalline PMN-PT. The deflection angle for the wavelengths of 532, 632.8, 1550, and 4500 nm was measured in the lab in terms of mrad.mm/kV at 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C temperatures. The results are given in Table 6. The measured deflection angles and the calculated refractive indices can define the EO coefficients of the polycrystalline PMN-PT using Equation (48). This EO beam steerer was also modeled in COMSOL Multiphysics to simulate its performance and compare it to the measured values. The calculated EO coefficients, the refractive indices, and the measured permittivity were used as the COMSOL model's inputs. The voltage and electric field distribution, the birefringence, and deflection angles were the COMSOL model outputs, as seen in Figures 20-22. Figure 19: Light deflection test with the 7.5 mm \(\times\) 7.5 mm \(\times\) 40 mm polycrystalline PMN-PT with voltage electrodes. Figure 18: The dielectric constant for the PMN-PT ceramic as a function of temperature. The peak dielectric constant of 11,688.9 is reached at room temperature, approximately 25 \({}^{\circ}\)C.  
Figure 21: The birefringence simulation of the EO steerer based on PMN-PT ceramic under 1 kV/mm at 25 \({}^{\circ}\)C using a COMSOL model for (**a**) 532 nm, (**b**) 633 nm, (**c**) 1550 nm, and (**d**) 4500 nm. Figure 20: (**a**) Voltage (**b**) electric field (E\({}_{2}\)) simulation of the EO steerer based on PMN-PT ceramic under 1 kV/mm at 25 \({}^{\circ}\)C using a COMSOL model. As seen in Table 6, the measured deflection angles for four wavelengths at 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C temperatures could also define the number of resolvable spots using Equation (2). The EO coefficients and the refractive indices can define the halfwave electric field using Equation (38). The halfwave electric field for the three different wavelengths at 25 \({}^{\circ}\)C and 40 \({}^{\circ}\)C was also measured in the lab. As seen in Table 6, the results are comparable to the calculated values. The wavelength-dependent birefringence (\(\Delta n\)) defines the phase retardance for those four wavelengths using Equation (37), as seen in Figure 23.  
The results show that although the EO coefficient of PMN-PT ceramic is not strongly varying by the temperature, it drops significantly from 15.68 \(\times\) 10\({}^{-16}\) for green laser (532 nm) to 2.18 \(\times\) 10\({}^{-16}\) for mid-IR (4500 nm). That resulted in reducing the number of resolvable spots of the EO beam steerer designed in this study from 1403 for 532 nm to 23 for 4500 nm at 25 \({}^{\circ}\)C.
## 4 Case Study
### Motion Sensors  
Room occupancy is monitored by presence sensors and cameras capable of using computer vision algorithms. All rooms are monitored by occupancy sensors, with occupancy counting delegated to cameras only for rooms where more than one person is expected to be present at the same time.
# An FCM-GABPN Ensemble Approach for Material Feeding Prediction of Printed Circuit Board Template
## 3 Methodology  
The procedure of the proposed approach (FCM-GABPN) is shown in Figure 2, and various aspects of FCM-GABPN are discussed in the following subsections.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**No.** & **Variable Name** & **Symbol** & **Description** & **Value Range** \\ \hline \multicolumn{5}{c}{Statistic items} \\ \hline
36 & Delivery unit in a panel & _Dup_ & Number of delivery unit in a panel & 1–262 \\ \hline
37 & Supplemental feeding frequency & _Supff_ & Material feeding frequency minus 1 & 0–14 \\ \hline
38 & **Required quantity** & _Reqq_ & Demand quantity of delivery unit minus delivery unit in inventory for the same order No. & 1–3000 \\ \hline
39 & **Required panel** & _Reqp_ & _Reqq/Dup_ rounded up to the nearest integer & 1–225 \\ \hline
40 & Feeding quantity & _Fedq_ & Feeding number of delivery unit & 2–6296 \\ \hline
41 & Least feeding panel & _Lfp_ & _Reqq/(1-scrap rate)_ rounded up to the nearest integer & 1–245 \\ \hline
42 & Feeding panel & _Fedp_ & Number of feeding panel & 1–308 \\ \hline
43 & Scrap quantity & _Scraq_ & Scrap number of delivery unit & 0–712 \\ \hline
44 & Qualified quantity & _Qualq_ & Qualified number of delivery unit & 1–6226 \\ \hline
45 & Surplus quantity & _Surpq_ & _Qualq–Fedq_ & 0–3226 \\ \hline
46 & Delivery unit area(m\({}^{2}\)) & _Duinta_ & Area of a delivery unit & 0.001–0.393 \\ \hline
47 & **Required area(m\({}^{2}\))** & _Reqa_ & _Reqq_\(\times\)_Duinta_ & 0.001–25.74 \\ \hline
48 & Feeding area(m\({}^{2}\)) & _Feda_ & _Fedq_\(\times\)_Duinta_ & 0.011–42.63 \\ \hline
49 & Scrap area(m\({}^{2}\)) & _Scraa_ & _Scraq_\(\times\)_Duinta_ & 0–15.39 \\ \hline
50 & Qualified area(m\({}^{2}\)) & _Quula_ & _Qualq_\(\times\)_Duinta_ & 0.009–37.49 \\ \hline
51 & Surplus area(m\({}^{2}\)) & _Surpa_ & _Surpq_\(\times\)_Duinta_ & 0–25.45 \\ \hline
52 & Supplemental feeding rate & _Supff_ & _Supff_ in a certain period/number of orders \(\times\) 100\% & 18.83\% \\ \hline
53 & Scrap rate & _Scrar_ & _Scraq/Feda_\(\times\) 100\% & 0\%–68.48\% \\ \hline
54 & Qualified rate & _Qualr_ & _Quala/Feda_\(\times\) 100\% & 31.52\%–100\% \\ \hline
55 & Surplus rate & _Surpr_ & _Surpa/Req_\(\times\) 100\% & 0\%–554.22\% \\ \hline
56 & Historical qualified rate & _Hquar_ & The _Qualr_ for the same order _No._ in the past 2 years & 8.824\%–100\% \\ \hline \end{tabular} Note: New orders having no _Hquar_ are replaced by the _Qualr_ for orders having the same layer number and surface-finishing operation during the past 2 years.  
\end{table}
Table 1: _Cont._