esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Background: Deep dental cavities can be restored through a single step according to the bulk-fill technique. Due to the great amount of resin to be cured, a main concern is the temperature rise occurring in the pulp chamber, potentially higher than that developed through the incremental layering technique. Temperature rise of bulk-fill composites have been evaluated. Methods: Bulk-fill composites, differing in material composition and viscosity, were used. Maximum temperature and temperature rate occurring in the composites were measured. Mesio-oclusal-distal cavities of human premolars were restored through the bulk-fill or the incremental layering techniques, and peak temperature and temperature rate occurring in the dentin, 1 mm below the cavity floor, were evaluated. Results: Temperature peak and temperature rise of flowable composites were significantly higher (\(p<0.05\)) than packable composites. For both the techniques, higher temperature peaks were recorded in the dentin for flowable composites. Peak temperatures higher than 42 \({}^{\circ}\)C were recorded for the incremental layering technique considering flowable composites. Conclusions: For all the composites, the light curing modality of 1000 mW/cm\({}^{2}\) for 20 s can be considered safe if the bulk-fill technique is performed. Instead, for the incremental layering technique, potentially dangerous temperature peaks have been recorded for flowable composites.  
bulk-fill composites; incremental layering technique; thermocouple; temperature rise; pulpar damage +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods  
### Experimental Protocol  
Twenty male subjects with no lower-extremity injuries, prior musculoskeletal injuries, or orthopedic abnormalities volunteered, with a mean age of 24 \(\pm\) 1.2 years, height of 172 \(\pm\) 2.7 cm, and body mass of 67 \(\pm\) 6.7 kg (normal body mass index (BMI) category). The subjects were informed and provided written consent prior to participating, in accordance with a protocol approved by the Ethics Committee of Universiti Malaysia Perlis. The subjects were then allowed to walk on a customized inclined platform to familiarize themselves with each experimental condition, as depicted in Figure 1. The custom-built wooden slope platform had a flexible angle feature to be adjusted. The slope was 5 m long with a 1 m broad walkway. Two force plates (Bertec Corporation, Columbus, OH, USA) with dimensions of 60 cm \(\times\) 40 cm \(\times\) 10 cm were embedded under the walking platform at a frequency of 200 Hz. Five Qus cameras in a motion-capture system (Qualysis, Gothenburg, Sweden) at a frequency of 200 Hz were used to obtain three-dimensional (3D) dynamic and static data. Ten reflective markers with a diameter of 20 mm wrapped with reflective tape were attached to the subjects using a revised form of the \"Plug-in Gait\" lower limb marker set, as shown in Figure 2. The subjects were instructed to walk along the platform through the force plate at their convenient walking speed without changing their steps to mimic normal walking style while wearing minimally shoes on a level and on three different incline platforms with angles of 5\({}^
## 3 Discussion
One explanation for this result could be that, at the final time, particles describe a limit cycle in which \(z(t)\) reaches a limit value \(z_{\pm\delta}^{\tau}\) (each enantiomer goes to a final z position that depends on their value \(\pm\delta\) and de-phase \(\tau\)) at the fixed value \(r=r_{p}\) (see Figure 2). In this case (when the limit cycle is reached \(z=z_{\pm\delta}^{\tau}\), \(r=r_{p}\)), Equations (28) and (30) can be approximated to:  
\[\dot{r}\approx 0\implies r=r_{p} \tag{31}\] \[\gamma r_{p}\dot{\theta}\approx\frac{-W_{s}(r_{p},z_{\pm\delta}^{ \tau})cos(\tau\,t-2k\,z_{\pm\delta}^{\tau}-\theta)+W_{c}(r_{p},z_{\pm\delta}^{ \tau})sin(\tau\,t-2k\,z_{\pm\delta}^{\tau}-\theta)}{r_{p}} \tag{32}\]  
\[\dot{z}\approx 0\implies \tag{33}\] \[-W_{s}(r_{p},z_{\pm\delta}^{\tau})cos(\tau\,t-2k\,z_{\pm\delta}^{ \tau}-\theta)+W_{c}(r_{p},z_{\pm\delta}^{\tau})sin(\tau\,t-2k\,z_{\pm\delta}^{ \tau}-\theta)=\frac{\partial_{z}W_{0}(r,z)|_{(r_{p},z_{\pm\delta}^{\tau})}}{2k}\]  
Introducing Equation (33) into (32), we finally obtain that:  
\[\dot{\theta}\approx(-2\,k\,r_{p}^{2}\,\gamma)^{-1}\,\partial_{z}W_{0}(r,z)|_{( r_{p},z_{\pm\delta}^{\tau})}=\omega \tag{34}\]  
As can be observed in Figure 5, the approximated function (34) qualitatively explains the final frequency values \(\omega\), although quantitatively, the predictions are overestimated be approximately 1 Hz. A possible explanation for this observed overestimation is that we have used the approximations:  
\[(\partial_{z}W_{s}(r,z)+2 kW_{c}(r,z))sin(\phi(z,\theta))\approx 2 kW_{c }(r,z)sin(\phi(z,\theta))\] \[(\partial_{z}W_{c}(r,z)-2 kW_{s}(r,z))cos(\phi(z,\theta))\approx-2 kW_{s}(r,z)cos(\phi(z,\theta))\]  
in order to obtain the analytical approximated Equation (34). Finally, Figure 6 shows the particle separation of particles with different chiral parameter \(\delta\) when we use the de-phasing parameter \(\tau=10\) Hz, taking in all cases a final time of 50 s.  
Figure 3:0 Angular frequency of the last circular trajectory of trapped enantiomers (\(\delta>\) 0 blue and \(\delta<\) 0 red) versus \(\tau\) (the continuous line shows the predicted results from Equation (34)). As can be observed, the particle separation is null for low values of \(\delta\), increasing for higher values of \(\delta\) until reaching a saturation zone for a fixed time of 50 s. It is important to note that at this time, particles with \(\delta>0.7\) have not reached their limit cycle.
## 2 Literature Review on the Australian HSR Risks and Barriers
### Cost Barriers in Implementation and Delivery  
The recent HSR study had projected the delivery cost to be $114 billion in 2012, equating to $152 billion in 2024, positioning it as Australia's most significant infrastructure investment if undertaken [5,21]. This represents a substantial investment even when viewed over a nominal 30-year project span. In a study conducted in the USA, [PERSON] and [PERSON] [24] discuss how the high capital costs of HSR infrastructure pose a significant barrier, especially in countries with tight budget constraints. The study also examines case studies where financial challenges hindered HSR development. A further study conducted by [PERSON] and [PERSON] [25] collected information on 166 HSR projects worldwide. They also presented that the high construction and land acquisition costs were frequently cited as major obstacles, particularly in countries without established HSR infrastructure, like Australia [19,24,25,26].  
Exacerbating this challenge, the literature notes that cost estimates during the conceptualisation phase tend to be unreliable [27,28], with megaprojects commonly experiencing cost overruns and schedule delays. Examples include the Metro project ($12 billion, 5 years delayed [27]) and Victoria's big build projects such as the Northeast Link ($10.3 billion [29]). This is a further barrier to HSR approval in Australia. [PERSON], Senior Vice President at WSP, suggests window and range-based budget estimates, claiming absolutes will always be wrong [2]. Renowned mega projects researcher [PERSON] is also in agreement that range or window-based estimates are most suitable [14,26,30].  
A 2017 study analysing public infrastructure megaprojects attributed cost overruns to various factors, including environmental concerns, claims and penalties, procurement challenges, and scope modifications [31], a notion supported by a recent University of Sydney investigation into the causes of the Sydney Metro cost escalation [32]. A Sydney Light Rail project case study confirms that unforeseen claims can particularly threaten cost control. In this project, undocumented utilities led to a substantial payout by the NSW Government [33,34].
## 4 Experimental Condition
### Metric Improvement  
This study found that routing has a great influence on the frequency of the ROs; the higher routing around the RO location, the less its frequency is. The highest routing density is at RO-14 using all patterns. This RO's average wire utilization is 22.8% (the highest), and the average routing hotspots is 59.8%. Upon realization, the RO produced the least pulses in all chips' overall patterns, which leads to the worst uniqueness. This finding explained the lower uniqueness of some reported RO-PUF qualities [21; 28; 40]. The study found that other logic existed inside the LAB of RO-14, leading to heavier routings. Hence, the RO exposed noises more than others [23; 24; 41].  
The unstable ROs were excluded to improve the PUF metric [5; 42; 43]. While in other designs, researchers considered only the RO pairs where their frequencies were far enough apart [43; 44], Meaning that the RO inclusion process was based on the resulting frequencies upon realization, varying from chip to chip, this work, on the other hand, improves the PUF metric (uniqueness) by excluding ROs based on routing density. The ROs are chosen before they are realized onto the chip. The ROs with heavy and light routing densities are excluded, or only ROs with narrow routing densities will be considered. For this purpose, this work applies four scenarios in excluding ROs.  
* The first scenario (named WR1); excludes ROs with heavy wire utilization (\(w_{h}\)).
* The second scenario (named WR2); excludes ROs with heavy wire utilization (\(w_{h}\)) and light wire utilization (\(w_{l}\)).
* The third scenario (named RH1); exclude ROs with heavy routing hotspots (\(r_{h}\)).
* The fourth scenario (named RH2); exclude ROs with heavy routing hotspots (\(r_{h}\)) and light routing hotspots (\(r_{l}\)).  
The thresholds (\(w_{h}\), \(w_{l}\), \(r_{h}\), \(r_{l}\)) for particular ROs are subject to RO-PUF design. Under these scenarios, fewer ROs are needed to determine the metrics. The thresholds in this work are applied equally to all patterns. This work chooses the thresholds of \(w_{h}\) = 5%, \(w_{l}\) = 3%, \(r_{h}\) = 50%, \(r_{l}\) = 20%, based on the medians of wire utilization (4%) and routing hotspots (28.1%).
###### Abstract  
This article presents the results of the tribological performance of 20 MnCr5 (1.7147) tool steel countersamples produced by Direct Metal Laser Sintering (DMLS), as a potential material for inserts or working layers of sheet metal forming tools. Tribological tests were performed using a roller-block tribotester. The samples were sheet metals made of materials with significantly different properties: Inconel 625, titanium-stabilised stainless steel 321, EN AW-6061 T0 aluminium alloy, and pure copper. The samples and countersamples were analysed in terms of their wear resistance, coefficient of friction (COF), changes in friction force during testing, and surface morphology after tribological contact under dry friction conditions. The tests were performed on DMLSed countersamples in the as-received state. The largest gain of countersample mass was observed for the 20 MnCr5/EN AW-6061 T0 friction pair. The sample mass loss in this combination was also the largest, amounting to 19.96% of the initial mass. On the other hand, in the 20 MnCr5/Inconel 625 friction pair, no significant changes in the mass of materials were recorded. For the Inconel 625 sample, a mass loss of 0.04% was observed. The basic wear mechanism of the samples was identified as abrasive wear. The highest friction forces were observed in the 20 MnCr5/Cu friction pair (COF = 0.913) and 20 MnCr5/EN AW-6061 T0 friction pair (COF = 1.234). The other two samples (Inconel 625, 321 steel) showed a very stable value of the friction force during the roller-block test resulting in a COF between 0.194 and 0.213. Based on the changes in friction force, COFs, and mass changes in friction pair components during wear tests, it can be concluded that potential tools in the form of inserts or working layers manufactured using 3D printing technology, the DMLS method, without additional surface treatment can be successfully used for forming sheets of 321 steel and Inconel 625.  
direct metal laser sintering; friction; marging tool steel; tool wear
## 3 Results
### Genological Characterization of Predominant Native _S. cerevisiae_ Isolated Strains
#### 3.3.3 Volatile Compounds  
The results of the main volatile compounds of wines fermented at 16 \({}^{\circ}\)C are reported in Table 5. The data show that strains belonging to different biotypes were characterized by the production of ester compounds. Biotype I exhibited a significant increase in isoamyl acetate (banana aroma) and ethyl octanoate (apple, pear, and peach aroma), while biotype II showed a significantly increased amount in ethyl hexanoate (apple aroma). _S. cerevisiae_ starter strains showed different aromatic profiles: OKAY(r) was characterized by a significant increase in ethyl butyrate content; EC1118 was characterized by relevant amounts of \(\beta\)-phenyl ethanol, and VIN13 showed a significantly high production of phenylethyl acetate. About hexanol, linalool, and geraniol, the results did not show a significant difference among the strains. The volatile compounds produced at 22 \({}^{\circ}\)C are reported in Table 6. The data show a different behavior of the starter strains in comparison with the 16 \({}^{\circ}\)C fermentation trials. At 22 \({}^{\circ}\)C, biotype I showed a significant increase in isoamyl acetate, while biotype II was characterized by a significantly high production of nerol and \(\beta\)-damascenone. Among the starter strains tested, OKAY(r) confirmed at 22 \({}^{\circ}\)C the high production of ethyl butyrate together with a relevant production of terpen, \(\beta\)-phenyl ethanol, and diethyl succinate. EC1118 was distinguished by the other strains by the production of phenylethyl acetate and ethyl hexanoate, while VIN13 in this fermentation condition did not show any relevant production of a particular compound. To assess the overall effects of yeast strains under different fermentation temperature, the main analytical characteristics and volatile compounds were analyzed by Principal Component Analyses (PCA) reported in Figure 2. The PCA analysis related to the fermentation carried out at 16 \({}^{\circ}\)C (Figure 2a) and showed that all _S. cerevisiae_ starter strains were grouped in the lower left quadrant, while the two dominant biotypes I and II were placed separate with the starter strains between them. Biotype I was in the lower right quadrant, while biotype II was in the upper right quadrant. Under 16\({}^{\circ}\) C of fermentation temperature, a greater differentiation of the analytical and volatile compounds was found. Otherwise, data obtained after fermentation carried out at 22 \({}^{\circ}\)C reported in Figure 2b showed a different distribution of the strains in comparison with fermentation carried out at 16 \({}^{\circ}\)C. Biotype I was located in the lower left quadrant with _S. cerevisiae_ VIN13. The other three strains were each in a different quadrant.
# A Performance Evaluation Approach for Satellite Attitude Control System in Tracking Mode
## 5 Evaluation Experiment and Analysis  
The satellite data utilized in this study originated from the telemetry data from a certain Earth observation satellite over the period August 2021 to October 2021. Prior to the assessment, data preprocessing was conducted based on operational modes. A total of 44 sets of complete operational data were extracted from sun-pointing modes under attitude tracking patterns. These data were used as a basis for conducting the evaluation experiments.
## 4 Multi-Antenna Frequency-Selective Channels
### Conventional Learning for LSTD-Based Prediction
} \left\{||X_{f}^{\text{tr}}(V_{f}^{(K)})^{k}-(Y_{f}^{\text{tr}})^{k}|| _{F}^{2}-\lambda_{1}\text{tr}\Big{(}(\hat{b}_{f}^{k})^{\dagger}(\hat{b}^{k}( \hat{b}^{k})^{\dagger})\hat{b}_{f}^{k}\Big{)}\right\},\] \[\qquad\qquad\qquad\qquad\qquad\qquad\text{subject to }(\hat{b}_{f}^{k})^{ \dagger}\hat{b}_{f}^{k}=1, \tag{43}\]  
until convergence. Closed-form solutions for (42) and (43) can be found in Appendix B, and the overall LSTD-based conventional learning scheme can be found in Algorithm 1. ```
Input: training data set \(\mathcal{Z}_{f}^{\text{tr}}\) at current frame \(f\); number of total features \(K\), hyperparameter \(\lambda_{1}>0\), \(\lambda_{2}>0\), \(\bar{b}^{1},\dots,\bar{b}^{K}\), \(\bar{\sigma}^{1},\dots,\bar{\sigma}^{K}\) Output: learned LSTD-based predictor \(V_{f}^{(K),*}\)
```  
LSTD-based conventional learning:  
initialize residual target matrix \((Y_{f}^{\text{tr}})^{k=1}\gets Y_{f}^{\text{tr}}\) for each feature \(k=1\dots,K\)do \(v_{f}^{k,*},\hat{b}_{f}^{k,*}=\texttt{ALS}(k,X_{f}^{\text{tr}},(Y_{f}^{\text {tr}})^{k},\lambda_{1},\lambda_{2},\bar{b}^{k},\bar{\sigma}^{k})\)  update residual target matrix \((Y_{f}^{\text{tr}})^{k+1}\leftarrow(Y_{f}^{\text{tr}})^{k}-X_{f}^{\text{tr}}v _{f}^{k}\otimes(\hat{b}_{f}^{k}(\hat{b}_{f}^{k})^{\dagger})\) end return\(V_{f}^{(K),*}=\sum_{k=1}^{K}v_{f}^{k}\otimes(\hat{b}_{f}^{k}(\hat{b}_{f}^{k})^{\dagger})\) \(\texttt{ALS}(k,X_{f}^{\text{tr}},(Y_{f}^{\text{tr}})^{k},\lambda_{1},\lambda_ {2},\bar{b}^{k},\bar{\sigma}^{k})\):  
while not converged do  first update \(v_{f}^{k}\) via (42) then, update \(\hat{b}_{f}^{k}\) via (43) end return\(v_{f}^{k},\hat{b}_{f}^{k}\) ```
**Algorithm 1**LSTD-based conventional learning for channel prediction for \(S\geq 1\)
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 2 Low-Power Rectifiers
### Matching Network Enhancement
#### 2.2.4 Low-Loss Structure Utilization  
Deploying a matching network can significantly reduce reflection losses caused by impedance discontinuities, but it inevitably introduces additional dielectric loss, conductor loss, and radiation loss. Choosing a low-loss dielectric substrate is one way to help improve overall performance [75]. Another way is to use alternative low-loss transmission line structures. The substrate-integrated suspended line (SISL) is a multilayer board structure evolved from the traditional suspended stripline [76]. The upper and lower surfaces of each dielectric layer of the SISL structure are covered with copper, and densely arranged metallized via holes are embedded to form metallic walls, which reduce radiation loss. Additionally, the second and fourth substrate layers are hollowed out to create air cavities for electromagnetic wave propagation, thereby reducing dielectric loss. As shown in Figure 12, a rectifier based on the SISL structure has been demonstrated to operate at extremely low power levels, achieving a rectification efficiency of 3.5% at \(-\)30 dBm input power [16]. Due to its ultra-low-loss characteristics, SISL technology has also found widespread application in the millimeter-wave frequency band [77].  
Figure 11: Photograph of tri-band flexible rectenna without impedance matching network [74].  
Figure 10: Prototype of single rectifying unit rectenna without matching network: (**a,b**) Top views, (**c,d**) Bottom views [73].
## 5 Thermal Analysis and Results Discussion
### Radiator Thickness and Thermal Coating Studies  
The following set of simulations were carried out for sun-synchronous orbits. The sun synchronous orbit's ascending node moves in the same direction and at the same average rate as the sun's motion about the ecliptic plane by selecting the right combination of altitude and inclination [41]. Beta angle does not vary much for a satellite in a sun synchronous orbit and it helps to ignore environmental heat variation, but the seasonal variations still need to be considered. A special orbit, dawn-dusk, is studied and analyzed for the same thermal design, satellite attitude, orientation, parameters that were used for the equatorial orbit study.  
From Figures 26 and 27, it can be proven that the radiator with higher thermal mass (higher thickness) exhibits better performance than lower thermal mass (lower thickness). Increasing the radiating panel thickness lowers the radiator maximum temperature in orbit while keeping the radiating surface area constant. However, the temperature range of the radiator panel is minimized with an increase in mass. The radiating capacity can be altered using specialized surface pains as discussed in Section 3.2. A few commercially available surface coatings are studied for the radiator performance, and the results are shown in Figures 28 and 29. The maximum radiator temperature can be lowered using low absorptivity pains and high emissivity pains which lowers the radiating temperature at hot and cold conditions. Both surfaces of the radiator are considered to have surface pains in the deployable radiator configuration as the panel radiates from both of its large surfaces, whereas in the body-mount configuration, only the radiating surface is applied with this coating and the other side is covered with a MLI. MLI in BMR helps to maintain the radiator at a steady state by avoiding radiation in and out from the radiator panel. Hence, body-mount radiator is considered to be a single-active face radiator, and deployable radiators are double-active face radiators. From the analysis, it can be said that the body-mount radiator panel can dissipate only 40-50% of the total heat that a full deployable radiator can dissipate. For the radiator mass of 0.4 kg, BMR's temperature range is 23.1 \({}^{\circ}\)C to \(-\)10.1 \({}^{\circ}\)C and DR's temperature range is 9.3 \({}^{\circ}\)C to \(-\)32.8 \({}^{\circ}\)C.  
Figure 25: DR temperature variation with inclination and beta angle—Worst cold condition.  
Figure 28: BMR Surface coating study.  
Figure 26: BMR —Thickness Study.  
Figure 27: DR—Temperature variation with radiator thickness.  
A constantly illuminated orbit with no eclipse phase is also simulated for the same thermal parameters and satellite configurations. This orbit provides constant power for the satellite components, but it increases the thermal load on different components exposed to the sun and this makes the thermal design more complex.
## 2 Improved Penzien Model
### Parameter Determination of the Improved Penzien Model
#### 2.3.2 Stiffness and Damping of the Equivalent Soil System  
To use an idealized model to more reasonably reflect the motion of the foundation soil near the piles in an actual soil and pile interaction system, and quantitatively determine the mass of the equivalent soil system, [PERSON] [51] put forward the energy equivalent principle to determine the mass of the equivalent soil system. Namely, under earthquake excitation, the produced motion energy of the actual boundary soil near the piles relative to the natural ground movement was equal to the motion energy of the equivalent mass in the same motion. The following formula was obtained.  
\[m_{ei}=\int_{-h}^{h}\int_{-\infty}^{+\infty}\int_{-\infty}^{+\infty}\frac{1}{3 }\Big{(}\psi_{u}^{2}+\psi_{v}^{2}+\psi_{w}^{2}\Big{)}\rho_{i}dxdydz \tag{19}\]  
where \(\psi_{u}=\psi_{u}(x,y,z)\), \(\psi_{v}=\psi_{v}(x,y,z)\), and \(\psi_{w}=\psi_{w}(x,y,z)\) are the displacement fields of the soil in the three directions \(x\), \(y\), and \(z\) under the interaction of the pile groups, which are determined by the Mindlin solution for an elastic half-space. \(h\) is the thickness of the soil.  
However, in practical analysis, Equation (19) is too complicated and contains two infinite integrals, so the calculation is too complex. In practical application, an empirical formula is often used to calculate the mass of the equivalent soil; that is, the mass of the equivalent soil is assumed to be the mass of the same area as the area of the pile cap and the same thickness as the actual thickness of soil layer. Therefore, the calculation formula for the mass of the equivalent soil is shown in Equation (20).  
\[m_{vi}=\rho_{i}Ah_{i} \tag{20}\]  
The interlayer shear stiffness of the equivalent soil system is determined by Equation (21). The damping of the equivalent soil system adopts proportional stiffness damping, and its calculation formula is given in Equation (22).  
\[k_{vi}=\frac{G_{i}A}{h_{i}} \tag{21}\]  
\[c_{vi}=\frac{2D_{i}}{\omega}k_{vi} \tag{22}\]