esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Evaluation of SIF  
The fatigue fracture morphologies of the specimens under different machining surface conditions were observed using SEM as presented in Figure 9. From the figure, it was found that all specimens exhibited a similar fracture surface pattern. The crack initiation occurred from the surface of the specimens and then propagated into the interior of the machined surface. A similar phenomenon was observed by [PERSON] et al. [28] who found that the crack initiated from the surface of machined Inconel 718 and presented multiple crack sources. Figure 10a shows a typical crack initiation region  
Figure 8: Residual stresses versus fatigue life: \(\sigma_{\text{max \it local}}\)= \(\sigma_{\text{max \it applied}}\) + \(RS\). Figure 7: The relationship between the fatigue life and the total stress. under higher magnification of the SI surface condition. Figure 10b exhibits small facets in the crack initiation region. The facet area, the maximum equivalent stress \(\sigma_{v,\text{max}}\), the minimum equivalent stress \(\sigma_{v,\text{min}}\), and the effective stress range \(\Delta\alpha^{{}^{\prime}}_{\textit{eff}}\) for each machining condition are summarized in Table 5. The threshold stress intensity factor \(\Delta K_{th}\) was obtained according to Equation (28). Figure 10: Crack initiation region under the SI surface condition: (**a**) crack initiation site and (**b**) small facets. Figure 9: Fracture morphology of the specimen under different surface conditions: (**a**) SI, (**b**) SII, (**c**) SIII, and (**d**) SIV. The values of \(\Delta K_{th}\) for the [PERSON] model and the proposed model can be calculated according to Equations (26) and (28), respectively. Figure 11 shows the relationship between the fatigue life \(N_{f}\) and SIF range \(\Delta K_{th}\). From the figure, it can be deduced that the \(\Delta K_{th}\) calculated by both [PERSON] et al. [16; 17] and the proposed model maintained a constant regardless of fatigue life. It can be seen that the values of \(\Delta K_{th}\), calculated by the [PERSON]'s model, were in the range of 15.45-19.72 MPa \(\sqrt{\text{m}}\) regardless of fatigue life. The mean value of \(\Delta K_{th}\) was 18.11 MPa \(\sqrt{\text{m}}\) approximately. Whereas the values of \(\Delta K_{th}\), calculated by the proposed model, were in the range of 11.19-14.78 MPa \(\sqrt{\text{m}}\) regardless of fatigue life. The mean value of \(\Delta K_{th}\) was approximately 13.45 MPa \(\sqrt{\text{m}}\), which is much lower than the calculated result by [PERSON]'s model. Figure 12 shows the comparison of the crack propagation curves of Inconel 718 at the same stress ratio \(R=0.1\). [PERSON] et al. [29] revealed that the threshold value of the SIF range was approximately  
Figure 11: The relationship between the threshold stress intensity factor \(\Delta K_{th}\) and the fatigue life.
## 5 Conclusions  
In the study, simulated BDS-3 ISL data and different LEO constellations were used for integrated POD with ground stations to analyze the accuracies of POD and time synchronization. In the case of a small number of ground monitoring stations, the enhancements of ISL observations and LEO satellite onboard observations for BDS-3 POD are analyzed, respectively.  
The results show that the three-dimensional average orbit accuracy is 95.1 cm, 3.1 cm, 2.4 cm, and 2.0 cm for the BDS-3 constellation in the four schemes of 7 sta, 7 sta + 12 LEO, 7 sta + 30 LEO, and 7 sta + 60 LEO, respectively. The clock offset RMS1 is 0.71 ns, 0.37 ns, 0.25 ns, and 0.1 ns, and the RMS2 is 0.6 ns, 0.33 ns, 0.21 ns, and 0.07 ns, respectively. The introduction of LEO satellites has significantly improved the BDS-3 orbit accuracy. With the increase in the number of LEO satellites, BDS-3 orbit accuracy improvement becomes smaller and smaller, while the time required for calculation increases. In the ground station and ISL-integrated orbit determination experiments, using seven ground-monitoring stations and ISL data, the average orbit accuracy of BDS-3 can reach the centimeter level and the time synchronization can reach the nanosecond level. The average orbit accuracy of the seven sta scheme is 74.8 cm, the RMS1 is 0.89 ns, and the RMS2 is 0.83 ns. The average orbit accuracy of the seven sta and ISL scheme is 3.5 cm, the RMS1 is 0.3 ns, and the RMS2 is 0.29 ns. The combination of the ground station and the LEO satellite can greatly improve the time synchronization accuracy and POD accuracy of the BDS-3 satellites. The orbital accuracy of the 7 sta + ISL scheme and the 7 sta + 12 LEO scheme is comparable. This is because of the low orbit of LEO satellites, their fast speed, and the large amount of observation data that can be observed, while the amount of data in the ISL depends on the number of satellites in the BDS-3 constellation.  
Conceptualization, X.S. and Z.L.; methodology, X.S. and B.X.; software, B.X. and X.S.; validation, J.C., Q.L., M.S. and Y.X.; formal analysis, Q.L. and Y.X.; investigation, J.C.; resources, X.S. and T.G.; data curation, B.X.; writing--original draft preparation, B.X.; writing--review and editing, B.X. and X.S.; visualization, Z.M.; supervision, Z.L.; funding acquisition, X.S. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the Ministry of Education-China Mobile Scientific Research Fund Project (MCM2020-J-1) and the Shandong Provincial Natural Science Foundation (Grant number ZR2021 QD131).  
The authors declare no conflict of interest.
## 3 System Identification for Position Model
### Model Validation  
The validation process determines whether or not the model obtained during the model estimation process is reliable. Equation (3) depicts the Final Prediction Error (FPE) formula, which can be used to obtain model acceptance results after analysing the best fit percentage and FPE data.  
\[FPE=V\frac{(1+n_{a}/N)}{(1-n_{a}/N)} \tag{3}\]  
The derivation of \(V\) is stated in Equation (4),  
\[V=\frac{e^{2}(k)}{N}=\frac{e^{T}(k).e(k)}{N} \tag{4}\]The error vector is represented as \(e(k)=[e_{k}\;e_{k-1}\;\ldots\;e_{k-N}]^{T}\), \(V\), as in Equation (4), is the loss function, the number of the approximated parameter is represented as \(n_{a}\), and the sample number is represented as \(N\). To obtain the most reliable transfer function, the model with the lowest FPE or Akaike's Information Criteria (AIC) is chosen from among those with different equation orders. Equation (5) depicts the AIC formula.  
\[AIC=\log\left[V.\left(1+\frac{2n_{a}}{N}\right)\right] \tag{5}\]  
When selecting the best model generated by the system identification technique, the best fit criterion is frequently used. As shown in Equation (6), the best-fit shows the percentage of how the model was generated compared to the actual model. The greater this percentage, the greater the accuracy and precision of the simulated model.  
\[fit=100\left[1-\frac{norm(\hat{y}-y)}{norm(y-\overline{y})}\right]\% \tag{6}\]  
\[\begin{array}{c}y:True\;Value\\ \hat{y}:Approximate\;Value\\ \overline{y}:Mean\;Value\end{array}\]  
ARX221, ARX331, and ARX441 were the three ARX models obtained with different set of orders. To obtain the best fit percentage, the ARX models were validated using validation data. According to the observations, the fourth-order model ARX441 provided the best fit with a value of 94.45%, as illustrated in Figure 5. This demonstrates the mathematical model's ability to represent the FBBA actual plant. Although ARX441 provided the best fit, ARX331 was selected with a best fit of 94.34%. This is due to the fact that higher order is more complex, and the difference in best fit between ARX331 and ARX441 was minimal. As a result, ARX331 was chosen to portray the FBBA real plant. Equation (7) depicts the transfer feature of the FBBA. Because all of the poles were within the unit circle, as shown in Figure 6, the system was considered stable.  
\[\mathrm{G(z)}=\frac{0.1135\mathrm{z}^{-1}+6.543\mathrm{z}^{-2}+15.5\mathrm{z}^ {-3}}{1-0.6154\mathrm{z}^{-1}-0.0714\mathrm{z}^{-2}+0.09229\mathrm{z}^{-3}} \tag{7}\]  
Figure 4: Input and output data recorded in model estimation.
## 2 Materials and Methods
### Information Sources and Search Strategy  
With the help of a senior librarian specialized in health sciences database searches, an electronic search was undertaken using the following databases: PubMed (MEDLINE), Clarivate Analytics' Web of Science, Elsevier's Scopus, Google Scholar, and Embase, without no restrictions on publication year and language. The following search terms were used alone or in combination: \"ceramic\", \"dental ceramic\", \"monolithic ceramic\", \"stained ceramic\", \"ceramic stain\", \"color\", \"color stability\", \"gloss\", \"surface hardness\", \"surface roughness\", \"ageing\", \"optical\", \"translucency\", \"translucent\", \"crystalline\", \"hydrothermal\", \"zirconia\", \"monolithic zirconia\", \"zirconium\", \"zirconium oxide\", \"Y-TZP\", \"yttra-partially stabilized zirconia\", \"yttra stabilized polycrystalline tetragonal zirconia\", \"yttra-stabilized tetragonal zirconia\", \"all-ceramic\", \"light scattering\", and \"light transmission\", either as Medical Subject Heading (MeSH, National Library of Medicine, Bethesda, MD, USA) terms or keywords. These terms were utilized with Boolean operators \"OR\", \"AND\", and \"NOT\". Reference lists of the selected studies were subjected to thorough searches to identify potentially relevant studies. The cross-checking of the retrieved articles was performed to identify and remove duplicate articles, and the eligibility criteria (as determined by the PICO format) were applied to search for the studies.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 4 Proposed EMS for SEEB
### Data Acquisition Design and Development Using IoT and WSN  
In SEEB, EMS requires data gathering in the \"Plan Phase\" and appliance control in the \"Do Phase\". To this end, the EMS relies on two primary layers to interact with the building for data gathering and control. First, the physical layer interacts with the building's appliances, doors, windows, and environmental conditions. This layer is composed of WSN and IoT, such as sensors and actuators (Figure 2). On the one hand, sensors monitor physical phenomena, for example, temperature, humidity, light, motion, human occupancy, CO\({}_{2}\) concentration, and power consumption. On the other hand, actuators control the building's appliances by executing commands mandated by the EMS software, such as switching the heaters ON and OFF.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Plan** & **Do** & **Check** & **Act** \\ \hline In this first step, we went through: & As a result of the previous plan step, we started the implementation: & Here we check the previous implementations periodically in order to update and maintain the EMS: & In the Act phase: \\ \(\bullet\) Energy planning & \(\bullet\) & Implementation of the plans. & Here we check the previous implementations periodically in order to update and maintain the EMS: & In the Act phase: \\ \(\bullet\) Commitment of top management & \(\bullet\) & Internal/external communication. & \(\bullet\) & Results were translated into updates and maintain the EMS: & Results were translated into reports for further studies to improve the Energy \\ \(\bullet\) Energy review & \(\bullet\) & Managing documents and records. & \(\bullet\) & Action plans were periodically checked to make sure they were effective. & ManagementSystem. \\ \(\bullet\) Energy policy definition & \(\bullet\) & Auditing the operations consuming energy. & \(\bullet\) & The decisions were made following a comparison with the objectives stated in the first phase. & \\ \(\bullet\) Defining objectives, tasks, and plansEnergy management establishment & & & \(\bullet\) & Data analytics \\ \hline \hline \end{tabular}
\end{table}
Table 2: The steps in each phase of the Plan-Do-Check-Act (PDCA) development cycle.  
Figure 2: Information and Communication Technology (ICT) components for data acquisition in Smart Energy-Efficient Buildings (SEEB).  
The second component is the software layer, which is the decision-making part of the EMS. In this layer, the software makes decisions based on two parameters: the received data from sensors and the energy policy applied. The communication between both layers is ensured via a network that can be a mixture of various technologies such as WiFi, Zigbee, Z-wave, Ethernet, Bluetooth, and Infrared.
# The Additive Manufacturing of Glass: A Critical Review
## 3 A Focus on Semi-Solid Processing for Glass AM
### High-Viscosity Glass Feedstock Methods
#### 3.3.2 The Production of Glass Filament  
To fabricate glass filament for high-viscosity methods, glass is typically 'pulled' mechanically from a bulk mass into a rod. This process is often given different (and essentially interchangeable) names in the glass art community, with the glass rod being known as a cane, stringers, or filament. Filament production may be performed in numerous ways. Traditionally, the Venetian style of glass cane production involves two people 'hand-pulling' a mass of glass in opposite directions, to thin the glass out into strands. Hand-pulled filament is compatible with some glass 3D printers; however, for precise additive manufacturing, it remains crucial to utilise filament that is consistent in size, to allow the accurate control of flow rates and extruded geometry. High-tolerance glass filament can be difficult to fabricate with manual techniques. Instead, draw towers and vitrigraph kiln [94] systems may be used to produce high-quality glass filament suited for additive manufacturing. A relatively recent instrument known as the MGP Vitri-GlassTM, was designed with the primary application of recycling glass and producing precision filament for glass AM, as depicted in Figure 24. In addition to dimensional accuracy, the material quality of filaments is a consideration. As filaments are directly extruded, impurities within the filament such as air bubbles or foreign materials will be present in the final printed object.
# Mechanical Properties and Flexural Behavior of Sustainable Bamboo Fiber-Reinforced Mortar
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.3 Flexural Tensile Behavior  
The control mix showed linear elastic behavior up to the peak load, which was followed by an abrupt failure, resulting in complete separation of the specimens into two parts. In contrast, the bamboo fiber mixtures exhibited improved post-crack behavior, resulting in a strain-softening behavior, where a single crack occurred within the central third of the prismatic samples. This effect can be seen in Figures 5 and 6 where the pictures of the tested specimens taken after the bending tests are shown for both sets of samples containing 300 \(\upmu\)m and 500 \(\upmu\)m fibers. Within these figures, it is possible to visualize the fibers bridging the developed crack, which was much more evident as the fiber content increased from 4 V% to 8 V%.  
Figure 4: Compressive strength (**a**) and splitting tensile strength (**b**) of the bamboo fiber-reinforced mortar mixtures.  
Figure 5: Photographs of test samples after bending tests involving 300 \(\upmu\)m fibers. Fiber content: (**a**) 4 V%, (**b**) 6 V% and (**c**) 8 V%.  
The load-deflection behaviors of all test specimens are shown in Figure 7. Test results with different fiber contents are plotted with an offset of 0.1 mm from each other for better visual comparison. Both fiber groups involving the use of 300 \(\upmu\)m and 500 \(\upmu\)m fibers, revealed similar trends, during which 8 V% mixtures exhibited the highest peak load and the mixtures with 4 V% the lowest. Furthermore, the bamboo fibers were able to bridge the developing crack, resulting in a strain-softening behavior in all bamboo mixtures. The post-crack behavior was more pronounced for mixtures with higher fiber contents, which was in line with the findings with [PERSON] and [PERSON] et.al. where it was shown that a higher fiber content results in a higher load-carrying capacity due to the crack bridging behavior of the fibers [58; 59].
## 2 Modeling of Novel Electrical Load Resources in UDNs
### Electrochemical Energy Storage Devices  
Electrochemical energy storage devices can participate in multiple scenarios, such as peak shaving and valley filling as flexibility resources [27]. The constraints for electrochemical energy storage are mainly as follows:  
\[\left\{\begin{array}{l}\Lambda_{\text{ES}}^{t}=\Lambda_{\text{ES}}^{t-1}+ \eta_{\text{ES}}^{\text{ch}}P_{\text{ES}}^{\text{ch},t}-P_{\text{ES}}^{\text{ dis},t}/\eta_{\text{ES}}^{\text{dis}}\Delta t\\ 0\leq P_{\text{ES}}^{\text{ch},t}\leq\upsilon_{\text{ES}}^{\text{ch},t}P_{\text {ES}}^{\text{ch},\text{max}}\\ 0\leq P_{\text{ES}}^{\text{dis},t}\leq\upsilon_{\text{ES}}^{\text{dis},t}P_{ \text{ES}}^{\text{dis},\text{max}}\\ v_{\text{ES}}^{\text{ch},t}+v_{\text{ES}}^{\text{dis},t}\leq 1\\ \Lambda_{\text{ES},\text{min}}\leq\Lambda_{\text{ES}}^{\text{end}}\leq\Lambda _{\text{ES},\text{max}}\\ \Lambda_{\text{ES},0}=\Lambda_{\text{ES}}^{\text{end}}\right. \tag{21}\]  
where \(\Lambda_{\text{ES}}^{t}\) is the energy stored in the storage device at time \(t\); \(\Lambda_{\text{ES},\text{min}}\) and \(\Lambda_{\text{ES},\text{max}}\) are the lower and upper limits of the energy storage, respectively; \(P_{\text{ES}}^{\text{ch},t}\) is the charging power of the storage at time \(t\); \(P_{\text{ES}}^{\text{dis},t}\) is the discharging power of the storage at time \(t\); \(P_{\text{ES}}^{\text{ch},\text{max}}\) and \(P_{\text{ES}}^{\text{dis},\text{max}}\) are the maximum charging and discharging powers, respectively; \(\eta_{\text{ES}}^{\text{ch}}\) and \(\eta_{\text{ES}}^{\text{dis}}\) are the charging and discharging efficiencies, respectively; \(\upsilon_{\text{ES}}^{\text{ch},t}\) and \(\upsilon_{\text{ES}}^{\text{dis},t}\) are the state variables for charging and discharging, respectively; \(\Lambda_{\text{ES},0}\) and \(\Lambda_{\text{ES}}^{\text{end}}\) are the initial and final energy storage levels, respectively. \(\Lambda_{\text{ES},0}=\Lambda_{\text{ES}}^{t,\text{end}}\) indicates that the storage should return to its initial state at the end of a scheduling period, and \(\Delta E\) is the margin left for the next scheduling cycle.  
The adjustable power of the electrochemical energy storage device is given by the following:  
\[P_{\text{ES},\text{DR}}^{t}=P_{\text{ES},\text{ini}}^{\text{ch},t}-P_{\text{ES }}^{\text{ch},t}+P_{\text{ES}}^{\text{dis},t}-P_{\text{ES},\text{ini}}^{\text{ dis},t} \tag{22}\]  
where \(P_{\text{ES},\text{DR}}^{t}\) is the adjustable power of the storage device at time \(t\); \(P_{\text{ES},\text{ini}}^{\text{ch},t}\) and \(P_{\text{ES}}^{\text{dis},t}\) are the charging and discharging powers when the energy storage is not participating in the regulation of the VPP, respectively.
# Journey to the Market: The Evolution of Biodegradable Drug Delivery Systems
## 2 The Evolution of Biodegradable Polymeric Drug Delivery Systems
### Polyesters  
Since the pioneering work of [PERSON] and [PERSON] in the 1970's, biodegradable polymers have been developed and become widely accepted as a mature system to deliver encapsulated drugs in a controlled manner [16]. Synthetic biodegradable polymers such as polyesters are widely used due to their biodegradability, biocompatibility, and their ease of processing [17,18]. They have glass transition temperatures or melting points above 37 \({}^{\circ}\)C, which enable them to retain a solid form in the in vivo environment. Among them, poly(lactide-co-glycolide) (PLGA) is the most popular polymer carrier used in microparticle systems. PLGA polymers degrade by random hydrolysis of ester bonds with encapsulated drug released by bulk erosion mechanisms. Their degradation periods vary from days to years according to their molecular weight and lactic acid to glycolide residue ratios [19].  
Figure 1: Schematic diagram summarizing the evolution of polymeric drug delivery systems (modified from [PERSON] et al., 2016 [10]).  
Generally, a higher proportion of lactic units, high molecular weight, and increasing crystalline content leads to a longer degradation time [20]. This means that various polymeric carriers can be used to design and fabricate particles to achieve a range of different release durations and so produce the desired therapeutic drug concentration profile.
## 3 Eye-Machine Interaction Technology Based on the YOLOv5 Network
### Analysis of Eye-Machine Interaction Experiment Results  
As shown in Figure 18, the performance verification results of the eye movement control technology in practical applications are critical for evaluating the model's performance in the real world. The chart includes accuracy, response time, and other key performance metrics for eight different testers using eye movements to control the arm, giving us a visual representation of the model's adaptability and reliability for different users and different tasks.  
Figure 17: Complete human–computer interaction process.  
Figure 18: Test results.  
Figure 19 offers a detailed analysis of the improved YOLOv5 model's performance during the eye movement-controlled grasping task. The experiment involved four distinct shapes--square, cylinder, pyramid, and sphere--with each shape being picked up 25 times. Tasks were labeled from 1 to 4 to represent different trials. According to the analysis in Figure 18, as the number of tests increased, the grasping accuracy of this method showed consistent improvement, stabilizing at around 95% after the eighth test. These results not only validate the effectiveness of the proposed method but also highlight its potential for practical applications in object recognition and grasping, making it a promising avenue for further research and development.