esa-sceva/satcom-chunk-collection huggingface dataset:
It presents good dimensional stability and a lack of residual monomers, reducing the risk of chemical irritation or allergies [13; 15]. There is a growing research trend aimed at improving 3D printing techniques and materials. Critical parameters that have to be improved for successful clinical performance are mechanical properties, biocompatibility, water absorption and solubility, and optical properties [16]. Color stability is crucial for esthetic reasons because its absence promotes patient dissatisfaction and reflects possible structural modification [17]. Intrinsic material features such as resin type, composition, polymerization mode, and aging, as well as oral environmental aspects, including food, beverages, wine, nicotine, and poor oral hygiene or use of denture cleaning agents, can influence the materials' color stability. There are also specific denture-related parameters, such as surface abrasion, surface roughness, and water absorption [18]. Few studies compare traditional heat-polymerized resins with newer, 3D-printed materials for staining evaluation after exposure to different coloring media [12; 19; 20; 21; 22]. In most studies, specimens of the tested materials are immersed for definite periods of time in various potentially staining media (coffee, red wine, tea, Coke(r), cleaning agents, etc.) in order to simulate the influence of daily use by the patient (references). According to the authors' knowledge, no study has yet evaluated the soft urethane-based resins in such protocols. The aim of the present in vitro study was to evaluate the influence of immersion time and coloring solution on the color stability of three denture base resins via spectrophotometry. The null hypothesis was that there is no difference in the color stability of the three resins immersed in five staining solutions for three immersion periods.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
Chitosan-citric acid nanoparticles for the removal of chromium (VI) have the lower adsorption capacity compared to those in other studies [17]. This was because chemical reagents reacted with the active functional groups of chitosan and could reduce the adsorption capacity of heavy metal ions, while the unreacted chemical required additional processing and possessed potential toxicity [18; 19; 20]. Therefore, in order to improve the adsorption capacity of chitosan adsorbents, a chitosan adsorbent with a high specific surface area structure was made. Porous chitosan or chitosan-based materials were prepared through a freezing-drying method, which was simple to operate and usually used non-toxic water as the pore-forming agent. Chitosan/gelatin/Ag porous sponge compound was used as a wound healing material and was prepared by freezing the mixture solution at \(-80\)\({}^{\circ}\)C for 24 h and then freeze-drying for 48 h [21]. To form a porous scaffold for tissue engineering, a chitosan-poly(vinyl alcohol)-methylcellulose mixture was refrigerated at 4 \({}^{\circ}\)C, frozen at \(-20\)\({}^{\circ}\)C in a deep freezer for 24 h and then placed in a lyophilized at \(-40\)\({}^{\circ}\)C until dried porous scaffolds were obtained [22]. Chitosan/tricarboxylic acid composites with different concentration ratios for tunable dye release were manufactured by freeze-drying a hydrogel mixture for 48 h and the pore shapes and sizes of the composites were related to the concentration ratio [23]. [PERSON] et al. [24] produced porous chitosan scaffolds for tissue engineering by controlling the freezing temperature of chitosan solutions; the porous chitosan scaffolds exhibited different exterior structures, pore shapes and sizes, and better mechanical properties than non-porous chitosan materials. This research focused on chitosan porous scaffolds or microspheres that were composited with other organic/inorganic materials at lower freeze-drying temperatures. However, there are no reports regarding the use of pure chitosan-based porous microspheres produced via a freezing-lyophilization drying method at \(-20\)\({}^{\circ}\)C as adsorbents to remove a toxic iron metal. The main aim of this work is to prepare chitosan porous microspheres without any other materials using a freezing-drying process. Various factors, including the freezing time and temperature, the diameter of the injection needle, and chitosan concentration, were taken into consideration and the microspheres were characterized in terms of their morphologies and FTIR. Finally, an adsorption test for Cr(VI) was carried out to estimate whether the porous chitosan microspheres could serve as an effective adsorbent to remove Cr(VI) compounds from wastewater.
# Mode Coupling Properties of the Plasmonic Dimers Composed of Graphene Nanodisks
## 1 Introduction
The proposed plasmonic molecules could find broad areas of application in fields including tunable filters [23], optical sensors [24] and optical switchers [25].
# Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection
## 4 Experiments
### Performance Comparison
#### 4.3.2 Instance Segmentation  
We also conduct comprehensive experiments to confirm the superiority and generalizability of MSBA in the context of instance segmentation tasks. As shown in Table 7, our approach significantly enhances performance in both detection and instance segmentation tasks, exhibiting substantial advancements when contrasted with various robust models. Mask R-CNN achieves 41.7 AP on detection and 37.3 AP when equipped with MSBA based on ResNet-101. Despite the complexity of potent methods like HTC, MSBA exhibits a notable enhancement of 1.6 points in detection AP and 1.4 points in instance segmentation AP, both based on ResNet-50. Furthermore, MSBA achieves superior performance on large ob  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **Backbone** & **MSBA** & \(\mathbf{AP^{b}}\) & \(\mathbf{AP^{b}_{5}}\) & \(\mathbf{AP^{b}_{M}}\) & \(\mathbf{AP^{b}_{L}}\) \\ \hline \multirow{4}{*}{RetinaNet} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 36.5 & 20.4 & 40.3 & 48.1 \\  & & & 38.0 & 22.3 & 41.6 & 48.8 \\  & & & **(4-1.5)** & **(4-1.9)** & **(4-1.3)** & **(4-0.7)** \\ \cline{2-7}  & & & 38.5 & 21.7 & 42.8 & 50.4 \\  & & & 39.7 & 22.9 & 43.5 & 51.2 \\  & & & **(4-1.2)** & **(4-1.2)** & **(4-0.7)** & **(4-0.8)** \\ \hline \multirow{4}{*}{Faster R-CNN} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 37.4 & 21.2 & 41.0 & 48.1 \\  & & & 39.5 & 22.6 & 42.9 & 52.3 \\ \cline{1-1}  & & & **(4-2.1)** & **(4-1.4)** & **(4-1.9)** & **(4-4.2)** \\ \cline{1-1} \cline{2-7}  & & & 39.4 & 22.4 & 43.7 & 51.1 \\ \cline{1-1}  & & & 40.7 & 23.4 & 45.0 & 53.4 \\ \cline{1-1} \cline{2-7}  & & & **(4-1.3)** & **(4-1.0)** & **(4-1.3)** & **(4-2.3)** \\ \hline \multirow{4}{*}{Cascade R-CNN} & \multirow{4}{*}{R50} & \multirow{4}{*}{\(\surd\)} & 40.3 & 22.5 & 43.8 & 52.9 \\  & & & 41.9 & 23.9 & 45.5 & 55.4 \\ \cline{1-1} \cline{2-7}  & & & **(4-1.6)** & **(4-1.4)** & **(4-1.7)** & **(4-2.3)** \\ \cline{1-1} \cline{2-7}  & & & **42.0** & 23.4 & 45.8 & 55.7 \\ \cline{1-1} \cline{2-7}  & & & 42.6 & 23.8 & 46.8 & 57.0 \\ \cline{1-1} \cline{2-7}  & & & **(4-0.6)** & **(4-0.4)** & **(4-1.0)** & **(4-1.3)** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Object Detection: Performance comparisons with typical detectors based on FPN. “MSBA” represents our proposed adapter. “\(\surd\)” denotes the methods equipped with MSBA.  
Figure 4: Example pairs of object detection results. (**Top row**) The outcomes are obtained using Faster R-CNN with FPN. (**Bottom row**) In contrast to Faster R-CNN with FPN, our MSBA method markedly enhances the localization capability of multi-scale objects through substantial interaction across diverse levels, as illustrated qualitatively.  
jects in both tasks, owing to substantial interaction and rich semantic information at higher levels. In addition, as shown in Figure 5, MSBA captures global semantic information, enabling accurate classification predictions and maintaining segmentation completeness.
## 4 Results
### Experiment 2 Results: One Device, Distance 20 m  
The results obtained in the experiment at 20 m (between gateway and LoRa device) showed that the best RSSI levels were obtained for high bandwidth values. The best RSSI levels were obtained on average in settings with a BW of 500 kHz.  
In contrast with Experiment 1, packets were received at the gateway when BW = 125 kHz, with SF equal to 10 or 12, but in most cases the packets were damaged.  
The percentage of packets received in each setting, as in the previous experiment, was approximately 100%. The cases where the data payload was corrupted must be considered, though.  
Figure 9 shows the RSSI level for the best settings of this experiment. These values have a greater variability than those of Experiment 1, between \(-63\) and \(-72\) dBm.  
The baseline setting obtained an average RSSI value of \(-75\) dBm and 100% of received packets, which was close to the best setting (SF = 7, BW = 500 kHz, CR = 4/7), with an average RSSI value of \(-64.20\) dBm and 100% packets received.  
Figure 8: Contour graphs for settings evaluated in Experiment 1.  
Figure 9: RSSI values for the best 10 settings in Experiment 2.  
In this second experiment, there was also an attempt to fit the behavior of the RSSI level to a linear regression model combining the LoRa transmission parameters. Results show that the relationship between RSSI and BW is statistically significant, with a confidence level of 90% and shows a stable behavior. The linear equation found was: RSSI = 78.595 - 0.02155 BW.
# Improved Cutting Force Modelling in Micro-Milling Aluminum Alloy LF 21 Considering Tool Wear
## 2 Prediction of Tool Wear Based on Simulation
### Prediction of Tool Wear Based on Deform  
Deformation and heat transfer are selected in simulation control. Sparse and Newton-Rapnson are selected for solver and iterative methods to ensure convergence under large deformation at the initial stage of simulation. At the later stage of simulation, Conjugate gradient and Newton-Rapnson are adopted to improve solution speed. After the set of the incremental step and simulation time, the set of the preprocessing of the model is finished. Submit the calculation and obtain the cloud map of tool wear depth based on simulation of micro-milling LF 21 processes, as shown in Figure 7.  
Figure 6: Boundary condition setting and partial enlarged detail.  
Assume the tool wear changes linearly with time, and then obtain the cumulative cutter wear depth:  
\[W=\frac{\Delta W_{pre}}{\Delta t_{pre}}t \tag{6}\]  
where, \(\Delta W_{pre}\) is the tool wear depth output by DEFORM 3D simulation; \(\Delta t_{pre}\) is the simulation time; \(\Delta W_{pre}/\Delta t_{pre}\) is the average cutter wear rate; \(t\) is the cutting time.  
DEFORM 3D postprocessing gives the simulation results of the tool wear depth and instantaneous wear rate of any node. However, the tool wear depth cannot be measured directly. Refer to the tool wear evaluation method proposed by [PERSON] et al. [20], and use the reduction rate of the tool diameter to characterize the micro-milling tool wear, as shown in Equation (7):  
\[\text{Diameter reduction rate}\%=\frac{D_{new}-D_{worm}}{D_{new}}\times 100 \tag{7}\]  
in which, \(D_{new}\) is the diameter of a new cutter; \(D_{worm}\) is the diameter of the tool after wear.  
Assume the micro-milling tool wear changes linearly with time, that is, the average wear rate of the wool is constant. Figure 8 shows the relationship between tool wear depth and the reduction rate of the tool diameter. Based on the simulated tool wear depth and cutting time, the average tool wear rate can be calculated. The relation between the reduction rate of the tool diameter and tool wear depth at any time is shown in Formula (8):  
\[\frac{D_{new}-D_{worm}}{D_{new}}=\frac{D_{new}-2\sqrt{\left(D_{new}/2-W\cos( \xi)\right)^{2}+\left(W\sin(\xi)\right)^{2}}}{D_{new}} \tag{8}\]  
where, the cumulative tool wear depth is calculated by Equation (6). \(\xi\) is the half angle of the cutter tip arc corresponding to the central angle, which is \(35^{\circ}\).  
According to Figure 8, the relationship between the arc radius of the cutting edge and cumulative tool wear depth can also be obtained, as shown in Equation (9):  
\[r_{e}=\frac{\sin(\xi)}{1-\sin(\xi)}W \tag{9}\]  
where, \(r_{e}\) is the arc radius of the cutting edge; \(W\) is the cumulative cutter wear depth.  
Figure 7: Tool wear depth based on simulation of micro-milling process.
## Appendix B Iterative Reweighted Least Squares Algorithm (IRWLS)  
1. Defined convergence constant \(k_{cc}\) and calculate initial estimate using weighted least squares using \(X_{0}=\left(\mathbf{H}^{T}\Sigma\mathbf{H}\right)^{-1}\Sigma\mathbf{H}^{T}\rho\)
2. Compute initial residual vector using \(\mathbf{r}_{0}=\rho-\mathbf{H}X_{0}\)
3. Calculate initial standardized residuals; \(\widetilde{\mathbf{r}}_{s,0}=\frac{\mathbf{r}_{0}}{\widetilde{s}_{0}}\)
4. Tukey bi-weight function type1 with tuning constant \(\alpha\) is applied. 1. \(w_{l,0}=1\) if \(\left|\widetilde{r}_{s,l}\right|<\alpha\) 2. \(w_{l,0}=0\) if \(\left|\widetilde{r}_{s,l}\right|\geq\alpha\)
5. \(\Sigma_{0}=diag\big{(}\left\{w_{l,0}:l=1,2,\ldots,n\right\}\big{)}\)
6. _while_ 1. \(X_{i}=\left(\mathbf{H}^{T}\Sigma_{i-1}\mathbf{H}\right)^{-1}\mathbf{H}^{T} \Sigma_{i-1}\rho\) 2. If \(\frac{\|X_{i}-X_{i-1}\|}{\|X_{i}\|}<k_{cc}\); break 3. If \(\frac{\|X_{i}-X_{i-1}\|}{\|X_{i}\|}\geq k_{cc}\); continue 4. \(\mathbf{r}_{i}=\rho-\mathbf{H}X_{i}\) 5. \(s_{i}=1.4826\times\mathrm{median}|\mathbf{r}_{i}|\) 6. \(\widetilde{\mathbf{r}}_{s,i}=\frac{\mathbf{r}_{i}}{\widetilde{s}_{i}}\) 7. Calculate \(\Sigma_{i}=\left\{w_{1,i},\ldots,w_{n,i}\right\}\) using Tukey bi-weight function type 2. \(n\) is the number of observations. \(w_{l,i}=\left\{\begin{array}{ll}\left\{\left[1-\left(\frac{\widetilde{r}_{s, l}^{l}}{\alpha}\right)^{2}\right]^{2}\right.&\left|\widetilde{r}_{s,l}^{l} \right|\leq\alpha\\ 0&\left|\widetilde{r}_{s,l}^{l}\right|>\alpha\end{array}\right.\) Here \(l=\left\{1,\ldots,n\right\}\) 8. go to step a.
7. end
## 2 Experimental Setup and Data Acquisition  
### Hardware Platform Description  
The acoustic sensor used in the NSA for neck-skin vibration monitoring was a miniature accelerometer (BU-27135, Knowles Inc., Itasca, IL, USA). One customized peripheral circuit, consisting of one power supply module and one amplifier module, was fabricated using the Printed Circuit Board (PCB) technique. Four lithium coin batteries (CR2032, Panasonic Inc., Japan; nominal voltage and capacity: 3 V and 225 mAh) were used as the power source. The interface between the peripheral circuit board and the accelerometer was one 3.5-mm stereo audio cable, of which the three wires corresponded to the positive, ground and signal channels. One voice recorder (ICD-UX565F, Sony Inc., Tokyo, Japan) was employed to record the acceleration data with high fidelity. The hardware components are shown in Figure 1. To increase the sensitivity of the accelerometer, one silicon pad (Ecoflex 00-30, Part A and Part B with a 1:1 ratio, Smooth-On Inc., Macungie, PA, USA) was fabricated to encapsulate the accelerometer and increase the contact area between the accelerometer and neck skin. The silicon pad was moulded to obtain a flat (diameter: 28 cm) and thin (thickness: 1.2 mm) circular pedestal. An adhesive was used to mount the silicon pad on neck skin firmly. The Sony voice recorder supports multiple options for sampling rate, encoding format (MP3: 48 kbps/128 kbps/192 kbps; Linear Pulse Code Modulation (LPCM): 44.1 kHz, 16-bit) and compatible memory cards. The voice data were saved in WAV format audio files and were transferred to a PC for analysis through the USB interface. The uniformity of the NSA frequency response was verified using Laser Doppler Velocimetry (LDV) in a Gaussian white noise test. The transfer function between the LDV and the NSA signals is shown in Figure 2. The frequency bandwidth of the NSA was around 3000 Hz.
## 2 General ESS-Based VSM with Energy Recovery Control  
### Significance of General ESS-Based VSM  
As most of the previous work on ESS-based VSM is restricted to specific ESS type or application, the proposed general ESS-based VSM expands the candidates for VSM.  
Given the high cost of energy storage, utilizing \"general\" types of energy storage in the power system is essential for the large-scale grid-forming converters, such as virtual synchronous machine. At present, energy storage in the narrow sense, such as battery, is technically immature for large-scale application in the power system, while the dominant renewable energy resources such as PV generator do not have the energy reserve for VSM realization. For a desired virtual inertia and damping value, there may be scenarios that the conventional energy storage is insufficient to synthesize it. As such, general types of energy storage such as an electrochemical battery, a supercapacitor, a flywheel, compressed air storage, a hydro-pump and the energy in a wind turbine rotor can all be considered as candidates for VSM implementation, thus broadening the candidates for VSM implementation. Microgrids, cloud energy storage, electric vehicle fleet, HVDC, wind farm, FACTS, etc., can all be utilized to realize VSM. The general VSM based on various forms of ESS is illustrated in Figure 1.  
However, in generalizing energy storage for VSM, not all storage types are suitable for the VSM application, due to their constraints in energy capacity and response speed [27]. The frequency support from VSM can be temporally decomposed into two parts, namely inertial response and governor control. The former is more speed demanding while the latter is more energy capacity demanding. The inertia and governor emulation control in VSM can be assigned to hybrid ESS or a single ESS. The hybrid ESS usually comprises fasting-acting ESS (FAESS) and slow-acting ESS (SAESS). A comparison of different energy storage techniques in response speed vs. power capacity, and power density vs. energy density, are presented in Figures 2 and 3[28,29]. FAESS such as a supercapacitor, flywheel or lithium-titanite battery, features high power capacity, fast response but small energy capacity. Therefore, it is more suitable for inertial response. By contrast, SAESS such as  
Figure 1: Diagram of general VSM based on various forms of energy storage.  
lead-acid, sodium-sulfur (NaS) or nickel-cadmium (NiCd) batteries, have a larger energy capacity but respond relatively slower. Hence, it is more suitable for virtual governor control. As inertial response is the core function of VSM, FAESS is the indispensable part.
# Data Analysis of the TK-1G Sounding Rocket Installed with a Satellite Navigation System
## 4 Data Processing and Analysis
### Result Analysis  
#### 4.2.1 Analysis of Body-Parachute System's Fall Velocity  
There is no comparability of the fall velocities in the range of 56.8 km to 59.6 km, due to the fact that the flight trajectory vertex could only reach 59.6 km and the body-parachute system got its balanced fall velocity at the height of 56.8 km during this test. The fall velocities below 56.8 km are smaller than the statistical data of the TK-1 sounding rockets because the weight of the body-parachute system is reduced from 3.4 kg to 3.0 kg. TK-1G dovetails with TY-4B sounding rocket system in the magnitude of fall velocity. The comparison of the fall velocities of different sounding rockets at major heights is listed in Table 3.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Plant Extract**} & \multirow{2}{*}{**Bacterial Strain**} & \multicolumn{5}{c}{**Mean Zone of Inhibition (mm) \(\pm\) SD**} \\ \cline{3-7}  & & **S0.0 mg/mL** & **25.0 mg/mL** & **12.50 mg/mL** & **6.25 mg/mL** & **3.125 mg/mL** \\ \hline \multirow{5}{*}{_V. dominant_ ethanolic leaf extract_} & _E. coli_ & - & - & - & - \\  & _E. coli_ ATCC 11775 & - & - & - & - \\  & _S. aureus_ & 15.5 \(\pm\) 2.12 & 13.0 \(\pm\) 0 & 10.0 \(\pm\) 1.41 & 7.0 \(\pm\) 0 & - \\  & _S. aureus_ ATCC 12600 & 14.5 \(\pm\) 0.71 & 12.5 \(\pm\) 0.71 & 10.5 \(\pm\) 2.12 & - & - \\  & _P. aeruginosa_ & - & - & - & - & - \\  & _P. aeruginosa_ ATCC 10145 & - & - & - & - & - \\ \hline \multirow{5}{*}{_P. macrophyla_ ethanolic leaf extract_} & _E. coli_ & 15.0 \(\pm\) 1.41 & 13.0 \(\pm\) 0 & 10.5 \(\pm\) 2.12 & 7.0 \(\pm\) 0 & - \\  & _E. coli_ ATCC 11775 & 14.5 \(\pm\) 2.12 & 11.5 \(\pm\) 0.71 & 10.5 \(\pm\) 0.71 & 8.0 \(\pm\) 1.41 & - \\  & _S. aureus_ & 20.0 \(\pm\) 1.41 & 15.5 \(\pm\) 0.71 & 11.5 \(\pm\) 0.71 & 10.0 \(\pm\) 0 & 8.5 \(\pm\) 0.71 \\  & _S. aureus_ ATCC 12600 & 12.5 \(\pm\) 0.71 & 9.5 \(\pm\) 0.71 & 8.5 \(\pm\) 2.12 & - & - \\  & _P. aeruginosa_ & 18.5 \(\pm\) 0.71 & 15.0 \(\pm\) 1.41 & 11.5 \(\pm\) 2.12 & 9.5 \(\pm\) 0.71 & - \\  & _P. aeruginosa_ ATCC 10145 & 17.0 \(\pm\) 0 & 14.0 \(\pm\) 1.41 & 11.0 \(\pm\) 1.41 & - & - \\ \hline \multirow{5}{*}{_V. dominant_ ethanolic stem bark extract_} & _E. coli_ & - & - & - & - & - \\  & _E. coli_ ATCC 11775 & - & - & - & - & - \\  & _S. aureus_ ATCC 17.5 \(\pm\) 0.71 & 14.0 \(\pm\) 0 & 10.0 \(\pm\) 1.41 & 7.0 \(\pm\) 0 & - \\  & _S. aureus_ ATCC 12600 & 20.0 \(\pm\) 1.41 & 16.5 \(\pm\) 0.71 & 11.0 \(\pm\) 1.41 & 8.5 \(\pm\) 0.71 & 7.0 \(\pm\) 0 \\  & _P. aeruginosa_ & - & - & - & - & - \\  & _E. coli_ & 17.0 \(\pm\) 0 & 12.0 \(\pm\) 1.41 & 11.5 \(\pm\) 0.71 & 8.5 \(\pm\) 0.71 & - \\  & _E. coli_ & 17.5 \(\pm\) 0.71 & 17.0 \(\pm\) 0 & 13.0 \(\pm\) 1.41 & 9.0 \(\pm\) 0 & - \\  & _S. aureus_ & 18.5 \(\pm\) 0.71 & 11.5 \(\pm\) 0.71 & 9.5 \(\pm\) 0.71 & 7.5 \(\pm\) 0.71 & - \\  & _S. aureus_ ATCC 12600 & 23.0 \(\pm\) 2.83 & 15.5 \(\pm\) 0.71 & 12.5 \(\pm\) 0.71 & 10.0 \(\pm\) 0 & 8.5 \(\pm\) 0.71 \\  & _P. aeruginosa_ & 20.0 \(\pm\) 1.41 & 18.0 \(\pm\) 0.71 & 16.0 \(\pm\) 0.71 & 13.0 \(\pm\) 1.41 & 10.5 \(\pm\) 0.71 \\  & _P. aeruginosa_ ATCC 10145 & 19.5 \(\pm\) 3.54 & 14.5 \(\pm\) 0.71 & 12.5 \(\pm\) 0.71 & 10.5 \(\pm\) 0.71 & 8.0 \(\pm\) 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Bacteria growth inhibition zones of ethanol extract of _V. Montana_ and _P. macrophyla_.