esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Measurement of Photodissociation with a Static Modulated Fourier Transform Spectrometer
## 4 Conclusions  
When the polymerized solid dye was irradiated with a laser, the fluorescence intensity rapidly became worse due to photodissociation. It was difficult to measure in real time and remotely with a conventional spectrometer. In this paper, we demonstrated real-time remote sensing with a static modulated Fourier transform spectrometer based on the modified Sagnac interferometer for the purpose of chasing the rapidly changing spectral information. The relationships between the optical path difference, the spectral resolution, the number of sampling data, and the measured wavenumber band were taken into account to prevent aliasing. The data sampling frequency was inversely proportional to the optical path difference. Since a long optical path difference is required to obtain high resolution, there is a problem in that the data sampling frequency decreases. Therefore, the spectral resolution was adjusted so that the data sampling frequency was twice the maximum wavenumber in the measured wavenumber band.  
The solid dye was fabricated in the laboratory, and the fluorescence decreased due to photodissociation and was immediately observed when an interferogram measurement was Fourier transformed into a fluorescence spectrum. It was also observed that the real-time changes in temperature contribute to the fluorescence decrease in the solid dye.  
When the pumping laser was irradiated to the solid dye, the fluorescence decrease was observed for up to 900 s. In turn, the greatest decrease in fluorescence was 65%, and the shift in the fluorescence peak wavelength was observed from 590 to 580 nm. The results of this study proved that real-time remote observations are possible using a static modulation Fourier transform spectroscopy. For this study, it is expected to be applied in the investigation and measurement of various fields, even in circumstances where the spectral information of inaccessible objects changed rapidly.  
Conceptualization, W.K.J.; methodology, W.K.J. and S.L.; validation, W.K.J., J.Y.C., H.K. and S.L.; formal analysis, J.Y.C.; investigation, W.K.J., J.Y.C., H.K. and S.L.; writing--original draft preparation, W.K.J. and J.Y.C.; writing--review and editing, W.K.J., J.Y.C., S.L. and H.K.; supervision, W.K.J.; project administration, W.K.J. and J.Y.C.; funding acquisition, W.K.J. and J.Y.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A6A3A01099403; NRF-2021R11A3052065).  
Not applicable.  
Figure 6: The temperature gradient of the surface of the solid dye. The irradiated pumping laser pulse was adjusted to (**a**) 2.5 ms, (**b**) 5 ms, and (**c**) 10 ms for 900 s. (**d**) The variation in the highest temperature of the irradiated surface of the solid dye.
## 3 Results
### Immunohistochemistry Marker Levels
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Markers (pg/mL)** & **Side** & **Baseline** & **12 Months** & **24 Months** \\ \hline IL-4 & Test & 0.04 (0.07) Ba & 0.31 (0.16) Aa & 0.01 (0.01) Ca \\ (Anti-inflammatory) & Control & 0.03 (0.05) Ba & 0.18 (0.16) Aa & 0.01 (0.02) Ca \\ \hline \(p\)-value & & \(p\) (side) = 0.0823; \(p\) (time) = 0.0002; \(p\) (interaction) = 0.0882 \\ \hline IL-10 & Test & 0.34 (0.33) Ba & 0.78 (0.35) Aa & 0.42 (0.24) Bb \\ (Anti-inflammatory) & Control & 0.43 (0.32) Ba & 0.76 (0.35) Aa & 0.69 (0.45) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.0165; \(p\) (time) = 0.0026; \(p\) (interaction) = 0.0645 \\ \hline IL-17 & Test & 1.23 (0.72) Aa & 1.59 (0.19) Aa & 1.13 (0.72) Ab \\ (Pro-inflammatory) & Control & 1.19 (1.19) Ba & 1.15 (0.370 Bb & 3.16 (2.38) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.1857; \(p\) (time) = 0.1015; \(p\) (interaction) \textless{} 0.0001 \\ \hline IL-1\(\beta\) & Test & 3.59 (2.16) Aa & 3.48 (0.32) Aa & 3.57 (2.24) Aa \\ (Pro-inflammatory) & Control & 2.52 (1.91) ABa & 2.38 (0.89) Bb & 3.55 (2.31) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.0003; \(p\) (time) = 0.0242; \(p\) (interaction) = 0.1444 \\ \hline IL-33 & Test & 0.29 (0.29) Aa & 0.48 (0.15) Aa & 0.30 (0.27) Ab \\ (Pro-inflammatory) & Control & 0.24 (0.23) Ba & 0.35 (0.28) Ba & 0.64 (0.49) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.3987; \(p\) (time) = 0.0848; \(p\) (interaction) = 0.0128 \\ \hline IL-21 & Test & 1.05 (0.63) ABa & 0.97 (0.12) Ba & 1.18 (0.98) Ab \\ (Pro-inflammatory) & Control & 1.14 (0.99) Ba & 0.90 (0.23) Ba & 3.11 (2.20) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.0583; \(p\) (time) = 0.0032; \(p\) (interaction) = 0.0092 \\ \hline IFN-\(\gamma\) & Test & 1.28 (0.69) Aa & 1.39 (1.01) Aa & 1.59 (1.11) Ab \\ (Pro-inflammatory) & Control & 1.21 (1.19) Ba & 0.88 (0.76) Ba & 3.46 (2.62) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.9145; \(p\) (time) = 0.0110; \(p\) (interaction) = 0.0188 \\ \hline IL-23 & Test & 0.09 (0.11) Aa & 0.17 (0.14) Aa & 0.10 (0.11) Ab \\ (Pro-inflammatory) & Control & 0.06 (0.06) Ba & 0.13 (0.07) Ba & 0.22 (0.18) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.5411; \(p\) (time) = 0.0264; \(p\) (interaction) = 0.0063 \\ \hline IL-6 & Test & 0.75 (0.68) Aa & 0.50 (0,33) Aa & 0.00 (0.00) Bb \\ (Pro-inflammatory) & Control & 0.52 (0.48) Ba & 0.47 (0,43) Ba & 1.73 (1.35) Aa \\ \hline \(p\)-value & & \(p\) (side) = 0.0001; \(p\) (time) = 0.1409; \(p\) (interaction) \textless{} 0.0001 \\ \hline TNF-\(\alpha\) & Test & 0.24 (0.13) Ba & 0.50 (0.09) Aa & 0.23 (0.17) Bb \\ (Pro-inflammatory) & Control & 0.22 (0.23) Ca & 0.48 (0.03) Ba & 0.90 (0.6) Aa \\ \hline \(p\)-value & & \(p\) (side) \textless{} 0.0525; \(p\) (time) = 0.0005; \(p\) (interaction) = 0.0004 \\ \hline \hline \end{tabular} Different letters (capital letters horizontally and lowercase letters vertically) indicate statistically significant differences (\(p\leq\) 0.05).  
\end{table} 
Table 5: Level of inflammatory markers for test and control groups over time (pg/mL).
# An Improved Q-Learning-Based Sensor-Scheduling Algorithm for Multi-Target Tracking
## 4 Detailed Description of the Proposed Algorithm
### Target Prioritizing
#### (2) Distance to cluster center  
Distance to cluster center \(\mathfrak{D}\) is obtained by calculating the Euclidean distance between the target and its corresponding cluster center. When this target moves to the boundary of its cluster, it may activate new clusters. In this case, the system needs a precise estimate of the target position to prevent target loss caused by activating the wrong clusters.
## 4 Discussion
In fact, on dedicated software, it is possible to virtually insert the implant screw to visualise its ideal position, to assess if the residual bone quantity is sufficient to safely accommodate the implant or if there is a need to plan regeneration procedures, and to plan the future definitive prosthetic crown [26]. Furthermore, having a safe thickness of the buccal cortical and knowing its thickness protects us from dehiscence or other cosmetic complications. [PERSON] et al. found that vestibular cortical bone loss after implant placement was greatest when the initial vestibular cortical thickness was less than 1.4 mm, while it decreased to a true possibility of bone gain when the thickness increased to approximately 1.8-2 mm [8]. Although computer-guided implant surgery seems to be a predictable and accurate technique, it must be considered that a deviation between preoperative digital planning and implant placement may occur due to the clinician's learning curve or due to possible errors in the various steps of the digital planning workflow [27]. Some errors may occur during intraoral scanning or during cone-beam analysis and may be operator-dependent or due to low accuracy of the devices used [28; 29]. Another limitation that may undermine the precision and predictability of a fully digital workflow of computer-guided implantology concerns the 3D printing step of the tooth-supported templates. These undergo a minimal but present contraction of the material that could affect the position of our guide during implant insertion. It is therefore very important to choose a very accurate 3D printer that produces a template with as little error as possible, so that it is clinically insignificant [30]. Another consideration to be made concerns the error resulting from the placement of the guide used for the implant placement. In an observational study, [PERSON] et al. analyzed the mean deviation between the preoperative digital planning and the implant insertion referring to the error in the positioning of the surgical guide and how this is influenced by the maxillary or mandibular arch [31]. It is possible that the small differences between the measurements found in our study could be due to this type of error. To reduce it, especially in cases of a bone-supported surgical guide, [PERSON] et al. suggested the use of stabilising screws [32]. Undoubtedly, computer-guided implant surgery has several advantages and makes implant placement more predictable. However, it should not be considered easier to perform, but a procedure that makes implant placement more precise. Some limitations include the acquisition of some specific skills such as training in the use of preoperative planning software and experience in traditional implant surgery, as well as the use of expensive equipment such as a 3D printer and intraoral scanner for digital acquisition for impressions of the patient's jaws. This study presents some limitations. It is a retrospective study and has no long-term follow-up. Further studies are needed to determine whether computer-guided implant placement provides advantages in terms of success rate and esthetics over time.
## 6 Discussion
### Exclusion of Low SNR Data  
It is interesting to know the SNR of our method and whether it is related to the accuracy of the authentication. The distribution of SNRs of our measured ear canal acoustic characteristics is shown in Figure 11a. In our experience, the SNR of 20 dB is excellent. We verified whether SNR affects the accuracy of authentication by excluding signals with SNR less than 30 dB. Undersampling was performed to equalize the number of trials when the signal of SNR below 30 dB were excluded. Other conditions are the same as in Section 5. Since the correspondence between SNR and authentication accuracy is clearer for single ear authentication, we calculated the accuracy of single ear authentication. Figure 10a,b shows the result of EER and AUC, respectively. In Figure 11a,b, \"SNR-\" are the results when signals with SNR less than 30 dB are excluded. Excluded results are slightly lower for EER. The excluded results are smaller and less accurate with respect to the AUC. Based on these results, it can be concluded that excluding signals with SNR less than 30 dB does not improve the accuracy.  
Figure 11: **(a) Histogram of SNRs. (b) EER when signals with SNR less than 30 dB are excluded. (c) AUC when signals with SNR less than 30 dB are excluded.**  
Figure 10: FAR and FRR curves for bilateral and single ear acoustic authentication. (a) FAR curve. (b) Enlarged FAR curves. (c) FRR curve.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 2 Design of Space Mirror  
### Selection of Mirror Material  
Currently, the materials that can be used as mirror blanks for space mirrors are Fused Silicon [6], Zerodur, Silicon carbide, Beryllium and Ultra-Low-Expansion Glass (Corning). The material parameters are shown in Table 1.  
From Table 1, it can be seen that the ultra-low-expansion glass thermal expansion coefficient tends to be close to zero from 5 to 35 \({}^{\circ}\)C. From the material science aspect, the ultra-low-expansion glass belongs to the titanium dioxide-silicate glass, which has the property of being easy to be fused, allowing the optical component to have more processing possibilities, as well as to reduce its own weight [7; 8; 9].  
The base structure design of the mirror follows the basis of ensuring rigidity and face shape accuracy, reducing weight, and possessing stress release capability. According to the characteristics of easy fusion bonding of ULE, the mirror is designed as three parts: top mirror, middle mirror and bottom mirror, which are combined together by means of fusion bonding, as shown in Figure 1.
## 3 Experimental Results  
### Relationship of Lateral Load-Drift Angle  
Figure 5 shows the relationship of lateral load-drift angle of specimens based on the experimental results. As shown in Figure 5, the longitudinal reinforcement of the H-series specimens experienced flexural yield at a drift angle of 0.5% regardless of the axial force ratio or yield strength of transverse reinforcement. Specimens with an axial force ratio of 30% had a higher yield load compared to those with an axial force ratio of 20%, but the change in initial flexural rigidity was not large. The yield strength of transverse reinforcement did not cause significant differences in flexural yield load. The peak load of specimens was high when the axial force ratio increased from 20 to 30%, but the difference due to yield strength of transverse reinforcement was not significant. Regardless of yield strength, after reaching the peak load, specimens with an axial force ratio of 30% showed a rapid decrease in strength compared to those with an axial force ratio of 20%.  
Similar to H-series specimens, S-series specimens underwent flexural yield of longitudinal reinforcement at a drift angle of 0.5%. The yield load of specimens increased with axial force ratio, but was not significantly influenced by yield strength of transverse reinforcement. The peak load of specimens increased with axial force ratio, but was not influenced by yield strength of transverse reinforcement. The difference in yield strength according to transverse reinforcement configuration averaged around 3%, while peak load differed by only about 4%. As shown in Figure 5, the rigidity of yield load and failure  
Figure 4: Test setup of specimen: (**a**) View of test setup; (**b**) Loading history; (**c**) Measurement of displacement.  
of specimens after reaching the peak load were similar regardless of configuration of transverse reinforcement.  
Figure 5: Relationship of lateral load-drift angle of specimens: (**a**) NH20; (**b**) NH30; (**c**) UH20; (**d**) UH30; (**e**) NS20; (**f**) NS30; (**g**) US20; (**h**) US30.
# ARCFIRE: Experimentation with the Recursive InterNetwork Architecture
## 3 Scaling up RINA Networks
### Resiliency within a Single DIF  
If a failure occurs in the current internet, there is no unified mechanism to signal a failure between layers, which hampers fast recovery. RINA has the concept of a flow, which can be marked as down upon failure. We implemented a Loop-Free Alternates (LFA) [11] policy for RINA, which has the goal to reduce failure reaction time to tens of milliseconds by using a pre-computed alternate next-hop that can be used as soon as a failure of the primary next-hop is detected. We deployed a network that uses the physical connectivity graph of the GEANT network on FIRE using Rumba. As depicted in Figure 4, the minimum degree of the graph is 2, the maximum 5 and the average degree is 2.93. Each link is implemented on the testbed as an ethernet DIF and a normal DIF is run on top of them. In total, the experiment has 30 nodes and 44 links, so there are 30 normal IPCPs, and 88 ethernet IPCPs.  
An application sending data at a constant bit rate has been deployed on the \"exterior\" nodes of the network, i.e., those geographically far from the core of the continent. The nodes selected as \"end-nodes\" are Ankara, Dublin, Lisbon, Ljubljana, Moscow, Nicosia, Oslo, Riga, Stockholm, and Valleta. Several links have been selected to represent faulty links: every 30 s one of them would go down for 15 s and come back up. Several 50 Mbps connections are created, emulating a realistic load on the net. Figure 5 shows the running average over 1 s of the RTT (Round Trip Time) of several of the paths starting from Ljubljana for a total duration of 270 s. The link down and link up events are marked with vertical lines. We can see that around those times, some of the paths suddenly increase or decrease their RTT, as some flows change their states to down, forcing the normal IPCP to reroute packets via an alternate path. The recovery is almost instantaneous, it takes around 15 ms to recover, well below the carrier grade recovery requirement of 50 ms.
# A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography
## 3 Results
### RF-Induced Acoustic Computed Tomography (RACT) of the Breast Phantom  
The radio frequency source for the breast phantom, similar to the homogeneous phantom, operates at 434 MHz. The field distribution is provided in Figure 8.  
The forward simulation of the electric field is able to directly detect the tumor and an estimation of its location. The conductivity has a much more significant effect on the wave, since the wavelength  
Figure 6: Total optical fluence distribution using an 800 nm wavelength source on the breast phantom. The background absorption coefficient is \(\mu_{a}=0.0005\) mm\({}^{-1}\), with a reduced scattering coefficient \(\mu_{s}^{\prime}=1.5742\). Glandular tissue (\(\mu_{a}=0.0059\) mm\({}^{-1}\), \(\mu_{s}^{\prime}=1.12\) mm\({}^{-1}\)) surrounds a tumor (\(\mu_{a}=0.0021\), \(\mu_{s}^{\prime}=0.625\) mm\({}^{-1}\)) located 32.5 mm deep from the right side of the phantom.  
Figure 7: Photoacoustic computed tomography (PACT) simulations of the breast phantom at 800 nm wavelength; (**a**) the true optical absorption; (**b**) the initial pressure rise induced by the photoacoustic effect; (**d**) the reconstructed pressure, calculated using the time reversal algorithm; and (**c**) the reconstructed absorption, obtained by dividing the reconstructed pressure with the fluence. The elliptical region similar to the background is the tumor, surrounded by the glandular tissue.  
is larger than the region of interest. In the large wavelength regime, scattering due to dielectric contrast does not distort the electromagnetic wave as much as the substantial conductivity. While the approximate location of the tumor can be inferred from the electric field intensity, the specific shape and glandular tissue identification requires further processing, such as the RF acoustic simulation. Figure 9 shows the initial pressure and the resulting reconstructed pressure and conductivity maps of the breast phantom.  
Figure 8: Total electric field intensity distribution of a 434 MHz radio frequency source inside the breast phantom. The background has a conductivity \(\sigma=0.0353\) S/m and dielectric constant \(\varepsilon_{r}=5.51\). Galland tissue (\(\sigma=8.86\) S/m, \(\varepsilon_{r}=61.3\)) surrounds a tumor (\(\sigma=13.03\) S/m, \(\varepsilon_{r}=25.25\)) located 32.5 mm deep from the right side of the phantom.  
Figure 9: RF-induced acoustic computed tomography (RACT) simulations of the breast phantom using a 434 MHz RF source; **(a)** the true RF conductivity; **(b)** the initial pressure rise induced by the thermoacoustic effect; **(d)** the reconstructed pressure, calculated using the time-reversal algorithm; and **(c)** the reconstructed absorption, found by dividing the reconstructed pressure by the electric field intensity. The elliptical region with significantly larger conductivity is the tumor, surrounded by the glandular tissue.
## 3 Numerical Example 1
### Discussion
After a thorough review of the graphs (see Figures 14-16) presented for cases with 4, 8, and 20 TMDs, conclusions can be drawn regarding the application of the \(J1\) objective function based on the \(9t_{2}\) norm and the \(J2\) function based on the \(9t_{\infty}\) norm. As far as the tuning distribution is concerned, we can observe increased factor \(\beta_{k}\) for TMDs tuned above and below the \(\omega_{1}\) frequency. The area of TMDs tuned to values beyond \(\omega_{1}\) exhibited a significantly higher growth of the value \(\beta_{k}\), especially for the parameters obtained from the \(J1\) objective function, which is particularly well illustrated by the graphs \(\beta_{k}-\beta_{k-1}\). If we consider the optimal tuning ratio graphs for individual \(\zeta_{k}{}^{t}\), we can observe a derivative characteristic, but only for the \(J1\) function, although the increments \(\zeta_{k}{}^{t}-\zeta_{k-1}^{t}\) no longer show a similar dependency as \(\beta_{k}-\beta_{k-1}\). On the other hand, the optimal parameters \(\zeta_{k}{}^{t}\) obtained based on the \(J2\) function look completely different. In this case, the graph shows a significant drop in the \(\zeta_{k}{}^{t}\) values for \"central\" TMDs tuned on \(\omega_{1}\). Figure 14: (**a**) Optimal tuning ratios \(\beta_{k}\) and optimal tuning spacings \(\beta_{k}-\beta_{k-1}\) (**b**) optimal damping ratios \(\zeta_{k}{}^{t}\) and damping ratios’ spacings \(\zeta_{k}-\zeta_{k-1}\) of the individual TMD for \(N_{k}=4\), \(\mu=0.02\), and \(\zeta_{1}=0.02\). Figure 13: Objective function value, in the case of primary system mistuning (10% increase and drop of mass), based on the optimal parameters obtained from \(J1\) and \(J2\), depending on the number of TMDs: (**a**) value of \(J1\); (**b**) value of \(J2\).
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
## 4 Results
### Shadow Intensity Estimation  
We evaluated the performance of the proposed method in the estimation of intensities of shadows. Since we do not have the ground truth for shadow intensities, as a novel indicator, we calculated the correlations of the brightness of the input image and the estimation with respect to the area labeled as shadows. More specifically, given an input image \(x\), a pixel-label \(l\), and a shadow estimation \(\hat{s}\), the indicator is calculated as follows;  
\[\rho(x,l,\hat{s}):=\frac{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw})(x_{hw}-\bar{ x})(\hat{s}_{hw}-\bar{s})}{\sqrt{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw})(x_{hw}- \bar{x})^{2}}\sqrt{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw})(\hat{s}_{hw}-\bar{s} )^{2}}}, \tag{15}\]  
\[\bar{x}=\frac{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw})x_{hw}}{\sum_{l=1}^{H} \sum_{w=1}^{W}(1-l_{hw})},\;\bar{s}=\frac{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw })\hat{s}_{hw}}{\sum_{l=1}^{H}\sum_{w=1}^{W}(1-l_{hw})}, \tag{16}\]  
which is Pearson's correlation coefficient [36] that is masked using the label \(l\). Table 2 shows the results. Since the conventional methods are designed for detecting shadows and not estimating their intensities, the coefficients for them were just for benchmarks. It illustrates that the proposed method achieved the largest coefficients for all the numbers of labeled data. This indicates that our method estimated the shadow intensities the most precisely among the methods we examined. The coefficients of U-Net are lower than that of the proposed method but it was stable. The methods based on image processing, the geometric method and the random walk, performed worse. Especially, the estimation of the random walk method is the worst and had almost no correlations to the input image, despite its better performance for shadow detection than the geometric method. The standard deviation of the random walk method was larger than other methods and the method seemed to be unstable for this indicator.