esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Reduced EMMPRIN Expression Inhibits Angiogenesis  
To exit from a state of dormancy, tumor cells need to increase their metabolism, and therefore their supply of oxygen and nutrients, through angiogenesis. The angiogenic potential of the CT26 cells was assessed by the functional wound assay that demonstrated the ability of conditioned media (CM) to affect the migration of the bEND3 mouse endothelial cells, as well as by the CT26 cells' ability to secrete the pro-angiogenic factors VEGF and MMP-9 to the CM. The CT26-WT cells enhanced their migration rate upon co-culturing, addition of TGF\(\beta\) or low amounts of EMMPRIN (Figures 6B,E,H and S4), the latter again exhibiting a hormetic response. A similar response was observed for the secretion of VEGF (Figure 6C,F,I). However, the secretion of MMP-9 was strongly induced in the co-culture, unchanged with the addition of TGF\(\beta\), and slightly elevated in a non-hormetic dose-response manner with the addition of EMMPRIN (Figure 6D,G,J). The strong induction of MMP-9 in the co-culture can be explained by the enhanced secretion of TNF\(\alpha\) (Figure 2C), which is a very strong inducer of MMP-9 [50], and the relative mild response to soluble EMMPRIN at high doses demonstrates the ability of EMMPRIN to induce MMP-9 secretion from tumor cells even in the absence of TNF\(\alpha\).  
In contrast, CM derived from the CT26-KD cells inhibited the migration of bEND3 cells, while CM derived from the co-culture, TGF\(\beta\), or EMMPRIN experiments restored migration to the levels of the similarly treated CT26-WT cells (Figure 6B,E,H). Secretion of VEGF was also reduced in the CT26-KD cells, and CM from co-cultures or from TGF\(\beta\) addition, but not from the EMMPRIN experiments, increased its levels (Figure 6C,F,I), but not to the levels of the treated CT26-WT cells. As VEGF is regulated by both TGF\(\beta\) and EMMPRIN, it is likely that addition of TGF\(\beta\) could enhance VEGF. However, the reduced expression of the membranal EMMPRIN also reduced the homophilic interactions with the added soluble EMMPRIN, possibly leading to no effect of the latter on the CT26-KD cells. The secretion of MMP-9 from CT26-KD cells was minimal, and was increased in the CT26-KD cells only in the co-culture, but not by the addition of TGF\(\beta\) or EMMPRIN. Despite the presence of equal amounts of TNF\(\alpha\) secreted by both CT26-WT and CT26-KD in co-culture (Figure 2C), we still observed an inhibition in the MMP-9 levels in the co-cultured CT26-KD cells. This could be explained by the crosstalk with other signaling pathways, such as the ERK1/2 kinases or PI3K pathways. Thus, while the CT26-WT cells exhibit enhanced angiogenic potential when co-cultured with macrophages or simulated by TGF\(\beta\) or EMMPRIN, the CT26-KD cells with reduced EMMPRIN expression demonstrated reduced angiogenic potential that was in accordance with their enhanced angiogenic dormancy. These CT26-KD cells were pushed to exit this angiogenic dormant state by stimuli provided by the co-culture, TGF\(\beta\) or EMMPRIN, that were partially EMMPRIN-independent.  
Figure 6: _Cont._
## 4 Performance
### Routing Recovery  
Figure 10 shows the packet reception ratio and the number of broadcast packets during the routing recovery stage. The failures occurred at 200 s when 11 sensor nodes were simultaneously turned off. Out of the 144 sensor nodes, 72 were affected by the failure.  
Figure 10: Packet reception ratio and number of packets in routing recovery stage.  
ARF showed the fastest routing recovery, requiring only 50 s, from 200 s to 250 s. ARF began the routing recovery when the failure was detected. Thus, packets were received immediately. ARF only selects a parent from the highest level of the routing tree and thus could detect the failure quickly. Moreover, the sensor nodes in the boundary area transmitted routing control messages at short intervals, allowing the sensor nodes near the boundary area to recover the routing tree quickly. As a result, the number of packets broadcast by ARF increased drastically at 200 s, but decreased again as the routing tree was being recovered between 200 s and 250 s. After 250 s, the entire routing tree was properly reconstructed, all of the data packets were received by sink node, and the number of broadcast packets decreased to the lowest level because the routing control messages were transmitted at 20,000 ms intervals. Proc with the 5,000 ms interval showed the next best routing recovery speed, maintaining a high rate of broadcast packets. However, it took packets 80 s to arrive at the sink node. During these 80 s, sensor nodes in the failure affected area were busy finding a new parent from all of their neighbor nodes. Proc with the 10,000 ms interval showed low rate of broadcast packets but the routing tree was recovered at a lower speed. MintRoute showed a very slow routing recovery speed for both the 5,000 ms and 10,000 ms intervals. As a result, ARF could recover the routing tree fast with fewer routing control messages in case of large area failure.
# Investigation on Cutting Blasting Efficiency of Hard Rock Tunnels under Different Charge Diameters
## 4 Cutting blasting Experiments  
### Experimental Design and Implementation  
In order to further explore the influence of charge diameters on the actual cutting blasting results, the hard rock cutting blasting experiments under the three different charge diameters were carried out. Because it was extremely difficult to implement and examine the cutting blasting separately without delaying the construction period of the rock tunnels of the coal mines, a hard rock vertical slope with high rock integrity was selected to replace the driving face of the underground mine tunnels.  
The rock vertical slope used for the cutting blasting experiments was located in the Shenshan open-pit limestone mine, Chizhou, China. The main physical and mechanical parameters of the rock had been tested previously, and the testing results have been listed in Table 2. It was observed that the compressive strength of the rock was 92.4 MPa, that is, the hardness coefficient of rock greater than 9, which indicated that the rock belonged to the hard rock category. The rock vertical slope employed for one cutting blasting experiment is presented in Figure 9.  
During the cutting blasting experiments, three kinds of drilling bits with diameters of 32 mm, 42 mm, and 50 mm were employed for the drilling constructions, and three kinds of water-gel explosive cartridges with diameters of 27 mm, 35 mm, and 42 mm were used as the blasting materials. It should be noted that the blasting hole arrangement and blasting parameters mentioned in the previous numerical simulation were taken from the cutting blasting experiments, thus the relevant information of the blasting scheme does not need to be repeated here.
## 4 Empirical Results
### Simulation Result
#### 4.2.2 Simulation metrics
The farthest junction vehicles and border vehicles are prone to go out of transmission range frequently due to the high mobility of the traffic environment. The performance of all the state-of-the-art protocols is approximately same for the speed range of 5- 20 Km/h. The performance of V-GEDIR degrades at a higher rate as the vehicle speed goes above 20 Km/h because it does not consider the speed of vehicles for selecting a forwarding vehicle. Thus the proposed protocol outperforms the compared state-of-the-art protocols. Figure 10: Impact of speed in (**a**) end-to-end delay; (**b**) throughput and (**c**) one-hop link disconnection. In Figure 10b, the results show the comparison of throughput between SLD-GEDIR and those of other protocols under consideration for varying speed. The results demonstrate that the throughput of SLD-GEDIR decreases linearly with increasing vehicle speed. However, the throughput decreases sharply for the state-of-the-art protocols. This can be attributed to the fact that the state-of-the-art protocols need to re-discover one-hop links more frequently because of high rate of link breakage with increasing vehicle speed. The link selected by SLD-GEDIR is based on various quality computations that make it more reliable. Therefore, SLD-GEDIR always outperforms the considered state-of-the-art protocols. The comparison result of one-hop link disconnection between SLD-GEDIR and the state-of-the-art protocols has been shown in Figure 10c. It can be clearly observed that one-hop link disconnection of SLD-GEDIR is nominal in comparison with the state-of-the-art protocols with the speed range of 5-40 Km/h. The reason behind this is that the degree of connectivity and the one-hop link quality prediction selects the most reliable NHV in the proposed protocol. However, the one-hop link disconnections suddenly increase at speeds above 40 Km/h because the NHV selection process could not be completed within the available time. P-GEDIR is the next best performer in terms of number of one-hop link disconnections compared to J-GEDIR and V-GEDIR. The one-hop link disconnections in P-GEDIR and J-GEDIR start growing rapidly at speeds above 20 Km/h because the forwarding technique used in these protocols does not consider speed information in NHV selection. V-GEDIR performs the worst as compared with other protocols for all speed ranges due to its larger hop count from source to destination region. Thus, the performance of SLD-GEDIR is better than the compared state-of-the-art protocols in terms of one-hop link disconnections.
## 5 Results and Discussion
The parameters of the RSOA used here are listed in Table 3. Figures 8 and 9 illustrate the BER performance of the downstream and upstream FSO traffic, respectively, over a variety of link ranges. The system's BER performance degrades as transmission distance increases, as shown in Figures 8 and 9. Moreover, we notice that as the FSO traffic advances to longer wavelengths gradually, the corresponding BER performance becomes relatively poor, as seen in the results obtained. Therefore, shorter wavelength FSO traffic can achieve better BER performance and a longer range, as illustrated in Figures 8 and 9. At the FEC limit, for instance, 1552.52 nm (\(\lambda_{1}\)) could reach \(\approx\)1200 m downstream and \(\approx\)1000 m upstream, respectively, whereas 1540.16 nm (\(\lambda_{32}\)) could reach \(\approx\)1400 m downstream and \(\approx\)1300 m upstream, respectively. Consequently, the aggregate FSO signal was capable of 1000 m bidirectional FSO transmission. The proposed WDM-FSO network had no visible crosstalk since the downstream, and the upstream signals traveled on different paths across the FSO link. The results illustrate that downstream signals can achieve a longer transmission distance than upstream signals due to the improved link performance that results from the use of OFDM formats. Additionally, the results demonstrate that the upstream signals are relatively unaffected by the Rayleigh backscattering noise caused by the downstream signals. This has been achieved by operating the RSOA in the gain saturation region to remove the downlink modulation on the seeding wavelength. Moreover, the results indicate that shorter wavelength FSO traffic can achieve better BER performance and an extended range than longer wavelengths. This is consistent with the experimental results obtained in [19] and can be explained by the fact that at longer wavelengths, the attenuation resulting from atmospheric absorption or scattering increases slightly, degrading the performance of the system.
## 3 Results  
The study participants were seven healthy term neonates. One of the seven neonates was born via forceps delivery but the others were born via a normal vaginal delivery. Two neonates were excluded from the analysis because the \(\mu_{\text{s}}\)' was abnormal according to previous work [10]. However, none of the neonates needed any respiratory support, had abnormalities, or required hospitalization. The gestational ages of the neonates were 38-40 weeks, and their Apgar scores at 1, 5 min were 8 or more (Table 2). Five neonates (V1-V5) did not need resuscitation until 15 min after birth.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **oxyHb** & **deoxyHb** & **Water** \\  & **(mM\({}^{-1}\)cm\({}^{-1}\))** & **(mM\({}^{-1}\)cm\({}^{-1}\))** & **(cm\({}^{-1}\))** \\ \hline
762 nm & 1.4320 & 3.8145 & 0.0272 \\
800 nm & 1.9924 & 1.9339 & 0.0204 \\
836 nm & 2.4985 & 1.7974 & 0.0363 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Extinction coefficients for oxyHb, deoxyHb, and water.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Neonate** & **Gestational** & **Body** & **Apgar** & **Apgar** & **pH Umbilical** & **Venous** \\
**No.** & **Age** & **Weight (g)** & **1 min** & **5 min** & **Artery** & **Hemoglobin at** \\  & & & & & & **2 h (g/dL)** \\ \hline V1 & 37 wk 3 d & 3,212 & 8 & 9 & 7.309 & 15.8 \\ V2 & 39 wk 6 d & 3,170 & 8 & 9 & 7.322 & 13.2 \\ V3 & 38 wk 5 d & 2,830 & 8 & 9 & 7.371 & 20.3 \\ V4 & 39 wk 4 d & 3,406 & 8 & 8 & 7.345 & 18.9 \\ V5 & 39 wk 3 d & 2,894 & 8 & 9 & 7.256 & 19.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of neonates’ clinical data in this study.  
Within 2-3 min after birth, CBV showed various changes such as increases or decreases, and was then followed by a gradual decrease until 15 min, and was then stable (mean (SD) mL/100 g brain: 2 min, 3.09 (0.74); 3 min, 3.01 (0.77); 5 min, 2.69 (0.77); 10 min, 2.40 (0.61); 15 min, 2.08 (0.47)) (Figure 2).  
ScO\({}_{2}\) showed a gradual increase, then kept increasing or became a stable reading (mean (SD)%: 2 min, 48.0 (12.3); 3 min, 53.9 (14.2); 5 min, 62.5 (13.3); 10 min, 67.2 (10.2); and 15 min, 64.3 (4.7)) (Figure 3).  
The values of DPF, \(\mu_{\mathrm{a}}\), and \(\mu_{\mathrm{s}}\)' are shown in Table 3 and correspond to those of a previous report [10]. The optical properties were stable for the first 15 min after birth: DPF (mean (SD) at 762, 800, and 836 nm), 4.47 (0.38), 4.41 (0.32), and 4.06 (0.28)/cm; \(\mu_{\mathrm{s}}\)': 6.54 (0.67), 5.82 (0.84), and 5.43 (0.95)/cm.  
Figure 3: Cerebral hemoglobin oxygen saturation (ScO\({}_{2}\)) in five neonates during the first 15 min of life. Values are means. ScO\({}_{2}\) showed the same pattern as arterial Hb oxygen saturation with a gradual increase, peak at 5–10 min, and then stable values.  
Figure 2: Cerebral blood volume (CBV) in five neonates during the first 15 min of life. Values are means. Compared with the reference value at 15 min, a significant decrease in CBV was observed at each time point.
# Hybrid Dual-Link Data Transmission Based on Internet of Vessels
## 3 Algorithm Design
### Spatial Crowdsourcing Task Allocation
#### 3.2.4 Comprehensive Evaluation  
A comprehensive scoring function is defined based on the ship's location, credibility value, and ship density. This function is designed to comprehensively evaluate the multiple characteristics of each candidate ship and calculate its overall suitability score. The score of each ship reflects its overall advantage as a relay node, with the ship having the highest score being selected as the optimal relay.  
Considering the impact of ship density around a node on communication effectiveness, communication transmission is more likely to be completed when the ship density is higher. The logistic function \(DE_{i}=\frac{1}{1+e^{-k\alpha}}(n>0)\) is used as the ship density scoring function. \(DE_{i}\) is the ship density score, \(n\) is the number of ships within the communication range of the node, and \(k\) is the adjustment parameter. When the number of surrounding ships is low, the density score approaches 1, while as the number of neighboring ships increases, the density score gradually increases. The synthetic scoring function is as follows:  
\[F(S_{i})=\gamma\cdot Q_{i}+\delta\cdot DE_{i}+\eta\cdot D_{i} \tag{12}\]  
where \(F(S_{i})\) is the comprehensive score of the ship, while \(Q_{i}\) and \(D_{i}\) represent the credibility score and the distance score, respectively. \(\gamma\), \(\delta\), and \(\eta\) are the weight coefficients for the corresponding factors. The comprehensive score function allows the algorithm to comprehensively evaluate the potential of each candidate ship as a relay node. It selects the ship with the highest comprehensive evaluation as the relay node to improve the overall communication network performance. The algorithm chooses the ship with the highest score as the final relay node. Then, the position coordinates of the information-carrying ship are updated, and the optimal relay ship for information transmission is selected, ensuring that the selected relay ship performs optimally in the current maritime communication environment. If a suitable relay ship is always available for selection, the relay selection strategy will quickly transmit the data to the target node. See Figure 6.
# Broadband Millimeter-Wave 5G Power Amplifier Design in 22 nm CMOS FD-SOI and 40 nm GaN HEMT
## 3 Results
### Differential GaN (GaN #2) PA PEX EM Simulation Results  
The single-ended broadband GaN PA discussed above was then used to form a differential broadband mm-Wave 5G PA as shown in Figure 9, where the CS topology is used and the neutralization capacitors are added for improved MSG and for comparison purposes against a reasonably similar differential FD-SOI CMOS PA that will be presented later in this paper. Additional RC traps were added at the gates of these GaN transistors to further stabilize the PA, and this differential design has an area of 1.94 mm\({}^{2}\) (with pads), slightly larger than the 1.85 mm\({}^{2}\) area of the single-ended design. PEX EM simulations indicate that this differential GaN PA achieves a maximum \(S_{21}\) of 13.4 dB, with a 3-dB BW from 20.1-44.3 GHz (or an absolute BW of 75.2%) at \(V_{DD}\) = 6 V, of which the BW is comparable to the single-ended GaN #1 design, but with a significantly higher gain and \(P_{OUT}\).  
Figure 8: Measured ACLR+/ACLR- vs. 5G NR instantaneous signal BW (PAPR = 8 dB) for the broadband single-ended GaN PA with \(V_{DD}\) = 4 V at (**a**) 24 GHz and 28 GHz, and at (**b**) 37 GHz and 39 GHz; and with \(V_{DD}\) = 6 V at (**c**) 24 GHz and 28 GHz, and at (**d**) 37 GHz and 39 GHz.  
The large-signal EM simulation results for this differential GaN PA are shown in Figure 10, which indicates that this PA can achieve a maximum PAE of 24.9%/26.6%/23.3% and \(P_{OUT,SAT}\) of 23.5/23.6/23.3 dBm at 24/28/44 GHz. Figure 11 shows the broadband nature of this PA, as it is able to achieve >20% maximum PAE across the 20-44 GHz band, and has a very high simulated 1-dB \(P_{OUT,SAT}\) BW and also \(P_{OUT,SAT}\) larger than ~23 dBm in the 5G FR2 band of interest. Compared to the measured GaN #1 PA, it achieves a similar maximum PAE, while having ~3 dB greater \(P_{OUT,SAT}\) than the single-ended design. We will discuss another broadband differential mm-Wave 5G PA designed in 22 nm FD-SOI CMOS next.  
Figure 10: _Cont._  
Figure 9: (a) Simplified schematics and (b) the layout of the broadband differential GaN PA (2.0 \(\times\) 0.97 mm\({}^{2}\) with pads); and (c) its _S-Parameter_ EM PEX simulation results.
# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 6 Evaluation
### Privacy  
To evaluate privacy, we measured TP for the four individuals. Figure 3 shows the results of this measurement. Based on our expectation, TP should decrease with increasing \(\epsilon\) as weaker and weaker noise is added in the generation process. It can be observed that the median values of DP-CTGAN and PATE-CTGAN in the box plots decrease with increasing \(\epsilon\), so the generated data become more and more accurate. With MWEM, on the other hand, the \(\epsilon\) parameter seems to have at most a minor influence on the achieved TP.  
For individual 1 in Figure 3i, the highest TP value is reached with DP-CTGAN and \(\epsilon=2\). The TP value is approx. 0.9 but should be viewed as an outlier. Considering only the median values, DP-CTGAN yields the highest value of approx. 0.87 at \(\epsilon=1\). The smallest median TP occurs for \(\epsilon=5\) when using DP-CTGAN, but the outer range with \(\epsilon=10\) includes smaller values. Individual 2, which is illustrated in Figure 3 ii, exhibits the phenomenon that the median TP peaks at \(\epsilon=2\) and decreases thereafter as expected. Here, the smallest median value is obtained in the case of PATE-CTGAN and the highest \(\epsilon\). The participant with ID 3 in Figure 3 iii shows similar results as the first two. As mentioned before, the raw data of individual with ID 4 differ from the other individuals. This also impacts the TP metric which can be seen in Figure 3 iv. The number of outliers for all generators is much higher compared to the other individuals.  
Figure 2: Box plots showing the Tachycardia Privacy (TP) and Tachycardia Utility (TU) for one individual using plain DP with Laplacian-distributed noise at 20 runs using a \(PIF=3\) for TP.  
In summary, PATE-CTGAN shows the expected downward trend, which is also not consistent overall \(\epsilon\) values. Additionally, a similar trend for DP-CTGAN can be observed but with a growing dispersion, the larger \(\epsilon\) gets.
# Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
## 3 Example Analysis  
### Early Warning Indicator System for Rock Bursts  
In the light of the dynamic phenomenon data of the Yanshtiai Coal Mine for more than 30 years, and referring to the research results of [PERSON] and other scholars, this paper comprehensively selected 10 main factors that affect the occurrence of rock bursts in the Yanshtiai Coal Mine as the risk assessment indicators of rock bursts, which can be presented as the coal thickness (\(X_{1}\)), dip angle (\(X_{2}\)), burial depth (\(X_{3}\)), structure (\(X_{4}\)), dip angle change (\(X_{5}\)), coal thickness change (\(X_{6}\)), gas concentration (\(X_{7}\)), roof management (\(X_{8}\)), pressure relief (\(X_{9}\)), and sound of the coal cannon (\(X_{10}\)). To facilitate model learning, it is necessary to quantify the non-quantitative indicators [40]. The quantification results are listed in Table 1.  
According to the measured data [40] from 35 groups of hazardous mines of rock bursts in the Yanshtiai Coal Mine, the rock bursts in the Yanshtiai Coal Mine were divided into four grades: no rock burst (I), weak rock burst (II), medium rock burst (III), and strong rock burst (IV). The first 27 samples were used as the training set for the model (Table 2), whereas the last eight were chosen as the model test set (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Assignment** & \multicolumn{6}{c}{**Qualitative Index of Rock Bursts**} \\ \cline{2-9}  & \(X_{4}\) & \(X_{5}\) & \(X_{6}\) & \(X_{6}\) & \(X_{7}\) & \(X_{8}\) & \(X_{9}\) & \(X_{10}\) \\ \hline
0 & Simple & Unchanged & Unchanged & No support or & No pressure & & \\  & & & & poor support & & relief measures & & \\
1 & Average & Minor change & Minor change & Average & & & & \\  & & & & & & & & \\
2 & Relatively complex & Relatively large changes & Relatively large changes & Relatively good & Relatively good & & & \\
3 & Complex & Severe change & Severe change & Good & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Assignment of the qualitative indicators of the rock burst risk grade.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Sample** & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) & \(X_{6}\) & \(X_{7}\) & \(X_{8}\) & \(X_{9}\) & \(X