esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Prediction Model of Annular Pressure Buildup for Offshore Wells
## 6 Model Validation
### Annular Pressure Buildup Calculation  
According to casing and annulus fluid temperature changes, annular pressure buildup was iteratively solved for the well, and annular pressure buildup simulation analysis was carried out in three cases:  
(1) Only the expansion and compression effect of the liquid is considered;  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Name** & **OD (in)** & **Line Weight (lbm/ft)** & **Depth (ft)** & **TOC (ft)** \\ \hline Conductor & 30 & 200 & 377 & \\ Surface casing & 20 & 133 & 3025 & 374 \\ Intermediate casing & 13 3/8 & 72 & 8258 & 3525 \\ Productor casing & 9 5/8 & 53.5 & 10,456 & 8000 \\ Liner & 7 & 38 & 11,900 & \\ Tubing & 3 1/2 & 9.2 & 11,000 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Well structure.  
Figure 4: Wellbore structure diagram.  
(2) Considering the expansion and compression effect of liquid and the deformation of the casing-free section;  
(3) Consider the expansion and compression effect of the liquid, the deformation of the casing-free section, and the sealing section.  
The annular pressure buildup values are shown in Figure 5.  
As can be seen from Figure 5, for annulus A and B, C1 and C2 were basically equal because there was little difference in deformation of the free section of the casing in the inner and outer layers and little change in annulus volume.  
For the C annulus, only the inner casing had a free section of pressure, and the annulus volume was reduced; therefore, C2 was greater than C1.  
If the deformation of the sealed section is considered, it can be seen from the figure that the C3 values were all lower than the C1 and C2 values, and the predicted value of the trap pressure was smaller than that without considering the deformation of the sealing section.  
Due to the different annular cement return heights, the sealing section had different influences on the trap pressure of A, B, and C and had the greatest influence on the C annular pressure buildup and the least influence on the A annular pressure buildup.  
Specifically, the annular trap pressure predicted by the case of B annulus C3 was better than that predicted by the case of C1 and C2, which decreased by 14.9% and 13.6%, respectively. The predicted annular trap pressure of the C annulus C3 was 13.2% and 14.8%, which was lower than that of C1 and C2, respectively.
# An Adaptive Kriging-Based Fourth-Moment Reliability Analysis Method for Engineering Structures
## 3 Results
### Reliability Analysis with Explicit State Function
When the critical value of the U function is set to a certain level (e.g., greater than 6) and the number of predicted samples is large enough, the reliability results will eventually converge to the theoretical true value. In the case of the explicit nonlinear state function, the overall reliability calculation results obtained using the AK-FM method proposed in this paper are in good agreement with those obtained using the MCS method, and the number of calls to the state function \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **State** \\ **Function** \\ \end{tabular} & **Method** & \begin{tabular}{c} **Total** \\ **Sample Size** \\ \end{tabular} & \begin{tabular}{c} **Initial** \\ **Sample Size** \\ \end{tabular} & \begin{tabular}{c} **Increased** \\ **Sample Size** \\ \end{tabular} & \begin{tabular}{c} **U Function** \\ **Value** \\ \end{tabular} & \
\begin{tabular}{c} **Reliability** \\ **Value** \\ \end{tabular} &
\begin{tabular}{c} **Number of** \\ **State Function** \\ **Calls** \\ \end{tabular} \\ \hline Exponential & AK-FM & 1000 & 25 & 7 & 2.3870 & 0.8144 & 32 \\  & MCS & \(10^{6}\) & \(\backslash\) & \(\backslash\) & 0.8249 & \(10^{6}\) \\ \hline Power law & AK-FM & 1000 & 25 & 4 & 3.5760 & 0.7220 & 29 \\  & MCS & \(10^{6}\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.7193 & \(10^{6}\) \\ \hline Trigonometric & AK-FM & 1000 & 25 & 6 & 3.0884 & 0.7852 & 31 \\  & MCS & \(10^{6}\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.7681 & \(10^{6}\) \\ \hline Segmented & AK-FM & 1000 & 25 & 47 & 3.1502 & 0.8183 & 72 \\ functional & MCS & \(10^{6}\) & \(\backslash\) & \(\backslash\) & \(\backslash\) & 0.7901 & \(10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A comparison of reliability in the case of explicit state function. Figure 2: Predicted response surfaces and actual sample points. (**a**) Exponential. (**b**) Power law. (**c**) Trigonometric. (**d**) Segmented functional. is greatly reduced, although the cost of the explicit state function calls in this case is very low, and the difference between the two methods in terms of the total computation time is not obvious. However, when taking into account the high cost of the complex state function calls in actual engineering, the proposed method is able to significantly improve the efficiency of the reliability calculation.
## 5 Results
Figure 9: Sequence diagram of the experiment used to investigate latency spikes by keeping track of packet numbers and ACK messages. broker. P1 is then forwarded by the NB-IoT board and is correctly acknowledged; therefore, \(n\)ACK1 is also set to 1 and sent to Client 2. The difference between T1 and the timestamp of reception (T2) is computed by Client 2 and is referred to as T21 in the figure. The delay in this case is below a reasonable value, i.e., not corresponding to a latency spike. During the following transmission, no ACK is received for packet P2, and thus, nACK is not increased and P2 is reinserted in the output queue. At the third transmission, the board sends both P2 and the new P3 packet and receives an ACK for both messages. \(n\)ACK for P3 is not set to 3 in this case to keep track of the previously lost ACK message, meaning that both P2 and P3 have an associated \(n\)ACK \(=n\)ACK2 \(=2\). By looking at log entries on Client 2 where a packet has an associated packet number that is different from its ACK number, it is therefore possible to find packets for which the previous ACK message was not received by the board. These packets also correspond to spikes in latency in the resulting latency graph, meaning that the spikes are most likely due to the development of an output queue in the NB-IoT board. The loss of an ACK message could be because the publish message did not reach the broker or the ACK message from the broker was lost in transmission. In this last case, since MQTT QoS is set to 1, there is no guarantee that each message will not be forwarded twice toward Client 2. Client 2 does not receive any duplicate message, meaning that the reason for a lack of ACK messages on the NB-IoT board is most likely because the original publish message traveling on the NB-IoT network was lost. The average loss across the two experiments is about 0.8%. The latency for the second experiment was in line with the previous one, even if slightly lower (0.3845 s).
## 2 Materials and Methods  
### FD-FLIM and Spectroscopy Imaging Setup  
FD-FLIM and spectroscopic data were acquired with a multimodal surgical microscope at a working distance of 200 mm (OPMI Visu 200, Carl Zeiss Meditec AG, Jena, Germany). The interested reader is referred to our previous work for a detailed description of the system [15,23]. In brief, fluorescence was excited by a 405 nm laser diode (phoxX-405, Omicron Laserage, Rodgau, Germany), which was modulated at a frequency of 10 MHz and raster-scanned across the tissue. Fluorescence lifetime maps were acquired across a field of view of 6.5 \(\times\) 6.5 mm\({}^{2}\) by employing a homodyne detection scheme. FD-FLIM acquisitionswere performed in 16 seconds with a lateral resolution of 25 um (256 \(\times\) 256 pixels) at a laser power of 6 mW. Our system allowed for steering the laser to specific regions of interest on the specimens to acquire complementary spectroscopic information. The imaging protocol can be found elsewhere [15]. Fluorescence emission was filtered by either a 590 nm to 740 nm (665/150 BrightLine HC Semrock, Rochester, NY, USA) bandpass filter or a 615 nm long-pass filter (ET615 LP, Chroma, Bellow Falls, Vermont). We installed a multi-position slider with resonant piezoelectric motor (ELL9K, Thorlabs Inc., Newton, NJ, USA) for fast switching between the emission filters.
# Modeling and Analysis of High-Power Ti:sapphire Laser Amplifiers-A Review
## 2 Amplification Model
### Pumping and Spontaneous Emission
In a large-diameter gain medium, a parasitic oscillation in the transverse direction can occur with the stored energy increase [48, 49, 50, 51]. The amplifier shown in Figure 7b has a higher possibility of experiencing a parasitic oscillation than that in Figure 7c. A method of the multiple extraction during pumping [48, 49, 47], which can control the relative delays between the pump pulses and/or seed and pump pulses, is proposed to reduce the peak value of the stored energy during the amplification process. A simulation including instantaneous pumping would be useful to optimize the delays and each pump pulse energy. Figure 6: Change of stored energy over time when a 10-ns (FWHM) pump pulse is applied to Ti:sapphire. Change in the stored energy in a time domain of the same order (**a**) as the input pulse and pulse width and (**b**) as the fluorescence lifetime. Figure 7: (**a**) Multi-pass Ti:sapphire amplifier. (**b**) setting 1 simulation. (**c**) setting 2 simulation. In contrast to Ti:sapphire amplifiers, there are microsecond [51; 52] or millisecond [53; 54; 55] lasers, which are used in medical or industrial applications. For these long-pulse lasers, the spontaneous emission term given in Equation (18) should be included in the calculation, because the duration of the amplification process is similar to or longer than the fluorescence lifetime of the gain medium [43]. In addition to the effects discussed in this review, the consideration of the losses induced by the amplification of a spontaneous emission would be important depending on the applications [56; 57]. In the next section, we reviewed the influence of the beam profile on the amplifier efficiency and corresponding modeling method.
## 4 Materials and Methods
### Plasmid Construction and Lentivirus Transduction  
Two individual _SAMM50_ shRNA target sequences were selected from the Genetic Perturbation Platform (GPP) (Table S2) and then cloned into a commercial pLKO.1-puro lentiviral vector (Agel/EcoRI) (gifted from [PERSON], Addgene plasmid #10879; [[http://n2t.net/addgene:10879](http://n2t.net/addgene:10879)]([http://n2t.net/addgene:10879](http://n2t.net/addgene:10879)); RRID:Addgene_10879) [38], and the non-targeting shRNA (NT) pLKO.1 vector (gifted by [PERSON], Addgene plasmid #1864; [[http://n2t.net/addgene:1864](http://n2t.net/addgene:1864)]([http://n2t.net/addgene:1864](http://n2t.net/addgene:1864)); RRID:Addgene_1864) was used as a control [39]. The HEK93 FT cells (Thermo Fisher Scientific, Waltham, MA, USA) in 100 mm plates were transfected with 1 \(\mathrm{\SIUnitSymbolMicro g}\) of pLKO.1-NT-puro, 1 \(\mathrm{\SIUnitSymbolMicro g}\) of pLKO.1-shSAMM50-puro, and the lentiviral packaging vectors (3 \(\mathrm{\SIUnitSymbolMicro g}\) of CMV delta 8.9 and 4 \(\mathrm{\SIUnitSymbolMicro g}\) of VSV-G), respectively, using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's protocol, and the lentiviral packaging vectors were kindly provided by Dr. [PERSON] (Korea University, Seoul, Korea). The lentiviral medium for shSAMM50 was harvested at 48 h after transfection. The hADSCs were infected with lentiviral supernatants in the presence of 6 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of polybrene (Sigma-Aldrich, St. Louis, MI, USA). The infected hADSCs were selected with 2 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of puromycin (Sigma-Aldrich, St. Louis, MI, USA) over 7 days.  
For the overexpression, human _SAMM50_ cDNA was obtained through PCR amplification and cloned into a pLL-EF1\(\alpha\)-FLAG-PGK-BSD, as described previously [40], with the plasmid kindly provided by Dr. [PERSON] (Korea University, Seoul, Korea). The HEK293 FT cells were transfected with 1 \(\mathrm{\SIUnitSymbolMicro g}\) of pLL-EF1\(\alpha\)-FLAG-PGK-BSD, 1 \(\mathrm{\SIUnitSymbolMicro g}\) of pLL-EF1\(\alpha\)-FLAG-SAMM50-PGK-BSD, and the lentiviral packaging vectors (3 \(\mathrm{\SIUnitSymbolMicro g}\) of CMV delta 8.9 and 4 \(\mathrm{\SIUnitSymbolMicro g}\) of VSV-G) using Lipofectamine 2000 (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's protocol. The lentiviral medium for FLAG-SAMM50 was harvested at 48 h after transfection, and the infected hADSCs were selected with 4 \(\mathrm{\SIUnitSymbolMicro g}\)/mL of blasticidin S (Sigma-Aldrich, St. Louis, MI, USA) over 14 days.
## 3 Results and Discussion
### The Effects of Helix Angle on Scaffling Load Capacity
#### 3.3.3. Tooth Flash Temperature and Scaffing Safety Assessment  
Table 12 illustrates the calculated flash temperature distribution on the tooth surfaces of five gear sets with different helix angles. The left column represents the highest  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gearset** & **Tooth Surface Load Distribution** & **Tooth Surface Load Distribution** \\  & **Peak Speed Load Case: 99 Nm, 25,000 rpm** & **Peak Torque Load Case: 450 Nm, 5000 rpm** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Tooth surface load distribution of variable pressure angle gearsets.  
speed operating conditions, while the right column represents the peak torque operating conditions. Under the highest speed operating conditions, the maximum flash temperature on the tooth surface decreases with an increasing helix angle, from 58.8 \({}^{\circ}\)C to 31.5 \({}^{\circ}\)C, which represents a 46.4% reduction. Similarly, under peak torque conditions, the maximum flash temperature decreases from 208 \({}^{\circ}\)C to 122.6 \({}^{\circ}\)C, representing an 85.4 \({}^{\circ}\)C reduction and a 41% decrease. It is evident that increasing the helix angle represents an effective design strategy for reducing the tooth surface friction flash temperature under extreme operating conditions.  
Figure 13 indicates that the scuffing safety factor exhibits a fluctuating pattern of improvement with an increase in the helix angle. At a helix angle of 10\({}^{\circ}\), the scuffing safety factor reaches its minimum value of 1.118. Upon increasing the helix angle to 30\({}^{\circ}\), the factor rises to 1.602, representing a 43.3% improvement. While an increased helix angle enhances the scuffing load-carrying capacity, as previously discussed, it also increases the axial force on the gear, which may result in a reduction in the bearing's lifespan. Therefore, in order to ensure the service life of the bearings, it is possible to utilize a larger helix angle in order to enhance the gearsets scuffing load-carrying capacity.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gearest** & **Tooth Surface Load Distribution** & **Tooth Surface Load Distribution** \\  & **Peak Speed Load Case: 99 Nm, 25,000 rpm** & **Peak Torque Load Case: 450 Nm, 5000 rpm** \\ \hline B10 & 12.2 & 12.2 \\ B15 & 12.8 \\ B20 & 13.6 \\ B25 & 13.6 \\ B30 & 14.1 \\ B30 & 14.
## 2 Related Works
### Non-EH Clustering Routing Protocols
#### 2.1.1 Uniform Clustering Routing Protocols  
Uniform clustering routing protocols can be further categorized into centralized, distributed, and hybrid types based on their clustering approach.  
1. Centralized protocols, such as Fuzzy C-means [11], ions motion optimization-based clustering routing protocol (IMOCRP) [12], CogLEACH-C [13], and artificial bee colony clustering protocol [14], utilize CHs to reduce energy use and improve data efficiency while simplifying network administration, though they are limited by potential single point of failure and communication bottlenecks, which can hamper network scalability.
2. Distributed protocols like CogLEACH [15], distributed spectrum-aware clustering (DSAC) protocol [16], spectrum-aware cluster-based energy-efficient multimedia protocol [17], network stability-aware clustering (NSAC) protocol [18], and energy aware cluster based routing protocol [19] allow for more flexible CHs selection based on node energy levels, thus enhancing reliability and scalability, reducing bottlenecks and failures, and enabling more even energy consumption distribution across the network.
3. The spectrum-aware clustering algorithm based on weighted clustering metric (WCM) [20] and energy efficient spectrum aware clustering algorithm based on reinforcement learning (EESA-RLC) [21] are representative hybrid clustering routing protocols. Specifically, WCM achieves optimal clustering by solving optimization problems, selecting CHs and CMs based on temporal-spatial correlation, confidence levels, and residual energy, while minimizing energy consumption by limiting spectrum sensing to CHs. EESA-RLC protocol introduces a reinforcement learning-based clustering algorithm for spectrum sensing, establishing an energy consumption model that takes into account channel sensing, intra- and inter-cluster data transmission, and formulates the clustering process as a Markov decision problem to achieve optimal cluster configuration. This algorithm performs well in terms of energy efficiency, channel sensing accuracy, and computational complexity.
required concerning the mentioned issues and challenges. The primary factor determining a routing protocol is selecting the relaying node. This node selection mechanism depends on several factors, such as distance, hop count, and residual energy.  
Nodes in UWSNs have limited battery power, and battery replacement is nearly impossible in such a restricted environment. A node's limited-power battery must be considered while developing a routing protocol. The proper deployment of sinks, sensor devices, and other equipment is critical in increasing the network's life [3]. Furthermore, the network topology also plays an essential role in reducing energy consumption in UWSNs. It means less energy will be used if the network topology is well designed and managed; otherwise, more energy will be wasted, and the nodes will die early. The structure of UWSNs is shown in Figure 1.  
The designed protocols have mainly focused on improving the lifetime of the network. As previously mentioned, replacement of the sensor node battery and recharge are extremely challenging in sensor devices, so routing protocols must address energy consumption balance. There are two ways to transfer the data from the source to the destination: noncooperative or using a single node for transferring the data. The second one is cooperative communication or using the relay nodes instead of a single node. Cooperative communication is the best way to provide reliable communication between the nodes and minimize data failures in these networks. Cooperative communication transfers data via numerous routes to increase the possibility of receiving it correctly at its final destination [4; 5].  
Cooperative methods are also divided into incremental relaying nodes and fixed relaying nodes. The fixed relying method refers to data improvement by the relay for data reliability. During incremental collaboration, delivery occurs when the receiver requests it. The relay either boosts or decodes data before sending it to the forwarder/receiver. The cooperative methods improve data receiving and reduce packet loss rate. However, significant energy and consumption of time make them difficult or impossible. It is more difficult in the case of acoustic waves due to limited energy resources and communication speed.  
In the noncooperative method, data delivery through a single link uses less energy and time than cooperative approaches. However, these techniques are unreliable and have a higher probability of dropping data packets. Noise sources damage data in noncooperative methods and have a more significant impact on data loss. Retransmission is not appropriate for data received over the same noisy link. Additionally, many antennas are expensive and impractical in the underwater environment [6]. Cooperation is the optimal strategy for ensuring reliable and effective communication in UAWSNs.  
Figure 1: UWSN architecture.
### Multivariate Data Analysis of UPLC-MS Dataset  
To aid in identifying further markers for each squill variety in an untargeted manner, unsupervised principle component analysis (PCA) and orthogonal projection to latent structures analysis (OPLS-DA) were attempted in both negative (Figure 4) and positive ionization modes (Figure 5). Complete segregation between the two squill varieties was observed, highlighting that RS was more rich in phenolics, whereas WS was abundant in bufadienolides.  
A score plot model derived from the negative ionization mode of UHPLC/MS prescribed by PC1 and PC2 accounted for 72 and 17%, respectively, of the total variance (Figure 4A), with clear segregation of RS from WS. As for revealing the metabolites mediating RS and WS segregation, a PCA-loading plot (Figure 4B) revealed an abundance of flavanols, identified as dihydrokaempfer-O-hexoside and taxifolin-O-hexoside derivatives, in RS, which are likely to serve as precursors for anthocyanins solely found in RS variety. The OPLS-DA model (Figure 4C) further confirmed the PCA results from the \(S\)-plot (Figure 4D) revealing that dihydrokaempfer-O-hexoside, its aglycon, and vanillin rhamnoglucide, were abundant in RS compared to WS.  
The results from the UHPLC/MS-derived model in the positive ionisation mode were comparable to those in the negative mode (Figure 5A). The two squill varieties, RS and WS, were clearly separated in the PCA model, which was prescribed by PC1 6% and PC2 23%. Furthermore, according to Figure 5B, in accordance with the negative ionisation mode, RS was more abundant in dihydrokaempfer, which is a biosynthetic precursor for anthocyanins in RS [53]. On the other hand, WS had higher concentrations of bufadienolides, such as hydroxy-scilliglaucosidin-O-rhammoside, desacetylscilliglaucosidin-O-rhammoside, and bufotalidin-O-hexoside as well as oxylipids, such as linoleic acid and linoleicyl alcohol. Supervised OPLS-DA model. Figure 5C,D confirmed linoleic acid enrichment in WS and identifying dihydro-kaempfer \"aromandrin\" and the bufadienolide \"scillipheoside-O-glucoside\" as markers for RS.
## 5 Technical Challenges and Ethical Issues
### Key Challenges and Requirements
#### 5.1.7 Costs and Scalability  
High deployment costs represent another significant barrier to DT adoption. Implementing DT solutions requires substantial investment in technology platforms, infrastructure, maintenance, and security. These high fixed costs may deter smaller healthcare organizations from adopting the technology, limiting scalability [71].