esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mechanical Effect Factors
### Quantitative Analysis of Frictional Force \(f_{d}\)  
Based on the integral region shown in Figure 7, frictional force \(f_{d}\) between the shield working face and the soil is expressed as Equation (12), which can be decomposed into horizontal load \(f_{dH}\) and vertical load \(f_{dV}\), as Equation (13). The solution of total stratum displacement can be obtained by compositing the displacements of Equation (10).  
\[f_{d}=[\gamma(H-\rho\sin\theta)\frac{\mu}{1-\mu}+q]\times\varphi_{f} \tag{13}\]  
\[\left\{\begin{array}{l}f_{dH}=f_{d}\sin\theta\\ f_{dV}=f_{d}\cos\theta\end{array}\right. \tag{14}\]  
\[\left\{\begin{array}{l}S_{x}^{f_{d}}=S_{x,H}^{x,f_{dH}}+S_{x,V}^{f_{dV}}\\ S_{z}^{f_{d}}=S_{z,H}^{x,f_{dH}}+S_{z,V}^{f_{dV}}\end{array}\right. \tag{15}\]  
where \(\varphi_{f}\) is interface friction angle; \(q\) is additional frontal thrust on the working face; \(\rho\) is the distance to the center of cutter head; \(H\) is burial depth of tunnel center; \(\mu\) is Poisson's ratio of soil.  
The contour map of ground settlement and horizontal displacement caused by frictional force \(f_{d}\) is shown in Figure 10a,b. The distributions of displacement show good regularity. Although, as the displacement values and action range are too small (less than 0.01 mm) even in deep soil quantitative analysis need not be carried out.  
Figure 10: Ground movements caused by \(f_{d}\): (**a**) Settlement; (**b**) Horizontal displacement.  
Figure 9: Horizontal displacement of stratum caused by \(q\).
# Construction of a Chemical Kinetic Mechanism of Five-Component Surrogate Fuel for RP-3 Kerosene
## 3 Modification of Kinetic Parameters for Mechanism of Surrogate Fuel Mixture  
### Verification of Multi-Component Mixture Mechanism  
Ignition delay time is a significant phenomenon in the autoignition and subsequent combustion of fuel. The laminar flame speed can reflect the characteristics of chemical reactions and transport in premixed flames and is one of the most crucial parameters for the fundamental combustion behavior of fuel. Hence, for the obtained multi-component fuel mechanisms, the zero-dimensional homogeneous module, and the laminar premixed flame module in Chemkin Pro 17.0 were, respectively, employed to predict the two parameters, namely the ignition delay time and the laminar flame speed.  
Figure 3 presents the outcomes of simulating the ignition delay time of real fuel using the surrogate fuel mixture mechanism. The experimental data are from the research of [PERSON] [22], with the experimental conditions being P = 1.0 MPa, a temperature range of 600 to 1100 K, and \(\varphi\) = 0.5 and 1.0. As can be observed from the figure, although the simulation values are in accordance with the experimental data in the overall trend, at temperatures ranging from 750 to 900 K, the simulated ignition delay time is larger than the experimental data and fails to reflect the negative temperature coefficient (NTC) behavior of the fuel; in the higher temperature range above 1100 K, the simulation values are smaller compared to the experimental data.  
Figure 4 presents the comparison of the predicted and experimental values of the laminar flame speed of real RP-3 fuel using the initial mechanism. The experimental conditions were 400 K to 480 K, with \(\varphi\) from 0.7 to 1.5, and P = 0.1 MPa and 0.3 MPa. The experimental data were from [PERSON] [14]. The results demonstrate that the mechanism overestimated the experimental values to different extents under all working conditions, particularly when \(\varphi\) was greater than 0.8, with considerable errors.  
Figure 5 depicts the verification outcomes of the ignition delay times for certain single-component fuels. Taking the ignition delay times of n-dodecane and isododecane as instances, the experimental data were derived from the measurement results of [PERSON] [19] and [PERSON] [40]. The results indicate that within the temperature range above 750 K, the mechanism underestimates the ignition delay time of n-dodecane; within the temperature range above 900 K, the ignition results predicted with the mechanism are smaller.  
Figure 6 presents the predicted outcomes of the laminar premixed flame propagation speed for certain single-component substances. The experimental data were sourced from the research of [PERSON] [23] and [PERSON] [41]. As depicted, although the mechanism can  
Figure 3: Verification of the ignition delay time of RP-3 [22].
## 2 Related Works
[8] introduced a practical Byzantine fault tolerance (PBFT) model at the same time. Certain violations discovered by ByzzFuzz were not detected by it. These problems involve a process error that corrupts a message's sequence number with the sequence number of a future round, which a twin duplicate cannot provide. Although PBFT permits copies to fall behind by losing some process information, it does not introduce incorrect messages with protocol message field values in the future. ByzzFuzz and PBFT, we think, may be used in tandem, complementing each other by methodically evaluating Twin's collection of adversary behaviors and ByzzFuzz's sampling from a broader variety of problematic process behaviors. In December 2022, a model for optimizing the node structure of a big consortium chain was suggested. This was accomplished by splitting the vast network nodes into various institutions to form autonomous consensus groups using clustering characteristics. A respectable rating incentive system was also presented to boost the consortium chain's consensus efficiency. This system featured a formula for calculating reputation scores, which was used to pick master nodes with high reputations. A replacement cycle was performed to improve the chain's consensus efficiency even further. This cycle replaced nodes with high reputation with nodes with poor reputation. Feature grouping and credit optimization Byzantine fault tolerance (FCBFT) have lower latency and greater throughput of the transaction processing system (TPS) than PBFT, according to experimental data [9]. The researchers presented a solution in 2023. Algorithms for fault identification are often based on statistical approaches such as repeated testing or machine learning. These procedures, however, can be difficult and time-consuming. To solve these concerns, the researchers suggest a one-shot likelihood ratio test (LRT) for evaluating a sensor node's failure state in a wireless sensor network. The suggested approach entails computing the mean and variance of the received data over time and then comparing the probability ratio with a threshold value linked with a specific tolerance limit. As a result, they were able to detect intermittent problems in the nodes with high accuracy, without the need for repetitive testing or computationally intensive machine-learning algorithms [10]. In August 2023, [PERSON] et al. [11] outlined the three approaches utilized by WSNs to recover from failed nodes: LDTR, FNR, and GD algorithms. They compared these techniques to the proposed system, which incorporates a path arbitrator and the RAFT blockchain consensus methodology. According to the article, the proposed failure recovery technique, which is based on the RAFT blockchain consensus mechanism, can aid in the control of Byzantine failures in wireless sensor networks as well as the recovery from malicious activities. In simulations, the suggested algorithm was assessed based on energy utilization, defect detection precision, packet delivery ratio, number of dead nodes, and time number of neighbor nodes.
## 3 Results  
### Clinicopathological Characteristics of UMs  
Comparing patients without metastasis and with metastasis, no significant difference was observed in median age, site (choroid or choroid/ciliary body), thickness, cell type, extrascleral extension, and pTNM: patients with metastatic melanoma showed a greater median largest diameter (15.6 mm vs 11.9 mm, \(p\) = 0.007) and higher median mH2A.1 expression (12 vs 2, \(p\)\(<\) 0.001). They also had lower median disease-free survival (25 months vs 81 months, \(p\)\(<\) 0.001) (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **Sex** & **Age** & **Location** & **Thickness** & **Largest** & **Cell Type** & **Extrascalenal** & **Pathological** & **DFS** & **Follow-Up** & **Macro** \\  & **m-\(t\)** & **(Year)** & & & **Diameter** & **Cell Type** & **Extension** & **T Stage** & **(Months)** & **(Months)** & **H2A** \\ \hline All & & 67 & Ch 40 & 9.7 & 14.1 & Ec 15 & No 54 & pT1a.7 & 42 & 60 & 12 \\ (\(n=55\)) & & (29-85) & ChVCB 15 & (32-16.3) & (6.4-20.8) & Sc 12 & Yes 1 & pT1b1 & (0-138) & (8-138) & (0-12) \\  & & & & & & & & & pT2k.8 & & \\  & & & & & & & & & pT2k.1 & & \\  & & & & & & & & & pT3a.8 & & & \\  & & & & & & & & & pT3b.5 & 5 & & \\  & & & & & & & & pT4a.1 & & & & \\  & & & & & & & & & pT4b.3 & 3 & \\ \hline Metastasis-free & 64 & Ch24 & 9.5 & 11.9 & Ec 6 & pT1a.6 & 81 & 81 & 2 \\ (\(n=32\)) & & (29-84) & ChVCB 8 & (32-16.3) & (6.4-20.8) & Sc 10 & & pT1b1 & (12-138) & (8-138) & (0-12) \\  & 17-15 & & & & & & & & & & pT2k.6 & & \\  & & & & & & & & pT3a.4 & & & \\ \hline Metastasis & 72 & Ch 16 & 9.7 & 15.6 & Ec 9 & No 22 & pT1a.1 & 25 & 42 & 12 \\ (\(n=23\)) & & (50-88) & ChVCB 7 & (3.5-15.5) & (6.5-20) & Sc 2 & Yes 1 & pT2a.9 & (0-63) & 13 deaths & (0-12) \\  & & & & & & & & & pT2k.2 & & (28-81) & \\  & 11-12 & & & & & & & & pT3a.4 & & \\  & & & & & & & & pT3b.5 & & & \\ \hline \(p\) (metastasis-free vs metastasis) & _0.400_* & 0.762 \({}^{\circ}\) & _0.911_* & _0.007_* & _0.400_* & _0.418_* & _0.560_* & _<0.001_* & _0.001_* & _<0.001_* \\ \hline \hline \end{tabular}
* Kolmogorov-Smirnov test.
* [PERSON]’s exact test. Abbreviations: Mc; mixed cell; Sc; spindle cell; Ec, epitheloid cell.  
\end{table}
Table 3: Medians (range) of demographics, tumor parameters, disease-free time, follow-up, and macroH2A expression in primary UMs without and with systemic metastasis.
# Exploring the Mechanisms of Humidity Responsiveness in Plants and Their Potential Applications
## 3 Results and Discussion  
### Internal Structure of Humidity-Sensing Plants  
Cross-sections of plant leaves were observed under a light microscope to determine their internal structures and the unique features of tissues related to water absorption (Figure 2A). Structurally, the epidermis was covered by a cuticle layer on the leaf surface, which facilitates survival when the external environment becomes dry; the epidermal layer was developed under the cuticle layer [24; 25]. The internal structure of _S. involvens_ was composed of xylem, phloem, and trabeculae inside a vein [26]. The interiors of _S. rossi_ and _P. lingua_ showed xylem and phloem surrounded by a bundle sheath. Further, the empty space at the center of the leaves was found to be air space. The structure of the internal tissue of the leaf was altered very distinctly depending on the humidity conditions. In particular, xylem enlarged markedly when moisture was absorbed. Thus, xylem and trabeculae were the main reasons underlying the change in the external shape of plants, depending on the amount of moisture in the surrounding environment. Regarding the internal structure of moss, the leaf midrib comprises irregular long squares (or hexagons) in the center (Figure 2B). The inside of the moss leaf is close to the stem, and sclerenchyma tissue development was determined to play a supporting role. Moss leaves are closely attached to the main stem and are overlapped, and short protrusions densely form on the surface. However, owing to the ambiguity of the tissue position, the names of the tissues are not accurately indicated in the data.  
Figure 1: Wood samples with dissimilar materials and without additional material (**A**); and cypress woods under dry (**left**) and moist (**right**) conditions (**B**).  
The internal and surface structures of leaves, such as cell density and stoma, were observed using FE-SEM. When the environmental conditions were dry and moist, the stoma on _S. involvens_ leaves closed and opened, respectively (Figure 3A).  
Figure 2: Internal leaf structures of _Selaginella involvens_, _S. rossii_, _Pyrosia lingua_ (**A**), and _Racomitrium canescens_ (**B**).  
In _S. rossii_, leaves stuck closely to the stems in dry conditions, whereas under moist conditions, leaves were away from the stems (Figure 3B). Further, the xylem size of the stem was dependent on the water conditions. The xylem under wet conditions showed a dense structure, whereas the xylem under dry conditions was loose. Although this study mainly focused on xylem tissue, the plant cell walls are known to expand and loosen under wet and dry conditions, respectively [27]. A clear change in the leaf surface of _P. lingua_ was observed depending on humidity and dryness (Figure 3C). To determine the structural specificity of moss leaves (Figure 3D), the density and surface structures of moss leaves were analyzed; contaminants such as moisture and dust were determined to be adsorbed and easily removed, respectively, because of the surface tension of numerous microscopic protrusions.
## 4 Conclusions  
This study proposed a simplified calculation method for estimating the leakage of a slope-wall rock-fill dam with a horizontal blanket in a steady state, mainly based on Darcy's law and the assumption of porous continuous media. The proposed method is available for dams with different water levels. Moreover, the shapes of the slope wall and blanket, dam height, and dam slope ratios are considered in the proposed method.  
Subsequently, two engineering examples in this study are used to investigate the accuracy of the proposed method. The results obtained from the proposed method are in agreement with those of the numerical method, and the relative errors between them are less than 5%, which is acceptable. This result illustrates that the proposed method is capable of dam seepage estimation and can be used in initial dam designs. The analytical methods introduced by other researchers were also compared with the numerical results. Some of these methods are comparatively unreliable, as there are considerable discrepancies between their results and the numerical results, which may lead to uneconomical designs.  
Ultimately, the proposed method can estimate and predict the seepage flux of embankment dams with adequate accuracy and rapidity and could be applied to practical engineering. On the one hand, seepage estimation using the proposed method could be employed to demonstrate the performance of seepage prevention measures for dams in the initial stages of dam design and is more convenient than the numerical simulations used in the past. On the other hand, estimations can be used approximately for safety evaluations and benefit assessments to avoid uneconomical dam designs during the initial feasibility research into dam construction.  
Conceptualization, G.L. and Z.S.; formal analysis, G.L.; funding acquisition, Z.S.; validation G.L.; writing--original draft, G.L; writing--review and editing, Z.S. and C.Y. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Project of the National Natural Science Foundation of China/Valong River Joint Fund [U1765205], the Priority Academic Program Development of Jiangsu Higher Education Institutions [YS11001], and the Fundamental Research Funds for the Central Universities [20188630X14].  
The authors declare no conflict of interest.
###### Abstract  
Optical microcavities have been widely used in nonlinear optics, quantum optics, and laser technologies. Here we demonstrate the self-injection locking of a distributed feedback diode laser using home-made high-finesse Fabry-Perot microcavity. The Fabry-Perot microcavity is fabricated from an x-cut lithium niobate crystal with highly reflective coatings. Frequency pulling effect can be observed for a successful locking, and results in a single-longitudinal mode lasing with narrow linewidth. The lasing wavelength and output power are found robust to the laser-diode current and temperature variations, in comparison to the free-running case. We further characterize the laser linewidth with beat note measurement with a high-performance external cavity diode laser, with beat-note linewidth of 601.85 kHz. This results shows a new method for laser frequency stabilization in a simple setup, and may find applications in telecommunication and spectroscopy.  
self-injection locking; Fabry-Perot microcavity; Distributed Feedback +
Footnote †: journal: Applied Physics Letters
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 1 Introduction  
A new era in X-ray science has been launched by the development of X-ray free-electron lasers (XFELs) which provide ultrafast coherent X-ray pulses with a peak brightness that is approximately one billion times higher than previous X-ray sources. The FLASH facility at DESY in Hamburg (the first extreme ultraviolet XFEL) [1], the LCLS facility at SLAC National Accelerator Laboratory (the first hard X-ray XFEL) and the SACLA facility in Japan [2] represent the first generation of XFEL sources, and they have already demonstrated the tremendous scientific potential and impact across broad areas of science.  
A comprehensive overview of the scientific impact of the first five years of operation of LCLS was published in Reviews of Modern Physics in 2016 [3]. This paper will highlight some of the more recent accomplishments from LCLS in the areas of atomic, molecular, and optical science; chemistry; condensed matter physics; matter in extreme conditions; and biology. This paper also presents an outline of the upgrade plans for the facility along with the motivating science opportunities. In particular, LCLS is now in the middle of an upgrade project (LCLS-II) which will provide ultrafast X-rays in the 0.25 keV-5 keV range at repetition rates up to 1 MHz with two independent XFELs based on adjustable-gap undulators: 0.25 keV-1.25 keV soft X-ray undulator (SXU) and 1 keV-5 keV hard X-ray undulator (HXU) [4]. LCLS-II is based on a new continuous-wave radio-frequency superconducting accelerator (CW-SCRF) operating at 4 GeV, and first-light is projected for 2020. A second phase upgrade is envisioned to extend the X-ray energy reach to beyond 13 keV (<1 A) at a high repetition rate by doubling the CW-SCRF linac energy to 8 GeV. This is motivated by compelling new science of structural dynamics at the atomic scale.
## 5 Key 6G Technologies
### Improved Wireless Interface
#### 5.2.8 Intelligent Reflecting Surface (IRS)  
Using frequencies over 10 GHz to free up more capacity for faster data transfer introduces difficulties such as more signal loss, less ability to bend around obstacles, and higher interference. Massive MIMO utilizes active beamforming in the mmWave frequency band to efficiently mitigate channel loss. However, researchers are actively seeking alternative approaches in pursuing the future 6G spectrum, and IRS has emerged as an up-and-coming technology.  
IRS, also known as reconfigurable intelligent surface (RIS) [166], consists of programmable sheets that can adaptively modify their radio-reflecting attributes. Applying IRS on walls and ceilings creates a smart radio environment (SRE) [167] by converting some areas of the wireless environment into smart reconfigurable reflectors. This passive beamforming enhances channel gain cost-effectively and with lower power consumption compared to active massive MIMO. Additionally, SREs, implemented on large surfaces away from user equipment, facilitate accurate beamforming with ultra-narrow beams. Unlike active mMIMO arrays tailored to specific RATs, IRS's passive reflection mechanism works universally across RF and optical frequencies, a cost-effective advantage for 6G systems operating in an ultra-broad spectrum.  
While IRS demonstrates technical competitiveness in the 6G spectrum, challenges include the need for accurate modeling and estimation of channels and surfaces, particularly in the near-field range. The successful implementation of commercial deployment is contingent upon the resolution of business problems, such as dependence on outside evaluations, such as buildings that are not owned by mobile network operators (MNOs). Therefore, to allow for the general access and use of IRS-equipped devices in both public and private domains [166], careful design and standardization of frameworks with necessary interfaces, agreements, and signaling protocols are important.
###### Abstract
Likewise, the inhalation of \(d\)-limonene (\(\sim\)0.3 ppmv) was reported to induce a comfortable and soothing state [28]. Further, the inhalation of \(d\)-limonene (2.5 ppmv) for 90 s was reported to provide physiological and psychological relaxation [29]. However, those tests were conducted using MTs prepared at artificially high concentrations (e.g., at sub-ppmv levels) rather than at those which are actually present in ambient air. No research has been conducted to directly assess the effects of ambient-level MTs on human psychology using standard samples made of three major MTs (\(\alpha\)-pinene, \(\beta\)-pinene, and \(d\)- limonene) at \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Site** & **\(\alpha\)-pinene** & **\(\beta\)-pinene** & **\(d\)-Limonene** & **3-Carene** & **Myrence** & **Camphene** & **etc.** &
\begin{tabular}{c} **\(\Sigma\)M.** \\ **Tepene** \\ \end{tabular} & **Ref.** \\ \hline Castelporiano, Italy & 97 (33\%) & 36 (12\%) & 162 (55\%) & bdl & bdl & bdl & 0 & 295 & [8; 9] \\ Chisotou, Taiwan & 100 (28\%) & 253 (27\%) & bdl & bdl & bdl & bdl & 0 & 353 & [8] \\ Balbina, Amazonia, Brazil & 1100 (44\%) & 500 (20\%) & 350 (14\%) & bdl & 50 (2\%) & 100 (4\%) & 400 & [16\%) & 2500 & [10] \\ Iomantsi, Finland & 100 (64\%) & 16 (10\%)* & 10 (6\%) & 22 (14\%) & - & 2 (1\%) & 6 (4\%) & 156 & [7] \\ Austin Cary Forest, FL, USA & 125 (59\%) & 86 (41\%) & bdl & bdl & bdl & bdl & bdl & 211 & [11] \\ Blodgett Forest, CA, USA & 104 (14\%) & 311 (43\%) & 76 (11\%) ** & 210 (29\%) & 10 (1\%) & 6 (1\%) & 5 (1\%) & 722 & [6] \\ Mainro, Germany & 117 (28\%) & 98 (23\%) & 74 (18\%) & 49 (12\%) & 24 (6\%) & bdl & (14\%) & 421 & [14] \\ Dequequ, Benin & 300 (55\%) & 200 (36\%) & 50 (95\%) ** & bdl & bdl & bdl & 0 & 550 & [12] \\ Oshshp Juzena, Japan & 70 (41\%) & 26 (15\%) & 74 (44\%)* & bdl & bdl & bdl & 0 & 170 & [13] \\ Jonkeping, Sweden & 9730 (50\%) & 320 (2\%) & 1300 (7\%) & 7000 (86\%) & 210 (1\%) & 700 (4\%) & 340 & [28] & 19,600 & [3] \\ Odae Chaman forest, Korea & 133 (45\%) & 55 (19\%) & 61 (21\%) & bdl & bdl & 44 (15\%) & bdl & 293 & [15] \\ Seenan temple forest, Korea & 32 (31\%) & 38 (37\%)* & 13 (13\%) & bdl & - & 7 (7\%) & (11\%) & 107 & [15] \\ Juknokova forest, Korea & 77 (27\%) & 103 (60\%)* & 74 (26\%) & bdl & - & 12 (4\%) & 24 (8\%) & 291 & [15] \\ \hline \hline \end{tabular}  
* \(\beta\)-pinene+myrorene, ** \(d\)-limonene + \(\beta\)-phellandrene, bdl: below detection limit, **-”: not detected, pptv: part per trillion by volume.  
\end{table} 
Table 1: Comparison of concentration levels (pptv) and composition ratios (%) of monoterepne compounds emitted from forests in different studies. low ppbv levels. Accordingly, we used a cohort of 59 subjects to investigate the human psychological response to inhaling the three major MTs at low ppbv levels by monitoring two parameters: alpha waves and stress index.