esa-sceva/satcom-chunk-collection huggingface dataset:
# Causes of Multi-Mechanism Abnormal Formation Pressure in Offshore Oil and Gas Wells
## 2 Mechanisms of Abnormal Formation Pressure
### Data Collection for the Target Work Area  
(1) Basic Data  
Comprehensive and accurate input data can reflect the overall characteristics of a block and assist researchers in analyzing specific wells. The input data collected in this study primarily include the stratigraphy of the Rio del Rey Basin in Cameroon, a comprehensive geological logging database for key wells, drilling fluid usage, drill bit usage, and records of drilling complexities.  
(2) Logging Data  
The conventional method for calculating formation pore pressure using logging data involves calculating effective stress, using sonic time delay data, and the overlying formation pressure, using density logging data. The pore pressure is then determined by combining these values through effective stress theory. Two main factors influence the reliability of formation pressure calculations: 1 the quality of logging data and the pre-processing methods used, and 2 the model used to derive the formation pore pressure. Therefore, it is important to use high-quality sonic time delay logging data (generally with a clear mudstone trend and minimal borehole enlargement) and available density logging data, with sufficiently long logging intervals. Additionally, it is crucial to eliminate interference factors and false data points as much as possible. By preliminarily processing the original sonic time delay and density data, it is possible to obtain relatively pure mudstone sonic time delay and mud density data, which are closer to the true formation conditions. Based on this, a pore pressure calculation model that matches the actual regional conditions can be selected to achieve ideal detection results. In the studied block, collected logging data include well depth, sonic time delay, natural gamma, P wave velocity, porosity, shale content, density, and overlying formation pressure [23, 24, 25].  
(3) Seismic Data  
Two-dimensional seismic data inversion requires seismic line data and individual well logging data. Preferably, seismic lines that pass through wells or are adjacent to wells (if no lines directly pass through wells) should be used. Post-stack migration pure wave seismic data and corresponding wells should be selected, with priority given to two cross-lines that intersect at wells and their corresponding wells, lines passing through multiple wells, or lines near many wells. The selected lines should reflect the research area's spatial arrangement. Corresponding interpreted horizon data, geological layering, and time-depth conversion data (VSP, geological layering data such as boundary depths, formation codes, or names) should also be used [26, 27, 28].
## 2 Therapeutic Applications of Peptides
Most research performed revolving around RNA manipulation within the MF system is based upon RNA sequencing, which is an extremely effective tool [45; 46]. Discovering the heterogeneity of cellular information is essential to many biomedical applications, for instance, vaccine development, suggesting that MFs could provide a \"door-to-door\" approach for vaccine production, as MFs can provide both the target identification platform via sequence isolation, as well as a synthesis system to formulate the final vaccine. The system's high throughput capacity allows multiple tests to be conducted within a limited timeframe, which would have been perfect in response to developing a COVID-19 vaccine. One of the key elements inhibiting using peptides as nanocarriers is the lack of reliable formulation reproducibility and general disadvantageous nanocarrier properties, such as large sizes and high PDI values. By utilizing the controlled, small volume environment provided by the MF platform, researchers have begun to observe a change in this trend. The self-assembly of peptide chains from amino acids highlighted by [PERSON] et al. and proven by [PERSON] et al. indicate a huge potential for implementing peptides into the nanocarrier field. Frequently, the function of peptides for nanoformulations is to act as membrane epitopes attached to a functional polymer, such as chitosan or polylactic co-glycolic acid (PLGA) [47; 48]. However, due to the emergence of MFs, it is starting to become feasible \begin{table}
\begin{tabular}{c c c} \hline \hline
**Microfluidics** & **Unilamellar Vesicle Fusion** & **Thin-Film Hydration** \\ \hline \hline  & **Advantages** & \\ \hline Allows synthesis at room temperature & Simple method & Simple method \\ Good control over PDI and particle size & Process can be controlled via electrostatic & Acceptable encapsulation efficiencies for \\  & manipulation [40] & smaller peptides \\ Easy method to scale-up & & \\ High encapsulation efficiencies [41] & & \\ Continuous process & & \\ \hline  & **Disadvantages** & \\ \hline Material interactions with MF chip & Less control over PDI and particle size & Less control over PDI and particle size \\  & & Produces heterogeneous particle \\ Requires initial high-cost system & Affected greatly by temperature & population that requires \\  & & extrusion/sonication [42] \\  & Batch process & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Advantages and disadvantages of three methods used for peptide nanomedicine synthesis. to produce NP membranes consisting fully of peptides. These peptide nanocarriers can possess desirable properties, including increased cell permeation [49], region-specific delivery [50] and a high degree of potential for modification based upon their unique chemical makeup. As a result, of the controlled synthesis, using extrusion and sonication is rarely needed for formulations produced by MFs, which ensures that the time taken for production is minimal.
## 2 Materials and Methods
### Test Methods and Sample Preparation  
The chemical composition of the major elements in cement, FA and MIBA was determined by the X-ray fluorescence technique (XRF). Likewise, to determine the main crystalline phases present in the materials X-ray diffraction (XRD) was used. Additionally, for the characterization of MIBA, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and metallic aluminium quantification tests were carried out. The pozzolanic activity of FA and MIBA was quantified using the Chapelle test [25; 26], by means of which the consumption of calcium oxide (CaO) per gram of by-product (MIBA and FA) was determined.  
Fluidity and flow velocity of fresh mixes were evaluated in accordance with the slump-flow test, viscosity and filling capacity through the funnel V test and flowability through confined spaces through the L-box flow test, in accordance with EN 12350 [27].  
The compressive strength of hardened specimens was evaluated according to EN 12390-3 [28]. This test was performed at 7, 28 and 91 days on 150 mm cube specimens and 28 and 91 days on 150 mm diameter \(\times\)300 mm high cylindrical specimens. The splitting tensile strength test followed the method presented in EN 12390-6 [29]. This test was performed on 150 mm diameter \(\times\)300 mm high cylindrical specimens at 28 and 91 days. The test procedure used to determine the secant elastic modulus of elasticity is described in LNEC E 397 [30]. This test was performed on 150 mm diameter \(\times\) 300 mm high cylindrical specimens at 28 and 91 days. An ultrasonic propagation velocity test, described in EN 12504-4 [31], was carried out to determine the quality of the concrete produced. The test procedure used to determine the shrinkage of SCC concrete is described in LNEC E 398 [32]. This test was performed on 100 mm \(\times\) 100 mm \(\times\) 400 mm prismatic specimens. The test started after unmoulding the specimens with a total duration of 90 days, with daily measurements during the first 14 days and weekly between 14 and 91 days, having been stored at a temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and a relative humidity of 50 \(\pm\) 5%.  
The following tests were carried out to determine the durability of mixes: Carbonation, according to LNEC-E391 [33]; immersion water absorption test, according to the specification LNEC E394 [34]; chloride ion diffusion coefficient per non-stationary migration test,  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Constituent** & & & & & \\ (kg/m\({}^{3}\)) & & & & & \\ \hline Cement & 482 & 467 & 467 & 267 & 267 \\ FA & 151 & 49 & 98 & 98 & 195 \\ MIBA & 0 & 106 & 53 & 212 & 106 \\ S\({}_{\text{p}}\) & 4.8 & 5.6 & 5.6 & 5.2 & 5.1 \\ Water & 176 & 191 & 191 & 191 & 191 \\ Fine sand 0/2 & 354 & 342 & 342 & 342 & 342 \\ Fine sand 0/4 & 350 & 338 & 338 & 338 & 338 \\ Gravel 1 & 384 & 384 & 384 & 384 & 384 \\ Gravel 2 & 391 & 391 & 391 & 391 & 391 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of concrete.  
according to the specification LNEC E 463 [35] and electrical resistivity tests following the procedure of the GRD1 71808 [36].
# A Quantitative Evaluation of UAV Flight Parameters for SfM-Based 3D Reconstruction of Buildings
## 4 Validation
### Discussion
According to the TOPSIS analysis, a multi-criteria decision-making technique, a specific composite pattern combination (e.g., C_O40_L_O30) demonstrated the highest overall efficiency (TOPSIS Score: 0.691) when considering multiple performance metrics comprehensively (Table 10). This suggests that the complementary nature of information provided by different shooting paths can contribute to improving overall model quality. Additionally, Pareto optimization analysis of composite pattern data identified efficient alternative sets for key conflicting goal pairs, such as time-error and time-point count, providing useful information for finding optimal trade-offs under specific constraints (Figures 7 and 9). Figure 9: Results of 3D reconstruction with optimized parameters. It is also important to contextualize our findings. The quantitative guidelines presented herein were derived from a specific, controlled experimental setup: a mid-rise, concrete-facade building and a single commercial UAV under consistent overcast lighting. This controlled environment, while necessary for isolating variable effects, means that the direct applicability of our numerical results--such as specific optimal overlap ratios or'sweet spot' coordinates from RSM--may be limited in more challenging, real-world scenarios involving, for instance, high-rise glass facades or variable lighting. Therefore, our findings should be regarded as a foundational baseline that requires calibration and validation when applied to projects with significantly different characteristics.
## 4 Simulation Setup  
The simulation setup is the illustration of the converged mm-wave 60 GHz A-RoF system with offline signal processing in Matlab. The setup used is shown in Figure 1, while detailed figures of the different components are shown in Figures 3-5.  
Figure 3: Block diagram of the implementation of the Orthogonal Frequency Division Multiplexing (OFDM) transmitter. PRBS: Pseudorandom Binary Sequence; RF: Radio Frequency.  
The transmitter in the OFDM module (detailed in Figure 3) consists of a PRBS module for the generation of the bit rate. The digital binary bits are encoded in the coder block. After the coding stage, pulse-shaping is performed by a raised cosine filter. By filtering the transmitted pulses, the ISI caused by the channel can be regulated. Next, an OFDM signal is up-converted to an RF carrier frequency of 7.5 GHz, with multilevel QPSK and 16 QAM coded symbols.  
After up-conversion, the signal goes to MZM. The signal after the MZM is comprised of many sub-bands, as indicated in Figure 5, with the frequency division between two subsequent bands being equal to 15 GHz. The modulated signal is then transmitted over an arrayed waveguide grating (AWG) block, and among successive channels the frequency separation is modified. Finally, a 'Bus Selector' block is employed to choose the correct bands separated by 60 GHz, as shown in Figure 6.  
Figure 4: Block diagram of the generation of millimeter waves. CW: Constant Wave; MZM: Mach-Zehnder Modulator; AWG: Arrayed Wavelength Grating; WDM: Wavelength Division Multiplexing; MUX: Multiplexer.  
Figure 5: Optical Spectrum obtained after the Mach-Zehnder modulator (MZM). The spectrum consists of sidebands with frequency spacing equal to 15 GHz.  
The two sidebands are then merged into a single sideband by a 'WDM_MUX' module. Subsequently, OFDM signals and the generated mm-wave 60 GHz are sent through an SSMF with a dispersion value of 17 ps/nm/km. A single photodiode is used to catch the optical signal, and then the electrical signal is sent to an OFDM receiver--as shown in Figure 7--to recover the subcarriers.  
The electrical signal is down-converted to a baseband signal, and pulse shaping is performed in the next stage. After pulse shapingthe signal is then sent to an OFDM decoder. After the completion of the decoding process, the distorted symbols from the OFDM constellation analyzer are captured and then equalized in Matlab by the LMS, CMA and AMF algorithms. Table 2 summarizes the simulation parameters used in this work.  
Figure 6: Spectrum obtained after the selection of mm-wave frequency (60 GHz) sidebands by a bus selector module from different sidebands.  
Figure 7: Block representation of the implementation of the OFDM receiver consisting of pulse raised cosine filters, an OFDM decoder and a constellation viewer. RF: Radio Frequency.
## 1 Introduction
### Literature Review
#### 1.1.2 Methodology for On-Site Construction Planning Automation and Optimization  
To investigate the methods related to creating assembly sequence plans, this study examined the algorithms and approaches used for automating scheduling and optimization in construction. Traditional methods such as the CPM and PERT acknowledge the precedence relationships between activities but face challenges in integrating constraints such as limited labor and equipment [15].  
Recent research has highlighted the efficacy of algorithms for automated scheduling optimization. [PERSON] et al. (2023) [16] innovatively leveraged a game engine to create a reconfigurable assembly simulator by deploying Deep Reinforced Learning for automated assembly plans using robots on robot-based construction sites. [PERSON] et al. (2023) [22] employed a Graph-Based Automated Scheduling method, rearranging construction schedules by extracting activity characteristics to maximize efficiency in terms of cost and time. Moreover, Building Information Modeling (BIM) serves as a pivotal tool for extracting inter-component relationships and determining optimal assembly schedules, as demonstrated by [PERSON] et al. (2014) [23]. Additionally, metaheuristic algorithms such as Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and GA play significant roles in schedule optimization within the construction environment [24]. ACO mimics the food-searching behavior of ants by leaving pheromones on efficient paths, effectively modeling the time-cost relationship in construction optimization [25]. PSO, inspired by the social behavior of birds, amalgamates evolutionary optimization techniques to find optimal solutions by amalgamating information from various sources [26]. These methods perform iterative calculations within specified constraints and generate multiple alternatives that mirror the intricate nature of construction environments to derive optimal schedules. However, the obtained optimal values tend to converge prematurely, leading to local optimal points [27].
# A Low Phase Noise Frequency Synthesizer with a Fourth-Order RLC Loop Filter
## 5 Experimental Results
### Loop Capture Time Test  
The loop capture times of the loop output signal of both loop filters are evaluated with an output frequency of 1426 MHz. From Table 7, it can be seen that the capture times of the two filter circuits are equal, about 94 us. This is because the filter types are similar, and the loop bandwidths are the same at 160 kHz. The loop capture time indicates when the loop reaches the locked state from the starting unlocked state. The tested trigger level is the level falling edge trigger after completely writing the MCU program to the PLL.  
The loop capture time of the designed frequency synthesizer can reach 2.5 ms and 14.4 ms using active second-order filters in [24; 27], respectively. As shown in Table 7, and since the loop capture time of the frequency synthesizer in this design is superior to the mentioned references, this design has better loop capture performance.
## 5 Experiments and Results
### Planning for the Two-Resource-Two-Agent-Type  
We also evaluated SC-M* in more complex environments: two agent types requesting two resources. This experiment considered both Wi-Fi and space capacity (i.e., \(A=\{A_{1}:``WiFi\",A_{2}:``Space\"\}\)). Type I agents use \(f_{1}\) in Figure 2 as the collision CDF for the Wi-Fi resource, and the linear CDF \(f_{2}\) for the space resource, implying that they treat Wi-Fi and space as important and trivial, respectively. On the other hand, Type II agents use \(f_{1}\) for space and \(f_{2}\) for Wi-Fi. Each agent has a 50% chance of being Type I. Both CDFs are adjusted using the same \(\delta\) at each trial, as illustrated in Figure 2.  
Figure 6a shows the success rate of the two-resource-two-type SC-M* with different thresholds \(T=0\) (equivalent to the basic M*), 0.2, 0.35, and 0.45, and with a fixed offset parameter \(\delta=9.0\). Table 2 (left) shows the run time for the experiments. As can be seen from the results, in general, SC-M* can handle the two-resource-two-type systems and plan for more than 80 agents. Because more resources contribute more factors to increasing the collision score, a relatively large offset (\(\delta=9.0\)) is needed to achieve comparable performance to the one-resource-one-type SC-M*.  
\begin{table}
\begin{tabular}{c c c c c|c c c} \hline \hline \(m\) & \(T=0\) & \(T=0.2\) & \(T=0.4\) & \(T=0.45\) & \(m\) & \(\delta=3.0\) & \(\delta=6.0\) & \(\delta=9.0\) \\ \hline
5 & 0.556389 & 0.52489 & 0.5472 & 0.3616 & 5 & 0.506 & 0.3616 & 0.575 \\
10 & 1.25143 & 1.18687 & 0.7057 & 0.7965 & 10 & 1.0765 & 0.7965 & 1.0427 \\
20 & 403.3011 & 2.72513 & 1.4871 & 1.4488 & 20 & 2.2578 & 1.4488 & 2.1034 \\
40 & \textgreater{}1000 & 56.1898 & 4.2336 & 4.4318 & 40 & 17.201 & 4.4318 & 4.525 \\
70 & \textgreater{}1000 & 370.059 & 257.59 & 255.78 & 80 & 477.96 & 292.17 & 59.31 \\
95 & \textgreater{}1000 & \textgreater{}1000 & 951.34 & 774.40 & 120 & \textgreater{}1000 & \textgreater{}1000 & 857.0 \\ \hline \multicolumn{8}{c|}{Left: \(\delta=6.0\)} & \multicolumn{8}{c}{Right: \(T=0.35\)} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Run time of one-resource-one-type SC-M* under different parameters.  
Figure 6: Impact of the collision threshold \(T\) (given \(\delta=9.0\)) and offset parameter \(\delta\) (given \(T=0.35\)) on two-resource-two-type SC-M*.  
Figure 6b and Table 2 (right) present the impact of the offset parameter \(\delta\) on performance. Different from the first experiment, SC-M* with the above configurations is less sensitive to \(\delta\), when compared to Figure 5. The reason is that 50% of the agents are insensitive to one of the resources because of the linear CDF \(f_{2}\), thus increasing \(\delta\) does not contribute to a significant reduction in collisions. This property implies that we can control the importance levels of resources efficiently through the design of collision CDFs. This experiment demonstrates that, with the proper parameter settings, SC-M* can feasibly handle a complex environment with multiple resources and multiple agent types.
# Stochastic Natural Vibration Analyses of Free-Form Shells
## 2 Shape Generation of Free-Form Shells
### Optimization Algorithm  
In this study, the free-form shape is generated through an optimization approach that minimizes the strain energy. A gradient-based method is employed to solve the problem, in which a gradient descent, or negative gradient direction of the objective function is selected as the search direction of each iteration step, and it gradually approaches the minimum function value. In gradient-based optimization, it is indispensable to solve the derivatives of the objective and/or constraint functions with respect to the design variables, which could be computed while using analytical, semi-analytical, or numerical methods, depending on the problem itself and the available resources [15]. Here, a numerical differentiation method is selected to compute derivatives, since we have relatively small number of design variables.  
Combining the finite element (FE) forward analysis with the shell shape being parameterized by NURBS, the gradient-based optimization procedure is obtained. The code is programmed while using the FORTRAN language. The FE modeling is based on the Kirchhoff-Love plate theory and the plane triangular shell element with 3 nodes is employed. For better understanding, the algorithm is described in Algorithm 1.  
Figure 1: Example of a NURBS (Non-Uniform Rational B-Spline) surface: (**a**) Control-point grid; (**b**) Corresponding NURBS surface.
## 3 Theoretical Analysis
### Protocol Design  
Next, we propose a method for determining the gain matrix of the multi-AUV implementations in Equation (13) of the time-varying formations in Equation (17).  
**Theorem 2**.: _In Theorem 1, when Equation (12) is held, let_  
\[K_{2}=[\mathrm{Re}(\lambda_{2})]^{-1}B_{2}^{T}\overline{P}, \tag{50}\]  
_The multi-AUV in Equation (13) obtains the time-varying formation under Equation (17), where \(\overline{P}\) is the positive-definite solution to the algebraic Riccati equation:_  
\[\overline{P}\Big{(}B_{2}K_{1}+B_{1}B_{2}^{T}\Big{)}+\Big{(}B_{2}K_{1}+B_{1}B_ {2}^{T}\Big{)}^{T}\overline{P}-\overline{P}B_{2}B_{2}^{T}\overline{P}+I, \tag{51}\]  
The proof process is as follows:  
Define the Lyapunov function candidate by considering the \(N-1\) subsystems described in Equation (48):  
\[V_{i}(t)=\xi_{i}^{H}(t)\overline{P}\xi_{i}(t)(i=2,3,\ldots,N), \tag{52}\]  
Taking the derivative of \(V_{i}(t)\) with respect to \(t\), then:  
\[\dot{V}_{i}(t)=-\overline{\xi}_{i}^{H}(t)\overline{\xi}_{i}(t)+\Big{(}1-2 \mathrm{Re}(\lambda_{2})[\mathrm{Re}(\lambda_{2})]^{-1}\Big{)}\times\overline {\xi}_{i}^{H}(t)\overline{P}B_{2}B_{2}^{T}\overline{P}\overline{\xi}_{i}(t), \tag{53}\]  
Note that \(0<\mathrm{Re}(\lambda_{2})\leq\ldots\leq\mathrm{Re}(\lambda_{N})\), which makes \(\dot{V}_{i}(t)\leq-\overline{\xi}_{i}^{H}(t)\xi_{i}(t)\) (\(i=2,3,\ldots,N\)) under Equation (53).  
Now, the \(N-1\) subsystems detailed by Equation (43) are asymptotically stable according to the [PERSON]'s second method for stability. Multi-AUV in Equation (13) establishes a time-varying formation through Equation (17), with the proof shown in Theorem 1. Thus, the proof of Theorem 2 has been completed.  
So, the procedure of the multi-AUV protocol in Equation (17) is confirmed. First, setting the appropriate \(K_{1}\) allows us to design the motion modes of the formation center by allocating the eigenvalues of \(B_{2}K_{1}+B_{1}B_{2}^{T}\) at the desired locations in the complex plane. As \(\left(B_{1}B_{2}^{T},B_{2}\right)\) is controllable, \(K_{1}\) is always present. At last, we design \(K_{2}\) to satisfy Equation (30) by using the conclusion of Theorem 2.