esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Origin of Low-Resistivity Oil Reservoirs
### Irreducible Water Saturation  
Irreducible water refers to the non-flowable water present in the reservoir pores under certain production pressure conditions. It is a relative concept and varies as production conditions and process improvements evolve. Typically, irreducible water consists of two components: film-stagnant water adhering to the surfaces of rock particles (including non-clay and clay particles) and capillary-stagnant water within the pores of capillaries. Once the external conditions change, the irreducible water formed by capillary stagnation may transform into mobile water, while the irreducible water resulting from film stagnation remains permanently non-flowable [22].  
A reservoir's immobile water content is controlled by many factors, such as the rock particle size, the amount of mud in the composition, and the complexity of the pore structure, and its value is often the result of several factors [23]. Generally, reservoir rocks with a fine lithology and a rich, argillaceous composition are usually developed towards microporosity, and the immobile water saturation in the reservoir increases obviously and coexists with the seepage pores, resulting in a conductive network \"extending in all places\". This results in a significant decrease in the resistivity of the oil and gas reservoir. For a reservoir with a complex pore structure, the greater the proportion of capillary stagnant water, the higher the immobile water content will be [24].  
According to the NMR T2 spectrum measurements, the core water saturations after the centrifugation can be calculated, as depicted in Figure 12. It was evident that the post-centrifugation core saturations were predominantly in the range of 0.6 to 0.8, indicating a relatively high saturation of irreducible water in the cores.  
Figure 11: Simulation results of the resistivity and water saturation for a formation with high-salinity water using the Waxman–Smits equation.  
Figure 12: The core water saturations after the centrifugation.  
As the saturation of irreducible water increases, the reservoir resistivity decreases. As observed in Figure 13, when the saturation of irreducible water exceeds 60%, the impact on the reservoir resistivity is relatively small. However, when the saturation of irreducible water is below 60%, the influence on reservoir resistivity becomes more significant, and an exponential decrease in the reservoir resistivity with increasing irreducible water saturation may occur. The experimental results indicated that the change in the resistivity index was substantial with the transition from a low to high irreducible water saturation, highlighting it as a crucial factor contributing to the low-contrast characteristics of the oil reservoir.
# A Geometric Error Measurement System for Linear Guideway Assembly and Calibration
## 2 Measurement Principles  
In this study, a commercial laser interferometer (5529A, Agilent Tech., Santa Clara, CA, USA), which is commonly utilized as a measuring instrument by machine tool industry, is used as a dual-frequency laser source. The PSDs used in this study are dual lateral detectors (DLS-10, OSI Optoelectronics, Hawthorne, CA, USA), which are utilized for detecting the 2-D position deviation of the incident beam spots on the surface of each PSD. The calibration experiment result shows that the repeatability of the dual lateral detector is up to 1.0 \(\upmu\)m with a distance of one meter.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 5 Experiments and Results
### Implementation Details  
In this experiment, the backbone networks of all algorithms are loaded with parameters pretrained on the ImageNet dataset. Among them, the backbone networks of the algorithms are ResNet50 except for YOLOv4 whose backbone network is CSPDarkNet53 [41]. Faster R-CNN and RetinaNet additionally add FPN as the neck of the network.  
The optimizer used in the training process is SGD, whose parameters _momentum_ and _weight decay_ are set to 0.937 and 0.0001. The training epochs of the other three algorithms are 25, except for YOLOv4 where the training epochs are 200. The data augmentation strategies used in training the four algorithms are RandomFlip and Resize. MixUp and Mosaic data augmentation strategies are additionally used in the YOLOv4 training, where Mosaic is only used in the first 70% of epochs (140 epochs).
# Periodic Monitoring and Filtering Suppression of Signal Interference in Mine 5G Communication
## 2 Related Work  
The existing 5G anti-interference schemes follow the 3D model of the overground environment, which are mainly based on MIMO technology and smart antenna technology.  
By adding another spatial dimension to the traditional time dimension and frequency dimension, MIMO technology can improve system performance and increase the processing flexibility [13]. There are two basic elements: multiplexing and diversity. While multiplexing transmits different data along multiple independent paths to increase the system's capacity, diversity transmits the same data along multiple independent paths to resist channel fading and improve transmission reliability [14].  
Smart antenna technology is mainly used to solve problems such as Rayleigh fading, multi-user interference, and delay spread in the mobile communication environment. It can also improve the mobile communication quality [15]. Smart antennas work by guiding wireless electromagnetic wave signals in the effective direction, mainly to generate a directional beam in free space. Therefore, the main beam of the antenna is aimed in the direction of the user, and the side lobes and nulls are aimed toward the interference signal so as to achieve efficient signal transmission and reception. The deployment process of smart antennas in mobile base stations should be combined with system planning, rather than individual design. The realization of a smart antenna not only involves the process of sending and receiving electromagnetic waves, but also considers the filtering characteristics, gain, and interference-suppression effectiveness of the antenna system [16; 17].  
According to the regulations of coal mine safety and the requirements of safety certification in China, mine 5G communication systems shall meet the requirements of GB 3836.1-2021 National Standard--radio frequency power \(\leq\) 6 W. At present, plate-shaped directional antennas with a gain of around 8 dBi are generally used in mine 5G base stations, which seriously restrict the application of massive MIMO technology and smart antenna technology in underground explosive environments.  
If only frequency domain spurs need to be suppressed, notch filtering is also an effective method, and many implementations have appeared and been verified [18]. It can provide strong attenuation to a specific frequency, namely band-stop filtering with a very narrow bandwidth. However, there are two difficulties in the application of notch filtering. One is the selection of the number of filter taps, which requires a trade-off between the attenuation capability and computational complexity. The other is the dynamic adjustment of frequency bands in response to changes in the interference conditions. Since notch filtering can only deal with spurs in the frequency domain, and not broadband crosstalk, it is not enough to properly solve the interference problem faced by mine 5G systems.  
To sum up, none of the aforementioned methods are sufficient to meet the interference suppression requirements of mine 5G systems.
## 8 Conclusions  
This paper addresses the complex scenario of large-scale remote sensing constellation and communication satellite network integration, proposing a multi-modal deep reinforcement learning-driven proactive remote sensing data push algorithm, multi-modal-MAPPO, which effectively resolves the challenge of collaborative decision-making for multi-modal heterogeneous data in large-scale dynamic network environments. The algorithm innovatively integrates satellite cache states, user cache states, and multi-modal data features into a Markov decision process (MDP), while introducing the MAPPO framework to satellite-terrestrial network push tasks. At the architectural level, it designs a dual-branch actor network to achieve the dynamic fusion of image-attribute modalities: a lightweight  
Figure 8: Different learning rate settings.  
Figure 7: Hit ratio of the three algorithms.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MAAC** & **MAPPO** & **Multi-Modal-MAPPO** \\ \hline Time (s) & 0.0633 & 0.0717 & 0.08654 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Average time overhead of decision making.  
MobileNet-v3-small extracts semantic features from remote sensing images, combined with MLP-encoded static attributes (e.g., payload type, geographical location), and employs LSTM to capture temporal patterns in user cache evolution. A specially designed dynamically weighted attention mechanism significantly enhances semantic correlation modeling in complex scenarios through adaptive modality confidence evaluation. To tackle high-dimensional action space challenges, a multi-dimensional discretized policy decomposition mechanism is proposed to reduce the computational complexity. Simulations demonstrate that, in large-scale satellite-terrestrial networks, multi-modal-MAPPO reduces the average content access latency by 53.55% and 29.55% while improving the push hit rates by 0.1718 and 0.4248 compared to MAPPO and MAAC single-modal algorithms, respectively. This provides a scalable, highly adaptive theoretical framework and engineering paradigm for real-time intelligent services in large-scale satellite-terrestrial integrated networks.
## 2 Methods  
### Model Geometry and Material Properties  
The target PSW is a typical small jetty in Indonesia, consisting of the deck and spun pile foundation. The pile configuration of the PSW varies, using both batter pile and vertical pile foundation systems. The target PSW and ground configurations are depicted in Figure 1. The crown height of the wharf is +3.00 m at low water spring (LWS). For the PSW with vertical piles, the deck is supported by four rows of vertical spun piles (Pile A) with a diameter (\(\O\)) of 700 mm. For the PSW with batter piles, the deck is supported by three rows of vertical spun piles (Pile B) and two rows of battered spun piles (Pile C) with a diameter (\(\O\)) of 600 mm. The angle of the batter pile is 5 degrees. Figure 2 shows the cross-sections of Piles A, B, and C. The top 2 m of each pile is filled with concrete. The initial material properties of the spun pile and deck members of the PSW used in the FB-FEA and FA are shown in Table 1. The initial material properties data utilized in this study correspond with the material specifications typically employed in the construction of port facilities in Indonesia. The initial material property is defined as the state of the material upon the recent completion of PSW construction.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Properties** & & \\ \hline \(f_{\text{co}}\) & Cover concrete compressive strength (MPa) & 52 \\ \(\varepsilon_{\text{co}}\) & Strain at cover concrete peak strength & 0.00275 \\ \(f_{\text{cc}}\) & Core concrete compressive strength (MPa) & 53.95 \\ \(\varepsilon_{\text{cc}}\) & Strain at core concrete peak strength & 0.00323 \\ \(f_{\text{c}}\) & Deck and infilled concrete compressive strength (MPa) & 30 \\ \(\varepsilon_{\text{c}}\) & Strain at deck and infilled concrete peak strength & 0.00275 \\ \(f_{\text{y-p}c}\) & PC bar yield strength (MPa) & 1275 \\ \(f_{\text{u-p}c}\) & PC bar ultimate strength (MPa) & 1420 \\ \(E_{\text{s}}\) & PC bar elastic modulus (MPa) & 190,000 \\ \(\sigma_{ini}\) & PC bar initial stress (MPa) & 781 \\ \(f_{\text{y-}in}\) & Infilled concrete rebar yield strength (MPa) & 400 \\ \(f_{\text{u-}in}\) & Infilled concrete rebar ultimate strength (MPa) & 620 \\ \(E_{in}\) & Infilled concrete rebar elastic modulus (MPa) & 200,000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Initial material properties of PSWs.  
Table 2 displays the soil properties obtained from a soil investigation. The ground consists of two clay layers and three sand layers, with CL01 being a soft clay layer at the seabed and SS05 being a dense sand layer at the base of the spun pile. The shear wave velocity (Vs) of each soil layer was computed utilizing the correlation equation proposed by [PERSON] and [PERSON] [37], and the N-value was determined from the standard penetration test (N-SPT). The average shear wave velocity in the top 30 m (Vs30) at the site was 205.48 m/s, placing it in site class SD (medium soil), according to the Indonesian seismic code [38].  
Figure 1: PSW configuration. (**a**) Plan view; (**b**) side view of PSW with batter pile; (**c**) side view of PSW with vertical pile.
## 3 The Procedure of TC Center Estimation  
If a TC exhibits a circular wind pattern, the perpendicular lines of the sea surface wind directions (SSWDs) all pass through the TC center. However, the actual wind patterns of TCs are characterized by spiral formations rather than perfect circles, resulting in SSWDs to deviate inward from the expected circumferential orientation. These deviations are commonly referred to as inflow angles [29]. The inflow angles for a TC in an SAR image are unknown. Hence, the algorithm applied in this study employs a series of compensation angles to offset the deviation of the sea surface wind directions (i.e., inflow angles). It then identifies the TC center position that yields the highest concentration of the perpendicular lines of compensated SSWDs.  
The flowchart of the TC-center estimation algorithm is shown in Figure 1, and the specific procedures are clarified in Sections 3.1-3.3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & \multirow{2}{*}{**Classification a**} & \multirow{2}{*}{**UTC Time**} & \multicolumn{4}{c}{**SAR**} \\ \cline{3-6}  & & & **Resolution (m)** & **Mode** & **Orbit Direction** & **Location** \\ \hline Noru & Tropical Storm & 23 September 2022, 13:23 & 10 & NS b & Ascending & Philippine Sea \\ Doksuri & Tropical Storm & 23 July 2023, 01:36 & 10 & TP c & Descending & Philippine Sea \\ Koinu & Severe Typhoon & 04 October 2023, 02:41 & 10 & NS & Descending & Philippine Sea \\ \hline \hline \end{tabular}  
* The classification is derived from CMA. b NS represents Narrow ScanSAR mode. c TP represents TOPSAR mode.  
\end{table}
Table 1: The information of TCs and corresponding SAR images.
# Reliable Fault Tolerant-Based Multipath Routing Model for Industrial Wireless Control Systems
## 3 Methodology and System Model  
This section illustrates the network deployment mechanisms and network planning procedures for ensuring transmission reliability in WirelessHART networks. Furthermore, the simulation setup is also described in this section.
## 2 Quantum-Field Theory for Photonic Transport
### Coupling to the Four Level Laser Rate Equations for Quantum Cascades  
The lasing dynamics in our theoretical framework are included by a four-level laser rate equation system, which is well known from quantum cascade lasers [35; 36; 45]\[\frac{\partial N_{3}}{\partial t} = \frac{N_{0}}{\tau_{P}}-\frac{N_{3}}{\tau_{32}} \tag{16}\] \[\frac{\partial N_{2}}{\partial t} = \frac{N_{3}}{\tau_{32}}-\left(\frac{1}{\tau_{21}}+\frac{1}{\tau_{ nr}}\right)N_{2}-\frac{(N_{2}-N_{1})}{\tau_{21}}n_{ph}\] (17) \[\frac{\partial N_{1}}{\partial t} = \left(\frac{1}{\tau_{21}}+\frac{1}{\tau_{nr}}\right)N_{2}+\frac{( N_{2}-N_{1})}{\tau_{21}}n_{ph}-\frac{N_{1}}{\tau_{10}}\] (18) \[\frac{\partial N_{0}}{\partial t} = \frac{N_{1}}{\tau_{10}}-\frac{N_{0}}{\tau_{P}}\] (19) \[N_{tot} = N_{0}+N_{1}+N_{2}+N_{3}, \tag{20}\]  
All transition times \(\tau_{i}\) here are given as the inverse of the transition rates \(\gamma_{i}\), see for details Figure 1b, \(\gamma\,=\,1/\tau_{i}\). The numbers represent the laser levels. \(\gamma_{21}\,=\,1/\tau_{21}\) represents the transition from upper to lower laser level, \(\gamma_{P}\) is the pump rate, \(\gamma_{nr}\) represents nonradiative decay. While we included nonradiative decay processes in previous work [36] for polydisperse ZnO powders, they are neglected in this work here. The numbers \(N_{i}\) represent the level occupation and their resummation \(N_{tot}\) ensures energy as well as particle conservation. The number \(n_{ph}\) represents the photon numbers due to spontaneous emission and due to lasing. The coupling to the transport theory is given by the stimulated decay procedures, i.e., by Equation (18), which is connected to Equation (15). In due course of this article the transition rate \(\gamma_{21}\) serves as the measure of the pump strength \(P\).
# Current Biomedical Applications of 3D Printing and Additive Manufacturing
## 3 Biocompatible 3D Printing Materials
### Metal Implants  
Metallic implants for orthopedic, dental and craniofacial applications have traditionally been manufactured from stainless steel, cobalt chromium molybdenum and titanium alloys by methods such as casting, forging and machining. Developments in AM technology have now enabled the production of implants designed from reconstructed 3D imaging data to produce patient-specific implants. A key area of recent research has been in adding functionality to these implants. A recent study showed how an antibiotic-eluting cement could be loaded into a central cavity within metallic implants that could not be manufactured by conventional methods [78]. Due to the high resolution achievable by selective laser melting (SLM) of metallic powders, intricate lattices can be produced [9; 34]. This has been investigated to overcome issues surrounding stress shielding in hip implants. Finite element analysis (FEA) was initially used to determine the theoretical mechanical performance, as well as theoretical reductions in bone loss, of porous implants. They were then produced using SLM and FEA was confirmed by mechanical testing [9].
## 1 Introduction  
The transient analysis of dynamic systems plays a crucial role in understanding system behavior and is equally important as steady-state analysis. While steady-state analysis focuses on the equilibrium conditions of a system, transient analysis examines the rapid variations that occur within the system over time [1, 2, 3, 4, 5]. These rapid changes, however, cannot happen instantaneously due to the energy exchange processes involved, typically between the magnetic fields in inductors and the electric fields in capacitors. If energy were to change instantaneously, it would imply infinite power, which contradicts the laws of physical reality [1, 2].  
To analyze transient phenomena, several methods have been developed, including classical techniques, the Cauchy-Heaviside (\(\mathcal{C}\)-\(\mathcal{H}\)) operational method, the Fourier transform, and the Laplace transform. The (\(\mathcal{C}\)-\(\mathcal{H}\)) operational method, sometimes referred to as the symbolic method, simplifies the analysis by replacing derivatives with symbolic operators (such as s or p) [2].  
Transient processes are particularly significant in power electronic systems (PES), as they occur over short time intervals and are characterized by substantial energy exchange [2]. The Fourier series has wide-ranging applications across various fields in the natural sciences and mathematics [3]. In [6], two novel methods for analyzing circuits with semiconductor elements are introduced: the \(\Phi\)-function method and the complex conjugate amplitude method. The work [6] does not deal with or investigate nonlinear functions, nor does it address the Fourier transform. In our case, the function under study does not necessarily have to be analytic, or even strictly periodic, i.e., it does not have to meet the Dirichlet conditions, except in the cases of Fourier analysis, as mentioned in the conclusion of our paper.  
This paper introduces two novel and effective approaches for transient analysis within the field of electrical engineering (EE):  
1. A method that employs the Laplace-Carson (L-C) transform in the complex domain, significantly simplifying the computation of the original function.
2. The use of Fourier transform techniques to analyze transient states, particularly in electrical and electronic applications.  
This study covers three major aspects of Fourier analysis: Fourier series/expansions, application within the orthonormal complex domain, and Fourier integral transforms. The paper is structured as follows:  
* Introduction.
* Fourier Analysis in Time and Complex Domains, including the method of complex conjugate amplitudes and transient analysis under non-harmonic excitation.
* Transient Analysis of Power Electronic Systems (PESs) using Fourier integral transforms, illustrated with two application examples from the EE field.
* Validation of System States using Matlab/Simulink.
* Discussion and Conclusion.
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 5 Adaptive Technology of Fiber Optic Current Sensor
### Real-Time Signal Processing System
\(P_{2m}>Set_{T}\) indicates that the temperature has changed, and the detection unit outputs \(W_{T}=1\). \(W_{T}=0\) when \(P_{2m}\leq Set_{T}\). (4) Signal selection unit  
The sensor operates stably when \(W_{B}=0\) and \(W_{T}=0\), and the average fiber optic current sensor signal value and the magneto-optical signal are selected as the output value. Here one can define \(Y=(Y_{1}+Y_{2})/2\) and the proportionality coefficient is set to \(M=(M_{1}+M_{2})/2\). The filtered output AC signal is accurately converted to the input current signal. The magnetic field is stable when \(W_{B}=0\) and \(W_{T}=1\), and the output from the fiber optic current sensor fluctuates due to temperature drift. Thus, the magneto-optical signal corrects the fiber signal, setting the proportionality coefficient to \(M=M_{1}\), which is subsequently input to the filtering unit. There is a transient magnetic field when \(W_{B}=1\) and \(W_{T}=0\), and the fiber optic current sensor output value will not be corrected by the magneto-optical glass output signal. The fiber AC output is stable, the fiber AC signal \(Y_{2}\) is selected as the final output signal, and the current proportionality coefficient is set to \(M=M_{2}\). (5) Filtering unit  
The filtering unit filters the output from the signal selecting unit, the noise in the current sensor is mainly white noise [19], and the output signal is filtered using linear Kalman filtering. The specific steps are as follows:  
Figure 9:2  
The output current signal is obtained by setting the proportionality coefficient \(M\) from the signal selection unit:  
\[\left\{ \begin{array}{l}i=Y_{out}/M\\ \varphi=\arctan(x_{2}/x_{1})\end{array} \right. \tag{28}\]  
where \(i\) is the real-time AC current value and \(\varphi\) is the current phase angle.