esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Discussion
### Effect of Pipeline Arrangement and Fixation  
The important lesson gained in the slurry shield tunneling project concerns the pipeline arrangement and fixation of the slurry circulation system. The slurry feed pipeline and discharge line, approximately 842 m in length, were put in an existing tunnel next to the new tunnel. The secondary lining of cast-in-situ concrete of the finished tunnel was completed, and no damage to the lining was allowed. Therefore, the in-tunnel pipelines were simply laid on brackets on the tunnel floor, as shown in Figure 16. Not enough space was left under the pipeline to perform repair work, and a window had to be cut at the pipeline top, as shown in Figure 9a. In the window, welding steel pads onto the broken pipeline was carried out from within the pipeline. Performing repairs in this fashion undoubtedly created excess work and delayed project construction.  
To address the issues of in-tunnel slurry discharge pipeline leaks, a new mounting method was adopted to arrange the pipelines on the redesigned brackets, as shown in Figures 17 and 18. This new arrangement allowed more efficient welding work to occur on the broken pipelines. Additionally, the pipelines could be more tightly fixed with the new brackets, thus reducing the transported rock particle-induced vibrations and wear on the pipelines.  
There is a strong correlation between the pipeline leaks and the finished segment rings per month. As an indicator of the pipeline leak frequency, the damage ratio in Table 2 is defined as the ratio of the number of pipeline leaks to the finished segment rings per month. The calculated pipeline damage ratios have experienced sharp increases since December 2020, and very high ratios of 59.02% and 66.67% were reached in April 2021 and May 2021, respectively. The high increases were closely related to the ground encountered. In December 2020, the tunneling ground changed from a pebble layer to rock ground. Fragmented rock particles due to rock cutting by the disc cutters of the slurry shield machine when tunneling in the rock ground exhibited sharp angularities, which undoubtedly caused severe damage and wear to the slurry discharge pipeline and consequently more pipeline leaks. In April 2021 and May 2021, fresh sandstone and quartz sandstone were encountered, and hard rock particles intensified the damage and wear of the slurry discharge pipeline and increased pipeline leaks. Meanwhile, the long service life of some pipeline segments also contributed to the high increase in pipeline leaks.
# Smart Healthcare Network Management: A Comprehensive Review
## 6 Architecture Implementation Plan
### Smart Healthcare Context Management  
SDN may also provide substantial help in configuring smart healthcare contexts and switching between contexts by exploiting the capability of running software applications on top of the controller [106]. All elements of the context can be configured and managed directly or indirectly by the controller as long as they are under the administrative authority of the managing entity. The configuration and management of the context elements can be realized by providing a management plane over the control plane. Context management may also include mobility and handover between different network architectures as well as the same network architecture. Mobility is best handled by a mobile IP in the case of all-IP networks.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 3 Atmospheric Influence on Grapevine Development
### The Role of Water  
Precipitation is another key atmospheric variable in viticulture, as it has a large footprint on soil water balance, determining soil water availability for the plant and its corresponding water status. Water stress leads to a wide range of effects that are also dependent on the grapevine development stage [52]. For instance, moderate-to-high soil moisture during budburst and shoot/inflorescence development is critical for vine growth [53]. Water stress at this stage may lead to stunted shoot growth, as well as poor flower-clustering development and berry set [54]. However, excessive humidity during early development stages may also overestimate growth, which may lead to excessively vigorous and dense canopies, thereby potentiating the risks of diseases. From flowering to berry ripening, severe water stress may lead to reduced leaf area, thus limiting photosynthesis, as well as promoting flower abortion and cluster abscission [55]. Nevertheless, dry weather during ripening is generally favourable to high-quality wine production [56; 57; 58]. Slower leaf development also promotes higher water use efficiency [59]. Conversely, excessive precipitation is commonly unfavourable to maturation [2], for instance due to sugar dilution [60] or to bunch rot [61]. Recent studies indicated that water deficit affects grape and wine composition [62; 63]. Regulated deficit irrigation has been used to improve berry and wine quality [64], increasing the concentration of terpen by modulating structural and regulatory genes involved in volatile organic compounds biosynthesis [62]. Water deficit early in the season, before veraison, also stimulated increased anthocyanins and phenolic concentrations [65]. Furthermore, the timing and intensity of water deficits influence the extent of changes in berry metabolism and in wine colour, aroma, and flavour by modifying berry size and/or the synthesis of berry compounds, with a positive contribution to the fruit and wine organoleptic properties. Indeed, a water-deficit treatment typically increases the skin to pulp ratio in the berries, when compared with well-watered grapevines [66], increasing the amount of skin tannins and anthocyanins. Colour differences may result from increased anthocyanin synthesis caused by water deficit during the fruit development [67].
# Study on the Evolution Law of Overlying Rock Collapse Induced by Mining Based on BOTDR
## 5 Vertical Optical Fiber Strain Monitoring Analysis
### Optical Fiber Strain Characterization at Different Stages  
As the working face advances, the strain recorded by vertical optical fibers varies accordingly at different excavation distances. To facilitate observation, strain measurements were compared at three fiber locations.  
The evolution of optical fiber strain clearly reflects the impact of working face advancement on both the surrounding and overlying strata. This evolution can be divided into three distinct stages:  
Stage 1 (Before reaching the fiber position): Before the working face reaches the fiber, the surrounding rock mass remains intact, with no visible fractures. However, due to stress redistribution caused by coal seam extraction, the optical fiber records compressive strain, which stabilizes once the pressure reaches a threshold (as shown in Figure 12a). Thisindicates that in this initial stage, the overlying rock can still withstand mining-induced stress, and the structure remains undamaged.  
Stage 2 (fiber within the working face): When the working face reaches the fiber, fractures begin to develop inside the surrounding rock, particularly horizontal tensile fractures. The subsequent overlying rock collapse further intensifies stress changes. During this stage, the optical fiber strain transitions from compressive to tensile strain, with the height of tensile strain influence increasing as the collapse extends upward (as shown in Figure 12b). This suggests that as the working face advances past the fiber location, the rock mass shifts from a compressed state to tensile failure.  
Stage 3 (fiber positioned after the working face has passed): As the working face moves away, the characteristics of the overlying rock collapse change. The goat rock mass enters the stages of fracturing, collapse, and compaction. In the vertical direction, the rock mass within 0-100 m of the goat has fully collapsed and entered a stable compaction state, whereas rock beyond 100 m is still in the early stages of fracturing and collapse. Consequently, the optical fiber strain curve exhibits alternating positive and negative variations (as shown in Figure 12c). This indicates that optical fiber monitoring not only captures the failure process of the rock mass but also reveals the stress evolution characteristics at different excavation stages.  
Throughout the working face advancement, optical fiber strain monitoring effectively captures the entire rock deformation process--from stability and compression to fracturing and tension, followed by collapse and compaction. This provides reliable data for studying overlying rock collapse mechanisms and further validates the scientific feasibility of using optical fiber monitoring for evaluating mining-induced rock deformation.
## 3 Results
### Resistivity Test Analysis
#### 3.3.1 Influence of Moisture Content and Pollutant Concentration on Resistivity  
The pollution concentration and water content of contaminated soil samples were controlled and variation relationship diagrams of resistivity of soil samples under different conditions were obtained (Figures 9 and 10).  
Figure 8: Change in shear strength of Pb heavy metal-contaminated soil under different coaxial loads.  
Figure 9: The resistivity of zinc-contaminated soil varies with water content.  
For the studied heavy metal-contaminated soil samples, the invasion of heavy metal ions enhances the pore fluid conductivity, which further affects the overall soil conductivity. From the results in the above figures, it can be seen that the resistivity of soil samples contaminated with the same concentration of heavy metals shows a decreasing trend with the increase in water content, and the magnitude of the change is higher in the low-contamination concentration soil samples than in the high-contamination concentration soil samples. Zinc-contaminated soil samples and lead-contaminated soil samples show a similar trend.  
In the case of the same water content, the resistivity of the soil sample decreases with the increase in pollutant concentration and the change range decreases gradually. The reason for this phenomenon may be that heavy metal pollution penetrates pore water in soil samples. In the saturated state, the conductivity channel mainly conducts through the pore water path, which maintains the relative stability of the resistivity of the contaminated soil sample to a certain extent [22].
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 3 Switchable Receiver Front-End Architecture: HARRF LNA and SPDT Integration
### Mathematical Model of the Receiver Frontend  
The total noise figure (\(F_{total}\)) of the cascaded system, incorporating the SPDT switch and LNAs, is derived using Friis' equation:  
\[F_{total}=F_{1}+\frac{F_{2}-1}{G_{1}}+\frac{F_{3}-1}{G_{1}G_{2}}+\cdots+\frac{ F_{n}-1}{G_{1}G_{2}\ldots G_{n-1}}, \tag{4}\]  
where:  
* \(F_{i}\) represents the noise figure of each stage,
* \(G_{i}\) denotes the gain of the preceding stages.  
For a two-stage receiver frontend comprising an SPDT switch and an LNA, this equation simplifies to:  
\[F_{total}=F_{SPDT}+\frac{F_{LNA}-1}{G_{SPDT}}, \tag{5}\]  
where \(F_{SPDT}\) and \(F_{LNA}\) are the noise figures of the SPDT switch and the LNA, respectively, while \(G_{SPDT}\) represents the gain of the SPDT switch.  
The input impedance (\(Z_{in}\)) of the cascaded system is expressed as:  
\[Z_{in}=Z_{0}\bigg{(}\frac{1+\Gamma_{in}}{1-\Gamma_{in}}\bigg{)}, \tag{6}\]  
where \(Z_{0}\) is the characteristic impedance (typically \(50\,\Omega\)), and \(\Gamma_{in}\) is the input reflection coefficient defined as:  
\[\Gamma_{in}=\frac{Z_{L}-Z_{0}}{Z_{L}+Z_{0}}. \tag{7}\]  
The gain of the cascaded system is given by:  
\[G_{total}=G_{SPDT}\cdot G_{LNA}. \tag{8}\]  
The power delivered to the receiver frontend is calculated using:  
Figure 2: Proposed SPDT switch-based Reconfigurable Low Noise Amplifier Architecture.  
\[P_{in}=\int_{f_{1}}^{f_{2}}S_{21}(f)df, \tag{9}\]  
where \(S_{21}(f)\) represents the forward transmission coefficient of the system over the frequency range \(f_{1}\) to \(f_{2}\).  
The operation of the SPDT switch is governed by a voltage-controlled mechanism where the transfer function can be expressed as:  
\[V_{out}=\begin{cases}G_{X}V_{in},&\text{if switch in X-band mode,}\\ G_{K/Ka}V_{in},&\text{if switch in K/Ka-band mode,}\end{cases} \tag{10}\]  
where \(G_{X}\) and \(G_{K/Ka}\) represent the gain factors for the respective frequency bands.
# Energy-Efficient User Pairing for Downlink NOMA in Massive MIMO Networks
## 4 Proposed User Pairing Solutions
### Correlation-Based Pairing  
Here, the pairing is based on the similarities between channels of cell-center users and cell-edge users [21]. After the estimation of channel vectors, the BS forms NOMA groups based on correlation between the channels estimates. The high correlation value indicates that the beamforming vector will match both users in the NOMA pair. The correlation between the channel of cell-center user \(u_{c}\) and cell-edge user \(u_{e}\) is given as  
\[\rho_{u_{c},u_{e}}=\frac{|\hat{\mathbf{h}}_{u_{c}}\hat{\mathbf{h}}_{u_{c}}^{H}|}{\|\hat{ \mathbf{h}}_{u_{c}}\|\|\hat{\mathbf{h}}_{u_{c}}\|}. \tag{16}\]
# Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming
###### Abstract
Therefore, the computational complexity of the navigation resource allocation problem in the LEO constellation is extremely high. At present, there is almost no research on the optimal allocation of navigation resources in LEO constellations. Based on the characteristics of the LEO navigation augmentation system, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. The structure of this article is as follows: Section 2 establishes a network model for LEO constellation navigation resource allocation and gives the constraints and objective functions of resource allocation. Section 3 proposes a navigation resource allocation algorithm based on Dynamic Programming (NRAA-DP). Section 4 analyzes the performance of the algorithm through simulation, including resource utilization and computational complexity. Section 5 summarizes the work of this paper.
# Measuring Method of Node Importance of Urban Rail Network Based on H Index
## 3 Method for Calculating the Importance of Urban Rail Network Nodes Based on \(h\)-Index
### Relevance Analysis of Node Passenger Flow in Urban Rail Network
#### 3.2.2 Node Reference Coefficient  
After calculating the correlation coefficient matrix of the urban rail stations, the correlation coefficient is assigned and the node reference coefficient between the stations is obtained. Node reference coefficient is defined as:  
\[h_{ij}=\left[\left(r_{ij}-minr_{ij}\right)/\frac{1-minr_{ij}}{m}\right] \tag{8}\]  
Among them, \(h_{ij}\) is the coefficient of node \(i\) and node \(j\); \(r_{ij}\) is the correlation coefficient between stations; \(m\) is the number of stations. \(\lceil\rceil\) takes an integer up, and if it is given an integer, it returns the integer itself. For example, \([3,4]=4\), \([3]=3\).  
After calculating the node reference coefficient between nodes, the node reference coefficient matrix \(H\) between nodes can be obtained.  
\[H=\left(h_{ij}\right)_{m\times m} \tag{9}\]  
The \(H\) matrix represents the \"citation frequency\" of each station cited by other stations, corresponding to the citation frequency of articles defined by the \(h\)-index.  
**Property 1.**_Node reference matrix is a symmetric matrix, that is \(H^{T}=H\)._  
**Proof.** Undirected network \(G=\) <\(V\),\(E\)> has n nodes \(V=\{v_{1},v_{2},\cdots,v_{n}\}\), \(n\)-order matrix \(H=\left(h_{ij}\right)\) is the node reference matrix of graph \(G\).  
\[h_{ij}=\begin{cases}\left[\left(r
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 4 Resource Management Algorithm
### Middle Loop Procedure  
**Theorem 1**.: _When \(\mathbf{t_{0,i}}=\mathbf{t_{1,i}}=\cdots=\mathbf{t_{M,i}}\), the minimum delay of the system in the \(\mathbf{i_{th}}\) slot is achieved._  
Proof.: Details are given in Appendix A.1.  
According to Theorem 1, we further reduce delay by reducing the data offloading ratio for the SS with the highest delay while increasing its transmission power, and by increasing the data offloading ratio for the SS with the lowest delay while decreasing its transmission power.  
We denote \(\alpha_{max}\) and \(P_{max}\) as the data offloading ratio and transmission power of the SS with the highest delay, and \(\alpha_{min}\) and \(P_{min}\) as the data offloading ratio and transmission power of the SS with the lowest delay. Then we have  
\[\alpha_{max} =\alpha_{max}-\Delta_{1} \tag{47a}\] \[P_{max} =P_{max}+\Delta_{2}\] (47b) \[\alpha_{min} =\alpha_{min}+\Delta_{1}\] (47c) \[P_{min} =P_{min}-\Delta_{2} \tag{47d}\]  
where \(\Delta_{1}\) and \(\Delta_{2}\) are constants greater than zero.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
## 2 \(\Phi\)-OTDR System Principle  
### Vibration Sensing Principle  
The vibration sensing principle of the \(\Phi\)-OTDR system is based on the phenomenon of Rayleigh backwards scattering (RBS) light, which is produced by the elasto-optical effect in the inner of optical fiber, when the pulsed light is injected into and propagates along the optical fiber. The result of the mutual interference of numerous Rayleigh scattering centers within the injected pulse duration lead to the formation of random up and down fluctuations in the RBS curve. When a light pulse with a frequency of \(w\) is injected into the sensing optical fiber at the moment of \(t=0\), the amplitude of backscattered signal without vibration is shown in the following equation [33]:  
\[r(t)=\sum_{p=1}^{\mathrm{M}}I_{p}\times\exp\left[j\omega\left(t-\tau_{p} \right)\right]\times\exp\left(-\beta\frac{c\tau_{p}}{n}\right)\times\mathrm{F} \left(\frac{t-\tau_{p}}{\mathrm{L}}\right) \tag{1}\]  
where \(I_{p}\) and \(\tau_{p}\) are respectively the amplitude and the time delay of the \(p\)-th RBS curve; \(L\) means the pulse width of incident pulse; \(\beta\) is the fiber attenuation constant; \(c\) is the velocity of light in a vacuum; \(n\) is the refractive index of the fiber; \(\mathrm{M}\) is the number of scatters within the sensing fiber. The \(\mathrm{F}(t/\mathrm{L})\) is a rectangular function, which can be expressed as:  
\[\mathrm{F}\left(\frac{t}{\mathrm{L}}\right)=\begin{cases}1,&0\leq\frac{t}{ \mathrm{L}}\leq 1\\ 0,&\frac{t}{\mathrm{L}}\leq 0,\frac{t}{\mathrm{L}}\geq 1\end{cases} \tag{2}\]  
When an external vibration is applied at certain positions in the sensing optical fiber, the light phase will be modulated, which could be collected by the intensity detection. In general, the acquired data should be processed by a differential accumulation algorithm and an FFT algorithm in order to  
Figure 1: Contrast diagram of field-programmable gate array (FPGA), central processing unit (CPU) and graphics processing unit (GPU) internal structure: (**a**) the internal structure of FPGA (**b**) the internal structure of CPU (**c**) the internal structure of GPU.  
demodulate the vibration location and frequency information [34; 35]. Eventually, the detection and location of external vibration could be achieved based on the RBS curves.
# Design and Dynamic Simulation Verification of an On-Orbit Operation-Based Modular Space Robot
## 2 Configuration Design of Reconfigurable Modular Space Robot
### Unit Module Mechanism Design  
This scheme adopts a quasi-tetrahedral configuration design. As can be seen in Figure 5, the sub-module is composed of two closed hemispherical shells. Each part is composed of a hemispherical shell and a supporting plate. The supporting plate is used to carry the transmission system, communication components, power supply system, etc. The hemispherical shell is designed based on a tetrahedral configuration, and the cross section can be equipped with a docking mechanism to connect other main joint modules or other non-joint modules.  
It can be seen from Figure 6 that the hemispherical shell adopts an integrated design, and the hemispherical shell can support the plate so that it can be connected through the fixing hole. Each hemispherical shell has three mutually perpendicular annular surfaces, and the docking surface can be fixed through this annular surface.  
Due to the complex shape of the hemispherical shell and the difficulty of metal cutting process, 3D printing rapid prototyping technology is used to process it. After processing and assembling the above parts, the assembly result of the main structure is shown in Figure 7.  
Figure 4: Comparison of sub-module solutions.  
Figure 5: Main structure design drawing.
# Detection of Respiratory Phases in a Breath Sound and Their Subsequent Utilization in a Diagnosis
However, breath sounds may be abnormal in certain pathological conditions of the airways or lungs [2,5]. The respiratory diseases can affect the timing of sound transmission from the airways to the chest surface (typically prolonged expiratory phase or cessation of breath [12]) and also it can change amplitude composition of the frequency spectrum of breathing due to the presence of bronchial obstruction in the airways [2,5]. The respiratory sound then carries adventitious sounds with high frequencies and amplitudes called wheezes, crackles, rhonchus or snores [2,5]. The mechanisms of genesis of these noises, their frequency range and duration times can vary. The wheezes, caused by airway wall flutter and vortex shedding [2], are sounds, at the bandwidth of 100 to 1600 Hz with the common duration from 80 to 250 ms [2,5,13]. On the other hand, the crackles are discontinuous adventitious lung sounds in frequency range from 100 to more than 2000 Hz and with the duration less than 20 ms [2,4,5,8,13]. It is very important to diagnose these adventitious sounds correctly to be able to start treatment as early as possible. Before detecting any adventitious sounds, the automatic detection of respiratory phases (areas of inspiration and expiration) in the sound of breath is very useful. If the detected borders of the respiratory phases are plotted over the frequency spectrum it allows an easier orientation in the frequency spectrum of the sound recording (see Section 3). Detection of the respiratory phases also offers a possibility to create a specific character of the respiratory sound which can be further compared with another recording. This approach also enables the automatic detection of respiratory phaseseven in the recordings with chaotic characteristics. This article introduces a simple and computationally inexpensive method for detecting the respiratory phase during lung auscultation and further discusses the possibility of using the alternation of inspiratory and expiratory phases based on the movement of air in the airways while the air movement is minimal or zero. This graphical interpretation is a great help for physicians while detecting the bronchial obstructions and relaying on temporal information.