esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Distance Calculation  
The distances between the sender, the potential next node, and the sink are computed. Let \(dist_{1}\) represent the distance between the recent source and the probable next nodes and let \(dist_{2}\) denote the distance between the next node and the sink. The matching distance from the sender \(dist_{1}^{M}\) and the matching distance from sink \(dist_{2}^{M}\) are considered.  
\[dist_{1}^{M}=1-dist_{1},\quad dist_{2}^{M}=1-dist_{2} \tag{10}\]  
Hence, the distance of the potential next node associated with matching distances \(dist_{1}^{M}\) and \(dist_{2}^{M}\) can be determined as follows:\[T_{dist}=\frac{\alpha\cdot dist_{1}^{M}+\beta\cdot dist_{2}^{M}}{\alpha+\beta} \tag{11}\]  
where \(\alpha\) and \(\beta\) are the adjustment parameters for \(dist_{1}^{M}\) and \(dist_{2}^{M}\).
###### Abstract  
Mesenchymal stem cells (MSCs) reside quiescently within a specialised 'niche' environment in the bone marrow. However, following appropriate signalling cues, MSCs mobilise and migrate out from the niche, typically toward either sites of injury (a regenerative response) or toward primary tumours (an intrinsic homing response, which promotes MSCs as cellular vectors for therapeutic delivery). To date, very little is known about MSC mobilisation. By adopting a 3D MSC niche model, whereby MSC spheroids are cultured within a type I collagen gel, recent studies have highlighted interleukin-6 (IL-6) as a key cytokine involved in MSC migration. Herein, the ability of IL-6 to induce MSC migration was further investigated, and the key matrix metalloproteinases used to effect cell mobilisation were identified. Briefly, the impact of IL-6 on the MSC migration in a two-dimensional model systems was characterised--both visually using an Ibidi chemotaxis plate array (assessing for directional migration) and then via a standard 2D monolayer experiment, where cultured cells were challenged with IL-6 and extracted media tested using an Abcam Human MMP membrane antibody array. The 2D assay displayed a strong migratory response toward IL-6 and analysis of the membrane arrays data showed significant increases of several key MMPs. Both data sets indicated that IL-6 is important in MSC mobilisation and migration. We also investigated the impact of IL-6 induction on MSCs in 3D spheroid culture, serving as a simplistic model of the bone marrow niche, characterised by fluorescently tagged magnetic nanoparticles and identical membrane antibody arrays. An increase in MMP levels secreted by cells treated with 1 ng/mL IL-6 versus control conditions was noted in addition to migration of cells away from the central spheroid mass.  
magnetic nanoparticles; matrix metalloproteinase; tissue inhibitor of metalloproteinase; mesenchymal stem cell; type I collagen; spheroid +
Footnote †: journal: Applied Sciences
## 2 Literature Review
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm/Method** & **Advantages** & **Disadvantages** \\ \hline Traditional methods (physics-based and rule-based expert systems) [26] & Precision in identifying and characterizing faults. They provide a robust framework for fault analysis. & Computational demands. Rigidity in adapting to emerging or unconventional fault patterns. Difficulty distinguishing benign operational changes from real faults in real-time scenarios. \\ Machine learning models [20] & Increasing the accuracy of fault detection. More adaptability. & The method performance depends on the quality of the data and requires significant computing resources. \\ RNNs (i.e., LSTM and GRU) [30] & Suitable for sequential data. They can capture dependencies over time. & They may have problems with very long sequences or when long-term dependencies need to be fetched. \\ Autoencoders (i.e., VAEs and VRAEs) [31] & Useful for dimensionality reduction. They can capture the distribution of normal data. & They may require significant training data. \\ CNNs [32] & They can learn spatial patterns in time series. & They are primarily designed for image data. Adaptation to time series can be non-trivial. \\  & Efficient processing through parallelization. & They may require special adjustments for specific time-series characteristics. \\ TCNs [33] & Capture long-range dependencies using dilated convolutions. & Their complexity can lead to longer training times and may require fine-tuning. \\  & They combine features of multiple models. & Their complexity can lead to longer training times and may require fine-tuning. \\ Hybrid models [30,31] & Versatility in feature extraction and modeling. & Their complexity can lead to longer training times and may require fine-tuning. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of different algorithms/methods applied for fault detection in gas turbines. gas turbines, especially when the balance between computational efficiency and diagnosis accuracy is considered.
## 4 The Proposed ECHR Algorithm
### Energy-Aware Hierarchical Routing Algorithm  
Like the multi-hop transmission mechanism mentioned earlier, the communication range \(C_{r}\) of any sensor node in network can be dynamically adjusted to reduce the power dissipation in data transmission. By shrinking the communication range \(C_{r}\), the sensor nodes are not able to transmit the sensed data to distanced nodes. Hence, all sensed data will be routed to the neighboring nodes of the sensing node by the multi-hop mechanism. Due to the hop-count information provided in the beacon message, the sensor nodes are able to determine which neighboring nodes are closer to the root node. The closest neighboring node can be chosen as the parent node to relay the sensed data. In each round, each sensor node transmits a sensed data to the root node. First, we compute the path weight for transmitting the data from a source node \(s_{i}\) to a destination node (or relay node) \(s_{p}\) by:  
\[\beta_{i,p}=\left(\frac{1}{d\left(s_{i},s_{p}\right)}\right)^{\lambda_{i}} \times\left(q_{p}\right)^{\lambda_{2}}, \tag{7}\]  
where \(d(s_{i}\), \(s_{p})\) is the Euclidean distance between nodes \(s_{i}\) and \(s_{p}\), and \(\lambda_{1}\) and \(\lambda_{2}\) are weight coefficients to adjust the relative importance of the distance factor and the residual energy factor, respectively. After the values of all \(\beta_{i,p}\) are determined, we can organize them in a set of weights \(\mathbf{\beta}_{i}\) by:  
\[\mathbf{\beta}_{i}=\left[ \begin{array}{c}\beta_{i,1}\\ \beta_{i,2}\\ \vdots\\ \beta_{i,|\mathbf{N}(i)|}\end{array} \right]. \tag{8}\]  
where \(||\mathbf{N}(i)||\) is the number of parent candidates of node \(s_{i}\). Subsequently, we can select the \(G_{i}\)-th node to be the parent node for data transportation by:  
\[G_{i}=\text{argmax}\mathbf{\beta}_{i}=\underset{p\in\mathbf{N}(i)}{\arg\max}\,\beta_{ i,p}\,, \tag{9}\]  
where \(\mathbf{N}(i)\) is the set of parent candidates of node \(s_{i}\). Hence, the sensor node \(s_{i}\) is likely to choose a closer node with greater residual energy. The task of data relaying that requires high energy consumption can be then assigned to a possible neighboring node without creating any hot-spot in the network. After all of the sensed data are collected by the root node, the root node aggregates the data, and then transmits it to the _BS_. The ECHR algorithm utilizes the multi-hop transmission mechanism as a spanning tree topology to reduce the power dissipations in the packet transmission phase. The pseudo code of the proposed ECHR algorithm is shown in Figure 2. In the ECHR algorithm, \(s_{i}\)_energy_ is the residual energy of node \(s_{i}\), and \(s_{i}\)_level_ is the number of hops when transmits data to the root node. Figure 3 shows the data transmission paths of nodes using the ECHR algorithm for a specific network topology.
## 5 Results and Analysis  
### Qualitative Failure Process from DIC Analysis  
The dynamic fracture process under spalling conditions was characterized using the ultra-high-speed DIC system described in Section 4.3. Figure 17 illustrates the typical temporal evolution of the axial displacement (\(u_{x}\)) and axial strain (\(\epsilon_{xx}\)) fields on the surface of a representative Deformed UDRC specimen, Z003-25.  
Figure 17a shows the displacement contour maps at four key time instances. At \(T=0\) us, the specimen is in its initial state. By \(T=136\) us, the incident compressive wave is traveling from left to right, indicated by the smoothly varying displacement field. At \(T=298\) us, after wave reflection from the free end, a region of rapidly changing displacement becomes visible. By \(T=360\) us, a clear discontinuity in the displacement field is established near the free end, with the spalled fragment reaching a maximum displacement of approximately 0.4 mm, signifying the opening of a macroscopic spall crack.  
The corresponding axial strain field evolution, shown in Figure 17b, provides a clearer view of the fracture initiation process. At \(T=136\) us, the specimen experiences compression from the incident wave. At \(T=298\) us, the superposition of incident and reflected waves creates a distinct band of high tensile strain near the free end. This strain localization intensifies by \(T=360\) us, clearly delineating the primary spall fracture location at approximately X = 165 mm from the free end. The peak tensile strain reaches 0.021, while adjacent regions show compressive strains of approximately 5 \(\times\) 10\({}^{-5}\). This visualization confirms that fracture initiates in regions of highest localized tensile strain, validating the spalling mechanism.  
Figure 17: DIC analysis for specimen Z003-25 showing the temporal evolution of (**a**) the axial displacement field and (**b**) the axial strain field. The images capture the wave propagation and subsequent strain localization leading to spall fracture near the free end.  
Beyond the strain localization captured by DIC, the final failure modes observed in post-test photographs offered direct experimental validation of the reinforcement's role. Figure 18 presents a stark visual contrast in the post-fracture behavior between the specimen types. The plain concrete specimen (Figure 18a) shows three distinct, catastrophic fractures, resulting in complete fragment separation. In contrast, while the UDRC specimen (Figure 18b) exhibits at least four visible cracks across the matrix, its structural integrity is maintained by the internal steel reinforcement, which bridges the cracks and prevents fragment detachment. This direct observation strongly corroborates the numerical simulation results and highlights the critical function of reinforcement in providing residual capacity after initial failure.
# Experimental Study on Fluid Dissipation Effects in Core Samples by NMR Measurement
## 3 Experimental Workflow
### Experimental Instruments, Materials, and Measurement Parameters  
The experimental devices include a MesoMR12-06H-I NMR core analyzer (Niumag Analytical Instrument Corporation, Suzhou, China) (the instrument operates at a magnetic field strength of 0.25 T, the frequency commonly used in laboratory core nuclear magnetic resonance measurements, and features a high signal-to-noise ratio, exceptional accuracy, and rapid measurement speed), a LY-BH-50 vacuum pressurization saturation device (Jiangsu Lianyou Scientific Research Instrument Co., Ltd., Jiangshu, China), a UPCP-100 porosity and permeability measuring instrument (Jiangsu Lianyou Scientific Research Instrument Co., Ltd., Jiangsu, China), a CSC-12S centrifugal machine (Shanghai Luxiangyi Laboratory Instrument Co., Ltd., Shanghai, China) (the instrument features temperature control and can exert a 6.9 MPa displacement force, enabling rapid core dehydration), a milligram precision electronic balance, and NMR-specific sample tubes (Niumag Analytical Instrument Corporation, Suzhou, China).  
The materials used consist of two processed sandstone core samples (labeled A and B) and crude oil of varying viscosities (42 mPas and 240 mPas). Additionally, plastic wrap and polytetrafluoroethylene (PTFE) film are employed for sample packaging.  
Regarding the measurement parameters, the magnetic field strength of the NMR core analyzer used in the experiment is 0.25 T, corresponding to a resonance frequency of 12 MHz. The CPMG and IR-CPMG sequences are employed to collect T\({}_{2}\) distribution and T\({}_{1}\)-T\({}_{2}\) two-dimensional distribution at different dissipation stages. T\({}_{2}\) distribution measurement parameters are set as follows: the number of echoes (NECH) is 12,000, the number of superpositions (SCAN) is 32, the echo interval (TE) is 0.1 ms, and the waiting time (TW) is 4000 ms. T\({}_{1}\)-T\({}_{2}\) distribution measurement parameters are set as follows: the number of echoes (NECH) is 12,000, the number of superpositions (SCAN) is 16, the echo interval (TE) is 0.1 ms. The waiting times (TW) are as follows: 0.08 ms, 0.13 ms, 0.30 ms, 0.69 ms, 1.57 ms, 3.60 ms, 8.17 ms, 18.66 ms, 42.56 ms, 97.10 ms, 221.50 ms, 505.32 ms, 1152.82 ms, 2630.00 ms, and 6000.00 ms. The waiting time TW is set using a logarithmic uniform distribution with a base of ten. This approach spans multiple orders of magnitude, enabling the T1 relaxation time to correspond to different pore sizes and fluid properties. Additionally, it ensures an even distribution along the timeline. Using a linear interval would overly concentrate sampling points within a short time range, insufficiently describe the long time range, and reduce inversion accuracy. The dissipation exposure times for the core samples are set to 1 h, 2 h, 4 h, 8 h, 16 h, 32 h, 64 h, 96 h, 160 h, and 247 h to investigate the impact of different dissipation times on NMR measurement results.
## 2 Mathematical Principles  
### Turbulence Modeling Equations  
Due to the irregular rod-like external structure of the plunger, the fluid dynamics around it become highly complex. This complexity stems not only from the plunger's geometry but also from the fluid flow behavior both around and within the plunger. To effectively simulate and analyze this intricate flow scenario, the RNG k-\(\varepsilon\) model, an enhanced version of the standard k-\(\varepsilon\) model, provides an ideal solution. By incorporating additional correction terms, this model can accurately handle strong vortex flows and flow distortions around curved surfaces, thereby improving the precision of turbulence simulations. The RNG k-\(\epsilon\) model excels in simulating flow fields both inside and outside the plunger, effectively capturing subtle flow variations and vortex structures [17].  
RNG k-\(\epsilon\) model equations:  
k equation:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{i}) =\frac{\partial}{\partial x_{j}}(\alpha_{k}\mu_{eff}\frac{\partial k}{ \partial x_{j}})+G_{k}+G_{b}-\rho\epsilon-Y_{M}+S_{k} \tag{1}\]  
\(\epsilon\) equations:  
\[\frac{\partial}{\partial t}(\rho\epsilon)+\frac{\partial}{\partial x_{i}}( \rho\epsilon u_{i})=\frac{\partial}{\partial x_{j}}(\alpha_{k}\mu_{eff}\frac{ \partial\epsilon}{\partial x_{j}})+C_{1\epsilon}\frac{\epsilon}{k}(G_{k}+C_{ 3\epsilon}G_{b})-C_{2\epsilon}^{*}\rho\frac{\epsilon^{2}}{k} \tag{2}\]  
In these equations, \(G_{k}\) represents the turbulent kinetic energy produced by the mean velocity gradient  
\[G_{k}=-\rho\overline{u_{i}^{\prime}u_{j}^{\prime}}\frac{\partial u_{j}}{ \partial x_{i}} \tag{3}\]  
\(G_{b}\) is the turbulent kinetic energy generated by buoyancy.  
\[G_{b}=\beta g\dot{u}_{i}\frac{\mu_{i}}{\text{Pr}_{t}}\frac{\partial T}{ \partial x_{i}} \tag{4}\]  
\(Y_{M}\) represents the contribution of pulsating expansion to the total dissipation rate in compressible turbulence.  
\[Y_{M}=2\rho\epsilon M_{t}^{2} \tag{5}\]  
\(S_{k}\) represents the source term defined by the use.  
The reciprocal of the effective Prandtl number, \(\alpha_{k},\alpha_{\epsilon}\), is analytically determined using RNG theory, with \(\alpha_{k}=\alpha_{\epsilon}=1.393\).  
In Fluent, the default constants of the RNG k-\(\epsilon\) turbulence model are modifiable, including: \(C_{u}=0.0845,C_{1\epsilon}=1.42,C_{2\epsilon}=1.68,Pr_{t}=0.85\).
# Performance Evaluation of Routing Protocols for Underwater Wireless Sensor Networks
## 3 Performance Evaluation and Simulation Results
### Performance Metrics  
The performance metrics for all compared protocols are defined as follows:  
* **Residual Energy:** It describes the distinction between the startup nodes' power and the nodes utilized during the operation.
* **Network Lifetime:** The overall time spent running the network is referred to as the network lifetime.
* **Packet Data Ratio:** The packet delivery ratio (PDR) can be measured as the number of packets delivered in total to the total number of packets sent from the source node to the destination node in the network.
## 5 Discussion
### Methods: Algorithms and Machine Learning Models
#### 5.1.4 Comparison of Models  
The use of one algorithm or another is determined by different factors, such as computational resources, the amount of data to process, and the type of infrastructure (rooms, tables, walls...) where a fingerprinting-based system is to be implemented.  
If we focus on computational resources, RF requires fewer resources than SVM algorithms. In fact, SVM-based algorithms tend to be almost unusable on large datasets because the training complexity of SVM is highly dependent on the size of dataset used.  
At the level of infrastructure complexity, the situation is similar. SVM algorithms work very well for mitigating the NLOS of signals; therefore, they are ideal in small and complex sites. On the other hand, NNs are more configurable, and their usage can be adjusted for better performance based on lower precision. If not much precision is needed and speed is preferable, the number of nodes and neurons involved in the network can be adjusted [39].  
In the case of large spaces, RFs have an advantage over SVMs, because these algorithms are appropriate on models that have been clustered, which is helpful in large scenarios. It is important to note that RFs are Decision Trees optimized to work with large amounts of data. ANNs are particularly suitable in situations where there is noise and multipath propagation, as well as where there are a large number of APs [18].  
In summary, in small spaces and with little computational capacity SVM is the best option, while in complex situations and with large datasets, RF and NN are more complex to implement; however, they are more adaptable due to their great configuration capacity.
# Evaluating the Vulnerability of Several Geodetic GNSS Receivers under Chirp Signal L1/E1 Jamming
## 5 Data Analysis, Results and Discussion
### Positioning Results
This finding will be the focus of further research in which the authors will include a larger number of instruments from many other manufacturers. However, due to malfunctioning of the Galileo system during the jamming campaign, the signal was useless for the position determination. The two Javads completely ignored Galileo, while the other three gave an elevation of 0\({}^{\circ}\), probably due to the invalid ephemeris information (see Figure 15). However, the information could be retrieved only when the jamming was not too strong. Galileo signals are transmitted in the same frequency band as GPS. Therefore, they are expected to be affected by the jammer in a similar way. Table 5 summarises a review of the effective positioning of both the code and carrier phase, the latter using ambiguity fixed values, for each of the receivers from 08:20 to 10:00 UTC, while static and kinematic jamming were performed. Furthermore, the quality of positioning is given within the accuracy of RTK positioning. Table 6 summarises maximal deviations in the code- and carrier-phase-based positioning, obtained from 08:20 to 10:00 UTC, during static and kinematic jamming. It is obvious that Trimble R8s and Trimble 4000 SSi reported completely wrong code-based positions for some moments. Moreover, Trimble R8 was giving a false carrier-phase solution for some time, which differed by a few metres from the exact solution and which should be considered a concern for geodetic use. Interestingly, small errors in carrier-phase positioning existed for the outdated instrument Trimble 4000 SSi, which is based only on GPS. Figure 15: Carrier-to-noise ratio during the dynamic jamming for the Galileo ready receivers: (**a**) Leica GS15, (**b**) Leica GS18 and (**c**) Trimble R10 (the two [PERSON]’s instruments are not shown since they ignored Galileo because it was not operational). All the Galileo satellites (annotation E in front of the number) reported an elevation of 0\({}^{\circ}\), which can be attributed to malfunctioning of the Galileo system during the jamming campaign (own study). As mentioned before, Trimble R10 had problems in positioning, which failed immediately after the first jamming in the vicinity of the receiver. Obviously, the instrument recognised some malicious events and stopped reporting positions, which could be treated as beneficial. Unfortunately, it did not recognise the end of the jamming. However, other code and carrier-phase solutions, acquired during short kinematic jamming events, were within the expected positioning accuracy. Positioning results for other receivers were within the expected range of the accuracy.
## 3 Experimental Setup and Data Processing
### Data Processing
The power \(W\) of the compressor is measured using a power meter, so the enthalpy at point 2 can be calculated using the following heat balance equation:  
\[W=(h_{2}-h_{1})\times m_{1,rG} \tag{13}\]  
where \(h_{1}\) is the enthalpy at the inlet of the compressor, kJ/kg; \(h_{2}\) is the enthalpy at the outlet of the compressor, kJ/kg; \(W\) is the compressor power, kW;  
The volumetric efficiency \(\eta_{\
u}\) of the compressor can be obtained from Equation (1):  
\[\eta_{\
u}=\frac{60\times v_{1}\times m_{1,rG}}{N\times V_{d}} \tag{14}\]  
where \(v_{1}\) is the specific volume at point 1 (compressor suction), m\({}^{3}\)/kg. The isentropic efficiency \(\eta_{is}\) of the compressor is  
\[\eta_{is}=\frac{h_{2,is}-h_{1}}{h_{2}-h_{1}} \tag{15}\]  
where \(h_{2,is}\) is the theoretical enthalpy at the outlet of the compressor, kJ/kg; \(h_{2}\) is the actual enthalpy at the outlet of the compressor, kJ/kg. The mass flow rate and mixture density at point 3s (at the outlet of subcooler) are obtained through a mass flow meter. The mass flow rate of the mixture is the sum of the mass flows of the lubricating oil and the refrigerant liquid, as shown in the following equation:  
\[m_{mix}=m_{o}+m_{rL} \tag{16}\]  
where \(m_{o}\) is the mass flow rate of the lubricating oil, kg/s; \(m_{rL}\) is the mass flow rate of the refrigerant liquid, kg/s; \(m_{mix}\) is the total mass flow rate of the mixture, kg/s. The density of the mixture is obtained by mixing the liquid phase refrigerant and the compressor oil and is calculated as follows:  
\[\rho_{mix}(T,P)=\frac{m_{o}+m_{rL}}{V} \tag{17}\]  
where \(\rho_{mix}(T,P)\) is the density of the mixture, kg/m\({}^{3}\); \(V\) is the volumetric flow rate of the mixture, m\({}^{3}\)/s. Assuming that the volume of the mixture after combining the compressor oil and refrigerant is the sum of the volumes before mixing (with theoretical errors), Formula (18) is calculated as follows:  
\[\rho_{mix}=\frac{m_{o}+m_{rL}}{V_{o}+V_{rL}}=\frac{m_{o}+m_{rL}}{m_{o}/\rho_{ o}+m_{rL}/\rho_{rL}} \tag{18}\]  
where \(\rho_{mix}\) is the density of the mixture, kg/m\({}^{3}\); \(m_{o}\) is the mass flow rate of the lubricating oil, kg/s; \(m_{rL}\) is the mass flow rate of the refrigerant liquid, kg/s; \(\rho_{o}\) is the density of the lubricating oil, kg/m\({}^{3}\); \(\rho_{rL}\) is the density of the refrigerant liquid, kg/m\({}^{3}\). From Formula (19):  
\[\omega=\frac{m_{o}}{m_{o}+m_{rL}} \tag{19}\]  
Formula (20) is calculated as follows:  
Figure 2:0  
where \(\omega\) is the oil circulation rate. Thus, the refrigerant flow rate \(m_{rL}\), oil flow rate \(m_{o}\), and oil circulation rate \(\omega\) under state 3s can be determined. The properties of the R290 refrigerant used in the equations above, such as enthalpy, can be obtained using REFPROP 9.0 [16].