esa-sceva/satcom-chunk-collection huggingface dataset:
# Routing Path Assignment for Joint Load Balancing and Fast Failure Recovery in IP Network
## 2 Requirements of Loop-Free Rerouting
### Requirement to Reroute Traffic without Generating Traffic Loops
Because A is not an upstream node of U, the rerouted traffic will not go back to U. It guarantees loop-free rerouting. The R3 neighbor requirement still holds for handling a single link failure. If the failure link is (U, Z), node U can forward the affected traffic to A for failure recovery. A fake node associated with link (U, A) needs to be inserted to realize the reroute. The cost introduced by the fake node need to be higher than the path cost seen by U. Consequently, U sends the traffic with destination D to node Z in the normal state. When Z or (U, Z) is failed, node A becomes the new next-hop node by the LFA rule. For a node N1, if its neighbor node N2 is not an R1, R2, or R3 neighbor, N2 must be an upstream node in the acyclic graph generated by the working paths. Therefore, to guarantee 100% survivability, the assignment of the working paths needs to be carefully designed to ensure that every node in the acyclic graph must have at least one of the three types of neighbors. The requirement is used in our problem formulations shown in the next section, so any single node or link failure can be recovered in our networks. Figure 4: Upstream node has a downstream neighbor node. Figure 5: Reroute through a neighbor node in another branch.
## 2 Materials and Methods
### Sentinel-1 Soil Moisture and Indices Retrieval  
From the downloaded Sentinel-1 time series we extracted the radar signal corresponding to bare soils and calculated three radar indices (S1-indices) from its two polarizations (Table 1). For each sampling point, the radar signal was averaged over its corresponding polygon of Thiessen, in the linear scale, then converted to the logarithmic scale and used for S1-indices calculation. If the Sentinel-1 image was acquired on the same date as a Sentinel-2 image, we automatically used it. If not, we calculated the mean radar signal of the two closest dates before and after the Sentinel-2 visit, with an interval not exceeding two days. If only one date was available in the two-day interval, either before or after the Sentinel-2 visit, we used it. In addition, we used estimates of volumetric soil moisture (%) derived from the Sentinel-1 time series (S1-soil moisture) using the approach of [PERSON] et al. [72] and validated for our study area by [PERSON] et al. [73].
# Feasibility, Design, and Deployment Requirements of TCR for Bistatic SAR radiometric Calibration
## 2 Requirements of Calibration Target for Radiometric Calibration
### The Stability of RCS Requirement  
The RCS of the target determines the power density returned to the radar for a particular power density incident on the target [19], so a natural requirement for a calibration target is that its RCS should be stable to allow radiometric calibration results with repeatability, where the stability of RCS refers to variation over frequency, due to the frequency having a great influence on the target RCS. For passive calibration targets, according to electromagnetic theory, the target RCS or backscattering characteristic can be divided into three regions, including the Rayleigh region (\(ka<0.5\)), resonance region (\(0.5<ka<20\)), and optical region (\(ka>20\)) [20], where \(k=\frac{2\pi}{\lambda}\) is the wavenumber, and \(a\) represents the feature size (e.g., radius of a sphere, interleg length of a TCR) of the passive target. Passive calibration targets will show a stable RCS when they work within the optical region. As shown in Figure 1, the target RCS is stable, and can be estimated accurately in the optical region. Therefore, the requirement for the stability of the RCS is that the feature size of the TCR must meet the optical region. We calculated that \(a>0.1\) can ensure RCS stability at the X-band.
## 2 Materials and Methods
### Model Updating Principle Using RBF Neural Network  
RBF neural network consists of two layers: a hidden radial basis layer of \(S^{1}\) neurons (radial basis function is activation function), and an output linear layer of \(S^{2}\) neurons (linear function is activation function). It belongs to the feedforward neural network, which can approximate any nonlinear continuous functions with arbitrary precision, deal with the law that is difficult to analyze in the system and have good generalization ability and fast learning speed. Therefore, the RBF neural network is selected as the finite element model updating algorithm. Its network structure is shown in Figure 2[35].  
In Figure 2, \(\mathbf{P}\) is the input vector; \(\mathbf{R}\) is the number of elements in each input vector; \(S^{1}\) and \(S^{2}\) are the number of neurons in the radial basis layer and linear layer, respectively; \(\mathbf{I}\mathbf{W}^{1,1}\) is the input weighting matrix; \(b^{1}\) is the deviation of the radial basis function, which is able to adjust the sensitivity of the function; \(\mathbf{L}\mathbf{W}^{2,1}\) is linear function weighting matrix; \(b^{2}\) is the deviation of the linear function; \(\mathbf{y}\) is the output vector.  
After the input vector \(\mathbf{P}\) is trained by the radial basis function and the output is:  
\[y=\sum_{i=1}^{S^{1}}\mathbf{L}\mathbf{W}^{2,1}\varphi\Big{(}{i}\mathbf{I}\mathbf{W}^{1,1}-\bm {P}\Big{)}{b_{i}}^{1}+b^{2} \tag{7}\]  
where \({i}\mathbf{I}\mathbf{M}^{1,1}\) denotes the weighting vector between the \(i\)th neuron of the radial basis layer and input vector \(\mathbf{P}\); \(\varphi\) denotes the selected nonlinear radial basis function. It can be seen that the purpose of the radial basis neural network training is to use training samples to determine the final weight value \(\mathbf{I}\mathbf{W}^{1,1}\), \(\mathbf{L}\mathbf{W}^{2,1}\) and deviations \(b^{1}\) and \(b^{2}\) of the radial base layer and the linear layer, and establish the mapping relationship between the output vector and input vector.  
The mapping relationship between the updating parameters \(\mathbf{P}\) in the finite element model and the structural characteristic quantities \(\mathbf{y}\) can be expressed as [32]:  
\[P=f^{-1}(y) \tag{8}\]  
The RBF neural network is used to establish a mapping relationship \(f^{-1}\) between the updating parameters \(\mathbf{P}\) as the output vector and the structural characteristic quantities \(\mathbf{y}\) as the input vector. When \(\mathbf{y}\) (resonance frequencies and antiresonant frequencies) are known, \(\mathbf{P}\) can be obtained through the RBF neural network, and the model updating comes down to solving the positive problem of the updating parameter \(\mathbf{P}\).
## 2 Materials and Methods
### Gene Expression Study  
Gene expression analyses were carried out on fresh tissue samples harvested during surgical resection from each patient. RNA isolation was performed using Qiagen's (Germany) RNA extraction kit, adhering strictly to the manufacturer's protocols to ensure the integrity and quality of the RNA. The concentration and purity of isolated RNA were assessed using a NanoDrop (Thermo Fisher Scientific, Waltham, MA, USA) spectrophotometer, with only high-quality RNA samples proceeding to the next stage of analysis. For the quantification of ARSB, TP53, and Maspin gene expressions, reverse transcription quantitative PCR (RT-qPCR) was conducted using Qiagen's (Germany) RT-qPCR kit, following the manufacturer's instructions. This approach allowed for the precise quantification of mRNA levels of the target genes, with GAPDH serving as an internal reference gene to normalize the expression data. The relative expression levels of ARSB, TP53, and Maspin were calculated by employing the \(2^{-\Delta\text{ACt}}\) method, which facilitates the comparison of gene expression among the patient samples in a relative manner. For the purposes of this study, gene expression levels were categorized based on their relative quantification (RQ) values: an RQ less than 1 indicated low gene expression, while an RQ greater than 1 signified high gene expression. This binary categorization allowed for a straightforward interpretation of the data, facilitating the subsequent statistical analysis to discern the correlation between gene expression levels and various clinical outcomes (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Marker** & **Antibody Type** & **Clone** & **Antigen Retrieval** & **pH** \\ \hline Arylsufatase B & Rabbit polyclonal & Ab 181410 (Abcam) & High pH (Dako) & 9 \\ \hline TP53 & Mouse monoclonal & Ab-5 (ThermoScientific) & 10 mM citrate (Dako) & 6 \\ \hline Maspin & Mouse monoclonal & BSB-92 (BioSB) & High pH (Dako) & 9 \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of immunohistochemical markers.  
Figure 1: Immunohistochemical staining: (**A**) TP53 positivity over 50%; (**B**) ARSB positivity over 50%; (**C**) TP53 negativity, (**D**) ARSB-negative normal tissue, in addition to positive cancer cells; (**E**) Maspin positivity; (**F**) Maspin negativity.
## 3 Results and Discussion
### Analysis of the Battery Charging, Discharging, and Cycling Performance  
Lithium metal as the negative electrode, LiFePO\({}_{4}\) as the positive electrode, and PU and PU/PAN lithium-ion battery diaphragms were used to assemble lithium-ion batteries. An analysis was conducted to determine whether PU/PAN fiber diaphragms could be applied to rechargeable lithium-ion batteries by comparing the charge and discharge performance and cycle stability of the batteries. The first discharge performance of the PU and PU/PAN composite diaphragm was compared with the commercial Celgard 2400 diaphragm at a current density of 0.2 C rate, as shown in Figure 10. The first discharge-specific capacities of PU, 18% PU:PAN = 8:2, and 18% PU:PAN = 7:3 nanofiber diaphragm batteries were 148.1 mAh/g and 152.6 mAh/g. [PERSON] et al. [31] prepared porous polyacrylamide(PAN) nanofiber diaphragm by electrostatic spinning method, and its first discharge specific capacity at 0.1 C magnification was only 130 mAh/g. The high porosity of the PU diaphragm enables it to absorb more electrolyte, which provides an efficient channel for lithium-ion transfer between positive and negative electrodes, and at the same time, the interface stability between PAN and electrode material The high porosity of the PU diaphragm enables it to absorb more electrolyte, providing an efficient channel for the transfer of lithium-ions between the cathode and cathode, and the good interfacial stability between the PAN and electrode materials, which gives the PU/PAN diaphragm a high first discharge capacity. With the increase in charging and discharging times, the porosity, liquid absorption rate, and ionic conductivity of the diaphragm decreased, and the discharge-specific capacity of lithium-ion batteries decreased [34]. The cycling performances of the PU/PAN fiber diaphragms were compared with that of the commercial Celgard 2400 diaphragm at a current density of 0.2 C rate, as shown in Figure 11. Due to the poor absorption of electrolytes by the Celgard 2400 diaphragm, the specific capacity of the first discharge was only 144.6 mAh/g. After 50 cycles, the discharge-specific capacity remained at 137 mAh/g, with a capacity retention rate of only 94.7%, while the remaining discharge-specific capacities of 18% PU:PAN = 8:2, 18% PU:PAN = 7:3, and 18% PU after 50 cycles were 145.3 mAh/g, 147.1 mAh/g, and 140.5 mAh/g, respectively, with capacity retention rates of 95.2%, 95.8%, and 94.9%. The capacity retentions of the PU/PAN fiber diaphragms were higher than that of the Celgard 2400 diaphragm, with an excellent charge/discharge performance and cycle stability. This was due to the better compatibility of the PU/PAN fiber diaphragms with the electrolyte, and the existence of large interconnecting voids and  
Figure 9: Linear scan diagram of the commercial Celgard 2400 and PU/PAN fiber diaphragms.  
the 3D mesh structure of the PU/PAN fiber diaphragms, which increases its ionic conductivity and suppresses the decrease in discharge specific capacity. Therefore, the PU/PAN fiber diaphragms have the potential to be used in rechargeable lithium-ion batteries.
## 4 Simulation Results and Analysis  
Considering the strong nonlinear characteristics of the ball-bearing force and UMP, the numerical simulation is conducted by MATLAB, and the differential equations are solved by the fourth-order Runge-Kutta method. Furthermore, in order to eliminate the influence of the transient responses in the free vibration, the results of former periods are discarded and the last 400 periods are retained for analysis. The specific system parameters used are as follows: \(m_{d}=25\) kg, \(m_{A}=m_{B}=4\) kg, \(c=100\) N\(\cdot\)s/m, \(c_{b}=200\) N\(\cdot\)s/m, \(EI=0.819\)\(\times\)\(10^{4}\) N\(\cdot\)m\({}^{2}\), \(k_{A}=k_{B}=2.5\)\(\times\)\(10^{7}\) N/m, \(R_{r}=0.08\) m, \(L=0.165\) m, \(l=0.75\) m, \(e_{0}=10^{-4}\) m, \(\Omega=25\) Hz, \(\delta^{\prime}=0.012\) m. The main parameters of the ball bearings are given in Table 3.  
\begin{table}
\begin{tabular}{l l} \hline \(m_{A}\), \(m_{B}\), mass of the bearings & \(m_{d}\), mass of the rotor \\ \(a\), distance between the rotor and the left bearing & \(l\), length of the shaft \\ \((x,y)\), position of rotor in coordinate system & \((\theta_{x},\theta_{y})\), rotational angles of rotor \\ \((x_{A},y_{A})\), position of left bearing in coordinate system & \(e_{0}\), mass eccentricity of the rotor \\ \((x_{B},y_{B})\), position of right bearing in coordinate system & \(\Omega\), rotating speed of the rotor \\ \(J_{d}\), moment of inertia of rotor & \(J_{p}\), polar moment of inertia of rotor \\ \(E\), Young’s modulus of the shaft & \(l\), moment of inertia of shaft \\ \(c\), damping of rotor & \(c_{b}\), damping of bearings \\ \hline \end{tabular}
\end{table}
Table 2: Parameters involved in the system.  
Figure 5: Dynamic model of the rotor bearing system.
# Numerical Simulation Study on the Performance of Buried Pipelines under the Action of Faults
###### Abstract  
The present paper investigates the mechanical behavior of buried steel pipelines crossing an active fault. Permanent ground deformation induced by an earthquake will cause serious damage to buried steel pipelines, resulting in buckling failure or even cracking damage to pipelines. Based on ABAQUS software, version 6.13., the model of an interacting soil-pipeline system is established, accounting for large strains and displacements and nonlinear material behavior, as well as contact and friction at the soil-pipeline interface. Numerical analysis is conducted through the incremental application of fault displacement. Combined with the force and deformation characteristics of buried pipelines, a strain-based design criterion is chosen to study the vertical displacement, axial compressive, and tensile strain of buried pipelines, etc. This paper focuses on the effects of horizontal fault displacement, fault type, and fault angle on the structural response of the pipe. The failure of the pipeline, such as wall wrinkling, local buckling, or rupture is identified. Furthermore, the effects of the pipeline internal pressure and pipe wall thickness are investigated. The results show that, when the pipeline depth is 1.5 m under the action of the fault, the buried pipeline will not be subject to beam buckling damage, and both tensile damage and shell buckling damage will occur. In this case, the critical displacement of the tensile failure is more than three times that of the shell buckling failure, which indicates that shell buckling damage is a greater threat to the pipeline. The pipeline is most susceptible to damage under the action of a strike-slip reverse fault. When the fault angle is equal to 45 degrees, the pipeline is more likely to be damaged, while it is relatively safe at a fault angle with 90 degrees. The results of this investigation can determine the fault displacement during pipeline failure and provide some reference for pipeline design.  
buried pipeline; fault displacement; buckling; failure mode; finite elements Article
## 1 Introduction
The ultimate objective is to adhere to legal requirements [37] that ensure the discharge of leachate has minimal environmental and health impacts on humans. A diverse range of treatment methods exists, often necessitating multiple stages that integrate physical, chemical, and biological processes. These include sedimentation, filtration, coagulation, aerobic and anaerobic biological treatments, constructed wetlands, membrane technologies, and advanced oxidation processes [38; 39; 40; 41]. In landfill leachate treatment, it is essential to emphasize sustainable development and circular economy principles. For instance, humic substances can be recovered from leachate and utilized as liquid fertilizers [42], while anaerobic fermentation can be employed to produce methane [43], achieving energy self-sufficiency. These practices not only transform waste into valuable resources but also reduce environmental impacts and promote economic benefits. This paper comprehensively reviews the main techniques employed for landfill leachate treatment. Specifically, the composition and characteristics of landfill leachate are discussed and subsequently, various landfill leachate treatment methods and their treatment efficiencies are provided. In addition, this paper further analyzes the advantages and limitations of each treatment technology, emphasizing the necessity of a synergistic approach that integrates different methods to ensure effective purification of leachate, as well as highlighting the importance of innovating existing technologies. Unlike previous reviews, this study systematically evaluates landfill leachate treatment technologies, emphasizing the challenges of emerging contaminants and the integration of novel membrane-based and oxidation techniques. Additionally, it highlights recent advancements in analytical methods for detecting persistent pollutants, providing a broader perspective on future research needs.
# LoRa Mobile-To-Base-Station Channel Characterization in the Antarctic
## 2 Materials and Methods
### Base Station Transceiver  
At the Princess Elisabeth Station, two receivers are mounted on the roof, each connected to a Yagi-Uda antenna. The base station height is 1382 m above sea level, with the Yagi-Uda antenna mounted 8 m higher. The type 434 A 434-MHz-antenna, manufactured by LPRS (Low Power Radio Solutions [15]), consists of six parasitic radiators, being one reflector and five directors, besides the folded dipole that acts as a feed. The type 868-914 A 868-MHz-antenna by the same manufacturer contains eight parasitic elements, being one reflector and seven directors, besides the folded feed dipole. Both directional antennas point along the path followed by the mobile transmitter. The trajectory along which the expedition takes place aims to stay within the main beam widths of both directional antennas during the full duration of each experiment. This is possible because expeditions in the Antarctic are performed more or less in a straight line from the base station to the destination, only deviating to avoid cracks in the ice. The base station antennas are shown in Figure 4. The base station receivers are connected to a laptop to record all valid LoRa packets, including the SNRs detected by the receiver, the packet's serial number, and time stamp.  
\begin{table}
\begin{tabular}{c c} \hline Modulation & LoRa \\ Power & \(+14\) dBm \\ Bandwidth & 125 kHz \\ Coding Rate & 4/5 \\ CRC & On \\ Preamble length & 10 \\ Spreading factor & 12 \\ Payload & 3 Bytes \\ Frequency 1 & 868.1 MHz \\ Frequency 2 & 434.1 MHz \\ \hline \end{tabular}
\end{table}
Table 1: LoRa parameters selected for the measurements. CRC: cyclic redundancy checksum.  
Figure 3: Detail of the 868 MHz antenna mounted on the 4 \(\times\) 4 vehicle.  
The radiation pattern of the 868 MHz Yagi-Uda antenna is displayed in Figure 5, showing a \(-3\) dB beam width of \(\pm 27.6^{\circ}\) in the H-plane. As for the E-plane, the manufacturer specifies a \(-3\) dB beam width of \(42^{\circ}\). At the start of each expedition, the base station Yagi-Uda antennas are pointed in the direction of the path to be traveled, and expeditions aim to always stay well within the main beam's width. Note that the 868 MHz pattern shown in Figure 5 corresponds to the most narrow beam width. The 434 MHz antenna contains less elements, and therefore has a substantially larger \(-3\) dB beam width, being \(50^{\circ}\) in the H-plane and \(75^{\circ}\) in the E-plane, as specified by the manufacturer. Hence, alignment errors will have a larger impact in case of the 868 MHz Yagi-Uda antenna. Therefore, it will be more difficult to obtain the maximum antenna receive gain along the path traveled by the mobile user at 868 MHz.  
Figure 4: Fixed base station setup with directional antennas for the 434 and 868 MHz bands.  
Figure 5: Measured H-plane radiation pattern of the 868 MHz Yagi-Uda antenna.
# Multi-Satellite Task Parallelism via Priority-Aware Decomposition and Dynamic Resource Mapping
## 5 Experiment Results
### Numerical Analysis
Figure 7 presents the normalized communication cost across all 14 datasets for each algorithm. From the graph, it is clear that HADRM consistently outperforms the other algorithms in minimizing communication cost, especially in large-scale environments. For example, in dataset \(D_{5}\), HADRM reduces the communication cost by approximately 2% compared to IDCE-GA, and by over 4% compared to MILP-STUDIO. Additionally, in the dataset \(D_{13}\), HADRM achieves a significant reduction of around 5% compared to PA-LBIMM, and a 3% improvement over RA-CNP. These results highlight HADRM's superior efficiency in managing communication resources, especially when task scale increases. In contrast, algorithms like IDCE-GA and TS-SVMPP, although they perform well in certain datasets, exhibit a higher communication cost due to the inefficiencies in their communication scheduling mechanisms. Similarly, RA-CNP and VNE-AC struggle with communication overhead, while HEFT-LA and PA-LBIMM, though competitive in some aspects, still lag behind HADRM. This is primarily due to HADRM's advanced dynamic resource mapping and task scheduling mechanisms, which optimize both task execution and communication cost effectively in large-scale environments. \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithms**} & \multicolumn{3}{c}{\(D_{5}\)} & \multicolumn{3}{c}{\(D_{9}\)} & \multicolumn{3}{c}{\(D_{13}\)} & \multicolumn{3}{c}{\(D_{14}\)} \\ \cline{2-13}  & **EQ** & **UQ** & **SR** & **EQ** & **UQ** & **SR** & **EQ** & **UQ** & **SR** & **EQ** & **UQ** & **SR** \\ \hline HEFT-LA [26] & 77 & 3 & 96.22\% & 144 & 16 & 89.99\% & 210 & 30 & 87.45\% & 261 & 39 & 87.01\% \\ PA-LBIMM [32] & 77 & 3 & 95.77\% & 142 & 18 & 88.55\% & 208 & 32 & 86.59\% & 256 & 44 & 85.30\% \\ IDCE-GA [30] & 75 & 5 & 93.75\% & 138 & 22 & 86.25\% & 206 & 34 & 85.83\% & 250 & 50 & 83.33\% \\ TS-SVMPP [31] & 77 & 3 & 96.25\% & 143 & 17 & 89.38\% & 210 & 30 & 87.50\% & 253 & 47 & 84.33\% \\ MILP-STUDIO [25] & 76 & 4 & 95.00\% & 144 & 16 & 90.00\% & 210 & 30 & 87.50\% & 260 & 40 & 86.67\% \\ RA-CNP [29] & 77 & 3 & 96.25\% & 143 & 17 & 89.38\% & 209 & 31 & 87.08\% & 261 & 39 & 87.00\% \\ VNE-AC [36] & 73 & 7 & 91.25\% & 134 & 26 & 83.75\% & 199 & 41 & 82.92\% & 243 & 57 & 81.00\% \\
**HADRM** & **78** & **2** & **97.50\%** & **146** & **14** & **91.25\%** & **215** & **25** & **89.58\%** & **264** & **36** & **88.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Task execution performance across 4 datasets. Figure 7: Communication Cost of eight algorithms across 14 datasets.