esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Practicality Experiment Results of Micro Reacting Pipe
#### 3.1.3 Test Results of Physical Mixing Efficiency of Micro Reacting Pipe  
In the experiment of mixing efficiency, 11.2406 g boric acid powder, 3.6357 g sodium hydroxide thin tablet, 1.973 g potassium iodide powder, and 0.4985 g potassium iodide powder were dissolved in enough deionized water, and then slowly poured sodium hydroxide into boric acid solution and stirred to make a buffer. Then, the potassium iodide solution and potassium iodate solution were poured into the buffer in turn. Then, 98% concentrated sulfuric acid was diluted to 0.025 mol/L and 0.03 mol/L to obtain the solution b. The initial concentrations of each component in solution a and b are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Mass before the Experiment (g)** & **Mass after the Experiment (g)** & **Mass Loss (\%)** & **Duration (h)** & **Medium** \\ \hline Sample 1 & 9.5814 & 9.5810 & 0.0042 & 24 & PBr\({}_{3}\) \\ Sample 2 & 9.5810 & 9.5810 & 0.0000 & 24 & PBr\({}_{3}\) \\ Sample 3 & 9.5810 & 9.5753 & 0.0595 & 24 & 3\% HCl \\ Sample 4 & 9.5753 & 9.5737 & 0.0167 & 24 & 3\% HCl \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of corrosion resistance of IN718.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ID**} & **Mass before the Experiment (g)** & **Mass after the Experiment (g)** & **Mass Loss (\%)** & **Duration (h)** & **Medium** \\ \hline Sample 1 & 5.0909 & 5.0881 & 0.055 & 24 & PBr3 \\ Sample 2 & 5.0881 & 5.0881 & 0 & 24 & PBr3 \\ Sample 3 & 5.0881 & 5.0727 & 0.3027 & 24 & 3\% HCl \\ Sample 4 & 5.0727 & 5.0573 & 0.3036 & 24 & 3\% HCl \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of corrosion resistance of 316 LSS.  
Figure 6: Welded micro reacting pipe before the micromixedness testing.  
The solution a and b were injected into each inlet of the micro reacting pipe simultaneously by the volume flow rate of 100 mL/min by the advection pump, the absorbance of the effluent was measured, and the absorbance was converted into the micromixedness ratio. The experimental results are as shown in Figure 7. The change of micromixedness ratio with the determining size was consistent at 0.05 and 0.06 mol/L H\({}^{+}\) concentration, and accorded to the law of theory that the micromixedness ratio of 0.05 to 0.06 mol/L is higher than that of 0.06 mol/L, so the experimental results are correct.
# POU-SLAM: Scan-to-Model Matching Based on 3D Voxels
## 4 Scan-To-Model Matching with POU Implicit Surface Representation
### POU Implicit Surface Representation for Planar Features
#### 4.2.3 Motion Estimation  
With distances from feature points to their correspondences in hand, we assign a bisquare weight [7] for each feature point. The rules are twofold. In general, when the distance between the feature point and its correspondence is below a certain threshold, the weight is assigned inversely proportional to the distance. However, when the distance is greater than the threshold, the feature point is regarded as an outlier. We proceed to recover the LiDAR motion by minimizing the overall distances.  
First, the following equation is used to project feature point \(x\) to the map, namely \(\widetilde{x}\),  
\[\widetilde{x}=Rx+\tau \tag{11}\]  
in which, \(R\) and \(\tau\) are the rotation matrix and translation vector corresponding to the pose transform \(T\) between current scan and the map. By combining the distances and weights from feature points to their correspondences and Equation (11), we derive a geometric relationship between feature points and the corresponding model,  
\[f(x,T)=d \tag{12}\]  
where each row of \(f\) corresponds to a feature point, and \(d\) contains the corresponding distances. Finally, we obtain the LiDAR motion with the Levenberg-Marquardt method [33]. We do scan matching between the current scan with the last \(n\) scans, and the final result is obtained by aggregating the successfully matched \(n_{m}\) scans. Literally, by matching the current scan with the historical \(n\) scans, the error propagation problem can be suppressed. As for blunder, we limit the scale of the relative transformation between the current scan and the model. When the blunder occurs, we will abandon the result of the current scan and subsequent scan-matching implementation would be slightly affected. After computing the transformation between the scan and model, we add feature voxels corresponding to current scan to the map and remove the feature voxels corresponding to the oldest scan to always keep \(n\) scans for scan matching.  
Figure 3: Illustration of POU implicit surface. Two cells, \(A\) and \(B\), are associated with their support radius \(R_{A}\) and \(R_{B}\), respectively. The value of a point \(x\) in the slashed region can be evaluated by \(I^{M^{E}}(x)=\frac{W_{A}(x)Q_{A}+W_{B}(x)Q_{B}}{W_{A}(x)+W_{B}(x)}\), \(W_{A}(x)=b(\frac{3d_{1}}{2R_{A}})\), \(W_{B}(x)=b(\frac{3d_{2}}{2R_{A}})\), where \(d_{1}\) and \(d_{2}\) are the distances from the point \(x\) to the \(c_{1}\) and \(c_{2}\), respectively, \(R_{A}\) and \(R_{B}\) are the support radius, \(Q_{A}\) and \(Q_{B}\) are local plane functions.
# Influence of Preconditioning and Tunnel Support on Strain Burst Potential
## 3 Methodology
### Numerical Modeling of the Strain Burst and Mitigation Measures
#### 3.1.1 Characteristics of the Models  
The models represent a circular tunnel of radio five meters, included in a box of dimension 30 \(\times\) 30 m to ensure that there is no influence of the boundaries (Figure 4). The models are meshed using triangular nodes. The size of the elements is defined with a gradation from the cavity toward the limits. They are smaller close to the tunnel and grow larger close to the boundaries. To define the stress environment, two tunnel depths were considered: 1000 and 2000 m. As strain bursts occur in brittle rock, it can be assumed that the intact rock density is 2700 kg/m\({}^{3}\)[51]. Considering a stress ratio, K, of 1.5, the stress environment corresponding to a tunnel of 1000 m depth presents an in situ major stress, \(\sigma\)1, equal to 40 MPa and an in situ minor stress, \(\sigma\)3, equal to 27 MPa. In the case of the 2000 m deep tunnel, the in situ major stress, \(\sigma\)1, is equal to 80 MPa and the in situ minor stress, \(\sigma\)3, is equal to 53 MPa.
# A General Calibration Method for Dual-PTZ Cameras Based on Feedback Parameters
## 6 Results and Discussion
### Extrinsic Parameter Calibration Results of the Dual-PTZ Camera  
To verify the extrinsic parameter calibration results, we set the left and right cameras to different angles and zooms and compared the calculated results from (24) with [PERSON]'s method. The results are shown in Table 5.  
\begin{table}
\begin{tabular}{c} **Average** \\ **Error** \\ \end{tabular} \\ \hline
2.6 & 3946.161 & 0.082\% & 4054.10 & 2.73\% & 4135.32 & 4.79\% \\
4.2 & 6273.145 & 0.159\% & 6454.95 & 2.89\% & 6617.45 & 5.49\% \\
5.7 & 8634.695 & 0.194\% & 8659.83 & 0.29\% & 8901.21 & 3.09\% \\
8.6 & 13,026.34 & 0.167\% & 13,025.48 & 0.00\% & 13,197.83 & 1.32\% \\
11.3 & 17,067.33 & 0.340\% & 16,818.90 & \(-\)1.46\% & 17,057.53 & \(-\)0.06\% \\
13.5 & 19,597.12 & 0.452\% & 20,179.47 & 2.97\% & 20,102.23 & 2.58\% \\
16.6 & 24,024.33 & 0.549\% & 24,236.96 & 0.88\% & 24,239.70 & 0.90\% \\
19.3 & 28,424.45 & 0.642\% & 27,377.25 & \(-\)3.6\% & 27,697.69 & \(-\)2.56\% \\ \hline \end{tabular}
\end{table}
Table 4: Comparison between focal lengths estimated by different methods and the ground truth.  
Taking the extrinsic parameters obtained with [PERSON]'s method as the ground truth and comparing them with the extrinsic parameters calculated using our method, it can be seen from Table 5 that the cosine similarity of the rotation matrix reached more than 99.8%, and the translation vector error was less than 1%. Considering that the PTZ camera remained fixed in its roll direction, we only took the recalculated Euler angles in the horizontal and pitch directions into comparison. The errors are less than 1 degree, which can basically meet the calibration requirements of the extrinsic parameters of dual-PTZ cameras.  
Figure 8: Quadratic (**a**) polynomial and cubic (**b**) polynomial fitting situations.
## 3 Materials and Methods
### Thermal Conductivity  
A commercially available measurement device was used, (Trident Thermal Conductivity Instrument, C-Therm Technologies Ltd., Fredericton, NB, Canada), in which the so-called Modified Transient Plane Source (MTPS) method is employed. According to the method, a spiral metallic element is used as heater. The element is covered by a polymeric film, so it is electrically insulated but does not thermally conduct. A constant current is applied to the element, and a subsequent amount of heat evolves. Around the heating element, there is a guard ring that prevents the in-plane heat transport, thus the produced heat is transferred vertically, with respect to the spiral plane. Since the element exhibits a finite resistance, the produced heat is proportional to the voltage applied across the element. Apparently, the produced heat leads to a change in the temperature of the element, and such a change can be recorded by the element itself, which can be used as a temperature sensor as well. Therefore, the temperature change is related to the voltage across the heater/sensor. By attaching a material above the element, a rise in the temperature at the interface between the sample and the sensor occurs, which induces a change in the voltage drop across the heater. The rate of increase in the sensor voltage is used to determine the thermal properties of the sample. The voltage is factory-calibrated to temperature. The thermal conductivity is inversely proportional to the rate of increase in the temperature at the point of contact between the sensor and the sample.  
Each sample was measured several times, and the measured values were directly extracted by the C-Therm v 3.6.1.57 software. Finally, it must be noted that before measuring the samples of interest, the device was calibrated against standard materials, as proposed by the manufacturer.
## 3 Test and Verification
### Calculation of Test Results  
#### 3.2.1 Calculation of Angle Test Results  
Based on the collected experimental data, we first calculate the average displacement value of the laser spot and the average heating value of the support frame, as shown in Table 2. Then, using the symmetry of the support frame mirror image, we calculate the relative displacement value of the bivariate laser spot. The displacement vector calculation diagram is shown in Figure 23, and the displacement calculation is shown in Equation (19).  
\[\left\{\begin{array}{l}x_{1,vertical}=x_{1}\cos\beta,\\ x_{1}^{\prime}=x_{1,vertical}\\ x_{2}^{\prime}=-x_{1}^{\prime}\\ x_{1}=1.24622,\beta=3.355^{\circ}\\ \Delta S=\sqrt{\left(x_{1}^{\prime}-x_{2}^{\prime}\right)^{2}}\\ \end{array}\right. \tag{19}\]  
We insert the test parameters \(H\) = 3435 mm, \(n\) = 2, and \(\Delta S\) into Equation (4) and obtain the relative angle change value of the two variables as:  
\[\Delta\theta=18.676^{\prime\prime}\]
# Roll Eccentricity Signal Detection and Its Engineering Application Based on SFFT-IAA
## 3 Roll Eccentricity Signal Detection Based on SFFT-IAA
### Frequency Band Partition Based on SFFT  
Considering the sparsity of the roll eccentricity signals, SFFT is an effective measure for processing such signals. SFFT is a sublinear algorithm that runs 10 to 100 times faster than the conventional FFT [21; 22]. For roll eccentricity signals, SFFT is used to quickly find the distribution range of each harmonic frequency and then, based on the frequency distribution, several small frequency bands are set, with each containing similar harmonic frequency components. Since each small frequency band has a small range, the calculation amount is greatly reduced by IAA, which greatly improves the computational speed of the whole detection process and meets the requirements for real-time performance.
# Joint Maintenance Strategy Optimization for Railway
## 4 Numerical Analysis
### Numerical Results for Wheelset Maintenance  
In order to ensure safe operation and prolong the service time of the wheel, with the above performance parameters, the maintenance cycle of the wheel and the reproffiling threshold of the flange thickness are optimized. With the constraint of failure risk, the change rule of the maintenance cost rate with the maintenance cycle and reproffiling strategy is obtained, as shown in Figure 9. Comparing Figure 9 with Figure 7, it can be seen that some reproffiling strategies are filtered out due to the failure risk constraints. The optimal maintenance interval is \(\Delta t\) = 6. The selected reproffiling strategy is \((\mathit{Sd}_{L}\), \(\mathit{Sd}_{H})\) = (28.5, 31), the minimum maintenance cost rate is \(C^{\ast}(t)\) = 78.12 and the number of reproffiles is 5, i.e., in practical operation, when the mileage of the train running reaches 60,000 km, periodic inspection is performed. When the thickness of the wheel flange is less than 28.5 mm, it is reprofiled and restored to 31 mm. The expected mileage with the proposed maintenance strategy is \(284.99\times 10^{4}\) km.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Items** & \(\mathit{C}_{i}\) & \(\mathit{C}_{m}\) & \(\mathit{C}_{p}\) & \(\mathit{C}_{e}\) & \(\mathit{C}_{l}\) & \(\mathit{C}_{r}\) \\ \hline
**The Cost Value** & 20 & 50 & 400 & 1060 & 50,000 & 12,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The value of the different maintenance costs.  
Figure 8: Threshold values of wheel flange reproffiling strategy.  
By observing the trend of maintenance cost rates in Figure 8, it can be seen that, when \(\Delta t\) and the \(\mathit{Sd}_{H}\) are fixed, the cost rate declines first and then rises with the decrease in \(\mathit{Sd}_{L}\), or it keeps declining due to some reprofiling control strategies being filtered out. Frequent reprofiling leads to higher maintenance costs when \(\mathit{Sd}_{H}\) and \(\mathit{Sd}_{L}\) are close. When the gap between \(\mathit{Sd}_{H}\) and \(\mathit{Sd}_{L}\) is large, the reduction in wheel diameter is also large for each reprofiling. This results in wheelset life reduction and a cost rate increase.  
The variation in the expected life of the wheel is shown in Figure 10. It can be seen that the trend of the expected life of the wheelset is roughly opposite to that of the maintenance cost rate trend. There are small fluctuations in the middle due to reasons such as the random value of the proportional coefficient.
## 2 Overview of Retrieval-Augmented Generation
### Retrieval
#### 2.1.4 Pre- and Post-Retrieval Enhancement  
Pre-retrieval enhancement encompasses strategies and techniques applied to an input query or retrieval system prior to a RAG system initiating its retrieval process. Pre-retrieval enhancement primarily aims to refine or enrich the original query, thereby ensuring efficient retrieval of the most relevant information. Key techniques in this domain include query expansion, query rewriting, and query augmentation.  
One query-expansion approach, Query2 doc [35], generates pseudo-documents using LLMs with few-shot prompting. These pseudo-documents aid query disambiguation, thus enhancing performance of both sparse and dense retrievers. Indeed, experiments demonstrate improvements in BM25 performance ranging from 3% to 15%. Another technique, Rewrite-Retrieve-Read [36], employs query rewriting by prompting an LLM to generate a revised query that bridges the gap between user input and retrieval requirements; then, it uses a web search engine to retrieve context for the RAG system. Experimental results indicate that this approach can enhance RAG performance when black-box LLMs or trainable small language models act as query rewriters. Additionally, the BlendFilter framework [10] implements query augmentation by incorporating both external and internal knowledge sources; it filters retrieved information to eliminate irrelevant content before combining it with the original query to generate a final answer.  
Post-retrieval enhancement in RAG involves refining information retrieved from a knowledge base before a language model uses it to generate a response. Post-retrieval enhancement primarily endeavors to improve the relevance, coherence, and utility of retrieved documents, ensuring that they contribute meaningfully to the final output.  
Several methods illustrate this approach. For instance, the aforementioned BlendFilter framework [10] integrates both pre-retrieval query augmentation (for improved information retrieval) and post-retrieval knowledge filtering (to remove noise) for achieving comprehensive enhancement. Likewise, the Retrieve-Rerank-Generate (Re2G) method [37] enhances retrieval robustness by assembling documents retrieved from different retrievers (such as BM25 and Dense Passage Retrieval (DPR)) through a reranking operation. Additionally, the RECOMP method (Retrieve, Compress, Prepend) [38] compresses retrieved documents into textual summaries before in-context augmentation occurs during generation. This approach not only reduces computational cost but also alleviates the language model's burden by filtering relevant information from lengthy retrieved documents.
## 4 Discussion
### In-Vitro Setup  
An in-vitro model was built following anatomical characteristics of the upper-limb circulatory system. This section of the body was chosen since PRV is usually measured from the finger or the wrist, and because blood pressure is usually assessed on the arm, both invasively and non-invasively. Although it is not possible to completely mimic the anatomy and physiology of the circulatory system in an in-vitro model, the length of the arterial segments, the diameter of the vessels and the behaviour of the pulsatile pump were designed and selected to be as similar as possible to the average human being. Also, the artificial blood used was shown to have an optical response partly similar to the optical spectrum of blood. Hence, optical signals can be acquired using the proposed artificial blood recipe, with better performance for red wavelengths, where the absorbance peak is at its maximum.  
The behaviour of the system was validated by measuring pressure signals at each arterial segment, PPG signals at the radial and deep palmar arch segments, and by altering stroke rate, target flow, and hence stroke volume. It was observed that the system behaved as expected. From the PPG signals, it was possible to determine stroke rate reliably, although the signals became noisier as the stroke volume was decreased, lowering the signal-to-noise ratio in these signals and making it harder to properly determine stroke rate. From the pressure signals, the expected performance was observed: As stroke volume increased, pressure increased in all measurement sites. Also, there were differences in the magnitudes of the pressure signals that were expected given the differences in resistance to flow due to the different vessel diameters.  
As explained by [PERSON] et al. [15] and [PERSON] et al. [10], using this kind of setup has become increasingly useful in the study of PPG signals and light-tissue interaction, since it allows for a dynamic simulation of the circulatory system as well as the investigation of several processes that are difficult to investigate in in-vivo studies due to the large number of uncontrolled variables. Moreover, using these in-vitro setups allows for the investigation of extreme conditions that can be difficult or unethical to simulate in-vivo circumstances, such as the complete absence of autonomic regulation. To the best of our knowledge, this is the first study that aims to investigate PRV in an in-vitro model in the absence of HRV or any other physiological processes, such as respiration.
###### Abstract  
Fractures are widely distributed in the subsurface and are crucial for hydrocarbon, CCS, offshore infrastructure (windfarms), and geothermal seismic surveys. Seismic anisotropy has been widely used to characterize fractures and has been shown to be sensitive to background matrix porosities in theoretical studies. An understanding of the effects of background porosity on seismic anisotropy could improve seismic characterization in different fractured reservoirs. Based on synthetic rocks with controlled fractures, we conducted laboratory experiments to investigate the influence that background porosity has on P-wave anisotropy and shear wave splitting. A set of rocks containing the same fracture density (0.06) with varying porosities of 15.3%, 22.1%, 26.1% and 30.8% were constructed. The P- and S-wave velocities were measured at 0.5 MHz as the rocks were water saturated. The results show that when porosity increased from 15.3% to 22.1%, P-wave anisotropy and shear wave splitting exhibited slight fluctuations. However, when porosity continued to increase to 30.8%, P-wave anisotropy declined sharply, whereas shear wave splitting stayed nearly constant. The measured results were compared with predictions from equivalent medium theories. Qualitative agreements were found between the theoretical predictions and the measured results. In the Shelby-Cheng model, an increase in porosity reduces fracture-induced perturbation in the normal direction of the fracture, resulting in lower P-wave anisotropy. In the Gurevich model, an increase in porosity can reduce the compressional stiffness in parallel directions to a larger extent than that in perpendicular directions, thus leading to lower P-wave anisotropy.  
Keywords:anisotropy; fractures; porosity; rock physics +
Footnote †: journal: Applied Sciences
# Environmental Effects on HV Dielectric Materials and Related Sensing Technologies
## 3 Conclusions  
This review covered some key environmental effects on the dynamics and life-time of ceramic and polymeric insulators. The study shows that high pollution levels are non-uniformly distributed and are correlated with, for example, industrial and marine regions. In these regions, the pollution,  
Figure 3: Flow chart of a maintenance prioritization method for characterizing insulators [116].  
aging and degradation of insulator materials is accelerated compared to relatively unpolluted regions, where the degradation is more predictable over time. The rate of the environmental pollution of HV insulators depends on the weather conditions, surface roughness wettability, and insulator geometry. The mechanism of pollution of HV insulators is mainly governed by the gravity, wind forces, electrostatic forces, humidity, particle size and mass, and chemical properties of the insulators. The analyses indicate that the presence of pollutants on insulator surfaces leads to different types of electrical discharge such as corona, partial discharge and dry band arching which result in the accelerated degradation of insulators and the surrounding infrastructure and ultimately can cause flashover and blackouts. Although multiple methods were devised to improve the material surface of insulators, the surface pollution associated with aerial deposits and electromagnetic radiation cannot be totally avoided. To that end, methods to analyze, characterize, monitor and predict the state of the systems were devised. The most common technologies to carry out these tasks rely on sensors that operate in microwave, ultraviolet and visible spectra. The sensors are integrated in sensor networks or IoT systems to provide a more comprehensive picture of larger power networks. The sensing data streaming from these sensors are filtered and analyzed using statistical methods and artificial intelligence (AI) algorithms to make predictions of the health state of insulators under specific environmental and power-network-related stresses. The review also highlights the importance of taking multiple factors and technologies into account when designing the power grids of the future to alleviate some of these adverse effects.  
All the four authors have contributed equally to conceptualization, writing, review and editing.  
This research received no external funding.  
There is no conflict of interests.