esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Method Validation
#### 3.2.3 Selectivity  
Selectivity was assessed by using both PDA and MS detectors in 2D and 3D modes, with no interfering peaks detected. Additionally, testing on serum samples from 50 healthy donors (control serum), one hemolyzed, two lipemic (mild and severe lipemia), and one icteric serum revealed no impact on AA identification and quantification. The measured concentrations of AA in normal serum, hemolytic, mild and severe lipemic, and icteric were 2.05, 2.22, 4.32, 5.15, and 1.78 \(\upmu\)g/mL, respectively. The deviations from control serum samples did not exceed 15% for samples with hemolysis and high bilirubin. There were no significant variations in the peak areas of AA and the IS, nor ion suppression or enhancement. No interferences were found on the retention time of the analyte and of the IS in the blank samples due to the presence of bilirubin and hemoglobin in the tested serums. The concentration of free AA in the lipemic samples was elevated more than two-fold. Data in the literature examining AA in lipemic, icteric, and serum samples with high hemolysis are incredibly scarce. It could be assumed that elevated serum TAGs are likely a serious interfering factor in the determination of free AA. Therefore, it is advisable to avoid lipemic blood serums for AA analysis.
# Communication Fault Maintenance Decision of Information System Based on Inverse Symmetry Algorithm
## 3 Experimental Analysis  
In order to test the effectiveness of the information system's communication fault maintenance decision-making based on the inverse symmetry algorithm, the information system based on the inverse symmetry algorithm is applied to the power information system, which includes dozens of power information systems, dozens of servers, switches, and other information equipment, nearly 2000 km of communication network lines and its ancillary communication equipment. When a subordinate city visits an information system, it is difficult to determine whether it is the communication equipment or the information equipment software and equipment. Taking the access fault of the communication fault in a power information system as an example, this paper uses the diagnosis model to diagnose the fault cause and make maintenance decision.
## 2 Background and Related Work  
This section begins with a concise background and overview of recent studies focusing on the optimization of energy consumption in the SDWSN. We conclude this section by reviewing recent research efforts on machine learning-based energy-efficient routing schemes applied in this domain.
# Quantification and Correction of Wave-Induced Turbulence Intensity Bias for a Floating LIDAR System
## 2 LIDAR Theory
### Floating LIDAR Systems (FLS)  
In the case of a buoy mounted LIDAR (FLS), the motion induced by the dynamic response of the buoy to waves affects the wind LIDAR measurement. As the LIDAR principle is defined for a stationary position, a motion-induced variation in the velocity measurement contaminates the FLS measurements. The measurement contamination arises from two causes: the instantaneous tilt of the LIDAR due to roll, pitch and yaw angles, and the translational movement of the platform modifying the perceived velocity at the measurement point [21]. In order to quantify the effect of motion on the wind LIDAR measurement, it is essential to measure the movement of the buoy using an Inertial Measurement Unit (IMU). The IMU measures the translational and rotational motion of the buoy in the three axes using accelerators and gyroscopes, providing six degrees of freedom of motion. The orientation of the motion frame used in this study is shown in Figure 1.  
Roll, pitch and yaw measurements (\(\Phi,\beta,\gamma\)) from the IMU can be used to correct the direction of the floating LIDAR's measurement beams. The rotation matrix allows to turn the rotated LIDAR reference frame into a non-rotated reference frame [21]. It is defined by:  
\[T_{rot}=\left(\begin{array}{ccc}cos\gamma cos\beta&-sin\gamma cos\Phi+cos \gamma sin\beta sin\Phi&-sin\gamma sin\Phi-cos\gamma sin\beta cos\Phi\\ sin\gamma cos\beta&cos\gamma cos\Phi+sin\gamma sin\beta sin\Phi&-sin\gamma sin \beta cos\Phi+cos\gamma sin\Phi\\ sin\beta&-cos\beta sin\Phi&cos\beta cos\Phi\end{array}\right) \tag{8}\]  
The rotation into the non-rotated reference frame is applied to a wind vector measured in the LIDAR reference frame by a dot product with the rotation matrix \(T_{rot}\). The use of the IMU's rotational and translational measurements allows the physics of the buoy's motion, induced by its geometry, weight and anchorage, to be taken into account in constructing a correction to the motion-induced wind measurement contamination. The use of an IMU for FLS measurements correction requires two conditions. The first is the alignment of the coordinate systems from the IMU and the wind LIDAR, a re-alignment can be performed in case of an offset [20]. The second is an equivalent position of the two devices relative to the center of gravity of the buoy, this induced error can also be corrected [21]. In this study, the two devices are aligned and close enough not to need these corrections.
## 2 Materials and Methods
### Monte Carlo Simulation: GEANT4 Simulations  
A key challenge of this study lies in integrating machine parameters from the cyclotron accelerator, such as the average beam energy (E\({}_{mean}\)), the energy spread (\(\sigma_{E}\)) and the influence of the transverse beam size, dose, and LET, into the simulation. These parameters are rigorously examined, and their impacts are presented in this paper. In the simulation, the source of the beam is defined as a virtual source located at the treatment head before the exit window. As can be seen in Figure 2, the distance from the source to the phantom surface was set at 420 mm. For this study, a cubic water phantom (\(50\times 50\times 40\) cm\({}^{3}\)), which matches the dimensions of a standard phantom used in beam commissioning, was modeled. A virtual sensitive detector, positioned immediately in front of the exit window, was used to capture the beam profile data.  
The simulation was performed using GEANT4 (version 11.0) [20], a widely recognized open source tool, to model the treatment process. The QGSP_BIC_HP reference physics list was used for all simulations in this study, which is a comprehensive physics list that covers a wide range of hadronic interactions. It is used to simulate the interaction of high-energy protons and neutrons with tissue in radiation therapy applications. To measure depth-dose  
Figure 2: A diagram of the setup in the GEANT4 simulation, illustrating the positioning based on the actual experimental configuration, with a distance of 420 mm from the source, where the beam exits from the vacuum into the air, to the target in air.  
and beam profiles, the water phantom was subdivided into 1400 thin slabs, each 0.25 mm thick, arranged across a total depth of 350 mm, sufficient to cover the full range of proton energies used in this study. Proton particles were generated using the G4 ParticleGun() class. The particle gun was positioned at the center of the vacuum tube to generate a focused beam with a defined beam size and energy spread.  
To generate particles with a Gaussian distribution of beam size and energy spread, the function G4 RandGauss::shoot(mean, sigma) was employed, where the mean and sigma were defined to cover the values of interest in this study.
## 3 Results
### Mechanical Properties
#### 3.1.4 Flexural Strain  
Typical stress-strain curves in the three-point bending test are illustrated in Figure 4. The higher force values at the same deformation (deflection) in Scots pine than in Norway spruce indicated higher MOE and MOR values. The corresponding stored energy values at maximum stress for the curves presented in Figure 3 were 0.81 MJ/m\({}^{3}\) (spruce, burned), 0.58 MJ/m\({}^{3}\) (spruce, reference), 1.16 MJ/m\({}^{3}\) (pine, burned), and 0.73 MJ/m\({}^{3}\) (pine, reference).  
The flexural strain at maximum force during the three-point bending test is presented in Table 12. Strain and air-dry density had a modest negative correlation in spruce (-0.266) and a negligible correlation in pine (-0.071). The highest strain values at maximum force were observed in the reference trees in pine sapwood in the top logs and the lowest in the slightly burned trees in spruce heartwood in the butt logs. In Norway spruce, strain generally decreased towards the top, but in Scots pine, the lowest values were observed in most cases in the middle logs. In the middle and top logs, the strain values were, on average, higher in pine than in spruce in all groups.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Norway Spruce**} & \multicolumn{4}{c}{**Scots Pine**} \\ \hline
**Variable** & **Estimate** & **Std. Error** & **95\% CI** & **Estimate** & \multicolumn{2}{c}{**Std. Error**} & \multicolumn{2}{c}{**95\% CI**} \\ \hline  & & & **Lower** & **Upper** & & & **Lower** & **Upper** \\ \hline Intercept & 15.511 & 19.585 & \(-\)23.266 & 54.288 & 42.146 & 11.974 & \(-\)23.266 & 54.288 \\ Fire damage class (ref: no damage) & & & & & & & \\ More damaged side & 3.505 & 3.198 & \(-\)2.827 & 9.838 & \(-\)11.485 & 4.285 & \(-\)2.827 & 9.838 \\ Less damaged side & 4.496 & 3.288 & \(-\)2.013 & 11.006 & \(-\)9.319 & 4.316 & \(-\)2.013 & 11.006 \\ Height class (ref: top log) & & & & & & & \\ Butt log & 6.391 & 2.865 & 0.719 & 12.064 & 2.830 & 2.894 & 0.719 & 12.064 \\ Middle log & 8.474 & 2.834 & 2.864 & 14.084 & 3.137 & 2.899 & 2.864 & 14.084 \\ Radial position (ref: heartwood) & & & & & & & \\ Sapwood & \(-\)3.182 & 19.017 & \(-\)40.833 & 34.470 & \(-\)57.080 & 12.576 & \(-\)40.833 & 34.470 \\ Air-dry density & 0.108 & 0.038 & 0.033 & 0.183 & 0.065 & 0.021 & 0.033 & 0.183 \\ Interaction of radial position with air-dry density (ref: heartwood \({}^{*}\) density) & & & & & & & \\ Sapwood \({}^{*}\) density & 0.027 & 0.043 & \(-\)0.058 & 0.112 & 0.144 & 0.026 & \(-\)0.058 & 0.112 \\ \hline \hline \end{tabular}  
* refers to interaction.  
\end{table}
Table 11: Fixed coefficients in the linear mixed model (Equation (9)) for the modulus or rupture (MOR).  
Figure 4: Typical flexural stress–strain curves of sapwood in burned wood (heavily burned side) and reference wood of Norway spruce (**left**) and Scots pine (**right**) in butt logs.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 3 The Purpose of the Investigations  
### Smart Street Lighting  
Currently, the smart street lighting industry is in a phase of rapid development. The United States, Germany, and other places have implemented pilot projects and small-scale deployments to construct smart streetlights, with the main objectives of saving energy, enhancing urban security, sensing urban information, and meeting charging needs. Delft University of Technology (TU Delft) equipped ordinary LED streetlight poles on campus with motion sensors and wireless communication systems to transform them into smart streetlights [25]. In 2014, Ubitricity company completed the first retrofit of smart streetlights with charging posts and other equipment to meet charging needs [26]. In the same year, San Diego became the first US city to widely use GE's networked LED lighting fixtures, deploying 4000 smart streetlights [27]. In 2017, the city of Patras used Flash-Net-based smart controllers and LED lighting systems to build smart street lights [28]. In addition, as of 2020, Los Angeles retrofitted about 200,000 smart streetlights, involving nearly 7200 km of roadways, with data collection and intelligent analysis through the installation of cameras and other sensing devices on light poles [29].  
However, unlike the general street lighting control systems with mature system design standards, there are no standards to guide the siting planning and system construction of smart street lighting because it involves multiple fields such as transportation, energy, and security. This study was based on the 5G micro-base station location selection model for smart street lighting, which not only provides help for the siting of smart streetlights but also provides a theoretical basis and support for introducing related standards.
# Balancing Distributed Key-Value Stores with Efficient In-Network Redirecting
## 3 KVSwitch Overview
### Processing Query  
The processing procedure for incoming packets varies according to their operation types and to whether they are requesting a hot item (see Algorithm 1). Each KVS query first enters the _Hot Key Lookup_ module to determine whether the requested item is hot. KVSwitch exerts no influence over queries for cold items other than updating the counting information in the _HashPipe_ and _Server Load Count_ modules. Alternatively, if this is a query for a hot item, the process varies between the _Get_ and _Set_ queries, also shown in Figure 3.  
**Handling _Get_ queries of hot items.** First, the _Hot Key Lookup_ and _Reassign Server_ modules will assign a new destination server for this packet in a round-robin manner across all servers containing this item (Section 4.2.2). The _Replica Manager_ module then checks whether the replica on the new destination server is valid (Section 4.2.3). If valid, the destination IP will be rewritten with the address of the new destination server. Finally, the _Server Load Count_ module increases the load on the destination server (Section 4.2.6).  
**Handling _Set_ queries of hot items.** The core job of handling a _Set_ query in KVSwitch is to update all replicas of this key. To do this, the valid flag of this item is first set to invalid on all servers that contain its replicas (Section 4.2.3). The _Set_ query itself will be forwarded to its destination server to update the key-value pair as for a normal packet. Secondly, a packet is sent to each server containing a replica of this item to update the value (Section 4.2.4). After the server updates its replica, it sends a confirm packet back to KVSwitch to validate the replica on the corresponding server (Section 4.2.3). Finally, the _Server Load Count_ module increases the load on the final destination server in the same way as that for the _Get_ queries (Section 4.2.6).  
**Handling _Delete_ queries of hot items.**_Delete_ queries can be treated as special _Set_ queries. All valid flags for the item in question are set to false and all servers containing this item will receive  
Figure 3: Logical view of KVSwitch.  
a _Delete_ packet from KVSwitch to remove the data from KVS. Due to these similarities, we will henceforth focus on discussing _Set_ queries and leave out _Delete_ ones.
# Grid Nodes Selection Strategies for Power Quality Monitoring
## 2 Literature Review
### Scientific Papers and Practical Reports
According to CEER guidelines (published in 2012), the solution is: (1) 20 sites if averages of all locations will be (must be) reported; (2) 200 sites if 95 th percentile values will be reported; (3) 1000 sites if 99 th percentile values will be reported. However, concerns can be raised, because despite giving the exact numbers of sites, the recommendation has not been verified in practice, and it is still not verified to this day. 2. If population has a normal distribution, the number of samples can be determined by the following equation: \[n=\bigg{(}\frac{Z\cdot\sigma}{E}\bigg{)}^{2},\] (5)where \(n\)--number of sites; Z--z-test critical value for required (chosen) confidence level; \(\sigma\)--population standard deviation; \(E\)--accepted error value. However, it is unclear how to apply the approach in the specific PQ case. In addition, there is limited literature in the field of PQ parameters probability density functions, in other words, usually it is unknown whether a population has a normal distribution. In [24] (2021), the monitoring campaign carried out in Great Britain is presented. \"Western Power Distribution\" company, one of the British DSOs, has been executing the \"Primary Networks PQ Analysis\" study since 2018. The reports are publicly accessible, and according to our opinion, currently, this project is one of the best PQ studies examples, both novel and non-resembling. The main aim of the project is to reduce uncertainties around the PQ within Primary Networks, and facilitate increased integration levels of low carbon technologies (LCT). A total of 24 monitors were installed during the first stage of the project (mainly at 33 kV nodes). The following criteria were used for trial sites prioritization: the main criterion is LCT penetration; criterion No. 2--additional features such as rapid electric car chargers, new LCTs about to connect, and the presence of existing PQ issues; criterion No. 3--similar networks selection (considering total circuit length, the proportion of overhead lines, infeed substation demand) in order to compare them; criterion No. 4--remote PQ monitoring feasibility (voltage and current transformers and their secondary terminals availability, mobile communication signal strength, substation layout, etc.). In the second study stage, sites containing onshore wind, battery energy storage, and large EV charging stations were included. A total of 46 monitors were installed in total. Despite the low scale of the campaign, many communication problems were faced and solved or discussed. In addition, the study is innovative not only due to communication aspects and frequency response laboratorial research of existing measurement transformers (in order to accurately determine harmonics levels), but also due to attempts and contributions to laboratory test methodology improvements.
## 3 Methods
### Simulation Modeling
#### 3.1.1 Model Description
Thus, these values are not entered but are determined by the simulation logic. However, the uncontrollable vessel waiting times at destination Ports B and C are input, following the specified distribution functions fitted to historical data. Furthermore, the voyage times between ports are set to be constants using average values from the assumption that the voyage time between ports is relatively stable. The model also considers the regularly scheduled maintenance time (e.g., six hours every three months) for vessels by using the reliability logic in the transporter module. The bad weather conditions for high wind and low visibility are modeled as discrete random events following the distribution according to the historical weather data. For example, a high-wind weather entity randomly arrives in the system according to the distribution for interarrivals. Then, it stays in the \"High wind\" condition during the period specified by the fitted distribution. While the high wind condition is in effect, the vessels are unable to leave port. However, vessels already underway will continue to move to the destination port regardless of the weather condition. Once the high wind condition is cleared, all voyage operations return to normal. The same logic is applied to a low visibility random event with the different distributions for interarrival and duration, using the \"Visibility\" condition. The bad weather conditions are illustrated in the upper part of Figure 2.  
The developed model assesses several performance metrics of the system. It provides details on the amount of product produced and transported, counts the number of trips by each vessel, accounts for operating costs (including voyage and port dues), and estimates GHG emissions. To estimate such performance metrics which are not provided by Simio, several variables are defined as user-defined statistics in the model. The generation of a simulation model that mimics a real-world situation requires a set of data. The used data have been collected from the internal documents of the studied company, the public weather database, and interviews with field experts. The following is a list of data used in the designing process of the simulation model: production volume; vessel operating times (i.e., voyage and tugging times); loading and unloading times at terminals; vessel capacity, and operating costs (i.e., fuel cost per round trip and port dues). Processes probability distributions are fitted to each of the following processes: vessel waiting at the destination ports; high-speed wind (interarrival time and duration), and low visibility (interarrival time and duration) (see the details in Section 4.2).
# Ultrasound Elastography: Methods, Clinical Applications, and Limitations: A Review Article
## 3 Elastography Techniques
### B: Shear Wave Imaging
#### 3.2.1 One-Dimensional Transient Elastography (1D-TE)  
One-dimensional transient elastography (1D-TE) is the first shear wave imaging technique developed, and it is also the most widely available and used scheme of the three. The main use of 1D-TE is for assessing liver fibrosis, which later led to the development of Fibroscan(r) (Echosens, Paris, France), a diagnostic non-invasive ultrasound-based imagingtechnique that measures fibrosis and other liver pathologies. The Fibroscan(r) probe includes a mechanical vibrating device and an ultrasound transducer that generate shear waves through external vibration, propagating through the tissue. Although one-dimensional transient elastography is US-based, it is functional without direct B-mode usage and instead uses A-mode ultrasound that measures the shear wave speed and therefore calculates [PERSON]'s modulus [43; 44; 45].
## 3 Methodology
### Uncertainty Propagation with Triangular Distributions  
To better account for real-world variability in key input parameters, a supplementary stochastic modeling layer was integrated into the optimization framework. This was motivated by the fact that several operational variables--such as transport distance, energy intensity of processing, and electricity emission factors--are known to fluctuate due to seasonal, spatial, and regulatory dynamics. Ignoring such inherent variability could lead to overly deterministic and potentially misleading optimization outcomes.  
To address this, triangular probability distributions were defined for the three most influential variables identified via SHAP analysis: Transport Distance (x\({}_{1}\)), Energy Intensity (x\({}_{2}\)), and Grid CO2 Emission Factor (x\({}_{5}\)). Each distribution was centered at the original deterministic value (mean) and bounded by \(\pm 10\%\), with the highest probability density at the mean. This distribution choice balances realism and simplicity, ensuring computational tractability while capturing plausible variability ranges without overfitting to tails.  
Latin Hypercube Sampling (LHS) was employed to efficiently generate 500 quasi-random combinations from the joint distribution space of the three variables. This approach ensures uniform stratification across each input's uncertainty range while maintaining independence between variables [58]. For each sampled realization, the trained Gradient Boosting Regression (GBR) surrogate model was used to rapidly predict the associated cost and emission outputs. The resulting dataset of 500 cost-emission pairs was subsequently processed through the NSGA-II algorithm to reconstruct a stochastic Pareto front under uncertainty.  
This procedure enabled the construction of probabilistic envelopes (i.e., uncertainty bands) around the original deterministic Pareto frontier. These envelopes allow assessment of both the variability in solution space and the robustness of previously identified optimal configurations--particularly the TOPSIS-optimal recommendation. By observing the spread and overlap between the deterministic and probabilistic Pareto fronts, we quantified the sensitivity of optimality to real-world operational deviations. This analysis not only strengthens the credibility of our results but also provides practical insight for decision makers operating under uncertainty.