esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Monitoring Data Analysis
### Interactions between Twin Tunnels and Their Influence on the above Tunnel  
The Gaussian distribution curve was first developed to represent the land subsidence profile, and it has since been confirmed by numerous researchers in a variety of practical engineering and laboratory tests [25].  
Figure 12: The opening width of the segment joint versus time at CS12.  
Figure 13: The opening width of the segment joint versus time at CS6.  
Furthermore, the existing large-diameter shield tunnel is a flexible construction, so the Peck equation can be utilized to characterize the settling trough generated by tunnel excavation [26]. However, in a complex metropolitan setting, the construction of double tunnels might have a greater impact on neighboring existing tunnels, and the ultimate settlement is not a simple superposition of two Gaussian distribution curves. The interaction between new twin tunnels is also the primary source of the asymmetric settlement profile. The settlement trough induced by the first tunnel excavation may be calculated using the Peck equation. Figure 14 depicts separate settlement profiles generated by Line 12's twin tunnel excavation.  
As shown in Figure 14, the volume loss due to the second tunnel's construction (0.14%) was found to be less than the volume loss due to the first tunnel's construction (0.19%). The width of the settlement tank induced by the second tunnel, on the other hand, was slightly greater than that induced by the first tunnel.  
A large number of examples investigated the changes in the sedimenting tank generated by the first and second tunnel excavations. However, in this case, it is also observed that there was a difference between the settlement trough induced by the construction of the first tunnel and that of the second tunnel. The largest settlement induced by the second tunnel construction occurred at a distance from the center of the new twin tunnel rather than on its central line. The reason for this may be because the soil in the middle part was reinforced before the construction of both the left and right lines of Line 12, resulting in multiple reinforcements that greatly changed the stiffness of the soil.
## 1 Introduction
A key parameter of this method is the stomatal slope (g\({}_{1}\)), which is not easily available, and neglecting its temporal variations can significantly affect the accuracy of T/ET estimation [32]. All of the above ET partitioning methods, regardless of their complexity or application scope, show certain limitations. Therefore, there is still a lack of a simple and widely applicable ET partitioning method. In contrast, machine learning models have demonstrated significant advantages as data-driven approaches. Machine learning models can capture complex nonlinear relationships between feature variables and target variables without relying on prior knowledge [33]. Moreover, these models are simple and easy to apply widely. Therefore, machine learning models provide a new research path for ET partitioning [34; 35]. The transpiration estimation algorithm proposed by [PERSON] et al. (hereafter, N18) is an ET partitioning method based on the RF model [36]. They employed the RF model to train WUE during periods when T was close to dominate ET (i.e., T \(\approx\) ET) and then used the trained RF model to predict WUE (=GPP/T) to calculate T. [PERSON] et al. used the ANN model with various environmental variables to predict daytime soil evaporation from nighttime ET data [37]. Ecosystem T was then calculated as the difference between total ET and the predicted daytime soil E. Although several studies have proved the effectiveness of machine learning models in estimating ecosystem T, the applicability of machine learning models for cropland T estimation with different crop is still unclear. Although the performances of machine learning models in ET partitioning have been validated by various studies [36; 37; 38], most of these studies have focused on an individual machine learning model or a single natural ecosystem, and there is a lack of validation by direct measurement of T or comparison with other ET partitioning methods. To address these limitations, our study provided comprehensive insights on ET partitioning using six machine learning models across 16 global cropland flux sites, encompassing 163 site-year data points and diverse crop rotations. Due to the lack of a method for the direct measurement of T, we compared our T estimates from the machine learning model with those from three other ET partitioning methods (i.e., Z16, N18, and Y22). Specifically, the objectives of this study are to (1) evaluate the performances of different feature combinations and different machine learning models (i.e., ANN, ExtraTrees, GBDT, LightGBM, RF, and XGBoost) on E prediction and identify the best model for ET partitioning; (2) validate the accuracy of T estimations based on the best machine learning model by comparison with three other ET partitioning methods (Z16, N18, and Y22); (3) estimate the T/ET ratios of typical crops, including maize, potato, rapeseed, paddy rice, soybean, sugar beet, winter barley, and winter wheat, using four distinct ET partitioning methods in order to examine the differences among these crops in water use and facilitate improvements in cropland water use efficiency.
# A Collision Risk Assessment Method for Aircraft on the Apron Based on Petri Nets
## 3 Quantitative Evaluation Method for Aircraft Collision Risk  
The overall framework of this article is shown in Figure 1. First, a Petri net for apron operations is established based on the operational network of a hub airport, discretizing the continuous operation system. For aircraft taxiing on the apron, the potential movement space at different times is calculated, treating the taxiing movement of the aircraft as a bounded Brownian motion. The modified probability density function is used to calculate the probability of an aircraft arriving at any position on the route at different times. The arrival probabilities at different positions are then mapped to the corresponding places in the Petri net, representing the probability of an aircraft arriving at those places. The collision risk for each place is determined by multiplying the arrival probabilities of different aircraft for that place. Based on these results, a classification evaluation of the collision probabilities is conducted, and the XGBoost algorithm is used to assess the effectiveness of the classification. Furthermore, by taking the collision risks corresponding to different aircraft paths as the objective function, relevant path planning algorithms can be employed to further optimize the taxiing paths of the aircraft, reducing collision risks while ensuring operational efficiency, thus providing valuable insights for apron operation management.
## 4 Applications for IoT Technology in Smart Buildings
### Virtual BAS  
Smart building technology plays a significant role in the retrofit industry, especially with older buildings. IoT allows virtual BAS (Building Automation System) technologies to use cloud-based controls to handle all other HVAC systems [98]. This cloud-based controller will enforce the weekend and holiday schedules. It will also apply complicated control routines in heating/cooling zones. Modern data models such as RDF (Resource Description Framework), JSON (JavaScript Object Notation), and IFC (Industry Foundation Class) can be utilized for implementing virtual BAS in an intelligent building. Improved integration of building automation technology into the IoT is possible because of new capabilities such as more significant addresses, self-configuration, QoS measures, and security [99].
## 2 Security Requirement and S-Box Design Methods
### S-Box Properties
#### 2.1.6 Fixed Points (Fp) and Opposite Fixed Points (Ofp)  
For an S-box, a fixed point is defined as \(f(x)=x\). This mean if an input \(x\) is given, the output is also \(x\). An opposite fixed point is defined as \(f(x)=\bar{x}\), where \(\bar{x}\) denotes the bit-wise complement of x. The number of \(Fp\) and \(OFp\) should be kept as low as possible to avoid any leakage in statistical cryptanalysis.
## 5 Conclusions
### Nomenclature
\begin{tabular}{l l l} Symbols & & \\ EF\({}_{\text{SOx}}\) & SO\({}_{\text{x}}\) emission factor & [g\({}_{\text{SOx}}\)/kg\({}_{\text{Fuel}}\)] \\ eta\_el & electric efficiency & \\ eta\_HTW & water boilers integrative thermal efficiency & \\ eta\_orc & ORC electrical efficiency & \\ eta\_St & steam integrative thermal efficiency & \\ eta\_str & Stirling electrical efficiency & \\ ER users & thermal power for engine rooms & [kW] \\ Fuel & engine fuel consumption & [kW] \\ Fuel\_BHt & fuel consumption of integrative water boilers & [kW] \\ Fuel\_BSt & fuel consumption of integrative steam & [kW] \\ Fuel\_TOT & total fuel consumption & [kWh] \\ g & generic day & \\ GNpv & photovoltaic panel data vector & [W/m\({}^{2}\)] \\ GNst & solar collector data vector & [W/m\({}^{2}\)] \\ Hexg & heat recovered from the ICE gases & [kW] \\ Hexg\_str & thermal input of Stirling groups & [kW] \\ HTh & heat recovered in form of HTW & [kW] \\ HTh\_abs & thermal input of absorption chillers & [kW] \\ HTh\_orc & thermal input of ORC groups & [kW] \\ Hotel NOT Hot water & thermal power for swimming pool heaters and laundry & [kW] \\ Hotel ONLY- HotWater & thermal power for sanitary use & [kW] \\ i & generic hour & \\ Input\_CW & power input in the chilled water storage & [kW] \\ Input\_HTW & power input into the HTW storage & [kW] \\ Input\_St & power input in the steam storage & [kW] \\ Int\_HTW & water boilers integrative thermal power & [kW] \\ Int\_St & steam integrative thermal power & [kW] \\ j & generic ICE & \\ Lev\_HTW & energy stored in HTW storage & [kWh] \\ Lev\_HTW\_size & energy stored size in HTW storage & [kWh] \\ Lev\_St & energy stored in the steam storage & [kWh] \\ Lev\_St\_size & energy stored size in the steam storage & [kWh] \\ MCR\% & engine load as a percentage of the nominal load & [\%] \\ OBJFunc & general objective function & [kWh] \\ Opput\_CW & power output in the chilled water storage & [kW] \\ Output\_HTW & power output in the HTW storage & [kW] \\ Output\_St & power output in the steam storage & [kW] \\ PE\({}_{\text{h}}\) & generic pollutant emission & [g\({}_{\text{pollutants}}\)] \\ PEN\({}_{\text{NOx}}\) & NO\({}_{\text{x}}\) emission & [g\({}_{\text{NOx}}\)] \\ PE\({}_{\text{SOx}}\) & SO\({}_{\text{x}}\) emission & [g\({}_{\text{SOx}}\)] \\ Pel/Pelect & electrical power & [kW] \\ Pel\_com & compression chillers electrical power & [kW] \\ Pel\_orc & ORC electrical power & [kW] \\ Pel\_str & Stirling electrical power & [kW] \\ PEN\_abs & penalty function for absorption chillers & [pt/kW] \\ PENALTY & penalty function & [pt] \\ PENconv & penalty function conversion factor & [kWh/pt] \\ PEN\_HTW & penalty function for HTW & [pt/kWh] \\ PEN\_orc & penalty function for ORC groups & [pt] \\ PEN\_st & penalty function for solar thermal collectors & [pt/m\({}^{2}\)] \\ PEN\_ST & penalty function for steam storage & [pt/kW] \\ PEN\_str & penalty function for Stirling groups & [pt] \\ PEN\_pv & penalty function for photovoltaic panels & [pt/m\({}^{2}\)] \\ \end{tabular}
## 2 Design of the Generator
### Structure of the Electric Pulse Generator
#### 2.1.1 The High-Power Unit  
The variable AC/DC 220/4000 V high-voltage unit, model CCPF-500-4P, Schulz-Electronic GmbH, Baden-Baden, Germany (A1), serves as the supply source (see Figure 1) for both modules. The output voltage of this supply source is controlled by an analog 0-10 V signal from the microcontroller, which corresponds to an output of 0-4 kV. The analog control signal is transmitted via the linear optocoupler unit (A5) and generated in the control unit.  
The activation of one of the two modules is controlled by the control unit via a relay, which connects the power supply to the selected module. A more detailed circuit of one of the modules is shown in Figure 2a. The first module consists of twelve 0.25\(\upmu\)F (5 kV) polypropylene capacitors, while the second module contains twelve 1\(\upmu\)F (5 kV) polypropylene capacitors. These capacitors offer a high repetition rate and a rise time of 11 kV/\(\upmu\)s.  
During operation, the capacitors are charged in parallel via diodes (diodes D2-D25 for the module). The capacitors are then discharged by connecting them in series through the load. Thyristors (TA20401203 SQ, Powers Inc., Youngwood, PA USA) with driver modules are used as the switches. These thyristors have a maximum operating voltage of 4000 V and a maximum operating current of 1200 A. Each thyristor is individually controlled by a separate driver, which is controlled by an A4 drive control unit via an optical interface. A more detailed circuit of the thyristor driver is shown in Figure 2b.  
Each driver is isolated from the others and is powered by the voltage that drops across the thyristor (between the anode and cathode). The electrical circuit of the drivers generates the pulses required for optimum and simultaneous opening of all thyristors. To protect  
Figure 2: Topology of one of the modules of the Marx generator (**a**) and thyristor driver (**b**).  
the thyristors in the event of a short circuit, resistors R2, R3 and inductances L2, L3 are connected in series with the load. In this scenario, resistors limit the maximum current, while inductances regulate the rate of current rise.
###### Abstract  
This paper presents a novel, measurement-driven method for the simultaneous estimation of the User Coordinate System (UCS) and Tool Center Point (TCP) in industrial robot systems, formulated as a solution to the AX = YB calibration problem. The proposed algorithm minimizes the discrepancy between simulated and real-world robot poses by iteratively refining both coordinate transformations using external laser tracker measurements. A comparative evaluation was conducted using two ABB IRB-6640 robots and four calibration strategies, including internal three-point measurement, absolute orientation, geometrical referencing, and the proposed method. Experimental results show that the proposed approach outperforms standard industrial techniques, achieving an average translational error below 0.4 mm, compared to over 2 mm for conventional methods. The geometric method, while widely used for its simplicity, demonstrated the highest error and instability due to poor rotational alignment. The findings validate the proposed solution as an accurate, non-invasive alternative for implementing simulated programs in real production environments, particularly where direct access to robot controllers is limited.  
Keywords:base frame calibration; AX = YB problem; industrial robot; offline robot programming +
Footnote †: journal: Applied Sciences
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ARM & Atmospheric Radiation Measurement \\ ASCAT & Advanced Scatterometer \\ AWDP & ASCAT Wind Data Processor \\ C-MAN & Coastal-Marine Automated Network \\ CSU & Colorado State University \\ DYNAMO & DYNAMICS of Madden-Julian Oscillation \\ ECMWF & European Centre for Medium-Range Weather Forecasts \\ EUMETSAT & European Organization for the Exploitation of Meteorological Satellites \\ GF & Gradient Feature \\ GMF & Geophysical Model Function \\ L1B & Level 1b \\ L2 & Level 2 \\ MERRA-2 & Modern-Era Retrospective Analysis for Research and Applications \\ MetOp & Meteorological Operational \\ MCS & Mesoscale Convective System \\ MLE & Maximum Likelihood Estimation \\ NASA & National Aeronautics and Space Administration \\ NCEI & National Centers for Environmental Information \\ NDBC & National Data Buoy Center \\ NOAA & National Oceanic and Atmospheric Administration \\ NWP & National Weather Prediction \\ Py-ART & Python ARM Radar Toolkit \\ RAP & Rapid Refresh \\ RJJ & Rear-Inflow Jet \\ SRF & Spatial Response Function \\ SST & Sea Surface Temperature \\ TRMM & Tropical Rainfall Measurement Mission \\ UHR & Ultra-High Resolution \\ UTC & Universal Time Coordinated \\ VAD & Velocity Azimuth Display \\ VarQC & Variational quality control flag \\ WRF & Weather Research and Forecasting \\
2 DVAR & 2-D variational \\ \end{tabular}
## 5 Conclusions  
The new signal processing technique has been proposed to enhance the detection reliability of low-amplitude transients in noisy acoustic emission time series. Using the logarithmic derivative of the AE power spectral density with respect to time, the mathematical expression for the signal detector termed \(\phi\)-parameter has been obtained considering the transfer function formalism. The method was compared with the most widespread benchmarking techniques-fixed amplitude threshold and optimised STA/LTA. The greatly enhanced detection reliability of the \(\phi\)-trigger compared to others has been demonstrated on two AE representing datasets simulating different signal-to-noise ratios of AE waveforms: application of the \(\phi\)-algorithm renders the much higher rate of correctly identified low-amplitude events while the rate of false alarms is consistently smaller than that for other methods used for comparison at any tested SNR.  
Despite a wealth of available advance methods overviewed briefly in the present work, unfortunately, all commercially available AE systems today use the primitive amplitude thresholding technique for signal detection, which is significantly compromised in comparison to many modern detection algorithms, and which strongly limits the capacity of the existing apparatus used in NDT and condition monitoring practice. We do believe that this practice will be changed in favour of higher accuracy and reliability of signal detection for every-day AE practice both in laboratories and in in-field inspections.  
Although the proposed algorithm was designed for AE monitoring and, in the present work, probed on an AE time series, the method is nonspecific to the nature of noisy time series and, without limitations, it can be applied widely in a new generation of condition monitoring and diagnostic systems where the sensors with different frequency bands are used and where early and reliable detection of possible damage is of paramount importance.  
Conceptualization and writing, A.V.; methodology, validation, formal analysis, software, E.A.; project administration and discussion the results, D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Russian Ministry of Higher Education and Science under the State Assignment, grant number 11.5281.2017/8.9.  
The authors declare no conflicts of interest.
# Haskap Juicing Method Effects on Haskap Juice Quality
## 3 Results and Discussion
### Total Phenolics Contents and Antioxidant Activities Affected by Juicing Methods
For total phenolics assays, in alkaline conditions (Na\({}_{2}\)CO\({}_{3}\) in this research), the dissociation of phenolic proton resulted in the creation of a phenolate ion, which could reduce the Folin-Ciocalteu reagent. Therefore, the electron from the phenolic antioxidants was the assay target. In some reports, the total phenolic assay results can be overstated when compared to data from HPLC techniques [63]. The DPPH assay also screens many bioactive substances, such as polyphenols and flavonoids. Limitations of DPPH include potential reaction with weak antioxidants and the influence from the reaction with solvents [64]. Figure 3: Antioxidant activity assays in haskap juices. The two different antioxidant activity assays are the DPPH assay and FRAP assay. Treatments (x-axis) for processing haskaps are steamer, press, and centrifugal juicer. Total phenolic assay is expressed as gallic acid equivalent (GAE mg/mL); DPPH assay was measured as radical scavenging activity (RSA%); FRAP assay results were converted into nmol/mL ferrous equivalent. Each value of each treatment is an average of three replicate measurements \(\pm\) standard deviation. Different letters above each bar indicate statistically significant difference (\(p<0.05\)) separated via [PERSON]’s HSD. The major limitation of the FRAP assay is that antioxidants must be water soluble. It has been reported that FRAP assay results can be less associated with other antioxidant activity assays [65]. In our research, both the total phenolic assay and DPPH assay indicated there were significant differences (\(p<0.05\)) among haskap juices from different processing treatments. Although FRAP also indicated the highest antioxidants from juicer-processed juices, followed by press, and then steamed haskap juices, juicing methods were similar in this assay, which might be due to lower effect size or the amount of water-soluble antioxidants (Figure 3). In conclusion, steam extraction might dilute or break down more antioxidants compared to press and juicers, whereas the centrifugal juicer-processed haskap juice had more antioxidants compared to press-processed haskap juices in the same volume.
# A Review of Low-Frequency Active Vibration Control of Seat Suspension Systems
## 2 Seating Systems with Active Suspension
### Experiments with Prototypes
#### Hydraulic Actuator  
[PERSON] and [PERSON] (1991) [56] developed an active seat suspension (Figure 10) using a hydraulic actuator. In this system, the hydraulic actuator is controlled by a solenoid valve to control the direction of the force generated by the actuator. The control system generates a corresponding compensation force to reduce the vibration based on signals collected by two acceleration sensors and a displacement sensor. A PI controller was designed for the active seat suspension. Since the acceleration sensor and amplifier are integrated for the displacement of the positioning system, the system can be simplified using only one accelerometer placed on the chassis. This structure greatly simplifies the control system. This structure can also be achieved by connecting two first-order low-pass filters in series or by using a second-order analog circuit to adopt a very low resonant frequency (Figure 11).  
In this study, the delay of hydraulic performance was also considered and solved by using the cut-off frequency method. In addition, the hysteresis effect of the hydrodynamic device was also carefully considered in the process of controller design. Depending on the result, the active seat suspension with the controller can reduce the acceleration Power Spectral Density (PSD) amplitude up to 16 dB at a frequency of 2 Hz compared with a passive system.  
Figure 11: Schematic of the control system [56].  
Figure 10: Active hydraulic control of a seat suspension system [56].  
Active hydraulic control has the advantages of a large output force and high control precision, but the huge hydraulic pipeline and servo system of hydraulic control also greatly limit its application.