esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Model for Angular Distortion in Multilayer Welding under Constraints
## 3 Results and Discussion
### Applications
When one-sided welding at the bottom is completed, as shown in Figure 13, the angular distortion on the upper side can be calculated while using Equation (11) by defining the filled thickness as initial thickness \(h_{0}\) and setting it equal to root face \(f\). Initial angular distortion \(\theta_{0}\) is adopted as final value after filling the bottom (lower) side. The negative angular distortion is given when welding the opposite (upper) side. Figure 12: Comparison of experimental and predicted values of V butt welding experiment with 50 mm thickness for welding conditions of 280 A, 30 V, and varying travel speed. Figure 14 shows a diagram of the specimen for the X butt welding experiments. We considered a welding current of 285 A and voltage of 29 V at varying travel speeds. The root gap was 4 mm and the root face was 0 mm. For the welding sequence, the upper side in Figure 14 was welded and filled first, and the lower side was welded after one pass was removed by arc gouging. The other welding conditions were the same as those for the previous experiments. The angular distortion was measured for each layer. The prediction model assumed that welding was resumed after removing one pass one layer by gouging when the bead thickness exceeded 18 mm, as it is not possible to predict the amount of gouging. Figure 15 shows the experiment and prediction results. When the bead thickness increased to 18.8 and 18.2 mm for travel speeds of 30 and 60 cm/min, respectively, it was reduced once to 14.9 and 15.4 mm due to gouging, respectively, corresponding to one layer height. The initial thickness and its angular distortion measured were 11 mm and 0.1\({}^{\circ}\) for welding speed of 30 cm/min, and 7 mm and 0.15\({}^{\circ}\) for 60 cm/min, respectively. The degree of constraint was calculated by inputting the angles that were measured before and after the third layer, being 1.04 for 30 cm/min and 1.45 for 60 cm/min. The degree of constraint increased with decreasing heat input, as with the prediction results for the V groove without strongback. When compared to the conventional V groove, there was a large prediction error of the final angular distortion after gouging, but the slope was similar for the X groove. This might be due to the amount of gouging, which is difficult to predict. Still, the overall trends suitably agreed, indicating that accurate prediction can be expected while using the proposed method for thicker plate welding under small change in distortion by gouging. Figure 14: Diagram of specimen for X butt welding experiments. Figure 13: Geometry of X-groove butt welding joint.
# IoT Open-Source Architecture for the Maintenance of Building Facilities
## 3 Materials and Methods  
### IoT and BIM System Architecture  
Automated data acquisition (DAQ) technology has seen significant advancements in hardware and software in recent years, even if the majority of available technologies are still expensive and not open-source, namely, a sort of \"black box\" where users have no access to alter and modify the implemented algorithms. Furthermore, a free access to the data previously stored is frequently impossible without buying a specific software. To address the aforementioned issues and overcome the limits of commercial technologies, studies were recently carried out to investigate and develop customized design of automated DAQ systems. This chapter proposes an automated data acquisition system within the IoT and BIM integration methodology based on open-source technologies.  
All the sensors for building facilities and rooms are hosted by Arduino based microcontrollers and a Raspberry Pi single board computer with integrated Wi-Fi module to acquire the planned data in real time and store them on the server. The collected data is then processed to monitor and detect anomalies in building facilities operation.  
A digital twin is next implemented by linking the stored data with a dashboard for trend visualization; a BIM model is used to visualize the component position and to have an overall view of the building.  
The general architecture of the IoT integrated into the BIM is shown in Figure 1. The architecture exhibits IoT building facilities' sensors continuously sending data to the sensor nodes. In the hardware and networking section, raw data coming from sensors are processed by the sensor nodes' microcontroller and raw data becomes human-readable data. The following sensor nodes send processed data to the server that is connected to the gateway. Users can gain access to the local and cloud sensor node dashboards through this gateway providing data visualization and fault detection features. In the last section of the architecture, IoT data is integrated into BIM and combined IoT-BIM data is visualized on the BIM dashboard.
# A Power-Gated 8-Transistor Physically Unclonable Function Accelerates Evaluation Speeds
## 3 Evaluations and Results Analyses
### Power Gating Parameter Evaluation  
A DC sweep and post-layout transient simulations were performed to evaluate the size of _SUPPLY_ transistors for a cluster of 128 PUF cells and the corresponding behaviour of the PUF cells.  
For two-phase power gating, the phase one _SUPPLY_ transistor _PM4_ must be small enough to prolong the metastability process. Meanwhile, the phase two _SUPPLY_ transistor _PM5_ should be large enough to supply power to 128 PUF cells quickly. As presented in Figure 5, there is a roughly linear relationship between the _SUPPLY_ transistor width and the approximate value of the _vddv_ plateau while metastability resolves. There is also a nearly quadratic dependence between the _SUPPLY_ transistor width and the time duration for _vddv_ to reach 1.2 V or the metastability resolving time duration. Hence, by varying the size of the _SUPPLY_ transistor, the metastability resolving time can be manipulated.  
Phase I _SUPPLY_ transistor widths of 80 nm to 320 nm and phase II _SUPPLY_ transistor widths of 1 \(\upmu\)m to 4 \(\upmu\)m were picked to evaluate various combinations of two-phase power gating. Figure 6 illustrates the comparison of the current peaks of different signal edges in different combinations of _SUPPLY_ transistors whilst power gating a 128-bit PUF. It can be seen that a higher power-up current corresponds to a larger _SUPPLY_ transistor. The phase II current also relates to the _vddv_ value at the start point of phase II. The energy consumption of a PUF cell for each reading cycle exhibits minimal variation, i.e., from 5.7 f to 6.2 f). These adjustable power-up currents can limit the in-rush current and be exploited further as a candidate against side channel attacks. In our design, the SUPPLY  
Figure 5: _SUPPLY_ transistor sizes vs. metastability starting vddv and time to reach 1.2 V.  
Figure 6: _SUPPLY_ transistor sizes vs. currents and energy consumption.  
of 80 nm for PM4 and 4 um for PM5 is implemented to emphasise the prolonged PUF metastability-resolving process and the rapid reset process.  
The power consumption of a power-gated PUF array is proportional to the chosen PUF percentage, in addition to the power consumption of the power-gating cells. The post-layout simulation results show that the power consumption is about 36.5% when only a quarter of PUF rows are enabled. For example, a row of 128 8T PUFs consumes 954 fj in an array of four PUF rows, which consume 2.613 pJ under the same conditions. The dynamic power of one two-phase power gating cell is about 812.5 aJ.
# Adaptive Estimation Algorithm for Photoplethysmographic Heart Rate Based on Finite State Machine
## 2 Algorithm
### Signal Denoising
#### 2.2.2 RLS Filter  
When it is a strong MA, the signal is first denoised by RLS filtering. Adaptive filtering minimizes the difference between the reference signal and the input signal by continuously and iteratively adjusting the parameters of the filter. Here, the Acc\({}_{\text{com}}\) and PPG\({}_{\text{com}}\) in Section 2.1 are taken as the reference a(n) and input signal x(n), respectively. The estimated MA signal is y(n), the error signal is e(n) = x(n) - y(n), and the framework is shown in Figure 3. The parameters of the RLS are set as follows: order N = 55, forgetting factor \(\lambda\) = 0.999, and initialized covariance estimate \(\theta\) = 0.1.  
In Figure 4a,b, the spectrograms of the PPG signal before and after RLS filtering in the same window are shown, respectively, where the marked red points are the spectral peaks of the true heart rate. It is obvious from the figure that there are two large MA spectral peaks before RLS filtering, and the spectral peak of the true heart rate may not be found if the spectral peaks of this spectrum are tracked directly. After RLS filtering, the MA is significantly suppressed to highlight the spectral peaks corresponding to the true heart rate.  
Figure 2: Spectral comparison of the strength of motion artifacts. (**a**) Spectrogram corresponding to Acc = 887.27; and (**b**) spectrogram corresponding to Acc = 1382.88.
# Analysis of Movement Law and Influencing Factors of Hill-Drop Fertilizer Based on SPH Algorithm
## 3 Results and Discussion
### Fertilizer Offset Distance  
The offset distance of fertilizer in each layer is shown in Figure 9. When the soil depth was 120, 140 mm, 160 and 180 mm, the average displacement of fertilizer was 29, 24.8, 18.8, and 15.5 mm, respectively. The results show that the offset distance of fertilizer decreased as the soil depth increased. The standard deviation of the offset distance of fertilizer at different soil depths was calculated. The results show that the dispersion degree of the offset distance of fertilizer was smaller as the soil depth increased. When the soil depth was 120 mm, there were two breaks in the broken line in the figure. The main reason was that there was no fertilizer at 120 mm due to the vibration of the planter and the uneven terrain, etc. The reason for the large offset distance and dispersion degree of the upper layer was analyzed. The main reason was that the designed cover restricted the soil displaced by the opener from moving to both sides and the soil gathered between the two covers. The disturbance degree of the upper layer of fertilizer was increased due to soil backflow.  
The simulation results and field experiment results showed that the offset distance of fertilizer in the upper layer was larger, and the offset distance of fertilizer in the lower layer was smaller. As soil depth increased, the amount of fertilizer displacement decreased. Both had the same movement trend and law. The comparative analysis of the simulation and experiment results demonstrates the reliability of the simulation.  
Figure 9: Fertilizer offset distances at different soil depths: (**a**) 120 mm; (**b**) 140 mm; (**c**) 160 mm; (**d**) and 180 mm.
## 2 Application to Flexible Structures  
The concept on the active vibration control of flexible beams using an SMA actuator was initiated in the 1990s [11; 12]. In these articles, simple control strategies such as constant amplitude controller and proportional amplitude controller have been implemented to reduce transient vibrations by operating an SMA actuator installed at the outside of the beam. This type of system architecture was reported until the mid-2000s [13; 14]. Thereafter, vibration control of flexible structures has been working in three different ways as shown in Figure 1. As shown in Figure 1a, controlling the vibration of a structure by attaching a wire or spring type SMA actuator to the outside of the structure is the most proposed configuration. It has the advantage of simple system configuration and intuitive control, but also has the disadvantage that the actuator can be affected or damaged by the external environment. As shown in Figure 1b, another configuration is to insert SMA wires or springs into the structure to form a composite material. Although there is an advantage in that the SMA actuator can be protected from the external environment, there is a disadvantage in that the difficulty of manufacturing the system is high. Although not common, it is possible to attach an SMA actuator in the form of a film or strip to the surface of a structure, as shown in Figure 1c.
# Real-Time Compensation for Thermal Errors of the Milling Machine
## 2 Thermal Model of a Machine Tool  
### Basic Theory  
There are three ways for transmitting the heat from one site to another in a structure.  
1. Heat Conduction: heat can transfer from higher temperature to a lower one mainly by means of the solid medium, which can be described by following equation: \[q=kA\frac{\mathrm{d}X}{\mathrm{d}T}\] (1) where \(q\) is heat conduction, \(k\) is conduction factor of the medium, \(A\) is the area of contact surface, \(\mathrm{d}X\) is the distance between the ends, and \(\mathrm{d}T\) is the temperature difference between the ends.
2. Heat convection: heat is delivered naturally from one side to another by affecting the volume and density of the media, which can be described by the following equation: \[q=hA\Delta T\] (2) where \(q\) is heat convection, \(h\) is convection factor, \(A\) is sectional area, and \(\Delta T\) is temperature difference between the ends.
3. Heat irradiation: Heat is delivered through electromagnetic wave, independent on any media, which can be described by following equation: \[q=\sigma A(T_{1}^{4}-T_{2}^{4})\] (3) where \(\sigma\) represents Stefan-Boltzmann constant, \(A\) is the radiating surface area, and \(T_{1}\) and \(T_{2}\) are the temperatures at two radiating surfaces.  
Generally, there are three kinds of heat sources inducing the thermal deformation (Figures 1 and 2). Firstly, the thermal deformation exists in the rotating spindle and ball-screw, which causes the thermal expansion of the internal components and hence the structure bending deformation of the machine tools. Secondly, there are errors of volume induced from structure construction in assembly error and between different mechanisms. Finally, errors of the controlled system are due to the interpolation errors of the CNC
# A Switching Observer for State-of-Charge Estimation of Reconfigurable Supercapacitors
## 4 Experimental Validation
### Structure of Hardware Platform  
The hardware setup is illustrated in Figure 5 and consists of the following key components: a control board, three supercapacitors, voltage and current data acquisition units, and a 12 V DC power source.  
Control Board:The platform's controller is a Raspberry Pi 4B with 8 GB RAM running Raspberry Pi OS, featuring GPIO output, SPI communication, and serial communication.  
Supercapacitors:Three Maxwell BCAP0310 supercapacitors (Yongin-si, Republic of Korea) are connected in series, balanced using RPi Relay Board (B) relay modules and RX24-50W equalizing resistors.  
Voltage Sensor:A high-precision AD/DA board, equipped with an ADS1256 chip, provides 8 channels of 24-bit ADC (4 differential) at a maximum sampling rate of 30 ksps, along with a DAC8532 chip for 2 channels of a 16-bit DAC.  
Current Sensor:An NAA238 Current Sensor, with a 2 m\(\Omega\) sampling resistance, detects currents up to 30 A with 0.05% accuracy, communicating data via serial communication.  
Power Source:The Tektronix PWS4305 power supply (Beaverton, OR, USA), known for its 0.03% voltage accuracy and 0.05% current accuracy, provides charging current for the supercapacitors, while an external 12 V DC power supply powers the control board and sensors.  
The operation process of the platform is as follows: The Raspberry Pi 4B runs the control program for the entire balancing and charging process. During charging, the controller uses SPI to monitor the voltage of each supercapacitor via the AD/DA Board. When a cell's voltage exceeds the set threshold, the controller activates the corresponding relay on the RPi Relay Board (B) via GPIO for balancing protection. The INA238 Current Sensor measures the charging current for further analysis.
## 3 Results  
### Experiment on Static Objects  
To test the optimization effect of the proposed scheme on the EIT acquisition system, static experiments were conducted to test the SNR of the current system using the optimized EIT acquisition system. The output waveform frequency of the FPGA DDS IP core is set to 160 kHz, and the sampling frequency of AD9764 and AD9244 is set to 20 Mhz. At this time, the excitation frequency of the entire system is 160 kHz. The inner diameter of the static water basin device is 16 cm, and the liquid phase background is tapping water with a conductivity of 260 \(\upmu\)S/cm. The entire testing device is shown in Figure 11.  
Figure 11: Experimental device for the test of static objects.  
Figure 10: Module diagram of digital-phase sensitive demodulation.  
Set the excitation size to 2 V and continuously collect 1000 sets of measurement values in the pure water. After performing SNR analysis on the current system using continuous multiple sets of measurement data, the calculation formula for SNR is as follows:  
\[SNR=20\lg(\sigma_{x}/\overline{x}) \tag{8}\]  
where \(\sigma_{x}\) represents the standard deviation of the measured values and \(\overline{x}\) represents the average of the measured values.  
The measurement value sequence consists of 480 measurements, and they are shown in Figure 12. To measure the anti-interference ability of the entire EIT system, it is necessary to calculate the SNR of the EIT acquisition system for 480 measurements separately and then calculate the expected values of all results to obtain the SNR of the entire system. The SNR of some measured values is shown in Table 5.  
From Table 5, it can be seen that the SNR of most measured values is above 65 dB, and some measured values have no fluctuations in 1000 sets of data, with a standard deviation of 0, indicating a good SNR. When calculating the expected SNR of all measured values, the parameter with an SNR of \"/\" is set to 100 dB, resulting in an SNR of 73 dB for the current system. Compared to other EIT systems, both domestically and internationally, it can reach a higher level [25; 26]. The EIT system in the article is optimized based on our previous work. The original EIT system has an SNR of 52 dB. The comparison results show that the optimized EIT measurement system has significantly improved SNR.
# GNSS-IR Soil Moisture Retrieval Using Multi-Satellite Data Fusion Based on Random Forest
## 5 Discussion
### Performance Analysis of the RF Model Based on the MDI Algorithm  
To further investigate the impact of MDI on the performance of the RF model, this study replaced the MDI algorithm based on Mean Squared Error (MSE) with a Mean Decrease Accuracy (MDA) algorithm based on Mean Absolute Error (MAE). Like MDI, the MDA algorithm uses the reduction in MAE before and after node splits to select the optimal splitting node. It measures the importance of features by observing changes in model accuracy when a specific feature is shuffled, using MAE as the accuracy metric. Figure 15 displays the soil moisture retrieval results at the P041 site using the two different algorithms, Figure 16 shows their linear regression analysis, and Table 3 lists the specific values of the accuracy metrics for the retrieval values produced by the two algorithms.  
From Figures 15 and 16, it can be seen that the soil moisture retrieval results based on the MDA algorithm align with the trend of the reference values, but compared to the MDI algorithm the results are more dispersed relative to the reference values. The R between the MDA-based retrieval results and the soil moisture reference values is 0.91, with an RMSE of 0.042, and the overall MAE is 0.029. Compared to MDA, the MDI retrieval results show a 31.2% reduction in RMSE and a 16% reduction in overall MAE. The experimental results indicate that the performance of the RF model based on MDI is superior to that based on MDA. This is because the MDA algorithm uses the reduction in MAE before and after node splits to select the optimal splitting nodes, and MAE, having linear characteristics, is not sensitive enough to larger errors. In contrast, the non-linear characteristics of MSE can capture more complex data structures and more accurately select the optimal splitting nodes, thus the RF model based on MDI exhibits stronger retrieval performance.  
Figure 15: A comparison of the soil moisture retrieval results from the RF models based on MDI and MDA at the P041 site against the reference values.
# Sensor-Data-Driven Prognosis Approach of Liquefied Natural Gas Satellite Plant
## 2 Related Work
The problem of predicting or forecasting the future evolution of the storage level can be solved by using prognosis algorithms. In the prognostics literature, the concept of remaining useful life (RUL) appeared, defined as the time from the current instant to the time of end of life. Reliable RUL predictions provide valuable information for condition-based maintenance, thus improving LNG regasification terminal performance. Prognostics plays an important role in health-management systems and have been used for predicting the RUL for plant equipment, such as compressors [20], pipelines [21], and pumps [22], a reviewed of the main approaches to Prognostics and Health Management (PHM) and to predict RUL can be found in [23]. The single and double exponential smoothing method are currently one of the most popular time series forecast methods and had been successfully applied in diverse fields such as water quality [24], air quality index [25], petroleum demand [26], among others. As in the case of consumption prediction, prognosis has been implemented using approaches that are data-driven, physics-based, or hybrid approaches [27]. During prognosis and health-management (PHM) analysis, historical knowledge carried by data is systematically fed into a decision-making process by sophisticated data modelling techniques [28]. However, it is frequently observed that collected (raw) data are redundant, incomplete, or inaccurate, as well as heterogeneous and false due to faulty sensors [29]. For this reason, PHM contains four main components: data processing, feature extraction, fault diagnosis, and failure prognosis [30]. The methods for consumption prediction or LNG storage prediction may produce satisfactory results depending on data availability. In general, the sensor-data validation process consists of two main steps: faulty-sensor-data detection and correction or data reconciliation. Faulty-sensor-data detection identifies incorrect values in the sensor data, and the correction process provides methods to deal with incoherent sensor data. In each category, a number of different tools and methods exist, a exhaustive review of these techniques can be found in [31]. There is no perfect or universal tool for this task, and the success of the considered approach depends on a number of factors, such as the type of monitored variable, overall measurement conditions, the used sensor, and the characteristics of the phenomenon being captured. Most of these methods are difficult to perform with asynchronous so it is desirable to convert them to uniform sampled data using resampling algorithms. For example, in [32], two algorithms were proposed for an asynchronous-to-synchronous resampling scheme. Signal resampling is one of the steps of the data-preprocessing process, used in many applications, such as prognosis and health management (e.g., [33]) and state identification (e.g., [34]).