esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation Results
### Grout-Injection Material Physical Property Change Measurement Comparison Results  
In order to provide an easier relative comparison, ratios derived from the test methods results of the respective test methods were calculated into percentage index (out of 100%) based on the performance criteria outlined in the national standard test methods through Equations (1)-(6). Refer to the following Table 10 for details. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Specimens**} & \multicolumn{1}{c}{\begin{tabular}{c} **Thermal** \\ **Stress** \\ **(Leakage** \\ **Time, min)** \\ \end{tabular} } & \begin{tabular}{c} **Chemical** \\ **Erosion** \\ **(Mass** \\ **Loss, \%)** \\ \end{tabular} & \begin{tabular}{c} **Water** \\ **Flow** \\ **Erosion** \\ **(Mass** \\ **Loss, \%)** \\ \end{tabular} & \begin{tabular}{c} **Wet Surface** \\ **Adhesion** \\ **Strength** \\ **(Leakage** \\ **Time, s)** \\ \end{tabular} & \begin{tabular}{c} **Hydrostatic** \\ **Behavioral** \\ **Stress** \\ **Time, min)** \\ \end{tabular} \\ \hline \multirow{3}{*}{\begin{tabular}{c} SPRG \\ A \\ \end{tabular} } & 1 & 60 & 3.1 & 5 & 14 & 60 & 52 \\  & 2 & 46 & 4.8 & 4 & 12 & 41 & 43 \\  & 3 & 60 & 3.0 & 7 & 36 & 60 & 60 \\  & Avg & 55.33 & 3.7 & 5 & 20.67 & 53.67 & 51.67 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} SPRG \\ B \\ \end{tabular} } & 1 & 36 & 2.1 & 3 & 60 & 60 & 60 \\  & 2 & 37 & 0.6 & 5 & 60 & 60 & 39 \\  & 3 & 42 & 2.7 & 7 & 60 & 60 & 49 \\  & Avg & 38.33 & 1.8 & 5 & 60 & 60 & 49.33 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} SPRG \\ C \\ \end{tabular} } & 1 & 48 & 3.4 & 21 & 24 & 60 & 60 \\  & 2 & 60 & 2.4 & 7 & 32 & 60 & 60 \\ \cline{1-1}  & 3 & 35 & 3.1 & 1 & 6 & 60 & 60 \\ \cline{1-1}  & Avg & 47.67 & 3.0 & 10 & 20.67 & 60 & 60 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The evaluation results of SPRG grout-injection materials. A radar chart for the respective grout-injection material type physical properties measurement was derived to allow a comprehensive comparison of the independent property changes after subjection to the six degradation conditions. The variation in the index of each axis indicates the degree of expected properties change with respect to each degradation factors. A higher percentage indicates a greater degree of physical properties change due to exposure to the specific degradation condition. In the case of chemical exposure resistance testing and water flow erosion resistance testing results, a mass loss ratio determined to be more than 100% relative to the standard mass loss threshold was not expressed past the 100% maximum in the final results to establish a regime for a relative comparison with the other properties. A higher percentage in the radar axis indicates a greater degree of physical property changes, which subsequently indicates the risk of waterproofing performance deterioration. Refer to the following radar charts below from Figures 13-16 for details.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 4 CEHBC System Measurement and Performance  
### Measurement Environment of the CEHBC System  
Figure 9 indicates the measurement environment of the proposed CEHBC system for performance observation. Figure 9a is a block diagram showing the configuration of the CEHBC transceiver system. Figure 9b displays the measurement environment of the CEHBC system implemented using hardware devices.  
The CEHBC system included the CEHBC Tx capsule endoscope, human body channel, CEHBC Rx board, and an image viewer software. A water tank filled with saline water was used as the communication channel of the CEHBC system to realize the In-body to On-body channel environment. The CEHBC-TXIF/CEHBC-TXD and CEHBC-RXD/CEHBC-RXIF were designed with digital logic and implemented using field programmable gate array (FPGA) chips.  
The output signal of the CEHBC-TXD was connected to the electrode of the capsule, which was placed in the saline tank. The signal was transmitted to the electrodes through saline channels. Signals received from the eight electrodes attached to the water tank were transferred to the CEHBC-AFE Board. CEHBC-AFE converted the received analog signal into a 1-bit digital signal using the CDR (etc.) and supplied the input data to the CEHBC-RXD. The CEHBC-RXD restored the image signal from the received signal and transferred it to the PC. The PC processed the transferred data and then displayed the image on the screen, which was captured by the image sensor.  
The animal clinical experiment is needed for realistic results. However it costs a lot of money to experiment with animals. Since the performance of the capsule endoscope system should be measured by an indirect experiment before proceeding with such animal clinical experiment, we used the signal channel model with the saline tank used in the reference papers [24; 25; 26] below. The CEHBC capsule endoscope uses a galvanic coupling signaling system and uses a 32 MHz clock. This environment is similar to the experimental conditions presented in the reference paper [24].  
Figure 9: Measurement environment: (**a**) Block-diagram of the CEHBC transceiver system for measurement; (**b**) Measurement environment of the implemented CEHBC system.
# Toxic Metals Migration from Plastic Food Contact Materials in Romania: A Health Risk Assessment
###### Abstract  
Food packaging plays an essential role in preserving food quality. However, heavy metals found in packaging materials--whether intentionally incorporated or not--can migrate into food. This study aims to evaluate the migration of specific heavy metals (Ba, Co, Cu, Zn, Al, Ni, Li, Fe, Pb, Cd, Cr, Sb) from plastic food packages (films and bags) obtained from various materials (PE, PP, PVC, composite materials) into food simulant B (3% acetic acid) using inductively coupled plasma mass spectrometry (ICP-MS). Migration tests was conducted according to EU regulations, using OM2 conditions (10 days at 40 \({}^{\circ}\)C). The obtained results were lower than the specific migration limits set by EU Regulation no. 10/2011 (Annex II). Both carcinogenic and non-carcinogenic risk assessments were carried out based on the specific migration data, estimating the exposure, average daily dose (ADD), hazard quotient (HQ), hazard index (HI), cancer risk (CR), and total cancer risk (TCR). The exposure values were found to be below the recommended tolerable daily intake (TDI) levels for each metal tested. Both HQ and HI values were under the limit value of 1. The average total cancer risk was \(1.73\times 10^{-4}\), indicating that approximately 1.73 consumers out of 10,000 may develop a type of cancer due to chronic exposure to the tested metals. These results highlight the importance of continuous monitoring of chemical migrants from food contact materials.  
food simulants; specific migration; plastic food packages; risk assessment; toxic metals Article
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 4 Dispatch Strategy Based on the Information Sharing System
### Level One and Corresponding Transportation Method  
In Level one, there is no information sharing among aircraft dispatchers, shelters, and aircraft pilots. This means that aircraft dispatchers and pilots do not know any information beforehand. Moreover, the shelters have no device to report the number of injuries to the aircraft dispatchment centers. In this case, the information about injuries can be obtained if the dispatchment center sends aircraft to a random rescue shelter to conduct \"reconnaissance and rescue\" [1; 34], as shown in Figure 4.  
Under this condition, the dispatch center could dispatch the aircraft to perform two kinds of missions: First, the center could assign all the available aircraft to obtain information about the rescue shelters, and if any injuries are present, the aircraft could transport the injured back to the airport. The second is that the dispatch center could dispatch aircraft to undertake rescue missions.
## 4 Results  
### Characteristics of Change  
#### 4.1.1 Breakpoints and Direction of Change  
The number of significant breakpoints detected (increased and decreased productivity) and precipitation anomalies between 1982 and 2015 were shown in Figure 3. Out of all the analyzed pixels (i.e., 4454), 814 (18.3% or -50,000 km\({}^{2}\)) had significant breakpoints--with either increasing or decreasing trends--were detected over different land cover types. The remaining pixels that showed insignificant change (NSC), indeterminant, or identified as Agricultural represented about 55.6% (158,591 km\({}^{2}\)), 10.8% (30,656 km\({}^{2}\)) and 3.2% (9122 km\({}^{2}\)) of NM's area (-315,900 km\({}^{2}\)). The distribution of these pixels is shown in Figure 4. Out of the 814 significant breakpoints, 52.3% (26,176 km\({}^{2}\)) and 46.5% (23,232 km\({}^{2}\)) had negative (decreasing) and positive (increasing) change in productivity, respectively (Table 2). The areas that showed significant increasing trends in productivity as 11, 12 and 13 were about 10,752 km\({}^{2}\) (3.8%), 9408 km\({}^{2}\) (3.3%), and 3072 km\({}^{2}\) (1.1%) respectively. The areas that showed decreasing trend in productivity with D1, D2, and D3 level of significance were about 9088 km\({}^{2}\) (3.2%), 13,056 km\({}^{2}\) (4.6%), and 3776 km\({}^{2}\) (1.3%), respectively.  
From all significant breakpoints, 58%, 20%, and 15.7% were observed in shrubland, grassland, and evergreen forest ecosystems, respectively (Figure 5). The highest number of detected breakpoints was 250 (or 31% out of 814) in 2005. Among them, shrublands and grasslands combined accounted for 87.6% (219), out of which 72.8% and 14.8% were over shrublands and grasslands, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Direction of Change (\% Relative to 4454 Pixels)**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-6}  & **Deceasing** & & & & **NSC \({}^{*}\)** & **Agriculture** & **Indeterminant** \\ \hline
**TSS-** & & & & & & \\
**RESTREND** & 17.6 & 12.8 & 55.6 & 3.2 & 10.8 & 100 \\ \hline \hline \end{tabular} \({}^{*}\)NSC = Non-Significant Change.  
\end{table}
Table 2: A summary of the identified direction of change in productivity based on the TSS-RESTEND method in New Mexico during the 1982–2015 period.  
Figure 4: The spatial distribution, significance, and direction of change in productivity using TSS-RESTEND (1982–2015). Bands of red (D1, D2, D3, D4) and green (I1, I2, I3, and INC) pixels indicate those with decreasing and increasing trends in productivity, respectively. NSC (yellow) and indeterminant (gray) pixels were those with non-significant and unidentified change, respectively.  
Figure 3: A summary of the number of detected significant breakpoints along with corresponding precipitation anomaly (mm) based on a 33 year mean from PRISM. Top and bottom panels show pixels with decreasing (red line) and increasing (green line) trends in productivity, respectively.
# Legendre Multiwavelet Transform and Its Application in Bearing Fault Detection
## 2 Legendre Multiwavelet Transform
### The Proposed Method  
There is a rare number of wavelet functions that have rich regularities and orthogonality at the same time, which may lead to the loss of useful information for fault detection frequencies after multiwavelet decomposition. To overcome this drawback, this paper employs Legendre multiwavelets for decomposing the signals related to bearing faults. Figure 1 illustrates the signal decomposition process using three wavelets in the Legendre multiwavelet transform. In this figure, we first replicate the original signal into three copies (assuming a total of three Legendre multiwavelets), each utilized for the decomposition of the Legendre multiwavelets. After each level of decomposition, we obtain two components: the low-frequency and high-frequency signals. We retain the high-frequency part and use the low-frequency signal for the next level of decomposition, continuing this process until we reach the specified number of levels. In order to obtain enough fault frequency features from the wavelet decomposition coefficients, there is no post-processing in the traditional wavelet transform. Additionally, to accurately locate signal components sensitive to fault frequencies, the relative energy ratio [32] is introduced, with the specific formula as follows:  
\[r=\frac{\max{[A(f_{ch})]^{2}}}{\sum_{f}^{f_{i}}[A(f)]^{2}},\qquad f_{ch}\in(f_ {ch}-\delta,f_{ch}+\delta) \tag{9}\]  
where \(f_{ch}\) represents the characteristic frequency, \(A\) is the amplitude of the multiwavelet coefficients's envelope spectrum, \(\delta\) is the frequency interval, and \(0-f_{i}\) refers to the range of the frequency band.
# Interval Extended Kalman Filter--Application to Underwater Localization and Control
## 5 Simulation Results
### Internal Analysis Observer  
Regarding the Interval Analysis Observer, its performances are similar in both scenarios (see Figures 4 and 7). At the beginning of the simulation, the Interval Filter estimates two coherent sets (see the sets \(a\) and \(b\) in Figure 7). Figure 10 details this behavior. The robot is in position \(\mathbf{p}_{1}\), and then moves to position \(\mathbf{p}_{2}\). While the robot is in \(\mathbf{p}_{1}\), the contraction and sub-paving result in two sets of solutions, the magenta boxes. As a consequence, the estimated position can jump from one to the other set. However when the robot reaches \(\mathbf{p}_{2}\), the bottom magenta box move to the black box by dead reckoning and then contracts  
Figure 8: Scenario 2 with extended Kalman filter.  
Figure 9: Scenario 2 with modified extended Kalman filter.  
to an empty box. Meanwhile, the top magenta box moves and contract into the yellow box, giving a better estimation of the robot's position.  
Then, in Figures 4 and 7, the estimated position (magenta) follows the true position (yellow) with a precision of about 1m and with small jumps. Therefore, this localization is not optimal, but robust in the two scenarios.
## 3 Results and Discussions
### X-ray Photoelectron Spectroscopy (XPS) Results  
Figure 4 shows the XPS spectra of Cr2p core levels on the Cu-Cr-Zr thin film surface before and after annealing at 300 \({}^{\circ}\)C. The intensity of Cr2p signals for the annealed sample is stronger than that for the as-deposited sample, but the signal at 574 eV is still weak because of the tiny amount of Cr. This is attributed to the low solid solubility of chromium in Cu, which can easily migrate toward the interface and react to form an oxide layer with the annealing process. The oxide layer enhances the oxidation resistance of Cu in air. Another added metal element in the Cu alloy is zirconium, which exhibits the same function as Cr but at a concentration too small to be detected.  
Figure 3: Adhesion on various substrates at different annealing temperatures with the contrast of Cu/Ti on glass: (**a**) pure Cu; (**b**) Cu-Cr-Zr.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Substrates** & **Room Temperature (RT)** & **300 \({}^{\circ}\)C** & **350 \({}^{\circ}\)C** & **400 \({}^{\circ}\)C** & **450 \({}^{\circ}\)C** \\ \hline Glass & 2B & 4B & 5B & 4B & 5B \\ Al\({}_{2}\)O\({}_{3}\)/Glass & 4B & 4B & 5B & 5B & 5B \\ SiO\({}_{2}\)/Glass & 4B & 5B & 4B & 4B & 5B \\ \hline \hline \end{tabular}
\end{table}
Table 3: The grades of Cu-Cr-Zr thin film adhesion on various substrates.
## 3 Results and Discussion
### Partial Correlation Matrix among PMs of Gangneung (G) and PMs of Beijing (B)  
Table 2 presents the partial correlation coefficients (Pearson r) of PMs associated with the meteorological parameters of Gangneung (Korea) and PMs of Beijing (China), before, during, and after the dust periods and all periods. The partial correlation coefficients among PM\({}_{10}\), PM\({}_{2.5}\) and PM\({}_{1}\) concentrations in Table 2 are much lower than the multiple correlation coefficients among them in Table 1, before, during and after the dust periods, except for the correlation coefficients of PM\({}_{10}\)(G) and PM\({}_{2.5}\)(G). This implies that the calculated values of each PM concentration using multiple regression statistical models are much closer to the observed values for non-dust and dust periods, in Table 1. The highest coefficient (the lowest) between PM\({}_{10}\) and PM\({}_{2.5}\) at Gangneung city was 0.913 before the dust period (0.725 during the dust period), and the highest coefficient (the lowest) between PM\({}_{2.5}\) and PM\({}_{1}\) was 0.997 after the dust period (0.989 before the dust period). Before the dust period, PM\({}_{10}\) of Gangneung city is positively influenced by the local air humidity, but it is negatively by the local air temperature, wind speed, PM\({}_{10}\) and PM\({}_{2.5}\) of Beijing. PM\({}_{2.5}\) of Gangneung city is positively influenced by its relative humidity and the PM\({}_{10}\) of Beijing city, but it is negatively by others. PM\({}_{1}\) is negatively influenced by the local air temperature and wind speed, except for others. During the dust period, PM\({}_{10}\) of Gangneung is positively influenced by the local air temperature, wind speed and PM\({}_{10}\) of Beijing, except for others. The PM\({}_{2.5}\) concentration is negatively influenced by the local wind speed and relative humidity, except for others. PM\({}_{1}\) of Gangneung city is positively influenced by all variables. After the dust period, PM\({}_{10}\) of Gangneung city is negatively influenced by the local relative humidity, except for others. PM\({}_{2.5}\) and PM\({}_{1}\) at Gangneung city are negatively influenced by the local wind speed and relative humidity, except for others. For all periods, PM\({}_{10}\) of Gangneung city is negatively influenced by the local relative humidity, except for others, but PM\({}_{2.5}\) and PM\({}_{1}\) are negatively influenced by the local wind speed, except for others.
## 2 Control and Modeling of the OSWF System  
Based on the largest market share across the world, four types of wind turbines are defined [15]. Type 1 is a fixed-speed induction machine with a capacitor bank. It is the oldest type, with a simple structure and control system. In Type 2, a variable rotor resistor is added to control rotor speed. The main feature of Types 3 and 4 is the use of an AC-DC-AC power converter. Both types can control rotor speed and output turbine power. The difference between Types 3 and 4 is the full coverage of turbine power by a power converter.  
A variable-speed wind turbine has several advantages compared to fixed-speed wind turbines [16], as it can operate under its rated speed and can extract maximum power by a control named maximum power point tracking (MPPT). The control can be achieved by adjusting rotor speed to the maximum power tracking point according to varying wind speed.  
Specifically, a Type-3 doubly-fed induction generator (DFIG) has some beneficial characteristics. It can control its rotor speed, so variable speed operation is attainable. Moreover, it has a small-scale power converter to control active-reactive power from the turbine. Although there is a full-scale converter-connected permanent magnet synchronous generator (PMSG)-type wind turbine, a DFIG costs less because of its small power rating. Furthermore, as PMSG power converters should be rated the same as generator capacity, the size and complexity of the overall system increases, and the losses are higher, which leads to low efficiency [17].  
In transient conditions, PMSG-type wind turbines can cope with fault conditions by controlling their full-scale converter. However, for a DFIG, direct connections of the grid to the stator side can damage the equipment. Therefore, special protection schemes, like a DC chopper or crowbar, are used. Moreover, because a DFIG is a commonly-used device in the commercial wind turbine market, it is helpful to use a DFIG for analyzing off-shore wind farm systems.  
The off-shore wind farm-connected MMC-HVDC is schematically shown in Figure 1. Since the off-shore side system topology does not change and the focus of this study is limited to a maximum of a few hundred milliseconds of on-shore fault, the DFIG wind turbine models are aggregated in the simulation [18]. MMC HVDC connects the on-shore side system to the off-shore side system. The MMC HVDC off-shore side converter outer-loop controller forms the off-shore grid and controls the off-shore AC voltage. Simultaneously, the on-shore side converter outer-loop controller controls the DC voltage of HVDC and the AC voltage of the on-shore grid.  
As introduced in [14], there are various studies of controlled off-shore frequency for various purposes [19; 20; 21]. In [19], a Line Commutated Converter HVDC-connected off-shore wind farm PMSG inverter controlled off-shore frequency for smooth LCC HVDC operation. In [20], HVDC controlled off-shore frequency for optimal operation of wind turbines. A Q/frequency droop control was used in [21] to obtain a reference frequency at PCC.
## 1 Introduction
The model's strength lies in its ability to manage missing data, provide feature importance, and avoid overfitting, making it particularly well-suited for real-time monitoring of tool wear where data may be noisy or incomplete. Gradient Boosting is another powerful Machine Learning method that builds models sequentially, with each new tree attempting to correct the errors of the previous one. This method is known for its high predictive accuracy and ability to model complex relationships between variables [13]. Gradient Boosting is particularly useful when the data are noisy or contain outliers, as it reduces both bias and variance in predictions, making it a solid choice for tool wear forecasting where environmental conditions can vary. Long Short-Term Memory networks are a type of recurrent neural network designed to handle time-series data, making them particularly well-suited for applications where wear evolves over time. Milling operations often involve continuous data streams, such as cutting forces and temperatures, that need to be analyzed over time to predict tool wear. The ability of LSTM to capture temporal dependencies is very relevant in tool wear prediction, where past operational conditions influence future wear patterns. Unlike traditional models, LSTM can detect subtle, time-dependent changes that may indicate impending tool failure, enabling proactive maintenance measures [14]. However, despite advancements in monitoring and analyzing wear using Machine Learning algorithms, challenges remain. For instance, the complexity of the generated data, the need for more robust and generalizable models, and the effective integration of these systems into real production environments are areas that require further research and development to achieve even greater precision and efficiency. This research was performed with data from insert milling tools, which are widely used in industry today. With these approaches, more accurate and efficient results than in previous studies are expected, optimizing both the relevance of the analyzed parameters and the precision in wear prediction. An artificial intelligence pipeline was used to analyze key parameters such as cutting speed, feed per tooth, and cutting depth, as its models are widely accepted in the scientific field for their simplicity and effectiveness in translating various signals into reliable wear forecasts [6]. The results can improve knowledge about tool wear and lead to longer tool life, reducing downtime, tool costs, and, consequently, overall process costs.