esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Test Scheme
#### 2.3.3 Disintegration Test  
In order to determine the disintegration speed of solidified soil in water, the SHY-1 was used for this test. To ensure consistent sample production, a sample of \(\Phi\) 5 cm \(\times\) H 5 cm was placed in the center of the stencil. A mesh plate was hung under the buoy, and the sample was quickly immersed into the water cylinder by holding the neck of the buoy. Electronic equipment was used to record the disintegration of the solidified soil. Data were recorded 1 min before the start of the test, every 10 s, and then every 1 min thereafter. The time interval of recording can be adjusted based on the disintegration speed of the solidified soil. The test is considered complete when the sample falls completely through the grid, or when the sample does not collapse significantly for a long time and the buoy scale does not change significantly within the 30 min time interval. The situation of the sample on the grid and in the water should be described and recorded, and real-time photos should be taken. The trial is terminated at this point [37]. The disintegration calculation can be seen in Formula (1).  
\[A_{t}=\frac{R_{t}-R_{0}}{R_{e}-R_{0}}\times 100 \tag{1}\]  
In the formula, \(A_{t}\) represents the amount of disintegration at time \(t\) in percentage, \(R_{0}\) represents the scale reading of the buoy at the water surface at the beginning of the test, \(R_{t}\) represents the scale reading of the buoy at the water surface at time \(t\), and \(R_{e}\) represents the scale reading of the buoy at the water surface at the end of the test. The disintegration rate, expressed in min\({}^{-1}\), indicates the speed of specimen disintegration over time. It serves to reflect the rate of disintegration transformation and, consequently, the erodibility of the soil.  
The disintegration rate calculation can be seen in Formula (2).  
\[v_{t}=\frac{A_{t}^{i+1}-A_{t}^{i}}{t_{i+1}-t_{i}} \tag{2}\]  
In the formula, \(v_{t}\) represents the average disintegration rate of the test piece during a specific time period from \(t_{i}\) to \(t_{i+1}\), measured in min\({}^{-1}\), \(A_{t}^{i+1}\) represents the amount of disintegration at time \(t_{i+1}\), \(A_{t}^{i}\) represents the amount of disintegration at time \(t_{i}\).
## 3 Results
### Selection of Input Features  
#### 3.2.1 Preliminary Analysis  
After a preliminary analysis of the dynamics of the time-series data with a subsample of _full control_ and _loss of control_ braking trials, we decided to include the differential of the collected signals roll rate, yaw rate and steering angle (i.e., roll and yaw acceleration and steering rate), as input features of the model. Figure 3 shows two examples of both _full control_ and _loss of control_ emergency braking representing the speed, the steering and roll angle and the yaw and roll rate.
## 3 Results and Discussion
### Amperometric Fatty Acid Biosensor Design and Characterisation
Figure 2: Schematic showing the sequential deposition of the enzyme and cross-linking agent onto Table 3. Schematic diagram of the amperometric fatty acid biosensor design and the sequence of reactions involved in its operation; SPCE = screen-printed carbon electrode; LOX = lipoxygenase; GLA = glutaraldehyde. Optimisation of Applied Potential and Other Conditions Using Amperometry in Stirred Solution and Calibration Studies with Linoleic and \(\alpha\)-Linoleic Acids  
In order to deduce the optimum potential for the operation of the biosensor, hydrodynamic voltammograms (HDVs) were constructed for solutions containing either linoleic acid, or \(\alpha\)-linolenic acid. To construct these HDVs, the biosensor was placed in a stirred solution of the selected fatty acid and left for 600 s at open circuit; the potential was then stepped between 0 V and +1.0 V (vs. Ag/AgCl), in 50 mV steps. After each change in potential, the steady state currents were measured after a waiting time of 600 s. These currents were then plotted against the corresponding applied potential to produce the HDV (Figure 5). Clearly, both compounds exhibited one well-defined voltammetric response,  
Figure 4: Scanning electron microscopy images obtained of the working electrodes of: (i) initial cobalt phthalocyanine screen-printed carbon electrode (CoPC-SPCE); (ii) CoPC-SPCE with lipoxygenase (LOX) deposited on the surface; (iii) CoPC-SPCE with LOX and glutaraldehyde deposited on the surface. Figure 3: Schematic diagram of the amperometric fatty acid biosensor design and the sequence of reactions involved in its operation; SPCE = screen-printed carbon electrode; LOX = lipoxygenase; GLA = glutaraldehyde. which concurs with that found earlier in our cyclic voltammetric study (Figure 1). From the position of the plateau, we deduced that the optimum operating potential for both fatty acids was +0.5 V (vs. Ag/AgCl). It should be noted that this potential is less positive than that seen in Figure 1; this is probably due to the cyclic voltammetric responses being directly related to scan rate [21], and as our HDVs are obtained with a very low scan rate (0.083 mV/s), the oxidative responses occur at lower applied potentials.
## 3 Materials and Methods
### Data Description
#### 3.2.1 AE Feature Extraction  
To ensure the features of the AE signal's quality, the AE data were recorded using identical AE sensors, which were adhered to the specimen's front surface by thermal bonding. The sensor model used was the PXR50 (Changsha Pengxiang Electronics Technology Co., Ltd., Changsha, China), a resonant-type sensor with a resonance frequency of 500 kHz and a frequency bandwidth of 300-1000 kHz. The signal amplifier model used was the PXPA3 (Changsha Pengxiang Electronics Technology Co., Ltd., Changsha, China), providing a fixed gain of 40 dB (100 times signal amplification) and a bandwidth of 4 kHz to 2 MHz. Among the most crucial equipment for AE signal collection experiments is the signal acquisition instrument, which consists of a PXI 1033 (National Instruments, Austin, TX, USA) chassis and a two-channel PXI 5122 (National Instruments, Austin, USA) high-speed digitizer board. Before AE data extraction, we conducted pencil lead breakage tests following the ASTM-E976 standards [39] to calibrate the AE measurement. The obtained  
Figure 3: Test material image. (**a**) Center side concave material morphology image; (**b**) center perforated material morphology image; (**c**) tensile testing machine picture.  
AE data were continuously monitored and recorded, capturing AE signals from the application of loading to the point of material failure. We obtained sound signal features from each AE event [40; 41], and, as illustrated in Figure 4, the features of individual AE events were extracted.  
Additionally, to ensure the completeness of the material's dynamic response features and to further analyze the material performance changes and failure mechanisms, we constructed some extended parameters for AE data as follows:  
Peak frequency is the frequency with the highest amplitude after using fast Fourier transform (FFT) to analyze signal coefficients within its frequency domain. A low peak frequency is associated with matrix cracking, while a high peak frequency is related to fiber damage.  
The \(RA\) value is the ratio of the _Rise Time_ to the _Amplitude_ of the AE signal. The \(RA\) value under tensile failure conditions is smaller than that under shear failure conditions. The calculation method for the \(RA\) value is expressed by Equation (1).  
\[RA=\frac{RiseTime}{Amplitude}\text{.ms/V} \tag{1}\]  
The \(RMS\) value is the root mean square value of the AE signal over the sampling time. The larger the \(RMS\) value, the higher the energy released by the AE events, which may correspond to more severe material damage. The calculation method for the \(RMS\) value is expressed by Equation (2).  
\[RMS=\sqrt{\frac{1}{T}\int_{0}^{T}(p(t))^{2}dt} \tag{2}\]  
where, \(p(t)\) represents the function of the signal amplitude as it varies with time, and \(T\) represents the observation time.  
**Peak frequency** is more decisive for AE features, as it is unaffected by the sensor's position. Both \(RA\) and \(RMS\) values are unaffected by the threshold and are useful in evaluating the mechanisms and extent of material damage.
###### Abstract  
Detecting pharmaceuticals in environmental matrices, particularly in wastewater, is crucial due to their potential environmental occurrence and unpredictable ecological and health-related consequences. These substances, often present in trace amounts, require highly sensitive and selective analytical methods for effective monitoring. A modified version of the QuEChERS (Quick, Easy, Cheap, Effective, Rugged, and Safe) method was evaluated to evaluate 18 pharmaceuticals and 2 metabolites in wastewater samples using liquid chromatography with tandem mass spectrometry (LC-MS/MS). The method's performance was assessed using linearity, recovery, precision, limits of quantification (LOQ) and detection (LOD), and the matrix effect (ME). The final method was based on acetonitrile, Na\({}_{2}\)EDTA, citrate buffer, and graphene oxide (GO). Finally, the calibration curves prepared in acetonitrile and the matrix extract showed a correlation coefficient of 0.99. Most of the compounds had LOQ values lower than 0.5 \(\upmu\)g-mL\({}^{-1}\). Recoveries were achieved in the 70-98% range, with RSD lower than 13%. GO allowed the elimination of the ME, which occurred in the range of \(-11\%\) to 15%. The results indicate that a low-cost and straightforward method is suitable for routinely monitoring pharmaceuticals in wastewater, which is crucial for minimizing the impact of pollutants on aquatic ecosystems.  
p _Applied sciences_  
2025158441
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 3 Heuristic Methods on a Simple Thermal Model Reduction and Correlation
### Model Parameters
#### 3.7.3 Thermal and Radiative Properties  
* Emissivities: \(\epsilon_{11}=0.08\), \(\epsilon_{21}=0.05\), \(\epsilon_{31}=0.10\);
* Radiative areas: \(A_{11}=A_{21}=A_{31}=1.0\,\text{m}^{2}\);
* Thermal conductances: \(GL_{0,10}=0.05\,\text{W}/^{\circ}\text{C}\), \(GL_{10,20}=0.5\,\text{W}/^{\circ}\text{C}\), \(GL_{20,30}=0.05\,\text{W}/^{\circ}\text{C}\), \(GL_{10,11}=GL_{20,21}=GL_{30,31}=2.5\,\text{W}/^{\circ}\text{C}\);
* Satellite mechanical interface temperature: \(T_{0}=25.0\,^{\circ}\text{C}\);
* Space radiative interface temperature: \(T_{99}=-270.0\,^{\circ}\text{C}\).  
A short summary of both thermal models, the DTMM considers separately the internal nodes (10, 20, 30) and surface nodes (11, 21, 31), including both conductive and radiative heat transfer. Regarding the RTMM, it combines each internal-surface node pair (10-11, 20-21, 30-31) into a single lumped node, simplifying the analysis while retaining the essential thermal dynamics.  
Both systems of ordinary differential equations (ODEs) can be solved numerically (e.g., using odeint from the scipy library) to obtain transient temperature profiles.
# A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
## 1 Introduction
A recent survey reveals the penetration of deep-learning techniques entirely into the field of medical image analysis in CAD; classification, detection, segmentation, registration, retrieval, image generation and enhancement, whereas the successful application of deep learning to medical imaging tasks is thoroughly examined [22; 23; 24; 25; 26]. Specifically, deep learning in medical imaging was achieved using convolutional neural networks (CNNs) [27; 28; 29; 30]. CNNs appertain to deep, feed-forward artificial neural networks, able to extract the feature of an image and use this feature to classify the image. The aim of CNNs in all application fields is to convert the input into a feature vector by means of a matrix and to match it with trained feature vectors [31]. Some major and popular CNN models, introduced in published research articles in the context of image processing and deep learning, are: the AlexNet (2012) network [32], which has a very similar architecture to LeNet, by [PERSON] et al., but is deeper, with more filters per layer and includes stacked convolutional layers, the VGGNet16 (2014), that consists of 16 convolutional layers and its very uniform architecture makes it very appealing [33], the ResNet model, that introduces the residual learning building block for extremely deep convolutional networks [34], the DenseNet (2017) [35] that offers the main advantage of alleviating the gradient vanishment problem with the direct connection of all the layers and, finally, the Deeplab, which deals with the introduction of convolutional layers atrous for semantic image segmentation in deep convolutional neural networks [36]. All the important and most interesting applications of deep learning in medical image analysis and segmentation are gathered in recent review studies [22; 23; 24; 29].
# Estimating Eddy Dissipation Rate with QAR Flight Big Data
## 1 Introduction
It is expressed as the kinetic energy per unit mass per second, with units of velocity squared per second (m\({}^{2/3}\)s\({}^{-1}\)) [21]. EDR represents the transmission rate of the disturbance energy from the larger-scale vortex to the smaller-scale vortex. It characterizes the disturbance state of turbulence in the inertia sub-region and is the key parameter for describing atmospheric turbulence. The larger the EDR is, the stronger the turbulence is [22]. Therefore, EDR is often used to describe the vertical gust spectrum of turbulent air around the aircraft [6]. According to the EDR value, the turbulence intensity is divided into different levels, as shown in Table 1.  
Quick access recorder (QAR) is an airborne flight data recorder designed to provide quick and easy access to raw flight data [23]. It provides environmental parameters, equipment parameters, and operating parameters of the aircraft during the flight. The data enjoy high time accuracy and strong sequence because of the short sampling interval. Meanwhile, QAR data have the characteristics of being complete, high-precision, real-time, and fine-grained, thus it provides a good data foundation for AT-related research. The Civil Aviation Administration of China (CAAC) has been collecting aircraft QAR data from major domestic airlines since 2016, which laid a good data foundation for this research. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Turbulence Indicator**} & \multicolumn{8}{c}{**Turbulence Peak (\(\epsilon^{\frac{1}{3}}\) unit: m\({}^{2/3}\)s\({}^{-1}\))**} \\ \cline{2-10}  & & **\(\boldsymbol{<0.1}\)** & **0.1–0.2** & **0.2–0.3** & **0.3–0.4** & **0.4–0.5** & **0.5–0.8** & **\(\boldsymbol{>0.8}\) \\ \hline \multirow{6}{*}{**Turbulence mean EDR**} & \(<\)0.1 & 0 & 1 & 3 & 6 & 10 & 15 & 21 \\ \cline{2-10}  & 0.1–0.2 & & 2 & 4 & 7 & 11 & 16 & 22 \\ \cline{1-1} \cline{2-10}  & 0.2–0.3 & & & 5 & 8 & 12 & 17 & 23 \\ \cline{1-1} \cline{2-10}  & 0.3–0.4 & & & & 9 & 13 & 18 & 24 \\ \cline{1-1} \cline{2-10}  & 0.4–0.5 & & & & & 14 & 19 & 25 \\ \cline{1-1} \cline{2-10}  & 0.5–0.8 & & & & & & 20 & 26 \\ \cline{1-1} \cline{2-10}  & \(>\)0.8 & & & & & & & 27 \\ \hline \multirow{2}{*}{**Turbulence intensity**} & \multicolumn{2}{c}{steady flight weak turbulence} & \multicolumn{2}{c}{moderate turbulence} & \multicolumn{2}{c}{strong turbulence} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Turbulence intensity scaling with respect to eddy dissipation rate (EDR) values. In this paper, we outline a simple method for estimating EDR with QAR data. In Section 2, we introduce the QAR data, three different methods to measure flight turbulence intensity, and present the procedure for estimating EDR with QAR data. The EDR estimating results are given in Section 3 and the conclusion is made in Section 4.
# Deep Neural Network-Based Guidance Law Using Supervised Learning
## 4 DNN-Based Guidance (DNNG) Law  
In this section, we explain the DNN-based guidance (DNNG) law, which begins with the acquisition of training data from missile systems with the PNG law. The DNNG law is a deep neural network that deduces one output variable for two input variables, and has the same principle as the PNG law. This law outputs an acceleration, \(a_{m}\), by inputting the LOS rate, \(\Omega\), and the speed of the missile, \(V_{m}\). Figure 4 shows a schematic diagram for the approach of this paper, indicating that the DNNG replaces the PNG. This missile system consists of three main modules: the seeker module, the guidance module and the dynamics module. The seeker module identifies the target position, and then this outputs the LOS rate and the speed of the missile. The guidance module then generates the acceleration commands for the missile in accordance with the guidance law. The dynamics module calculates the state of the missile through numerical integration based on the missile dynamics using control values.  
Figure 5 shows the process of supervised learning for the DNNG. The learning process consists of a collection of the training data, the DNN architecture design and optimization. The details of the learning process are covered in the following paragraph. The performance evaluation of DNNG is  
Figure 4: Schematic diagram of the missile system.  
Figure 3: Conceptual diagram of proportional navigation guidance.  
carried out in the missile system simulator established in this study, and its performance is compared with the PNG.
## 2 Single-Pile Composite Foundation Load Test
### Single-Pile Composite Foundation Load Test  
The test methods and data analysis followed the specification [14]. The pressure and displacement values under different load levels were recorded using pressure transducers and displacement transducers through graded loading with an oil pressure jack. The load-settlement curve (P-s curve) was generated, as shown in Figure 4, and the on-site view of loading test is shown in Figure 5. The settlement of the composite foundation at each location and its corresponding allowable bearing capacity are illustrated in Table 1.  
In Figure 4, the eight curves are all slow-varying. When the 4 composite foundations with bag grouting piles were loaded to the maximum test load of 300 kPa, the settlements were stable. From Table 1, the accumulated settlements of the four composite foundations with bag grouting piles were 12.64-17.19 mm. The pressure (190-208 kPa) corresponding to the deformation value \(s=0.006b\) (7.8 mm) can be taken as the allowable bearing capacity of the composite foundation of the bag grouting pile, according to the specification [14] C.0.12, paragraph 3, clause 4. Similarly, the settlements of the four cement mixing pile composite foundations were stabilized when loaded to the maximum test load of 300 kPa, and the  
Figure 4: P–s curve of bag grouting pile and cement mixing pile.  
Figure 5: On-site view of loading test.  
accumulated settlements of the four composite foundations were 9.64-13.11 mm. The test did not show the situation described in paragraph 1, 2, and 3 of Article C.0.10 from the specification [14], the bearing capacity did not reach the limit, and the pressure-settlement (P-s) curve does not show obvious proportional limits. The pressure (172-192 kPa) corresponding to the deformation value \(s=0.006b\) (6 mm) can be taken as the allowable bearing capacity of the composite foundation of the cement mixing pile, according to the specification [14] C.0.12, paragraph 3, clause 4.  
The comparison analysis demonstrates that the bag grouting pile exhibited a slightly smaller pile diameter (0.4 m), a slightly larger pile spacing (1.3 m), and the former provided an 8.8% increase in the bearing characteristics, compared to the cement mixing pile with a pile diameter of 0.5 m and a pile spacing of 1.0 m. Moreover, the construction period of bag grouting pile is shorter, the cost is lower, and the process requirement is simpler, so the treatment effect of bag grouting pile can be considered to be slightly better than that of cement mixing pile.
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 3 Two Opposite IMDs
### Parametric Studies  
Figure 3 presents the supplemental modal damping ratio of a cable with two symmetric identical VDs versus damping coefficients. For the convenience of comparisons, the results of a cable with a single VD are also shown. It is observed that symmetrically attaching two VDs on the opposite end of a cable is favorable to increasing the maximum supplemental damping ratio of the cable, and its maximum supplemental modal damping ratio is asymptotically the sum of contributions from each VD separately. The findings above are quite consistent with those reported in previous studies [76,77].  
Figure 3: The supplemental modal damping ratio curve of a cable with a single viscous damper (VD) or two symmetric identical VDs (\(x=x_{1}=x_{2}^{*}=1\%L\)).  
Figure 2: Comparison of asymptotic and numerical complex wavenumbers of a cable with two symmetric identical inertial mass dampers (IMDs) (\(x=x_{1}=x_{2}^{*}=1\%L\)).  
Figure 4 presents the supplemental modal damping ratio of a cable with two symmetric identical IMDs or a single IMD versus damping coefficients for various inertial masses. It is clear that two opposite IMDs can provide superior control performance to the cable over a single IMD. Figure 5 directly compares the maximum supplemental modal damping ratio of a cable equipped with a single IMD or two symmetric identical IMDs. For two symmetric identical IMDs with small or medium inertial mass, similarly to the case of two symmetric identical VDs, the maximum achievable supplemental damping ratio is approximately doubled with that provided by a single IMD. It indicates that two opposite IMDs are almost independent from each other. This finding may explain why the optimal damping coefficients of the IMDs for both the single configuration and two-symmetric configuration are similar to each other in magnitude, as shown in Figure 4. Moreover, the maximum achievable supplemental modal damping ratio of a cable provided by two symmetric identical IMDs is larger than that provided by a single IMD or two symmetric identical VDs.  
Figure 4: The supplemental modal damping ratio curve of a cable with a single IMD or two symmetric identical IMDs (\(x=x_{1}=x_{2}^{*}=1\%L\)).
# Mid-State Kalman Filter for Nonlinear Problems
## 3 Mid-State Kalman Filter for Nonlinear Problems  
The KF is proposed based on the least mean square error criterion. Under the assumption that the observation noise obeys the Gaussian distribution, the KF is a constant minimum variance and linear unbiased estimate that can recursively obtain the state. In this paper, a Mid-state Kalman Filter is proposed based on the KF method. A MSKF selects the observed state and its first-order state derivative as the mid-state vector and transforms the filter update process into the measurement space. The proposed method guarantees the Gaussian distribution of the measurement and correspondingly transforms the measurement equation into a linear form.