esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Effect of O\({}_{2}\) Diffusion on the Retention Time of Inert Agents
## 2 Theoretical Background
### Definition of the Interface Slope \(\alpha\)  
In this study, a method to calculate the theoretical retention time is proposed using the interface slope \(\alpha\) measured by experiments and numerical analysis. Therefore, it is necessary to define the interface slope \(\alpha\). The definition of the interface slope \(\alpha\) can be seen in Figure 4.  
This figure shows the O\({}_{2}\) concentration profile for the enclosure height. Here, the interface slope \(\alpha\) is defined as follows:  
\[\alpha=\frac{\partial wO_{2}}{\partial z} \tag{7}\]  
That is, the magnitude of the interface slope \(\alpha\) is proportional to the magnitude of the z-direction diffusion flux \(J_{d}\), which is [PERSON]'s first law [18].  
\[J_{d}=-D_{ij}\frac{\partial\varphi_{o_{2}}}{\partial z}\propto\alpha \tag{8}\]  
\[D_{ij}=0.0018583\frac{T^{\frac{3}{2}}}{p\Big{(}\sigma_{ij}\Big{)}^{2}\Omega_{ ij}}\sqrt{\left(\frac{1}{M_{i}}+\frac{1}{M_{j}}\right)}\left[cm^{2}/s\right] \tag{9}\]  
where  
\[\sigma_{ij}=\frac{1}{2}\Big{(}\sigma_{i}+\sigma_{j}\Big{)},\ \Omega_{ij}= \int\left(\frac{k_{B}T}{\varepsilon_{ij}}\right),\ \frac{\varepsilon_{ij}}{k_{B}}=\left(\frac{ \varepsilon_{i}}{k_{B}}\frac{\varepsilon_{j}}{k_{B}}\right)^{\frac{1}{2}}\]  
That is, interface slope \(\alpha\) means z-direction diffusion flux \(J_{d}\) at the end, and comparison of the interface slope \(\alpha\) for each inert agent means comparison of the z-direction species diffusion.  
Figure 4 shows the indication of O\({}_{2}\) concentration for enclosure height. Here, the \(y\)-axis is represented by the enclosure height (independent variable) and the \(x\)-axis is represented by the O\({}_{2}\) concentration (dependent variable). This is for comparison with the same axis as Figure 4, which shows the cross-section of the enclosure. This expression method has already been used in several studies [1; 2; 3; 4; 9; 12]. Therefore, this is applied equally to all graphs representing the experimental and numerical analysis results of this study.  
Figure 4: Schematic diagram for the O\({}_{2}\) concentration distribution profile for calculating interface slope \(\alpha\).
## 3 Materials and Methods
### Blackbox Devices' Concept and Development
With a low power consumption appropriate for wearable usage, its modular and adjustable architecture enables the EMG module to be tailored for a variety of health monitoring applications, such as neuromuscular evaluations and rehabilitation situations [61]. * _DS18B20 Digital Temperature Sensor_: The DS18B20 temperature sensor operates using a 1-wire interface, allowing multiple sensors to share a single data line. It provides accurate temperature readings with a configurable resolution (9-12 bits) and dig ital transmission to minimize errors compared to analog sensors, making it ideal for biomedical and environmental applications. The sensor includes an integrated temperature compensation feature, ensuring stable readings even under varying environmental conditions. It supports cyclic redundancy check for error detection, enhancing data integrity. The DS18B20 sensor was chosen for its networking advantages, using a 1-wire interface that allows multiple sensors to share a single data line, simplifying wiring in large networks. Data from the sensors were collected with a microcontroller and transmitted via a wireless connection to a cloud platform. The digital transmission of data reduces errors compared to analog sensors, providing accurate and reliable measurements over time [62]. * _MAX30100/MAX30102_: Pulse Oximetry and Heart Rate Sensors utilize photoplethysmography (PPG) to measure blood SpO\({}_{2}\) and heart rate. In our project, the integration of the MAX30102 biosensor with the ESP32 microcontroller utilizes the Inter-Integrated Circuit (I\({}^{2}\)C) protocol to facilitate seamless data transfer, which is essential for monitoring key health parameters such as heart rate and SpO\({}_{2}\)[63]. The sensor includes infrared and red light emitters for dual-wavelength analysis, improving accuracy by compensating for motion artifacts. An integrated ambient light cancelation system enhances signal clarity. The MAX30100 sensor measures PPG signals to determine the SpO\({}_{2}\) levels and heart rate. Data are then sent wirelessly (via Bluetooth or Wi-Fi) to cloud storage, enabling real-time remote monitoring. This setup allows healthcare professionals to access patient data remotely and make timely interventions if necessary [64].
# Raman 2D Peak Line Shape in Epigraphene on SiC
## 2 Materials and Methods  
The epitaxial graphene was grown on a Si-face of 6H-SiC by thermal decomposition [31; 32; 33; 34]. We grew three samples, each under different growth conditions. The sample grown in vacuum [31; 32] at 10\({}^{-5}\) mbar was heated to 1600 \({}^{\circ}\)C for 5 min. The sample grown in argon [33] at 1050 mbar was heated at 1650 \({}^{\circ}\)C for 5 min. The quasi free-standing monolayer graphene (QFMLG) [35; 36] was grown in two steps. First, the graphene buffer layer was grown in 1050 mbar of argon at 1550 \({}^{\circ}\)C for 5 min. The second growth step was the hydrogen intercalation at 1120 \({}^{\circ}\)C for 5 min followed by a 2 hours long cooling to 600 \({}^{\circ}\)C. The growth temperature was adjusted with respect to the sublimation rate of silicon from the heated SiC wafer. The growth in high vacuum allows high silicon sublimation rates, hence, the temperature is reduced to 1600 \({}^{\circ}\)C in contrast to growth in 1050 mbar of argon, where the growth temperature has to be increased by 50 \({}^{\circ}\)C to 1650 \({}^{\circ}\)C to promote silicon sublimation comparable to the sublimation rate in high vacuum. The growth of QFMLG requires to grow so called buffer layer first. The buffer layer grows at even lower temperature than the single layer graphene. For this reason, the first growth step is performed at 1050 mbar of argon at 1550 \({}^{\circ}\)C for 5 min. The buffer layer is the graphene lattice, where about 30% carbons are sp\({}^{3}\) bonded to the underlying SiC substrate [37]. This sp\({}^{3}\) bonding was switched into sp\({}^{2}\) bonding by hydrogen intercalation [17; 38]. More details on graphene growth and hydrogen intercalation can be found in our previous works [17; 18; 39]. Raman spectra were measured by WITec alpha300 (WITec, Ulm, Germany) micro-Raman confocal microscope. The Raman spectra were excited by 532 nm laser light. We used 25 mW laser power and the laser spot diameter was 1 \(\upmu\)m in the focal plane. The spectra were acquired in two 30 s accumulations to achieve low level of noise in the tails of the 2D peak. The spatial Raman maps were measured on the area \(4\times 4\)\(\upmu\)m\({}^{2}\) and we accumulated Raman spectra twice 5 s to optimize lateral resolution, noise level and the total measurement time. The Atomic Force Microscopy (AFM) and Lateral Force Microscopy (LFM) were measured by WITec alpha300 (WITec, Ulm, Germany) AFM. We measured AFM and LFM in the contact mode, and, the scanned area was \(20\times 20\)\(\upmu\)m\({}^{2}\). The 2D peak was fitted by Lorentzian and Voigt line shapes. The Lorentzian line shape was taken in the form of Equation (1)  
\[G_{L}=\frac{\gamma}{\pi[(x-x_{0})^{2}+\gamma^{2}]}, \tag{1}\]  
where \(x_{0}\) is the spectral position of the peak, \(\gamma\) determines the width of the 2D peak. The Equation (1) is scaled by a factor \(I_{0}\) describing the intensity of the Lorentzian peak. Parameter \(\gamma\) is related to the more experimentally accessible Full Width at Half Maximum (FWHM) of the Lorentzian peak \(f_{L}\) by Equation (2).
# Speech Recognition for Task Domains with Sparse Matched Training Data
## 3 Proposed Methods  
### Active Learning Using Latent Variables with Disentangled Attributes  
In this section, we describe the proposed method of actively selecting training data for a target domain using attribute-disentangled latent variables. As described in the Introduction, there are tasks that require large amounts of speech data that are difficult to collect. These include non-native speech recognition and call center recording speech recognition. The proposed active learning method is meant to effectively obtain training data close to the acoustic characteristics of these target domains from other domains, such as native speech recognition and broadcast speech recognition. In these other domains, we can obtain relatively large amounts of labeled speech data, although the overall acoustic characteristics, such as speaker and channel/noise environments, are somewhat different.  
For this purpose, we designed an integrated system consisting of factorized hierarchical VAEs, [31] with an encoder that infers latent variables with disentangled attributes from the input speech, and a DNN-based classifier that selects training data with attributes matching the target domain using the preceding encoder output as input [36]. Each input speech subject to ASR contains various acoustic attributes. Among these attributes, phonetic content information changes at segmental level time scales. Additionally, environmental information such as channel/noise; speaker information such as individual speaker identity and gender/age; and prosodic information such as accent, tone, and speaking rate, change at sequence level time scales. VAEs that infer latent variables with disentangled attributes, and a DNN-based classifier are trained using a small amount of the speech data obtained from the target domain and other general domains. Here, the VAEs are trained by an unsupervised learning method using speech input/output. The classifier is trained via supervised learning by inputting attribute-disentangled latent variables that are the output of the preceding VAEs and outputting a label indicating whether the input is from the target domain or other general domains.  
Figure 2 illustrates the learning stage of an integrated system consisting of the VAEs and classifier using the example training data. In the VAEs' learning step (a), VAEs, which generate latent variables with disentangled attributes, are trained through the process of inferring the attribute-disentangled latent variables in the encoder and generating the speech again in the decoder. For example, the VAEs can use paired non-native and native speech data as input/output training data. In the classifier learning step (b), the classifier is trained by supervised learning, using latent variables with sequence-level attributes of the training data with label information identifying whether it is the target domain or general domain.  
Finally, the data selection step, which involves the selection of training speech data with the desired attributes matched for the target domain is shown in Figure 3.
## 4 Result of the Experiment and Discussion
### Scene Imaging Simulation  
In the scene face target simulation, the real SAR image is used as the image source to simulate the echo, and the simulation parameters are consistent with Table 1 except for the motion error. Figure 8 shows the imaging results for the range of 130-131.4 km. Figure 8a-c shows the imaging results of the traditional TDCM with different motion errors, and Figure 8d shows the imaging result after signal error estimation and compensation by the method proposed in the paper. If there is only channel error, the traditional TDCM and OSM can meet the accuracy requirement of channel error estimation. However, the traditional TDCM and OSM ignore the motion error, and the deterioration of the imaging result is similar. Therefore, the traditional TDCM is taken as an example for comparison.  
Figure 8: \(Cont\).  
Defocusing is severe in Figure 8a,b due to the uncompensated motion error. Figure 8c is partially focused but has obvious azimuth ambiguity. By using the proposed method, the imaging is well focused and the ambiguity is well suppressed, as shown in Figure 8d.  
The entropy of the imaging is used to evaluate image quality, as shown in Table 2. When the motion error is large, the imaging is defocused and the entropy is large. The entropy decreases when the motion error is smaller. The motion error is compensated by the proposed method. Therefore, the entropy is the smallest.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 6 Proposed Protocol
### Informal Analysis
#### 7.2.2 Physical Drone Capture Attacks  
This work assumes that an attacker (\(A\)) physically captures a drone and obtains the secret values of \(\{K_{D}^{*},r_{D}\}\) from its memory. To generate the session key, \(A\) must know the random numbers \((r_{3},r_{4},r_{5})\). However, \(A\) cannot determine these random numbers  
Figure 5: Simulation results under OFMC and CL-AtSe.  
because \(A\) cannot compute the private keys of the drone and mobile device (\(K_{D}\) and \(K_{M}\), respectively). Furthermore, the PUF challenge-response pairs (\(C_{j}\) and \(R_{j}\)) depend on the intrinsic physical variations in the drone's IC chip; thus, they are unique and independent for all deployed drones. Therefore, the publicly known \(C_{j}\) value does not help compute the session key between a mobile user and other non-compromised drones. Thus, the proposed protocol can prevent physical capture attacks on drones.
# Study on Mechanical Properties of Simply-Supported Composite Beams Considering Creep and Slip
## 3 ANSYS Finite Element Software Modeling Analysis
### Implicit Creep Method  
The implicit creep analysis method supports the following elements: Plane42, Solide45, Beam188, and Beam189.  
In the implicit creep method, ANSYS uses the TBOPT command to select its own equations in the software, such as the following.  
(1) TBOPT = 1 Initial creep equation  
\[\varepsilon_{\rm cr}=C_{1}\sigma^{C_{2}}\varepsilon_{\rm cr}{}^{C_{3}}e^{-C _{4}/T} \tag{52}\]  
(2) TBOPT = 6 Initial creep equation  
\[\varepsilon_{\rm cr}=C_{1}\sigma^{C_{2}}\varepsilon_{\rm cr}{}^{C_{3}+1}e^{- C_{4}/T}/(C_{3}+1) \tag{53}\]  
(3) TBOPT = 11 Initial creep equation + second-order creep equation  
\[\varepsilon_{\rm cr}=C_{1}\sigma^{C_{2}}t^{C_{3}+1}e^{-C_{4}/T}/(C_{3}+1)+C_{ 5}\sigma^{C_{6}}te^{-C_{7}/T} \tag{54}\]  
According to the 11 th calculation formula of the implicit creep equation, when TBOPT = 11,  
\[\varepsilon_{\rm cr}=C_{1}\sigma^{C_{2}}t^{C_{3}+1}e^{-C_{4}/T}/(C_{3}+1)+C_{ 5}\sigma^{C_{6}}te^{-C_{7}/T} \tag{55}\]  
where \(\varepsilon_{cr}\) is the equivalent creep strain and \(\sigma\) is the equivalent stress.  
It can be seen from the Dischinger algorithm that  
\[\frac{d\varepsilon_{(t,t_{0})}}{dt}=\frac{1}{E_{\rm c(t_{0})}}.\frac{d\sigma_ {(t_{0})}}{dt}+\frac{\sigma_{(t_{0})}}{E_{\rm c(t_{0})}}.\frac{d\phi_{(t,t_{0 })}}{dt} \tag{56}\]  
Figure 6: ANSYS finite element model.  
For structures under a constant load \(d\sigma_{(t_{0})}=0\), Equation (56) can be changed to \(\frac{d\varepsilon_{(t_{0})}}{dt}=\frac{\sigma_{(t_{0})}}{E_{\mathrm{c}(t_{0})}} \cdot\frac{d\phi_{(t_{0})}}{dt}\).  
That is,  
\[\dot{\varepsilon}_{\mathrm{cr}}=\frac{\sigma_{0}}{E_{\mathrm{c}}}\cdot\frac{d \phi_{(t_{0})}}{d_{t}} \tag{57}\]  
Derivation of Equation (55):  
\[\dot{\varepsilon}_{\mathrm{cr}}=C_{1}\cdot\sigma^{C_{2}}\cdot t^{C_{3}}\cdot e ^{-C_{4}/\mathrm{T}}+C_{5}\sigma^{C_{6}}e^{-C_{1}/\mathrm{T}} \tag{58}\]  
Let \(C_{2}=1\), \(C_{3}=0\), \(C_{4}=0\), \(C_{5}=0\), and the constant load \(\sigma=\sigma_{0}\); then, Equation (58) can be expressed as follows:  
\[\dot{\varepsilon}_{\mathrm{cr}}=C_{1}\sigma_{0} \tag{59}\]  
From Equations (57) and (59),  
\[\left\{\begin{matrix}C_{11}=\frac{\phi_{1}}{t_{1}}\frac{1}{\mathrm{C}}\\ C_{1i}=\frac{\phi_{i}-\phi_{i-1}}{t_{i}-t_{i-1}}\frac{1}{E_{\mathrm{c}}},i\geq 2 \end{matrix}\right. \tag{60}\]  
In the formula, \(\phi_{i}\) is the creep coefficient at time \(i\).  
In comparison, the implicit method is more convenient and accurate, so this paper adopts the implicit method to simulate shrinkage and creep.
# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 2 Materials and Methods  
ML methods can be classified into supervised or unsupervised techniques based on the availability of a training dataset. Supervised ML is designed to generate desired outputs in response to a given set of inputs during training. This technique is commonly employed for modeling and controlling dynamic systems, classifying noisy data, and making predictions about future events. On the other hand, unsupervised ML is trained in a manner where the network continuously adapts to novel inputs. It has the ability to reveal non-linear relationships within the data and establish classification schemes that adjust to variations in the added data. In this study, supervised ML techniques are utilized. There are 4 kinds of supervised ML techniques selected to explore the best suitable methods for predicting time-series database, i.e., PWP.  
Most deep learning methods are good at training time series datasets; two kinds of them are chosen in this study, including RNNs and GRU. RNNs are neural networks suitable for processing sequence data that can effectively capture the temporal information in the sequence. RNNs use the same parameters at each time step, which can effectively share weights and reduce the complexity of the model and the number of parameters for training. RNNs can memorize previous information and use it to make predictions or decisions in subsequent time steps, which is very useful for handling context-dependent tasks. The disadvantage of RNNs is that they often yield vanishing gradients and gradient explosions during training.  
In order to avoid the drawbacks of RNNs, GRU has been employed in this study. GRU has a simpler structure compared to LSTM, solves the problem of vanishing gradients compared to RNNs, and has been shown to be more effective for smaller sequences.  
Deep learning suffers from a uniform common problem, i.e., training is inefficient, time-consuming, and computationally demanding. In order to compare the computation time and find the most accurate and efficient training method for the case of this paper, the two most commonly used ML methods, RF and MLP, are selected for this paper.
# Late Line-of-Sight Check and Prioritized Trees for Path Planning
## 6 Experiment
### Complex Street Test  
In the previous subsection, we did some benchmark tests on various maps. Each type of map had different scenarios, and each scenario had different pairs of start and goal points. The average planning results, including path length and execution time, showed that the LLA* algorithm ran the fastest among the four given planners, and it generated shorter paths than Lazy Theta* and A*. It seemed that LLA* had a good performance in both the planning speed and quality. However, the average results could not display the performance of LLA* in some special situations. Thus, we selected six complex street maps from the benchmark maps mentioned in Section 6.1, which are shown in Figure 8. These maps included thumbnails of the streets in Berlin, Boston, London, Milan, Moscow, and Paris, which were all of a large size, 1024 \(\times\) 1024. These complex street tests contained difficult tasks, where the start position and goal position were both set at some narrow corners. We performed this kind of test to explore the extreme cases and limits of these planners. Table 2 shows the planning results of the four planners on the six complex street maps, including path length, execution time, and LoS-Check number. In the original design, we aimed to speed up the planning algorithm by reducing the LoS-Check number. Therefore, we provided the LoS-Check number to reflect the relationship between execution time and the line-of-sight check operation. From the planning results on the six complex city street maps, we can find that LLA* still ran the fastest among the four planners, and it always generated shorter paths than Lazy Theta* and A*. Moreover, it is interesting that LLA* sometimes generated shorter paths than Theta*, such as the planning results of the street tests in Milan and Paris (this is similar to the results in Figure 6). LLA* performed the least LoS-Check, so it ran faster than the other planners. Meanwhile, because of using the discriminatory updating strategy in the planning process, it also generated shorter paths than Lazy Theta* and sometimes generated shorter paths than Theta*. The discriminatory updating strategy helped the planner focus on exploring the states that have the potential to improve the final paths.
# Bending Behavior of Corroded H-Shaped Steel Beam in Underground Environment
## 3 Analysis of Results
### Bending Test of Corroded Beam
#### 3.2.3 Analysis of Load-Midspan Displacement Curve  
The load-midspan deflection curve of all corroded H-shaped steel beams is shown in Figure 11(a). Evidently, the shape of the load-midspan displacement curves of all tested beams is similar and the curve can be divided into three stages. The first stage is the elastic stage, wherein the curve is essentially linear. The second stage is the yield stage, wherein the edge of the beam part in the tension zone (or compression zone) yields first and then the entire cross-section gradually yields as the load increases, which is reflected as an arc in the curve. The third stage is the strengthening stage, which features a large deflection increment with a small load increase. In this stage, the steel beam eventually undergoes overall bending failure after losing its bearing capacity.  
Figure 12: Load-displacement curves of corroded beams: (**a**) Group A; (**b**) Group B; (**c**) Group C.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimens** & \(\mathbf{\eta}\) & \(\mathbf{P_{y}/kN}\) & \(\mathbf{P_{u}/kN}\) & **Decrease in \(\mathbf{P_{y}}\)** & **Decrease in \(\mathbf{P_{u}}\)** \\ \hline G-0-0 & – & 224.84 & 274.77 & – & – \\ GA-60-0 & 3.65\% & 197.09 & 261.61 & \(-\)12.34\% & \(-\)5.31\% \\ GA-60-3 & 4.75\% & 195.31 & 254.75 & \(-\)13.13\% & \(-\)8.08\% \\ GA-60-6 & 5.85\% & 184.56 & 237.26 & \(-\)17.91\% & \(-\)15.14\% \\ GB-120-0 & 8.51\% & 186.83 & 248.00 & \(-\)16.91\% & \(-\)10.80\% \\ GB-120-3 & 9.72\% & 184.39 & 237.77 & \(-\)17.99\% & \(-\)14.93\% \\ GB-120-6 & 10.35\% & 176.22 & 225.45 & \(-\)21.62\% & \(-\)19.91\% \\ GC-180-0 & 11.92\% & 178.38 & 237.23 & \(-\)20.66\% & \(-\)15.15\% \\ GC-180-3 & 12.06\% & 170.17 & 230.32 & \(-\)24.32\% & \(-\)17.94\% \\ GC-180-6 & 13.07\% & 162.57 & 217.99 & \(-\)27.70\% & \(-\)22.92\% \\ \hline \hline \end{tabular} Notes: \(\mathbf{P_{y}}\) is the yield load; \(\mathbf{P_{u}}\) is the ultimate load.  
\end{table}
Table 5: Bending test results.  
Figure 12a-c shows the load-midspan deflection curve of Group A, Group B, and Group C, respectively. As shown in Figure 12a, the slope of the load-midspan displacement curve of the corroded beam before the yield stage was slightly smaller than that of the uncorroded beam. The reason for this is that the corrosion led to a slight decrease in the section size of the test beam; therefore, the stiffness of the test beam decreased. When the load-midspan displacement curve entered the strengthening stage, the curve of GA-60-0 contained the curve of GA-60-3 and the curve of GA-60-3 contained the curve of GA-60-60. When the tested beam reached the ultimate load, the displacement of GA-60-0 was larger than that of GA-60-3 and the displacement of GA-60-3 was larger than that of GA-60-6. These two phenomena indicate that the combined action of the sustained load and corrosion reduced the ductility of the tested beam.