esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comprehensive Evaluation Model for Optimizing the Sensor Array of Electronic Nose
## 3 Validation of the Evaluation Model
### Experimental Procedures  
In order to compare the recognition accuracy of the E-nose before and after the sensor array optimization, the above experimental system was used to identify the CO-CH\({}_{4}\) gas mixtures. The compositions and concentrations of the gas samples are listed in Table 2. A total of 10 rounds of testing were carried out, and each round of testing included all types of gas mixtures, with the order of gas samples in each round of testing randomized. The temperature of the experimental environment was \(25\pm 1\)\({}^{\circ}\)C, and the humidity was \(60\pm 5\%\). The data collection steps were as follows:  
* Preheat the sensor array for 30 min, and flush the sensor array chamber with ambient air (300 mL/min) until the sensor baseline stabilizes.
* Pump the target gas into the sensor array chamber, the gas pumping time was 5 s and the response time was 120 s, and collect the response signal data of the sensor to the target gas during this whole process.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Sensor No.** & **Main Detected Gas** & **Response Time** & **Recovery Time** \\ \hline S1 & Benzenes, VOCs & \textless{}10 s & \textless{}60 s \\ S2 & Air quality control, nitrogen oxides & \textless{}5 s & \textless{}30 s \\ S3 & VOCs, ethanol, acetone & \textless{}10 s & \textless{}60 s \\ S4 & Ethanol, methane, VOCs & \textless{}5 s & \textless{}30 s \\ S5 & Hydrogen, hydrogen sulfide, methane, & \textless{}10 s & \textless{}60 s \\ \multicolumn{4}{c}{Formaldehyde, carbon} & \textless{}5 s & \textless{}20 s \\ \multicolumn{4}{c}{monoxide, methane} & \textless{}5 s & \textless{}30 s \\ S7 & Carbon monoxide, ethanol, methane, & \textless{}5 s & \textless{}30 s \\ S8 & Ethanol, ammonia & \textless{}10 s & \textless{}30 s \\ S9 & Acetone, hydrogen sulfide, ethanol & \textless{}5 s & \textless{}30 s \\ \multicolumn{4}{c}{Hydroogen sulfide, ethanol,} & \textless{}10 s & \textless{}60 s \\ \multicolumn{4}{c}{carbon monoxide} & \textless{}10 s & \textless{}60 s \\ \hline \end{tabular}
\end{table}
Table 1: Characteristics of the employed gas sensors in initial sensor array.  
Figure 1: Schematic diagram of the experimental E-nose system.  
Purge the sensor array chamber with ambient air until all sensors return to their original baseline, then start the detection for the next gas sample.
# An Accelerated Maximum Flow Algorithm with Prediction Enhancement in Dynamic LEO Networks
## 3 System Model and Problem Formulation
### Predictive Maximum Flow in e-TEG  
To efficiently compute the maximum flow in a dynamic LEO satellite network, we leverage a predictive flow \(\hat{f}\) as an initial estimate to reduce the number of iterations needed to reach convergence, thereby accelerating the solving process.  
The predictive maximum flow problem is defined on the e-TEG \(G=(\mathcal{V}^{*},\mathcal{A}^{*})\), which represents the network's topology across a time horizon \(T=[t_{0},t_{k})\). The objective is to calculate the maximum flow \(f\) from the source satellite \(s_{1}^{1}\) to the ground station \(g_{1}^{h}\) while satisfying the following constraints:  
\[0\leq f(\mu,\
u)\leq c(\mu,\
u),\;\;\;\forall(\mu,\
u)\in \mathcal{A}^{*} \tag{15}\] \[F=\sum_{\mu\in\mathcal{V}^{*}\setminus\{s_{1}^{1}\}}f(s_{1}^{1},\mu)\] (16) \[\sum_{(\
u,\mu)\in\mathcal{A}^{*}}f(\
u,\mu)=\sum_{(\mu,\
u)\in \mathcal{A}^{*}}f(\mu,\omega),\;\;\;\forall\mu\in\mathcal{V}^{*}\setminus\{s _{1}^{1},g_{1}^{h}\}\] (17) \[\sum_{(\
u,\mu)\in\mathcal{A}^{*}}\hat{f}(\
u,\mu)=\sum_{(\mu, \omega)\in\mathcal{A}^{*}}\hat{f}(\mu,\omega),\;\;\;\forall\mu\in\mathcal{V}^{ *}\setminus\{s_{1}^{1},g_{1}^{h}\} \tag{18}\]  
Here, \(f(\mu,\
u)\) denotes the data flow transmitted or stored on edge \((\mu,\
u)\in\mathcal{A}^{*}\), while \(c(\mu,\
u)\) represents the capacity of the edge. Equation (15) ensures that the flow on each edge does not exceed its capacity. Equation (16) defines the total flow \(F\) from the source \(s_{1}^{1}\) to the ground station \(g_{1}^{h}\), which is the objective to maximize. Equation (17) enforces flow conservation, ensuring that the total inflow to any intermediate node equals its total outflow.  
The predictive flow \(\hat{f}\) provides an initial estimate that satisfies flow conservation, as described in Equation (18). By initializing the algorithm with \(\hat{f}\), the solver can begin with a  
Figure 4: The process of dynamic updating in a dynamic satellite network, where \(\tau_{i}\) represents the next time slot, and the network within the box corresponds to the network that will be updated in the next time slot. The residual network is indicated by the orange dashed box.  
near-feasible solution, reducing the number of iterations needed to compute the optimal maximum flow.  
This predictive framework offers a significant computational advantage in dynamic networks, where frequent updates are required. The ability to incorporate predictive flows into the e-TEG model ensures that the computation remains efficient, even as the network evolves over time.  
By employing this update mechanism, the e-TEG remains adaptable to the dynamic environment of LEO networks. The approach minimizes computational overhead by building on previously computed residual networks rather than reconstructing the graph from scratch for each time slot. This efficiency is critical for real-time applications requiring rapid response to changing network conditions.
## 3 Results and Discussions
### Result of RSM/CCD Analysis
#### 3.3.1 ANOVA Result
strength) were estimated with high accuracy. Additionally, the compressive strength model showed the highest performance with a value \(R^{2}\) = 0.9469. The standard deviation values can be considered when evaluating the models' performance concerning datasets obtained from experimental studies. The models' mean values were compared to standard deviation values to determine how accurately the data fit the developed models. Furthermore, adequate precision (AP) value was needed with the appropriate signal as achieved from the developed model were greater than 4. As a result, the established models might be used to explore the design space as specified in CCD [57]. Moreover, the model's performance was assessed using the coefficient of variation, which reflects the scattering of the data. The models' COV results of 4.87% and 5.75% demonstrated that the scattering in the dataset is insignificant. Figures 10 and 11 illustrate the dataset of the established model for 3D and 2D response surface plots showing the relationship among the input parameters against the responses. The surface plots also clearly noted the reinforcing effect of PU binder and NS on the cement mortar. As depicted in Figure 10, the flexural and compressive strength increases with the increase in NS, and the peak strengths were obtained by adding 3% NS (the red color portion in the plot). The strength improvement was promoted by adding 10 to 15% PU binders, and then the strengths tended to decline with a further increasing PU binder content, as revealed against 25% PU binders (the blue color portion). From Figure 10a, it can be observed that the mitigating effect of NS is more pronounced in the flexural strength compared with compressive strength, which showed a flatter convex curve. The modeling result agreed with the experimental result regarding the mechanical properties of cement mortar due to the combined effect of the PU binder and NS. The 2D response surface plots of the strength of the PUCM are shown in Figure 11. The plots also reveal the combined effect of the polymer material and NS with optimum PU binder content of 10 to 15%. Figure 9: Relationship between actual and predicted values using CCD/RSM model for (**a**) flexural strength and (**b**) compressive strength.
## 4 Experiments and Results
### Prediction
#### 4.2.4 Heuristic Baseline  
To provide a point of comparison, we implement a simple, non-machine-learning baseline that uses only the performance (P) feature. Specifically, we utilize the student's weighted average grade at a given \(\text{CWA}_{U}\) threshold. The baseline predicts that a student will not receive a certificate of participation (\(\hat{y}=1\)) if their weighted average grade is below 3.5 and will receive one (\(\hat{y}=0\)) otherwise. We apply this heuristic to the same 10-fold used  
Figure 8: Confusion matrix of a random forest model at different \(\text{CWA}_{L}\) thresholds.  
\begin{table}
\begin{tabular}{l l l} \hline
**Classifier** & **Hyperparameters** & **Values’ Interval** \\ \hline \multirow{4}{*}{RF} & n\_estimators & [200, 250, 300, 350, 400, 450] \\  & max\_depth & [None, 10, 20] \\  & min\_samples\_split & [10, 20, 30] \\  & min\_samples\_leaf & [1, 2, 5] \\  & max\_features & [‘sqrt’, ‘log2’, None] \\ \hline \end{tabular}
\end{table}
Table 3: _Cont._in the outer loop of the nested cross-validation process described in Section 4.2.2 to ensure a fair comparison with the machine learning models. We then compute the mean (\(\mu\)) and standard deviation (\(\sigma\)) of the F1-score, as shown in Table 4.  
As illustrated in Table 4, the baseline model's F1-score improves as the CWA\({}_{U}\) threshold increases, underscoring the relevance of the performance feature in predicting whether a student will achieve the certificate of participation. The baseline's competitive performance, even at the 20% CWA\({}_{U}\) threshold, provides a valuable benchmark for evaluating the machine learning model results.
# Dynamic Response of Railway Subgrade Under Train Load and Freeze-Thaw Action
## 5 Discussion  
Our research is centered around a numerical simulation of railway subgrades, which, although powerful, is based on certain assumptions. One major limitation is the simplification of complex real-world geological conditions. In the model, soil heterogeneity and the presence of hidden geological structures are not fully considered. These simplifications may lead to deviations between the simulation results and the actual situation. For example, in real-world railway subgrades, the varying properties of soil layers and the presence of faults or underground cavities can significantly affect the dynamic response of the subgrade under train loads and freeze-thaw cycles. However, our model overlooks these factors, potentially leading to less accurate predictions. Another shortcoming is the lack of comprehensive incorporation of long-term environmental factors. Climate change, for instance, can influence the frequency and intensity of freeze-thaw cycles over time. Rising temperatures or changing precipitation patterns can alter the freeze-thaw process, but our current model does not account for these long-term changes. This omission may limit the model's ability to predict the long-term performance of railway subgrades accurately.  
Figure 13: Maximum vertical acceleration of points with soil temperature.  
To address the limitations and further advance the research in this area, several future research directions are proposed. Firstly, more in-depth studies on the complex interaction mechanisms between train loads and freeze-thaw cycles under different geological conditions are essential. This could involve collecting a large amount of field-monitoring data. By comparing the simulation results with real-world measurements, we can validate and improve the existing numerical models. For example, setting up monitoring stations along railways in different geological regions to record parameters such as subgrade displacement, stress, and temperature during train operations and freeze-thaw cycles. Another, exploring the application of advanced monitoring technologies, such as distributed optical fiber sensing, holds great potential. This technology can provide real-time monitoring of the state of railway subgrades, detecting early signs of damage or deformation. By integrating such advanced monitoring technologies with numerical simulations, we can achieve a more comprehensive understanding of the performance of railway subgrades.
## 3 Experiment on Low-Temperature Battery Characteristics
### Low-Temperature Calibration Experiment of the Battery Capacity  
The activity of the electrolyte of the lead-acid battery is affected by low temperatures, which affect the capacity of the battery. A battery-capacity at low temperatures experiment was designed and implemented.  
In the experiment, to calibrate battery capacity, we placed the self-developed lead-acid battery into a GDJS series low-temperature test chamber, which can provide stable environmental temperature. The temperature range for the experiment was from \(-\)50 \({}^{\circ}\)C to 0 \({}^{\circ}\)C. The ideal capacity of the battery is 13.6 V and 75 Ah under a normal temperature environment. We discharged the battery at 7.5 A at different ambient temperatures (\(-\)50-0 \({}^{\circ}\)C) to determine battery capacity. The voltages of battery were measured by an oscilloscope (MSO70404C, Tektronix, Beaverton, OR, USA), and the discharge cut-off voltage was 10.5 V. The interval for the experimental temperature change was set to 10 \({}^{\circ}\)C. At each temperature in the experiment, the low-temperature test chamber maintained the temperature at which battery voltage reached the discharge cut-off voltage. We took the average values of the voltage to minimize statistical error and uncertainty.  
The experiment results are shown in Figure 7 and demonstrate that, when a lead-acid battery is discharged with a constant current in a low-temperature environment, the overall trend of the discharge process is similar. As temperature decreased, the discharge time of the battery also decreased at a constant current. This finding indicates that the discharge capacity of the battery was weakening, simultaneously, the amount of discharged electricity decreased. Lead-acid batteries have a capacity of 75.6 (100%), 65 (85.9%), 51.25 (67.8%), 37.5 (49.6%), 28.75 (38%), and 22.5 Ah (29.7%) at 0 \({}^{\circ}\)C, \(-\)10 \({}^{\circ}\)C, \(-\)20 \({}^{\circ}\)C, \(-\)30 \({}^{\circ}\)C, \(-\)40 \({}^{\circ}\)C, \(-\)50 \({}^{\circ}\)C, respectively. When the temperature was higher than 0 \({}^{\circ}\)C, battery capacity remained nearly constant.
# Probabilistic Condition Monitoring of Azimuth Thrusters Based on Acceleration Measurements
## 2 Materials and Methods
### Classification Tests
#### 2.3.1 Classification Approaches for the Proposed Method  
The first approach utilizes probabilistic thresholds \(\alpha\) to classify individual samples in classes. If a sample defined by squared Mahalanobis distance (\(D\)) is below the threshold defined by \(\alpha\), the sample comes from the defined distribution and is a member of the negative class. Otherwise, it is a member of the positive class. Three different thresholds \(\alpha=\{0.1,\,0.05,\,0.001\}\) were tested to separate the classes from each other. The models were trained by using only the data from the negative class. This approach is named as \"probabilistic threshold\" here.  
To obtain a more equal approach to the actual classifiers from the training perspective, an alternative approach called \"two distributions\" was used as well. Two different models were trained by using the samples of the positive and negative classes in separate models. New samples were then classified by estimating \(D\) values using both models and by labeling the sample based on the lower value. For example, if the model trained based on the positive class produced a lower value than the model trained based on the negative class, the sample belonged to the positive class.  
Figure 9 illustrates the flowcharts for both approaches. The quality control of signals was done in advance with parameter values shown in Table 2. The shaft speed range of the samples was defined before classification tests based on the identification data, introduced in Section 2.1.3, by using the approach presented in Section 2.2.4. The training of the model included the identification of the parameters presented in Table 5.
## 3 Effects of TBR on Thermal Management in GaN-on-SiC and GaN-on-Diamond Devices
### Effects of TBR on Thermal Management in GaN-on-SiC Devices
In addition to the decrease in the TBR, the thermal conductivity of the GaN layers is higher than that of the GaN layers grown by MBE, which is beneficial for the heat dissipation of GaN-on-SiC devices. Theoretical and computational methods have been employed to calculate the TBRs of GaN/SiC interfaces. The TBR at the GaN/SiC interface at room temperature was calculated at \(\sim\)1.2 m\({}^{2}\) K/GW using the DMM [91]. In comparison, the calculated TBR of the GaN/SiC interface was indicated as \(\sim\)2 m\({}^{2}\) K/GW using nonequilibrium MD simulations [92; 93; 94]. The measured TBR of GaN/SiC interfaces with different interlayers and different growth or integration methods, and the TBRs predicted using simulation methods are summarized in Table 1.  
In summary, SiC substrates are conventionally used for GaN-based HEMTs because of their advantages, which include a small mismatch in the lattice and thermal expansion coefficient with GaN and high thermal conductivity. However, the TBR between the GaN device layer and SiC substrate has a significant effect on the thermal management of GaN-on-SiC devices. The TBRs of the GaN/SiC interfaces were measured as exceeding 100 m\({}^{2}\) K/GW in some devices, which is two orders of magnitude higher than the values calculated by the DMM and MD simulations (1-2 m\({}^{2}\) K/GW). The measured TBRs were significantly higher than the intrinsic thermal resistances of the GaN device layer and SiC substrate. An AlN nucleation layer is conventionally used to accommodate the lattice mismatch between the GaN and SiC substrates, resulting in high-quality GaN heteroepitaxy and reduced TBR. However, the TBRs measured by different research groups differed significantly, ranging from 5 to 100 m\({}^{2}\) K/GW, although the same growing methods were used for the AlN nucleation layers. Furthermore, the TBRs of the GaN/AlN/SiC stacksshowed no thickness dependence on the AlN nucleation layer. Some samples with an extremely thin AlN nucleation layer showed a high TBR. Most measured TBRs fell within the range of 10-40 m\({}^{2}\) K/GW, as shown in Figure 6. This finding indicates that the intrinsic thermal resistance of the AlN layer contributes little to the overall thermal resistance of the GaN/AlN/SiC stacks, which is attributed to the high thermal conductivity of the crystalline AlN nucleation layer. These results demonstrate that the growth method and conditions have a significant impact on the microstructure and defect density near the AlN nucleation layer, resulting in a major difference in the measured TBR. In comparison, GaN/SiC interfaces fabricated by the MBE and room-temperature SAB methods with no AlN interlayer have a much lower TBR, which is close to the TBR values predicted by DMM and MD simulations. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
## 2 Expansion Strategies
### Ex Vivo Expansion Using mAbs  
Alternative strategies for expanding or activating \(\gamma\delta\) T cells that do not respond to pAgs or N-BP, such as the use of monoclonal antibodies (mAb) are in development [41]. A clearer understanding of the V82 TCR signaling and its interaction with BTN3A has led to the development of activating mAbs that potentially remove the need for direct TCR stimulation [103]. [PERSON] et al. demonstrated that 20.1, a weak agonist Ab specific for CD277 (a member of BTN3 subfamily), mimics pAg-induced V\(\gamma\)9V82 T cell activation [104]. These antibodies may simulate a conformational change in the CD277 molecule that is recognized by V82 TCR [103].  
One of the first studies using mAb for expansion of \(\gamma\delta\) T cells, was performed by [PERSON] et al. They developed an expansion strategy based on anti-CD2-mAb which generated IL-21-dependant signals. This approach led to the expansion of a large population of viable and functional \(\gamma\delta\) T cells as well as protecting the \(\gamma\delta\) T cells from mitogen-induced apoptosis (ADCC). The \(\gamma\delta\) T cells expanded by this approach, retained their anti-tumor activity against a broad range of hematologic and solid primary tumors and cell lines [105]. To date, the use of anti-\(\gamma\delta\) TCR mAbs for expansion of \(\gamma\delta\) T cells has not been successful in vitro. ImCheck Therapeutics is focused on therapeutic Abs that target different members of butyrophilins (BTN/BTNL) family for \(\gamma\delta\) T cells-based cancer treatment. The activating Abs have been designed to bind certain BTN/BTNL molecules, thereby activating V\(\gamma\)9V82 T cells for cancer immunotherapy. The antagonist Abs targeting BTN/BTNL molecules are underway for treating auto-immune disease. Currently, there are several companies focusing on generating mAbs for the selective expansion of \(\gamma\delta\) T cells subset for adoptive cell immunotherapy, but no phase I clinical trials have been opened with these products.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 4 Conventional Image Watermarking Schemes
### Transform Domain
#### 4.2.2 Discrete Fourier Transform (DFT)
Further, [PERSON] zet al. [94] designed a blind color-image watermarking scheme using quaternion discrete Fourier transform (QDFT). The proposed method combines a number of algorithms, such as extreme pixel adjustment (EPA), mixed modulation (MM), multi-bit partly sign-altered mean modulation (MPSAM), and particle swarm optimization (PSO). In this approach, these techniques are collectively referred to as EMMQ. Due to the weakness of QDFT-based techniques against JPEG compression and contrast enhancement attacks, EMMQ has been employed to provide good robustness against these types of attacks. MPSAM is used to map multiple bits within a single non-overlapping image partition block. In contrast, the MM is used to embed the watermark so that the trade-off between robustness and image quality can be achieved, while PSO has been used as a swarm intelligence technique to optimize the EMMQ parameters, which generally optimizes the results in both robustness and imperceptibility compromises. Experiment results prove the resistance of the watermarking scheme to several image-processing attacks. However, the embedding and extracting operations required a high computational cost, and geometric attacks were not considered in this work. In [95], the amounts of DFT bands and the frequency coefficients, as well as watermark strength, were optimized using particle swarm optimization. The visual quality metric called VIF was suggested to obtain better performance in terms of imperceptibility. In order to implement the algorithm in large-scale image datasets, 1000 color images with 24-bit/pixel resolution were used for testing imperceptibility and robustness. Additionally, PSO is employed in DFT watermarking to guarantee security by locating the specific secret key for each image in a given dataset. This approach also offers advantages in terms of imperceptibility and robustness. Nevertheless, the compromise between imperceptibility and robustness needs to be improved; meanwhile, the use of VIF may result in significant dB-value loss for the images being examined. More recently, [PERSON]. [96] proposed a novel discrete Fourier watermarking method based on gray component replacement (GCR) masking for printed images. The watermark was concealed in the amplitude coefficients in the Fourier domain. The key innovation of this work is the use of a GCR mask to hide the artifacts produced by watermark embedding, which improved the visual quality of the watermarked images. Due to the magnitude invariance characteristics, DFT-based watermarking techniques in general provide great resilience to a wide range of attacks, particularly geometric ones. As a result, the DFT domain is an important field of study. Higher watermark robustness, however, frequently produces perceptible visual artifacts. This makes it difficult to achieve a good balance between robustness and other watermarking requirements, such as imperceptibility and capacity.