esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### GO and KEGG Pathway Enrichment Analysis of DEGs  
The DEGs were enriched in three GO categories: biological process, cellular component, and molecular function. In the W vs. B group, the top 50 most significantly enriched GO terms were as displayed in Figure S6A, among which the DEGs annotated in the biological process category mainly include the aminoglycan biosynthetic process, carbon fixation, cellular response to alkaline pH, cellular response to light intensity, and another 30 GO terms; the DEGs in the cellular component category contained photosystem, photosystem I, photosystem II, and plastoglobule; and the DEGs in the molecular function category involved adenosyl homocysteine activity, chlorophyll binding, gibberellin binding, histone acetyltransferase active, etc. The top 50 most significantly enriched GO terms in the W vs. R group are shown in Figure S6B; 28 biological process terms were most enriched, including mucilage extrusion from seed coats, negative regulation of anion channel activity, negative regulation of anion-channel activity by blue light, and negative regulation of anion transport; the DEGs in the cellular component category included amyloplast, DNA packaging complex, nucleosome, and another four terms; the DEGs in the molecular function category consisted of 15 terms, including 3\(-\)oxo\(-\)arachidoyl\(-\)CoA synthase activity, 3\(-\)oxo\(-\)cerotoyl\(-\)CoA synthase activity, adenosyl homocysteine activity, trialkyl sulfo  
Figure 7: Venn diagram showing the overlapping and unique differentially expressed genes (DEGs) amongst the three comparison groups (W vs. R, W vs. B, and R vs. B).  
nium hydrolase activity, etc. In the B vs. R group, 31 biological process terms, 11 cellular component terms, and 8 molecular function terms were enriched in the top 50 GO terms (Figure S6).  
KEGG pathway enrichment analysis was performed to uncover the specific metabolic pathways that the DEGs participated in. In the W vs. B pair, the top 20 enriched pathways were mainly involved in carbon fixation in photosynthetic organisms, carbon metabolism, plant hormone signal transduction, MAPK signaling pathway-plant, terpenoid backbone biosynthesis, fatty acid elongation, etc. (Figure 8A). Pathways such as circadian rhythm-plant, aminoacyl-tRNA biosynthesis, selenocompound metabolism, isoflavenoid biosynthesis, and ubiquitin-mediated proteolysis were significantly enriched in the W vs. R group (Figure 8B). The top 20 enriched pathways in the B vs. R group are displayed in Figure 8C, including biosynthesis of secondary metabolites, metabolic pathways, plant-pathogen interaction, alpha-linolenic acid metabolism, and 16 other pathways.
## 4 Discussion and Future Directions
### Implications for Understanding Artificial Reasoning  
This theoretical reinterpretation, while compelling, requires empirical validation. Future research should directly compare LRM computational patterns with human physiological markers during analogous tasks, testing whether the proposed correspondences hold quantitatively. Additionally, interventional studies that manipulate perceived task difficulty or success probability could test whether LRMs exhibit the same strategic effort allocation patterns observed in human cognition.  
The framework also requires extension beyond the specific puzzle environments examined by [PERSON] et al. [1]. Testing whether dual-process interpretations apply to LRM behavior across diverse reasoning domains would strengthen the generalizability of this theoretical approach. Although the present analysis centers on well-controlled puzzles such as the Tower of Hanoi and River Crossing, these tasks sample only a narrow corner of the reasoning landscape. To establish the generality of the dual-process account, future work should probe ill-structured or creative challenges. Such challenges could involve open-ended analogy generation, insight problems, and multi-step design tasks.
## 3 Results
### ANN Model  
The surface roughness of machined Al6061 aluminum blocks has been predicted using the ANN model. The 30 data given in Table 1 have been employed to train the model. Here, 70% of the data have been used for training, 15% of the data used for testing, and the 15% remaining used for validation. The neural network structure describing four inputs, one hidden layer that contains 10 hidden neurons, one output layer that contains a single neuron, and one output is shown in Figure 6. The results generated by different ANN configurations are reported in Table 2 in terms of root mean square error (RMSE) and regression values (R\({}^{2}\)).  
Figure 5: Flowchart of the study procedure.  
Figure 6: ANN structure.  
The ANN model trained and tested using the Levenberg-Marquardt training algorithm, with 12 as the number of neurons and 1 layer, has yielded better results (RMSE of 0.03653, R\({}^{2}\) of 0.8587 for training, and R\({}^{2}\) of 0.9984 for testing). Figure 7 shows the regression lines of the model, where the targets are plotted against the outputs, with both experimental and predicted mean arithmetical deviation Ra (\(\upmu\)m).
# XTTS-Based Data Augmentation for Profanity Keyword Recognition in Low-Resource Speech Scenarios
## 2 Proposed Methods
### Ethical Considerations  
The proposed speech recognition method, utilizing XTTS for synthetic speech generation and targeting profanity recognition, raises ethical concerns due to its potential for misuse and privacy implications. The ability of XTTS to generate highly realistic synthetic speech could enable voice-based fraud or deceptive audio content, potentially compromising personal privacy and societal trust. To mitigate these risks, we advocate for robust ethical safeguards, including limiting synthetic speech data to specific research purposes, implementing secure access controls, and developing advanced real-versus-synthetic speech detection techniques, as demonstrated in this study's high-accuracy identification approach. Additionally, profanity recognition, intended to monitor verbal aggression in contexts such as school safety, may raise concerns about privacy or inappropriate surveillance. To address this, its deployment must be restricted to clearly defined scenarios, with transparency ensured through user notification and explicit consent obtained prior to implementation. By integrating real and synthetic speech data and achieving robust real-versus-synthetic speech classification, this study contributes to ethical safeguards for secure speech applications, balancing technological advancements with responsible use.
## 2 Experimental Setup and Methods  
### Experimental Setup  
Figure 1 shows a schematic diagram of NTP-assisted ammonia synthesis. The reactor is a coaxial DBD. Stainless steel rods are used as high voltage electrodes. A pulsed power supply (HVP20P, Xi'an Smart Maple Electronic Technology Co., Xi'an, China) capable of delivering voltages up to 30 kV and operating in the frequency range of 100-3000 Hz was used for the experiments. An 80 mm long copper mesh served as the grounding electrode, while a stainless-steel electrode with a 6 mm diameter acted as the high voltage counterpart. The quartz tube featured a 1 mm wall thickness and the gap for discharge was set at 2 mm. Hydrogen was obtained from a hydrogen generator (TH-300, Beijing Institute of Cogeneration Analysis Technology, Beijing, China). A gas mixture of nitrogen (99.999%), hydrogen (99.99%), and argon (99.999%) or helium (99.999%) was introduced into the reactor. Its flow rate and volume ratio were adjusted by a mass flow controller. To capture the electrical characteristics of the plasma within the reactor, an oscilloscope (TDS 3054B, Tektronix Inc., Beaverton, OR, USA) equipped with a current monitor (TCP312A, Tektronix Inc., USA), a voltage probe (P6015A, Tektronix Inc., USA) all supplied by Tektronix, was utilized. OES were recorded and transferred to a grating monochromator (ANDOR SR-750I, Andor Technology, Belfast, UK), and the optical signals were converted to digital models by a CCD (Newton 920, Andor Technology, UK).
# Wavelet-Based Estimation of Damping from Multi-Sensor, Multi-Impact Data
## 4 Proposed Algorithms
Conversely, in the CTWT-2 approach, the averaging is performed after converting the complex values to **real values** by taking their absolute values. This distinction underscores the importance of the location where averaging occurs within the process, affecting the accuracy and reliability of the resulting damping ratio estimates. By employing Equations (3) and (31), the damping coefficient and damping ratio are obtained.
## 4 Results and Discussion
### Linear Inertance of Communication Port  
The relationship between the linear inertance of the communication port and the oscillatory flow velocity amplitude under different oscillatory flow frequencies is shown in Figure 13a.  
This indicates that, at the same oscillating flow frequency, the flow patterns of the communication port flow may have similarities and are independent of the oscillatory flow velocity amplitude. Thus, the ratio of the communication port linear inertance to the orifice linear inertance of the same size should be a single-valued function of the oscillatory flow frequency, as follows:  
\[L^{\prime}_{\text{LA}}=L_{\text{LA}}H(f) \tag{12}\]  
where \(H\) is a monotonic function of oscillation flow frequency, which is only meaningful within the pogo frequency range. As depicted in Figure 13b, its value shows a good linear relationship with oscillatory flow frequency within the pogo frequency range. The fitting results of functions \(H\) obtained from the simulation results are the follwing:  
\[H(f)=12f \tag{13}\]
## 4 Experiments
### Highway Crack Detection Experiments
#### 4.1.4 Comparison Experiments
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**UAVRoadCrack**} & \multicolumn{4}{c}{**CRKWH100**} & \multicolumn{4}{c}{**CrackLS315**} \\ \cline{2-10}
**Model** & **ODS** & **OIS** & **AP** & **ODS** & **OSI** & **AP** & **ODS** & **OIS** & **AP** \\ \hline DeepCrack [27] & 0.601 & 0.613 & 0.608 & 0.909 & 0.917 & 0.931 & 0.845 & 0.867 & 0.877 \\ ResNet [36] & 0.442 & 0.505 & 0.402 & 0.844 & 0.853 & 0.869 & 0.804 & 0.813 & 0.826 \\ FCN [37] & 0.341 & 0.347 & 0.364 & 0.789 & 0.827 & 0.774 & 0.700 & 0.713 & 0.692 \\ SegNet [38] & 0.449 & 0.480 & 0.395 & 0.818 & 0.852 & 0.849 & 0.761 & 0.795 & 0.779 \\ UNet [39] & 0.352 & 0.413 & 0.355 & 0.846 & 0.854 & 0.902 & 0.672 & 0.702 & 0.740 \\ CrackNet & 0.661 & 0.672 & 0.665 & 0.933 & 0.936 & 0.942 & 0.863 & 0.882 & 0.895 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The results of the comparison experiments. \begin{table}
\begin{tabular}{l c c} \hline \hline  & \multicolumn{2}{c}{**Predicted Results**} \\ \cline{2-3}
**Real Results** & **Cracks** & **Non-Cracks** \\ \hline Cracks & 68.3\% & 31.7\% \\ Non-Cracks & 33.4\% & 66.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The confusion matrix for the accuracy of CrackNet on the UAVRoadCrack dataset. The confusion matrix for the accuracy of CrackNet on the CrackLS315 dataset is shown in Table 7. For real cracks, 87.3% were detected as cracks and 12.7% were detected as non-cracks; for real non-cracks, 79.6% were detected as non-cracks and 20.4% were detected as cracks. Figure 12: The comparison experiments on the UAVRoadCrack dataset. Based on comprehensive observation of the three confusion matrices, it is clear that the CrackNet network has a higher accuracy in identifying crack and non-crack pixels in two public datasets representing close-range shooting scenes. Additionally, in the UAVRoadCrack dataset representing high-altitude aerial shooting scenes by unmanned aerial vehicles, the lower clarity of crack images results in a lower accuracy in identifying crack and non-crack pixels compared to the public datasets. Nevertheless, the accuracy still falls within the error range required by the highway management department.
## 3 Hardware Architecture  
This section describes the main hardware components of the embedded underwater localization system. The beacons and the receiver unit are the main modules. In the following subsections, the underwater antenna design, the setup of the fixed beacons and the mobile receiver unit are introduced.
## 3 Experiment and Results  
### Simulation Experiment  
#### 3.1.1 Simulation Experiment Results  
The experiment first simulates the coupled AC signal to analyze and verify the accuracy of the system's measurements. The frequency of the coupled AC signal should be the standard 100 Hz. The simulated AC signal from the cigarette lighter is shown in Figure 4.  
The cigarette lighter emulation signal in Figure 4 is processed using the algorithm in Section 2. In the actual engine speed measurement, the sampling window length cannot be an exact integer multiple of the signal period. To simulate real-world scenarios where the signal contains a non-integer number of periods, a segment of T = 1/3 s (33.3 signal cycles, i.e., 1667 sampled points) was extracted from the standard 100 Hz signal. Both Fast Fourier Transform (FFT) and moving sinusoidal fitting analyses were performed, and their results were rigorously benchmarked. The calculation result of FFT is shown in Figure 5. Since only the data within 1/3 s were intercepted, Equation (1) was used, where \(f_{\rm s}\) = 5000 Hz and \(N\) = 1667, so the actual frequency should be three times the main frequency shown in the figure. It can be seen from the figure that, under non-integral period sampling, the main frequency of the directly calculated FFT is \(f_{\rm FFT}\) = 34 \(\times\) 5000 \(\div\) 1667 = 101.98 Hz, and the error is 1.98%.  
Figure 6 shows the FFT spectrum diagram of the cigarette lighter emulation signal. Although the precision of the short-time sampling signal frequency calculated by FFT is insufficient, it can be used for moving sine fitting. The amplitude phase and other  
Figure 4: Cigarette lighter emulation signal.  
Figure 5: FFT spectrum of the vibrometer signal.  
information of the fitted signal can be obtained by fitting the above signal using the algorithm in Section 2.2.3. The fitted \(a\) and \(b\) values are shown in Figure 6, and the fitted wrapping phase is shown in Figure 7.  
Figure 7 shows the parcel phase diagram obtained by fitting \(a\). As can be seen from Figure 7, compared with the original signal, the fitted signal can effectively improve the smoothness of the amplitude and increase the signal-to-noise ratio. Meanwhile, the obtained phase information is also relatively smooth, which can provide a reliable guarantee regarding the measurement of frequency. The continuous phase of each point of the whole signal was obtained via unwrapping operation, as shown in Figure 8. The main frequency after fitting was 100.06 Hz and the accuracy was 0.06%, as obtained through calculating the phase difference, shown in Equation (9).  
By comparison, it can be seen that the accuracy of the direct calculations obtained using FFT was 1.98% when the data were collected at a frequency of 5 kHz within 1 s. After the speed calculation formula was amplified 30 times, the precision of the measured speed was 59.4%. The theoretical accuracy of the rotational speed obtained by this method is 1.8%, and the accuracy is improved by about 33 times.  
Figure 6: Plot of the magnitude of fitting results.  
Figure 7: The fitted wrapped phase of the a-value.
# Reducing the Complexity of Musculoskeletal Models Using Gaussian Process Emulators
## 3 Results
### Model Reduction  
Of the 29 muscles in SSM, Gluteus Medius 1 was the most influential (Figure 4): with VRSI \(\geq\) 0.1 for more than 40% of the gait cycle this muscle was the first to be personalised. Gastrocnemius Medialis, Rectus Femoris, and Tensor Fasciae Latae followed, being influential for 19%, 16%, and 10% of the gait cycle, respectively. Semimembranosus and Iliacus were influential for 9% of the gait cycle, Soleus for 8%, Iliacus for 6%, while Tibialis Anterior, Biceps Femoris Longus, and Tibialis Posterior were influential for less than 5% of the cycle. The remaining nineteen muscles never reached the threshold of VRSI = 0.1 and thus were not deemed as influential, and therefore their Fmax was never personalised.  
RMSDs between JCFs estimated with SSM and SSMm (reported as a function of \(m\) in Figure 5) were overall very small (below 0.1 BW). The highest RMSD values were found at the knee joint when low degrees of personalisation were employed (RMSD = 0.08 BW, against 0.05 BW for both hip and ankle), and decreased when more muscles were personalised. The same trend was observed for hip and ankle. Eventually, the deviation between the reduced models and SSM reached zero when all the muscles in SSMm were personalised.  
Similarly to RMSD, \(\Delta\)max between SSM and SSMm's JCFs were always small (up to 0.2 BW). A value of 0.2 BW of difference was found at the knee when no muscles were personalised. This dropped to 0.07 BW with the personalisation of 9 muscles. Similarly, hip \(\Delta\)max dropped from 0.14 BW to 0.06. At the ankle joint, the behaviour was more complex, with an initial reduction in \(\Delta\)max followed by a steep increase first, and then a final decrease.  
Figure 4: Percentage of gait cycle where VRSI \(\geq\) 0.1 for the muscles included in the analysis. The chosen model reduction strategy prioritises the personalisation of muscles at the top of the plot, where VRSI is above 0.1 for a larger percentage of gait cycle.
## 2 Study Area and Data
### Data
#### 2.2.2 Assessment Indicators  
In order to objectively evaluate the EGER of Wangwa mining area, Fraction Vegetation Coverage (FVC), Remote Sensing Ecological Index (RSEI), precipitation, and temperature are selected to reflect the ecological environment conditions of Wangwa mining area. FVC is an indicator of vegetation coverage, used to describe the distribution of surface vegetation of the study area. RSEI combines greenness, wetness, dryness, and heat through principal component analysis, which can provide a more comprehensive reflection of the ecological quality of mining areas. Precipitation and temperature have a significant impact on the health of the ecological environment in mining areas. Slope is selected to reflect the geological environment conditions of the Wangwa mining area and is closely related to the occurrence of geological hazards. In addition, the time series InSAR processing and remote sensing image interpretation of the compression damage data will be used as the indicators for evaluating the mining-induced ground compression and damage and subsidence, which can reflect the impact of coal mining activities on the surface. The geographic coordinate system of the layer of indicators was unified as WGS84, and the projected coordinate system was WGS_1984_UTM_Zone_48N, and the evaluation indicator grid was divided into a 50 m \(\times\) 50 m cell grid.
## 4 Experiments and Findings
### Experimental Setting  
The deep learning networks employed in this study were exclusively developed and trained on the Google Colab Pro+ platform. To expedite the model training procedure, we utilized the Tesla V100-SXM2-16 GB graphics processor component. The proposed model and the standard deep learning models were implemented using the Python programming language, with Tensorflow and CUDA backend frameworks serving as the backbone. Throughout the investigation, we focused on the following Python libraries:  
* To facilitate the comprehension, manipulation, and analysis of sensor data, we employed Numpy and Pandas for efficient data manipulation.
* For effective presentation and visualization of data exploration and model evaluation results, we utilized Matplotlib and Seaborn.
* In our experimental procedures, we leveraged the Scikit-learn library as a tool for data sampling and generation.
* The instantiation and training of the DL models were carried out utilized the TensorFlow, Keras, and TensorBoard frameworks.
# Development of Inkjet Printable Formulations Based on Polyorganosilazane and Divinylbenzene
## 3 Results and Discussion
### Thermal Analysis and Ceramization Process  
Thermogravinetric analysis was performed for all cured samples, including pure UV-cured OPSZ and DVB (DVB0 and DVB100), and DVB15-DVB75 to analyze the thermal decomposition of the samples during pyrolysis and to measure the ceramic yield. Figure 5 shows the thermograms.  
All cured samples show a similar two-step decomposition process. The first step occurs between 100 and 300 \({}^{\circ}\)C, which is caused by the evaporation of non-crosslinked volatile low-weight oligomers. The mass loss in the first step is observed, because of the crosslinking process, which was performed completely by UV irradiation. If the samples were cured thermally [48], the evaporation of the volatile low-weight oligomers will take place during the crosslinking process and will not be visible in the thermogram. That is the reason why the ceramic yield result of sample DVB60 is around 10% lower in comparison to  
Figure 4: FTIR spectra of uncured, cured DVB15, and the corresponding amorphous ceramic SiCN pyrolyzed at 1100 \({}^{\circ}\)C (red curve).  
Figure 5: Thermograms of the samples cured by UV-LED source (\(\alpha_{\text{max}}\): 385 nm, 300 s, N\({}_{2}\)).  
the literature [48]. The second step occurs between 400 and 650 \({}^{\circ}\)C for the samples DVB30, DVB45, DVB60, and DVB75, whereas for samples DVB0 and DV15, it begins around 550 \({}^{\circ}\)C. The mass loss is caused by the pyrolysis of the organic skeleton, which was induced by the decomposition of the crosslinked OPSZ and DVB resulting in the organic-inorganic transformation of the thermosel leading to amorphous SiCN. The weight changes above 800 \({}^{\circ}\)C remained constant, which indicates that the transformation from a thermoset to a ceramic was complete. By increasing the amount of DVB, the second step starts earlier due to the higher number of volatile organic components and the residual mass decreases after the pyrolysis. Table 4 lists the residual mass for each sample as percentage of the total mass at 1100 \({}^{\circ}\)C.  
As expected, DVB75 exhibits the highest mass loss regarding the OPSZ/DVB inks, resulting in a ceramic yield of 42%. The reason for this result is due to the low amount of the preceramic polymer OPSZ in the formulation. The residual mass of DVB100 is the lowest mass, as it consists of pure cured DVB. Since the samples were measured in a nitrogen atmosphere, the residual mass is supposed to be carbon.
## 2 Materials and Methods
### Meta-Classification Using Machine Learning Algorithms
#### 2.4.2 Decision Tree (DT)  
Decision Tree, a machine learning and data mining algorithm [49], is a predictive model for classifying objects or predicting outcomes based on input characteristics. This hierarchical structure starts with a root node representing the entire dataset and branches representing possible outcomes linked to input feature values. The leaves signify the final classification or prediction, constructed from a relatively simple set of rules. DT algorithm is a supervised learning method for classification and regression tasks. In this method, each inner node represents a \"test\" attribute, branches depict the test result, and leaves represent class labels. The algorithm traverses the tree, making decisions at each internal input attribute. It concludes with a prediction determined by the leaf node reached.  
DT can handle numeric and categorical data, and ensemble DTs enhance performance by combining predictions from multiple trees [50]. Ensemble methods, incorporating techniques such as bagging, boosting, and bootstrapping, use multiple algorithms to achieve better predictive performance than a single algorithm. Ensemble DTs are commonly used in various applications, such as image and text classification, regression, and anomaly detection.