esa-sceva/satcom-chunk-collection huggingface dataset:
# Addressing Text-Dependent Speaker Verification Using Singing Speech
## 6 Conclusions  
This study attempted to investigate the short-time text-dependent ASV between the singing and normal reading speech. A new corpus was released to the public for pure research. Detail comparison and analysis between MFCC and NCCF feature were presented. Preliminary experiments were performed on both 1 s-1 s and 3 s-3 s speaker verification conditions, either for gender-dependent or gender-independent tasks. We found that the speaker identity information extracted from the singing speech was more distinguishable than the one extracted from reading speech. Furthermore, it is worth noting that, in our current corpus, all of the speakers are very familiar with the singing text and original songs, thus the melody of these original songs may guide the speaker to sing with a similar singing style. This similar singing style may also reduce the discriminative information between different speakers. The data amount of our current RSS corpus is limited because creating a high-quality speech database for thousands of hours takes a very long time and a huge amount of money. Because until now there is no publicly released corpus designed for our purpose, we chose to put our preliminary works in this study first. We hope that creating and releasing the RSS would promote the research work in this field. Since, in this study, the performance improvements were only observed on the limited RSS dataset, we cannot guarantee their generalization and significance. Therefore, in our future works, we will focus on recording a larger corpus on reading, singing and humming speech using speakers' personalized password texts. They may sing and hum using their own styles. All observations obtained from this study will be examined in our future larger database. New features related to the singing and humming speech will also be considered.  
[PERSON] mainly built the GMM, DTW systems and wrote this paper. [PERSON] collected the RSS corpus and revised this paper. [PERSON] was the supervisor of this research. [PERSON] advised about the X-vector implementation and experimental analysis. [PERSON] was the co-supervisor of this research.  
This work was funded by the Project 61701306 supported by National Natural Science Foundation of China.  
The authors declare no conflict of interest.
## 5 Discussion
Looking back at the results in Figures 5 and 6, showing the variation of the model prediction for the coefficient of determination metric, the best results are obtained by OC-SVM when \(c=0.3\) (\(R^{2}=0.95\) for both XBGT and HGBR), and LOF when \(c=0.2\) (\(R^{2}=0.943\) for XBGT and \(R^{2}=0.945\) for HGBR). For EE there is a local peak at \(c=0.1\), and a maximum value at \(c=0.5\), while IF improves as \(c\) is increased (with  
Figure 10: Actual vs. predicted yield for the models with missing data removed (**left**) and for the best result obtained by the HGBR model, with preprocessing OC-SVM and RF (**right**). Figure 9: Histogram for yield values with missing data instances removed (**left**) and with all data (**right**). a maximum at \(c=0.5\)). More information on the initial data and the collection context could help inform the actual anomaly percentage present in the data, and what approach would indeed improve the model. Another aspect to discuss is in relation to the regression algorithms XGBT and HGBR, which seem to be quite robust, showing little impact from unsupervised outlier removal, as opposed to the other regression algorithms which are more susceptible to data changes. However, a main finding of our study is that it is feasible and we can reliably use these algorithms for crop yield prediction even with relatively small datasets. However, we must provide a minimum amount of data to ensure performance, thus demonstrating the importance of using imputation. The most important contribution we bring is related to the problem of missing data, which can be handled effectively even if it affects 80% of the instances, resulting in an increased performance from 0.723 to 0.938 (and even up to 0.95 if we use OD with \(c=0.3\)). Our findings suggest that the best method for imputation is prediction using RF. Given that historical crop data are reduced and it would take many years to collect a designated dataset (which we have started as a work in progress), we are able to address the climate change effects in agriculture now, by modelling the correlation between sowing dates and weather forecast to identify the best planting window that maximises yield production.
# Ship Detection in Sentinel 2 Multi-Spectral Images with Self-Supervised Learning
## 3 Results  
Our evaluation has three objectives: (1) study the impact of the SSL pre-training strategy of the backbone on the final performance of the ship detector, (2) compare our SSL-trained U-Net to the baselines and, (3) analyze the few-shot performance of SSL.
# Developing a Universal Mirror-mirror Laser Mapping System for Single Event Effect Research
## 3 Results
### Sensitivity Mapping for Operational Amplifiers
#### 3.1.2 Sensitivity Mapping Results  
OP77 Testing  
The size of the ROI was \(1.39\times 1.36\) mm, with a step size of 0.07 mm and dwell-time of 0.1 s. Based on the method described above, the total number of steps was 400, meaning a mapping time of 40 s. Figure 14 shows the sensitivity mapping result.  
To verify the mapping result, specific ROIs were drawn corresponding to each sensitivity area in the mapping results. Figure 15 shows the results, which verify that this mapping result was accurate.  
Figure 14: Sensitivity map for OP77 amplifier (laser energy: 4 nJ).  
Figure 15: SET results in each ROI. (**a**) distribution of ROIs; (**b**) SET pulse in ROI_1; (**c**) SET pulse in ROI_2; (**d**) SET pulse in ROI_3; (**e**) SET pulse in ROI_4; (**f**) SET pulse in ROI_5.  
AD711 Testing  
The AD711 amplifier was smaller than OP77, with a size of \(1.13\times 1.20\) mm. The step size used was 0.056 mm, with a dwell-time of 0.1 s, corresponding to 462 scanning steps and a 46.2 s scanning time. Similar to the previous experiment, the sensitivity map is shown in Figure 16.  
Applying the same process as before, specific ROIs were drawn corresponding to each sensitivity area observed in the map. As shown in Figure 17, each ROI generated SET pulses, validating the sensitivity mapping result was accurate.  
Figure 16: Sensitive map for AD711 amplifier (laser energy: 4 nJ).  
Figure 17: SET results in each ROI. (**a**) distribution of ROIs; (**b**) SET pulse in ROI_1; (**c**) SET pulse in ROI_2; (**d**) SET pulse in ROI_3; (**e**) SET pulse in ROI_4.  
As demonstrated by the OP77 and AD711 amplifiers test results, sensitive areas can be located with high accuracy and velocity by this mapping system. Since this system keeps the stage still and moves the laser beam, there was no external vibration introduced to the DUT. This feature provides much benefit to decrease the time consumption for generating the sensitivity map.
## 3 Results and Discussion
### H\({}_{2}\)O\({}_{2}\) Production  
To further assess the performance of ht-pDA/ACF cathode, we set an undivided cell to practically produce H\({}_{2}\)O\({}_{2}\) at a voltage of 2.0 V. For comparison, we also prepared the ao-pDA/ACF cathode by air autoxidation and analyzed the difference between the two as follows. The H\({}_{2}\)O\({}_{2}\) production of CF cathode reached 30 mg/L in the first hour and remained stable in the other 5 h (Figure 3a). However, the H\({}_{2}\)O\({}_{2}\) production of the ACF cathode increased to 99 mg/L in 6 h, which could be attributed to the oxygen-containing group introduced by acid treatment. After modification, the H\({}_{2}\)O\({}_{2}\) production of the ht-pDA/ACF cathode and ao-pDA/ACF cathode both increased compared to that of the CF and ACF cathode. As shown in Figure 3a, the curve of the ht-pDA/ACF cathode showed linear growth to 201 mg/L in the first 4 h, after which the growth rate slowed down and the H\({}_{2}\)O\({}_{2}\) production remained at 220 mg/L after 6 h of reaction, which is over seven times higher than that of the CF and are comparable or even favorable to many carbon-based catalysts (Table S1). A similar H\({}_{2}\)O\({}_{2}\)-increasing trend was found in the line for ao-pDA/ACF, but the amount of H\({}_{2}\)O\({}_{2}\) was lower than the ht-pDA/ACF, which stood at 155 mg/L. The above outcome suggests that pDA modification by either hydrothermal process or air autoxidation has a positive influence on the 2e\({}^{-}\) ORR to produce H\({}_{2}\)O\({}_{2}\). Meanwhile, the H\({}_{2}\)O\({}_{2}\) production discrepancy between the ao-pDA/ACF cathode and ht-pDA/ACF cathode also implies that there are some differences between the two preparing routes during polymerization.  
We also tested the performance of ht-pDA/ACF under different cell conditions and the results are displayed in Figure 3b and Figures S1-S3. It is clear that when the voltage increases, the yield of H\({}_{2}\)O\({}_{2}\) production improved from 220 mg/L at 2.0 V to 348 mg/L at 2.4 V, which suggests that the higher voltage has a positive influence on the reaction. The reason is that 2.0 V was low and can hardly support the H\({}_{2}\)O\({}_{2}\) synthesis reaction, whereas a higher potential could provide more energy to run the 2e\({}^{-}\) ORR for H\({}_{2}\)O\({}_{2}\) production. The corresponding results of the H\({}_{2}\)O\({}_{2}\) concentration in the electrolyte with different pH values at a potential of 2.0 V (Figure S1) showed that the H\({}_{2}\)O\({}_{2}\) concentration remained stable regardless of whether an acid or alkaline electrolyte were used, implying that the ht-pDA/ACF cathode has good adaptability in different harsh reaction environments. The effect of the amount of dopamine and reaction time in the hydrothermal process for H\({}_{2}\)O\({}_{2}\) electrocatalysis synthesis was also investigated. As shown in Figures S2 and S3, the best condition for electrode modification is 70 mg dopamine for a 6-h hydrothermal reaction.
###### Abstract
Nevertheless, many existing methods are designed and validated under controlled conditions, often on small experimental plots with low-altitude UAV flights. This limits their transferability to large-scale commercial farming environments, such as those in Southern Dobrudja, Bulgaria, where real-world variability in field conditions must be considered. Furthermore, the high cost of advanced sensors and the limitations of index-based methods like NDVI underscore the need for scalable and affordable solutions that are compatible with the operational capacities of small- and medium-sized farms. In light of these challenges, this study proposes and evaluates a practical methodology for yellow rust detection using UAV-acquired RGB imagery combined with deep learning-based pixel-level classification. The objective is to provide a scalable and accessible tool for early-stage disease detection that supports timely agronomic decision-making, reduces dependency on costly equipment, and promotes sustainable crop management in real-world field conditions.
# Extracellular Vesicles and Artificial Intelligence: Unique Weapons against Breast Cancer
## 2 EVs in Breast Cancer
promoted the involvement of the pluripotent factor Lin28B-high, which is particularly upregulated in triple-negative BC, and low-let-7 exosomes (sourced from Lin28B-induced breast cancer stem cells) to generate lung metastases in breast cancer patients via exosomes, supporting cancer progression [60]. Great progress has been made in recent years to characterize new pathways and identify new biomarkers in tumors using machine learning approaches. A very interesting example is a study using a machine learning approach that identified COL11A1 as a hub gene that is highly expressed in breast cancer samples and associated with a poor prognosis [64]. On the other hand, several studies reported EV proteomic signature profiling, which established a strong application of EV cargo in predicting clinical outcomes and monitoring disease progression and evolution. Using label-free quantitative phospho-proteomics, 144 phospho-proteins were identified in plasma EVs, the levels of which were found to be higher in BC-positive patients compared to healthy controls, showing the utility of EVs in cancer monitoring and screening [65]. Several proteins correlated with the tumor context were reported as EV cargo and found to be useful for tumor/health discrimination and tumor stage identification. For example, EGFR, epithelial cell adhesion molecule (EpCAM), and HER2 on circulating EVs were investigated as candidates to distinguish malignant BC patients from healthy controls; they were found to have sensitivities of 97.37% and 92.31% in detecting early stage I BC [66]. Moreover, a liquid biopsy was used to identify CD63/EpCAM/mucin 1-triple-positive circulating EVs in BC-positive patients, discriminating between patients and healthy samples with a sensitivity of 91% [67].
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 4 Verification of Numerical Examples
### Example Two  
A continuous beam with a cantilever is subjected to a concentrated load, \(F=1\times 10^{-2}\) kN, as shown in Figure 7. The flexural stiffness, \(EI=k(x)\), of the continuous beam is simulated as a Gauss random field with a mean value of \(E_{k}=100\) kN\(\cdot\)m\({}^{2}\), standard deviation of \(\sigma_{k}=10\) kN\(\cdot\)m\({}^{2}\), and correlation length of L = 8 m. The correlation models contain four commonly used models: Triangular (T), second-order AR (AR), exponential (Exp), and Gaussian (G). The limit state functions of the deflection and bending moment are described as follows:  
\[G_{1}=1.2\overline{\delta}-\delta_{0}, \tag{17}\]  
\[G_{2}=1.1\overline{M}-M_{0}, \tag{18}\]  
where \(\overline{\delta}\) and \(\overline{M}\) represent the deflection at the free end and the moment at the fixed end of the beam, respectively, when the mean values of the random variables are taken; and \(\delta_{0}\) and \(M_{0}\) indicate the deflection at the free end and the moment at the fixed end of beam, respectively.  
The effect of the correlation model on structural reliability is discussed in this example. The mesh discretization of the random field is consistent with that of the FE model of the beam. For the four  
Figure 6: Deflection reliability vs. the number of finite elements.  
Figure 7: Continuous beam subjected to a concentrated load.  
correlation models, the deflection and bending moment reliabilities are calculated by the proposed method. The results are compared with those obtained from the PSFE method and the Monte-Carlo (MC) method [34], as shown in Figure 8.  
Under different correlation models of a random field, the deflection and bending moment reliability indexes obtained by the proposed method are consistent with the results of the PSFE method and the MC method (10,000 samples), as shown in Figure 8. The MC method requires a large number of samples to estimate the structural reliability, especially for large and complex bridges with a small failure probability. The computation time using the MC method, PSFE method, and proposed method is 2736, 79, and 45 ms, respectively. Compared with the MC method, the other two methods have higher computational efficiency. However, the estimation accuracy of the PSFE method cannot be guaranteed when the random variability of the structural parameters is large. Compared with the PSFE method, the proposed method improves the computational accuracy because its predicted results are not limited by the variability in the random variables. Figure 8 shows that the correlation models have some influence on the structural reliability. The reliability index calculated by the EXP correlation function is larger than those of the other three functions because the coefficient of variance reduction is small, as shown in Figure 2. In other words, the discreteness of the random variables obtained by the LAM is relatively lower.
# Improved Convergence Speed of a DCD-Based Algorithm for Sparse Solutions
## 2 Preliminaries
### DCD Algorithms
#### 2.2.3 Complexity Discussion  
Table 1 shows that when \(N_{u_{\text{{cyclic}}}}\) is much larger than \(M_{b}\), then the computational complexity of the cyclic DCD is dominated by \(2 UN_{u_{\text{{cyclic}}}}\) additions. When the \(N_{u_{\text{{cyclic}}}}\) value is small, then the computational complexity is upper bounded by the term \(UM_{b}\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Step** & **Input \(\beta\)**, \(\mathbf{R}\), \(\boldsymbol{M_{b}}\), \(\boldsymbol{\zeta}\), \(N_{u_{\text{{leading}}}}\); output: \(\mathbf{x}\), \(\mathbf{\Gamma_{\text{{leading}}}}\) & **Addition** \\ \hline \multicolumn{4}{c}{Initialization: \(\mathbf{x}=0\), \(\mathbf{\Gamma_{\text{{leading}}}}=\beta\), \(\sigma=\zeta\), \(m=1\)} \\ \hline \multicolumn{4}{c}{for \(p=1,\ldots,N_{u_{\text{{leading}}}}\)} \\ \hline \(1\) & \(n=\arg\max_{j=1,\ldots,U}\{|\Gamma_{\text{{leading}}_{j}}|\}\), goto step 4 & \(U-1\) \\ \hline \(2\) & \(m=m+1\), \(\sigma=\sigma/2\) & \\ \hline \(3\) & if \(m>M_{b}\), execution stops & \\ \hline \(4\) & if \(|\Gamma_{\text{{leading}}_{n}}|\leq(\sigma/2)R_{n,n}\), goto step 2 & \(1\) \\ \hline \(5\) & \(x_{n}=x_{n}+\operatorname{sign}(\Gamma_{\text{{leading}}_{n}})\sigma\) & \(1\) \\ \hline \(6\) & \(\mathbf{\Gamma_{\text{{leading}}}}=\mathbf{\Gamma_{\text{{leading}}}}- \operatorname{sign}(\Gamma_{\text{{leading}}_{n}})\sigma\mathbf{R}_{\cdot,n}\) & \(U\) \\ \hline \multicolumn{4}{c}{end for} \\ \hline complexity & \(\leq(2U+1)N_{u_{\text{{leading}}}}+M_{b}\) adds & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Leading DCD algorithm.
## 4 Findings and Discussion
### Apriori Algorithm Findings for Online Complaining Behavior
Disconfirmations (positive or negative) take place when there is a discrepancy between expectations and outcomes. A negative disconfirmation occurs when the outcome is worse than expected or when the service performance is less than anticipated [93; 94]. Negative disconfirmations are particularly prone to producing dissatisfaction or complaining behavior. [PERSON] and [PERSON] [88] also pointed out that guest satisfaction is largely based on meeting or exceeding expectations. In order to explain the results of the current study, guests may have developed expectations for high quality services from past experiences staying at higher-star-rated hotels. On subsequent travels if there is a gap between expectations and actual services, a negative disconfirmation will occur. When it comes to Room Space, expectations may vary from one individual to another--they may also be influenced by locational factors. Guests who travel to London, for instance, might be accustomed to living in less-crowded environments, and when confronted with the spatial limitations, they may find it an unhappy experience [62]. Finally, with respect to Miscellaneous Issues, negative results could be related to a wide variety of things, including insects in the room, mattresses that are too soft, pillows that are too hard, or sheets that are too rough. For these issues, hotel managers can only try to ensure that their tangible and intangible in-house facilities are always fully operational before welcoming new arrivals. When it came to online complaints made about on Cleanliness, but not about Customer Service, Room Space, or complaints made about Hotel Facility and Value for Money, guests were found to have stayed at lower star-rated hotels. This is comparable with results from [PERSON] and [PERSON] [45], [PERSON], [PERSON], [PERSON] and [PERSON] [46] and [PERSON] and [PERSON] [95], who asserted that cleanliness and dirty rooms caused the most dissatisfaction for guests staying in suite hotels. Since hotels are mainly \"accommodations-led\", the main reason for guests to visit or return is the guest rooms and their cleanliness. Simply stated, clean and comfortable rooms help satisfy customers, while dirty ones lead to guest dissatisfaction [95]. [PERSON], et al. [96], [PERSON], et al. [97], [PERSON], et al. [98; 99], and [PERSON], et al. [100] all point out that a comfortable bed, a clean room and bathroom, and a certain amount of aesthetic appeal are considered important and necessary features to achieve customer satisfaction--these are also factors that will influence customer evaluations of their staying experiences. Other studies on public transportation also concluded that cleanliness and comfort are the most important factors when it comes to (dis)satisfaction, although the impact of these factors does differ depending on the socio-economic characteristics of the users [89; 101; 102; 103; 104; 105; 106]. **Appendix A Table A1** demonstrates a total of 17 rules.
# MKD: Mixup-Based Knowledge Distillation for Mandarin End-to-End Speech Recognition
## 5 Results
### Results of MKD  
In order to testify the performance of proposed method, MKD is compared with previous softmax-level knowledge distillation (S-KD) and sequence-level knowledge distillation (SEQ-KD). The S-KD trains the model by Equation (6) directly and the training procedure  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **Stu1** & **Stu2** \\ \hline Encoder & 3 & 2 \\ Decoder & 3 & 2 \\ Head & 8 & 8 \\ Head size & 64 & 64 \\ Feed-forward & 2048 & 2048 \\ Parameter & 20 M & 10 M \\ Compression & 50\% & 75\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The settings of two student models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & \(\alpha\) & \(p\) & **CER** \\ \hline Baseline & 0.0 & 1.0 & 10.7\% \\ +Mixup & 0.3 & 1.0 & 9.2\% \\ +Mixup & 0.5 & 1.0 & 9.7\% \\ +Mixup & 0.3 & 0.25 & 9.8\% \\ +Mixup & 0.5 & 0.25 & **8.6\%** \\ +Mixup & 0.3 & 0.5 & 9.2\% \\ +Mixup & 0.5 & 0.5 & 8.8\% \\ +Mixup + LS & 0.3 & 1.0 & 11.7\% \\ +Mixup + LS & 0.5 & 1.0 & 10.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter search of Mixup in Speech-Transformer on Aishell-1.  
of SEQ-KD is the same as [45]. The Baseline is chosen as the teacher model. The size of Stu1 is 50% of teacher model, and the size of Stu2 is one third of teacher model. To make the results comparable, the teacher model and the student models are the same as the original one when training by S-KD, SEQ-KD and MKD. The Mixup strategy is adopted under previous settings.  
The results of MKD are shown in Table 3. The proposed MKD beats the existing method by 1.7% and 0.4% compared to S-KD and SEQ-KD, respectively. MKD is more effective for small-scale models when the proportion of augmented samples occupies 25%. MKD can achieve a lowest character error rate of 9.2% on Stu1 with \(p=0.5\), which is even better than the result of the teacher model. Such phenomenon demonatrates that Mixup improves the generalization of ASR model. As the number of model parameters decreases, the effect of MKD also decreases. The experiments also indicates that data augmentation has a potential to be combined with knowledge distillation.  
Table 4 exhibites the RERR of proposed MKD compared with S-KD. For MKD with \(p=0.25\), the relative improvement is 11.0% in Stu1 and 3.3% in Stu2. For MKD with \(p=0.5\), the relative improvement reaches 15.6% in Stu1. However, the CER increases in Stu2 when \(p=0.5\), the reason is that the small parameter scale leads the underfitting problem. The number of mixed samples is required to be controlled for small models.