esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiments on Speed and Accuracy of the Proposed Method Compared to Other Networks  
The aforementioned experiments validated the excellent overall performance of the Inception-ResNet101 backbone network when combined with our proposed method. To further evaluate the effectiveness of the integrated FAS-DDH-ECS structure, we compared it with current mainstream oriented object detection methods on the DOTA and HRSC2016 datasets, with the results presented in Table 7. As shown in the table, although single-stage methods such as RetinaNet and ReDet hold a slight advantage in inference speed (22.4 FPS and 24.5 FPS, respectively), our method maintains a competitive speed (24.4 FPS) while significantly outperforming these methods in detection accuracy. It achieves the highest mAP values, of 82.6% and 79.5%, on DOTA1.0 and DOTA1.5, respectively, and also attains an mAP of 89.1% on HRSC2016, surpassing all compared models. It is noteworthy that our proposed method not only comprehensively surpasses traditional two-stage methods such as R2 CNN, R3 Det, and Oriented R-CNN in terms of accuracy but also achieves significant improvements in speed. For instance, compared to Oriented R-CNN, our method improves the mAP by 0.8%, 1.1%, and 2.9% on the respective datasets while increasing inference speed by 3.3 FPS. Compared to R3 Det, the speed improvement reaches 12.4 FPS. Additionally, although S2A-Net achieves an mAP of 81.4% on DOTA1.0, its inference speed of only 2.0 FPS makes it unsuitable for real-time applications, highlighting the bottleneck of some high-accuracy models in engineering deployment. In summary, our method ensures high precision while maintaining inference efficiency, demonstrating excellent real-time detection capabilities and engineering adaptability.
# Cross-Lingual Named Entity Recognition Based on Attention and Adversarial Training
## 3 Model
### Output Layer  
Recurrent neural networks (RNN) usually experience gradient disappearance or gradient explosion during the training process [22]. To solve this problem, long short-term memory networks (LSTM) were born. LSTM can significantly improve the performance of the long-range dependence of the model. The difference between LSTM and the general RNN is that LSTM adds a memory block unit A, and this memory block A includes three parts: the input gate, forget gate, and output gate. The input gate determines how much new information needs to be added to the cell; the forgetting gate is mainly used to control the storage of information in the cell, i.e., to decide what information to discard; and the output gate determines what information is to be output from this cell A.  
The Bi-LSTM layer outputs the predicted scores of each label corresponding to each word, and the highest scores can be selected as the labels of the words. However, there are often some invalid label sequences [23]. Therefore, the CRF layer is added to the Bi-LSTM layer, and the CRF layer can obtain the binding rules from the training data, such as: the first word of the sentence starts with B/O, but not with I; in B- label1 and I-label2, label1 and label2 should be of the same type; and O and I-label cannot be combined together. The probability of illegal sequences appearing in the label sequence is greatly reduced, thus improving the accuracy of label prediction.  
The two-way LSTM only considers the long-term contextual information of the sentence but does not consider the dependencies between labels, and CRF can ensure the labels are valid by learning the adjacency relationships between labels. In this paper, we use the standard CRF layer on top of the model of named entity recognition to obtain the final sequence annotation.  
\[\text{Define prediction score}:\ S(X,y)=\sum\
olimits_{i=1}^{n}(O_{t,y_{t}}^{ner }+A_{y_{t-1},y_{t}}) \tag{8}\]  
\[O_{t}^{ner}=W^{ner}h_{t}^{ner} \tag{9}\]  
where \(W^{ner}\) are model parameters, \(A_{y_{t-1},y_{t}}\) is the transfer probability matrix from label \(y_{t-1}\) to \(y_{t}\), and n is the length of the input sentences.  
For the input \(X\!=\!\{x_{1},x_{2},\ \ldots,x_{n}\}\), the probability of the output best sequence labeled \(\hat{y}\) can be defined as  
\[p\ (\hat{y}|X)=\frac{e^{s(X,\hat{y})}}{\sum_{\hat{y}\in Y_{X}}e^{s(X,\hat{y})}} \tag{10}\]  
where \(Y_{x}\) denotes the set of all possible labels, the numerator s function denotes the score of the correct label, and the denominator s function denotes the sum of the scores of each possible label.  
In the CRF model training process, the loss function is defined as:  
\[L_{ner}=-\log p(\hat{y}|X) \tag{11}\]The loss function values are calculated, and the network parameters are continuously updated until the end of the iteration.
## 3 Materials and Methods
### BA-ELM for Systematic Error Compensation  
The BA-ELM model was utilized as the estimating function in our systematic error compensation method. In practice, the ideal star centroid position \(x_{0}\) in the captured image cannot be obtained, but we can calculate the actual star centroid position \(\hat{x}_{g}\) by the CoM method. According to Equation (24), the functional relationship between \(\sigma_{\hat{x}_{g}}\) and \(\hat{x}_{g}\) estimated by BA-ELM can be written as  
\[\sigma_{\hat{x}_{g}}=\sum_{j=1}^{L}\beta_{j}g_{h}(\mathbf{w}_{j}^{*}\cdot\hat{ x}_{g}+b_{j}^{*}). \tag{40}\]  
where \(\mathbf{w}_{j}^{*}\) and \(b_{j}^{*}\) are the optimal parameters in the ELM obtained by BA. In practical operations, if we input \(\hat{x}_{g}\) into the BA-ELM model, it will output the corresponding predicted systematic error \(\delta_{BA-ELM}(\hat{x}_{g})\), and then we have the compensated position \(\hat{x}_{g}^{c}\) by Equation (41). Through this way, the systematic error of the CoM method can be eliminated to the maximum extent.  
\[\hat{x}_{g}^{c} =\hat{x}_{g}-\delta_{BA-ELM}\Big{(}\hat{x}_{g}\Big{)} \tag{41}\] \[=\hat{x}_{g}-\sum_{j=1}^{L}\beta_{j}g_{h}(\mathbf{w}_{j}^{*}\cdot \hat{x}_{g}+b_{j}^{*}).\]
## Experimental Results
### Comparison of Motion Information Representation in the State Space
#### 5.2.3 Training Speed  
In addition to a certain improvement in performance, Tables 6 and 7 show that the training time of the four consecutive frames was the longest, whereas that of the single-frame image was the shortest.  
Figure 7 shows the training speed, revealing that the greater the number of stacked images, the more time the neural network requires for backpropagation, which increases the training time. Using the single frame image, gradient descent could be performed for 71 batches with an average batch size of 64 per second. Using the four consecutive frames, an average of only 40 batches were dealt with per second. The training speed of the other two methods was around 60 batches per second. The reason for the difference is that the input image depth of the neural network is reduced, which reduces the depth of the convolution kernel of the CNN, and merely convolution operations must be performed. When using the influence map superimposed image as the state space, the training speed was nearly 59% faster than that achieved using four consecutive frames, which means that the training time could be reduced by 38%. This is a considerable improvement for practical application. The training speeds of the four methods presented in Figure 7 are basically unchanged throughout the whole process, with a slight decline compared with the beginning because when the experience replay is full, some existing data are overwritten when new data are added. This operation takes slightly longer direct addition.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**State Space** & **Smooth Value** & **Actual Value** & **Training Time** \\ \hline Single-frame image & 3786 & 4153 & 5 h 23 m 26 s \\ Two consecutive frames & 4258 & 3733 & 6 h 17 m 25 s \\ Four consecutive frames & 5836 & 4860 & 9 h 42 m 56 s \\ Influence map superimposed image & 6529 & 7593 & 6 h 4 m 52 s \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ordinate comparison of four training scores at 1.4 M gradient descent.  
Figure 7: Training speed of four different image state spaces.
# An Efficient Multivariate Autoscaling Framework Using Bi-LSTM for Cloud Computing
## 4 Experiment and Evaluation
### Experiment Results  
#### 4.4.1 Feature Selection Results  
1. GWA-T-12 dataset:  
Table 3 shows the Pearson correlation values of the network received throughput metric with other metrics for the GWA-T-12 dataset. As can be seen in the table, memory usage had the highest correlation value of 0.746. Thus, the network received throughput and memory usage were used as inputs of the multivariate models to evaluate the prediction accuracy;  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Pair of** & **Pearson Correlation** \\
**Metrics** & **Value** \\ \hline corr(network, CPU) & 0.714 \\ corr(network, memory) & **0.746** \\ corr(network, disk read) & 0.637 \\ corr(network, disk write) & 0.63 \\ \hline \hline \end{tabular} The bold value indicates the best value.  
\end{table}
Table 3: Feature selection of the GWA-T-12 dataset.  
2. GWA-T-13 dataset: Table 4 presents the Pearson correlation values of the network received throughput metric with other metrics for the GWA-T-13 dataset. The memory usage metric in this dataset also had the highest correlation value. Thus, the memory usage and network received throughput were used as inputs to the multivariate models to evaluate their prediction accuracy.  
We preprocessed the dataset to a value in the range [0, 1] based on Equation (30). Subsequently, we divided each dataset into training (80%) and evaluation (20%) datasets for both datasets.  
As mentioned earlier, the feature selection can attempt to prevent overfitting of the prediction model. Figure 3 presents the model loss of the proposed multivariate Bi-LSTM model during the training process. The training dataset (which contains 80% of the entire dataset) was split into two parts: training (80%) and validation (20%). As shown in Figure 3, the loss during the training and validation processes decreased over time. Thus, the model did not have an overfitting problem.  
\[z=\frac{x-min(x)}{max(x)-min(x)} \tag{30}\]
## 4 Cardiovascular Pharmacology and Precision Medicine  
The clinical trials help us judge and predict drug outcomes with the best representative samples that evolve with phases of clinical trials, but the genetic variations, environmental factors, and idiosyncrasies are still strong enough to cause a fair number of adverse events and treatment failures.  
The evolving approach of precision medicine advocates the individualization of therapy, directed by local regulations and guidelines based on novel markers and gene targets, which can help us define reasons for failure, thus evolving a better tailored patient-centric approach to curing diseases. Numerous examples of genetic diversity and DNA variants determining the response to a drug are already in common parlance and are continually used to modify treatment. For instance, warfarin, the most commonly prescribed anticoagulant medicine, has a narrow therapeutic window and has shown wide inter-individual variations [114; 115]. Studies document around 10% to 50% variability in warfarin dose requirements per the patient genotype, notably SNPs in _CYP2C9_ (_CYP2C9_*2, _CYP2C9_*3) and _VKORC1_ (rs9923231) [115; 116; 117]. Additionally, genetic variants have been identified that show differences in the response to \(\beta\)-blockers (_ADRB1_, _ADRB2_, _GRKS_, _GRK4_); angiotensin-converting enzyme inhibitors (_ACE_, _AGTR1_); diuretics (_ADD1_, _NPPA_, _NEDD4L_); and Calcium Channel Blockers (_CACNB2_, _CACNA1C_) [26]. Further, clopidogrel, an antiplatelet medicine, is a P2Y12 inhibitor, and it shows great inter-individual variability-the clopidogrel non-responders [106; 118; 119]. The genetics purported behind this involve loss-of-function alleles in CYP2C19 (CYP2C19*2 and CYP2C19*3), which is thought to be associated with poor drug responsiveness, whilst the gain-of-function allele CYP2C19*17 is associated with increased bleeding risk [119; 120]. Now, the COAG (Clarification of Optimal Anticoagulation through Genetics) trial and the EU-PACT (European Pharmacogenetics and Anticoagulant Therapy-Warfarin) have produced RCTs advocating genotype-guided drug dosing for warfarin, which prescribe dosing based on _CYP2C9_ and _VKORC1_ genotyping [121; 122]. Precision medicine aided by pharmacogenomics and pharmacogenetic profiling poses to refine this area, bringing in next-generation care using enhanced phenotyping for disease stratification [123].
# Machine Failure Prediction Using Survival Analysis
## 3 Background
### Feature Selection  
Feature selection is a critical step in the development of machine learning models, such as random survival forest. It involves identifying a subset of relevant features from a larger set of input features. Optimal feature selection can lead to significant improvements in the performance of classification models, as well as providing insights into the underlying patterns and relationships in the data. Feature selection is crucial for several reasons, such as reducing the dimensionality of the problem, improving the interpretability and generalization performance of the model, and reducing the computational cost of training and prediction [26]. Therefore, proper feature selection techniques are essential to develop effective machine learning models that can accurately solve real-world problems.  
Recursive feature elimination (RFE) is a popular feature selection technique that recursively removes less important features from a dataset until a desired number of features is reached. RFE uses a machine learning algorithm to rank the importance of each feature and iteratively eliminates the least important features until the desired number of features is selected. The basis for RFE involves training a machine learning model on a subset of the features and evaluating the importance of each feature based on its contribution to the model's performance. The RFE model can improve the accuracy and efficiency of a prediction model by identifying the most important features, while also reducing the risk of overfitting [26].  
The results of RFE can be important in feature selection, as they can provide valuable insights into the underlying patterns and relationships in the data. By identifying the most important features, RFE can help to simplify the prediction model and improve its interpretability, while also reducing the computational complexity of the model.
## 3 Results and Discussion
### Assessment of the Matrix Effects  
With regard to the detection of volatile compounds, one of the challenges encountered when developing quantitative extraction methods is the influence of other matrix components; the headspace equilibrium of substances is greatly influenced by the presence of volatile compounds other than the selected substances [34]. The target analytes in our study were trace compounds with a content of less than 1 mg/L in Baijiu. However, these trace compounds were affected by competitive adsorption with high content compounds in the sample during quantification, resulting in a lower response. To compensate for such matrix effect, it was decided the IS method would be used to construct standard calibration curves to evaluate the headspace concentration of volatiles from GC peak area responses. Six ISs, including three isotopically labeled ones, were used in our study. The selection of the matrix for quantitative calibration curves played an important role in this method. It was necessary to make the response value of the target compound in the model synthetic matrix consistent with the response value of the real Baijiu sample, otherwise, it caused a large difference in peak area and inaccurate results.  
To check the matrix interference in detail, several solutions of different model Baijiu matrices were analyzed using HS-SPME-GC\(\times\)GC-TOFMS, and the resulting chromatographic peak areas were compared. These included 50% water/ethanol solution at pH 3.5, which is also a common model synthetic solution in the quantitative analysis of volatile compounds in Baijiu [22], referred to as SS (simple solution); 50% water/ethanol solution at pH 3.5 with some high content volatiles in Baijiu, referred to as SS + HCV; and a real Baijiu sample (50% ethanol). These matrices were spiked with the same amounts of analytes, and the final concentrations of analytes were close to those of the real Baijiu sample. Table 1 shows the relative response of the different classes of compounds in these matrices. The peak areas of analytes in SS + HCV were significantly lower than those in SS, indicating the presence of some type of competition between the interfering substances and analytes in the matrix. The chromatographic response of real Baijiu was close to the response in SS + HCV. The calibration plots of different chemical classes of trace compounds in SS and SS + HCV are shown in Figure 4. For SS, the low concentration mixed standard solution had a different trend from the high concentration mixed standard solution, which may be the reason for the inaccurate quantification of the corresponding peak overload. However, the linearity of the corresponding standard curve in SS + HCV was improved.  
Therefore, in the calibration and quantification steps, we worked with the model synthetic solution described in Section 2.2.2. The application of this model synthetic solution could not only avoid matrix effects but also expand the quantitative range of trace components in quantitative analysis using HS-SPME-GC\(\times\)GC-TOFMS.
## 3 Results
### Combustion Characteristics
#### 3.1.2 Maximum In-Cylinder Pressure  
Figure 3 shows detailed analysis of maximum pressure in the cylinder for all test fuels according to different engine speeds. As shown in Figure 3, it is obvious that the maximum pressure in the cylinder increases significantly with the increase of engine load. This can be attributed to the increase of load, which leads to more fuel being injected into the combustion chamber and more fuel participating in combustion, thus increasing the maximum pressure in the cylinder. On the other hand, with the addition of ethanol, the maximum pressure in the cylinder does not show a large change under the same load. At 30 Nm, the maximum pressure of D95E5, D90E10, and D85E15 is reduced by 0.67%, 0.99%, and 0.83% compared with that of D100, respectively. At 60 Nm, the maximum pressure of D95E5, D90E10, and D85E15 is reduced by 0.61%, 0.30%, and 1.06% compared with that of D100, respectively. At 90 Nm, the maximum pressure of D95E5, D90E10, and D85E15 is reduced by 3.49%, 0.81%, and 2.69% compared with that of D100, respectively. On the whole, the maximum pressure in the cylinder is slightly reduced by adding ethanol to the diesel fuel only under high load (e.g., 90 Nm), and the maximum pressure is only reduced by 1% at most under low load (e.g., 30 and 60 Nm). This can be attributed to the fact that although the cetane number and calorific value of ethanol are lower than that of diesel (see Table 1), the oxygen content carried by ethanol itself is as high as 35%, which leads to more sufficient oxygen to ensure complete combustion. At higher load, more ethanol is involved in the combustion, and the higher evaporative cooling effect of ethanol plays a major role, resulting in the reduction of the maximum pressure in the cylinder. This is consistent with the results reported by [PERSON] et al. [26]. Overall, the addition of bioethanol did not have a significant impact on the maximum in-cylinder pressures, which may be
# Fault Diagnosis System of Power Grid Based on Multi-Data Sources
## 2 System Framework and Operation Process
### System Operation Process  
The complete operation process of fault diagnosis and tracking is completed by the cooperation of the three subsystems, described as follows:  
Figure 1: System framework.  
After the fault occurs, the dispatching center first obtains accident-level information from the scene. A real-time diagnosis system makes the information representation of the power grid network topology information and accident-level information and then finds out the suspicious faulty elements through fault pre-diagnosis link. In the process of fault diagnosis and information loss analysis, faulty elements can be determined, and operation statuses of related protections and circuit breakers are analyzed. Then through the classification of action protections, this subsystem judge whether information missing occurs. When information loss occurs, a set of suspected faulty elements is given.  
After operation statuses of circuit breakers and protections having been analyzed, the incorrect action information of circuit breakers and protections are transmitted to corresponding substations. Each corresponding substation immediately starts a quasi-real-time processing system. This subsystem extracts fault features of relay protection devices and circuit breakers related to incorrect actions in warning-level information and compares them with the relation table between fault reasons and features. Through the fault reason tracking link, the possible fault reasons for incorrect actions are arranged in order to guide the maintenance work of corresponding relay protection devices and circuit breakers.  
After getting the diagnosis result, the dispatching center invokes related fault recording documents from the fault recording information net and initiates a batch processing system. When partial accident-level information is lost, this subsystem extracts relevant fault recording wave data of the suspected faulty elements outputted by a real-time processing system and diagnoses them. Subsequently, it is consistent with the situation when no information loss occurs: fault recording wave data of faulty elements that have been diagnosed are extracted to verify whether a fault occurs, and fault phases, types, times and locations of faulty elements are outputted to form a complete diagnosis report.
## 2 Results  
### DisA and CdaA Are the Main Sources of c-di-AMP in Vegetative Cells of Strain B. subtilis YB955  
DACs DisA and CdaA are implicated in DNA repair in vegetative cells of _B. subtilis_[16]. However, the contribution of these DACs and consequently the c-di-AMP levels produced by these proteins to mutagenic events occurring in vegetative cells from the exponential and stationary phase of growth has remained elusive. To investigate the effects of DACS on mutagenesis, we generated mutant strains of _disA_ and/or _cdaA_. To avoid potential polar effects, we designed gene constructs in the integrative plasmid pMUTIN4, which only disrupt _disA_ and/or _cdaA_ as described in Materials and Methods. The level of c-di-AMP was also determined by competitive ELISA in these strains. Our results revealed that the single disruption of _disA_ or _cdaA_ decreased the concentration of c-di-AMP by \(\sim\)75% and \(\sim\)89%, respectively, compared to the parental strain YB955 (Figure 1).  
Notably, vegetative cells of the mutant strain _AldisA_/_AcdaA_, almost completely lost their capability to produce c-di-AMP; this strain displayed a \(\sim\)96% reduction of the concentration of this second messenger relative to the parental strain proficient for both DACs (Figure 1). Also, the contributions of each DAC to c-di-AMP levels (1.41 and 1.22 pmol/10\({}^{9}\) cells for DisA and CdaA, respectively) amount to higher values than what is observed in the parental strain and suggest that activity of these enzymes is regulated when both are active. Altogether, these results strongly indicate that CdaA synthesizes more c-di-AMP than DisA and that both DACs are the main sources of this cyclic dinucleotide in vegetative cells of _B. subtilis_ YB955.