esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmission Line Fault Classification Using Conformer Convolution-Augmented Transformer Model
## 2 Methodology
### Data Generation for Training and Validation Datasets Using Simulation  
ML-based approaches typically require substantial training data to achieve optimal performance. However, in scenarios where historical data may not provide sufficient diversity or uniformity in fault types, simulations can serve as a valuable tool to generate additional training and validation data [22]. In this study, we use Simulink simulations to create the training and validation datasets. The power system configuration used in the simulations is a two-terminal transmission line, as depicted in Figure 3. When the power flows from the 'ABC' to 'abc,' of a block, the power measurement is positive. Otherwise, the power measurement is negative. Consequently, the orientations of the blocks directly influence the resulting measurements. The system parameters of the simulations are listed  
Figure 1: Fault type classification.  
Figure 2: Proposed fault classification scheme.  
in Table 1. The two-terminal transmission line configuration has been widely applied for the purpose of fault classification [5; 9; 12; 13; 14; 15]. Based on the analysis of data from historical events, various fault scenarios were considered, encompassing different fault impedances, fault distances, circuit breaker time delays, FIAs, and fault types. We designed the fault impedance ranging from 0.1 to 10 Ohms, and the fault distance varied between 0.5 and 81 km, with a total transmission line length of 100 km. The circuit breaker time delay ranged from 2 cycles to infinity after the fault occurred, and the FIAs spanned from 0 to 315 degrees. By incorporating these factors into the simulations, we aim to create a comprehensive and diverse dataset that captures a wide range of potential fault scenarios in a transmission line. This comprehensive dataset allows us to evaluate and test the performance of our fault classification method across different fault conditions and parameters, providing a realistic and robust assessment of its effectiveness in handling various fault scenarios.  
To assess the model's performance, a portion (15%) of the generated data was set aside as validation data, enabling the evaluation of the model's classification accuracy on unseen instances. The validation results demonstrated that the model successfully classified both the training and testing data generated from simulations. This indicates that the model can effectively generalize and accurately classify fault types beyond the training set, providing confidence in its performance when applied to real-world scenarios using historical data for inference.
# Machine Reading Comprehension Model Based on Fusion of Mixed Attention
## 7 Conclusions  
This study introduces an innovative machine reading comprehension (MRC) model, the BERT_hybrid model, which combines the pretrained BERT model with a hybrid attention mechanism. It aims to address the challenges of inadequate contextual semantic integration and the underutilization of information in question-answering tasks within MRC. Through extensive experiments and comprehensive evaluations on the DuReader2 dataset, the following conclusions are drawn:  
The BERT_hybrid model excels in the MRC domain: The experimental results demonstrate that the proposed model achieves significant performance improvements on the DuReader2 dataset. Significant accuracy is achieved, with ROUGE-L and BLEU-4 scores of 60.1% and 59.9%, respectively, on the development set, and 61.1% and 60.9%, respectively, on the test set.  
The BERT_hybrid model surpasses traditional models and BERT-based models: When compared to traditional models such as match-LSTM and BiDAF, as well as BERT-based models, the BERT_hybrid model exhibits superior performance in question-answering tasks. Furthermore, it demonstrates enhanced data fitting capabilities, providing higher accuracy in Chinese reading comprehension tasks.  
N.M.: conceptualization, methodology, and software. Z.G.: data curation and writing--original draft preparation. Y.W.: visualization and investigation. N.M.: supervision. Z.G.: software and validation. Y.W.: writing--reviewing and editing. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China (Grant No. 62466052) and the Fundamental Research Funds for the Central Universities (Grant No. 31920220059).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results
### Catalytic Performance Studies
Catalyst stability and reusability in the esterification of AA was evaluated over at least two cycles, and the results of these experiments are reported in Figure 10. As can be evinced from this figure, by the end of the second run, more than one third of the activity was lost with respect to the first cycle; after 160 min, the AA conversion into the corresponding ester decreased from 94% in the first cycle down to 55% in the second cycle. A comparable behavior has been reported by [PERSON] et al. [12] in the esterification of linoleic acid with ethanol. The cause of such deactivation was unclear but, it could be the result of incomplete regeneration of the H\({}^{+}\) active sites of the sulfonic groups those are apparently the most important parameter for the catalytic activity [10; 12], or of the detachment of the polymer chains from the silica support [1]. This phenomenon necessitates further future investigation. However, the regeneration protocols of alike solid catalysts reported in the literature suggest either the use of diluted acids such as HCl [2] or the sequential wash using polar and non-polar solvents such as methanol and \(n\)-hexane [12], respectively followed by water, to assure the removal of embedded residues of both reactants and products. Accordingly, the regeneration protocol of SBA-15-PSSA as well as its reusability conditions have to be optimized further in the future. Figure 10: Reusability of catalyst SBA-15-PSSA after two catalytic cycles. Reaction conditions: 0.1 g of catalyst, 80 \({}^{\circ}\)C reaction temperature, 160 min reaction time. SBA-15-PSSA/acetic acid (AA) = 10.7 wt%, \(n\)-heptanol/AA = 4.97 mol ratio. SBA-15: pore-expanded mesoporous silica; SBA-15-PSSA: SBA-15-supported polystyrene sulfonic acids. Figure 9: Acetic acid (AA) conversion in the catalyzed esterification reaction (with 0.1, 0.2, or 0.3 g of catalyst) at 50 \({}^{\circ}\)C as a function of time.
# Seismic Response Analysis of Buried Nuclear Power Plant Structures in Non-Bedrock Sites
## 4 Conclusions  
In this paper, a numerical analysis method was adopted to investigate the effects of different structural embedment depths on the seismic response of an embedded nuclear power plant structure under non-bedrock site conditions. This study shows the following:  
(1) The structural response is influenced by the embedment depth, with greater embedment depth being beneficial to the seismic response of the nuclear power plant structure.  
(2) The acceleration response and floor relative displacement of the nuclear power plant structure decrease significantly as the structural embedment depth increases. Different embedment depths have minimal influence on the floor response spectrum in the low-frequency range. However, the embedment depth significantly affects the frequency band corresponding to the spectral peak, causing an overall shift in this peak frequency toward the low-frequency range. The impact of different embedment depths on the high-frequency range of the floor response spectrum is relatively minor.  
(3) Under the same seismic excitation, the liquefaction potential of the liquefiable layer is found to increase as the liquefaction layer is positioned closer to the surface.  
(4) For embedded nuclear power structures, the seismic response of the structure is shown to be significantly influenced by site response characteristics.  
(5) The buried nuclear island structure does not require extensive component modifications on the original structure to eliminate liquefaction hazards, significantly enhancing the seismic performance of the nuclear power structure. Based on this research, it is recommended that regulations consider adding site selection and design requirements for buried  
Figure 37: Pore pressure ratio of site.  
nuclear power structures as a new structural form. This can serve as discussion content or a direction for future research.  
Conceptualization, [PERSON]; methodology, [PERSON]; software, [PERSON]; validation, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, [PERSON]; visualization, [PERSON]; supervision, X.D. All authors have read and agreed to the published version of the manuscript.  
This study is supported by the Key Program of the Natural Science Foundation of China (No. 52338001). This support is greatly appreciated.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 3 Review Results
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Reference** & **Type of Study** & **Number of Subjects** & **Summary** & **Main Outcomes** \\ \hline [PERSON] et al., 2020 [15] study & Theoretical planning & 50 (all with glioblastoma) & Semi-automated DTI-defined tumour volume. GTV manually defined using q map, which was automatically expanded using an expansion model on the p map to model tumour infiltration. Comparison made to manual segmentation of p and q maps. & Mean Dice coefficient of 74\% between manual and semi-automated method over all 50 patients. \\ \hline \multicolumn{4}{p{113.8 pt}}{**Applications in Gliomas**} \\ \hline \multicolumn{4}{p{113.8 pt}}{} \\ \hline [PERSON] et al., 2018 [9] study & Theoretical planning & 19 (all with HGG) & Tractography used to dissect multiple element tracts bilaterally (CST, SLF, IFOF, UNF). Tracts included as OAR during inverse planning with TomoTnerrapy. Treatment plans with and without tractography were compared. & Significant reduction in maximum and mean radiation dose to the tracts, particularly to contralateral tracts. \\ \hline [PERSON] et al., 2014 [29] study & Clinical feasibility study & NA & Integration of tractography into radiotherapy planning in glioblastoma by including the corticospinal tracts as OAR in planning. & Integration of tractography shown to be feasible and reduce radiation does in two cases. \\ \hline [PERSON] et al., 2020 [27] & Retrospective observational study & 35 (all with glioblastoma) & Use of deep learning to correct FA maps for free water and used to predict areas of glioma recurrence on follow up imaging. & Area under the curve for recurrence prediction of 0.77 using FA and 0.9 using free water-corrected FA values. \\ \hline [PERSON] and [PERSON] 2019 [19] & Systematic review & NA & Systematic review of literature on DTI in intracranial radiotherapy. & Finds only three articles evaluating DTI in radiotherapy that includes gliomas, none of which were case control trials or used prospective integration. \\ \hline \multicolumn{4}{p{113.8 pt}}{**Applications in Brain Metastases**} \\ \hline \multicolumn{4}{p{113.8 pt}}{} \\ \hline [PERSON] et al., 2013 [30] study & Theoretical planning & 25 (10 AVM, 3 CM, 12 brain tumours’ & fMRI and tractography of eloquent structures in close proximity to the lesions. Comparison of radiotherapy plans with and without integration. & Found an average reduction in radiation dose by 17\% to eloquent regions when fMRI and tractography integrated. \\ \hline [PERSON] and [PERSON] 2016 [20] study & Clinical feasibility study & 20 (5 VS, 5 AVM, 9 CM, 1 Menig) & DTI tractography used to guide GKRS. & Applied to nine cases of cerebral metastasis successfully; however, there was no comparison to a control group. \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.
# Solving Task Scheduling Problems in Dew Computing via Deep Reinforcement Learning
## 3 Related Work
### Human-Designed Heuristic Methods in SCEs  
In the context of SCEs, different algorithms have been proposed to optimize the system's utility and job execution time, using the mobile devices remaining battery as a formal constraint of the resource allocation problem formulation [34; 35; 36; 37; 38]. These algorithms, however, assume complete and accurate information in terms of the energy spent and the execution time for every candidate node, making it challenging to apply in real-life scenarios.  
[PERSON] et al. [39] and [PERSON] [40] addressed the previous limitation by proposing algorithms that do not rely on complete information. This approach seeks to exploit the nodes' proximity and cost-effectiveness of node-transferring capabilities. However, the focus of their study is to analyze the effect of nodes' mobility rather than balancing the load to efficiently utilize the battery and processing power of the available resources.  
[PERSON] et al. [31; 32] presented other types of heuristics, which distribute jobs by considering the mobile devices' battery level and computing scores obtained from benchmarks. These methods outperformed traditional scheduling algorithms (such as Round Robin) but had one limitation: they only consider battery-dependent devices. This problem was recently addressed by [PERSON] et al. [7]. [PERSON] et al. [7] studied hybrid-mobile topologies, which combine both battery-dependent and non-battery-dependent devices and proposed heuristic methods that consider the device's battery level, computing score, and current work load in order to distribute jobs in Dew environments.  
In contrast to those works, we propose to use RL to learn how to distribute jobs in a Dew environment. The main advantage of this approach is adaptability. As discussed by [PERSON] et al. [7], no current heuristic seems to dominate across all Dew environments. This makes sense, since each Dew environment might have considerably different characteristics. Therefore, instead of trying to figure out (by ourselves) a clever way to correctly distribute jobs in any Dew environment, we propose to let an RL agent do that work for us (automatically).
# Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication
## 4 Results
### Link Budget for FSO Nano-Satellite  
We present herein two simple link-budget formulations for FSO communication between an LEO nano-satellite and an optical ground station (as described in Section 2.1). The first formulation is based on \"photon-counting\" link budget (following the modeling of the \"NODE\" project [40]). The second formulation is based on standard RF link budget. We also provide an analysis of the overhead that stems from dealing with errors. In the following subsection, we add a generalization and improvement of the presented link-budget analysis in order to allow up to 1 Gbps laser communication link between a pico-satellite and an FSO ground station.  
The following parameters were used: Beam divergence of 1 mrad and a range of 1000 km (between the satellite and the ground station) implying a 1 km\({}^{2}\) \"spot\". A 14\({}^{\prime\prime}\) telescope has an area of 0.1 m\({}^{2}\), leading to a geometric ratio of \(Geo_{rt}=10^{-7}\). The modeled transmitter has a bit rate of 10 Mbps and power of \(Tx_{power}=100\) mW. Assuming a clear night sky, the expected loss is around 3 dB (about 50%, denoted by \(Atm_{loss}\)). The telescope losses are also bounded by 50% (denoted by \(Rx_{loss}\)).
# On Analyzing Beamforming Implementation in O-RAN 5G
## 4 O-RAN Beamforming
### Open Research Issues
#### 4.2.2 Zero-Forcing Beamforming Model Concept  
Implementing the precoding in O-RU will cost high complexity in the case of a high precoding stream number, leading to inter-symbol interference in O-RU by interference with non-desired users. To overcome this interference, we propose using zero-forcing beamforming.  
Zero-forcing beamforming (ZF-BF) is a method that is widely considered in massive MIMO systems [36]. The simplest approach is to invert the channel through the pseudo-inverse, which is referred to as ZF precoding; this technic allows transmitting and receiving data to desired users and nullifying the directions from the interference users. ZF-BF performance depends on the accuracy of received channel status information (CSI) from each user to determine the precoder and beamforming weight vector. This channel, also called estimated channel information, carries the information of the signal properties such as propagation features from the transmitter to the receiver, scattering, fading, and power decay with distance [37; 38; 39].
###### Abstract  
Recently, the usage of the automotive Electronic Control Unit (ECU) and its software in cars is increasing. Therefore, as the functional complexity of such software increases, so does the likelihood of software-related faults. Therefore, it is important to ensure the reliability of ECU software in order to ensure automobile safety. For this reason, systematic testing methods are required that can guarantee software quality. However, it is difficult to locate a fault during testing with the current ECU development system because a tester performs the black-box testing using a Hardware-in-the-Loop (HiL) simulator. Consequently, developers consume a large amount of money and time for debugging because they perform debugging without any information about the location of the fault. In this paper, we propose a method for localizing the fault utilizing memory information during black-box testing. This is likely to be of use to developers who debug automotive software. In order to observe whether symbols stored in the memory have been updated, the memory is partitioned by a memory map and the stack, thus the fault candidate region is reduced. A memory map method has the advantage of being able to finely partition the memory, and the stack method can partition the memory without a memory map. We validated these methods by applying these to HiL testing of the ECU for a body control system. The preliminary results indicate that a memory map and the stack reduce the possible fault locations to 22% and 19% of the updated memory, respectively.  
Electronic Control Unit (ECU); automotive software; fault localization; embedded testing; memory map; Hardware in the Loop (HiL); memory update Article
# Application of Transient Electromagnetic Method for Investigating the Water-Enriched Mined-Out Area
###### Abstract  
The water-enriched mined-out area, which led to frequent accidents and the serious destruction of ecological environment and serious threat to coal-mining practices. To ensure safe mining practices and to avoid the intrusion of water from the bases of coal deposits, the detection of the mined-out areas, especially the water-filled mined-out area in advanced, is one of the most important issues. This research focuses on detecting mined-out area enriched by water with the large-loop transient electromagnetic method. Data acquisition system was arranged along 23 survey lines, and the total of 1975 survey points were recorded. An inversion technique was used to interpret the TEM data. The inversion results suggested that the mined-out areas enriched by water always exhibit a very low-resistivity, the resistivity contour present closed circle sharp. There are three main water enriched mined-out areas, named JS1, JS2, and JS3 in the north part of the survey area, as well as three main water weak-enriched areas named RJS1, RJS2 and RJS3 in the west and southwest part of the survey area can be inferred by TEM, the interpretation results were verified by drilling. Large loop TEM is proved as an efficient way to investigate water enriched mined-out areas under the rugged topography conditions, and this work provides more detailed geological information to the coal mining enterprise for further coal-mining practices safety arrangement.  
transient electromagnetic method; mined-out area; resistivity; water-enriched area; mine
# Stability of Feature Selection in Multi-Omics Data Analysis
## 2 Materials and Methods
### Experimental Setup
An important part of our computational procedure was selecting the value of parameter \(C\) to ensure that the classifier utilizes the desired number of features. It should be noted, however, that in practice, it is not possible to select \(C\) to produce a classifier model that uses exactly the desired number of features. The number of features used by the classifier is influenced by the subset of data on which it is fitted. Nonetheless, it can be expected that classifier models fitted on similarly large subsets of the same dataset, with the same \(C\) value, will use a comparable number of features. In practice, we may find that these feature counts do not vary by more than 5%. Based on the above, the determination of parameter \(C\) is achieved by taking as input the type of classifier (LR, SVM, or Lasso), the desired number of features \(nf\) for the fitted classifier model, and the dataset \(D=\{\mathbf{x}_{j},y_{j}\}\) (or its omics layer). The computational procedure employs a gradual narrowing of the range of \(C\) values, training successive classifier models and checking the resulting feature subset size. This process continues until a classifier model is obtained that, for the tested parameter \(C=C_{opt}\), uses approximately the desired number of features \(nf\) (within a tolerance of 5%). The value \(C_{opt}\) is then returned as the determined \(C\) value, ensuring that classifier models fitted on similarly large subsets of the same dataset \(D\) will use approximately \(nf\) features. In our computational procedure, the determination of \(C\)-parameter values is performed separately for each dataset and its subsets containing features from a single omics layer. For each dataset, we first define the desired numbers of features \(nf_{low}\), \(nf_{med}\) and \(nf_{high}\) that the classification models should utilize. These feature counts correspond to approximately 7%, 30%, and 55% of the 80% of the total number of observations (to match the size of the training split used during 5-fold cross-validation). The C-parameter selection is carried out independently for each classifier type (SVM, LR, Lasso), and each desired feature count \(nf_{low}\), \(nf_{med}\), \(nf_{high}\). Additionally, a random 80% split of the observation set is used during the C-parameter determination procedure to reflect the training set size employed in the main computational workflow. As a result, different sets of \(C\)-parameter values (low, med, and high) are used for each classifier and each dataset or its omics subsets.
## 4 Results and Discussion
### Results of the Laboratory Experiments
#### 4.1.3 Results of SEM  
Figure 10 shows the microstructures of different WGP and mortar samples, exploring the chemical activation's effect. The relatively smooth surface of the nonactivated WGP was in striking contrast with the eroded surface shown in Figure 10c. This phenomenon indicates the susceptibility of the WGP to chemical activators. Moreover, more hydrates and finer fissures were revealed in the WGP mortar sample activated by 2% alkali and 2% Na\({}_{2}\)SO\({}_{4}\), compared with the nonactivated one. Figure 10d shows that fewer calcium hydroxide (CH) crystals were detected than in the sample without chemical activation, indicating a more intense pozzolanic reaction. Therefore, the chemical agents promote the pozzolanic activation of WGP and simultaneously increase its mechanical property, as described before, if the activators' content is moderate.