esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Representation Using Sub-Symbolic Knowledge in Commonsense Reasoning +
## 4 Experiments
### Experimental Results
#### 4.4.2 Adversarial Attack Test Using SRL  
To demonstrate the analysis of whether the pre-trained language models precisely comprehend the question, we used semantic role labeling (SRL) ([[https://docs.allennlp.org/models/main/models/structured_prediction/predictors/srl/](https://docs.allennlp.org/models/main/models/structured_prediction/predictors/srl/)]([https://docs.allennlp.org/models/main/models/structured_prediction/predictors/srl/](https://docs.allennlp.org/models/main/models/structured_prediction/predictors/srl/)) (accessed date in 1 April 2021)). SRL [39] labels the predicate and its arguments in a sentence. This study conducted an adversarial attack test on pre-trained language models based on SRL data. For the analysis, we replaced one candidate option, except for the correct answer, with the randomly selected argument related to the predicate of the question in Figure 0(a). The experiment assessed whether the model predicted the correct answer using common-sense knowledge or relied on the argument text in the question. We selected BERT as a representative of the pre-trained language models. We tested the model using the CSQA. We fine-tuned the BERT model using the original training dataset for QA tasks and obtained inference results  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Relation** & **Ntrain** & **Ndev** & **Ntest** \\ \hline ARG0 & 17,300 (22.70\%) & 2547 (22.73\%) & 2477 (23.09\%) \\ ARG1 & 24,673 (32.38\%) & 3566 (31.83\%) & 3521 (32.82\%) \\ ARG2 & 6001 (7.88\%) & 864 (7.71\%) & 829 (7.73\%) \\ ARG3 & 286 (0.38\%) & 37 (0.33\%) & 51 (0.48\%) \\ ARG4 & 587 (0.77\%) & 92 (0.82\%) & 59 (0.55\%) \\ \hline Total relations & 76,203 & 11,204 & 10,727 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of core roles from AMR graph in CommonsenseQA.  
\begin{table}
\begin{tabular}{l l c c} \hline \hline
**Language Model** & **Graph Type** & **Ndev-Acc(\%)** & **Ntest-Acc(\%)** & **Avg** \\ \hline \multirow{4}{*}{BERT-base-cased} & \multirow{2}{*}{-} & 51.81 & 51.59 & 52.70 \\  & & 53.48 & 53.10 & 53.29 \\ \cline{1-1} \cline{2-4}  & AMR-CN-Full(ACF) & 53.81 & 52.38 & 53.10 \\ \cline{1-1} \cline{2-4}  & AMR-CN-Pruned-ARG\({}_{0.1}\)(ACP-ARG-mini) & 53.89 & 52.54 & 53.22 \\ \cline{1-1} \cline{2-4}  & AMR-CN-Pruned-nonARG(ACP-nonARG) & 53.15 & 50.77 & 51.96 \\ \cline{1-1} \cline{2-4}  & AMR-CN-Pruned-ARG\({}_{N}\)(ACP-ARG) & **54.38** & **53.51** & **53.95** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experiments on diverse graph features.  
from the original and SRL-corrupted development datasets. Table 3 shows the results for each development dataset. They indicate that the performance of BERT decreases when one option other than the correct answer is substituted with the argument of the question. Thus, the decrease in performance suggests that BERT merely relies on superficial cues from the question. Our proposed model alleviated the performance of BERT, which showed a decrease of 1.06% compared with the decrease of the fine-tuning model of 5.78%.
###### Abstract  
FIDO (Fast Identity Online) is a set of network identity standards established by the FIDO Alliance. It employs a framework based on public key cryptography to facilitate multi-factor authentication (MFA) and biometric login, ensuring the robust protection of personal data associated with cloud accounts and ensuring the security of server-to-terminal device protocols during the login process. The FIDO Alliance has established three standards: FIDO Universal Second Factor (FIDO U2F), FIDO Universal Authentication Framework (FIDO UAF), and the Client to Authenticator Protocols (CTAP). The newer CTAP, also known as FIDO2, integrates passwordless login and two-factor authentication. Importantly, FIDO2's support for major browsers enables users to authenticate their identities via FIDO2 across a broader range of platforms and devices, ushering in the era of passwordless authentication. In the FIDO2 framework, if a user's device is stolen or compromised, then the private key may be compromised, and the public key stored on the FIDO2 server may be tampered with by attackers attempting to impersonate the user for identity authentication, posing a high risk to information security. Recognizing this, this study aims to propose a solution based on the FIDO2 framework, combined with blockchain technology and access control, called the FIDO2 blockchain architecture, to address existing security vulnerabilities in FIDO2. By leveraging the decentralized nature of the blockchain, the study addresses potential single points of failure in FIDO2 server centralized identity management systems, thereby enhancing system security and availability. Furthermore, the immutability of the blockchain ensures the integrity of public keys once securely stored on the chain, effectively reducing the risk of attackers impersonating user identities. Additionally, the study implements an access control mechanism to manage user permissions effectively, ensuring that only authorized users can access corresponding permissions and preventing unauthorized modifications and abuse. In addition to proposing practical solutions and steps, the study explains and addresses security concerns and conducts performance evaluations. Overall, this study brings higher levels of security and trustworthiness to FIDO2, providing a robust identity authentication solution.  
FIDO2; blockchain; access control; identity authentication +
Footnote †: journal: Applied
## 2 Materials and Methods
### Transfer Learning Based Snapshot Ensemble Method (TLSE)
#### 2.2.2 Snapshot Ensemble Phase for TLSE  
Current practices indicate that combining the outputs of different models can achieve a better accuracy [48]. The outputs of several CNN models are combined by averaging their soft-max class posteriors. This improves the accuracy due to the complementarity and diversity of different CNN models. However, training multiple deep networks with different architecture or initial weights for model averaging is computationally expensive and time-consuming since it needs to train several CNNs, respectively. To remedy this time-wasting and extensive workload problem, snapshot ensemble was brought forward, which can obtain the similar goal of combining networks without additional training cost.  
Figure 3: Overview of the transfer learning based snapshot ensemble method (TLSE).  
The snapshot ensemble phase for TLSE that is basically the same as the original snapshot ensemble. The snapshot ensemble develops an ensemble of accurate and diverse models within a single model training process. By using a cyclic learning rate schedule [49], snapshot ensemble enables the network to visit several local minima through the optimization process, and several model snapshots are saved at those local various minima.  
The snapshot ensemble divides the whole training epochs into M cycles, and the learning rate is forced to decline at a very fast pace, for the sake of enabling the model to reach a local minimum. Thus, at every end of the cycles, a snapshot of the model weights is taken and saved. After that, the learning rate is raised for the start of the next training cycle. After M training cycles, M model snapshots are obtained, which will be used for the final ensemble. It is important to emphasize that the total training time is the same as a standard schedule. The ensemble prediction at the testing process is the average of the last two models' soft-max outputs.
## 2 Materials and Methods
### Quantification of Aroma Compounds  
#### 2.3.1 Headspace-Solid-Phase Microextraction-Gas Chromatography-Mass Spectrometry (HS-SPME-GC-MS)  
The extraction and quantitation of aroma compounds were performed with HS-SPME-GC-MS according to our published method [7]. The analysis system was an Agilent 7890 GC gas chromatograph combined with an Agilent 5975 MS mass spectrometer. A 5 mL wine sample was mixed with 10 \(\upmu\)L of 4-methyl-2-pentanol (internal standard, 1.0086 g/L) and 1 g of NaCl. The mixture was placed into a 20 mL vial capped with a PTFE-silicon septum. The volatile compounds of wine samples were extracted with automated headspace solid-phase microextraction using CTC CombiPAL (CTC Analytics, Zwingen, Switzerland). The sample vial was heated and balanced at 40 \({}^{\circ}\)C for 30 min, and the heating tank was rotated at 500 rpm. Then, the activated SPME fiber (50/30 \(\upmu\)m DVB/CAR/PDMS, Supelco, Bellefonte, PA., USA) was inserted to extract the volatile compounds at 40 \({}^{\circ}\)C for 30 min. Then, the fiber was immediately inserted into the GC injector. The sample was injected at 250 \({}^{\circ}\)C in a split mode with a ratio of 5:1. The capillary column was HP-INNOWAX (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, J&W Scientific, Folsom, CA, USA). The carrier gas was high-purity helium, and the flow rate of carrier gas was 1 mL/min. The oven temperature was held at 50 \({}^{\circ}\)C for 1 min after injection, then programmed to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and held at 220 \({}^{\circ}\)C for 5 min. Qualitative and quantitative information for the major aroma compounds is listed in Table S2.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Groups \({}^{\text{a}}\)** & **Variety \({}^{\text{b}}\)** & **Winery** & **Region** & **Vintage** \\ \hline
1 & CS-1 & MA-1 & Chateau Yunmo Greatwall & Jiaodong Peninsula & 2016 \\
2 & CS-2 & MA-2 & Amethyst Manor & Huaizhuo Basin & 2015 \\
3 & CS-3 & MA-3 & Martin Vineyard & Huaizhuo Basin & 2017 \\
4 & CS-4 & MA-4 & Grace Vineyard & Loess Pplateau & 2013 \\
5 & CS-5 & MA-5 & Huangkou Winery & Eastern Foothill of Helan Mountain & 2017 \\
6 & CS-6 & MA-6 & Chateau Huahao & Eastern Foothill of Helan Mountain & 2017 \\
7 & CS-7 & MA-7 & Chateau Huahao & Eastern Foothill of Helan Mountain & 2018 \\
8 & CS-8 & MA-8 & Chateau Zhongfei & Xinjiang & 2015 \\
9 & CS-9 & MA-9 & Chateau Zhongfei & Xinjiang & 2016 \\
10 & CS-10 & MA-10 & Tiansai Vineyards & Xinjiang & 2015 \\
11 & CS-11 & MA-11 & Tiansai Vineyards & Xinjiang & 2016 \\
12 & CS-12 & MA-12 & CITIC Guoan Winery & Xinjiang & 2017 \\ \hline \end{tabular}  
\({}^{\text{a}}\) Wines are divided into 12 groups according to their origin. \({}^{\text{b}}\) CS, Cabernet Sauvignon; MA, Marselan.  
\end{table}
Table 1: Information of Cabernet Sauvignon and Marselan dry red wines from five regions in China.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 2 Current In-Car Network Architectures and Technologies
### Heterogeneity of Current In-Car Network Architectures  
Nowadays different network technologies are used in the different functional domains [24]. However, several distributed applications also require communication between these different domains, i.e., inter-domain communications. For instance, the active suspensions on several high-end vehicles require real-time interaction among ADAS cameras, powertrain sensors, and chassis actuators.  
Due to the existence of multiple not directly compatible networking technologies, the inter-domain communications have to be supported by complex gateways that require application knowledge. This is an issue, as the gateway has to be adapted every time a change in the application is made. Moreover, there is a performance penalty due to the protocol conversion operated by the gateways. For example, infotainment interfaces are required to provide the driver with prompt and timely interaction [25], however, sometimes such interfaces are slow to respond to the user's inputs because infotainment systems must wait to receive information from other components that belong to other subsystems in other functional domains. Another example is the lack of synchronization between the instrument clusters and the dashboard screens on some cars, which may cause occasional loss of visual consistency for indications and alerts, thus degrading the quality of the useexperience.  
A common networking technology offering high bandwidth, bounded latency, reliability, and synchronized operations of the ECUs through a common network time would solve the problems described above and simplify the car's electronic architecture.
# End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning
## 3 Methodology
### Enhanced MDD by Textual Information  
#### 3.2.1 The Framework of Enhanced MDD  
Although we have included a powerful pretrained acoustic model into our MDD system, there is still possibly much room for improvement by combining with reference texts. A common approach is to convert the reference text into phonemes and transform each phoneme into a high-dimensional linguistic feature vector via a text encoder. [PERSON] et al. [20] implemented such a MDD framework based on CNN and Bi-LSTM neural networks. As shown in the left part of Figure 2, their framework takes audio signals and phoneme sequences as inputs. The audio encoder built with CNN and Bi-LSTM and the text encoder built with Bi-LSTM convert the audio signal and phoneme sequence to acoustic and linguistic representations, respectively. Given a canonical phoneme sequence with length \(N\), the linguistic representation \(\mathbf{H}^{\text{text}}=[\mathbf{h}_{1}^{\text{text}},\mathbf{h}_{2}^{ \text{text}},...,\mathbf{h}_{N}^{\text{text}}]\) can be derived by the text encoder. After that, the model improves the aligned representation learning by performing attention for feature aggregation. The acoustic and linguistic representations are concatenated and fed into a linear layer for MDD. This model is referred to as BaselineConventional. We replace the audio encoder and text encoder with the pretrained wav2 vec2.0 model and transformer respectively while keeping the same fusion strategy, which is shown in the right part of Figure 2. Moreover, the concatenation of acoustic and textual representations is further fed into another transformer layer. This model is referred to as BaselineConcatenate.
## 3 Research Design and Methodology
### GSCPI Overview  
In January 2022, the Federal Reserve Bank of New York published the GSCPI through Beningo et al. [65]. The GSCPI comprehensively evaluates potential disruptions affecting global supply chains, representing bottlenecks on the supply side of global supply chains. It incorporates the Baltic Dry Index (BDI), the Harper Index, and airfreight cost indices from the U.S. Bureau of Labor Statistics (BLS) to reflect international transportation costs. BDI is an index that tracks the price of transporting dry bulk cargo such as cement, coal, iron ore, and grain on bulk freighters. The Harper Index tracks the cost of chartering container ships. BLS publishes international transportation services indexes that show changes in transportation costs by flight direction, as well as import and export indexes that show cross-border payments and receipts for services. Additionally, it utilizes various supply chain-related factors (e.g., delivery times, backlogs, and purchasing inventories) derived from the Purchasing Managers' Index (PMI) surveys, in which manufacturing companies from seven key economic regions (South Korea, China, Japan, Taiwan, the U.S., the EU, and the UK) participated. Delivery times are a measure of how much economic impact supply chain delays have on producers; this measure could be seen as identifying a purely supply-side constraint. The backlog is a measure of the number of orders that businesses have received but have not yet begun or finished. Finally, the amount of inventory that businesses in the economy have accumulated is measured by purchased stocks. The PMI data start only in 2007, so for the U.S. GSCPI, the PMI data are combined with those from the manufacturing survey of the Institute for Supply Management (ISM). Figure 1 shows the trend of the GSCPI from September 1997 to December 2023. In an attempt to give businesses a more accurate indicator of the state of the global supply chain, New York Fed policymakers combined manufacturing indicators and transportation cost data to create the GSCPI. It is published at 10:00 a.m. Eastern Time (U.S.) on the fourth working day of every month and is updated continuously.  
The GSCPI can rise due to various natural, political, social, and economic factors. While the GSCPI was \(-0.44\) in September 1997, it significantly increased following the COVID-19 outbreak, reaching 3.07 in April 2020 and 4.31 in December 2021, which indicates heightened global supply chain disruptions. The Federal Reserve Bank of New York noted that the closure of factories in Asia and travel restrictions exacerbated disruptions throughout the logistics network. Interruption and delays in global logistics led to the closure of major ports and airports in South Korea, China, and the U.S., which, in turn, limited the flow of consumer goods into key markets such as Europe, North America, India, and Southeast Asia. Notably, the closure of factories in China, the epicenter of the outbreak and a key player in the global value chain, negatively impacted production across multiple countries. Consequently, the GSCPI peaked at 4.33 in December 2021.
## 2 Materials and Methods
### Study Data
#### 2.2.3 Heatmap Data  
Population mobility refers to the migration and movement of people between different regions for reasons such as employment, education, and housing. It reveals the strength of connections between regions, the intensity of socio-economic activities, and the balance of resource distribution. In population mobility studies, heatmaps are a commonly used visualization tool, representing the density of certain phenomena in space through color gradients. Specifically, in population mobility research, heatmaps can illustrate high- and low-density migration areas, highlighting hotspots of population concentration within a specific timeframe and their changing trends. Compared to other forms of data representation, such as traditional point or line charts, heatmaps provide a more intuitive view of hotspots and spatial distribution patterns of population mobility, aiding in identifying regional activity levels and population aggregation trends [66].  
Numerous platforms provide heatmap data, such as Baidu Maps, Google Maps, and Tencent Maps. Among them, Baidu Maps, with its large user base, extensive coverage, and high-precision location information, offers real-time updates and API support on an open platform, making its heatmap data widely utilized for analyzing patterns of population concentration and movement within cities. In this study, we obtain 2023 heatmap data for the PRD urban agglomeration through Baidu Maps' open platform website ([[https://lbsyun.baidu.com/](https://lbsyun.baidu.com/)]([https://lbsyun.baidu.com/](https://lbsyun.baidu.com/)), accessed on 15 November 2024). To enhance data accuracy and effectiveness, we perform rigorous data cleaning and preprocessing. The specific steps included: first, removing outliers and noise, such as missing data or inaccurate geographic coordinates; second, resampling and interpolating the data to address inconsistencies in temporal and spatial resolution due to uneven data collection frequencies; finally, conducting spatial overlay and temporal alignment to produce the preprocessed heatmap data (Figure 4).  
Figure 3: Preprocessed LandScan Data for the PRD Urban Agglomeration.
## 3 Materials and Methods
### Model Evaluation Metrics  
The model with the best prediction results is selected based on mean absolute error (MAE) and relative mean error (RME) evaluation metrics.  
_MAE_  
If \(y_{i}\) is the actual value of the i_th sample and \(\hat{y_{i}}\) is the predicted value, then the mean square error MAE can be estimated as  
\[\text{MAE}(y,\ \hat{y}=\frac{1}{n_{\text{samples}}}\sum\
olimits_{i=0}^{n_{ \text{samples}}-1}\lvert y_{i}-\hat{y_{i}}\rvert \tag{7}\]  
_RME_  
The relative mean error (RME) is one of the most commonly used evaluation metrics in machine learning and is the average of the percentage error of the forecast.  
\[\text{M}=\frac{1}{n}\sum\
olimits_{t=1}^{n}\left|\frac{A_{t}-F_{t}}{A_{t}}\right| \tag{8}\]  
Figure 3: Training and testing process.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Set** & **Features** & **Description** & **Formula** & **Complexity** \\ \hline \multirow{5}{*}{Bw\_Avg} & nnz\_sd & Standard deviation of nonzero elements per row. & \(\sqrt{1/\text{N}\sum\limits_{i=1}^{\text{N}}(\text{nnz}_{i}-\text{nnz\_avg})^{2}}\) & \(\varnothing(2\text{M})\) \\  & & Average column distance between the first and & \(1/\text{N}\sum\limits_{i=1}^{\text{N}}\text{Bw}_{i}\) & \(\varnothing(\text{M})\) \\  & & the last nonzero element each row. & \(1/\text{N}\sum\limits_{i=1}^{\text{N}}\text{Bw}_{i}\) & \(\varnothing(\text{M})\) \\  & & Minimum column distance between the first and & \(\text{Min}(\text{Bw}_{1,.....}\text{Bw}_{n})\) & \(\varnothing(\text{M})\) \\  & & Maximum column distance between the first and & \(\text{Max}(\text{Bw}_{1,.....}\text{Bw}_{n})\) & \(\varnothing(\text{M})\) \\  & & the last nonzero element of each row. & \(\sqrt{1/\text{N}\sum\limits_{i=1}^{\text{N}}(\text{Bwi}-\text{Bw\_avg})^{2}}\) & \(\varnothing(2\text{M})\) \\ \multirow{2}{*}{Clustering} & \multirow{2}{*}{Clustering} & \(\sum\limits_{\text{N}}^{\text{N}}=0\text{Clus}_{i}\) & \(\varnothing(\text{nnz})\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: _Cont._* n = the number of errors;
* \(\Sigma\) = symbol of summation;
* A\({}_{\text{t}}\) is the actual value;
* F\({}_{\text{t}}\) is measure of forecast value.
## 5 Economic Opportunities and Benefits
### Cost-Benefit Analysis and Investment Case
A digital twin that clearly increases transfer capacity, reduces redispatch payments, or improves cross-border price convergence is well placed to meet these criteria and attract blended finance or grant support [16]. On the private side, technology vendors and cloud providers are enteringrevenue-sharing agreements with utilities, confident that the resulting efficiency gains will outweigh license and service fees. This early commercial engagement further reinforces the investment case [41]. Potential disadvantages, such as the accelerated depreciation of legacy information technology platforms that may become obsolete with the introduction of the digital twin, are relatively minor and can be addressed through phased deployment strategies that allow existing systems to operate until the end of their service life. Furthermore, early pilot projects indicate that the risk of stranded digital assets is significantly outweighed by the benefits of avoiding unnecessary physical infrastructure expansion. A well-structured cost-benefit analysis that includes deferred capital expenditure, reduced operational expenditure, enhanced reliability, carbon reduction, and innovation spillover effects is highly likely to demonstrate net positive returns. As empirical evidence from pilot initiatives grows and regulatory incentives begin to favor overall system efficiency, the investment case for the digital twin will become increasingly persuasive to transmission and distribution system operators, regulators, financial institutions, and policymakers. This will help transform the vision of a data-driven, integrated European grid into an economically viable and self-sustaining reality.