esa-sceva/satcom-chunk-collection huggingface dataset:
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
###### Abstract  
Technology of shield tunneling has been widely used in modern city subway system building, however, due to the limit of urban construction land resources, it is inevitable for the new building structures conflicted with existing one. Thus, it is essential to taking correct measures to guarantee the safety of existing structure. In this case, a typical case of metro shield tunnel crossing through city overpass bridge piles underpinning construction project is studied by the numerical method and site field monitoring. According to the existing overpass bridge structure, geological conditions and site operation environment, a suitable construction method of piles foundation underpinning plan for this project is adopted. In order to ensure the structure safety of the existed overpass bridge during the pile foundation underpinning construction, a numerical simulation model is established which takes the whole foundation underpinning structure and construction steps into consideration. The numerical simulation result shows that the stress and the settlement of the piles foundation underpinning structure is reasonable. After the comparing of the site monitoring data with the numerical model simulation results, it is found that the site settlement and stress results are highly consistent with the calculated results, and it proves the feasibility of the piles foundation underpinning construction scheme. Eventually, the site gauging settlement data of the overpass pier and the pile cap indicate that the original overpass structure is barely affected by the shield tunnel construction, the construction method can provide reference and experience for the future similar projects.  
s 2021 11 1611. [[https://doi.org/10.3390/app11041611](https://doi.org/10.3390/app11041611)]([https://doi.org/10.3390/app11041611](https://doi.org/10.3390/app11041611))  
1  
shield tunnel; city overpass; piles underpinning; site monitoring; settlement control
_Applied_  
_sciences_  
Analysis of the DC Bias Effects on the UHV Autotransformer with Rated Load Based on a Reduced-Scale Model Experiment  
[PERSON] *(), [PERSON], [PERSON] and [PERSON]  
State Key Laboratory of Alternate Electrical Supply System with Renewable Energy Sources, North China  
Electric Power University, Beijing 102206, China; [EMAIL_ADDRESS] (Z.W.); [EMAIL_ADDRESS] (M.L.); [EMAIL_ADDRESS] (S.G.)  
* Correspondence: [EMAIL_ADDRESS] check for updates  
Received: 28 January 2020; Accepted: 20 February 2020; Published: 24 February 2020  
**Featured Application: This paper proposes a new equivalent technique, the DC bias current equivalent relationship between the UHV transformer and the reduced-scale model (RSM) is established by means of the identical saturation degree, the proposed RSM is capable to reflect the actual operation of the UHV transformer under DC bias.**  
**Abstract: The DC bias of transformers as a result of geomagnetically induced currents (GIC), monopole operation of high voltage direct current (HVDC) transmission and the increasing power electronic devices in the power grid, creates a high magnitude of fundamental and harmonic components in winding currents, reactive power, and vibro-acoustic noise. For transformers with large capacity, it is very expensive and difficult to carry out an in-depth DC bias effects investigation on a practical equipment, especially for the ultra-high voltage (UHV) transformer. In this paper, a reduced-scale model (RSM) based on the principle of identical saturation degree is proposed, and the equivalent relationship of DC bias current is obtained. Furthermore, the DC bias platform with the RSM is established, experiments have been conducted to analyze the characteristics of the transformer under DC bias. It turns out from the experiments that the current of high voltage side and excitation are less tolerated to the DC bias current, the distortion occurs during the first half of the cycle and peaking at the quarter. Besides, the no-load loss and total harmonic distortion (THD) increase significantly with an increasing DC bias. The results of the characteristics of RSM with load under DC bias may provide great services in protecting and reducing the DC bias effects on the UHV transformer in actual operation.**  
**Keywords: UHV transformer; DC bias; reduced-scale model; rated load experiment**
# Comparative Analysis of Current Approaches to Quality Estimation for Neural Machine Translation
###### Abstract
Previous studies have used the input structure of _[BOS] Source sentence [EOS] [EOS] MT output [EOS]_ or _[BOS] MT output [EOS] [EOS] Source sentence [EOS]_ without a clear standard. Therefore, we investigate this process through a quantitative analysis by utilizing different input structures for all mPLMs. The contributions of this study are as follows:  
* We conduct comparative experiments on finetuning mPLMs for a QE task, which is different from research concerning the performance improvement of the WMT shared-task competition. This quantitative analysis allows us to revisit the pure performance of mPLMs for the QE task. To the best of our knowledge, we are the first to conduct such research;
* Through a comparative analysis concerning how to construct an appropriate input structure for QE, we reveal that the performance can be improved by simply changing the input order of the source sentence and the MT output;
* In the process of finetuning mPLMs, we only use data officially distributed in WMT20 (without external knowledge or data augmentation) and use the official test set to ensure objectivity for all experiments.
## 3 Proposed Receiver
### DFT Dispersion  
The V2V channel selectivity makes OFDM systems susceptible to detection errors because the instant power of some subcarriers can be low due to the deep fading, which makes it difficult to detect the transmitted data. To counteract this problem, the precoding by dispersion in frequency (Direct Fourier Transform Spreading: DFTS) technique is used in this work because it uniformly distributes each symbol's energy over the entire bandwidth and because its implementation, by means of the Fast Fourier Transform (FFT), is low in complexity. This operation can be represented formally on the transmitter side as follows:  
\[\mathbf{s}_{D}=\mathbf{F}_{D}\boldsymbol{\chi}. \tag{22}\]  
This is to say, the elements transmitted in frequency in the data positions are built by applying the Fourier matrix to the data symbols in vector \(\boldsymbol{\chi}\). The elements of the Fourier matrix are determined by:  
\[[\mathbf{F}_{D}]_{d^{\prime},d}=1/\sqrt{N_{D}}e^{\beta\pi t^{\prime}d/N_{D}}, \tag{23}\]  
where \(d,d^{\prime}=\{0,1,\ldots,N_{D}\}\). Due to the fact that the data detection will only be carried out in the data subcarrier indexes, the original signal model in (4) is reduced to the following expression:  
\[\mathbf{u}_{D}=\mathbf{G}_{D}\mathbf{F}_{D}\boldsymbol{\chi}+\mathbf{G}_{D,p} \mathbf{s}_{p}+\mathbf{z}_{D}, \tag{24}\]  
where \(\mathbf{u}_{D}\) and \(\mathbf{z}_{D}\) are the received signal vector and the noise vector, respectively, each in the position of the data subcarriers. The \(\mathbf{G}_{D}\) matrix is obtained by taking the rows and columns of the data carrier positions. The term \(\mathbf{G}_{D,p}\mathbf{s}_{p}\) represents the interference in the carriers, with data from the pilot carriers.
# Strategies of Automated Machine Learning for Energy Sustainability in Green Artificial Intelligence
## 3 Hyperparameter Optimization Problem
### Definitions of Hyperparameter Optimization  
In the domain of machine learning, hyperparameter optimization constitutes the process of identifying the optimal combination of hyperparameter values for a model, with the objective of maximizing its performance on a designated evaluation dataset. This pursuit can be framed as a mathematical optimization problem, as described in the work of [PERSON] et al. [57].  
The goal is to find \(T^{*}\) such that (Equation (1)):  
\[T^{*}=\arg\max_{T}(f(T)) \tag{1}\]  
subject to the following:  
\[T\in\mathcal{C}\]  
In this context, \(T\) denotes the set of hyperparameter configurations. The objective function \(f(T)\) assesses the model's performance based on a specific hyperparameter configuration \(T\). The constraints \(\mathcal{C}\) encompass any limitations or requirements that must be met by hyperparameter configurations. These constraints may involve allowable values for hyperparameters, restrictions on computational resources (such as maximum runtime or available memory), or any other relevant conditions for the given problem.  
The optimal hyperparameter configuration \(T^{*}\) is the one that maximizes the objective function \(f(T)\) while satisfying the constraints \(\mathcal{C}\).  
The proposal presents the search for the set of hyperparameters \(T^{*}\) that minimizes the equivalent carbon dioxide (\(CO_{2}e\)) emissions while maximizing the model's accuracy within an iterative hyperparameter optimization process (Equation (2)).  
\[T^{*}=\arg\min_{T\in\mathcal{C}}\Bigl{(}CO_{2}e(T^{*}_{\text{accuracy}}(T)) \Bigr{)} \tag{2}\]  
where \(T\) represents a set of hyperparameters.  
\(\mathcal{C}\) denotes the search space of all possible sets of hyperparameters.  
accuracy \((M)\) denotes the accuracy metric of a model \(M\).  
\(\mathcal{M}\) (\(T\)) represents the set of all possible models that can be trained given the hyperparameters \(T\).  
\(T^{*}_{\text{accuracy}}(T)\) denotes the set of hyperparameters \(T\) that maximizes accuracy within the search space \(\mathcal{M}(T)\). Formally, it can be defined as (Equation (3)):  
\[T^{*}_{\text{accuracy}}(T)=\arg\max_{M\in\mathcal{M}(T)}(\text{accuracy}(M)) \tag{3}\]  
This means that for a given set of hyperparameters \(T\), we select the model that has the highest accuracy.  
\(CO_{2}e\) denotes a measure of equivalent carbon dioxide emissions, which represents the environmental impact of training a model given a set of hyperparameters.  
This dual optimization approach ensures that the model not only achieves high performance in terms of accuracy but also minimizes its environmental impact, providing a balanced and sustainable solution in the field of IA and AutoML.
## 3 Results and Discussion
### Discrimination Analysis
#### 3.2.2 Discriminant Models Based on Full-Range Wavelength Variables
All the four models, KNN, SIMCA, PLS-DA, and SVM-DA, combined with their optimal parameters, were applied to the permutation tests. The average accuracy of these models was 51.96%, with a range of 50.03-55.04% for the training set, and the average was 50.17%, with a range of 49.98-50.57% for the testing set. The highest accuracy amongst all models could only obtain 60.16%. Accordingly, all the high accuracies of the previous models did not rely on overfitting or chance correlation for the classification of cultivars.
# A New Criterion for the Splashing of a Droplet on Dry Surface from High-Fidelity Simulations
## 4 Results and Discussion
### Critical Weber Number  
During the analysis of the simulation results, we observed that for a contact angle greater than 90\({}^{\circ}\), there exists a critical Weber number, \(We_{c}\), such that when \(We\) is less than \(We_{c}\), despite a large Reynolds number, the droplet does not undergo splashing, as depicted in Figure 6.  
When the contact angle is less than 90\({}^{\circ}\), the predominant splashing type is spreading splashing. This splashing occurs mainly due to the rapid movement of the triple contact line of the droplet with a small contact angle. The kinetic energy at the droplet's edge is larger compared to the center, resulting in a velocity difference between the edge and the center, leading to detachment of the droplet at the edge. During the spreading process of the droplet, the viscous forces and surface tension collectively dissipate the droplet's kinetic energy. Therefore, both Reynolds and Weber numbers determine whether the droplet will splash. When the contact angle is greater than 90\({}^{\circ}\), the primary splashing type is prompt splashing, occurring within a short time after collision. At this point, viscous forces contribute less to the dissipation of droplet kinetic energy, and the main influencing factors on splashing are surface tension and inertia. Hence, the Weber number has a greater effect on splashing than the Reynolds number. Therefore, as long as the Weber number is smaller than a certain value, splashing does not occur. From our simulation results, for the contact angle above 90\({}^{\circ}\), the critical Weber number is around 60.
# Research on Blockchain-Based E-Bidding System
## 6 Conclusions  
This paper proposes the design and implementation method of a distributed E-bidding system based on blockchain. Its core idea is to combine blockchain technology with privacy protection, so that all participants using this system can safely participate in the opening stage. Different from the current E-bidding system, this system still uses a trusted third party, replacing it with an SC with public and transparent attributes. SC is used to store records and check information during the bidding process.  
At the same time, this paper uses a \"Permissioned Blockchain\" such as Hyperledger to develop the bidding process. Among them, chaincode and MSP can control the access of a single user together and can also set a time limit for it. Therefore, the risk of distributed denial of service (DDoS) attacks entering the network is greatly reduced, thereby ensuring the extensive security, confidentiality and non-repudiation of the entire process.  
Although the system has solved many shortcomings in the existing system, there are still some limitations and challenges in the implementation. The details are as follows:  
1. HLF is a very new technology with rapidly changing functions. Establishing such a new network and getting other stakeholders to adapt to it is a huge challenge.
2. Compared with other E-bidding systems, this system requires more time and computing resources. After 50 experiments, the system takes an extra 2.3 s on average after adding the privacy protection mechanism. Among them, the highest extra cost is 3.6 s, and the lowest extra cost is 1.2 s. Therefore, there is a trade-off between resource consumption and security performance in this case.
3. The lack of skilled blockchain technical personnel to operate and maintain the system is also an obstacle.  
For now, this system has significant advantages different from other E-bidding systems and has a longer-term development prospect. In the future, in-depth research can be conducted from the following two aspects:Adding other privacy protection mechanisms to the bidding system, such as blind signature [36] and secure multi-party calculation [37], etc., can make the bidding process more fair and secure.
2. Secondly, the framework will not be limited to the field of bidding but will also be explored in other enterprise blockchain services, such as insurance, human resources, healthcare and more similar infrastructure.  
To sum up, the fundamental purpose of this paper is to provide a technical solution to make the participants' privacy information in the E-bidding system more secure and the bidding environment more reliable. Therefore, relevant organizations should identify opportunities, vigorously develop E-bidding systems, absorb advanced technologies and management concepts, keep up with the pace of the times, and promote the sustainable development of the industry.
## 2 Proposed Methods  
### Acoustic Feature Extraction  
In the field of audio classification, effectively extracting features that are rich in information and robust against noise and interference is crucial for improving the performance and generalizability of models [26]. To overcome the negative impact on environmental sound recognition in urban noisy environments, this paper proposes an innovative feature fusion representation strategy by combining the Patch-Mix feature fusion scheme to reduce the influence of noise and fully represent audio features. It aims to mitigate the influence of noise while comprehensively capturing the key audio features, thereby achieving outstanding and fabulous accomplishments.  
Introducing background noise during model training can help the model better adapt to various environments and noises [27]. The statistical characteristics of Gaussian distribution white noise are similar to those of background noise in many natural environments. Therefore, additive white Gaussian noise (AWGN) is often chosen as a high-quality option for simulating environmental background noise in this domain [27]. First, AWGN is incorporated into the original audio to serve as background noise. Secondly, Log-Mel spectrograms are extracted from both the original and the noise-added audio. The Log-Mel spectrograms are used as input for the Transformer model, which can be conceptualized as a temporal sequence, where each temporal instant encapsulates key features spanning diverse frequency dimensions [28]. The Transformer model enhances the Log-Mel spectrogram by incorporating positional information into the time dimension. It is quite convinced that this structure dynamically reallocates weights on a large scale to capture global information and long-term dependencies within the audio signal based on the adaptive attention mechanism. Therefore, using Log-Mel spectrograms as input for the Transformer model fully leverages the advantages of both, enabling efficient processing of audio signals. Figure 1 shows the extraction process of the Log-Mel spectrogram.  
Finally, the spectrogram is divided into multiple patches through Patch Embedding, and then Patch-Mix is used to implement patch-level fusion. Figure 2 shows the overall feature extraction process.
# Toward a Modern Last-Mile Delivery: Consequences and Obstacles of Intelligent Technology
###### Abstract  
Last-mile delivery (LMD) is essential in supply chains, and providers of logistics services are aware that they must adapt to changing customer and society expectations, competition challenges, and modern technologies. In light of the importance of artificial intelligence (AI) in modern technologies, this article employed a narrative literature review to recognize impacts of AI-powered technologies that aid in optimizing the LMD component. For this study, two possible classes of technologies were considered: tangible technologies, such as robots, drones, and autonomous vehicles, and intangible technologies, such as decision support tools and operating systems. Thus, this article frames potential developments in modern LMD, taking into account the fact that technological advancement brings both opportunities and challenges. According to this literature review, the modern version of LMD is capable of providing services that are both more productive and more sustainable, thus satisfying demands for better services. Last but not least, another goal of this article was to look into the challenges that limit the benefits of modern LMDs. Finally, suggestions are provided as a resource for practitioners and policymakers working in the supply chain to improve performance.  
last-mile delivery (LMD); artificial intelligence (AI); tangible and intangible technologies; benefits and challenges +
Footnote †: journal: Applied Systems
# Total Polypphenols and Antioxidant Properties of Selected Fresh and Dried Herbs and Spices
###### Abstract  
In this study, the content of total polyphenols (TP) and the antioxidant activity (AA) of fresh herbs (13 in total) and those subjected to the freeze-drying process (stems and leaves separately) were compared. Moreover, the content of TP and AA of retail, commercial food spices (19 in total) of the two leading companies on the Polish market were compared. The novelty of our studies is the comparison between fresh and dried forms of herbs and spices and additionally between dried in a freeze-drying process and commercially available (in dried forms). It was found that fresh herbs and spices showed a large accumulation of polyphenolic compounds (from 466.55 to 17.23 CAE/100 g, respectively, for lemon balm and ginger). For freeze-dried herbs and spices, the highest TP content was found for marjoram (3052.34 CAE/100 g--leaves). Among commercial herbs and spices, sage (971.28 CAE/100 g) deserves attention. Fresh herbal spices, in particular oregano, (236.21 uM TE/g) had the highest AA. AA of freeze-dried herbs and spices was much lower (5.27-1.20 uM TE/g). The average value obtained for commercially available herbs and spices purchased was 1.44 uM TE/g. In the case of AA measured by the DPPH radical, thyme was characterized by the highest activity among fresh marjoram for freeze-dried herbs and spices. For dried commercial spices, the highest levels of AA were found for cumin.  
fresh and dried spices; fresh and dried herbs; antioxidant activity; polyphenols; healthy properties +
Footnote †: journal: Applied Sciences
# Genetic and Environmental Factors Associated to Glutenin Polymer Characteristics of Wheat
## 3 Results and Discussion
### The Allelic Diversity of Glutenins had Limited Influence on Polymer Characteristics
When the cumulative daily average temperatures over the months (Tmeans of May, June, and July) computed in the 11 locations were added to glutenin alleles as explanatory variates, the percentages were two times those obtained with the rainfall averages. The total phenotypic variations explained were 60.5%, 73.6%, and 76.4%, for Mw, Rw, and PI, respectively (Table 2). The standardized coefficient attributed to each of the explanatory variates clearly evidenced the low impact of the different glutenin alleles compared to the monthly averaged temperature of the last three months of wheat crop in the 11 locations for Mw, Rw, and PI (Figure 1a-c, respectively). Then, among the three variates (Tmean May, Tmean Jun, and Tmean July,) the latest had, by far, the largest influence on increasing the polymer characteristics. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Explanatory Variate** & \begin{tabular}{c} **Mw** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Rw** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **PI = Mw/Mn** \\ **(\%)** \\ \end{tabular} \\ \hline PC + GH & & & \\ +HMW-GS & & & \\ +LMW-GS & & & \\ + WatSum May & & & \\ + WatSum Jun & 65.436 * & 79.146 * & 84.310 * \\ +WatSum Jly & & & \\ +Tmean May & & & \\ + Tmean Jun & & & \\ + Tmean Jly & & & \\ \hline \hline \end{tabular} (*) significant at \(p<0.05\); NS: not significant.  
\end{table} 
Table 2: _Cont._Figure 1: Standardized coefficients of the partial least square (PLS) regressions, explaining the molecular mass (Mw) (**a**), gyration radius (Rw) (**b**), and polydispersity index (PI) = Mw/Mn (**c**) characteristics of the polymers. The grain protein concentration (PC) (noted Protein) and grain hardness (GH) (noted Hardness) were explanatory variates first introduced in regression, followed by 14 alleles of high molecular weight glutenin subunits (HMW-GS); 17 alleles of low molecular weight glutenin subunits (LMW-GS); and the average temperatures of May, June, and July (noted Tmean May, Tmean June, and Tmean July).