esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Conclusions
### The Influence of English Learning Hedonic Motivation on Internet QoE  
Cultural intelligence, English self-efficacy, and hedonic motivation had been proposed as the antecedent elements of Internet QoE in this research model. Three of them were all examined significantly impacting Internet QoE statistically. The regression coefficient figures were 0.190, 0.213, and 0.405 respectively. Within them, hedonic motivation was the most influencing factor. Hedonic motivation impacted technology usage has been verified from previous studies [41; 47; 61; 42]. As [PERSON] et al. pointed out, when consumers intended to use technological equipment and services, they were directly linked to hedonic motivations [47]. This study also verified the importance of hedonic motivation while using the Internet-based resources to learn English. Especially, since the young generation grew up in the Internet age, Internet learning is a part of their usage of the Internet. Hedonic motivation seriously impacted Internet QoE. In addition, with the mediation effects examined in this study, hedonic motivation impacted Internet-based resources English continuous learning intention through Internet QoE significantly. The mediation effect of hedonic motivation was also stronger than cultural intelligence and English self-efficacy.  
The research results displayed the factors impacting Internet-based resources English continuous learning intention of China university students. To increase the English continuous learning intention, it is necessary to emphasize Internet QoE. Since hedonic motivation is the most important factor, English learning material should be attractive, fun, and enjoyable. From this aspect, the traditional, formal, standardized, lecture-based English teaching method is difficult to remain students' English continuous learning intention. Instead, question-based, interaction, student-center, story, drama, or even game-based approaches would be the direction of English learning. Internet-based resource English self-learning may not totally be considered as \"learning\" by the young generation. This may be because of an interesting English TV show, an attractive English movie, or an addictive online game. In the nature of languages, communication is the main function. Understanding interesting Internet-based English content keeps students learning. This is what teachers should think of and emphasize when to recommend learning material for students.
# Special Equipment Safety Supervision System Architecture Based on Blockchain Technology
## 2 Requirement Analysis
### Current Requirements
#### 2.2.3 More Transparent Supervision Information  
Based on the research on the loopholes of special equipment supervision, [PERSON] et al. [46] proposed that the transparency of safety supervision information in the traditional supervision mode is not enough. In the context of China's social relations, represented by the reciprocal social network, the supervision information is seriously interfered with by interpersonal relationships, resulting in low supervision efficiency. There is a strong requirement for a more transparent supervision mode to change this situation.
## 1 Introduction
This initiative has since expanded to include both concluded and ongoing research projects to address challenges like poor productivity and low-quality technical documentation [17]. In this scope, ASL (\"Application Specification Language\") is research that explores specification languages for the IT domain, mainly targeting the design of software applications [3]. Besides continuously improving as a platform-independent specification language, recent work surrounding ASL has explored model-to-model and model-to-code transformations, respectively, from ASL into ASL specifications and from ASL specifications into Django artefacts [3]. The main contribution of this paper is the proposal and discussion of a functional approach that combines model-driven techniques with low-code platforms. This approach allows developers to quickly write software application specifications in a platform-independent language mainly focused on these applications' data, user interface, and business aspects. ASL specifications can later be automatically converted to concrete low  
Figure 1: Transforming an ASL specification into a software application via a low-code platform (BPMN notation). code platform specifications to take advantage of the numerous features offered by these platforms. Our work differs from the work developed by [PERSON] and [PERSON] [3] because, in their approach, they discuss model-to-code transformations, i.e., directly from ASL specifications into Django/Python code. In contrast, our approach is based on the combination of model-to-model and model-to-code transformations, as suggested in Figure 1. Moreover, this paper presents and compares the metamodels underlying both ASL and Genio languages and proposes ASL extensions (developed during this research) to better support the alignment and transformation between ASL and Genio specifications or models. The rest of this article is organised as follows: Section 2 provides the background on domain-specific languages for the specification of software applications and low-code platforms. Section 3 compares the common concepts of ITLingo ASL and Quidgest Genio languages. Section 4 presents the extensions we propose to ASL based on the previous analysis and comparison findings. Section 5 overviews the ASL2 Genio transformation mechanism. Section 6 illustrates how the evaluation and testing of this research were conducted based on a simple yet representative business application. Section 7 identifies and discusses the related work. Finally, Section 8 presents the conclusion, discusses the advantages and limitations of the proposed approach, and identifies future research goals.
## 3 UAV Applications in Climate Change Research
### Urban Microclimate Assessment  
One of the main domains where UAVs play an integral role in climate change research in cities is mapping and analyzing localized phenomena, such as urban heat islands. UAVs with thermal infrared cameras can rapidly map fine-scale urban surface temperature variations to delineate heat islands and cooler areas linked to land cover. Flights at altitudes below 300 meters provide pixel resolutions down to 10-50 cm, capturing microclimate gradients within neighborhoods. A study by [PERSON] [68] leveraged drone thermal cameras to map micro-scale temperature variations down to individual city blocks in Texas, USA. By combining these UAV thermal data with land-cover information, the researchers identified localized hotspots correlated with limited greenery, ineffective roofs, and urban materials. This granular UHI quantification and diagnosis assisted targeted interventions in the built environment to mitigate heat risks. Moreover, UAV thermal data have been used to correlate temperatures with vegetation, impervious surfaces, building density, and road types across cities coupled with satellite imagery to provide a comprehensive assessment of the dynamics of urban microclimates [150]. This helps model microclimate interactions with the built environment and identify the localized areas most vulnerable to extreme heat.  
In addition to thermal imaging applications, UAVs have been deployed to collect high-resolution aerial imagery and LiDAR data to construct detailed 3D models of urban morphology. Photogrammetry processing generates point clouds, mesh models, and orthomosaic classifying features, such as buildings, roads, and vegetation [2]. LiDAR complements the visual data with urban elevation mapping [151]. Classified 3D models enable the analysis of urban geometry, including sky view factors, building heights, and canyon orientations influencing solar exposure and heat retention [152]. By correlating land-surface temperatures with land-cover types derived from UAV-based 3D models, the researchers can gain insights into the factors contributing to urban heat island formations and develop strategies for mitigating their effects [153]. Furthermore, a study by [PERSON] et al. [154] examined the spatial and temporal variations of heat islands in Zhengzhou, China, considering the influence of urbanization and urban forestry. The drone imagery helped assess the effectiveness of urban forest construction in mitigating the urban heat island effect.
# Day-Ahead PV Generation Scheduling in Incentive Program for Accurate Renewable Forecasting
###### Abstract  
Photovoltaic (PV) power can be a reasonable alternative as a carbon-free power source in a global warming environment. However, when many PV generators are interconnected in power systems, inaccurate forecasting of PV generation leads to unstable power system operation. In order to help system operators maintain a reliable power balance, even when renewable capacity increases excessively, an incentive program has been introduced in Korea. The program is expected to improve the self-forecasting accuracy of distributed generators and enhance the reliability of power system operation by using the predicted output for day-ahead power system planning. In order to maximize the economic benefit of the incentive program, the PV site should offer a strategic schedule. This paper proposes a PV generation scheduling method that considers incentives for accurate renewable energy forecasting. The proposed method adjusts the predicted PV generation to the optimal generation schedule by considering the characteristics of PV energy deviation, energy storage system (ESS) operation, and PV curtailment. It then maximizes incentives by mitigating energy deviations using ESS and PV curtailment in real-time conditions. The PV scheduling problem is formulated as a stochastic mixed-integer linear programming (MILP) problem, considering energy deviation and daily revenue under expected PV operation scenarios. The numerical simulation results are presented to demonstrate the economic impact of the proposed method. The proposed method contributes to mitigating daily energy deviations and enhancing daily revenue.  
photovoltaic power; energy storage system (ESS); renewable curtailment; renewable incentives +
Footnote †: journal: Applied Sciences
## 3 Correlation and Implementation
### CMV and PLT Relationship  
The recorded data of the CMV and PLT results of the same points were plotted with the CMV on the x-axis, the bearing coefficients were plotted on the y-axis, and linear regression analysis was performed (Figure 9). The R-squared values for the contaminated soil replacement site and event construction site are 0.855 and 0.862. The R-square value is an important parameter for correlation development and shows the goodness-of-fit for linear regression models. The value ranges from 0 to 1, or it can be written in terms of percentage. Higher R-square values mean a better relationship between the model and the dependent variable. For such study, the value of the coefficient of R > 0.7 indicates a strong correlation [26]. The R-square values of both construction sites clearly show a high correlation between the CMV and bearing coefficient from the PLT for compaction strength measurement. This study proves that the traditional compaction determination method can be replaced with a more effective CMV method for compaction quality management.  
Figure 8: CMV data recorded during the compaction operation.
## 4 Experimental Environment and Results
### Model Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Parallel Model & 96.03 & 95.70 & 93.73 & 94.67 \\ DenseNet169 [59] & 95.44 & 93.98 & 92.31 & 94.20 \\ Inception-V3 [60] & 90.06 & 88.73 & 87.12 & 89.44 \\ ResNet50 [61] & 89.41 & 86.81 & 87.29 & 88.52 \\ VGG16 [62] & 85.13 & 83.98 & 82.31 & 85.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification performance comparison between image classification models such as DenseNet169, Inception-V3, ResNet50, VGG16, and the parallel model. ing 1000 real images to obtain an FID score for each class separately. The score comparison for different classes and models can be seen in Table 5 and reconstructed sample images for all cases and the ablation study results can be seen in Figure 5.  
Figure 5: 9 original images for each class AD (**a**), MCI (**b**), and NC (**c**), respectively. (1) The original image, (2) the reconstructed image by the proposed model, (3) the CNN reconstruction, and (4) the DCN reconstruction images are given. (**a**) An original batch of AD images and the reconstructed counterparts by the proposed model and its sub-networks. (**b**) An original batch of MCI images and the reconstructed counterparts by the proposed model and its sub-networks. (**c**) An original batch of NC images and the reconstructed counterparts by the proposed model and its sub-networks. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **AD** & **MCI** & **NC** \\ \hline GANomaly [29] & 18.216 & 17.353 & 17.885 \\ EGBAD [32] & 20.815 & 21.621 & 19.134 \\ AnoGan [23] & 19.908 & 17.991 & 21.759 \\ Skip-GANomaly [56] & 11.892 & 12.516 & 11.908 \\ Ablation Conv. & 16.175 & 18.023 & 17.659 \\ Ablation Dilated Conv. & 21.762 & 18.895 & 21.342 \\ Proposed Model & 9.481 & 10.024 & 10.858 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Fréchet Inception Distance (FID) scores comparison for three different classes; AD, MCI, and NC using GANomaly, Skip-GANomaly, EGBAD, and AnoGAN along with the ablation study. Note that a lower score generally means better performance. An anomaly score [32] is used to detect the anomalies in a given test image. For an input image \(\hat{x}\) the corresponding anomaly score is calculated as:  
\[A(\hat{x})=(1-\lambda)\cdot R(\hat{x})+\lambda\cdot D(\hat{x}) \tag{7}\]  
where \(R(\hat{x})\) is the \(\mathcal{L}_{1}\) loss between the input image and the corresponding reconstructed image. \(D(\hat{x})\) is the latent vector score given in Equation (3). \(\lambda\) is the weighting parameter on emphasizing the importance of the scores. This experiment was performed based on \(\lambda=0.5\). The anomaly scores were then normalized. The anomaly score comparison for three different scenarios with the frequency graphs are shown in Figure 6.  
Figure 6: _Cont_.
# Dual-Probe Near-Field Phaseless Antenna Measurement System on Board a UAV
## 6 Patents  
The work presented in this contribution is related to the patent \"Airborne System and Method for the characterization and measurement of radiating systems or antennas\". Publication No. WO/2018/158472. International Application No. PCT/ES2018/000015. Priority date: 03/03/2017. International filing date: 02/03/2018. University of Oviedo, University of Vigo. Available online: [[https://worldwide.espacenet.com/publicationDetails/biblio?CC=WO&NR=2018158472A1&KC=A1&FT=D](https://worldwide.espacenet.com/publicationDetails/biblio?CC=WO&NR=2018158472A1&KC=A1&FT=D)]([https://worldwide.espacenet.com/publicationDetails/biblio?CC=WO&NR=2018158472A1&KC=A1&FT=D](https://worldwide.espacenet.com/publicationDetails/biblio?CC=WO&NR=2018158472A1&KC=A1&FT=D)) (accessed on 1 August 2019).  
The following are available online at [[https://youtu.be/k3](https://youtu.be/k3) CleGTKhrE]([https://youtu.be/k3](https://youtu.be/k3) CleGTKhrE), Video: video showing the UAV-based system for on-site antenna measurement under operation.  
Conceptualization, M.G.F., Y.A.L. and F.L.-H.; methodology, M.G.F., Y.A.L. and F.L.-H.; hardware, M.G.F. and Y.A.L.; software, M.G.F. and Y.A.L.; validation, M.G.F. and Y.A.L.; data curation, M.G.F. and Y.A.L.; writing--original draft preparation, Y.A.L.; writing--review and editing, M.G.F. and F.L.-H.; funding acquisition, F.L.-H. and Y.A.L.  
This research was funded by the Ministerio de Educacion--Gobierno de Espana under Grant FPU15/06341; by the Ministerio de Ciencia, Innovacion y Universidades--Gobierno de Espana under Project RTI2018-095825-B-I00 (\"MilliHand\"); by the Government of the Principality of Asturias (PCTI) and European  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Measurement** & **Directivity** & \(\mathbf{-3}\) **dB Beamwidth** \\ \hline Anechoic chamber measurements. NF-FF, complex NF. & 29.8 dB & 6.6\({}^{\circ}\) \\ Anechoic chamber measurements. NF-FF, & 29.5 dB & 6.8\({}^{\circ}\) \\ amplitude-only data. & 29.4 dB & 6.9\({}^{\circ}\) \\ On-site measurements. Previous prototype [12]. & 29.6 dB & 6.7\({}^{\circ}\) \\ Amplitude-only. & 29.6 dB & 6.7\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Radiation pattern parameters of the AUT.  
Union (FEDER) under Grant IDI/2018/00019; and by the Instituto Universitario de Investigacion Industrial de Asturias (IUTA) under Project SV-19-GJION-1-17 (\"RadioUAV\").  
The authors would like to acknowledge [PERSON] for her support during the measurement campaign conducted on 23 May 2019 at the airfield of the Technical School of Engineering of the University of Oviedo.  
The authors declare no conflict of interest.
## 6 Space Situational Awareness
DSMSs continuously process incoming data streams and produce output data streams to solve this problem (cf. Figure 10). The processing is data-centric, meaning that processing is triggered one element at a time as soon as a data element arrives into the system. The execution of one of the main SST functionalities, initial orbit determination, has successfully been evaluated using the DSMS approach and the execution performance has been benchmarked [39]. It has been found that initial orbit determination on raw measurement data output by a radar sensor poses no problem for a DSMS, even on consumer hardware. This leads to the idea that DSMS could be able to enhance conventional SST systems. Such so-called SST System Architecture Enhancements have been conceptualized and designed for a DSMS [40]. One specific SST System Architecture Enhancement where a DSMS aggregates sensor measurements on-the-fly has successfully been implemented and tested. In summary, the results show that DSMS can help conventional SST systems to cope with new requirements like (near-)real-time processing.
# Design of Magnetic Concrete for Inductive Power Transfer System in Rail Applications
## 4 Results and Discussion  
### Optimization of Magnetic Concrete Mix for IPT Systems  
In terms of workability and cost considerations, the choice of ferrite chips versus ferrite powder was a significant factor. Although ferrite chips were initially considered, the high cost and difficulty of crushing ferrite bars into chip form led to the preference of ferrite powder: Ferrite powder is not only less costly but also easier to mix and was  
Figure 8: Passive shielding methods for magnetic fields. (**a**) Passive shielding using conductive materials. (**b**) Passive shielding using magnetic material.  
used as a replacement for the fine aggregate typically found in regular concrete mixes. The resulting magnetic concrete mix consisted of 33% ferrite powder by weight, ensuring an optimal balance of structural integrity and magnetic properties. The water-to-cement (\(w/c\)) ratio was maintained at 0.57 to achieve satisfactory workability while retaining adequate strength.  
The inclusion of ferrite powder can complicate the water-cement ratio in the concrete mix, which is reflected in the reduction in concrete strength. Compression tests were conducted to confirm the structural viability of the magnetic concrete mix. The tests were performed using a Universal Testing Machine (UTM), as illustrated in Figure 9. The results presented in Tables 3 and 4 show that the compressive strength of the magnetic concrete mix is comparable to that of standard concrete. This indicates that the
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 4 ML-Based Designs
In contrast, if enough data for training are available, SL might allow for obtaining real-time adjustments for UAV locations. Strictly speaking, SL is faster than RL if a training dataset is available. In highly dynamic environments, there might exist unknown relationships between the variables in the input dataset. In a traditional neural network, all inputs (and outputs) are assumed to be independent of each other and, thus, such a network is unable to capture data interdependencies [1; 37; 63]. The idea behind recurrent neural networks (RNNs), instead, is to make use of sequential information, in order to capture long-term dependencies in the input dataset, such as UAV altitude and hovering time. RNNs are called recurrent because they perform the same task for every element of a sequence, with the output being dependent on the previous computations. It is worthwhile to note that data collected using RNN and RL can be used as input datasets for SL. The focus of the rest of the paper is on the use of SL and RL in all those cases where UAVs are employed as a part of a RAN. In the following subsections, applications and benefits of such ML tools in U-RANs are discussed in detail.
# Influence of Noise-Limited Censored Path Loss on Model Fitting and Path Loss-Based Positioning +
## 7 Positioning Simulation Results
### Example 1
In MLE-OTP, the positioning is based on the correct PL model but the censored PL is not used in the positioning phase. In OLS-MLE, the censored PL is used in the positioning phase with MLE, but the OLS fitting results in wrong parameter estimates, as shown in Section 4. As pointed out earlier, the OLS fitting result can be also interpreted as (a rather poor) educated guess in the absence of training data. Using MLE in the positioning phase compensates for the poor PL model. Therefore, it can be concluded that if the noise-limited censored PL is taken into account in the positioning phase, the training phase is perhaps not needed, or at least, the training phase is not very critical when MLE positioning is used.
## 4 Proposed Scheme
### Pseudonym Renew  
This phase indicates the main effect of the proposed scheme, which is renewing the pseudonym identity of the vehicle before the attacker could destroy the 5G-enabled vehicular networks by launching side-channel attacks. This phase is as follows. Before the short valid period, \(SVP_{v_{j}}\) is to expire; the registered vehicle needs to renew the inter-pseudonym identity \(IPID\) saved on its TPD through 5G-BS to resist side-channel attacks. Once this phase waits for many years, the attacker has enough time to disclose data saved for impersonating legal vehicles and sending fake messages in 5G-enabled vehicular networks. The following steps to renew inter-pseudonym identity \(IPID\) are saved in the TPD. Figure 4 explains the process of the pseudonym renew phase in detail.  
* The vehicle \(V_{i}\) randomly picks a value \(\alpha\), computes \(\xi_{1}=\mathcal{T}_{a}(x)mod\)\(P\) and computes public pseudonym identity \(\digamma_{v_{i}}=IPID_{v_{i}}\oplus h(A_{v_{i}}||T_{v_{i}}^{2})\), where \(T_{v_{i}}^{2}\) is the latest timestamp.
* The vehicle computes \(B_{v_{i}}^{1}=h(A_{v_{i}}||\xi_{1}||\digamma_{v_{i}}||T_{v_{i}}^{2})\) and sends the tuple \(\{\xi_{1},\digamma_{v_{i}},\,T_{v_{i}}^{2},\,B_{v_{i}}^{1}\}\) to the TA through the 5G-BS.
* The TA first verifies the timestamp \(T_{v_{i}}^{2}\) of the tuple \(\{\xi_{1},\digamma_{v_{i}},\,T_{v_{i}}^{2},\,B_{v_{i}}^{1}\}\) and then checks the vehicle by checking whether the equation \(B_{TA}^{-}=h(A_{v_{i}}||\xi_{1}||\digamma_{v_{i}}||T_{v_{i}}^{2})\stackrel{{?}}{{= }}B_{v_{i}}^{1}\) are equal.
* The TA seeks whether the member list has the inter-pseudonym identity \(IPID_{v_{i}}=\digamma_{v_{i}}\oplus h(A_{v_{i}}||T_{v_{i}}^{2})\). If it is false, the process will be ended. Otherwise, the TA continues by checking the validity of \(SVP_{v_{i}}\).  
Figure 3: Signing and verification phases.