esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 1 Introduction
ZnO was also utilized as a piezoelectric phase in magnetoelectric composites not only due to its piezoelectric properties but also due to its inherent ferroelectric properties [18] and the plethora of micro- and nanostructures that it can be built into. Additionally, ZnO is a bio-compatible [19] and cost-effective material that can become a promising candidate for magnetoelectric devices for biomedical applications. Thus, this article presents a comprehensive review on magnetoelectric composites based on ZnO micro- and nanostructures, starting with a brief introduction of the magnetoelectric effect and its major concepts. The next sections elaborate upon the different categories of magnetoelectric composites based on ZnO, including thin-film structures, either pure or doped with metallic or ferromagnetic elements, or nano- and microrods based on magnetostrictive materials and nano composites comprised of ZnO nanoparticles (NPs) doped or mixed with ferromagnetic nanoparticles. A discussion section provides information regarding the current ME composites, including the future development prospects and potential applications of ZnO-based magnetoelectric composites, followed by conclusions.
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 2 System Model
### Remaining Service Time
\begin{cases}x=(\dfrac{a}{\sqrt{1-e^{2}\text{sin}^{2}}\phi}+\zeta)\cos\phi \cos\varphi\\ y=(\dfrac{a}{\sqrt{1-e^{2}\text{sin}^{2}}\phi}+\zeta)\cos\phi\sin\varphi\\ z=((1-e^{2})\dfrac{a}{\sqrt{1-e^{2}\text{sin}^{2}}\phi}+\zeta)\sin\phi\end{cases} , \tag{6}\]  
where \(a\) is the length of the Earth's semimajor axis, and \(e\) is the eccentricity of the Earth. Use three coordinates of the satellite footprint \(\{(x_{t}^{s},y_{t}^{s},z_{t}^{s})|t=0,1,2\}\) to determine the satellite footprint trajectory plane equation  
\[Ax+By+Cz+D=0, \tag{7}\]  
where  
\[ \begin{cases}A=(y_{2}-y_{1})(z_{3}-z_{1})-(y_{3}-y_{1})(z_{2}-z_{1})\\ B=(z_{2}-z_{1})(x_{3}-x_{1})-(z_{3}-z_{1})(x_{2}-x_{1})\\ C=(x_{2}-x_{1})(y_{3}-y_{1})-(x_{3}-x_{1})(y_{2}-y_{1})\\ D=-Ax_{1}-By_{1}-Cz_{1}\end{cases} . \tag{8}\]  
According to the plane Equation (7), \(d_{\rm min}\) can be expressed as  
Figure 4: The geometric relationship of \(\rm Y_{min}\). \[d_{\min}=\frac{Ax^{u}+By^{u}+Cz^{u}+D}{\sqrt{A^{2}+B^{2}+C^{2}}}. \tag{9}\]  
Finally, the angular velocity \(\omega\) of the satellite in the ECEF coordinate system can be got according to the geometric relationship in Figure 5, which can be expressed as  
\[\omega\approx\omega_{s}+\omega_{e}\cos(\pi-\sigma), \tag{10}\]  
where \(\omega_{s}\) and \(\omega_{e}\) are, respectively, the angular velocity of the satellite and the Earth in the Earth-Centered Inertial(ECI) coordinate system, \(\sigma\) is the satellite orbit inclination, \(\omega_{s}\) can be expressed as  
\[\omega_{s}=\sqrt{\frac{\mu}{\left(R_{e}+h\right)^{3}}}, \tag{11}\]  
where \(\mu\) is the Kepler constant, \(R_{e}\) is the Earth radius, and \(h\) is the altitude of the satellite orbit. With Equations (4) and (5), \(\Gamma(t_{0})\) can be written as  
\[\Gamma(t_{0})=\arccos\biggl{(}\frac{\cos\mathrm{Y}(t_{0})}{\cos(\mathrm{Y}_{ \min})}\biggr{)}. \tag{12}\]  
Then, the maximum service time of satellite can be obtained by Equation (1), and the remaining service time at the current slot \(T\) can be expressed as  
\[T_{rem}=T_{\max}-(T-T_{0}), \tag{13}\]  
where \(T_{0}\) is the recorded service start time. Figure 5: Angular velocity of satellite.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Impact of Fusion Methods on Model Performance  
When we get the text features and image features through the text encoder and image encoder, we use that feature fusion method to fuse the features of the two modes, which also significantly impacts the final model's performance. We selected the following fusion methods for experiments, and the experimental results are shown in Table 9:  
(1) _T-V-concatenate_: The text features and image features are concatenated and input into the full connection layer to obtain the classification results.  
(2) _T-V-add_: The text and image features are added by elements and then input to the full connection layer to obtain the classification results.  
(3) _T-V-weight_: The weighted sum of text features and image features is input to the full connection layer to obtain the classification results.  
(4) _T\({}^{T}\)V-max_: After multiplying text and image features, use maximum pooling to reduce dimensions and input them to the full connection layer to obtain classification results.  
(5) _T\({}^{T}\)V-avg_: After multiplying text and image features, use average pooling to reduce dimensions and input them to the full connection layer to obtain classification results.  
(6) _W\({}_{I}\)V-W\({}_{T}\)T-add_: The text features and image features are input into the full connection layer to obtain the text and image classification results. Then the text classification results and image classification results are weighted sums to obtain the final classification results.  
Table 9 shows that the _W\({}_{I}\)V-W\({}_{T}\)T-add_ method achieves the optimal result, which is also the fusion method used in this paper. The difference between the accuracy of the best and worst fusion methods in Table 9 is nearly 17%, which is a big gap. Therefore, it is crucial to select an appropriate fusion method in our model to fuse the features of the two modes. Of course, the above six fusion methods only represent some fusion methods. Researchers have made many achievements in multimodality in recent years, and some fusion models based on Transformers have achieved good results in multiple tasks [53; 54; 55]. However, due to the limitations of the text encoder and image encoder we use, these fusion methods based on the Transformer cannot be used in our model. At the same time, as the focus of this article is to prove that the method of text classification assisted by text-generated images is effective, the selection of fusion methods is not our focus, so there is no further analysis. To obtain better results, subsequent researchers can use text encoders, image encoders, and fusion methods that are more suitable for their own tasks.
# A Review of Systems and Technologies for Smart Homes and Smart Grids
## 2 State of the Art
### The Role of the ICTs in Smart Homes and the Smart Grid  
As confirmed by recent studies [23; 24], some of the most important advantages provided by the smart grid derive from its capability of improving performance reliability and customers' responsiveness [22]. The rapid advancements in ICT solutions and smart metering are well suited to tackling the aforementioned limitations of existing power grids. The conversion of traditional electricity grids into smart grids ensures productive interactions among energy providers, users and other stakeholders [25]. The advent of smart grids has fostered the deployment of smart meters, low-cost sensors and smart load devices, and the integration of ICTs in residential energy management programs [19; 26]. The integration of advanced ICTs increases the efficiency of the traditional grid by providing more automation, a reliable forecast of electrical loads, and a safer operation of electrical appliances, resulting in a rise in the quality of the energy delivery service and a higher overall user satisfaction [27].  
New ICT infrastructures supporting a more efficient smart grid will also include frequent price updates to follow the evolution of the balance between supply and demand in near real-time [22]. They can be set to follow load-shifting programs that offer customers a more effectively way to manage the cost of their electricity bill. They work by storing relatively inexpensive electricity during off-peak demand periods and using this stored energy during peak periods, when electricity energy prices are high. These actions allow users to reduce the cost of their electricity bill and save energy [28].  
Another possibility that can consistently contribute to energy saving in buildings is to use more efficient appliances. A study conducted by Berardi's [29] estimated how much energy can be saved using highly efficient appliances in households: up to 20% in China [30], by 30% in India [31] and by 27% in Brazil [32]. The same strategy is being adopted in the US where, during the last few years, electricity consumed in buildings has slightly increased because the use of appliances, electronics, and electric lighting has risen from 24% in 1993 to more than 34% in 2009 [29].  
The new method of energy distribution made possible by the advent of the smart grid has contributed to improving energy savings in the building sector. In recent years, several studies in this field supported by the European Community focused on the analysis and optimal design of modern distribution systems that aim to minimize the operational cost and maximize the profit of the users [33].  
To achieve high energy efficiency in the smart grid, distributed energy resources have to be well managed and load can be reduced implementing a demand-response (DR) approach. Residential DR is strictly related to the users' behavior. It is also often linked to the price of energy during the day [34].
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Data Measurement  
To evaluate the performance of the deep learning models, several standard metrics were used, including accuracy, precision, recall, and F1-score, as commonly adopted in recent healthcare-related machine learning studies [38; 39; 40; 41].  
The mathematical formulations of these metrics are defined as follows:  
* Accuracy is calculated as shown in Equation (1), measuring the proportion of correct predictions among the total number of cases. TP denotes true positives, TN true negatives, FP false positives, and FN false negatives. \[Accuracy=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{FN}}\] (1)
* Precision, defined in Equation (2), measures the proportion of true positive predictions among all positive predictions made by the model. \[Precision_{i}=\frac{TP}{\text{TP}+\text{FP}}\] (2)Recall, presented in Equation (3), evaluates the proportion of actual positives that were correctly identified by the model. \[Recall_{i}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (3)
* The F1-score, given by Equation (4), represents the harmonic mean of precision and recall, providing a balance between the two. \[F1\ Score_{i}=2\times\frac{Precision\ \times\ Recall}{Precision+Recall}\] (4)  
These metrics provide a comprehensive assessment of the classification performance, particularly in multilabel classification tasks.
# Status of the SXFEL Facility
## 2 The SXFEL Test Facility
### Construction, Installation and Commissioning of the SXFEL-TF  
Construction of the SXFEL-TF started in December 2014. One year later, 547 pile foundations, a total area of about 7000 m\({}^{2}\) of civil construction, and a concrete tunnel were completed. The building was ready for machine installation in April 2016 and then installation started. Figures 9 and 10 show the status of the construction and installation at the SXFEL-TF site.  
Installation of the linac, the undulator system, and the diagnostic beamline was almost completed by the end of 2016. The linac RF conditioning and beam commissioning started in late December 2016. The beam was then successfully accelerated to 700 MeV at the exit of the C-band linac and sent to the undulator line to check the installation and equipment's function. The electron beam went through the radiator undulators and the spontaneous undulator radiation at wavelength about 15 nm was characterized with the photodiode and X-ray charge-coupled-device camera at the diagnostic beamline on 31 December 2016. The RF conditioning of the C-band linac and the commissioning of the S-band injector have been performed at the same time. The normalized emittance of the injector electron beam at about 200 MeV and after the magnetic bunch compressor BC1 is 1.2 mm\(\cdot\)mrad and 1.1 mm\(\cdot\)mrad in the horizontal and vertical directions, respectively. Further optimization to achieve FEL lasing is ongoing.  
Figure 8: Simulated FEL performances of a single stage EEHG at the SXFEL-TF: final output pulse at 8.8 nm in time (**left**) and spectral (**right**) domain.  
Figure 7: Simulated FEL performances of the cascaded echo-enabled harmonic generation (EEHG)-HGHG scheme at the SXFEL-TF: final output pulse at 8.8 nm in time (**left**) and spectral (**right**) domain.
## 4 Conclusions  
First-principles DFT calculations of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) were performed with fixed experimental lattice parameters to estimate their phase stabilities and to analyse the properties of the atomic structures and electronic band structures. For E\({}_{\rm xc}\), PBE and LDA were considered with or without SOI. From the structural analysis, PBE overestimated the distances between adjacent atoms along the \(c\)-axis in the intralayers of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) compared to LDA, and LDA overestimated the interlayer distances in (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) compared to PBE. From the electronic band structures, the electric transport types of (Bi\({}_{2}\))\({}_{\rm m}\)(Bi\({}_{2}\)Te\({}_{3}\))\({}_{\rm n}\) remained as semimetals or semiconductors regardless of whether the E\({}_{\rm xc}\) type or SOI was considered, except for Bi\({}_{8}\)Te\({}_{9}\) and BiTe. Our calculations revealed that Bi\({}_{8}\)Te\({}_{9}\), BiTe, and Bi\({}_{2}\)Te\({}_{3}\) are expected to be semiconductors. Consequently, Bi\({}_{8}\)Te\({}_{9}\) and BiTe, including the well-known thermoelectric Bi\({}_{2}\)Te\({}_{3}\), are expected to be potential thermoelectric materials for automobile energy harvesting technologies.  
[PERSON]. ([PERSON]) and [PERSON] conceived the idea. [PERSON]. ([PERSON]) performed all calculations. [PERSON]. ([PERSON]) and [PERSON] contributed to the discussion on the purposes of the manuscript. [PERSON]. ([PERSON]) contributed to the discussion on the research direction and the manuscript. [PERSON]. ([PERSON]) wrote the manuscript with revision from the other authors. All authors have read and agreed to the published version of the manuscript.  
Korea Institute of Energy Technology Evaluation and Planning (KETEP) [grant number 20188550000290]; Korea Electrotechnology Research Institute (KERI) primary research program [grant number 21A01003].  
Not applicable.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) in conjunction with the Ministry of Trade, Industry, and Energy (MOTIE) of the Republic of Korea [grant number 20188550000290: Development of Meta-Silicide Thermoelectric Semiconductor and Metrology Standardization Technology of Thermoelectric Power Module]; and the Korea Electrotechnology Research Institute (KERI) primary research program via the National Research Council of Science and Technology (NST) of the Republic of Korea [grant number 21A01003: Research on High-Power Low-Mid Temperature Thermoelectric Power Generator via Thermoelectric Data Manifold Exploration and Expedition].  
The authors
# Multi-Classifier Feature Fusion-Based Road Detection for Connected Autonomous Vehicles
## 4 Proposed TLFC-RD Method
### Two-Level Fusion of Classifiers for Road Detection
#### 4.5.4 Decision Fusion Using Multiple Classifiers  
The proposed TLFC-RD method uses the two-level fusion of diverse classifiers, which is performed based on a different set of feature vectors. At first, multiple classifier training is used to create the feature sets. Here, the feature maps are generated from the feature vector obtained for each vehicle in the CAVs. In the first level, feature maps are extracted from the fully connected layers of LeNet-5, the cell gate of LSTM and a residual output from ResNet. The extracted feature maps are fused together and given as an input to the next-level classifier.  
A multi-classifier feature fusion model [27] is used in this TLFC-RD, where the feature maps from the Lenet-5, LSTM and ResNet are used as inputs. This helps to improve the classification accuracy during the road detection. The reason for using multi-classifier feature fusion is that the pooling layer used in the deep learning classifier eliminates some information while performing the classification. However, the multiple features obtained from the Lenet-5, LSTM and ResNet have the supervision information about road scenes, which is utilized to differentiate the classes as drivable and non-drivable data. As the road scenes obtained from the vehicles are captured from different perspectives and angles, the proposed multi-classifier feature fusion model is used to extract appropriate feature maps from the road scenes. Therefore, the feature maps from Lenet-5, LSTM and ResNet are concatenated to fuse the features as shown in Equation (20), and the fused information is given as an input to the second-level classifier (i.e., SVM).  
\[FFM=\left\{fc_{n}^{ln},\ hlt,Re\right\} \tag{20}\]  
where \(FFM\) represents the fused feature map vector.
## 3 Results and Discussion
### Subsystem Evaluation Findings and Analysis
#### 3.2.4 Findings and Analysis of Technical Climate Assessment  
The technical climate evaluation result is shown in Figure 7 and Table 9. First of all, the Baltic States continue to have a relatively clear advantage in infrastructure quality, with Estonia ranking second among the 16 countries in terms of infrastructure quality scores in 2018. The Visegrad Group and the Slovenian segment are second only to the Baltic States, especially the Czech Republic and Slovenia, which ranked first and third among the 16 CEE countries with scores of 0.105 and 0.093, respectively, in 2018. The Balkan countries, on the other hand, lag significantly behind the rest of the CEE region in terms of infrastructure, with seven other countries, excluding Croatia, occupying the last seven positions in 2018, and Bosnia and Herzegovina, Albania and Serbia in the last three positions with scores of 0.016, 0.023 and 0.037, respectively. In 2018, these three countries also ranked low among the 16 countries in terms of average infrastructure scores, indicating a significant infrastructure gap between them and other CEE countries. Besides, about half of the CEE countries have made significant improvements in their infrastructure levels over the past 10 years. Their infrastructure quality and informatization scores have increased to varying degrees, with Albania ranking first with an increase of 122.4%. Montenegro's score has also increased by more than 30%, which is a remarkable improvement.  
Figure 6: Evaluation results of social climate of CEE countries from 2009 to 2018.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **Ave.** & **Rank** & \
\begin{tabular}{c} **Relative Change** \\ **(2009/2018)** \\ \end{tabular} & **Country** & **Ave.** & **Rank** &
\begin{tabular}{c} **Relative Change** \\ **(2009/2018)** \\ \end{tabular} \\ \hline Albania & 0.1604 & 2 & \(-\)5.4\% & Lithuania & 0.1441 & 7 & 9.2\% \\ Bosnia & 0.0538 & 16 & \(-\)73.5\% & Montenegro & 0.1451 & 6 & 4.2\% \\ Bulgaria & 0.0628 & 15 & 26.6\% & Macedonia & 0.1325 & 8 & \(-\)2.7\% \\ Croatia & 0.0767 & 14 & \(-\)36.1\% & Poland & 0.1274 & 9 & 6.0\% \\ Czech & 0.1468 & 5 & 4.5\% & Romania & 0.1175 & 10 & 57.2\% \\ Estonia & 0.2135 & 1 & 21.3\% & Serbia & 0.0910 & 13 & 25.9\% \\ Hungary & 0.1602 & 3 & \(-\)3.3\% & Slovakia & 0.1109 & 12 & \(-\)21.0\% \\ Latvia & 0.1560 & 4 & 3.4\% & Slovenia & 0.1121 & 11 & \(-\)16.9\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Average evaluation results of social climate in CEE countries.
# Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models
## 2 Literature Review
### Simple and Complex Human Activities  
The HAR domain has witnessed huge attention by research studies involving simple activity recognition. However, limited studies focus on complex activities. This is evident in several independent surveys conducted by other researchers [2; 6; 11; 12; 24; 25]. They have suggested that the lack of studies is attributed to the data associations inherent in complex activities. For instance, the authors in [2] categorized complex activities into three broad groups: composite activities comprising a series of simple activities; concurrent activities occurring when users engage in more than one simple activity at the same time; and multi-occupant activities suggesting more than one user is engaging in a set of activities in a multi-resident environment. Moreover, using an approach based on tree-structured taxonomy, [23] categorized human activities into two broad groups: single-layered and hierarchical activities. The single-layered activities are likened to simple activities, such as gestures and actions with sequential characteristics, while the hierarchical activities are high-level activities with multiple single-layered activities. Another definition given by the authors of [25] argues that the differentiating factor is the duration. In their paper, they referred to simple activities as single-person actions with a short duration, while complex activities are regarded as a complex sequence of actions performed by several individuals over a long time. Although several authors have used different terminologies for their categorization, the authors of [26] argue that the boundaries of these classes are still not clearly demarcated. One thing that these existing studies agree on is that human activities can be further categorized into simple and complex activities to better reflect the activities of daily living. In this article, we adopted the classification taxonomy developed by [2; 25].  
For HAR models to achieve state-of-the-art performance when discriminating between simple and complex activities, [PERSON] et al. [27] argue that collecting data from multiple sensors should be explored because of the hierarchical structures in determining the diverse levels of physical activities. Although the authors of [11] agree with this heterogenous data collection approach, they also identify the prevalent interclass similarity challengesassociated with HAR, particularly when the dataset comprises both simple and complex activities. They define interclass similarity as the potential for activity classes that are fundamentally different to exhibit similar signal patterns. Finally, they recommend applying an Activity Recognition Chain (ARC) to mitigate these challenges. An ARC is a specific activity recognition system behavior that comprises signal processing, pattern recognition, and machine learning algorithms.