esa-sceva/satcom-chunk-collection huggingface dataset:
#### Article
###### Abstract  
The problem of scheduling parallel tasks graphs (PTGs) represented by directed acyclic graphs (DAGs) in heterogeneous distributed computing systems (HDCSs) is considered an nondeterministic polynomial time (NP) problem due to the diversity of characteristics and parameters, generally opposed, intended to be optimized. The PTGs are scheduled by a scheduler that determines the best location for the sub-tasks that constitute the PTGs and is responsible for allocating the resources of the HDCS to the sub-tasks of the PTGs. To optimize scheduling and allocations, the scheduler extracts characteristics from the internal structure of the PTGs. The prevailing characteristic in existing research is the critical path (CP), which is limited to providing execution paths of PTGs; considering this limitation, we extend the array method proposed in [PERSON], which extracts two additional characteristics to the CP: the layering and the density of the graph for scheduling. These characteristics are represented as integer values of the PTGs to be scheduled; the values obtained from the characteristics are stored in arrays representing populations that are evaluated with the heuristic univariate marginal distribution algorithm (UMDA) and in terms of comparison with the genetic algorithm. With the best allocations produced by the algorithms, two performance parameters are evaluated: makespan and waiting time. The results indicate that when more PTGs characteristics are considered, resource allocations are optimized, and scheduling times are reduced. The results obtained with the heuristic algorithms show that UMDA provides shorter scheduling and allocation times compared with the genetic algorithm; UMDA widely distributes the sub-tasks in the clusters, whereas the genetic algorithm compacts the assignments of the PTGs in the clusters with a longer convergence time that translates into longer scheduling and allocation times. Extensive explanations of these conclusions are provided in this work, based on the conducted experiments.  
parallel tasks graphs; heterogeneous distributed computing systems; scheduling; critical path; layering; density; genetic algorithm; univariate marginal distribution algorithm
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 5 Discussion  
Existing studies [2; 3; 4] have tried to estimate the similarity of sentences mainly by using global features. We were able to improve accuracy by up to 8.32%p (Korean) by also utilizing local features. In addition, it was shown that an accuracy of up to 3.03%p can be achieved by simply changing the LSTM to Bi-LSTM (compare No. 1 to No. 4). A model capturing forward information and backward information simultaneously is more effective. Comparing No. 1 and No. 2, and No. 3 and No. 4, it can be seen that simply and mechanically combining the global features and the local features does not help improve performance. Rather, it only decreases the accuracy.  
A capsule network is designed to solve the problem of CNN's pooling method, but rather CNN's pooling shows higher accuracy not only for the English dataset but also for the Korean dataset (see No. 5 and No. 7. in Table 4). Furthermore, we can confirm that the capsule network model, No. 6, which does not use dynamic routing, has higher accuracy than the CNN based models, No. 5 and No. 7. Through this, it can be confirmed that dynamic routing considering the spatial relationship of local features is inappropriate for use in the task of semantic similarity estimation. Unlike images in which the relative positions of the pixel chunks are fixed to some extent, the position of words or phrases in a sentence are relatively free.  
As shown from No. 8 to No. 11, which are RNN-related models, it can be confirmed that the accuracy of the capsule network model with self-attention applied to Bi-LSTM is the best. In No. 4, No. 9, and No. 11, in Korean the degree of accuracy improvement by the capsule network and the self-attention is higher than in English. The semantics of Korean sentences is concentrated in a more local area than in English. In order to grasp the entire meaning of a Korean sentence, it is important to accurately understand the local meanings.  
No. 12 is a proposed model in which \(\alpha\) of Equation (13) is 0.8 in English and 0.5 in Korean. This can be seen that \(\alpha\) of the Korean dataset concerning global features is lower than \(\alpha\) of the English dataset. From the fact that the larger the alpha value, the higher the proportion of global features, we can infer that Korean depends more on local features since it has a freer order of words than English.
# Design of a Double-Matched Cross-Polar Single Antenna Harmonic Tag
## 3 Antenna Design
### Rationale of the Antenna Design Procedure
The inclusion of the feeding line and the stub (protrusion) does not perturb, at the fundamental frequency, the \(TM_{11}\) mode in Figure 4a since they are placed in the position where the \(TM_{11}\) mode has a minimum of electric field. Evidently, for exciting both modes \(TM_{11}\) and perturbed \(TM_{21}\) radiating orthogonal fields, two orthogonal feeding lines are required. Since the ARMSA has very different input impedance at the two frequencies (fundamental and second harmonic), a specific design procedure is required for fitting dimensions of feeding lines and stub (protrusion).
# Pilot-Assisted OFDM for Underwater Acoustic Communication
## 3 System Architecture
### Linear Equalization  
The fact that equalization can be done in the frequency domain makes OFDM an attractive choice for radio as well as UWA channels. In this work, two linear equalizers that operated in the frequency domain were evaluated. The performance of both equalizers was compared in terms of the BERs that they offered over a shallow UWA channel. For one equalizer, the channel was estimated using pilots and an LS estimator. For the other, it was assumed that perfect channel estimates were available and the ZF equalizer was used to estimate the transmitted sequence.
## 2 Related Work
However, the acquisition of the glare index may be difficult due to the subjective evaluations required, as mentioned above. Three algorithms, namely ANN, K-nearest neighbors, and random forest, were evaluated by [PERSON] et al. [20]. The daylight autonomy and daylight glare probability metrics and information about the building design, such as the size of the rooms, windows size, and viewing direction, were used for this method. The results indicated that machine-learning-based methods offer a good compromise between accuracy and computational expenses. Research based on machine learning was also presented in [21]. The proposed model was based on a machine learning algorithm that analyzed data from surveys and experiments to identify the factors that influenced visual comfort and develop a predictive model for individual preferences. The results of the study showed that the proposed model could accurately predict individual preferences for visual comfort in different environments, such as offices, classrooms, and homes. However, the model might not be directly applicable to other nations or cultural contexts where people may have different personal preferences and expectations for visual comfort, as the models were trained on individual preferences for visual comfort in China. As can be seen from the analysis of the most recent research presented in the above paragraphs, the existing literature has limitations in terms of the variables used, which can be difficult to acquire in some situations. Another limitation of the existing models is their applicability in different situations. Many visual comfort models have been created using standardized metrics and assumptions, which may not always be applicable to various indoor environments, such as offices, classrooms, and homes. Due to this lack of flexibility, it may be challenging to accurately predict occupant satisfaction and visual comfort in real-world situations. As may be observed from the summary of the literature analysis in Table 1, this paper presents the only method leveraging an AI model for visual comfort estimation while still using the least available inputs. Utilizing an AI model for visual comfort estimation offers distinct advantages over relying solely on a mathematical model. AI models' ability to learn from real-world data enables them to capture complex patterns and nuances, improving their accuracy over time. Their continuous learning capability allows them to adapt to changing environments and evolving standards. Furthermore, AI models leverage computational power for the efficient processing of large datasets, leading to quicker and more resource-efficient estimations. Overall, AI models provide superior accuracy, adaptability, continuous learning, and computational efficiency compared to traditional mathematical models, making them highly valuable for visual comfort assessment in indoor spaces.
# Modified 16-Quasi Log Periodic Antenna Array for Microwave Imaging of Breast Cancer Detection
###### Abstract  
In this paper, an effective system for microwave imaging of breast tumor detection using modified 16-planar log periodic antenna (PLPA) array is presented. The modified PLPA operates in the band from 2 to 5 GHz with stable directional patterns in the end-fire direction. Once the results of a single antenna element have been validated, the design is extended to include 16 antenna elements. All 16 transceiver antennas are vertically placed around the phantom in a circular manner where one antenna acts as a transmitter and the rest work as receivers. Delay and Sum (DAS) algorithm is used for post processing the acquired scattered signals from the sensors to reconstruct the image of the breast and to identify the existence of breast tumors. The electromagnetic simulators CST and HFSS are used to design the system, while MATLAB is used to process the data. The developed PLPA array-based microwave imaging system performs admirably, making it one of the most effective systems for detecting tumor cells.  
antenna array; breast cancer; DAS; log periodic antenna; microwave imaging; tumor detection +
Footnote †: journal: Applied  
Article
## 2 Preliminaries and Problem Description  
### Preliminaries  
Throughout this study, standard notation is employed. Consider a directed graph \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathcal{A})\), where \(\mathcal{V}=\{1,2,\ldots,N\}\) denotes the vertex set, \(\mathcal{E}\subseteq\mathcal{V}\times\mathcal{V}\) represents the edge set, and \(\mathcal{A}\) indicates the weighted adjacency matrix. An edge \((i,j)\) exists in \(\mathcal{E}\) if there is a reachable path from agent \(i\) to agent \(j\). The neighborhood of agent \(i\) is defined as \(N_{i}=\{j\in\mathcal{V}\mid(j,i)\in\mathcal{E}\}\). The adjacency matrix \(\mathcal{A}\) satisfies \(a_{ii}=0\) for all \(i\in\mathcal{V}\), and \(a_{ij}=1\) if \((j,i)\in\mathcal{E}\), otherwise \(a_{ij}=0\). The Laplacian matrix of the graph \(\mathcal{G}\) is defined as \(\mathcal{L}=\mathcal{D}-\mathcal{A}\), with \(\mathcal{D}=\operatorname{diag}\{d_{1}^{\operatorname{in}},d_{2}^{ \operatorname{in}},\ldots,d_{N}^{\operatorname{in}}\}\), and each element \(d_{i}^{in}=\sum_{j=1}^{N}a_{ij}\).
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 2 Materials and Methods  
### Study Area  
Our heat assessments took place in three mid-Atlantic cities in the USA: Richmond, VA, Baltimore, MD, and D.C. The city of Richmond, VA is located at approximately 37.5\({}^{\circ}\) North, 77.4\({}^{\circ}\) West, on the Falls of the James River just west of the Chesapeake Bay. Richmond covers approximately 162 km\({}^{2}\), with a population of nearly 223,000 as of 2016 [18]. Over the past 30 years, summer temperatures in the Richmond area have average monthly highs of 29.2 \({}^{\circ}\)C, 31.2 \({}^{\circ}\)C, and 30.3 \({}^{\circ}\)C for June, July, and August, respectively, and on average the city experiences approximately 14 days above 35 \({}^{\circ}\)C annually. D.C. is located at approximately 38.9\({}^{\circ}\) North, 77.0\({}^{\circ}\) West, at the confluence of the Anacostia and Potomac rivers. D.C. covers approximately 177 km\({}^{2}\), with a population of around 694,000 as of 2017 [18]. Summer temperatures in the D.C. area have average monthly highs of 29.0 \({}^{\circ}\)C, 31.3 \({}^{\circ}\)C, and 30.3 \({}^{\circ}\)C for June, July, and August, respectively, and on average D.C. experiences approximately 11 days above 35 \({}^{\circ}\)C annually. Baltimore, MD is located at approximately 39.3\({}^{\circ}\) North, 76.6\({}^{\circ}\) West, at the mouth of the Patapsco River west of Chesapeake Bay. Baltimore covers approximately 239 km\({}^{2}\), with a population of around 615,000 as of 2016 [18]. Summer temperatures in the Baltimore region have average monthly highs of 28.3 \({}^{\circ}\)C, 30.7 \({}^{\circ}\)C, and 29.5 \({}^{\circ}\)C for June, July, and August, respectively, and on average Baltimore experiences approximately nine days above 35 \({}^{\circ}\)C annually.
# Resource Allocation on Blockchain Enabled Mobile Edge Computing System
## 3 Resource Allocation Problem
### Reinforcement Learning Model
#### 3.3.4 Reward Function  
Reinforcement learning is a type of unsupervised learning, in which the reward function is used to back-propagate to update the parameters of the policy function and the state-value function, guaranteeing its policies would converge towards expectation.  
As mentioned before, action \(\mathbf{A}\) is chosen according to the current system state \(\mathbf{S}\). Corresponding to each \(s\in\mathbf{S}\) and \(a\in\mathbf{A}\), we obtain policy \(\mathbf{\Pi}\):  
\[\mathbf{\Pi}=\{\pi:s\to a;s\in\mathbf{S},a\in\mathbf{A}\} \tag{24}\]Then the reward is measured in four aspects: process delay of the task, mining delay, cost, and the mining reward. Generally, it is a comprehensive measurement of the reward, aiming to lower the latency, contain the cost, and guarantee the mining reward as expected.  
The process delay is regarded as a combination of two components: \(t_{q}\), \(t_{p}\). Considering that each task has been split into several servers, parameter \(t_{p}^{ki}\) is introduced to indicate processing delay of the task \(k\) in server \(i\). Thus, \(t_{process}\) can be described as:  
\[\begin{split} t_{process}&=\max_{i}[t_{q}+\sum_{k=1 }^{n}t_{p}^{ki}]\\ &=\max_{i}[\frac{b_{i}}{f_{i}}+\sum_{k=1}^{n}c_{ki}\frac{M_{k}}{f _{i}}]\end{split} \tag{25}\]  
The formula of mining delay is given in Equation (16).  
Then the cost of task \(k\) is measured:  
\[cost=\sum_{i}h_{i}\cdot u_{i}+t_{mining}\cdot\sum_{i=1}^{m}(e_{i}f_{i}u_{i}), \leavevmode\
obreak\ \leavevmode\
obreak\ h_{i}=\sum_{k=1}^{n}c_{ki}\cdot M_{k} \tag{26}\]  
where \(h_{i}\) denotes the resource consumed in server \(i\), \(u_{i}\) denotes the unit price of server \(i\).  
Lastly, the mining reward is measured according to Equation (17).  
From the analysis above, if time becomes longer, the cost could decrease, the mining reward would decrease. We want to reduce latency, reduce cost, and obtain more mining rewards, but these three targets cannot be obtained at the same time. Hence the reward function is given as follows:  
\[R=-\alpha t_{process}-\zeta t_{mining}-\beta cost+\gamma bonus \tag{27}\]  
where \(\alpha,\zeta,\beta,\gamma\) are weight coefficients of process delay, mining delay, cost, and mining reward, respectively. We expect to find a reasonable set of those coefficients through which the better policies could be obtained after training.
# Motor Vector Control Based on Speed-Torque-Current Map
## 4 Verification of the Proposed Vector Control Strategy
### Simulation Analyses
#### 4.2.1 0-100 km/h Acceleration Simulation  
In the simulation of 0-100 km/h acceleration, when the vehicle speed is lower than 60 km/h, the torque coupling mode is adopted; otherwise, the speed coupling mode is adopted. The results are shown in Figure 17.  
Response delay and torque fluctuation always exist in the actual process of motor speed regulation. Figure 17 shows that due to the response delay, the 0-100 km/h acceleration time resulting from the dynamic model is 14.22 s, which is 0.12 s more than that of the static model (14.10 s). The reason is that the speed and torque response characteristics of the dynamic model are closer to the actual motor operating characteristics. Different from the step response in the mode switching process of the static model, it can be seen from the speed diagram that the time required for the motor to complete mode switching is almost 0.28 s in the dynamic model, which is not shown in the static model. Torque fluctuation always exists in the dynamic model but not in the static model. The fluctuation in the process of mode switching is more apparent, which is shown in the enlarged red circle areas for the torques of MG1 and MG2. In this process, the torque of MG1 is greater than that of the static model, and the torque of MG2 is less than that of the static model. The lines of the torque aligned with the static model after 5.57 s because in the mode switching process, the speed regulation of the two motors caused the change of motor torque. This situation is similar to the actual motor operation, indicating that the dynamic model can well reflect the actual motor speed regulation process.  
Figure 17: Motor performance without considering bus voltage variation. Comparison of the results of the dynamic model and static model.
## 2 Materials and Methods
### Attribute Analysis and Quantitative Index of Road Network Node
#### 2.1.1 Characteristic Index of Complex Road Networks  
To comprehensively reveal the characteristics of the complex network structure of urban roads, the evaluation indexes of nodes are selected from the local attributes and global attributes of the network, and the importance of nodes in the network is comprehensively evaluated. The local attribute of the node focuses on the evaluation of the local connection attribute of the node, and mainly analyzes the direct connection state between the nodes, so the degree of the node is selected as the evaluation index; The global attribute of the node focuses on the global topology evaluation, which reflects the importance of the node in the whole network, so the betweenness of the node is selected as the evaluation index.  
* Local attribute--degree index  
The node degree is the number of edges connected to the node in the road network topology. It indicates the ability of a node to establish direct contact with neighboring nodes in the network or the complexity of the connection in the network [32]. The node degree is only a local feature of the network, so the index is one-sided. Although some important \"core nodes\" do not have a large degree of connectivity, they play an important connecting role in the whole road network. Therefore, from the local properties of the network, the higher the degree index, the more important the node. But from a global point of view, the importance of nodes with the same degree index is not necessarily the same [17]. It can be seen that there are limitations in evaluating the importance of road network nodes with a single index.  
To reflect the physical information of the connected edges of the road network, the connected edges are weighted according to the road grade. Then the weight of the node is defined as the sum of the edge weights connected to the node [15]. According to the standard of road grade of the urban road network, it is divided into four grades: expressway, trunk road, secondary trunk road, and branch road, with weights of 10, 8, 5, and 3, respectively.  
Global attribute-Intermediate Index  
The betweenness of nodes refers to the ratio of the number of shortest paths of a node to the number of all shortest paths, reflecting the importance of nodes in the path selection of the whole network [32]. The betweenness value of any node is defined as  
\[C_{\text{B}}(v)=\sum_{s\
eq t\
eq v}\delta_{st}(v) \tag{1}\]  
\[\delta_{st}(v)=\frac{\partial_{st}(v)}{\partial_{st}} \tag{2}\]  
wherein, \(\partial_{st}\) is the number of shortest paths from node \(s\) to \(t\). \(\partial_{st}(v)\) is the number of shortest paths through node \(v\) between pairs of nodes \(s\) and \(t\).
## Materials and Methods
### Expression Vector Construction and Transformation  
The expression vector was constructed using full-length _CpMYC2_ and _CpbHLH13_ gene sequences on a modified pCAMBIA 2300S vector by double digestion using Kpn1 and Xba1 digestion enzymes under a CaMV 35S promoter (Supplementary Figure S8a,b); 35S::_CpbHLH13_ was introduced into _Agrobacteriumum tumefaciens_ strain GV3101 by electroporation. The positively cloned Agrobacterium were selected and transformed into tobacco by using the leaf disk method [78]. The empty vector pCAMBIA 2300S was also introduced into tobacco plants as a control. The shoots of emerged explants were transferred into rooting media as described previously [78]. These explants were incubated in a growth chamber at 25 \({}^{\circ}\)C under a 16 h/8 h photoperiod. Homozygous arabidopsis Salk_083483 mutant plants were transformed by _CpMYC2_, while _CpbHLH13_ was transferred into the arabidopsis Col-0 cultivar via _Agrobacteriumum tumefaciens_-mediated transformation [79]. The pots containing the wild-type and mutant arabidopsis plants were carefully inverted and immersed in the infiltration medium containing Agrobacteriumium while stirring for 1 min. The seedlings were placed back on the shelf and covered by plastic bags. Three days after infiltration, the plastic bags were removed, and the seedlings were supported with sticks and allowed to grow till the first generation of seeds (T1). The seeds from the T1 generation of arabidopsis plants and the T0 generation from tobacco were grown in \({}^{1}\)/2 MS media plates with 50 \(\upmu\)M-mL-1 kanamycin for screening the positive seedlings. The surviving plants were transferred to soil pots, and their positivity was also confirmed by PCR with gene-specific primers using plant cDNA as a template (Table 5). These plants were self-crossed, and at least three plants from tobacco and 3 groups of 6 plants from arabidopsis of the T3 generation with the highest gene expression were used for further experiments.
## 4 STATCOM Performance and Results  
The transformer integrated seven level VSC based STATCOM was implemented and associated with a 132 kV, 50 Hz AC power system. The dynamic load progress of STATCOM was estimated with reactive power changes at a nominal point \({}^{\prime}\mathcal{O}_{5}^{*}\), and the terminal voltage changes at nominal point \({}^{\prime}\mathcal{O}_{t}^{*}\); then, quadrature current \({}^{\prime}I_{1q}^{*}\)' was evaluated for stable operation of the system with minimum harmonic distortion. The system parameters have been mentioned in Appendix A.  
_i. Dynamic Performance of Reference Reactive Power Changes_  
The binary weighted transformer integrated seven level VSC based STATCOM was examining with dynamic conditions of inductive and capacitive loads. The various continuous, cumulative and disintegration dynamic state loads were tested.