esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient LDPC Encoder Design for IoT-Type Devices
## 6 Numerical Results
### Memory Utilization  
Figure 6 shows the memory utilization observed in our experimental framework for eight different LDPC parity-check matrices (H1,..., H8), as well as their Reed-Solomon (RS) and Bose-Chaudhuri-Hocquenghem (BCH) counterpart block codes (with similar block lengths and code rates \(R\)). Three LDPC encoder implementation methods were investigated. The basic implementation refers to the direct \(\mathbf{H}\) matrix stored in the Boolean table. The memory requirement is too huge to be applicable with the STM32 device mentioned here due to the stack overflow error.  
The second solution, referred to as an efficient LDPC, is based on the index representation of the \(\mathbf{H}\) matrix and the developed encoding algorithm written in the C programming language. The algorithm is versatile. The memory utilization is roughly 20 times less than with the basic representation.  
Finally, the third solution, marked with the blue bar in Figure 6, involves the developed QC-LDPC encoding algorithm and the circular shift value representation of \(\mathbf{H}\). This algorithm is available to encode only QC-LDPC parity-check matrices, but with a broad range of code lengths and rates. The significant memory reduction in the proposed solution was confirmed by the experimental results. It can also be observed that the memory utilization was not greater than that of the RS encoder counterpart implemented with  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Matrix** & \(K\) & \(N\) & \(P\) & \(R\) & **Distribution** & \(N_{RS,BCH}\) & \(K_{RS}\) & \(R_{RS}\) & \(K_{BCH}\) & \(t_{BCH}\) & \(R_{BCH}\) \\ \hline H1 & 512 & 1024 & 32 & \(\dfrac{1}{2}\) & Reg., \(\lambda_{3}=1\) & 1023 & 511 & \(\approx\dfrac{1}{2}\) & - & - & - \\ \hline H2 & 512 & 1024 & 32 & \(\dfracthe same microcontroller equipment. In all the presented charts, the proposed encoder is shortly denoted as QC-LDPC, while the index representation solution is denoted as LDPC.
## 3 Results and Discussion  
### Comparison of Heavy Metal Uptake by Different Algae  
The removal efficiency of dried marine algae, _Hizikia fusiformis, Green graclaria_, and _Codium fragile_ for Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\) were shown in Figure 1 (initial metal concentration: 100 mg/L; pH: 4.0; adsorbent dosage: 1.67 g/L; reaction time: 24 h; agitation speed: 100 rpm; temperature: 25 \(\pm\) 0.5 \({}^{\circ}\)C). The Cd\({}^{2+}\), Cu\({}^{2+}\), Ni\({}^{2+}\), and Pb\({}^{2+}\) metal ion removal performances were 14.9%, 31.6%, 10.9%, and 10.9%, respectively, for _H. fusiformis_. _H. fusiformis_ had the highest removal efficiency, followed by C. _fragile_, and _G. graclaria_. Biomass from different algal species exhibited different metal-binding capacities under these experimental conditions (pH 4 and 25 \({}^{\circ}\)C). Differences between algal species in the magnitude of metal-binding capacity may have been due to the properties of the algae such as structure, functional groups, and surface area, depending on the algal division, genera, and species [38]. Based on these experimental results, _H. fusiformis_ was selected for further experiments.
## 4 Simulation Analysis  
To validate the effectiveness and correctness of the proposed TM-MDH error model and C-LM identification algorithm, numerical simulations were conducted using MATLAB R2016A software in this section. First, the source of the industrial robot tool end pose data in the simulation experiment is introduced. Secondly, according to the TM-MDH model proposed in Section 3.1 and the C-LM algorithm proposed in Section 3.2, all geometric parameters are identified, and their linear relationship is analyzed by observing the difference between them and the actual structural parameters of the industrial robot, and then a scheme for not identifying some geometric parameters under specific conditions is given. Lastly, the convergence of the proposed C-LM algorithm is analyzed, and measurement noise is introduced to evaluate the actual compensation effect of the TM-MDH model on pose accuracy.
# Service for Deploying Digital Twins of QKD Networks
## 4 Implementation  
To achieve the full functioning of the roles discussed in Section 3, three main software components have been developed [12]: an orchestrator application to manage the life cycle of the QKD network digital twins, and two Python packages, one for the QKD network modules and other for the QKD network clients, where the QKD API functions are defined to ease the development of applications. This section gives an overview of the most relevant implementation details of each of them.
## 3 High-Voltage Battery and Air Cooling System Modeling
### Single Temperature Lumped-Parameter Model and Battery Cooling System
#### 3.2.1 Air Cooling System  
The battery air cooling system has been assumed here to be capable of exploiting the cabin conditioned air, as suggested for example in the paper by [PERSON] et al. [37]. Air cooling is less effective than liquid cooling, however this approach is still widely used thanks to its limited cost and its ease of installation [40]. Moreover, several examples of its application can be found in the literature when aiming to evaluate the thermal behavior of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Battery pack mass & \(m_{bat}\) & 109.4 kg \\ Battery pack length & \(l_{bat}\) & 1050 mm \\ Battery pack height & \(H_{bat}\) & 240 mm \\ Battery pack width & \(w_{bat}\) & 400 mm \\ Battery side exchange surface & \(S_{bat}\) & 1.10 m\({}^{2}\) \\ Overall cooling exchange surface & \(S_{cooling}\) & 2.55 m\({}^{2}\) \\ Battery specific heat & \(c_{bat}\) & 1109.2 J/(kg\(\cdot\)K) \\ Air specific heat & \(c_{p,air}\) & 1005 J/(kg\(\cdot\)K) \\ Convective coefficient—side air & \(h_{side,air}\) & 10 W(m\({}^{2}\cdot\)K) \\ Convective coefficient—cooling air & \(h_{cooling,air}\) & 50 W(m\({}^{2}\cdot\)K) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Battery and air thermal parameters [26; 39].  
Figure 3: Schematic diagram of the arrangement of cells within the modeled battery pack.  
cylindrical Li-ion cells [41; 42]. Furthermore, the objective of this work does not relate to identifying the most suitable cooling system, but rather to analyze how the operation of a realistic cooling system impacts on the effective electrical power consumption during a given driving mission. A scheme of the cabin and battery air cooling system implemented here is reported in Figure 4.  
When the battery cooling system is activated, air flow is enabled between vehicle cabin and high-voltage battery thanks to Fan2 in Figure 4. In this case, air flows through the battery in the dedicated channels between the cells. A forced convection heat exchange occurs in this framework through the surface \(S_{cooling}\). cabin climate comfort of the passengers might be slightly affected by such air flow circulation between the cabin and high-voltage battery [40]. If cabin air conditioning is not activated by the HEV passengers, the air flow enabled by Fan2 to cool the battery will be at ambient temperature, thus considerably limiting the cooling effect. On the other hand, if cabin air conditioning is activated by the passengers, air flow will be enabled by Fan1 in Figure 4 from the _HVAC_ (Heating, Ventilation, and Air Conditioning) system to the cabin. The air flow coming from the _HVAC_ system will be at the temperature set by the HEV passengers for the air conditioning system. When the battery cooling system is not activated, the battery pack only dissipates heat with the surrounding air at cabin temperature by means of natural convection and passive cooling through its side surface \(S_{bat}\).
## 4 Results and Discussion
### DQ4: Top Publishing Venues  
The favorite venues for publication of the selected works were Conferences, with 64.4% of the primary works (Figure 6); the IEEE International Computer Software and Applications Conference, with four primary papers published, topped the list of the most cited (Table 15); together with the Workshops (10%), the Conferences cover 75% of the set of selected papers; however, 25.6% of the papers, published in Journals, is a good fact, and provides a solid empirical basis, given the quality and the recognized prestige of publication venues (Expert Systems, Requirements Engineering, etc.). Table 16 shows the complete list of mentioned journals.  
Finally, Figure 7 shows that the interest in CVs and RE activities has remained at a moderate level (seven papers per year, on average) since 2009, with higher peaks in 2012 and 2016.
# Testing and Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN Applications
###### Abstract  
A 3D electromagnetic circuit design and analysis of a MOSFET-based absorber active integrated antenna has been performed. It integrates a transmitting dual-band double material substrate (DMS) cylindrical surrounding patch antenna (CSPA) with a MOSFET-based absorber of reflected radio frequency power. It is a solution to the problem of performance degradation in the power amplifier (PA) resulting from antenna and PA impedance mismatch. This fully integrated MOSFET-based absorber antenna can absorb reflected RF power with a diode-based quasi-circulator as part of the integrated design circuitry. The antenna used for the proposed integrated design will operate at frequencies ranging from 2 GHz to 3 GHz and from 4.6 GHz to 6.1 GHz, thus providing a bandwidth of 1 GHz and 1.5 GHz at a resonance frequency of 2.5 GHz and 5.3 GHz, respectively. This makes it suitable for use in lower and upper bands of WLAN/WiMAX medium RF front-end applications. Furthermore, the condition for MOSFET connected to the absorber (\(l_{S}\leq l_{D}\) and \(V_{DS}=0\)) has been satisfied at both instances of resonance. In this proposed design, an antenna radiation efficiency of 84% has been observed.  
absorber; antenna; circulator; MOSFET; pulse generator; microelectronics; solid-state electronics Article Testing and Analysis of MOSFET-Based Absorber Integrated Antenna for 5G/WiMAX/WLAN
###### Abstract
The characteristics of non-imaging hyperspectral techniques are high resolution, a large number of bands with abundant information, being less influenced by the atmosphere and the external environment, and higher signal-to-noise ratio, which is closer to the true spectrum of the ground object [8]. Therefore, using non-imaging hyperspectral techniques to study the spectral features of FHB at the ear level is of great significance for the subsequent identification of FHB. The main purposes of this paper are: (1) to evaluate the correlation between spectral features and the severity of disease at different angles of FHB-infected ears; (2) to use correlation analysis and independent sample T-tests to extract spectral feature sets that are sensitive to FHB and have significant differences between different severity classes; and (3) to use the FLDA and SVM algorithms to construct an effective model for identifying the severity of wheat infected with FHB in a complex farmland environment. The results can provide a theoretical reference for the monitoring and identification of FHB at the canopy scale or regional scale.
## 6 Conclusions  
In this paper, a named entity recognition dataset is constructed in the electric power marketing domain. With the data annotated by using the BIO annotation method, nine entity types are classified, and a PMDNER model is established for named entity recognition in the electric power marketing domain. The RoBERTa pre-training model is employed to implement word vectorization of the electric power text data, and then the semantic representation vector of RoBERTa output is input to the constructed DFENN neural network to obtain both local and global features of the text in parallel for further fusion. Utilizing the output of the RoBERTa layer of the model as the auxiliary classification layer and the output of the DFENN layer as the master classification layer, the weights of the two layers are calculated using the attention mechanism, and new features are fused based on the weights to be input to the CRF layer to predict the labels. Additionally, a focal loss function is used to alleviate the problem of uneven sample distribution during training. This method can better tackle the challenges such as multiple meanings of words, incomplete feature extraction and poor recognition of specialized terms in the process of entity recognition. The experiments demonstrate that the recognition effect of the model is significantly improved, and the accuracy of the model is 89.22%, the recall is 87.95%, and the \(F1\) value is 88.58%, which is a 7.36% improvement compared with the baseline model BiLSTM-CRF.  
In future work, more suitable feature fusion methods can be explored to further strengthen the recognition effect of named entity identification in the power marketing domain, and the correlation between named entities and entity relationship extraction can be investigated to scientifically and precisely mine the potential knowledge in the field of power marketing. In addition, it is also expected to apply the proposed model to data from other domains to verify the generalization ability of the model.  
Conceptualization, Y.C.; methodology, Y.C. and Z.L.; software, Y.C., Z.L. and Z.T.; validation, Y.C. and D.L.; formal analysis, Z.T. and D.L.; data curation, Z.T. and D.L.; writing\(-\)original draft preparation, Y.C.; writing\(-\)review and editing, Z.L.; project administration, Z.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Guangxi Scientific Research and Technology Development Plan Project grant number AA20302002-3 and Innovation Project of China Southern Power Grid Co., Ltd. (202201161).  
Not applicable.  
Since the data use the real power data of Guangxi Power Grid Co., they are too sensitive and inconvenient to disclose.  
Thanks to [PERSON] of Guangxi Power Grid Co., Ltd. for helping with the data part of this study. The authors declare no conflict of interest.
## 3 Results and Discussion
### Influence of foliar Ca, Si and Ca + Si Treatments on Grape Phenolic Compounds
Foliar application of Si increased the concentration of delphinidin-3-glc and cyanidin-3-glc when compared with control and Ca grapes. In addition, the concentration of cyanidin-3-cmglc increased, although this increment was only significant for Ca foliar application because differences with control and Ca + Si foliar treatment in these compounds were not observed (Table 3). Variations on total anthocyanin contents among control and foliar-treated samples were not observed (Table 3). Therefore, foliar application of Ca, Si and Ca + Si did not improve the anthocyanin content of grapes, in contrast with some previous studies that described an increase in anthocyanins content in grapes after Ca or Si foliar treatments [47; 48; 49]. Anthocyanins are the compounds responsible for the red color in red grapes and wines, so the improvement of their content in grapes is a goal for winemakers. [PERSON] et al. [48] evaluated the effect of Si foliar treatment on four grape varieties and described a significant increase in the anthocyanin content of the Oseleta cultivar. However, the foliar application of Si stimulated specific metabolic changes in each particular cultivar analyzed. [PERSON] et al. [49] showed that foliar application of Ca increased anthocyanin, sugar and Ca contents in the grape skin, probably because of Ca effects for increasing the expression of genes related to anthocyanin biosynthesis, modification and transport. Unfortunately, in our study, this effect was not clearly observed as described above. Flavonols are compounds that can act as co-pigments and, thus, contribute to wine color stabilization [50]. For this family of compounds, the foliar application of Ca did not produce a significant effect in comparison with control grapes (Table 3). However, [PERSON] et al. [36] described an increase in some specific flavonoids in grapes after a Ca spray supply. Silicon foliar treatment led to grapes with a higher isorhammetin-3-glc concentration than
# Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies
## 3 Energy Management Proposal for 5G Network Slicing
### Architecture for Adaptive Energy Management
#### 3.3.1 Architecture Description and Operation
As a result of the analysis of service parameters (customer information), the available supply, and the possible management strategies at the service level (network slices), the MNO obtains the optimal service scheduling (number of processed services, service execution time, and power consumption levels) in each network slice that leads the optimization of power consumption (minimization of power from renewable energy consumptions) and the energy-efficient network slice creation and configuration. A summary of the energy-aware creation and configuration of network slices in the proposed architecture is shown in Figure 3.  
Once the general structure of the network slicing (network slice profile) has been selected, the MNO can deploy in the network slice MANO entity (at core network) the corresponding Network Slice Instance (NSI), which refers to a set of network function instances (physical or VNFs, e.g., belonging access, transport, and core networks), the connectivity between them, and the required resources (e.g., compute, storage, and networking resources) that form a deployed network slice [7]. Typically, a network slice instance is designed (preparation phase), then it is instantiated (instantiation, configuration, and activation phase), later it is operated (run time phase), and finally it may be decommissioned (decommissioning phase) when the slice is no longer needed [1]. The complete NSI lifecycle is managed by the MNO, specifically by the network slice MANO. In the process of configuring and deploying network slices, the MNO sends the NSI information to the second block of the architecture, specifically to the NFV Orchestrator (NFVO), which is in charge of coordinating with the VNF Manager (VNFM) and with the Virtualized Infrastructure Manager (VIM) the creation  
Figure 3: Description of the energy-aware network slice creation in the adaptive energy management architecture. The network slices and corresponding services are configured to achieve energy efficiency, considering a finite energy provisioning and the optimization of power consumption, specifically through the minimization of the consumption of non-renewable energy sources. of VNFs (that provide specific network capabilities to support and realize the particular service(s)) and virtual networks (access, transport, and core) need for the deployment of network slices. Then, the requests from the VIM entity are sent to the SDN controllers (access, transport, and core controllers), which coordinate with the Network Function Virtualization Infrastructure (NFVI) the creation of network slices on the underlying network infrastructure (physical network resources). At this point, all created slices compose a single (end-to-end) network slice for specific customers. All of the network slices are managed and orchestrated first by an NFVO and in an upper level by the NFV and 3 GPP MANO frameworks.
## 2 Related Works
### Congestion Control Methods
SenTCP [19] is an open-loop and hop-by-hop congestion control technique for regulating the transmission rate of the receiving node by continuously passing congestion status information to neighbor nodes in order to allow them to reduce network congestion. SenTCP calculates the congestion coefficient according to buffer status information, which is passed to the neighboring nodes. The node receiving the congestion information may then transmit to the other node depending on the information. Unlike CODA, SenTCP does not include techniques to ensure the reliability of the data, and has the disadvantage that the throughput control algorithm determines performance according to seven parameters. The priority-based congestion control protocol (PCCP) [20] gives priority on a node-by-node basis, and in the event of congestion, it transmits packets according to priority. If a packet cannot be sent to the parent node because of congestion, it will attempt to find a new parent node through multiple paths.
# Scene Text Detection Using Attention with Depthwise Separable Convolutions
## 3 Proposed Methodology
### Design of Text Attention Blocks  
Attention mechanisms have been successfully applied to improve the effectiveness of convolutional neural network architectures along with the careful selection of layers, filters, and channel dimensions. As shown in Figure 1, the proposed detector includes text attention blocks at three stages of the feature extraction. The objective of the attention block at the early stage of feature extraction is to channelize the feature extraction process toward capturing the text positional information. The subsequent attention blocks enables the feature extraction process to capture text attributes at all scales. The text attention blocks in the proposed detector are implemented using the efficient channel attention (ECA) mechanism by [PERSON] et al. [24]. The ECA mechanism exploits the local cross-channel attention implemented through one-dimensional convolutional operators with an adaptive kernel size. The kernel size \(k\) represents the neighbors to be considered for the cross-channel interaction exploration. Figure 2 shows the basic design of efficient channel attention blocks adopted from [24]. The channel dimension \(Ch\) and the kernel size \(k\) are related by the nonlinear mapping \(Ch=2^{\gamma*k-b}\) i.e.,  
\[k=\left|\frac{log_{2}(Ch)+b}{\gamma}\right|_{\text{nearest odd value}} \tag{5}\]  
The implementation of the ECA module includes a channel-wise feature aggregation layer using global average pooling followed by processing through a one-dimensional convolutional layer. The convolutional layer output is passed through a fully connected layer that outputs the weight of different channels. The **MobileTDNet** network uses a similar block structure. The input feature aggregation is an important step in the ECA mechanism. The ECA-Net in [24] builds on global average pooling for channel-wise feature aggregation following SENet [62]. The average pooling-based feature values correlate with the target shape extent in the given input, whereas the max pooling-based feature values correlate with detection object-specific attributes, which are effective in the case of target objects appearing at different scales.  
Therefore, the different feature aggregation strategies based attention blocks are applied. At the early stage of feature processing in the network, the **Text Attention 1** block is implemented with global average pooling-based feature aggregation to complement the text discovery process in the input. In later stages where the network generated feature maps capture the finer text segment details, the **Text Attention 2** and **Text Attention 3** attention blocks are implemented with the global max pooling-based feature aggregation, since this affects the detection of small text segments. The parameters \(\gamma\) and \(b\) in Equation (5) control the span of the channel neighborhood for attention analysis. Without loss of generality, the parameter \(b\) and \(\gamma\) are set to 2 and 3, respectively, for all experiments in this work.