esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 PV Generation Forecaster Models
### Error Metrics  
In order to understand and evaluate the operation of any model, it is necessary to establish certain metrics to calculate the error of the model's results. In this work, the  
Figure 7: Non-linear SVR, adapted from [29].  
developed models are assessed using three different metrics, which are widely used in the literature for accuracy evaluation purposes.  
So, the models designed to forecast the day-ahead PV production curve are assessed by calculation of MAE (Mean Absolute Error), RMSE (Root Mean Square Error), and R\({}^{2}\) (coefficient of determination).  
All three metrics show great potential for comparing the operation of different models, which is of vital importance in this work.  
The equations for each of them are shown below [31].  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert y_{j}-t_{j}\rvert, \tag{8}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\bigl{(}y_{j}-t_{j}\bigr{)}^{2}}, \tag{9}\]  
\[R^{2}=\left[\frac{\sum_{i=1}^{N}\bigl{[}\bigl{(}y_{j}-\overline{y_{j}}\bigr{)} *\bigl{(}t_{j}-\overline{t_{j}}\bigr{)}\bigr{]}}{\sqrt{\sum_{i=1}^{N}\bigl{(}y_ {j}-\overline{y_{j}}\bigr{)}^{2}}*\sqrt{\sum_{i=1}^{N}(t_{j}-\overline{t_{j}} )^{2}}}\right]^{2}, \tag{10}\]  
where \(y_{j}\) and \(t_{j}\) are measured and predicted values, respectively, in this case of the PV production and \(\overline{y_{j}}\) and \(\overline{t_{j}}\) the mean values of both. \(N\) represents the number of the used test samples.  
With the calculation of MAE, the uniform forecast error of the model results is evaluated. The RMSE calculates the general accuracy of the model. Large deviation errors are the ones more desirable to identify, and in this case, RMSE offers robustness in dealing with this kind of error. While both mentioned metrics can range in value from 0 to infinity, the coefficient of determination, R\({}^{2}\), takes values between 0 and 1 so that the assessment of model prediction accuracy becomes more intuitive. R\({}^{2}\) reflects the goodness of fit of a model to the variable it seeks to explain.
# Analysis and Design of Tunable THz 1-D Leaky-Wave Antennas Based on Nematic Liquid Crystals
## 2 Theoretical Analysis
### Tunable Leaky Waveguide Partially Filled with NLC  
The structure described in the previous Section 2.3 would not only be unrealistic (the _liquid_ crystal cannot touch the partially _open_ PRS) but also would exhibit poor radiation performance due to the non-negligible loss tangent of the NLC. This feature contributes to considerably raising the total attenuation constant, thus, increasing the beamwidth (see Equation (2)) and reducing the directivity (see Equation (3)) and, consequently, the total efficiency (see, e.g., Equation (4)).  
In order to mitigate the losses, one strategy is to reduce the volume of the NLC cell inside the waveguide and to simultaneously increase the volume of a spacer material with significantly lower losses. This is the approach that we exploit in this work and that is shown on the right side of Figure 1, where the NLC cell is sandwiched between two low-loss layers of the cyclo-olefin polymer Zeonor, whose relative permittivity at THz frequencies equals \(\varepsilon_{\rm{sp}}=2.35(1-\gamma 0.0013)\). The side effect of this strategy is the corresponding reduction of the tunability of the device: the lower the volume of the NLC, the lower the tuning range. In Section 3, we will explain how to properly design the structure to optimally deal with this kind of trade-off.
# Joint Uplink and Downlink Resource Allocation for D2D Communications System
## 2 System Model and Problem Formulation
### Problem Formulation  
In this paper, we propose the joint uplink and downlink resource allocation scheme in a single cell system with both CUs and D2D pairs. In order to maximize the total capacity of the system and guarantee the SINR requirements to both CUs and D2D pairs. The overall optimization problem about system capacity can be expressed as  
\[\max\limits_{{{\rho}_{mk}^{u}}+{{\rho}_{mk}^{d}}+{{\rho}_{mk}^{c}},{{\rho}_{mk}^{d}}}\sum\limits_{m\in M}{{{\sum}_{k\in K}}R_{sum}}\] (11) s.t. \[\gamma_{m,up}^{c}\geq\gamma_{mm}^{c},\gamma_{m,down}^{c}\geq\gamma_{ min}^{c},\ \forall m\in M \tag{12}\] \[\gamma_{k,up}^{d}\geq\gamma_{min}^{d},\gamma_{k,down}^{d}\geq \gamma_{min}^{d},\ \forall k\in K\] (13) \[0\leq P_{m}^{c}\leq P_{\max}^{c},0\leq P_{Bm}^{c}\leq P_{\max}^{ c},0\leq P_{k}^{d}\leq P_{\max}^{d},\ \forall m\in M,\ k\in K\] (14) \[\sum\limits_{m\in M}{{{\rho}_{mk}^{u}}}\leq 1,\ \sum\limits_{m\in M}{{{ \rho}_{mk}^{d}}}\leq 1,\ \forall k\in K\] (15) \[{{\rho}_{mk}^{u}},{{\rho}_{mk}^{d}}\in\{0,1\},\ \forall m\in M,\ \forall k\in K\] (16) \[\left(\sum\limits_{m\in M}{{{\rho}_{mk}^{u}}}\right)\left(\sum \limits_{m\in M}{{{\rho}_{mk}^{u}}}\right)=0,\ \forall k\in K \tag{17}\]  
where \(\gamma_{min}^{c}\) and \(\gamma_{min}^{d}\) denote the minimum SINR requirements of CU and D2D pair, respectively, \(P_{\max}^{c}\) and \(P_{\max}^{d}\) are the maximum transmission power of the CU and D2D pair, respectively. Constraints (12) and (13) are the QoS requirements of CUs and D2D pairs, respectively. Constraint (14) guarantees that transmission power of CUs, BS, and D2D pairs are within the maximum limit. Constraints (15) and (16) ensure that each uplink or downlink channel of CU can be shared by at most one D2D pair. Constraint (17) indicates that each D2D can only reuse one uplink or downlink channel. In the next section, joint uplink and downlink (JUAD) resource allocation algorithm is presented to effectively solve the optimization problem (11).
# IoT-Enabled Smart Cities: Evolution and Outlook
## 4 Open Challenges for IoT-Enabled Smart Cities
### Privacy Regulations on Data in Different Countries  
Because a smart city collects and utilizes massive amounts of data, it inevitably harbours the potential for personal information infringement. For example, sensors on IoT-based home appliances may be used to collect data from inside the home. Sensors in the interior of a smart car may also be used to observe the driving habits of the driver. Implementing data mining techniques on such information can lead to the identification of certain individuals. As such, governments are enacting strong laws on the protection of personal information to systematically protect personal information and prevent data-collecting service providers from abusing it [89].  
For example, Europe enacted GDPR to regulate personal information protection and utilization. GDPR scrupulously defines the rights and obligations of personal information holders and processors, thereby strengthening the duties of companies that collect and manage data. As this law applies to all systems handling data, data-based smart cities must naturally comply with it.  
Clauses of the GDPR [102] prescribe consent, right to be forgotten, pseudonymization, among others. These clauses must be reflected as system requirements and embedded into platform features, so that such clauses can be applied in a smart city platform. Doing so can enable various smart city services to process personal information in a manner compliant with GDPR. For example, consent management as defined by GDPR requires that personal information must be processed based on the consent of the data owner. However, existing smart city data platforms do not provide a feature that processes data owner consent. For this, the access authorization feature of the existing platforms should be expanded to implement the function of systematically acquiring user consent and classifying such data in the smart city system.
# Composition Diversity and Expression Specificity of the _Tps_ Gene Family among 24 _Ficus_ Species
## 1 Introduction
Class II TPSs, which only include diterpenes, contain only a conserved DXDD motif. In addition, the N-terminal domain of plant TPSs usually has a conserved R(R)X8W motif [21]. The second R of the motif R(R)X8W is not conserved, and some cases even feature the absence of the R(R)X8W motif [24]. At present, genome-wide comparative analysis of the evolutionary characteristics of the _TPS_ gene family has been performed in more than 50 species [22; 25]. Meanwhile, the number of plant species for which _TPS_ gene family information has been obtained, on the basis of individual genome screening, is increasing [9; 23; 26; 27; 28]. Previous studies showed that the plant _TPS_ gene family can be divided into seven subfamilies, namely, _TPS-a_, _TPS-b_, _TPS-c_, _TPS-d_, _TPS-e/f_, _TPS-g_, and _TPS-h_[21; 22]. These subfamilies are specific for angiosperms, gymnosperms, land plants, vascular plants, and _Selaginella moellendorffi_ Hieron, respectively [21]. _TPS-a_, _TPS-b_, and _TPS-g_ encode proteins that produce sesquiterpenes and monoterenes, respectively. Owing to the importance of terpenoids in plant growth, chemical interactions, and protection in the environment [15], the cloning, expression, and functional characterization of _TPS_ genes have also been completed in many plant species [16; 19; 26; 28; 29; 30]. For example, [PERSON] et al. [28] demonstrated the dynamic regulation of _TPS_ genes in the emission of herbivore-induced plant volatiles after studying the expression and promoter activity of cucumber _TPS_ genes in response to different herbivores. Recently, eight _TPS_ genes from _F. carica_ L. were isolated and confirmed to play a key role in their defense against pests during syconium development by triggering diverse and dynamic changes in volatile emissions [16]. Although numerous studies have provided an important basis for understanding the functions and contributions of _TPS_ genes, existing studies only focused on a few species and paid more attention to plant protection. In this study, to reveal the _TPS_ diversity of _Ficus_ and the evolutionary characteristics of the special gene family, we investigated the _TPS_ genes in 24 _Ficus_ species using RNA-seq. We aimed to (1) identify the potential _TPS_ genes that function in _Ficus_ pollination attraction, (2) determine the gene structures, phylogenetic relationships, and expression divergence of the _TPS_ genes among different _Ficus_ species, and (3) evaluate the probable effects of positive selection on the _Ficus TPS_ genes. The findings of this study could lead to an improved understanding of the production mechanism of VOCs in _Ficus_ and, most importantly, may shed light on the evolution of the _TPS_ gene family across the genus.
# Underwater Sensor Network Redeployment Algorithm Based on Wolf Search
## 1 Introduction
Many obstacles exist in actual UWSN deployment. These obstacles greatly affect the network topology. Therefore, this study considers obstacles as an important factor in the process of UWSN node deployment. To address the obstacles in actual water environments and the demand for simple calculation, this paper presents the underwater sensor network redeployment algorithm based on wolf search (RAWS) to obtain good underwater network coverage. The wolf search algorithm has two advantages over other swarm intelligence algorithms. These two advantages are independent search and predator avoidance. After the network model and underwater sensing node model are decided, it combines with the independent search characteristics of the wolf search algorithm. Each node can implement independent search in the deployment process. Any two nodes will not move in the same direction unless a common coverage target exists. They can rapidly search, and they have global optimization ability. Different from other swarm intelligence optimization algorithms, the proposed algorithm eliminates a wide range of internal communication among individuals and reduces computational complexity. Furthermore, it sets the escape mechanism in the process of node movement, and thus it avoids the aquatic organism obstacles. It allows the algorithm to jump out of the local optimal solution to maintain the diversity of the latter part of the solution space. The simulation results show that the distributed redeployment algorithm can effectively increase network coverage and avoid node failure resulting from external force. The rest of this paper is organized as follows: in Section 2, the related works are introduced; in Section 3, the three models of UWSNs and related definitions are described; in Section 4, the problem is analyzed, and the principle and the progress of the RAWS algorithm are implemented. In Section 5, the description of algorithm simulation and the detailed analysis of the simulation results are discussed. Finally, in Section 6, the conclusions and future work of our study are drawn.
## 3 Results and Discussion
### Glycosidally Bound Volatiles and Related Gene Expressions
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Volatiles** & **G2** & **XZZ** & **G4** & **BY** \\ \hline
3-methyl-1-Butanol & - & - & \(2.16\pm 0.10\) b  & - \\
3-methyl-3-Buten-1-ol & - & - & \(3.34\pm 0.28\) a  & \(0.63\pm 0.06\) b  \\
1-Octen-3-ol & \(1.60\pm 0.01\) b  & \(1.12\pm 0.56\) b  & \(2.61\pm 0.75\) a  & \(1.25\pm 0.12\) b  \\
1-Heptanol & \(1.35\pm 0.01\) b  & \(1.26\pm 0.09\) b  & \(2.46\pm 0.23\) a  & \(1.31\pm 0.04\) b  \\ Geraniol & - & - & \(0.24\pm 0.04\) b  & \(0.91\pm 0.13\) b  \\ Linalool & \(1.32\pm 0.01\) b  & \(1.20\pm 0.02\) b  & \(1.21\pm 0.03\) b  & \(2.51\pm 0.01\) a  \\ \(\alpha\)-terpineol & \(1.01\pm 0.02\) b  & \(1.21\pm 0.01\) b  & \(1.32\pm 0.04\) b  & \(3.21\pm 0.04\) a  \\ Citronello & \(2.31\pm 0.01\) c  & \(2.51\pm 0.01\) c  & \(3.01\pm 0.01\) b  & \(4.31\pm 0.01\) a  \\ Isogeraniol & \(1.31\pm 0.01\) b  & \(0.92\pm 0.01\) b  & \(2.14\pm 0.01\) a  & \(2.35\pm 0.01\) a  \\ Terpinolene & \(2.31\pm 0.11\) c  & \(3.21\pm 0.01\) b  & \(3.65\pm 0.20\) a  & \(4.31\pm 0.22\) a  \\ Benzyl alcohol & \(4.92\pm 0.01\) c  & \(4.20\pm 1.43\) c  & \(7.17\pm 1.16\) c  & \(11.82\pm 1.50\) b  \\ Phenylethyl alcohol & \(0.49\pm 0.01\) b  & \(0.66\pm 0.26\) b  & \(1.32\pm 0.20\) b  & \(0.69\pm 0.02\) b  \\
1-Hexanol & \(5.36\pm 0.08\) a  & \(1.46\pm 0.61\) d  & \(4.14\pm 0.26\) b  & \(3.08\pm 0.38\) c  \\
2-Ethyl-1-hexanol & - & - & \(4.27\pm 0.45\) c  & \(5.92\pm 1.09\) a  \\ (Z)-3-Hexen-1-ol & \(0.43\pm 0.09\) b  & \(0.17\pm 0.02\) b  & \(1.15\pm 0.14\) a  & \(0.62\pm 0.54\) ab  \\
**Total alcohols and** & 22.41 & 17.92 & 40.19 & 42.92 \\
1-Undecanol & \(7.45\pm 0.10\) a  & \(1.05\pm 0.25\) d [FONOTE:d]Footnote a: footnotemark: [ENDFOOTNOTE] & \(5.70\pm 1.21\) b  & - \\ Nonanal & \(2.22\pm 0.17\) b  & \(0.51\pm 0.18\) c  & - & - \\
2-ethyl-2-Hexenal & \(4.12\pm 1.12\) d  & - & \(5.25\pm 0.36\) c [FONOTE:c]Footnote b: footnotemark: [ENDFOOTNOTE] & \(2.15\pm 1.22\) e  \\
**Total aldehyde** & 13.79 & 1.56 & 10.95 & 2.15 \\ Hexadecanoic acid, ethyl ester & - & \(1.26\pm 0.09\) b  & - & \(1.20\pm 0.20\) b  \\ Methyl salicylate & \(9.54\pm 0.59\) a  & - & \(3.44\pm 0.15\) c  & \(10.00\pm 2.41\) a  \\ Dibutyl
# Efficient Removal of Lead Ions from Water by a Low-Cost Alginate-Melamine Hybrid Sorbent
## 3 Results and Discussion
### Adsorption Mechanism  
To reveal the adsorption mechanism of the Alg-Mel, we also monitored the changes in Ca\({}^{2+}\) and Pb\({}^{2+}\) concentrations in the adsorption process. The ICP-OES analysis indicated that the concentration of Ca\({}^{2+}\) increased with the reaction proceeded and the concentration of Pb\({}^{2+}\) decreased gradually. However, after the adsorption, the mole mass of the adsorbed Pb\({}^{2+}\) ions became greater than that of the desorbed Ca\({}^{2+}\) ions, indicating that the ion exchange is not the only adsorption mechanism in the whole process. By combined with the COO\({}^{-}\) (carboxylate) peak of the Alg-Mel shifted after the Pb\({}^{2+}\) adsorption (the carboxylate peak of the Alg-Mel shifted from 1329 to 1294 cm\({}^{-1}\)), which might be caused by the coordination of the carboxylate groups with the Pb\({}^{2+}\). Thus, we proposed a possible adsorption mechanism as depicted in Figure 8.  
\begin{table}
\begin{tabular}{c c c c} \hline
**adsorbent** & **Target Heavy Metals** & \begin{tabular}{c} **Maximum Adsorption** \\ **Capacity (mg/g)** \\ \end{tabular} & **Reference** \\ \hline Polyving alcohol/polyacrylic acid double & Cd\({}^{2+}\), Pb\({}^{2+}\) & 115.9 (Cd\({}^{2+}\)), 195.0 (Pb\({}^{2+}\)) & [28] \\ \
\begin{tabular}{c} Modified mesoporous SiO\({}_{2}\) \\ \end{tabular} & Pb\({}^{2+}\) & 147.5 & [29] \\
\begin{tabular}{c} Chitosan coated calcium alginate \\ \end{tabular} & Pb\({}^{2+}\) & 106.9 & [30] \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 8: Possible binding mechanisms of Alg-Mel with Pb\({}^{2+}\).
## 1 Introduction
This communication is only between the sensors and the sink, whose task here is to process the collected information and transmit it to Tier-2 through ZigBee, Bluetooth, Wi-Fi or some other short-distance transmission technology. Tier-2 is inter-WBAN communication,  
Figure 1: The basic idea of the WBAN system and its applications. which uses smartphones, personal computers or other intelligent electronic devices. Ad hoc architecture is distributed to communicate in this tier with a random topology. The function of Tier-2 is to forward the information sent by the sensor to Tier-3 (terminal center) through 3G/4G/5G, WLAN and other wireless technologies. Tier-3 is the beyond-WBAN communication, in which the terminal center is mainly composed of remote servers. The function is to store and analyze the received information, which can be used for monitoring, diagnosis and treatment of diseases. Especially when the received data are abnormal, an emergency response and alarm can be initiated, contributing to speeding up emergency care. Each tier has special technical difficulties and requirements that need to be solved. Numerous papers [12; 13; 14; 15; 16] have summarized the challenges faced by WBANs, but these surveys, which focus on some specific aspects of WBANs, are limited to systematically describing WBANs and only refer to several technical points, failing to provide solution schemes using integrated circuits (IC). In this paper, we denote the application and technical requirements of WBAN, and give the problems systemic, including the sensor nodes, frequency bands, channel models, ultra-short range communication systems, networking and protocols, safety and privacy protection and energy consumption. For each problem, we present a comprehensive and deep analysis and try to present some hints for technical solutions. In order to solve the problems of safety and energy consumption in particular, we put forward designs for application-specific integrated circuit (ASIC) schemes which not only achieved good performances but also can contribute to the future batch production and application of WBANs.
# Mobile Broadband Performance Evaluation: Analysis of National Reports
## 3 Case Studies
### White Paper on Information and Communications: Japan  
Regulator\(\rightarrow\) As to the state of communication in Japan, the Ministry of Internal Affairs and Communications (MIC) provides the evaluation report. The MIC of Japan's latest report was released in 2019, with most of the results covering up to 2018 [16]. The report dates back to 1993 in some different forms.  
Operators\(\rightarrow\) The three major mobile broadband service providers in Japan are NTTDO-COMO, KDDI, and SoftBank.  
Method_\(\rightarrow\) There is no clear statement in the report. The results indicate survey and MNO data have been used.  
Metrics_\(\rightarrow\) Most of the metrics presented in the report are not technical.  
* Number of subscribers;
* Average revenue per user (ARPU);
* Fixed vs. mobile communication services;
* Data vs. voice services;
* Market share.  
Evaluated services\(\rightarrow\) 3.9G or broadband wireless access (BWA) and 4G service.  
Result_\(\rightarrow\)
* The number of BWA users is estimated to be \(>\)136 million and 4G subscribers are more than 66 million.
* While the percentage of fixed-line communication sales has been shrinking over the years, those of mobile broadband have been growing.
* Although yearly ARPU is increasing, KDDI and SoftBank registered the highest and lowest ARPU, respectively.
* Voice service shrunk over the years while the usage of data services increased significantly over the years.
* As of 2019, NTT DOCOMO has the highest mobile subscribers market share with 39% total market. KDDI and SoftBank have 27% and 23% shares of the market, while the rest is shared among the subsidies of the three companies.  
Policies_\(\rightarrow\) A new comprehensive verification of competition rule is proposed by the organization to ensure the growth of telecommunication. The rule requires MNOs to have (1) local and global vision, (2) have enhanced communication maintenance, (3) maintain net neutrality, and (4) address future IT platform services.  
The report provided by MIC is exhaustive from a market metrics point of view. However, when it comes to technical mobile broadband service assessment and evaluation, it does not provide comprehensive results. Although a thorough investigation of other organizations that also provide a quality evaluation of mobile broadband services, such as Statistics Bureau (Japan e-stat accessed on 26 January 2022) and Ministry of Economy, Trade and Industry (Japan meti accessed on 26 January 2022), a useful technical evaluation result could not be obtained.