esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper has proposed using the threshold voltage and current value as a standard for coordinating and setting the instantaneous overcurrent relay protection. The simulated result was analysed to confirm the model's viability in calculating the tripping time, delay time and operating time of the high-voltage transmission line relay. It also analyses or detects the maximum and minimum threshold voltage and current suitable for optimising the relay and circuit breaker for optimal performance. This technique helps reduce the time delay and improve the relay's tripping time. One of the constraints of this technique is the inability to optimise the threshold current for different current and voltage types. However, this technique has poor discrimination in distinguishing between fault currents at different points when the fault impedance between two points is small.  
Additionally, coordinating is challenging and necessitates changes as the load increases and the optimisation of the model to accommodate different voltage inputs synchronously. This process can be used for all fault types. Therefore, it has a superior and effective tripping time compared to other techniques in the literature.  
**Author Contributions:** Conceptualization, methodology, software, formal analysis, investigation, data curation and writing, V.N.O. Validation, writing--review and editing, visualization, supervision, and project administration, S.H. and K.A.A.G. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**List of Acronyms**  
TL Transmission Line  
TEC Thevenin Equivalent Circuit  
WT Wavelet Transform  
DWT Discrete Wavelet Transform  
CWT Continuous Wavelet Transform  
SSU Supply and Switching Unit  
IDMT Inverse Definite Minimum Time  
PMU Phasor Measurement Unit  
TMS Time Multiplier Setting  
L-G Single Phase to Ground Fault  
LL-G Double Phase to Ground Fault  
LLL-G Three Phase to Ground Fault  
RBFNN Radial Bias Function Neural Network
## 4 Results and Evaluation
### Wilcoxon Test
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Scores**} \\ \cline{3-4}  & & **F1** & **AUC** \\ \hline \multirow{4}{*}{Logistic Regression (Ensemble BOW and Handcrafted)} & CSRRS & 0.6335 & 0.7357 \\ \cline{2-4}  & DDVHSM & 0.9433 & 0.9470 \\ \cline{2-4}  & Kaggle & 0.9428 & 0.9438 \\ \hline \multirow{4}{*}{Logistic Regression (Ensemble BOW)} & CSRRS & 0.6469 & 0.7395 \\ \cline{2-4}  & DDVHSM & 0.9026 & 0.9302 \\ \cline{2-4}  & Kaggle & 0.9236 & 0.9376 \\ \hline \multirow{4}{*}{Random Forest (parser-tree)} & CSRRS & 0.5231 & 0.6568 \\ \cline{2-4}  & DDVHSM & 0.7611 & 0.7115 \\ \cline{2-4}  & Kaggle & 0.6149 & 0.6606 \\ \cline{2-4}  & LOSADA2016 & 0.2797 & 0.6815 \\ \cline{2-4}  & LOSADA2018 & 0.2545 & 0.6771 \\ \hline \multirow{4}{*}{Naive-Bayes (VAD and Topics)} & CSRRS & 0.5740 & 0.6810 \\ \cline{2-4}  & DDVHSM & 0.9553 & 0.9502 \\ \cline{2-4}  & Kaggle & 0.7853 & 0.8011 \\ \cline{2-4}  & LOSADA2016 & 0.2392 & 0.6247 \\ \cline{2-4}  & LOSADA2018 & 0.2097 & 0.6404 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Mean F1 and AUC scores for each model tested. Figure 7: A comparison plot of the AUC for the best proposed representation using PBC4 cip, and other classifiers found in the literature.
# Toward Addressing Location Privacy Issues: New Affiliations with Social and Location Attributes
## 4 Discussion
### User's Representation through Socio-spatial Attributes  
Social actors are affected by previous experiences while making future choices [2], so tracking users' trajectory using location information facilitates assumptions about user's normativity, as future choices can be inferred through past records on social status, geographic places, and users' ambitions, which will have already defined users as biographical subjects [2; 10]. Users' online behavior that is illustrated in a specific place and time respectively will constitute an online habit, which will be descriptive enough to create affiliations and correlations for identifying not only a user's identity but also their future options and the relationships among them. That is why location-privacy protection mechanisms (LPPMs) use a number of techniques to protect this sensitive information [21; 43], while at the same time users tend to express their concerns on potential threats. Combining attributes of social identity to location attributes may disclose information concerning users' privacy in a way that arises additional privacy issues. Thus, taking these new affiliations into consideration while summarizing the reviewed papers for our analysis, the need for users to protect their privacy became evident. This need was important to provide the necessary questions to design our methodology before trying to address those privacy issues.
# Sildenafil Citrate Liposomes for Pulmonary Delivery by Ultrasonic Nebulization
## 1 Introduction
Sildenafil formulations based on solid lipid nanoparticles [19; 20] and polymeric biodegradable nanoparticles [21; 22; 23; 24; 25] have been recently developed and are currently being assessed as potential pulmonary delivery systems. However, liposomal formulations of sildenafil have only been proposed for vaginal delivery [26]. The results of an in vitro study on the stability of nebulized sildenafil citrate loaded liposomes [27] are the only reported data related to the use of liposomes for the pulmonary administration of sildenafil. Among the commercially available inhaler systems, ultrasonic nebulizers are frequently used for liquid formulations. The shearing provided during nebulization to convert the aqueous liposome dispersions into aerosol droplets may exert physical stress, causing drug leakage or changes in liposome morphology [28]. [PERSON] et al. [29] investigated the impact of atomization techniques on the stability and transport efficiency of liposomes showing different surface characteristics. The authors found that conventional liposomes were the most stable, while polymer-coated and positively charged liposomes were more prone to aggregation and drug leakage. Regardless of therapeutic aims, in most cases the methods used to prepare liposomes involve the use of organic solvents to obtain a lipid solution. From the original method of Banghan [30] to those based on simple or multiple emulsification [31; 32; 33], as well as modified ethanol injection methods [34], all require the use and later removal of an organic solvent. Little attention has been paid to solvent-free procedures, although high-pressure homogenization and supercritical fluid methods have been applied to prepare liposomes [35; 36; 37; 38]. Sonication is used as an additional step to homogenize and reduce the size of previously formed liposomes, but recent studies have probed that the sonication of components, as a single step, is a procedure suitable for the preparation of liposomes [39]. The aim of present study was to optimize sildenafil citrate-loaded liposomes for pulmonary drug delivery, and to evaluate their stability after lyophilization and ultrasonic nebulization. The liposomes were prepared by a solvent-free procedure based on direct sonication of components. A pH transmembrane gradient was applied to increase entrapment efficiency and drug loading. Sucrose and trehalose were used as lyoprotective agents, and the influence of these agents on the characteristics of liposomes before and after nebulization was evaluated.
## 2 Battery Model Settings
### Battery Models
#### 2.2.2 Battery Operation  
For the operation stage, the assumptions made are summarized in Table 1. They are made in accordance with the results of the experimental campaign for some batteries for operation life and round-trip efficiency given in Reference [36].  
The operation of the batteries is strongly related to their operational context as explained in Section 3 for the different scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Average Efficiency** & **Up to 3000 Cycles** & **Up to 5000 Cycles** & **Up to 13,000 Cycles** \\ \hline LFP & 90\% & 85\% & - \\ NCM & 90\% & 85\% & - \\ VRFB & 85\% & 85\% & 85\% \\ NIB & 90\% & 85\% & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average operating efficiency of the batteries based on the number of life cycles.  
Figure 1: Percentage mass composition of the batteries in this study [30; 31; 32; 33; 11].  
In the results presented in this work, for VRFB a life of 13,000 cycles is considered, while for the other batteries it is expected to be equal to 5000 cycles, and the life in years depended on the charge-discharge history of the batteries in each scenario.
## 3 The Control Scheme
Thus, the control input \(\boldsymbol{\tau}\) is derived from the following:  
\[\boldsymbol{\tau}=\widetilde{\mathbf{H}}(\mathbf{q}\dot{\mathbf{q}},\ddot{ \mathbf{q}})+\widetilde{\mathbf{M}}\mathbf{u} \tag{14}\]  
where \(\widetilde{\mathbf{H}}(\mathbf{q}\dot{\mathbf{q}},\ddot{\mathbf{q}})\) can be expressed based on (12), as follows:  
\[\widetilde{\mathbf{H}}(\mathbf{q}\dot{\mathbf{q}},\ddot{\mathbf{q}})=\mathbf{ H}(\mathbf{q}\dot{\mathbf{q}},\ddot{\mathbf{q}})_{t-L}=\boldsymbol{\tau}(t-L)- \widetilde{\mathbf{M}}\widetilde{\mathbf{q}}_{(t-L)} \tag{15}\]  
and the control law \(\mathbf{u}\) is expressed as follows:  
\[\mathbf{u}=\ddot{\mathbf{q}}_{d}+\frac{q_{2}}{p_{2}}[\mathbf{K}_{2}(\mathbf{I }+\boldsymbol{\alpha}\mathbf{D})]^{-1}\Big{[}\dot{\mathbf{e}}_{c}\big{]}^{p_{ 2}/q_{2}-1}\Big{]}^{-1}\bigg{(}1+\frac{p_{1}}{q_{1}}\mathbf{K}_{1}|\mathbf{e}_{ c}|^{p_{1}/q_{1}-1}\bigg{)}\dot{\mathbf{e}}_{c}+\mathbf{K}\mathbf{s}+ \mathbf{K}_{sw}\text{sign}(\mathbf{s}) \tag{16}\]  
with  
\[\mathbf{s}=\mathbf{e}_{c}+\mathbf{K}_{1}\mathbf{e}_{c}^{p_{1}/q_{1}}+\mathbf{K }_{2}\dot{\mathbf{e}}_{c}^{p_{2}/q_{2}} \tag{17}\]  
\[\widetilde{\mathbf{q}}_{t-L}=\frac{\big{(}\mathbf{q}_{t}-2\mathbf{q}_{t-L}+ \mathbf{q}_{t-2L}\big{)}}{L^{2}} \tag{18}\]  
\[\mathbf{e}_{c}=\mathbf{e}+\boldsymbol{\alpha}\mathbf{e}_{s}=(\mathbf{I}+ \boldsymbol{\alpha}\mathbf{D})\mathbf{e} \tag{19}\]and the parameters of \(\mathbf{u}\) can be found in Table 3, below. Furthermore, we realize that \(\widehat{\mathbf{H}}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) constitutes the DTE and \(\mathbf{\ddot{M}}\mathbf{u}\) constitutes Syn-NFTSMC, as seen in Figure 4.  
As shown in (14), the input \(\boldsymbol{\tau}\) to the manipulator consists of two primary components, a TDE-based input and an NFTSM input. The TDE-based component minimizes the influence of unknown dynamics in the manipulator dynamics, ensuring that the system can handle uncertainties in the model more effectively. On the other hand, the NFTSM component is designed to asymptotically drive both the cross-coupling errors \(\mathbf{e}_{c}\) and the tracking error \(\mathbf{e}\) to converge to zero, thereby enhancing the accuracy of the control system over time. Moreover, the application of TDE brings a further advantage to the control scheme. Instead of directly computing the complex and computationally intensive dynamic parameters \(\mathbf{H}(\mathbf{q},\ddot{\mathbf{q}},\ddot{\mathbf{q}})\), the TDE-based component can be derived with the estimate \(\widehat{\mathbf{H}}(\mathbf{q},\ddot{\mathbf{q}},\ddot{\mathbf{q}})\) of \(\mathbf{H}(\mathbf{q},\dot{\mathbf{q}},\ddot{\mathbf{q}})\) using (15). This approach significantly improves the efficiency of the control system by minimizing the computational burden. By combining both the TDE-based and NFTSM components, the control system is able to handle both model uncertainties and the convergence of errors, providing a robust solution for controlling the manipulator.
# Asymmetric Quantum Multicast Network Coding: Asymmetric Optimal Cloning over Quantum Networks
## 3 Results
### \(1\to 3\) Optimal Asymmetric Quantum Universal Clones Multicast Protocol
#### 3.2.1 \(1\to 3\) Quantum Multicast Protocol
**Step 5:** The target nodes apply \(U_{5,DM_{1}}^{(rs)}\otimes U_{5,EM_{2}}^{(rs)}\otimes U_{5,FM_{3}}^{(rs)}\) to system \(DM_{1}EM_{2}FM_{3}\), where \(U_{5}^{(rs)}\) is defined by Equation (44). **Step 6:** The target nodes apply \(U_{6,DM_{1}N_{1}}^{(rs)}\otimes U_{6,EM_{2}N_{2}}^{(rs)}\otimes U_{6,FM_{3}N_{ 3}}^{(rs)}\) to system \(DM_{1}N_{1}EM_{2}N_{2}FM_{3}N_{3}\), where \(U_{6}^{(rs)}\) is defined by Equation (46). **Step 7:** The target nodes apply \(U_{7,DM_{1}}^{(rs)}\otimes U_{7,EM_{2}}^{(rs)}\otimes U_{7,FM_{3}}^{(rs)}\) to system \(DM_{1}EM_{2}FM_{3}\), where \(U_{7}^{(rs)}\) is defined by Equation (48). **Step 8:** Using \(2\log_{2}3\) ebits of entanglement resource, subspaces spanned by \(\{|0\rangle,|1\rangle,|2\rangle\}\) of the systems \(N_{2}\) and \(N_{3}\) are sent from the target nodes \(t_{2}\) and \(t_{3}\) to the target node \(t_{1}\), respectively. The target node \(t_{1}\) applies \(U_{8,N_{1}N_{2}N_{3}}\) to system \(N_{1}N_{2}N_{3}\) and discards systems \(N_{2}\) and \(N_{3}\). **Step 9:** The target node \(t_{1}\) applies the projective measurement \(\{P_{k}\}_{k=0}^{2}\) defined by Equation (59) on system \(N_{1}\) in the basis and discards the quantum system \(N_{1}\). Then, depending on the measurement outcome \(k\), the target node \(t_{1}\) applies \(U_{9}^{(r,s,k)}\) defined by Equation (51) on system \(D\). **Step 10:** The target nodes \(t_{1}\), \(t_{2}\), and \(t_{3}\) measure system \(D\), \(E\), and \(F\) in the Fourier basis \(\{d^{-1/2}\cdot\sum_{x=0}^{d-1}\omega^{px}|x\rangle\}_{p\in\mathbb{Z}_{d}}\), respectively. Then, they apply \(Z^{(p_{1}+p_{2}+p_{3})}\otimes Z^{(p_{1}+p_{2}+p_{3})}\otimes Z^{(p_{1}+p_{2}+ p_{3})}\) to system \(M_{1}M_{2}M_{3}\), where \(p_{1}\), \(p_{2}\), and \(p_{3}\) are the measurement outcomes on the target nodes \(t_{1}\), \(t_{2}\), and \(t_{3}\), respectively.
# Advances in Quantitative Earth Remote Sensing: Past, Present and Future
###### Abstract  
A combination of multispectral visible, infra-red and microwave sensors on the constellation of international Earth-observing satellites are providing unprecedented observations for all Earth domains over multiple decades (i.e., atmosphere, land, oceans and polar regions). This Special Issue of _Sensors_ is dedicated to papers that describe such advances in the field of Earth remote sensing and their applications to advance understanding of Earth's planetary system and applying the resulting knowledge and information to meet the societal needs during recent decades. The papers accepted and published in this issue convey the exciting scientific and technical challenges and opportunities for remote sensing of all domains of Earth system, including terrestrial, aquatic and coastal ecosystems; bathymetry of coasts and islands; oceans and lakes; measurement of soil moisture and land surface temperature that affects both water resources and food production; and advances in use of sun-induced fluorescence (SIF) in measuring and monitoring the contribution of terrestrial vegetation in the cycling of carbon in Earth's system. Measurements of SIF, for example, has had a profound impact on the field of terrestrial ecosystems research and modelling. The Earth Polychromatic Imaging Camera (EPIC) instrument on the Deep Space Climate Observatory (DSCVR) satellite located at the Sun-Earth Lagrange Point One, about 1.5 million miles away from Earth, is providing unique observations of the Earth's full sun-lit disk from pole-to-pole and minute-by-minute, which overcomes a major limitation in temporal coverage of Earth by other polar-orbiting Earth-observing satellites. Active and passive microwave remote sensing instruments allow all-weather measurements and monitoring of clouds, weather phenomena, land-surface temperature and soil moisture by overcoming the presence of clouds that affect measurements by visible and infrared sensors. The use of powerful in-space lasers is allowing scientists and engineers to measure and monitor rapidly changing ice sheets in polar regions and mountain glaciers. These sensors and their measurements that are deployed on major space-based observatories and small- and micro-satellites, and the scientific knowledge they provide, are enhancing our understanding of planet Earth and development of Earth system models that are used increasingly to project future conditions due to Earth's rapidly changing environmental conditions. Such knowledge and information are benefiting people, businesses and governments worldwide.  
earth observations; multispectral remote sensing; satellites; sensors +
Footnote †: journal: Sensors
## 3 Proposed LeSSA Framework  
Formally, we describe the LeSSA framework in this section, the implementation and technical details in the next section. LeSSA is a novel unified SA framework, which has been designed with the intentions for multi-domain sentiment classification in a case when the labeled data is scarce or not available. LeSSA is also applicable for heterogeneous domain sentiment classification. The core architecture of the LeSSA is illustrated in Figure 1. LeSSA is composed of three main layers: Feature Engineering (FE), Multi-Model Sentiment Learning (MMSL), and Sentiment Classification (SC).  
FE layer is responsible for generating effective sentiment features from unstructured data. The construction of suitable feature vector creation from the unstructured textual data is a significant task for well-learning performance in sentiment classification. In the literature, various feature generation techniques such as BOW, n-grams, POS tagged features and semantic features have been presented [7; 9; 56; 57; 58; 59]. In this paper, we extract and select the most appropriate features from review texts, which are adjectives, adverbs, verbs and nouns for learning sentiment classifiers.  
MMSL Layer, uses five different sentiment learners, such as wide coverage sentiment lexicon-based sentiment learner (WCSL-SL), high ranking pseudo-labeled based sentiment learner (HRPL-SL), Self-Training based Sentiment Learner (ST-SL), Active-Self-Training based Sentiment Learner (AST-SL) and Active-Self-Co-training based Sentiment Learner (ASCT-SL). For the first two sentiment learners, we utilize the induced WCSL for learning, while for the remaining learners, we use SSL techniques with the K-means clustering algorithm to generate an initial training set for first classifier learning. In the SC layer, the final fully trained sentiment learners based on WCSL-SL, HRPL-SL, ST-SL, AST-SL, and AST-SL are applied to the test data for sentiment polarity prediction. Finally, classification fusion is presented that determines the final sentiment polarity based on majoring voting. In the proposed framework diagram, the solid arrow line represents the model training workflow, while the dotted arrow line represents the offline workflow for real-time prediction of SA.
## 2 Materials and Methods  
### Data Preparation  
A total of 363 pregnant women having a fetus with a normal heart or CHD underwent fetal cardiac ultrasound screening at 18-34 weeks. Patients were examined in the four Showa University Hospitals (Tokyo and Yokohama, Japan). All women were enrolled in research protocols approved by the Institutional Review Board of RIKEN, Fujitsu Ltd., Showa University, and the National Cancer Center (approval ID: Wako1 29-4). All methods were performed in accordance with the Ethical Guidelines for Medical and Health Research Involving Human Subjects, and with regard to the handling of data, we followed Data Handling Guidelines for the Medical AI project. Not only expert sonographers, but also obstetricians with at least three years of experience, obtained fetal ultrasound videos under the guidance of experts. A total of 772 screening videos were acquired using commercially available ultrasonography machines (Voluson(r) E8 or E10, GE Healthcare, Chicago, IL, USA) equipped with an abdominal 2-6 MHz transducer in accordance with the guidelines [17; 18]. A cardiac preset was used, and images were magnified until the chest fills at least one-half to two-thirds of the screen. Each video consisted of the sequential cross-sections from the level of the stomach, through the heart, to the vascular arches, mainly in apical view. All data consisted of 349 normal cases and 14 CHD cases, and were randomly assigned for deep learning, as shown in Figure 1. The characteristics of the CHD cases are listed in Supplementary Table S1.
## 2 Materials and Methods  
### Instrumentation  
All voltammetric and amperometric measurements were carried out with a \(\mu\)Autolab III potentiostat interfaced to a PC for data acquisition via NOVA v2.0 (Metrohm, Barendrecht, The Netherlands) or an AMEL Model 466 polarographic analyser attached to an ABB Gorez SE120 chart recorder. An in-house low pass filter (time constant 22 s) was incorporated between the potentiostat and the chart recorder to substantially reduce stirrer noise. SPCEs are commercially available and were supplied by Gwent Electronic Materials Ltd. (Pontypool, UK). For CoPC-SPCEs, the working electrode was fabricated using a carbon-based ink with CoPC (C2030408P3) and the reference electrode was fabricated using an Ag/AgCl ink (C2130809D5). The working electrode area (3 mm \(\times\) 3 mm) was defined using electrical insulation tape. For plain SPCEs, the working electrode was fabricated using a carbon ink (C2030519P4), and the reference electrode was fabricated using an Ag/AgCl ink as before. The working electrode area was defined as before. All pH measurements were carried out with a Testo 205 (Testo Limited, Alton, Hampshire UK) pH meter. Solutions were stirred using a colour squid (IKA, Tunbridge Wells, UK) and warmed using a HAAKE P5 water bath (Thermo Scientific, Loughborough, UK). Surface morphology and composition of the working electrode were analysed using a Quanta FEG 650 scanning electron microscope (FEI, Hillsboro, OR, USA) (4000\(\times\) magnification; samples were gold-coated).
## 4 Experiment Setup and Performance Analysis
### Experiment Results and Analysis in the Ocean Scenarios
#### 4.3.1 Performance Analysis of Macrodiversity Reception Scheme Using Different Number of Antennas  
Compared with the park environment, the marine environment is relatively open, and there is no blocking object between the sensor node and sink node. However, the reflection plane of the signal changes over time due to the ocean waves. The variation in the height and slope of the ocean waves causes the reflection plane to show different tilt angles, which causes severe multipath effects. The macrodiversity system proposed in this paper configures multiple hard-decision receivers at different locations to receive data. Different paths have different channel fading factors. The combination of multiple receiving copies can greatly improve the success rate of data transmission using diversity. The experiment analyzes the PER and FER of macrodiversity reception scheme with different number of antennas varying with the transmission distance in the ocean scenarios and the experimental results are shown in Figure 13.  
As we can see from Figure 13, the macrodiversity reception scheme can obtain 3-4 dB transmission gain compared with single antenna reception scheme at PER = \(10^{-2}\). In detail, the macrodiversity reception scheme can achieve error-free reception within 600 m in the ocean scenarios, and still has good reception performance (FER < \(10^{-2}\)) within 1000 m. With the increase of the transmission distance, the reception performance of each cluster starts to decline, which causes the performance of macrodiversity reception scheme to decrease rapidly.  
Figure 11: Locations of buoys and sink node in the ocean scenarios.  
Figure 12: The sink node and the sensor node in the ocean scenarios.