esa-sceva/satcom-chunk-collection huggingface dataset:
# Forecast Analysis of Pollutant Emissions of Cruise Ship Routes in Western Mediterranean
###### Abstract  
The cruise shipping industry has a steadfast commitment to the environmentally sustainable operation of ships through many initiatives to reduce greenhouse gas emissions. This paper presents a forecast analysis of the emission inventory of NO\({}_{x}\), SO\({}_{2}\), and CO\({}_{2}\) during the sailing phase associated with cruise routes in the Western Mediterranean. Five scenarios were raised to obtain projections about future emissions considering the evolution of cruise traffic; the implementation of the Sulfur Emission Control Area (SECA) in the Mediterranean by 1 May 2025, and variations in the specific fuel consumption of the engines of the cruise ships. To this extent, a time series of cruise calls during 2019 in Mediterranean ports is used to identify the main routes and their associated emission inventory. Network analysis is applied to identify the routes associated with the cruise calls, and the emission inventory is estimated using a bottom-up approach. A total of 292 cruise routes were identified. The estimations reveal that the emissions of the cruise activities assessed are approximately equivalent to 0.58% of NO\({}_{x}\), 0.28% of SO\({}_{2}\), and 1% of CO\({}_{2}\) of the entirety of the emissions associated with maritime transport at the European level in 2019. The projection of the scenario with the highest cruise activity and the SECA being enforced leads to a reduction of 77% of the emissions of SO\({}_{2}\) considering the activity of 2019. The research conducted enables us to assess the environmental impact of the intense cruise ship activity registered in the Mediterranean and the positive footprint of the implementation of new regulations on emissions.  
cruise ships; network analysis; activity-based method; shipping emissions; cruise routes; emission control area +
Footnote †: journal: Applied  
1
# Probing Collisional Plasmas with MCRS: Opportunities and Challenges
## 2 Theory
### Examples of Prior Studies of Collisional Plasmas  
To give an impression of typical MCRS results obtained while studying collisional plasmas, two prior studies of atmospheric-pressure plasma jets are discussed. In the first study [19] the electron dynamics of the spatial afterglow of a radio-frequency driven atmospheric-pressure plasma jet and in the second study [20] acoustic waves produced by such a jet were investigated. A schematic overview of the experimental setup used in both studies is presented in Figure 4. Plasma was created by applying an RF voltage on the central needle electrode with respect to the cylindrical shell-shaped grounded electrode. Between these electrodes, a helium gas flow was present which was (partially) ionized by the electric field between the electrodes. As MCRS is a differential measurement scheme, it requires periods in which no plasma is present for the measurement to be successful. For this reason, the plasma was produced for short periods, i.e., 50 \(\upmu\)s in these experiments, with a repetition rate \(f_{\rm rep}\).  
For the MCRS measurements, a cylindrical microwave cavity with a resonance frequency \(f_{\rm res}\) of 3.5 GHz for the TM\({}_{010}\) mode was used. This mode had a quality factor of \(Q=4\times 10^{3}\) and provided a temporal resolution of \(\sim 1\)\(\upmu\)s. The gas flow and discharge were able to enter and exit the cavity via concentric holes of 13 mm in diameter in the top and bottom flat walls. A microwave generator was used to create the probing signal with power \(P_{\text{in}}\) (1 mW) at frequency \(f=\omega/2\pi\). The microwaves travelled via a directional coupler to a straight antenna that protruded a few mm into the cavity. Ten percent of the reflected microwaves travelled via the directional coupler to a logarithmic power detector of which the output was sampled at 10 MHz using a transient recorder. To synchronize this sampling procedure with the discharges, the recorder and RF power delivery system were triggered by a pulse/delay generator. As the discharge dynamics were extremely reproducible, it was allowed to use multiple discharges to create a spectral response for each time step and to reduce noise by averaging. Setting the applied microwave frequency and saving the data of the transient recorder was done by in-house developed software.  
Figure 4: A schematic representation of the experimental setup comprising the plasma source, the resonance cavity, and the control and read-out electronics. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON], Applied Physics Letters, Vol. 115, Article ID 254103, 2019; licensed under a Creative Commons Attribution (CC BY) license.
## 3 Results and Discussion
### Analysis of Porosity  
The role of lithium-ion battery diaphragm is not only to separate the positive and negative electrodes but, more importantly, to provide a channel for the transport of lithium-ions, a higher porosity helps to promote electrolyte absorption, making lithium-ion transfer more efficient. The absorption rate of the diaphragm also indirectly reflects the porosity, and the performance of both has an important impact on the ionic conductivity, internal resistance, and cycle life of the battery [30]. The test results are shown in Table 3. As the commercial Celgard 2400 diaphragm is not hydrophilic, the affinity for n-butanol was relatively poor, and the diaphragm porosity was only 38%. [PERSON] et al. [13] prepared filled ceramic (Al\({}_{2}\)O\({}_{3}\)) materials in polyurethane (PU) and used an electrostatic spinning technique to prepare new PUC diaphragms with three-dimensional structure, whose porosity was only 63.7%. The centrifugal spinning process relies on its unique fiber film-forming mode. Both PAN and PU have excellent spinnability, and PU-based and PU/PAN composite diaphragms were obtained. The porosity of the PU/PAN fiber diaphragms were relatively high. The porosity of the pure PU diaphragm was 82.2%, and when 18% PU:PAN = 8:2 and 18% PU:PAN = 7:3, the porosities were 83.5% and 83.9%, respectively. In the PU/PAN composite diaphragm, as the percentage of PAN in the polymer solution increased, the porosity of the diaphragm increased accordingly. The porosity of the PU-based nanofiber diaphragm was higher than the commercial Celgard 2400 diaphragm, indicating that the PU-based diaphragm was more capable of preserving the electrolyte, which promoted the rapid transfer of lithium-ions. Therefore, the PU/PAN composite diaphragm also had a better electrochemical performance than the PU diaphragm.  
Figure 4: SEM images of an 18% PU:PAN = 7:3 fiber membrane.  
Figure 3: SEM images of an 18% PU:PAN = 8:2 fiber membrane.
# Improvement of Global Ionospheric TEC Derivation with Multi-Source Data in Modip Latitude
## 2 Data and Analysis Method
### Analysis Method
#### 2.2.2 Spherical Harmonic Function  
In our method, observations are combined and fitted by an SH function with a degree and order of \(n_{max}\) as an interpolation technique to derive the global ionosphere, which can be expressed as:  
\[VTEC(\phi,\lambda)=\sum_{n=0,m=0}^{n_{max}}\widetilde{P}_{nm}(\sin\phi)( \widetilde{A}_{nm}\cos(m\lambda)+\widetilde{B}_{nm}\sin(m\lambda)), \tag{8}\]where \(n_{max}\) is the maximum degree of the SH expansion, \(\widetilde{P}_{nm}\) is the normalized associated Legendre function of degree \(n\) and order \(m\), \(\widetilde{A}_{nm}\) and \(\widetilde{B}_{nm}\) are the unknown coefficients to be determined, \(\phi\) and \(\lambda\) are the latitude and longitude of IPPs.
## 5 Experiment and Result
### Naturalness  
For simplicity, we abbreviate the system with acoustic word embeddings to AWE. With the analysis above, we use Conv+Tranformer architecture as the word encoder and set the word frequency threshold to 50 in AWE. In this section, we compare AWE with three other systems in terms of naturalness. (1) None: As is described in Section 3, we first built a basic FastSpeech2-based TTS system without using word information. (2) GloVe: We followed the prior work [8] that extracted linguistic word embeddings from pretrained NLP tasks. Similarly, the word embeddings were encoded and then added to the phoneme encoder output. In our experiments, we obtained the linguistic word embeddings from GloVe [20]. (3) BERT: We followed prior work [11] that extracted word representations from pretrained BERT and then applied the word representations to the TTS system. It should be noted that BERT takes subword units as input, and each word often corresponds to multiple BERT hidden states. Thus, we applied an average pooling to the multiple states in order to obtain the word representations [21].  
An AB preference subjective listening test was carried out in terms of naturalness (The audio examples are available at [[https://cpdu.github.io/acoustic_word_embedding](https://cpdu.github.io/acoustic_word_embedding)]([https://cpdu.github.io/acoustic_word_embedding](https://cpdu.github.io/acoustic_word_embedding)) (accessed on 11 August 2021)). Ten listeners with no prior experience in TTS participated in this test. Ten pairs of synthetic speeches were provided to the participant in one trial to select the better one of each pair. Each pair consists of one speech randomly selected from 50 AWE synthetics and the other randomly chosen from 50 BERT/GloVe/None synthetics. Each participant went through three trials (BERT, GloVe, and None) and made 30 judgments in total. Figure 6 demonstrates the subjective results. As expected, the proposed system with acoustic word embeddings not only outperformed the baseline that does not use any word information directly, but also outperforms the systems that use linguistic word embeddings.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Word Freq Thres** & **Vocabulary Size** & **OOV Ratio** \\ \hline
10 & 2118 & 0.087 \\
50 & 529 & 0.227 \\
100 & 266 & 0.300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The vocabulary sizes and OOV ratios with various word frequency threshold.  
Figure 5: The log-likelihood curves of phone-level prosodies with various word frequency thresholds.
## 3 Results
### Machine Learning for Existing Buildings  
For the dataset containing variables describing existing buildings in Koszalin, a complementary analysis, similar to that in Section 3.5.3, was conducted. In Table 10, alongside the results of the existing baseline linear model (Model C) and the best LASSO-regularised model from [1], the performance of the best-obtained gradient-boosted tree model (_XGBoost-eb_) and the top neural network model (_NN6(10-6)eb_) is presented. Next, models were combined according to the principles described in Section 3.5.2. Table 10 presents the results achieved by three of such hybrids.  
The baseline linear model obtained in [1] had average characteristics, with a mean absolute error (MAE) of 20.95 m\({}^{2}\). The regularised model performed only slightly better in predicting the usable floor area of buildings.  
Gradient-boosted tree models significantly reduced the average errors in estimating the usable floor area of buildings compared to the baseline model. For the best-performing model, the MAE value dropped to 15.94 m\({}^{2}\)--an improvement of over 20%--while the maximum error was reduced to 30 m\({}^{2}\). These results were further improved with the use of neural networks. The best of the network architectures tested, featuring 6 inputs and 10 and 6 neurons in the hidden layers, delivered another significant improvement in estimation accuracy. For this model, the mean squared error (MSE) decreased to 10.81 m\({}^{2}\), with a maximum error below 21 m\({}^{2}\)--results that can already be considered satisfactory. They are comparable to those obtained for house design projects in Section 3.5.1, and even slightly better.  
Several hybrid models were also created, with the results of three of them presented in Table 10. Notably, even the model that averages the baseline model \(C\) and the LASSO-regularised model outperforms each of its component models. Thus, the simple act of combining two weaker models resulted in a clear benefit, significantly reducing MAE and noticeably lowering RMSE. This result is worth highlighting. Another hybrid model presented is a regularised linear model, where its predictions are replaced by those of the baseline model if the covered area exceeds 180 m\({}^{2}\). This model proved to be slightly better than the \(C+LASSO\) model in terms of RMSE, meaning the largest errors were reduced--particularly the maximum error, which was lowered to the level previously achieved by the \(LASSO\) model. The last model is a hybrid of a neural network and the baseline model \(C\) run for observations with a large covered area (exceeding 200 m\({}^{2}\)). This combination allowed for further improvements in the neural network model estimations. On the test set, it achieved a mean squared error (MSE) of 9.99 m\({}^{2}\) and a root mean squared error (RMSE) of 11.91 m\({}^{2}\), making its results comparable to the best models presented in Section 3.5.2.
_Article_  
A Method Validation for Simultaneous Determination of Phthalates and Bisphenol  
A Released from Plastic Water Containers  
[PERSON] 1, [PERSON] 2, [PERSON] 2, [PERSON] 3 and [PERSON] 1,* 4  
Received: 8 July 2019; Accepted: 19 July 2019; Published: 23 July 2019  
**Featured Application: If plastic bottles are left in the heat for a long time, they can be harmful to human health. One has probably seen pallets of water bottles in front of large supermarkets waiting for someone to take them away from the sun and heat. The main problem derives from the fact that the commonly defined plastic bottles, once in contact with heat sources, release both phthalates and bisphenol A. This means that great care must be taken not to expose water bottles to food, neither during transport, nor in supermarket stores in any way. For this reason, the authors propose an easy, robust, and rapid method to determine such compounds with high precision and accuracy.**  
**Abstract: Phthalates (or phthalate esters, PAEs) and bisphenol A (BPA) are widely used in various industries, particularly in the fields of cosmetics and packaging, and they increase the malleability and workability of materials. As a result of their use, some international health organizations have begun to study them. In this study, the authors developed a methodology for the simultaneous determination of dimethyl phthalate (DMP), diethyl phthalate (DEP), disobutyl phthalate (DiBP); dibutyl phthalate (DBP), bis(2-ethylhexyl) phthalate (DEHP); di-n-octyl-phthalate (D_n_OP) and bisphenol A (BPA) from drinking and non-potable waters. The extraction of PAEs and BPA was performed using a solvent-based dispersive liquid-liquid microextraction (SB-DLLME) method. The analytical determination was performed using a gas chromatography-ion trap mass spectrometry (GC-IT/MS) analysis. The entire procedure was validated as recoveries were studied according to the volume and the extraction solvent used, pH, and ionic strength. Dynamic linearity ranges and linear equations of all the compounds were experimentally determined as well as the limit of detection (LOD) (1-8 ng mL\({}^{-1}\)) and the limit of quantification (LOQ) (5-14 ng mL\({}^{-1}\)), reproducibility, and sensitivity. The method was applied to 15 water samples (mineral water and tap water) for determining PAEs and BPA released from the plastic container. After the release simulation, four PAEs (i.e., DiBP, DBP, DHEP, and D_n_OP) were determined at very low concentrations (below 1.2 ng mL\({}^{-1}\)) in two water samples from (sport) bottles.  
**Keywords: phthalates; bisphenol A; water; plastic container; DLLME; GC-IT/MS**
## 2 Drivers of Photovoltaic Technologies
### PV Module Costs  
Module costs are usually based on market prices and quoted as price per unit of power, measured under STC. They may be quoted in USD/W\({}_{\text{p}}\), or EUR/W\({}_{\text{p}}\). The price of a module may vary in different countries, due to the tax systems, the costs of transport and the volume of deliveries, the margins of resellers, etc. Differences in prices in different countries can be significant. For example, compared to module prices in Germany, module prices are 17% higher in Australia, 52% higher in the USA, and 60% higher in Japan [9].  
As cumulative production levels have increased, module costs have decreased, as shown in Figure 4 for crystalline silicon (c-Si) and thin-film modules (TF). Experience with the technology has resulted in improved yields, more efficient use of raw materials, and reduced labour costs. There have been step-changes through technological breakthroughs, the expansion of the production plants, and the use of increased automation. In addition, the costs of materials, components, and energy used in the production chain are important factors.  
A technological learning in PV modules [10] is demonstrated in Figure 4.
## 1 Introduction
A detailed analysis shows that even the existence of small nonlinear terms in the sensor models make the full-consensus-based algorithm inapplicable. The emphasis is placed on the algorithm derived by fixing the corrected sensor gain of one selected sensor, similarly as in the case of offset correction. It is proved in Theorem 3--using the arguments related to slowly varying linear dynamic systems--that the algorithm is B.I.B.O.-stable, and, therefore, applicable in practice. Section 5 is concerned with _simultaneous offset and gain calibration_. After deriving a general algorithm generating offset and gain corrections, we demonstrate that the algorithm with full consensus is again not reliable in practice, and that the algorithm with reference with regard to both offsets and gains ensures B.I.B.O. stability. In Section 6, attention is paid to the effects of the _stochastic environment_, encompassing _additive measurement and communication noises_. In the introductory part, we demonstrate how to extend the results related to the deterministic case (in Sections 2-5). A concise presentation is given for the offset and gain-correction algorithms under additive measurement and communication noises, indicating their mean square convergence to consensus. Section 7 is devoted to carefully chosen _simulation examples_ that illustrate the conclusions formulated in the preceding sections. Section 8 is devoted to the following: (a) a description of a newly developed _multichannel instrument for aerodynamic pressure sensing_, dedicated to aerodynamic testing of fast-moving objects, or large structures under the influence of wind; (b) a description of an implementation of the above-presented distributed blind calibration algorithm embedded in the newly developed instruments; (c) the presentation of the results of blind calibration of the newly developed instrument during normal system operation. We first provide an introduction into aerodynamic pressure measurements, followed by a short description of a novel multichannel pressure-sensing instrument with improved properties with respect to the existing ones. Then, we define the calibration problem to be solved, and introduce the corresponding assumptions based on both the instrument's design and the way the aerodynamic testing experiments are typically carried out. We point out that the developed calibration algorithms are well suited for online and real-time blind calibration of the developed instruments. After paying attention to the general aspects of the implementation of the proposed calibration algorithms, we present and discuss some initial results of calibration under normal system operation. These results represent an indication of how to calibrate networks of complex measurement instruments.
# A Framework for Fall Detection Based on OpenPose Skeleton and LSTM/GRU Models
## 3 Model Evaluation and Results
### RNN, LSTM and GRU Model Training Comparison
#### 3.3.2 Hidden Nodes Comparison  
This section examines the impact of different Hidden Nodes on the relative position normalization dataset. There are 64, 128, 256, 512 and 1024 different numbers, and the learning rate is 0.01. If there are too few Nodes, the problem may not be described. When there are more Nodes, a smaller error can be observed, but the convergence is slower. However, if the number of Nodes is continuously increased, the calculation time will increase, and smaller errors may not be found.  
As shown in Table 5, we can see that the accuracy of 1024 Nodes is 88% for RNN. Because of the simple structure, more Nodes are needed to improve learning performance. However, 1024 Nodes are less effective for LSTM, with an accuracy of 94.6%. In fact, LSTM reaches the highest accuracy of 98.3% at 512. We hypothesize that this may be due to the complex structure of the LSTM model, and when too many Nodes are used it may over-describe the event, thus causing a decrease in accuracy. We observe the same trend for GRU and LSTM. The highest accuracy of GRU is 96.6% with 512 Nodes.
# Review on the Electrical Resistance/Conductivity of Carbon Fiber Reinforced Polymer
## 2 Structure of CFRP and Its Electrical Conductivity
### Novel Methods  
Metal mesh increases the manufacturing cost and structural weight and lightning strike damage cannot be avoided by metal mesh even though LSP is adopted in CFRPs. Therefore, a new solution is highly desired for the purpose of improving the lightning damage resistance in the CFRP [6]. To reduce the structural weight, random Bucky paper, aligned carbon nanotubes (CNTs) and other approaches have also been considered. Material selection and protective measures are significant ways to reduce lightning strike damage. Aerospace structures should be developed based on the consideration of lightning strike protection. The main factor influencing the lightning strike damage in composite structures is the low electrical conductivity of resin. Several techniques were developed to increase the conductivity of epoxy. Carbon black was generally used as the reinforcement to increase the conductivity of epoxy in CFRPs. CNT reinforcement has been recently applied for this purpose since CNT has excellent mechanical properties and high electrical and thermal conductivities [93].