esa-sceva/satcom-chunk-collection huggingface dataset:
# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
###### Abstract  
Photovoltaic (PV) power is attracting more and more concerns. Power output prediction, as a necessary technical requirement of PV plants, closely relates to the rationality of power grid dispatch. If the accuracy of power prediction in PV plants can be further enhanced by forecasting, stability of power grid will be improved. Therefore, a 1-h-ahead power output forecasting based on long-short-term memory (LSTM) networks is proposed. The forecasting output of the model is based on the time series of 1-h-ahead numerical weather prediction to reveal the spatio-temporal characteristic. The comprehensive meteorological conditions, including different types of season and weather conditions, were considered in the model, and parameters of LSTM models were investigated simultaneously. Analysis of prediction result reveals that the proposed model leads to a superior prediction performance compared with traditional PV output power predictions. The accuracy of output power prediction is enhanced by 3.46-13.46%.  
photovoltaic plant; power forecasting; recurrent neural networks; LSTM Article
# TSCH Multiflow Scheduling with QoS Guarantees:
## 3 Results
### Multiflow Test  
Previous tests analysed the behaviour of the network independently for each traffic flow. However, due to the diversity of traffic flowing through the WSN, in industrial environments it is necessary to guarantee independently each of the flows. Therefore, in this section we will study the behaviour of the schedulers with multiple flows from the same node. For these tests, the two protocols with the highest overall performance, SDN WISE-TSCH and Orchestra, were used.  
The results for the packet inter-arrival time are shown in Figure 8, with a packet period of 70 ms for flow 1, 140 ms for flow 2 and 500 ms for flow 3. The nodes transmitting these flows are 8, 9 and 10, as shown in Figure 8. Figure 8a shows how SDN WISE-TSCH performs a clear differentiation of the packets, guaranteeing regular arrival for each data flow, obtaining a DSR of 100% for both flows. in contrast with Orchestra (b) where the flows have variations that exceed the packet period. The DSR values obtained are 17%, 41% and 58%, this performance is clearly lower than that shown in Figure 6, because the network has a higher saturation and the available bandwidth is the same in both cases.  
The results obtained are summarised in Table 3. SDN WISE-TSCH is the only system that can differentiate unicast flows, which allows QoS parameters to be guaranteed independently. AMUS performs similarly to WISE-TSCH SDN when there is only one unicast flow but loses determinism as the number of flows increases. This leads to increases in end-to-end delay and PIAT. Therefore, in a multiflow network its behaviour is similar to that of Orchestra, but with a much higher energy consumption. Therefore, in the testbed implementation, only the Orchestra and SDN WISE-TSCH schedulers will be used.
# Towards Sentiment Analysis for Romanian Twitter Content
## 3 Data Processing Methodology
### Feature Extraction  
Key to any NLP task is the document internal representation, i.e., properly selecting the features from the raw text and encoding them to numerical values, so as to keep the representation tractable or to enrich it with some language semantics [3]. In our work, we tested the most popular approaches as suggested by the NLP literature [2; 3]: TF-IDF, Word2 Vec and Doc2 Vec. We learned the embeddings on the English dataset and its Romanian translation and we restricted the vocabulary to contain only the tokens which appear at least three times, removing a large number of infrequent tokens or those which may have been erroneously built in the preprocessing step.  
We notice that the resulting English vocabulary contains around 3100 tokens, while the Romanian one contains around 4000, due to the fact that Romanian is more verbose than English.  
In order to learn the Word2 Vec and Doc2 Vec embeddings for our data, we used the Gensim library [61]. For Word2 Vec, we worked with the Continuous Bag-Of-Words (CBOW) architectural model. For learning the Doc2 Vec embedding, we used the Distributed Bag-Of-Words (DBOW) with hierarchical softmax architecture. In both cases, we set thevector embedding size for each token to 200 and we trained each model with the following parameters: learning rate \(\alpha=0.025\), \(window=5\), over 5 _epochs_.  
For all three embeddings, the trained models were then applied on the testing sets.
## 4 Experiment
### Cross-Model Evaluation with Human Users Involved  
#### 4.2.1 Users for Cross-Model Evaluation  
To access the performance on different dialogue managers, simulated users and human users take part in the cross-model evaluation.  
**A group of user simulators (Group-S):** This group of user simulators is obtained through changing the settings of adjacency pairs and is optimized with the dialogue manager in MADM as one-to-many strategy via multi-agent reinforcement learning. The Group-S is composed of five different simulators: all-simulator where all the types of adjacency pairs is applied to reward shaping,  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Dialogue Acts** \\ \hline Dialogue manager & ask\_date,ask\_location,ask\_attendance, \\  & confirm\_date,confirm\_location,confirm\_attendance,bye \\ \hline User simulator & inform\_date,inform\_location,inform\_attendance,update\_date, \\  & update\_location,update\_attendance,affirm,deny,error,hello \\ \hline \hline \end{tabular}
\end{table}
Table 1: lists all dialogue acts in details.  
ask-simulator where only ask-action adjacency pairs (e.g., ask_loc() to inform_loc()) is applied to reward shaping, confirm-simulator where only confirm-action adjacency pairs (e.g., confirm_loc() to affirm()) is applied to reward shaping, Dye-simulator where only Dye-action adjacency pairs (e.g., Dye() to Dye()) is applied to reward shaping and naive-simulator where no adjacency pairs is applied. The shaping reward \(r_{s}\) is set to \(+0.01\). The probability of simulating goal change is set to 0.5. Each slot is limited to change once to avoid overly complex conversations. For the NLG, the collected pre-defined templates are used to generate the user utterance through lexicalization as described in Section 3.2.2. Different dialogue managers are tested with each simulator in Group-S through interacting 200 episodes.  
**A rule-based user simulator (Rule-S):** This simulator is developed according to the mode proposed in Reference [25, 37]. The naive reward function is used in Section 3.3. The same settings in Group-S is used for goal generator and NLG. Different dialogue managers are tested with this Rule-S through interacting 200 episodes.  
**Human Users:** 25 graduate volunteers are recruited to conduct human users test. Comparing different model subjectively on human users always suffers from unfairness and human user may fit in the system gradually. Thus, human users test is conducted in a paralleled manner and is evaluated in objective assessment whether the system can help users accomplish tasks or not. Before testing, the specific user goals are allocated to each users. In the guide of the same allocated goal, the human users use the same natural language to interact with different dialogue managers. Each of the volunteers conducts two parallel tests on different dialogue managers.
## 1 Introduction  
Nowadays, 670.8-nm tunable lasers with high power, compact volume and high efficiency are demanded for experiments of lithium atomic physics, such as atomic cooling and trapping [1; 2; 3], and isotope absorption spectroscopy [4].  
Several approaches have been demonstrated previously. Solid state lasers usually yield 1342-nm lasing by Nd:YVO4 or Nd:YAP crystal at first, then shift to 671-nm lasing by second harmonic generation (SHG), which can achieve output optical power of 1.2 W [5; 6]. However, wall-plug efficiency (WPE) of solid-state lasers is very low and wavelength tuning is difficult to achieve. Dye lasers can directly yield 671-nm pulsed lasing by pumping light, without using SHG, which can achieve average optical power of 2 W and is commercially available [7]. However, they are usually large in volume and toxic. Laser diodes based on InGaP/AlGaInP quantum well can achieve continuous-wave (CW) output power of 5.6 W with very high WPE of 41% [8], although the optical spectrum is a multiple longitudinal mode. Commercial tunable lasers based on semi-conductor devices can only achieve a CW output power of 500 mW [9; 10; 11]. They usually use a very simple master oscillator power amplifier (MOPA) scheme, which includes only one tapered semi-conductor optical amplifier (SOA).  
In this letter, a specially designed semi-conductor MOPA system using tapered SOA array will be demonstrated. Our expanded scheme of laser optical system can achieve much higher CW output power than commercial products, meanwhile good performance of narrow linewidth and wavelength tuning remains.
# A Survey on Monitoring Quality Assessment for Wireless Visual Sensor Networks
## 4 Reviewing Content Quality  
While spatial coverage quality addresses the amount of visual data gathered from the visual nodes, the content quality expresses the application quality with respect to the utility of the gathered information, generally associated with the sharpness, definition or angle of view of the gathered images or videos. These aspects are commonly related to the distance and orientation from the camera to some point of interest, but it can also be affected by other elements, such as occlusion, luminosity (absence or excess), camera parameters (lens, distortion) or even weather conditions (in case of outdoor monitoring) like fog, rain, snow and dust.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Work** & **Coverage** & **Metric** & **Redundancy** & **Minimum Sensors Set** & **Occlusion** \\ \hline
[31] & Target & Coverage rate & – & Minimization & – \\ \hline
[32; 33; 34] & Target &
\begin{tabular}{c} Coverage rate \\ Power consumption \\ \end{tabular} & Reduced & Minimization & – \\ \hline
[36; 58] & Target & \(k\)-coverage & Implicit issue & Minimization & – \\ \hline
[37] & Target & \(k\)-coverage & Implicit issue & Minimization & Modelled \\ \hline
[38] & Target &
\begin{tabular}{c} \(k\)-coverage \\ Power consumption \\ \end{tabular} & Implicit issue & Minimization & – \\ \hline
[39; 40] & Target &
\begin{tabular}{c} Required coverage \\ quality \\ Lifetime \\ \end{tabular} & Reduced & Schedule of cover sets & – \\ \hline
[41; 42] & Target &
\begin{tabular}{c} Required coverage \\ quality \\ Lifetime \\ \end{tabular} & Implicit issue & Minimization & – \\ \hline
[43; 44; 45; 46] & Target &
\begin{tabular}{c} Required coverage \\ quality \\ Lifetime \\ \end{tabular} & Implicit issue & Schedule of cover sets & – \\ \hline
[47] & Target &
\begin{tabular}{c} Required coverage \\ quality \\ Connectivity \\ Energy harvesting \\ \end{tabular} & Implicit issue & Schedule of cover sets & – \\ \hline
[17; 52; 59] & Area & Coverage rate & Reduced & – & – \\ \hline
[60] & Area & Coverage rate & Reduced & Minimization & – \\ \hline
[6, 48; 49; 61] & Area & Coverage rate & Reduced & – & Modelled \\ \hline
[51; 62] & Area & Coverage rate & Implicit issue & – & – \\ \hline
[63] & Area & Coverage rate & Implicit issue & – & Modelled \\ \hline
[35] &
\begin{tabular}{c} Area \\ Target \\ \end{tabular} & Coverage rate & – & – & – \\ \hline
[64; 65] &
\begin{tabular}{c} Area \\ Barrier \\ \end{tabular} & Coverage rate & Reduced & – & – \\ \hline
[54; 66] & Barrier &
\begin{tabular}{c} Breadth of \\ coverage \\ \end{tabular} & Implicit issue & Minimization & – \\ \hline
[55] & Barrier & Quality of sensing & Implicit issue & Minimization & – \\ \hline
[56; 57] & Barrier & Barrier weight & Implicit issue & Minimization & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the performed spatial coverage quality reviewing.  
Next subsections discuss the coverage objectives in our proposed classification.
that are related to information leakage and tampering. However, since the deployment of current blockchain aims to protect communication among distributed components of a business, it fails to detect any threat that arises from any other layer of the architecture, e.g., application. In industry, most of the research has been focused on developing tools that use blockchain to support transparent exchange of information by automatically enforcing the transactions among parties. However, again these tools fail to detect any threat that targets business level agreements and other local threats.  
There are several literature studies available, discussing current research in this domain. However, current surveys and reviews on blockchain-based supply chain systems have focused on data driven protection of supply chain and involved stakeholders, compliance of supply chain with various regulations, interoperability among cross-chain and cross-border supply chains, to name a few. This survey will help the interested reader to find a systematic way through the diverse literature on the security of blockchain-based supply chain topics out there already. This survey provides a systematic investigation of various security issues by defining classification of different attacks contextualising blockchain and then investigating various academic and industrial efforts to handle them. Importantly, we are interested in end-to-end protection of the supply chain processes that involves security of all the involved stakeholders, their business processes and corresponding assets. We have identified the gaps that need to be addressed by the corresponding stakeholders and entities involved in the supply chain process to ensure end-to-end security of the process.  
Overall, the research so far has been focused to use blockchain in supply chain systems to handle different issues enabling trusted communication among interacting partners and stakeholders of the supply chain. However, with the introduction of the blockchain technology in the supply chain, new attacks and threats have emerged that needs to be considered, in particular the attacks that are related to the actual business and assets.  
To this end, we discussed our survey methodology in Section 2. We explained our study starting from an overview of related technologies like supply chain management, blockchain and block mining, smart contracts, and blockchain-based supply chain systems in Section 3. Initially, we defined the treat model for BC-SCM systems in terms of security requirement, possible attacks, and sources of attacks, in Section 4. Based on our threat model, we classified attacks on BC-SCM systems into computational and communication ones, and we have investigated current academic and industrial efforts to handle them in Sections 5 and 6. Finally, in Section 7, we identified research gaps that may help to build blockchain-based supply chain systems that support more rigorous protection to the supply chain and their associated partners and stakeholders. At last, we conclude our article in Section 8.
## 4 Our Modeling Approach
### Transition Modeling
#### 4.4.1 Scenario 1: An Event Is Followed by Two or More Other Events, Which Are Independent  
This scenario is typically described as follows:  
\[(A\to B)\ \ \text{OR}\ \ (A\to C)\ \ \text{OR}\ \ \cdots\ \ \ \text{OR}\ \ (A\to\cdots) \tag{1}\]  
The \"\(\to\)\" symbol is translated as \"is followed by\" and Equation (1) indicates that our data have presented patterns where an event \(A\) has been followed by other events \(B,C,\dots\), one at a time. In other words, at different times, we have discovered patterns in the form \(A\to B\), \(A\to C\),...etc., but not in the form \(B\to C\), \(B\to D\),..., etc. This means that, at a given time, event \(A\) may trigger either event \(B\) or event \(C\),..., etc. We use the term _successors_ to define the events that may follow \(A\) and the term _predecessor_ for \(A\). The successors of \(A\) do not trigger each other, that is, they are _independent_ of each other. A successor event may be implemented by different instances of the same entity. Figure 1 shows the modeling of this scenario.  
In Figure 1a, we note that there are three possible successor events that can be triggered when event \(A\) is executed. We assume that event \(A\) is the start of a transportation. Transition \(A\) will place a single token in place \(p\) as can be seen in Figure 1b. This token represents  
Figure 1: (**a**) Modeling Scenario 1, (**b**) the effects after a successor transition has been chosen, (**c**) pseudo-code for an entity declaration.  
an entity class named vehicle, which describes the means of transportation. This entity class is represented by a red token and its instances may be a lorry, a motorbike, and a car. Thus, one of the transitions \(B,C\), or \(D\) will fire and the event that will occur will be a transportation with a certain means. At a different time, event \(A\) will trigger a different transition, but, in any case, only one successor event may follow \(A\) at a time: \(B\) OR \(C\) OR \(D\), that is, transportation by lorry, motorbike or car. In addition, note that the notion of \"Colored\" in CPN formalism indicates rather a class of an entity than a color itself.
# A Study of Order Modification for Reactive Power Support by Wind Farm during Communication Failure
## 1 Introduction
We have studied the allocation of reactive power command to VSC based distributed sources inside a large farm network, and suggested significant advantage in several perspectives in Reference [19]. The described plan in Reference [19] focused on enhancing the efficiency of allocating references for each turbine. A feasibility study based on a composed management system was the main issue in the research. However, when we consider a large-scale wind farm, an error between the generated order and absorbed reference signal at each control section could exist, because the system is generally composed in an extensive area that can cause a communication error. If additional compensation response for preparation for the mentioned issues does not exist, an inappropriate result can take place in practical operation. In this paper, a new response plan for reactive power is presented, and applied on the previously described network. The focus in this plan is handling control reliability within a certain range, and reducing system error caused by mistreated communication. The purpose is to generate appropriate signal by solving a multi-objective model within the designated interval, involving the network characteristics of the inner system.
## 4 Experimental Research
### Multiscale Analysis  
The entropy of time series is usually used to characterize its complexity, but the entropy variation of some sequences may be inconsistent on different scale factors. If the majority of scales' entropy values are higher for one time series than for another, the former is considered more complex than the latter.  
We randomly selected two trials of MI-EEG signals on channel C3, in which one is derived from imaginary left hand movement and the other is derived from imaginary right hand movement. Then, we calculated MFE values of two trials of MI-EEG signals. At this time, \(\tau_{\text{max}}\) was equal to 4 and in the calculation of MFE values on different scale factors, the parameters \(m\), \(n\), and \(r\) were set to 2, 2, and 0.1 \(SD\), respectively. Here, \(SD\) was the standard deviation of the original MI-EEG signal. The experimental result is shown in Figure 5.  
From Figure 5, we can see that the FE of MI-EEG signal for imaginary left hand movement is smaller than the FE of MI-EEG signal for imaginary right hand movement when \(\tau=1\). This means that the latter is more complex than the former. However, when \(\tau=2\), 3 and 4, the FE values of the MI-EEG signal for imaginary left hand movement are all higher than those of the MI-EEG signal for imaginary right hand movement corresponding to one scale factor; this shows that the former is  
Figure 4: (**a**) The mean FE time series of MI-EEG on channels C3 and C4 for imaginary left hand movement; (**b**) the mean FE time series of MI-EEG on channels C3 and C4 for imaginary right hand movement.  
Figure 5: The MFE variation curves with scale factor \(\tau\). The pink dotted line and blue solid line represent the MFE of MI-EEG on channel C3 corresponding to imaginary left hand and right hand movement, respectively.  
more complex than the latter. So, it is unreasonable to analyze the complexity of a time series on a single scale with FE. In addition, it can be seen that the coarse-grained MI-EEG signal on each scale factor contains important information related to the imaginary task. To obtain more information, it is necessary for MI-EEG to conduct multiscale analysis.
## 4 Models of Implementing Vaccinations in European Pharmacies  
There are a lot of models of implementing vaccinations in European pharmacies. In 2019, flu vaccinations were offered in pharmacies in 40% of European countries, and in 17%, other vaccinations were also available (\(n=30\)) [28]. Currently, in Europe, vaccinations are administered in pharmacies in 13 countries (Figure 1), yet it should be noted that conducting vaccinations by pharmacies is not obligatory in any country. For example, in Great Britain and Portugal, vaccinations are available in about 78% of the pharmacies, and in Norway, in 60%. In some countries, the Czech Republic, Belgium,  
\begin{table}
\begin{tabular}{l l} \hline \hline  & **Advantages of Administering Vaccinations in Pharmacies** \\ \hline \multirow{4}{*}{Pharmacists} & – Increased responsibility for preventive actions within \\  & public healthcare \\  & – Professional development \\  & – Financial benefits \\ \hline \multirow{7}{*}{Healthcare system} & – Increased vaccination coverage level and herd immunity \\  & – Using pharmacists’ competencies and availability for preventive \\  & actions in the healthcare system \\  & – Counteracting anti-vaccination groups by increased involvement of \\  & pharmacists in the promotion of vaccinations \\  & – Increased state of readiness to respond in the event of epidemic \\  & or pandemic \\  & – Lower costs of flu treatment (direct and indirect) \\  & – Decreased number of secondary infections of healthy people visiting \\  & healthcare facilities \\  & – Decreased unjustified use of antibiotics \\  & – Increased potential for giving first aid as a standard element of \\  & training of pharmacists administering flu vaccinations \\  & – Obtaining potential for changes in financing flu vaccinations \\ \hline \hline \end{tabular} Source: [PERSON], [PERSON] [PERSON], [PERSON] [PERSON], [PERSON] [PERSON], [PERSON] [PERSON], [PERSON] [PERSON] Szczepienia przeciw grypie w aptekach. Raport opieka farmaecutyczna. Fundadja Nadzieja dla Zdrowia, Warszawa 2020.  
\end{table}
Table 2: Advantages of administering vaccinations in pharmacies for patients, medical staff and the healthcare system.  
Germany, and others, endeavors are being made to be able to administer vaccinations in pharmacies, but they face opposition from other medical professions conducting vaccinations [9; 10; 29]. Due to the fact that vaccinations are dispensed and administered on the basis of prescriptions, most vaccination models are based on doctor-pharmacic cooperation. Pharmacists are authorized to administer vaccinations in Portugal, Ireland, Great Britain, France, Switzerland, Denmark, and Greece. In many countries, vaccinations are not conducted by pharmacists but by nursing staff employed for this purpose--this is the case in Italy, the Netherlands, Sweden, Finland, and Estonia. In some countries, e.g., in Portugal, there is a mixed model.  
Selected models of implementing vaccinations in pharmacies, with the most advanced vaccination pattern in community pharmacies, where the health care model is similar to Poland's health care system.