esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Participatory Mapping  
The participatory mapping showed that the EE measures were the preferred choice compared with the HE and GE approaches (Figure 4, Table 4). When accumulating the results of all three workshops, the EE measures involving macrophytes (44%) and mussels (22%) were pinned the most. The notion that no mitigation measure was necessary or wanted was pinned only 7% of the time in comparison. It should be noted that only the selection \"no measures\" was made at the German workshop in Stralsund.  
Comparisons between the different nationalities of the participants were difficult to conduct, as the workshops in PM2 and PM3 were conducted on an international level (Table 1), and the participants involved were of different nationalities. However, a comparison could be made between the different results from PM1 and PM3, as local stakeholders (non-scientists: Germany = 69%; Lithuania = 50%) participated in both workshops. The comparison shows that the participants in Lithuania showed a much greater focus on the  
Figure 4: Locations of the flags for potential installation sites of sea-based measures for nutrient removal which the participants pinned during the workshops in Stralsund, Germany (23 January 2018; symbol: circle), in Athens, Greece (22 March 2018; symbol: square), and in Klaipeda, Lithuania (24 May 2018; symbol: triangles). (**A**) German and Polish coastal waters, with focus on the Greifswalder Bodden and the Szczecin Lagoon. (**B**) Lithuanian coastal waters, with focus on the Curonian Spit.  
GE (33%). The mussel cultivation approach was, on the other hand, less favored compared with the results from Stralsund.  
However, if we compared the results of the different workshops according to their stakeholder compositions, we saw that there were also differences in the choice of measures (Table 4). In PM2, where 95% of the participants were scientists, ecological and mechanical methods were equally pinned. During PM1, where 69% of the participants consisted of non-scientists, the ecological methods (macrohyptes (50%) > mussels (30%)) were predominated. At four locations, the wish that no measures at all be implemented was stated (Figure 4). During PM3, which consisted of 50% scientists and 50% other stakeholders, the ecological measures were also preferred, but the use of GE measures (chemical applications) also had a share of 33%.  
With the current conducting method, no homogeneous result could be obtained, and no recommendation could be derived. In order to achieve a homogenous result, it is necessary to define or provide homogenous background information (e.g., specify the target area, potential interactions with the environment, or define nutrient reduction scenarios). Here, a combination of the ESA approaches would be beneficial, since it supplies scenario and background information and guides the participants to reflect about possible environmental interactions.
# In-Band Pumped Thulium-Doped Tellurite Glass Microsphere Laser
## 5 Conclusions  
We have developed a simple theoretical model to describe thulium-doped glass microlasers generating in the 1.9-2 \(\upmu\)m range with in-band CW pump at 1.55 \(\upmu\)m. We have obtained an analytical expression for laser power assuming single-mode generation and further performed exhaustive search for each WGM from the gain band and after that chose the wavelength corresponding to a stable physically meaningful solution. This model can be used for microspheres made of different glasses such as silica, tellurite, fluorotellurite, fluoride, chalcogenide, and others. To describe the system, the following parameters are required: emission and absorption cross sections for the \({}^{3}\)F\({}_{4}\)\(\rightarrow\)\({}^{3}\)H\({}_{6}\) energy transition, the lifetime of the \({}^{3}\)F\({}_{4}\) level, linear refractive index, Tm\({}^{3+}\) ion concentration, Q-factor, coupling coefficient, microsphere diameter, and pump power. We have performed a theoretical analysis for tellurite glass microspheres promising for lasing in the two-micron range. It has been shown that laser generation is possible if the Q-factor exceeds a certain value depending on active ion concentration. The expected laser wavelength depends on thulium ion concentration and Q-factor. The higher the concentration and Q-factor, the longer the laser wavelength is. There is an optimal Q-factor to attain the maximum slope efficiency for a certain thulium ion concentration. Additionally, we have fabricated a 320-\(\upmu\)m tellurite glass microsphere doped with thulium ions with a concentration of 5\(\cdot\)10\({}^{19}\)cm\({}^{-3}\). We have attained laser generation at 1.9 \(\upmu\)m experimentally in the produced sample with a Q-factor of 10\({}^{6}\) pumped by a C-band CW narrow line laser at 1.55 \(\upmu\)m. The observed laser wavelength and slope efficiency agree with the theoretical calculation for the same Q-factor.  
Conceptualization, E.A.A. and A.V.A.; methodology, E.A.A., V.V.D. and A.V.A.; software, E.A.A. and A.V.A.; validation, E.A.A. and A.V.A.; investigation, E.A.A. and A.V.A.; resources, A.V.A. and V.V.D.; data curation, E.A.A.; writing--original draft preparation, E.A.A.; writing--review and editing, A.V.A.; visualization, E.A.A.; project administration, E.A.A.; funding acquisition, E.A.A. All authors have read and agreed to the published version of the manuscript.  
The work was funded by the Russian Science Foundation, Grant No. 20-72-10188.  
Not applicable.  
The authors declare no conflict of interest.
# Prediction of Wind Power with Machine Learning Models
## 2 Materials and Methods
### Proposed Model Architecture
#### 2.2.1 Artificial Neural Network Structure  
Artificial intelligence is dedicated to researching and developing methods that enable machines to exhibit human-like capabilities such as reasoning, judgment, emotional experience, language understanding, and problem-solving. One prominent approach in artificial intelligence is the ANN model, which is modelled after the structure of the human brain. However, the quantity of neurons within ANNs is established according to the demands of a given predicament, in contrast to the approximate 15 billion neurons within the human brain [32, 33]. ANNs can learn from data and apply acquired knowledge, leading to their widespread utilisation in various domains, including but not limited to forecasting, classification, identification, and control. In this study, a feedforward neural network was constructed for wind power estimation, employing the general structure of a feedforward multilayer neural network, outlined as follows [34, 35].  
A multilayer feedforward network consists of various layers, including an input layer, an output layer, and one or more hidden layers positioned in between. The input layer is responsible for receiving the input data, which is then processed through the hidden layers, ultimately resulting in the generation of the final output by the output layer. The hidden layers play a fundamental role in effecting the transformation of the input data through a set of weighted connections and activation functions, thereby facilitating the network's ability to comprehend intricate patterns and relationships within the data. Lastly, the output layer generates the final prediction based on the transformed information derived from the hidden layers. Table 1 shows that the ANN architecture begins with a dense layer in the first layer, consisting of 64 neurons and utilising the Rectified Linear Unit (ReLU) activation function. Following this, a second thick layer with 32 neurons and the ReLU activation function is employed. Finally, the model is completed with a dense layer with a single output and utilises the linear activation function.
# Hybrid NHPSO-JTVAC-SVM Model to Predict Production Lead Time
###### Abstract  
In the shipbuilding industry, each production process has a respective lead time; that is, the duration between start and finish times. Lead time is necessary for high-efficiency production planning and systematic production management. Therefore, lead time must be accurate. However, the traditional method of lead time management is not scientific because it only references past records. This paper proposes a new self-organizing hierarchical particle swarm algorithm (PSO) with jumping time-varying acceleration coefficients (NHPSO-JTVAC)-support vector machine (SVM) regression model to increase the accuracy of lead-time prediction by combining the advanced PSO and SVM models. Moreover, this paper compares the prediction results of each SVM-based model with those of other conventional machine-learning algorithms. The results demonstrate that the proposed NHPSO-JTVAC-SVM model can achieve further meaningful enhancements in terms of prediction accuracy. The prediction performance of the NHPSO-JTVAC-SVM model is also better than that of the other SVM-based models or other machine learning algorithms. Overall, the NHPSO-JTVAC-SVM model is feasible for predicting the lead time in shipbuilding.  
lead time; hybrid model; artificial intelligence optimization techniques; machine learning; support vector machine +
Footnote †: 2021: 11, 6369. [[https://doi.org/10.3390/app11146369](https://doi.org/10.3390/app11146369)]([https://doi.org/10.3390/app11146369](https://doi.org/10.3390/app11146369))
# Case Study of Power Plants in the Slovak Republic and Construction of Microgrid and Smart Grid
## 8 Conclusions  
In the introduction, the current situation of the location of power plants in the Slovak Republic was evaluated, where most of the installed capacity is in Western Slovakia. For this reason, Slovakia is not yet ready to create microgrids. Alternatively, the possibility of trial designs opens only in Western Slovakia as a microgrid model example.  
The aim of the publication was to highlight the need for a suitable energy mix of resources and to ensure it in all areas. Determining the appropriate mix of resources, we can proceed to the next step, to determine the size of the territory that will be controlled by the microgrid system. As soon as we successfully apply these steps in practice, we can approach the solution of the problem of the protection system and localization of faults, while the problem of measuring and evaluating the simulated data must be solved.  
The article provides design options for the RES model, where the next step will be to design a system using these models as a sample microgrid with the proposed protection system. Perspective control tool in the future to control and monitor microgrid and the smart grid is the wide area management system and control.  
Conceptualization, R.S., Z.C. and M.K.; software, R.S.; data curation, R.S. Z.C., and M.K.; formal analysis, R.S. Z.C.; funding acquisition, Z.C. and M.K.; investigation, R.S. Z.C.; methodology, R.S. Z.C.; project administration, M.K.; resources, M.K.; validation, Z.C.; visualization, R.S.; supervision, M.K.; writing--original draft, R.S. Z.C.. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Slovak Research and Development Agency APVV-19-0576 and the Ministry of Education, Science, Research and Sport of the Slovak Republic and the Slovak Academy of Sciences VEGA 1/0757/21.  
Not applicable.  
Figure 32: Location of biogas and biomass power plants of Slovak Republic [24].
## 5 Results and Discussions
### Average Traversed Distance  
Flight traversed distance reduction plays a key role in UAV route planning algorithm design. UAVs may traverse to discover the area, find SPs and/or setup the routing infrastructure. Increased traversed distance addresses enhanced battery consumption and increased ETE. Hence, a better route planning performance is achieved if the algorithm/technique is able to forward UAVs through minimized distances to complete a remote sensing mission.  
Figure 8 shows that Q-learning reduces the UAV's average traversed distance as compared to IEMF if SPs are dense and crowded. This is because Q-learning takes the flight distance into account during environment exploration. Hence, the UAV learns the environment and finds the best movement to go through if the reward (minimized distance) is maximized. However, IEMF is able to only heuristically discover the SPs using a greedy algorithm. Hence, this increases the UAV movement, especially when the number of SPs is increased.  
TBID outperforms both Q-learning and IEMF as this forms a spanning tree to forward the UAV through. This results in the reduced traversed distance because the UAV movements are restricted into the tree infrastructure. However, this forwards UAV through longer and re-established paths to collected data from unvisited SPs when the number of (mobile) SPs is increased.
## 3 Energy Trading in Local Energy Market
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Ref.** & **Trading Platform** & **Market Stakeholders** & **System Components** & **Primary Purpose** & **Secondary Purpose** \\ \hline [94] & P2P & DSO, prosumers & PV, BESS & Energy cost & Welfare maximization \\ \hline [95] & P2P & Prosumers, retailers & PV, BESS & Financial reward & Welfare maximization \\ \hline [96] & P2P & WEM, P2P platform agent, prosumers & PV, BESS & Financial reward & Welfare maximization \\ \hline [97] & P2P & DSO, prosumers, market agents & PV, BESS & Self-consumption & User preferences \\ \hline [98] & P2P & DSO, prosumers consumers consumers & PV, BESS, EVs & Financial reward & User preferences \\ \hline [99] & CO & DSO, community manager, prosumers & PV, BESS & Self-consumption & Fair cost distribution \\ \hline [100] & CO & DSO, community manager, prosumers & PV, community BESS & Price transparency and fairness & Self-consumption \\ \hline [101] & CO & DSO, prosumers & PV, auxiliary heating, BESS & Community preferences & Renewable energy adoption \\ \hline [102] & CO & DSO, community manager & Controllable appliances, PV, BESS & Energy efficiency and resilience & Energy independence and resilience \\ \hline [103] & CO & Community manager, prosumers, TSO & PV, BESS, EVs & Energy affordability & Energy equity \\ \hline [93] & TE & Consumer, prosumers, grid operators & PV, wind turbines, large-scale storage & Grid constraints & Resilience and flexibility \\ \hline [104] & TE & DERs, traditional generators, consumers & Combined heat and power & Ancillary services & Grid constraints \\ \hline [105] & TE & Hydropower plants, geothermal plants & PV, wind, energy storage systems & Grid imbalance & Energy-balancing services \\ \hline [106] & TE & Energy service providers, prosumers & Smart meters, blockchain & Ancillary services & Market efficiency \\ \hline [107] & TE & DERs, energy service providers & Distributed ledger technologies & Transparent and auditable transactions & Energy traceability and sustainability \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification of market stakeholders and system components in LEM energy trading platforms. and TE markets, a more limited body of literature specifically analyzes the CO market, focusing on community and locality aspects within energy markets. Moreover, P2P and CO markets primarily offer financial incentives to market participants, while TE markets are primarily oriented toward grid-related services.
## 4 Evaluation
### Experimental Setup  
#### 4.1.1 Data Collection  
To evaluate our TPL detection method, we collect two types of datasets from ModX: Dataset I and Dataset II. Dataset I is a binary target program dataset with a known TPL reused relationship, which includes programs built by the mix package manager and a set of manually building binaries on Ubuntu 20.04. The mix binary dataset is as shown in Table 1, which is classified into three classes according to binary program size. A brief description of the ubuntu dataset is as shown in Table 2, in which the first column presents the binary names, and the last two columns present the number of TPLs reused by these binaries and the specific information of TPLs. Dataset II is a TPL dataset, which is used to build a third-party library database.
## 3 Materials and Methods
### Technological and Research Areas  
The main technological and research areas related to \"Mergin' Mode\" implementation include:  
* Contemporary photogrammetric surveying technologies using high-capacity cameras and/or cameras on UAVs.
* Image processing technologies for object recognition in images and videos for MR rendering.
* Geoinformatics technologies for core GIS based functionalities, such as thematic layers overlaying and georeferencing.
* Global satellite system technologies, for the visitor's spatial reference in the archaeological-touristic site to achieve location-based servicing.
# Estimation of the Achievable Performance of Mobile Ad Hoc Networks with Optimal Link State Routing
## 3 Results
### Network Parameters Determined before Starting Work
#### 3.1.2 Network and Transport Layer Parameters  
Routing is performed using the OLSR protocol. Quality of service control is achieved through the RSVP protocol. The quality of service for transmitted packets is determined with ITU-T recommendation Y.1541 [35]. Table 3 presents the acceptable IP packet delivery delays.
## 3 Results  
### Univariate Data Analysis  
Each feature was subjected to a univariate data analysis to identify its level of significance with the studied classes, i.e., normal or seizure class, and to investigate its discrimination capability in characterizing seizure records. Tables 3 and 4 show the normalized values of the extracted measures (mean \(\pm\) standard deviation) for seizure and seizure-free segments. Significant \(p\)-values (obtained using the ANOVA test) indicate a high discrimination capability in characterizing seizure segments. The values displayed in Tables 3 and 4 correspond to the highest \(p\)-value per feature. It is evident from Table 3 that seizure-free records had lower complexity values compared to epileptic seizure records. Results in Table 4 show that the relative power in the alpha band had the lowest \(p\)-value. The results also show that oscillatory power change features had higher levels of importance than complexity measures (Tables 3 and 4), which indicates that oscillatory power change in all channels can contribute to better classification. In addition, Shannon entropy and relative power in the alpha band had lower \(p\)-values, which indicates that they had higher levels of importance than other features of this category.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Features**} & **Seizure-Free** & **Seizure** & \multirow{2}{*}{\(p\)**-Value**} \\  & **(Mean \(\pm\) Std)** & **(Mean \(\pm\) Std)** & \\ \hline Shannon entropy & 0.242 \(\pm\) 0.125 & 0.1556 \(\pm\) 0.095 & 6.159 \(\times\) 10\({}^{-15}\) \\ Sample entropy & 0.279 \(\pm\) 0.132 & 0.199 \(\pm\) 0.118 & 1.760 \(\times\) 10\({}^{-96}\) \\ Spectral entropy & 0.288 \(\pm\) 0.143 & 0.203 \(\pm\) 0.123 & 2.193 \(\times\) 10\({}^{-95}\) \\ Log energy entropy & 0.320 \(\pm\) 0.152 & 0.217 \(\pm\) 0.139 & 7.184 \(\times\) 10\({}^{-11}\) \\ Wavelet entropy & 0.262 \(\pm\) 0.125 & 0.181 \(\pm\) 0.112 & 6.657 \(\times\) 10\({}^{-11}\) \\ Multi-permutation 1 & 0.267 \(\pm\) 0.122 & 0.196 \(\pm\) 0.117 & 1.212 \(\times\) 10\({}^{-80}\) \\ Multi-permutation 2 & 0.296 \(\pm\) 0.145 & 0.207 \(\pm\) 0.129 & 4.130 \(\times\) 10\({}^{-96}\) \\ Multi-permutation 3 & 0.275 \(\pm\) 0.130 & 0.194 \(\pm\) 0.125 & 3.050 \(\times\) 10\({}^{-90}\) \\ Multi-permutation 4 & 0.291 \(\pm\) 0.141 & 0.196 \(\pm\) 0.123 & 3.467 \(\times\) 10\({}^{-12}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Range of values of the complexity measures for the two classes of seizure and seizure-free. The lowest \(p\)-value of 4.130 \(\times\) 10\({}^{-98}\) for multi-permutation entropy at level 2 indicates the great discrimination capability of this feature in characterizing seizure records.
# An Accelerating Approach for Blockchain Information Transmission Based on NDN
## 5 Simulation Results and Discussions  
Here we use ndnSIM as our simulation tool, which is a NS3 based NDN simulator. In order to better verify the performance of the proposed acceleration strategy, we propose three simulation scenarios and compare them:  
(1) Use NDN network and deploy acceleration strategy, record network latency and data overhead in a period of time.  
(2) Use NDN network but do not deploy acceleration strategy, record network latency and data overhead in a period of time.  
(3) Use TCP/IP network and do not deploy acceleration strategy, record network latency and data overhead in a period of time.  
We constructed a network topology to simulate and observe the performance of the acceleration strategy. In the process of building the topology, we refer to the real network topology provided by ndnSIM and the topology data in some references [17]. In a real network, the value of \(P\) is always between 0.1 and 0.2 (as the network scale expands, the value of \(P\) is different), so we set \(P\) to 0.15 and calculate \(L_{j}\) as 0.15. Therefore, in the simulation network discussed below, we will calculate the importance of each node according to Equations (2) and (3), and select the top 15% of the nodes as secondary nodes. The secondary nodes will be set to have twice the bandwidth of normal nodes and 100G of cache space.
## 2 Materials and Methods
### Characterization of Nanocellose Gels  
As starting materials, two nanocellulose gels (CNF\({}_{1}\) and CNF\({}_{2}\)) were used. Both gels were obtained from bleached chemical pulps of eucalyptus globulus; the first came from a sulfite pulp and the second from a sulfate pulp. Both pulps were subjected to a similar TEMPO (2,2,6,6,-tetramethylpiperidine-1-oxyl) oxidative process, with a hydmodulus of 1:100. For sulfite pulp, the following system was used: 0.016 g TEMPO/0.1 g NaBr/33.3 mL NaClO (Merck, Darmstadt, Germany). NaClO was added in four spaced additions of 1 h at room temperature and pH higher than 10.5. At the end of the last addition, the reaction was stopped with acid, followed by washing with distilled water and vacuum filtration. The system 0.016 g TEMPO/0.5 mL NaClO/0.9 g NaClO\({}_{2}\) was followed for sulfate pulp immersed in 0.1 M phosphate buffer (pH = 6.8) for 24 h at 60 \({}^{\circ}\)C, followed by cooling to room temperature, subsequently washed with distilled water and filtered under vacuum.  
Both suspensions passed twice through a GEA Panther NS 3006L homogenizer (GEA, Dusseldorf, Germany). This equipment has a volume of around 4 L and works with a consistency of 1.5%. The operating parameters were the following: two passes, the first at 500 bar and the second at 1000 bar.  
The gels were characterized in terms of dry matter content (% solids), rheology and FTIR. To evaluate dry matter content of the nanocellulose gel, the sample (2 mL) was previously weighed (\(w_{i}\)) and subsequently dried in an oven at 105 \({}^{\circ}\)C until constant weight. After the sample was cooled in a desiccator, it was weighed again (\(w_{f}\)). The determination of solids content was given in percentage according to the following equation:  
\[\% Solids=100-\frac{w_{i}-w_{f}}{w_{i}}\]  
The rheological study of the nanocellulose gels was performed on the HAAKE RS 150 rheometer (Haake, Vreden, Germany), using plate-cone geometry and sensor C35/2\({}^{\circ}\), that is, 35 mm in diameter and 2\({}^{\circ}\) angle. Tests were carried out at 23 \(\pm\) 2 \({}^{\circ}\)C and 50 \(\pm\) 3% relative humidity (RH). The control stress (CS) mode was performed in a measurement range between 0 and 200 Pa.  
To investigate the organic compounds of the gels, FTIR analysis was recorded in a Nicolet iS10 smart iTRbasic (Thermo Fisher Scientific, Waltham, MA, USA) in the range of 600-4000 cm\({}^{-1}\), with 64 scans and a resolution of 4 cm\({}^{-1}\), normalizing the results acquiring previously the background spectra.