esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
It was recommended that advanced ML ensemble methods such as bagging, boosting, and stacking need to be incorporated into future studies. As stated previously, SA can be beneficial for the time being due to COVID-19 conditions along with IDSS to support decisions taken [36]. Proposed analytics policy using citizens' e-participation through social media to support the decision-making process. Four cities in Belgium (Li'ege, Mons, Marche-en-Famenne, and Leuven) were subject to study by identifying decision-makers' requirements and the required information was extracted from social media for the study. Then, data analysis techniques were applied to the extracted data. This is followed by an actionable plan to facilitate the TM decision-making [2]. The authors of [2] conducted SA using NLP and ML on data extracted from Twitter to measure Indians citizens' satisfaction with governmental policies during the nation lockdown forced to control the spread of COVID-19. The results found that the majority of Indians supported the government's decision during this period. Furthermore, a DL-based SA was conducted in [37] on Arabic extracted data from Twitter to track citizens' emotions expressed during the COVID-19 spread period and the effect of these tweets on daily health behavior; 5.5 million tweets in the period from January to August 2020 were extracted, then a long short-term memory (LSTM) classifier was applied to classify emotions into six categories and two types (symptom and non-symptom tweets). Results showed that the top emotions categories were anger and fear. The study was found to be helpful for the government's TM to take decisions for educating people about the disease to reduce their fear and anxiety. Moreover, the authors of [38] conducted a SA on extracted data from Twitter about COVID-19 using DL techniques and fuzzy logic to examine the importance of data shared on social during this critical pandemic conditions and how it affects other users when includes unnecessary information about the disease. The study urged governments' on the importance of using data available on social media to prevent sharing unnecessary cases which may increase citizens' panic regarding the disease. Furthermore, it was recommended that laws should be designed to restrict useless, false, and exaggerated news through social media to control the current situation. Therefore, this work differs from the previous studies in combing the DSS with sentiment analysis to improve government decisions during the Covid-19 situation. The utilization of WOA-SVM offers more insights and obtained better performance than the state-of-art techniques. Moreover, several datasets were generated to study different scenarios that comprise various feature extraction and stemming methods. Additionally, investigates people's behavior to provide more efficient countermeasures and decisions. Finally, examine the most relevant features to obtain valuable, useful, and precise information faster.
# Investigation of Railway Network Capacity by Means of Dynamic Flows
## 3 Optimization Model for Determining a Network-Wide Capacity  
### Modeling of the Railway Network  
In order to investigate the capacity of a railway network using network flows, we define a directed graph \(G=(V,A)\) with vertices \(V\) and arcs \(A\). Vertices may represent either railway stations or junctions, whereas arcs depict the line sections between the stations. Each arc that connects two vertices \(i,j\in V\) is given as tuple \((i,j)\in A\). Because every line can be operated in either direction, we always define both a forward arc \((i,j)\in A\) and a backward arc \((j,i)\in A\) for each connection of two vertices \(i,j\in V:i\
eq j\). This applies to both single-track and double-track line sections. The capacity of single-track sections is represented in the model under the assumption that only one train can run on the section at any time. For this assumption to have a realistic effect on the investigation, the modeled network should therefore only contain shorter single-track line sections. Lines that have only minor connection points to the rest of the network can be neglected.  
The network \(G\) is divided into two subsets: the core network \(G_{c}\) on the one hand and the so-called linking lines \(G_{l}\) on the other hand. The network capacity defined in this paper is calculated for the core network \(G_{c}=(V_{c},A_{c})\). The linking lines \(G_{l}=(V_{l},A_{l})\) comprise all line sections \(A_{l}\subset A\) entering or leaving the core network as well as virtual vertices \(V_{l}\subset V\) at the end of these line sections. They are defined to connect the evaluated core network to the surrounding railway network to depict interactions and dependencies to the entire system.
## 4 Results and Discussion: Statistical Approach
### Simple-Regression Analysis  
The simplest type of statistical model to establish a relationship between two variables is a simple-regression model, which evaluates whether one variable can be estimated from another. The final objective of this study was to determine whether the UPV result can be expressed as a function in which all the mechanical properties are reflected. In a first approach to the analysis of this aspect, a simple-regression analysis was therefore conducted, so as to study whether a UPV reading could be expressed as a function that is dependent on the value of each mechanical property considered individually.  
In this simple-regression analysis, the R\({}^{2}\) coefficients of all the conventional simple-regression models were analyzed, yielding the result that the most accurate (higher coefficient R\({}^{2}\)) simple-regression model for all the mechanical properties was an inverse square-root model, as shown in Equation (1) (_UPV_ is ultrasonic pulse velocity, in km/s; _MP_ is each mechanical property, all in MPa units except the modulus of elasticity in GPa; \(a\) and \(b\) are adjustment coefficients). The adjustment coefficients and coefficient R\({}^{2}\) for each mechanical property are detailed in Table 3. The coefficients R\({}^{2}\) for the linear models are also provided.  
\[UPV=\sqrt{a-\frac{b}{MP}} \tag{1}\]  
The coefficients R\({}^{2}\), confirmed the above-mentioned aspects related to the correlations. On the one hand, the Spearman correlations were higher than the Pearson correlations, so a potential model, in this case an inverse square-root model, had a higher R\({}^{2}\) coefficient than the linear model. Therefore, the inverse square-root model was more accurate than the linear model for estimating the UPV through the mechanical properties. On the other hand, it can also be noted that while the coefficient R\({}^{2}\) for the square-root inverse model was very high, above 90%, for compressive strength and modulus of elasticity, it was much lower, around 65-70%, for splitting tensile strength and flexural strength, properties related to the bending-tensile behavior of concrete. This aspect was already observed in the correlations, which were lower for the bending-tensile-behavior-related properties.  
Based on these results, it can be stated that the UPV readings can indeed be expressed as a function of each mechanical property, which is an especially accurate relationship for the mechanical properties linked to compressive behavior. Therefore, the formula resulting from expressing the UPV as a function of the four mechanical properties will most likely depend more on the compressive strength and modulus of elasticity to achieve a higher accuracy.
## 3 Experimental Outcomes
### The Influence of Variables on CV Adsorption on NIFGS
#### 3.2.4 Temperature Influence  
Keeping in view our focus to scale to commercial applications, evaluation of the process of adsorption of dyes onto NIFGS in relevance to thermal influence was studied. Temperature influence on CV dye adsorption onto NIFGS is presented in Figure 2d. The data obtained for the classical thermodynamic parameters, namely, \(\Delta G^{\circ}\), \(\Delta H^{\circ}\) and \(\Delta S^{\circ}\) indicate the nature and type of reaction. For example, the positive \(\Delta H^{\circ}\) (enthalpy) values obtained from 303 to 323 K of NIFGS indicate an endothermic process. The overall negative values of \(\Delta G^{\circ}\) (free energy) obtained for CV-NIFGS system confirm the spontaneity and viability of adsorption. For spontaneous adsorption at reduced temperature, the extent of \(\Delta G^{\circ}\) values indicates that the process of adsorption is rapid. Further, it is inferred that the positive \(\Delta S^{\circ}\) value suggests more randomness at a solid solution interface and good affinity of CV towards the adsorbent. Similar observations were reported elsewhere [39].  
The uncertainty principle, also known as [PERSON]'s uncertainty principle, is a topic of quantum mechanics. Analytical chemists do not use this principle to determine the accuracy of the experiments carried out. Conversely, we report the accuracy in terms of the coefficient of variance which we incorporated in the text. The experiments which were carried out in our laboratory were performed in sets of three trials. The coefficients of variation in the results and uncertainty value for parametric influence such as pH grade of the solution, initial concentration of the dye, and the adsorbent quantity and temperature did not exceed \(\pm 2\%\) error. The error bars are incorporated for the Figures 2 and 3 respectively.
## 3 Results and Discussion
### Pigments  
The non-destructive analysis employed by Raman spectroscopy was addressed to characterize the main pigments and compounds involved in the execution of the mural painting. The data and spectra obtained are reported in Table 3 and in Figure 8.  
It can be stated that the palette involved in the execution of the wall painting, as is quite usual for paintings of the Medieval period, is not very variegated. It is essentially represented by red, pink, orange, grey and white. At the same time, the presence of colors obtained by mixing several pigments demonstrates the technical and scientific knowledge of painters who were able to create the shades that best suited the aesthetic choices of their time.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample ID** & **Measurement Point** & **Chemical Element by EDS** \\ \hline S\_1 & Plaster layer & **Ca, Si,** (S, K, Al, Mg, Cl, Na) \\ S\_4 & Plaster layer & **Ca, Si, Al, Mg, Na, S, K (Cl)** \\ S\_4 & Lump & **Ca, Si, Al, Na, Mg, S, Cl** \\ \hline \hline \end{tabular}
\end{table}
Table 2: EDS chemical analyses detected for plastered areas of samples S_1 and S_4. Notes: more abundant elements in bold; minor elements in brackets.  
Figure 7: EDS spectrum and SEM image of the lump observed in sample S_4 (highlighted by the red square).  
Figure 8: Raman spectra performed on the white, grey, orange, pinkish and red pictorial layers.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Color** & **Bands (cm\({}^{-1}\))** &
\begin{tabular}{c} **Identified** \\ **Compound/s** \\ \end{tabular} & **Ref. Bands (cm\({}^{-1}\))** \\ \hline Red & 224,290, 413, 500, 613, 713, 1086 & Hematite + calcite & 224, 244, 292, 408, 493, 610 (hematite); 280, 712, 1015, 1080 (calcite) \\ Pink & 226, 286, 1086 & Hematite + calcite & 224, 244, 292, 408, 493, 610 (hematite); 280, 712, 1015, 1080 (calcite) \\ Orange & 245, 281, 389, 718, 1088 & Goethite + calcite & 245, 299, 387, 480, 550 (goethite); 280, 712, 1015, 1080 (calcite) \\ Grey & 276, 1085, 1337, 1582 & Calcite + Amorphous & 280, 712, 1015, 1080 (calcite); 1348, 1585 (amorphous carbon) \\ White & 282, 715, 1085, 1337 & Calcite & 280, 712, 1015, 1080 (calcite) \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of the main colors investigated by Raman spectroscopy, identified compounds and reference bands.  
It is worth mentioning that calcite typical bands were detected for all the colored areas investigated. This evidence might be explained considering that calcium carbonate was used both in the plaster as a binder to fix pigments on the surface and as a white pigment to lighten pigments.
## 1 Introduction
The results are validated using observations from meteorological and wind towers, with a focus on the impact of different quality control methodologies on the data availability and inversion accuracy. The factors affecting the inversion results are also discussed. The inversion method proposed in this study can be used to obtain vertical wind profiles below the minimum range of a vertically operating scanning Doppler Lidar, providing support for the study of the impact of wind farm operation on wind resources and wind profiles. The structure of this article is as follows: Section 2 describes the experiment site and instruments, and introduces the method for wind speed inversion; Sections 3 and 4 provide the results of the study and discussions, respectively; Section 5 presents the conclusions.
# Phase Offset Tracking for Free Space Digital Coherent Optical Communication System
## 4 Number Results
To present the performance improvement of VVPE-KF quantitatively, Figure 7 demonstrates the estimation errors of the two methods. Comparing with Figure 4, the error of VVPE-MF is greater than Figure 4. Because of the atmosphere turbulence, there are channel gain samples moving towards zero and more low SNR symbols appearing. The greater estimation errors of VVPE-MF are caused by these low SNR symbols. However, the obvious advantage of VVPE-KF is kept. The intensity probability density of the received optical signal depends on the scintillation index. With the scintillation index increasing, there are more samples of channel gain near to zero. In Figure 8, the impact of various scintillation indexes on the estimation error is demonstrated and two SNR values (SNR = 8 dB, SNR = 12 dB) are employed. With the turbulence increasing, the errors of the offset estimation algorithm increase too. The performance of VVPE-KF is more stable and the estimation accuracy of VVPE-MF is more sensitive to atmosphere intensity. According to the number results, the performance of the VVPE-KF is better than the VVPE-MF, which results from the Kalman filter theory. Comparing with the information symbols rate, the phase offset changes much more slowly. Several adjacent phase offset values can be considered as the same approximately, thus, the prediction equation can be obtained precisely. The measurement values containing noise are forged with the prediction values with the least square error criterion by Kalman filter, by which the VVPE-KF can be considered to an optimized signal processing method. Figure 8: The impact of various scintillation indexes on the estimation error of VVPE-KF and VVPE-MF. The SNR values of (**a**) and (**b**) are 8 and 12 dB respectively. Figure 7: The error produced by VVPE-KF and VVPE-MF with atmosphere turbulence.
# The Influence of Bearing Clearance on the Load Capacity of Gas Polymer Bearings
###### Abstract  
In this paper, the influence of a nominal bearing clearance on the lift-off behaviour and the load-carrying capacity of gas polymer bearings (GPBs) is investigated. For this purpose, an experimental study with five different bearing gap configurations and a range of static loads is carried out. Furthermore, the test rig for the clearance and friction torque measurements is illustrated and discussed from a practical point of view. The experimental gap measurement results of all the bearing configurations and the method used are explained and discussed. Finally, the friction torque during the lift-off and run-up phases is measured for a range of static loads. A method that uses the time derivative of the measured friction torque during the run-up tests as a criterion for the lift-off detection is explained and applied to the measured data. The results of this study indicate that there are large differences between the most studied bump-type and gas polymer bearings in terms of the dependence of the load-carrying capacity on the bearing gap. Furthermore, the recorded effects are described, and possible explanations are given. Finally, the differences in characteristics from those of bump-type bearings are described and their importance for the design process of rotors supported in GPBs is explained.  
Keywords:load capacity; gas polymer bearing; gas foil bearing; bearing clearance; lift-off +
Footnote †: journal: Applied Sciences
## 2 Method
### Training  
The overview in Figure 9 describes the training process for a single level. Here, it is visible that during a batch iteration, the input data points are calculated randomly and then passed to the predictor as well as to the evaluators.  
Within one batch, the input data points are randomly generated, and the predictor creates the corresponding geometries \(\mathbf{x}_{i}\). Afterwards, the quality function is computed from the evaluators' losses, according to (30). The objective function \(J\) is then calculated for the whole batch. Then, the gradient \(\mathbf{G}_{\text{P}_{b}}\) of the objective function, with respect to the trainable parameters, is calculated. The trainable parameters of the predictor for the next batch are then adjusted according to the gradient descent method to decrease the value of the objective function. In order to apply the efficient gradient descent method, the functions must be differentiable with respect to the trainable parameters [32]. For this reason, the evaluators and the objective function use only differentiable functions.  
When the level increases, the predictor outputs a geometry with higher resolution, and the process starts again at batch \(b=1\).  
It is important to stress that, unlike conventional topology optimization, the PEN method does not optimize the density values of the geometry, but only the weights of the predictor.
# Review on Free-Space Optical Communications for Delay and Disruption Tolerant Networks
## 5 Conclusions  
With this paper, the authors provide a basis of knowledge in both FSO and DTN advances. A comprehensive survey on these topics is also presented, mainly on their recent applications. Table 1 presents a summary of the state-of-the-art studies presented throughout the article.  
On the topic of FSO communications, a detailed theoretical analysis is provided, followed by research and applications on the links and channel, the ground-station selection, and wireless satellite networks. Furthermore, an introduction to DTN is presented, with focus on the LTP and the state-of-the-art applications of this technology. These technologies show promise for collaboration, with benefits ranging from faster satellite networks to robust protocols for handling the high data-rates.  
The challenges proposed will provide a framework for implementing adequate protocols for these kind of networks. The next step for research will be the emulation of a DTN with optical links. This will provide questions and solutions to address obstacles that arise.  
All authors contributed equally to this work. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 D-MSR Protocol Stack Architecture
### Upper Data Link Sub-Layer (Resource Reservation Layer)
#### 3.2.2 D-SAR Module  
Real-time control applications require data to be transmitted over long distances through a multi-hop network in a reliable and timely manner. However, most recent studies [18; 19; 20; 21] on data link layer use the centralized resource reservation (scheduling) scheme to provide timely and reliable data delivery service. The centralized scheduling schemes have several disadvantages. They often perform poorly in terms of reaction time, as all updates need to be sent first to the base station for further processing. A distributed resource reservation algorithm is needed which would allow source nodes, based on the requirements of the application and traffic characteristic, to reserve network resources for its peer communications along their paths for addressing different QoS needs. Relevant techniques from other networking-related domains (e.g., Asynchronous Transfer Mode (ATM)) could potentially be adapted to develop solutions that are suitable for wireless sensor and actuator networks [7].  
D-SAR is a distributed scheduling algorithm that is based on concepts derived from ATM networks. This is because the ATM signaling protocols [11] also address performance issues in terms of reliability and timeliness of packet delivery.  
The D-SAR protocol is used to establish an end-to-end connection (for supporting point-to-multipoint or point-to-point traffic) and to reserve the communication resources based on the traffic characteristics requested by the source node, along the path toward the destination in different phases of the network operation. These traffic flows can be either network management traffic (e.g., network layer control messages) or sensor data traffic that are published periodically by the sensor nodes toward actuators or gateway. The D-SAR module in the upper data link sub-layer receives the request for establishing a connection from the _end-to-end connection manager_ in the transport layer. The D-SAR module in each device, reserves and releases the communication resources by modifying the link table and the superframe table in the lower data link sub-layer. The relation between the D-SAR module and the _end-to-end connection manager_ and communication table is shown in Figure 5(b).  
Before initiating the D-SAR protocol, the network is already established, all nodes have joined the network, the initial communication links have been established between neighbor nodes, and the routing layer has constructed the routes between network nodes. The details of the D-SAR protocol are discussed in Section 4.3.  
At different phases of the network operation, the D-SAR protocol allocates or releases the communication resources (links and superframes), based on a request that may be initiated either from the upper layers in the stack or received from the other neighbors. In Sections 5.4 (Phase-4), 5.5 (Phase-5), and 5.6 (Phase-6) the details of these procedures are explained.
## 7 Discussion Section  
### Achieved Performances  
From Figure 6, it can be concluded that three of the four control logics successfully performed levitation of the ball for the specified trajectory. Only the DNN control algorithm could not provide tracking performances in multiple repeated experimental cases: the levitation processes stopped in every attempt, and the ball fell to the ball holder (position 0.0128 on the y axis occurred after 3.7 s). Figure 7 presents an enlarged view of part of Figure 6 and provides better insight into the difference between the other three tracking signals compared with the reference signal. Finally, the numerical presentation of achieved results from Figure 6 and summarized results are presented in Table 2. The root mean square error (RMSE) was used to calculate the overall measure of achieved performances, keeping in mind it was calculated as a difference between the measured output position MLS signal and the desired signal (the reference signal).  
As can be concluded from Table 2, OENN + AOG provided similar results to the OEI controller, with a significant reduction in computational costs expressed through the training time. In comparison to the OEI controller, OENN + AOG provided similar performances with a simpler structure and lower computational costs. Further, the OEI controller was based on the combination of online and offline learning and required special attention for setting OENFIS. Finally, both examined structures showed smaller RMSE from the default LQ control. As final insights from conducted experiments, Figure 8 presents control signals generated from appropriate controllers. Figure 8 also presents unstable control from the DNN controller, resulting in uncontrolled oscillations and levitation process interruption. Additionally, it shows three successful control signals which enabled stable ball levitation.