esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Hierarchical Clustering Algorithm  
Clustering is the most well-known and frequently utilized unsupervised learning technique in data mining, pattern recognition, image segmentation, and so on [24; 25; 26; 27]. The cluster algorithm is a set of methodologies for automatic classification of samples into a number of groups using a measure of similarity, so that the samples in one group are similar to each other and samples belonging to different groups are not similar to each other. Usually, there are seven categories of clustering algorithms, in particular (i) graph-based algorithms, (ii) hierarchical clustering algorithms, (iii) partitioning clustering algorithms, (iv) density-based clustering algorithms, (v) grid-based algorithms, (vi) combinational clustering algorithms, and (vii) model-based clustering algorithms [28].  
Hierarchical clustering algorithms create a hierarchical decomposition of the given set of data objects. On the basis of how the hierarchical decomposition is formed, hierarchical methods can be classified into two categories: agglomerative and divisive hierarchical clustering algorithms. The agglomerative hierarchical clustering algorithm is known as a bottom-up approach. It starts with each object forming a separate group. It keeps on merging the objects or groups that are close to one another. It keeps on doing so until all of the groups are merged into one or until the termination condition holds [29]. The divisive hierarchical clustering algorithm is commonly known as the top-down approach. All objects initially belong to a single root group. Then the group is iteratively split up into subgroups until each object is in one cluster or the termination condition takes holds [29; 30]. In the paper, the agglomerative hierarchical clustering algorithm is selected because that it is usually more accurate than the divisive hierarchical clustering algorithm [30].  
For both agglomerative and divisive hierarchical clustering algorithms, the termination condition usually is the number of groups or the similarity threshold value. In engineering applications, the number of clusters are not always known a priori. Different similarity measures lead to different types of clusters, and the effectiveness of the clustering algorithm depends intensively on the definition of the similarity. In the next two sections, the similarity measurement and the decision of the cluster number are discussed.  
Figure 3: Schematic of coal seam floor clustering.
# Fog Computing: Enabling the Management and Orchestration of Smart City Applications in 5G Networks
## 3 A Fog-Based Management and Orchestration Framework for 5G Smart Cities
### Challenges of a Fog-Based MANO Architecture
#### 3.1.5 High Dynamicity  
Mobility is an important requirement of IoT scenarios since resources can be requested on-demand simultaneously by multiple devices at different locations. Furthermore, IoT introduces serious challenges on how to address seamless mobility in heterogeneous environments, since devices are constantly moving and accessing the medium through different technologies. To deal with these inherent mobility challenges, the fog computing paradigm has been introduced to provide resources and services on the edges of the network to effectively handle handover procedures.
## 7 Conclusions and Future Work
The use of a message predictor is almost independent of the routing protocol used in the islands of a HWSN, therefore this strategy can be easily reused in several other link state routing protocols. The use of this strategy can be combined with several other energy reduction proposals, and thus increasing energy saving. For instance, we can use the Mobile Gambler's Ruin (MGR) algorithm to predict the node mobility in a MANET (as proposed by [PERSON] _et al_. [35]), and the proposed predictor to save extra TC messages. Clearly, when two or more nodes move together, which is predictable by the MGR algorithm, the proposed predictor can also avoid disseminating TC messages because it infers that the nodes are moving in a group. This strategies combination contributes to save extra energy. The TC message predictor can also help mobile collaborative applications to save additional energy only by making an intelligent processing of the TC messages. For instance, using that information a node can identify appropriate periods to deliver messages to a destination node, and also avoid the message dissemination if the destination node is no longer part of the network. The amount of this additional saving will depend on both, the number of messages exchanged (at the application level) by the system and also on the policy that it uses to decide when delivering a message. The next steps in this initiative consider the evaluation of this prediction strategy in real scenarios, as a way to confirm the results obtained in the reported simulations. We will then explore the use of predictors in other routing protocols for opponents. Finally, it would be interesting to compare this proposal with others existing ones to trying identifying the best alternative to support each particular work scenario.
## 3 Results and Discussion
### Model Validation
#### 3.1.3 Reactor Temperature  
Figure 7 shows the average emission spectrum of N\({}_{2}\) SPS measured from the reactive zone for determining the rotational temperature. The red solid line labeled as \"T\({}_{rot}\)\" is the theoretical spectrum calculated from the commercial software Specair with theories of quantum mechanics for comparing with the average measured emission spectrum to determine the rotational temperature. The rotational temperature determined is 350 K.  
Figure 5: The Lissajous curve of a typical discharge cycle to evaluate the measured power consumption.  
Figure 6: The simulated and measured current densities of atmospheric-pressure helium dielectric-barrier discharges (APHeDBDs) generated in the planar reactor.  
The simulated heating power calculated by the 1D PFM in the reactive zone is around 1.8 W. In other words, around 90% of the average power consumption contributes to gas heating under current operating conditions, which is similar to the simulated result obtained for a helium microplasma operated under atmospheric pressure [9]. The volumetric heating source calculated by Equation (10) is adopted as the source term of the energy equation considered in the 3D GFM for modeling the temperature distribution of the reactor.  
Figure 8 shows the gas temperature in the symmetric plane of the reactor. The helium gas flows from the inlet on the reactor top and exits from the outlet on the reactor bottom. The helium flow is supplied at room temperature (300 K) and is heated as the flow passes the reactive zone. It is observed that the high-temperature zone moves slightly downward from the central region of the reactive zone because of the convective effect with the highest gas temperature reaching around 373 K. The average simulated gas temperature of the reactive zone is around 346 K which is close to the rotational temperature determined.  
Figure 8: The gas temperature distribution in the symmetric plane of the reactor.  
Figure 7: The rotational temperature of the reactive zone is determined as 350 K from the emission spectrum of the N\({}_{2}\) second positive system (SPS).  
To further validate the 3D GFM developed, the simulated surface temperature of the reactor is compared with that detected by the IR thermal imager as presented in Figure 9a,b, respectively. Similarly, the high-temperature zone of the simulated surface temperature moves downward from the central region of the reactive zone as explained previously, which agrees with the temperature distribution detected by the IR thermal imager. The simulated highest surface temperature is 362 K, which is in good agreement with that detected by the IR thermal imager as 365 K with an uncertainty of 2 K.  
Generally, the simulated results capture the thermal behavior of the APHeDBDs.  
Figure 9: The surface temperature of the reactor: (**a**) simulated by the 3D GFM; (**b**) measured by the infrared (IR) thermal imager.
# M-Shape PV Arrangement for Improving Solar Power Generation Efficiency
###### Abstract
[15] theoretically analyzed the study of the optimal tilt angle of the flat collector at different geographic locations and at different times of the year, which used three different models, from simple to complex. [PERSON] et al. [16] compared the accuracy of several solar radiation models, showing that the [PERSON] model [17] and [PERSON] model [18] are more accurate on cloudy days, with the [PERSON] model [19] being more accurate on sunny days. [PERSON] et al. [20] also made a very detailed summarized review of several PV panel configuration models. This paper mainly wants to control the external influence factors under the optimal conditions and compare the two types to show the advantages of the M-shape. A simple isotropic model developed is used to investigate the potential benefits of M-shape configuration of PV panel arrangement over the conventional S-shape arrangement [21]. To show the model's accuracy, the results obtained are compared with the optimal angles provided by the Industrial Technology Development Organization (NEDO), Japan. Six sets of solar radiation data were used from six regions in Asia. The data are from the Solar Radiation Data Base Reading System of NEDO [22]. In order to improve the efficiency of solar power generation, this paper adopts a PV arrangement that is different from the original standard. The optimal annual azimuth and tilt angle was calculated via the sky isotropic model. Using the PV model, two types of annual power generation were calculated. The main contributions of the proposed scheme are as follows:  
1. Novel design schemes are presented to reshape the solar panels configuration so as to improve the power generation efficiency;
2. The solar radiation model is introduced in detail. Six cities from six different Asian countries are used as a test model to confirm the robustness and effectiveness of the proposed control approach;
3. Detailed analysis is made to investigate the superiority of the presented scheme to the conventional one from different aspects like total cost, revenue, and area space. The remaining section of this paper is written as follows; in Section 2, the approximate astronomical knowledge is introduced by making the isotropic model in Section 3 easier to understand. Through the isotropic model in Section 3, the amount of solar radiations that are to be received by different azimuth and tilt angles is calculated to find the best azimuth and best tilt angles for an inclined plane of a PV array. Then, in Section 4, the PV model is used to calculate the annual power generation. Section 5 introduces the calculation method of the footprint of two PV arrangements. In Section 6, a simple optimizer and simulation condition is introduced. Section 7 is a comparison of two types of PV arrangement array in each region, while Section 8 is a summary of the full text and discussion of future work.
# On Site Investigation and Health Monitoring of a Historic Tower in Mantua, Italy
## 4 Continuous Dynamic Monitoring of the _Gabbia_ Tower  
A few weeks after the execution of the preliminary AVTs, a simple dynamic monitoring system was installed in the tower. The monitoring system is composed by: (a) three piezoelectric accelerometers (WR model 731A, 10 V/g sensitivity and \(\pm\)0.50 g peak, Wilcoxon Research, Germantown, MD, USA), mounted on the cross-section at the crowning level of the tower (Figure 6a); (b) one temperature sensor (TRAFAG, Legnano, Italy), installed on the S-W front and measuring the outdoor wall temperature; (c) one four-channel data acquisition system (24-bit resolution, 102 dB dynamic range, and anti-aliasing filters) (National Instruments, Austin, TX, USA); and (d) one industrial PC on site, for the system management and data storage. A binary file, containing three acceleration time series and the temperature data, is created every hour, stored in the local PC and transmitted to Politecnico di Milano for being processed.  
The continuous dynamic monitoring system has been active since 17/12/2012. The data files received from the monitoring system are managed by a LabVIEW toolkit [15] (National Instruments, Austin, TX, USA), including the (on-line or off-line) execution of the following tasks:  
* creation of a database, with the original data in compact format, for later developments;
* data pre-processing, _i.e.,_ de-trending, automatic recognition, and extraction of possible seismic events, and creation of one dataset per hour;
* statistical analysis of data. This task includes also the evaluation of the hourly-averaged temperature and the computation of the root mean square (RMS) accelerations associated to each 1-h dataset; and low-pass filtering and decimation of the each dataset and creation of a second database in ASCII format, with essential data records, to be used in the modal identification phase. In more details, the recorded accelerations were low-pass filtered--using a classic seventh-order Butterworth filter with a cut-off frequency of 20 Hz--and decimated five times, reducing the sampling frequency from 200 Hz to 40 Hz.
# An Emergency Alert Broadcast Based on the Convergence of 5G and ATSC 3.0
###### Abstract  
We propose a novel emergency alert broadcast mechanism for mobile phone users, which is based on the convergence of 5G and ATSC 3.0. Cellular networks including 5G adopt a broadcast technique for emergency alert. This technique just delivers a text-based message. Moreover, the message only includes a limited number of characters. Therefore, cellular networks cannot afford to provide abundant information in emergency cases. Broadcast networks such as ATSC 3.0 also offer an emergency alert broadcast service. This service can deliver a multimedia-based message in emergency cases. Therefore, the ATSC 3.0 supports more abundant information in the cases of emergency alert broadcasts. Especially, the ATSC 3.0 employs wake-up functionality and location information, which enables the delivery of emergency alerts to idle-state receivers in emergency areas. However, it is unlikely that the wake-up functionality and the location information are directly applicable to mobile phone users due to some practical issues. In order to improve the emergency alert broadcast service in mobile environments, we converge the 5G and the ATSC 3.0 networks, which effectively exploits the advantages of the networks. For the convergence network, we suggest a modified table, which associates the 5G message with the ATSC 3.0 message in the cases of emergency alerts. We also present a novel scenario for delivery of the emergency alert messages. Simulation results show that the convergence significantly enhances the receiver performance for emergency alert broadcast.  
emergency alert; broadcast; convergence; 5G; ATSC 3.0 +
Footnote †: journal: _electronics_  
0
Footnote 0: Copyright: © 2021 by the authors. Licenses MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).**
## 4 Results
### Self-Reported Valence and Arousal Data
change in emotion for the accident simulation situation. Moreover, there was no significant difference in VA mean value between medium-risk and low-risk conditions, but there was a significant difference in VA mean value between low-risk and no-risk conditions. In other words, we can simulate the different levels of risk through our motion simulation system. We also found that the level of information provision affected the emotion of participants. When the information was provided to the HUD, the participants showed low arousal and high valence. In other words, it was found that providing information to participants in an autonomous vehicle had a positive effect on participants' emotions. We also observed significant changes in VA mean values before and after the event. A t-test was conducted to confirm the significant relationship between data before and after the event. In the case of Arousal Mean, the value increased significantly after the event occurred than before the event occurred in 7 cases: high-risk and no-information, \(t(44)=-8.786\), \(p<0.001\); high-risk and information, \(t(44)=-8.238\), \(p<0.001\); medium-risk and no-information, \(t(44)=-7.008\), \(p<0.001\); medium-risk and information, \(t(44)=-7.561\), \(p<0.001\); low-risk and information, \(t(44)=-5.740\), \(p<0.001\); low-risk and no-information, \(t(44)=-6.738\), \(p<0.001\); no-risk and information, \(t(44)=-4.073\), \(p<0.001\); In the case of Valence Mean, the values after the event significantly decreased compared to that before the event occurred in 7 cases: high-risk and no-information, \(t(44)=5.175\), \(p<0.001\); high-risk and information, \(t(44)=4.462\), \(p<0.001\); medium-risk and no-information, \(t(44)=4.430\), \(p<0.001\); medium-risk and information, \(t(44)=5.045\), \(p<0.001\); low-risk and information, \(t(44)=3.137\), \(p=0.003\); low-risk and no-information, \(t(44)=3.078\), \(p<0.004\); no-risk and information, \(t(44)=2.706\), \(p=0.010\); Through the above statistical analysis, it was possible to decipher changes in the participants' emotions according to the risk level through simulation, and we observed that there were actually significant changes in the participants' annotation data and biometric data before and after events. Figure 7: A graph that compares the maximum and mean value of Arousal data and the minimum and mean value of Valence data according to risk level and whether or not information was provisioned through HUD. The use of asterisks in the figure indicates statistical significance. A single asterisk (*) denotes a \(p\)-value less than 0.05, while two (**) and three (***) asterisks denote \(p\)-values less than 0.01 and 0.001, respectively.
## 3 Results and Discussion
### The Effect of AsA Addition  
To test the effect of AsA, increasing AsA amounts varying from 10 to 200 umol L\({}^{-1}\) were added into Qt solutions, to provide a Qt/AsA molar ratio range from 10 to 0.5. In this case, the sigmoidal course of degradation was more evident as AsA concentration increased (Figure 5). Although AsA at levels of 10 and 25 umol L\({}^{-1}\) resulted in lower \(k\), an increasing trend observed for \(k\), from 10 and up to 100 umol L\({}^{-1}\) AsA (Table 3). However, the differences in \(k\) compared to control  
Such a phenomenon could be attributed to a stabilizing effect on Qt quinone, or, if Qt radical formation has preceded, donation of a H atom to convert Qt radical back to Qt. The latter would prevent O2-mediated oxidation of Qt, which would further decompose into simpler phenolics [29]. The fact that Qt was oxidized first could be supported by its lower oxidation potential, compared to AsA [32; 33].  
Therefore, it would be likely that, in a mixture of AsA and Qt, the latter is preferably oxidized, giving rise to Qt radicals and/or Qt quinone. Then, AsA might scavenge O2 (and/or H2O2), delaying to some extent further Qt oxidation and decomposition. This is likely the reason for determining increasing \(t_{1/2}\) upon addition of increasing AsA concentration.  
Upon depletion of AsA, Qt would react with O2 (or H2O2) generating eventually PCA and PGCA. To verify whether the presence of AsA could alter the mechanism of Qt decomposition, the mixture of Qt treated in the presence of 200 umol L-1 AsA for 50 min, at 90 \({}^{\circ}\)C, was analyzed by LC-MS/MS. Once again, as in the case of Qt alone, the major compounds detected were PCA and PGCA, but also another one with \(m/z=179\). The latter was tentatively assigned to 3,4-dione-2-oxo-phenylacetic acid (Figure 3), and it could derive from decomposition of 2,3,4-chalan-trione \(o\)-quinone, as previously proposed [31]. In such a case, formation of Qt quinone should precede and, therefore, it could be supported that, in the presence of AsA, Qt quinone formation and subsequent decomposition could be a plausible predominant pathway.
# Linguistic Patterns and Linguistic Styles for Requirements Specification: Focus on Data Entities
## 4 Data Entities' Linguistic Patterns and Linguistic Styles
### Data Attribute, DataPrimitiveType and DataEnumeration  
#### 4.2.1 Linguistic Pattern  
A DataAttribute denotes a particular structural property of a data entity. A data attribute has the following properties: identifier (id), name, type, an optional default value (i.e., the value assigned by default), and some predefined constraints (DataAttributeConstraint), such as NotNull, Unique, Derived, ReadOnly, Encrypted properties, multiplicity, or even other customized constraints.  
A DataEnumeration denotes a customized data type with a predefined set of string values.  
A DataType (i.e., an attribute type) might be a primitive type (DataPrimitiveType, e.g., Integer, Double, String, Date, DateTime) or a customized data enumeration (DataEnumeration).  
The following rules define the linguistic patterns DataAttribute, DataType, and DataEnumeration (lp2).  
DataAttribute:: <id:ID>  <name:String>? <type:DataType>  <defaultValue:STRING>?  <constraint:DataAttributeConstraint>?  //see Section 4.3 DataType::  DataAttributeType |  DataEnumeration enum  DataPrimitiveType:: Integer |  Double | Decimal | Boolean | Date  | Time | Datetime | String | etc.  DataEnumeration:: <id:ID>  <name:String>? <values:STRING>*