esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Controllability of Processes without Surprises
## 5 Sudden Termination Constellations and Patterns
### Sudden Termination Constellation and Pattern STP-4  
A _Sudden Termination Pattern STP-4_ (see Figure 7) is defined as follows: a contingent activity \(C\) and a semi-contingent activity \(S\) with constraints \(ubc(C.e,S.e,w)\) and \(lbc(C.e,S.e,v)\) as above are in an STP, if there is no way to schedule the start of \(S\) and the start of \(C\), such that the value for the end of \(S\) can be fixed, when \(S\) starts. This means that for all admissible  
Figure 6: Sudden Termination Pattern 3 (STP-3).  
start times of \(S\) and \(C\) and all possible durations of \(S\) there is a possible duration of \(C\) such that one of the constraints is violated.  
**Definition 10** (Sudden Termination Pattern 4).: _Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with the duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},\)\(C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.v)\) be constraints in \(P\)._  
\(S\) _and \(C\) are in a sudden termination pattern STP-4 iff \(\forall S.s,C.s\)\(\forall S.d_{min}\leq S.d\leq S.d_{max}\)\(\exists C.d_{min}\leq C.d\leq C.d_{max}:\ S.s+S.d>C.s+C.d+w\), or \(S.s<C.s+v\)._  
In the following we derive conditions to check whether an STP-4 might occur for a given pair of activities \(S\) and \(C\).  
**Theorem 7**.: _Let \(P\) be a dynamically controllable process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\), with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff \(S.d_{min}+v<S.e-C.s\leq C.d_{min}+w\) holds in \(P\)._  
Proof.: Please refer to Appendix A.3.  
**Theorem 8**.: _Let \(P\) be a process. Let \(S\) be a semi-contingent, and \(C\) be a contingent activity in \(P\) with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff the constraints \(lbc(C.s,S.e,S.d_{min}+v)\) and \(ubc(C.s,S.e,C.d_{min}+w)\) hold in \(P\)._  
Proof.: The constraints express exactly the condition described in Theorem 7. Hence in a process model \(P\) including these constraints \(S\) and \(C\) cannot be in an STP-4.
# A Unified Database Solution to Process BIM and GIS Data
###### Abstract  
For many years, the objective of spatial databases created using Geographic Information System (GIS) technology was to provide information about large spaces and areas outside of buildings. Building Information Modeling (BIM) technology focused mainly on small spaces, indoor and outdoor, targeted at other users and slightly different applications, was developed simultaneously for several dozen years. The significant development of GIS technology and new tools for quick data acquisition (e.g., laser scanning) and growing user needs resulted in the geoinformation modeling of the space inside buildings as well. BIM, on the other hand, began to be used for increasingly larger spaces outside buildings. Technology developers, users, and scientists started to notice that works turned out to be repetitive and that combining two different technologies is necessary; however, it is not simple. The research presented in the article is another attempt at connecting the world of BIM and GIS. The proposed integrated database environment of BIM/GIS spatial data makes it possible to store GIS and BIM data, enabling the use of the same data by both types of systems simultaneously and in a consistent manner. This allows BIM systems to to obtain simultaneous access to BIM and GIS data, which may be needed in, for example, the process of analyzing a building and its immediate surroundings. At the same time, GIS can obtain up-to-date building data necessary for spatial analyses, building management, or route mapping in navigation applications. The concept proposed in this article assumes a pragmatic approach, which is based on sharing Industry Foundation Classes (IFC) and CityGML schemas from a single database for BIM and GIS applications in their practically original form using an additional integrated BIM-GIS schema, called BIGI-S. The research joins some other works in this field, complementing them and adding a new perspective. This paper describes the concept of this solution, including specific data structures, data conversion algorithms, and a prototype solution. The tests carried out by the authors prove the robustness of the adopted concept and its technical feasibility.  
GIS; BIM; CityGML; IFC; indoor modeling; spatial modeling; building information +
Footnote †: journal: Applied Soft. 2020, 10, 8518; doi:10.3390/app10238518
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 7 Conclusions and Future Implementations  
The proposed routing protocol addresses existing limitations by effectively managing challenges related to delays, speed, link stability, network scalability, residual energy, and reliability, resulting in improved energy efficiency and overall reliability. However, further enhancements are necessary to ensure functional efficiency and accommodate diverse application requirements and communication environments. Notably, this protocol achieves stable cluster formation, reduces re-clustering issues, and improves communication efficiency. Through simulations, this article demonstrates superior performance compared to existing protocols, highlighting significant advancements in neighbor discovery algorithms and network optimization. While the current evaluation is based on simulations, this paper acknowledges the importance of validating the protocol in real-world environments and a up-to-date comparison table for significant enhance the clarity and impact. Due to time and budget constraints, hardware-in-the-loop (HIL) testing was not conducted within this study. As part of future work, HIL testing is planned to provide a more realistic evaluation of the protocol's performance. Additionally, expand the analysis to include smaller and larger packet sizes, aiming to assess their impact on throughput, latency, and overall network efficiency. This will allow for a more comprehensive evaluation of the proposed routing protocol's scalability and performance across a wider range of data payload conditions, enhancing the applicability and robustness of the findings in diverse UAV communication scenarios. This will enhance the reliability of the findings and further underscore the potential for the protocol to meet evolving communication demands and environmental conditions. These steps are critical for continued research and development aimed at improving the functionality and adaptability of CRAHNs.  
Figure 14: OLSR+GPSR vs. Proposed protocol End-to-End Delay.  
**Author Contributions:** Conceptualization, A.R. and N.M.; methodology, A.R., E.A. and N.M.; validation, A.R., E.A. and N.M.; formal analysis, A.R. and E.A.; investigation, A.R. and E.A.; data curation, A.R. and E.A.; writing original draft preparation, A.R. and N.M.; supervision, N.M.; project administration, A.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### Route Optimization Establishment
#### 2.5.1 Firefighting UGV Route Derivation  
The source UGV transmits a routing request to the ST after picking the intersection information for the source and destination. In the case of the DT information, it is available at the ST and is not out of date, the ST responds to S with a positive message; otherwise, the ST responds with non-active message then searches for the best tracking path. To determine the potential tracking route from the ST to DT, the ST sends a set of forwarding signals through the road's fire sensor, with the number of forwarding signals set to the UGV. Whenever a forward signal arrives at the fire sensor intersection li, it saves the transmission throughput before making a random choice to select, depending on the throughput, the next intersection recorded at li, assuming that li has K surrounding crossings, namely I1, I2,..., IK1, IK. The probability pij with the firefighting UGV picks the next \"Ij\", which is illustrated in Equations (9)-(11). When a forward signal from a firefighting UGV arrives at the DT with a delay of less than the Dth, the firefighting UGV's path is selected as an available candidate route [28].  
\[P_{ij}=\frac{LP_{ij}^{\infty}\cdot GP_{ij}^{\beta}}{\sum_{m=1}^{k}LP_{im}^{ \infty}\cdot GP_{im}^{\beta}} \tag{9}\]  
\[LP_{ij}=F\big{(}c_{ij}\big{)}=\varphi_{1}\cdot PC\big{(}c_{ij}\big{)}+\varphi_{2 }\cdot PRD\big{(}c_{ij}\big{)}+\varphi_{3}\cdot\frac{D_{th}-D\big{(}c_{ij}\big{)} }{D_{th}}\cdot\frac{1}{\big{(}1+D_{p}\big{(}c_{ij}\big{)}\big{)}} \tag{10}\]\[GP_{ij}=F\big{(}y_{ij}\big{)}=\varphi_{1}\cdot PC\big{(}y_{ij}\big{)}+\varphi_{2} \cdot PRD\big{(}y_{ij}\big{)}+\varphi_{3}\cdot\frac{D_{th}-D\big{(}y_{ij}\big{)}}{ D_{th}}\cdot\frac{1}{\big{(}1+D_{v}\big{(}y_{ij}\big{)}\big{)}} \tag{11}\]  
where:  
\(\alpha\) and \(\beta\): represent the weight values  
\(LPij\): represents the local throughput, which indicates the quality of service e\({}_{ij}\) (li-lj).  
\(GPij\): is denoted as the general throughput.  
\(PC(cij)\): connection probability, \(PDR(cij)\): packet delivery ratio, \(D(cij)\): delay, and \(Dv(cij)\): delay variance of road segment.  
The same QoS measures for route yij are represented by \(PC(yij)\), \(PDR(yij)\), \(D(yij)\), and \(Dv(yi)\). \(LPij\), which is a heuristic throughput that can assist forward the UGV in obtaining the most recent local QoS information and exploring the best routing pathways.  
\(GPij\): indicates the optimal QoS to the UGV, benefiting the algorithm convergence and avoiding routing difficulties on the road.  
\(p_{ij}\): is used to maintain the stability of both the new route exploration and the existing route utilization.
# Construction of a Chemical Kinetic Mechanism of Five-Component Surrogate Fuel for RP-3 Kerosene
## 4 Result and Discussion
### Verification of Single-Component Combustion Parameters
#### 4.2.4 Decalin  
Figure 19 shows the comparison outcomes of the ignition data of decalin with the reaction mechanism of surrogate fuels. The experimental data were measured by [PERSON] [54], with T from 769 to 1202 K, P from 1.17 to 5.12 Mpa, and \(\varphi\) = 0.5 and 1.0, respectively. As depicted in Figure 19a, although the predicted values of the mechanism are smaller than the experimental values under high-temperature conditions above 1000 K, it can, on the whole, effectively reproduce the variation trend of the ignition delay time of decalin with temperature. As shown in Figure 19b, at \(\varphi\) = 1.0, the predicted results of the mechanism are in good accordance with the experimental values, and it also well predicts the NTC behavior of the decalin component in the medium-temperature region.  
Figure 18: Verification of ignition delay times of iso-dodecane [19].  
The laminar flame speed of decalin was compared with the experimental data of [PERSON] [23] and [PERSON] [55], as depicted in Figure 20. As indicated in Figure 20a, within \(\varphi\) values of 0.7 to 1.5, the predicted results of the mechanism are all larger than those of the experimental data, whereas the good match with the experimental data in Figure 20b reflects the variation trend of the laminar flame speed of decalin with \(\varphi\) and enables reasonable predictions to be made regarding the ignition behavior of the fuel.  
Figure 21 presents the concentration changes in the major species during the oxidation process of decalin. The experimental data were the measurement results obtained by [PERSON] [56] using a McKenna burner in an O\({}_{2}\)/Ar atmosphere, with T = 300 K, P = 0.1 MPa, and \(\varphi\) = 0.75 and 1.8. The lines represent the simulation values, and the symbols represent the experimental values. The outcomes demonstrate that the mechanism constructed in this research can effectively predict the main products and their concentration distributions of decalin during the combustion process.  
Figure 19: Verification of the ignition delay time of decalin [54].  
Figure 20: Verification of laminar flame speed of decalin [23; 55].
# Sentinel-2 Reveals Record-Breaking Po River Shrinking Due to Severe Drought in 2022
## 3 Results
### Changes in Monthly Water Frequency
Figure 7: Spatio-temporal evolution of monthly water frequency anomalies (MWFAs) calculated against the monthly means for the period 2016–2023 at 1 km increments along the Po River. For example, the CVOWF calculated for the polygon about 1 km downstream of the reference hydrometer in Pontelagoscuro (Figure 9a) is equal to 5.21%. The trend of the MWF values over the period 2016-2023 for the area downstream of the Pontelagoscuro hydrometer is shown in Figure 9b. A significant decrease in the MWF values during summer 2022 is evident, as well as the relation of the MWF and monthly discharge, which results in a dynamic morphology characterized by a partly confined channel and the presence of a mid-channel bar. Figure 9: (**a**) Subdivision of the Po river area, in Pontelagoscuro (green rectangle in the overview map), into slices (polygons in red color) of 1 km length: the location of the reference hydrometer is indicated by a black triangle, while the selected polygon for the analysis of monthly water frequencies is highlighted in yellow color. (**b**) The trend of monthly average discharge measured by the Pontelagoscuro hydrometer and the trend of monthly water frequency (MWF) calculated for the selected area over the period 2016–2023. Figure 10 shows OWF and July 2022 MWFA calculated for the last river stretch before the confluence with the Po River of two tributaries: Trebbia River (originates from Apennine mountains, flows into Po River 42 m a.s.l. approximately 320 km from the river mouth), and Oglio River (originates from Alps mountains, flows into Po River 18 m a.s.l. approximately 183 km from the river mouth). For the considered stretches, OWF maps reveal a braided pattern of Trebbia River and partially confined channel forms for Oglio River, while the MWFA of July 2022 reveals the patterns of river shrinking, even at a channel width of about 20 m.
## 2 Modeling and Optimization Methodology  
### Configuration of the Hybrid PV-SOFC System  
The configuration of the hybrid PV-SOFC system is shown in Figure 1. It is modeled to fully satisfy the annual load profile of a small hotel in Cyprus. The load profile consists of several electric loads, which have been described by the authors in a previous publication [15]. The proposed system generates DC electricity through the SOFC and PV subsystems. When electricity generation from the PV is either unavailable, or inadequate, to fulfill the energy demand of the serviced buildings, the SOFC subsystem is operated. The latter is fueled with natural gas, which is compressed, desulfurized, and preheated through a heat exchanger. Steam is mixed with natural gas, and syngas is generated in the exit of the steam reformer. The hydrogen-rich syngas enters the fuel cell anode. Air is filtered and compressed through an air blower. Air is then preheated and directed to the cathode to generate DC electricity. The fuel cell exhaust mixture is directed to the catalytic combator. Additional natural gas and air is fed to the combustor, as needed. The combustor generates flue gas, which is used to preheat the natural gas fuel supply (HEx1), generate steam (HEx2), preheat the incoming air flow (HEx3), and generate useful heating through heat recovery (HEx4). The DC/AC inverters convert DC electricity to AC electricity. The main assumptions of the modeling methodology for the study can be found in [15].
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
###### Abstract  
The recent advancements in magnetoelectric (ME) materials have enabled the development of functional magnetoelectric composites for sensor applications in the medical and engineering sectors, as well as in energy harvesting and material exploration. Magnetoelectric composites rely on the interaction between piezoelectric and magnetoelastic materials by coupling the magnetization-induced strain to the strain-generated potential of the piezoelectric phase. This creates an increased interest around the development of novel piezoelectric materials that not only possess favorable piezoelectric properties but also fulfill specific material criteria such as biocompatibility, bioactivity, ease of fabrication and low cost. ZnO, and its nanostructures, is one such material that has been employed in the magnetoelectric research due to its remarkable piezoelectric, semiconducting and optical properties. Thus, this article provides a comprehensive review of the available literature on magnetoelectric composites based on ZnO micro- and nanostructures, aiming to present a concise reference on the methods, applications and future prospects of ZnO-based ME composites. Specifically, a brief introduction is provided, presenting the current research interests around magnetoelectric composites, followed by a concise mention of the magnetoelectric effect and its key aspects. This is followed by separate sections describing the relevant research on ZnO magnetoelectric composites based on ZnO thin-films, either pure or doped, and nano- and microrods composites, as well as nano composites comprised of ZnO nanoparticles mixed with ferromagnetic nanoparticles. Finally, the future prospects and the extension of ME ZnO research into nanowire and nanorod composites are discussed.  
magnetoelectric composites; ZnO magnetoelectric composites; ZnO/magnetostrictive thin films; ZnO magnetoelectric nanostructures +
Footnote †: journal: Applied Sciences
## 3 Methodology
### Feature Extraction
#### 3.3.2 Deep-Activated Features from Pre-trained CNNs  
Pre-trained CNNs are convolutional nets, trained on a large datasets and can classify 1000 or more natural objects. There are two ways to use pre-trained CNNs in specific tasks, in our case, medical image classification, transfer learning with fine-tuning as the classifier, and feature extractors along with supervised machine learning classifiers. If there is a limited amount of memory and computational resources, using them as the feature descriptors is a good choice. Here, we used nine different pre-trained CNNs: AlexNet [43], GoogLeNet [44], InceptionV3 [45], XceptionNet [31], ResNet-50 [46], Squeezenet [47], ShuffleNet [48], MobileNet [49] and DenseNet [50], as the feature descriptors to extract high-level deep-activated features. Here, we resized the segmented lung image to the input size, for each CNN, before extracting the features and feeding them to the network. The fully connected layer, which is the last layer before sigmoid classification neuron of each CNN, is retrieved and returns 1000 deep-activated features from each CNN, as listed in Table 1. Pre-trained CNN constructs a hierarchical representation of input images. The early layers extracted fewer low level features. The deep layers extracted high-level features, constructed using earlier layers. Figure 6 displays an example of activated feature maps of three different pooling layers: 'pool1', 'pool2' and 'pool3', of DenseNet. The pooling operation encapsulated the feature maps from convolution layers by highlighting the activated spatial locations, so that the features became more abstract in deeper layers of the CNN. These activation maps reveal the features the CNN learned by overlaying it with the original image.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## 7 Numerical Optimization  
Numerical optimization methods offer an alternative approach to solving the non-rest-to-rest flexible body rotation problem. However, these methods are often sensitive to the initial guess, the problem formulation, and the choice of algorithm parameters (e.g., mesh tolerance).  
The optimal control software GPOPS-II [28] is used to solve the minimum-time rest-to-rest and non-rest-to-rest problems given in Section 2 using the linearized equations of motion. The results are shown in Figures 8 and 9.  
Figure 8: Rest-to-rest case with minimum-time control found using GPOPS-II. The plot units are degree (\(\theta_{1}(t)\) and \(\theta_{2}(t)\)), degree/second (\(\dot{\theta}_{1}(t)\) and \(\dot{\theta}_{2}(t)\)) and N.m (\(u(t)\)).  
Figure 7: Non-rest-to-rest case with conventional ZVDD input shaping. The plot units are degree (\(\theta_{1}(t)\) and \(\theta_{2}(t)\)), degree/second (\(\dot{\theta}_{1}(t)\) and \(\dot{\theta}_{2}(t)\)) and N.m (\(u(t)\)).  
In both cases, GPOPS-II is able to find the minimum-time optimal control solution and achieve resting conditions. The solution of the rest-to-rest problem has three switches, while the solution of the non-rest-to-rest problem has five (\(>2n+1\)) switches.
# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 1 Introduction
* We achieve significant improvements in both performance and efficiency through our optimization strategy. The BLEU-4 score of enhanced BLIP-2 increases from 0.0117 (baseline BLIP-2) to 0.1609 (\(r=32,a=64\)), while the SPICE score improves from 0.1002 to 0.4642. Additionally, by leveraging LoRA-based optimization, we update only 0.1022% of the model parameters, demonstrating a highly efficient solution for domain adaptation. * We provide a detailed comparative analysis against existing captioning methods, including traditional optimization strategies and prior BLIP-2 implementations. Our results show that LoRA-based optimization significantly enhances model adaptability to structured driving environments while reducing computational overhead, making it more suitable for real-world deployment in autonomous systems. This paper provides a link between structured autonomous driving datasets and advanced vision-language models. The proposed method can facilitate the generation of more accurate and contextually enhanced scene captions, which would improve the overall reliability of autonomous systems.
# Enhancing the Accuracy of Advanced High Temperature Mechanical Testing through Thermography
## 3 Results
### Unique Benefits of Thermography
#### 3.3.2 Crack Growth Measurements  
Thermography has been utilised to measure crack growth at ambient, isothermal and dynamic temperatures. The infra-red camera was aligned to the tip of a machined 0.2 mm corner starter slit in a 7 \(\times\) 7 mm test specimen. Example thermographic images taken of crack growth at 750 \({}^{\circ}\)C under IP TMF loading are given in Figure 17. Crack growth down to 0.1 mm in length has been detected and recorded using this technique. Work is on-going comparing the visual non-invasive thermography measurements with the direct current potential drop (DCPD) technique [32], where a current is applied to the specimen and probes detect the potential difference across the cracked surface as damage progresses.