esa-sceva/satcom-chunk-collection huggingface dataset:
# A Trust-Based Model for Secure Routing against RPL Attacks in Internet of Things
## 5 Performance Parameters
### Packet Loss Rate  
The packet collision and network congestion become disastrous under Blackhole and Rank attacks. The packet loss rate (PLR) for MRHOF in Scenario I is 78% and 67.2%, in Scenario II is 80.5% and 72.7%, and in Scenario III is 83.3% and 76.2%, under Blackhole and Rank attacks, respectively, as depicted in Figure 9. The graphical representation shows that the packet loss percentage under Blackhole and Rank attacks is much less in SMTrustOF as compared to MRHOF in all three scenarios. The higher PLR in MRHOF is since there is no implementation for detection of attacks or trust among nodes. In addition to that, there can be several causes, including that the normal nodes may choose a malicious parent that intends to drop the packets, thus disrupting the topology. Whereas, in SMTrustOF, the route is established considering trust metrics such as, success rate, historical observation, feedback, and energy level, thus reducing the overall packet loss rate.  
Figure 8: Comparison of the Node Rank Changes (frequency).  
Similarly, as compared to DCTM, SMTrust shows better results in Scenario II (Figure 9) with only 17.9% and 10.7% PLR, under Blackhole and Rank attack, respectively. The logic behind better results of SMTrust as compared to DCTM is the difference in the trust metrics and computation. Moreover, in contrast to DCTM, SMTrust uses location and link stability to compute the trustworthy route, and mobility of nodes is necessary to be considered when sender nodes are mobile in an IoT environment. The better results of SMTrust thus, indicate the importance of mobility metrics for trustworthy parent selection in an IoT scenario of mobile sender nodes.  
For Scenario I, comprising static nodes, SMTrust also shows better results for PLR than SecTrust. This is because SMTrust has reduced node rank change frequency compared with SecTrust, thus causing a more stable topology and consequently reduced PLR. Moreover, the trust metrics and their computation in SMTrust is different as compared to SecTrust. Contrarily, SMTrust shows comparably acceptable results with MRTS. In MRTS, an IDS-based trust computation has been used which is computationally intensive in IoT scenarios and need an additional hardware module. Whereas, in comparison with MRTS, we show that keeping the trust computation simpler and selecting the crucial trust metrics for trust computation can improve the overall routing mechanism, particularly resulting in the reduced packet loss rate. Thus, indicating that the security from Blackhole and Rank attacks as well as a stable network topology can be achieved by carefully selecting the trust metrics instead of using computationally intensive IDS or hardware module.
## 4 Simulation Results and Analysis  
In order to verify the algorithm proposed in this research work, MATLAB2019a simulations were employed. The data presented in each result plot were subjected to statistical analysis, with a minimum of 400 repetitions, in order to obtain averaged values.  
This study follows the simulation configuration of the highway scenario outlined in 3 GPP TR 37.885. The model presented in this paper represents a single-directional highway consisting of three lanes, each with a width of 4 m. The overall width of the highway is 12 m, and it spans a length of 3000 m. All vehicles exhibit uniform velocity. The distribution of vehicle positions on the road can be modeled by the Poisson distribution, where the average inter-vehicle distance is influenced by the speed of the cars. The BS is situated in a central location with a radius of 500 m. Additionally, there is a distance of 40 m between the university and the adjacent highway. A random selection is made of X pairs of VUEs and N CUEs from the pool of created cars. It is important to note that VUEs are always formed by nearby vehicles. Table 1 displays the primary simulation parameters utilized in the investigation.
## 4 Conclusions
PSO again shows variance by providing a wide range of results for all cycles and missions; however, this technique meets the mission constraints for each run. Pigeon-Inspired Optimization provided optimum performances for the SC cycle only. Similarly to CSA, for the GG cycle, this technique struggled to meet constraints and provided a greater scatter effect in the results. GA provides results within a wider range of acceptability. However, the constraints were not met on multiple instances. Therefore, although this is a robust technique, the additional computational time negates the use of this technique. GA-Swarm, due to its hybrid nature, combines the search ability of GA by efficiently exploring the solution space with the fast convergence of PSO for all cycles and OFs. In conclusion, GA-Swarm performs better than GA for all cycles and both PSO and GA-Swarm provide a set of optimum results for each objective function for all cycles. Suggestions for future work would include running the optimizer using different propellant pairs to check if GG is still the more feasible cycle for this mission. This model can be modified to model a pressure-fed LPRE cycle and to incorporate multiple stage LVs. A database can be developed by comparing various LPRE cycles against each other for different missions. In addition, the application of GA-Swarm for a variety of engineering problems is also recommended in order to further establish its robustness. Conceptualization, I.Q. and S.S.K. ; methodology, S.S.K. and I.Q. ; software, S.S.K. and M.A.A. ; validation, S.S.K. ; formal analysis, S.S.K. ; writing--original draft preparation, S.S.K. ; writing--review and editing, M.U.S., R.F.S. and S.R.Q. ; supervision, I.Q. 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 conflicts of interest.
# Evolutionary Optimization of Spiking Neural P Systems for Remaining Useful Life Prediction
## 1 Introduction
[15] applied an attention mechanism to improve the RUL prediction accuracy. Another recent work [16] applied evolutionary computation to optimize the architecture of a multi-head CNN-LSTM model tailored to the RUL prediction task, which was previously handcrafted in [17]. More recently, a combination of an auto-encoder (AE) with a DL architecture has been proposed to improve the RUL prediction accuracy [18], whereas Extreme Learning Machines (ELMs) have been proposed in [19]. While a few previous works proposing SN P systems for pattern recognition do exist, they all use handcrafted systems for classification tasks, such as English letter recognition [1] or fingerprint recognition [20]. SN P systems have been used also for fault diagnosis [21; 22], but in most cases, these existing approaches require extensive domain knowledge for the design of the used SN P systems. Our work, instead, allows automatically designing SN P systems by using a neuro-evolutionary technique. Another novelty is that in our case, we apply SN P systems to a regression task, differently from the previous literature focused on classification tasks. To summarize, the main (to the best of our knowledge, novel) contributions of this work can be identified in the following elements:  
* We use a modified version of the NEAT algorithm to successfully evolve SN P systems applied to RUL prediction. * We obtain better performance than an MLP on the CMAPSS dataset, reducing also the number of parameters. * We obtain better-than-random performance on the new CMAPSS dataset while significantly reducing the number of parameters w.r.t. the state-of-the-art. The rest of the paper is organized as follows. The next section briefly presents the background concepts. Section 3 introduces the proposed method to optimize SN P systems based on evolutionary computation. Then, Sections 4 and 5 present the experimental setup and the numerical results, respectively. Finally, Section 6 concludes this work.
# Experimental Device to Evaluate Aerosol Dispersion in Venues
## 2 Materials and Methods
### Aerosol Generator  
The aerosol generator used for this study is the Atomizer Aerosol Generator ATM 230 (Topas GmbH, Dresden, Germany). The aerosol generator has an external compressed air supply (FK65 pro, 9 bar max, Weldinger, Oranienburg, Germany). It is also suitable for the generation of salt aerosols. The concentration of the NaCl solution used is decisive for the size of the resulting aerosol particles. Thus, the size distribution was measured by phase Doppler anemometry (PDA) [20] and the data were fitted through a t-location-scale distribution, which is useful for modelling data distributions with heavier tails than the normal distribution. The PDA measurements showed that at a nozzle pressure of 1.5 bar and a NaCl solution of 10% generate a particle size distribution that corresponds to that of sputum after evaporation (Figure A1) [21]. The emitted aerosol particles have a mean diameter of 2.4 um with a standard deviation of 1.1 um. This comes very close to the measured particle sizes of [PERSON] et al. (d = 2.44 um, standard deviation = 1.17 um) [22]. Aerosols with a diameter <5 um are of interest for the investigations because they can remain airborne for a long period of time under most indoor conditions and pathogens predominate in these aerosols [23]. Therefore, the tracer solution for the experiments was set to 10% NaCl in water. The NaCl within the emitted aerosol acts as a tracer particle. The aerosol is released with a controllable mass flow of 0.43 g min\({}^{-1}\). The aerosol generator operates in a continuous emission mode. The tracer solution is stored in a separate 5 L container and is pumped in a circuit between the atomizer container (500 mL filling volume) of the aerosol generator and the storage container with the aid of a peristaltic pump (DC 12 V, Vikye, Tiwu City, China). This ensures that the tracer solution has the same salt concentration throughout the dispersion and that the salt concentration of the tracer solution does not increase noticeably due to salt crystals which remain attached to the edge of the container. The tracer solution in the container is constantly heated to 37 \({}^{\circ}\)C.
## 5 Evaluation
### Dataset Generation and Model Implementation  
Given the experimental parameters \(\{C,A,\sigma\}\), we first applied the formulas in Section 4.1 to generate the codebook \(\mathcal{C}\) using \(C\). Next, we input the settings in Table 1 and \(A\) to the DeepMIMO dataset generator by modifying the parameters.m file, which is one of the supporting scripts provided by the DeepMIMO [38]. Then, after running another script DeepMIMO_Dataset_Generator.m, we obtained the channel matrices among BSs, DOI, and the RISs, along with the corresponding DOI locations, which are the labels in our problem. Finally, by applying the RSSI calculation pipeline presented in Section 3.1.1 using \(\mathcal{C}\) and \(\sigma\), we calculated the RSSI values, thereby constructing fingerprint datasets for evaluation.  
\begin{table}
\begin{tabular}{c c} \hline \hline Operating frequency & 3.4 GHz, 3.5 GHz, 28 GHz \\ Activated BSs & 1, 2, 3, 4, 5 (RIS), 18 \\ Antennas of BSs and RIS & \(4\times 4\) \\ Antennas of DOI & \(2\times 2\) \\ Bandwidth & 200 MHz \\ The number of OFDM sub-carriers & 512 \\ \hline \hline \end{tabular}
\end{table}
Table 1: DeepMIMO settings for our experiments.  
Figure 14: A top-view sketch map of the experimental area using the O1 scenario in the DeepMIMO.  
Here, we can simply assume that for all BSs we activated, \(G_{p}=40\) dB and \(L_{p}=20\) dB. We implemented these operations using MATLAB R2022a.  
For the training/test split, we first randomly sampled 80% of available codewords, then randomly sampled 80% locations in the testing area. Then, a data point, whose location and codeword were sampled, belonged to the training dataset. Conversely, if neither its location nor codeword was sampled, it was assigned to the test datasets. In other words, we tested the estimation model using fingerprints of unknown locations and unknown codewords. This way, the test data for online inference challenged the generalization capabilities for both locations and codewords.  
We implemented the proposed system using PyTorch [101] with the help of the DGL library [102] to build graph models for the feature extractor. All neural network layers contained 64-dimensional latent features, resulting in a training model with around 370,000 trainable parameters (float32). The whole system was trained using an Adam optimizer [103] with an initial learning rate of 0.01. We trained models and obtained simulation results on a laptop built by Dell Inc., Round Rock, TX, USA with an Intel(R) Core(TM) i7-10750H CPU and an NVIDIA GeForce GTX 1650 Ti graphic card. Using this hardware set, we easily achieved approx. 820 online inference per second without dedicated optimization.
# Framework for BIM-Based Repair History Management for Architectural Heritage
## 6 Results and Discussion
### Results  
Existing literature on BIM-based architectural heritage maintenance was reviewed, focusing on the use of maintenance data, and facility maintenance procedures were examined in accordance with BIM guidelines and case studies. Subsequently, a systematic development process for the management of architectural heritage facilities was formulated, spanning three stages: data collection and selection, mapping, and validation.  
In the data collection and selection phase, standards and systems for the management of diverse data types in architectural heritage were established. These included the data composition system for the repair history database, object classification code generation standards, and BIM LOD and LOI standards. As a case study, structured repair history records of the Great Hero Hall of Sudeoksa Temple in Yesan, South Korea, were utilized, adhering to the Cultural Heritage Administration's HBIM writing standards, with BIM following common family writing standards.  
The data mapping stage, central to our repair history database linkage system, featured three core automation sub-systems using the Revit API and Dynamo. The first sub-system assigned object classification codes automatically to BIM elements through Dynamo, while the second sub-system generated a data container mirroring the repair history database using the Revit API. The third sub-system synchronized repair history database information in spreadsheets based on object classification codes through another Dynamo script. The system for creating data containers using the Revit API was originally planned to be developed on Dynamo in order to simplify the entire workflow; however, due to encoding errors, the development had to shift to the API. While both the Revit API and Dynamo have their advantages and disadvantages, using the Revit API made the system usage steps more complex but also improved usability by allowing the system to be operated through a simple click of a ribbon tab command icon; however, modifying the hardcoded shared parameter information in the API's source code is difficult and poses a significant challenge to system maintenance. Therefore, it is necessary to simplify the entire process into a Dynamo-based automation system in the future, after correcting Revit's inherent encoding bugs. In the data validation stage, repair history records entered into the integrated HBIM were re-exported to the database, concluding the core process of the BIM-based architectural heritage management system through data validation and modification by the database manager.
# Exploring the Bioethanol Production Potential of _Miscanthus_ cultivars
## 3 Results and Discussion
### Sugar Profiles of Miscanthus Cultivars through a Growing Season  
Analysis of the sugars released from Cultivars C, D, E, F and G was conducted following acid pre-treatment and enzymatic saccharification; for each cultivar, the samples used were collected at different harvest points during the growing season (1 November 2018 to 07 February 2019). Determining sugar profiles for the cultivars over the growing season revealed that these cultivars had similar sugar profiles at the first point of harvest (31.6-35.4 g/L), with glucose levels of between 22.3 and 26.5 g/L. Analysis of the sugar profile for these cultivars over the growth period revealed that hydrolyses derived from the cultivars released progressively less sugar over time (Figure 4), with hydrolyses produced from cultivars harvested on 7 February 2019 having between 13.7-18.1 g/L of total sugars (Figure 4). The results also indicated that Cultivar D had a very rapid loss of released sugars between 26 November 2018 and 14 December 2018 and had the lowest released sugars by the end of the assay. Cultivars C, E, F and G, had a slower loss of released sugars following pre-treatment.  
The lignin content of selected samples in these cultivars was analysed with the aim of investigating whether the loss of monosaccharide is due to the accumulation of lignin along with _Miscanthus_ growth. As shown in Table 3, all late harvest samples, with the exception of cultivar C, contained similar or higher lignin content than that of early harvest samples. Similar results to several reports [14; 20] were also obtained when lignin content was analysed for _Miscanthus_ cultivars harvested in a different growing period (Table 2). The amount of lignin in the _Miscanthus_ samples could be a contributing factor to the prevention of monosaccharide release in the hydrolysis process. The cellulose and hemicellulose contents of these _Miscanthus_ samples were not determined. Previous results (Figure 1B) indicated that there was no significant difference detected between early harvest and late harvest. The reduction of monosaccharide obtained from _Miscanthus_ biomass could be due to the change of morphology of the grass and tighter binding of the cellulose, hemicellulose  
Figure 3: Scanning electron microscopy images of _Miscanthus spp._ (**A,B**) raw milled samples (cultivar C), (**C,D**) after 1% sulphuric acid pre-treatment (cultivar B); (**E**) after degradation of cellulose and hemicellulose in Klason lignin analysis (cultivar C); (**F**) after lignin extraction in cellulose measurement (cultivar B); (**G**) after lignin and hemicellulose extracted in cellulose measurement (cultivar B).  
and lignin with increasing age, which would require harsher hydrolysis/saccharification conditions for adequate degradation. For Cultivar 3, the lignin content of the early harvest sample was higher than that of the late harvest sample (Table 3). Two further repeat analyses were conducted, and similar results were obtained. It is not known why this sample displayed an opposite trend to other cultivars.
# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 3 Material and Method
### Dataset  
A new dataset was prepared because the open-source datasets prepared for forest fires are single-class, require very high processing power, owing to the excessive number of images, and have very long processing times. At the beginning of a fire, dense smoke rises, and after this rising smoke, flames begin to grow. Because our aim was early fire detection, the dataset was created from aerial smoke images and images of smoke-like objects in nature. First, 737 images were taken from the open-source Wildfire Smoke Dataset created by AI for the Makind technology community [15]. Then, 124 smoke-like images were downloaded from various open-source websites. By applying data augmentation to 72 downloaded images, 268 images were obtained and combined with the images of the Wildfire Smoke Dataset. A dataset consisting of two classes, divided into three classes, train, test, and validation, was prepared. The images in our dataset consist of two classes: smoke and normal. A large extended wildfire dataset [16] was created with 737 images containing smoke images and 268 images downloaded from open-source websites (istockphoto, pexels, shutterstock).
# Extended Reality in Applied Sciences Education: A Systematic Review
## 3 Results
### Integration of XR Technologies Across Scientific and Educational Disciplines  
Figure 5 illustrates the application of XR technologies across various scientific and educational disciplines. In the field of medical sciences and technology, particular emphasis is placed on the integration of XR technologies, including medical training simulations and surgical rehearsals. Within this field, MR technology is applied in 2 studies ([9; 64]), AR technology in 9 studies ([13; 38; 45; 48; 50; 57; 61; 63]), and VR technology is the primary technology utilized, appearing in 10 studies ([13; 19; 20; 29; 32; 33; 34; 38; 41; 42]).  
In the industrial design and engineering domain, VR technology is employed in six studies ([17; 25; 31; 37; 40; 43]), while AR technology is used in one study ([59]). In applied natural sciences, AR technology dominates with six studies ([16; 52; 53; 54; 55; 60]), followed by VR technology with five studies ([11; 16; 23; 27; 28]).  
In the architecture and civil engineering field, VR technology is applied in four studies ([18; 24; 30; 36]), AR technology in three studies ([36; 51; 56]), and MR technology in one study ([65]). In engineering education, AR technology is featured in four studies ([12; 46; 47; 62]), and VR technology in five studies ([5; 21; 22; 35; 39]). In the field of engineering mathematics, only two studies employ AR technology ([7; 58]). Additionally, one study in social sciences ([44]) and one study in marine sciences ([26]) utilize VR technology.  
Figure 5: Distribution of XR technology applications across educational and scientific fields.  
XR technologies are not only widely adopted in medical sciences and technology but are also increasingly used in engineering education and engineering mathematics as assistant teaching resources, reflecting the deep integration of XR technologies across diverse academic disciplines.