esa-sceva/satcom-chunk-collection huggingface dataset:
# Uncertainty Analysis Based on Kriging Meta-Model for Acoustic-Structural Problems
## 5 Numerical Examples
### Structural-Acoustic Model  
#### 5.1.1 Deterministic FEM  
The deterministic model consists of two aluminium flexible plates 1 and 2 with thickness \(t_{1}=2\) mm and \(t_{2}=4\) mm as shown in Figure 4[50]. These plates are imposed on the right surface and on the top surface of the acoustic cavity, respectively. The remaining surfaces of the acoustic cavity are perfectly rigid and the system composed of flexible plates and acoustic cavity presents a coupled structural-acoustic problem. Numerical simulation of the vibro-acoustic response are performed using the commercial software ANSYS-APDL. Regarding the discretization of the internal fluid and the plates, 3000 and 200 elements are adopted, respectively, for the fluid and the structure. The internal fluid is modeled by Fluid30 elements having 8-nodes with one degree of freedom per node: the acoustic pressure. The flexible plates are modeled by Shell181 elements having 4-node with six degrees of freedom at each node: 3 translations in the \(x\), \(y\) and \(z\) directions and 3 rotations about the \(x\), \(y\) and \(z\) axes (see Figure 5). The adopted boundary conditions were fluid-structure interface at all free nodes of the aluminium plates. All the nodes at the plates border were constrained.  
The finite element method (FEM) is used to study the sound pressure level (SPL) at point G2 as shown in Figure 6. This SPL can be determined using Equation (27) [10]:  
\[\text{SPL}_{dB}=20\log_{10}\left(\frac{P}{P_{ref}}\right) \tag{27}\]  
Figure 4: Acoustic-structural model.  
with \(P\) is the acoustic pressure at G2 and \(P_{ref}=20\) uPa represents the acoustic pressure of human hearing.
## 2 Background and Related Work
### Modelling Obstructions
#### 2.1.1 Modelling with 3D Vectors  
Figure 2(a), displays a LOD4 model, including interiors and exteriors of Maynooth University created by a team of land surveyors using a terrestrial laser scanner. Traditional survey methodologies such as these [16] or close-range photogrammetry [17] are time consuming and are not readily available for  
Figure 2: Mobile Mapping Systems and GNSS Quality (**a**) the XP1 MMS, designed and developed at the NCG (**b**) a 2D plot of GNSS satellite signal exhibiting signal loss due to obstructions during an MMS survey.  
most cities. More rapid methods of surveying urban areas are aerial LiDAR [18], mobile terrestrial LiDAR [19] or earth orbiting satellites. Data from satellites can be used to create 3D models of urban areas, [20] applied an object-based approach to extract buildings from multi-view, high-resolution satellite imagery captured by the PLEIADES constellation. Building height mapping surveys [21], cadastral surveys [22] and deformation studies in urban areas [23] have also been performed using space-borne Synthetic Aperture Radar (SAR) interferometry. The resulting models require powerful workstations to view or manipulate the data. The first innovative element of the novel approach being presented in this paper is a methodology that bypasses the requirement for these high detail models through creation of a 2.5D DSM from 2D vector mapping and low density elevation data at LOD0.
## 4 Methodology
### Features Extraction
#### 4.2.1 Pitch (F0)  
Pitch is a fundamental frequency of the voiced speech signal produced by the vibration of vocal cords while vocalizing any voiced phoneme. The periodicity of the signal makes pitch estimation easier. In other words, we can define the pitch of speech signals as the vocal cord's vibration rate. The reliability of pitch detection algorithms is why pitch is used in speaker recognition systems [25]. Pitch detection algorithms could be evaluated in various methods such as pitch estimation filter (PEF), cepstrum pitch determination (CEP), and normalized correlation function (NCF); e.g., in our current research, we used NCF to detect pitch. NCF calculates the pitch value in a sequence of steps as described in Figure 5, as follows [26]:  
Figure 4: The feature extraction process.  
Figure 5: Block diagram of the pitch detector.  
1. Filter the speech signal with a 1 kHz low-pass filter.
2. Emphasize the signal by the third power to raise the amplitude.
3. The pitch period duration is analyzed by short-time correlation.
4. The peak is chosen to produce a valid pitch, if applicable. This can be computed from the voiced segment of the spoken speech.  
In this research, we proposed novel features to develop a speaker identification system. The main concept is to use the acoustic features of speech signals along with their first and second derivatives, which means the pitch frequency (F0) and the first pitch derivative \(\Delta\)F0 and second pitch derivative \(\Delta\)\(\Delta\)F0 are considered. The following equations determine the derivative calculation.  
Equation (3) is the first derivative equation. Where x: is the pitch of the nth frame and n: is the current frame.  
\[\Delta\mathrm{x_{n}}=\mathrm{x_{n}}-\mathrm{x_{n-1}}. \tag{3}\]  
Equation (4) is the second derivative equation.  
\[\Delta\Delta\mathrm{x_{n}}=\Delta\mathrm{x_{n}}-\Delta\mathrm{x_{n-1}}. \tag{4}\]
## Materials and Methods
### Phylogenetic Analysis  
To check the taxonomic placement of the ZCR6 strain, the 16S rRNA gene was amplified with the universal bacterial primers 8F (5\({}^{\prime}\) AGTTTGATCATGCCTCAG 3\({}^{\prime}\)) and 1492R (5\({}^{\prime}\) GGTTACCTTGTTACGACTT 3\({}^{\prime}\)), targeting a fragment size of 1484 bp [107]. Next, the obtained sequence with a length of 1402 bp was compared with the EZBioCloud database. The phylogenetic analysis was performed based on the longest common fragment of the 16S rRNA gene sequences (1387 bp) selected from ClustalW alignment of ZCR6 strains and 13 type strains of other _Pseudomonas_ species. Sequences of 16S rRNA fragments of type strains were obtained from GenBank. The analysis was performed using Mega X software with the maximum likelihood method and the Tamura-3-parameter model, assuming that a certain fraction of sites are evolutionarily invariable (+I) and 1000 bootstrap replicates [138; 139]. The similarity of 16S rRNA sequences between tested and type strains was calculated using the average nucleotide identity (ANI) calculator ([[https://www.ezbiocloud.net/tools/ani](https://www.ezbiocloud.net/tools/ani)]([https://www.ezbiocloud.net/tools/ani](https://www.ezbiocloud.net/tools/ani)), accessed on 12 September 2021) [140].  
Detailed identification of the ZCR6 strain was obtained using multilocus sequence analysis (MLSA) of the partial sequences of 16S rRNA and two housekeeping genes (_gyrB and rpoD_) [20; 138]. After alignment using ClustalW, the concatenated sequences (2563 bp for 16S rRNA-_gyrB-rpoD_ genes) were compared with concatenated sequences of type strains obtained from GenBank (accession numbers of sequences are given in Supplementary Table S1). Phylogenetic analysis of the ZCR6 strain was performed using Mega X software with the maximum likelihood method with 1000 bootstrap replicates under the Tamura-Nei model using the discrete gamma distribution (+G) [138]. The similarity of concatenated gene sequences between tested and type strains was calculated using the ANI calculator. Additionally, using the TrueBac ID tool (truebacid.com, accessed on 16 November 2020), the isolate was identified based on the sequence of the entire genome [141]. The target genes were extracted from the entire genome using Geneious Prime software.  
A tetra-correlation search (TCS), a feature of JSpeciesWS ([[http://jspecies.ribobst.com/jspeciesws](http://jspecies.ribobst.com/jspeciesws)]([http://jspecies.ribobst.com/jspeciesws](http://jspecies.ribobst.com/jspeciesws)), accessed on 15 May 2020) [142], allowed comparisons of the genome sequenced of the ZCR6 strain against the genomes from the reference database GenomesDB, providing a list of the most similar genomes based on their resulting tetranucleotide signature correlation index.
## 4 Proposed Scheme
### Formation Control Algorithm Based on Reinforcement Learning
#### 4.2.1 Formation Generation Policy
\right.\)
14:for\(m=\left\{t-1,t-2,\ldots,t_{end}\right\}\)do
15:\(R_{i}=r_{m}+\gamma R_{i}\)
16: Update the critic by (9):
17:\(\theta_{i}^{V}\gets [PERSON]\left(\theta_{t}^{V},\
abla_{\theta_{i}^{V}}L( \theta_{t}^{V})\right)\)
18: Update the formation generation policy for USV \(u_{i}\) by (10):
19:\(\theta_{i}^{u}\gets [PERSON]\left(\theta_{t}^{u},\
abla_{\theta_{i}^{V}}L\left( \theta_{t}^{u}\right)\right)\)
20: Update the \"soft\" target networks for the actor and critic:
21:\(\theta_{i}^{V}\gets\tau\theta_{i}^{V}+(1-\tau)\theta_{i}^{V}\)
22:\(\theta_{i}^{U}\gets\tau\theta_{i}^{U}+(1-\tau)\theta_{i}^{U}\)
23:endfor
24:endfor
25:endfor
26:endfor
```  
**Algorithm 1**Formation Generation Policy Based on the Advantage Deep Deterministic Policy Gradient (ADDPG)  
The details of the proposed formation generation policies are shown in Algorithm 1, where \(M\) is the number of episodes, \(T\) is the maximum episode length, and \(N\) is the number of USVs. The inputs are the designed reward \(r_{1}\),\(r_{2}\) for the formation generation scenario and the formation shape \(F_{s}\). The output are the formation generation policies for the USV team. In each episode, every USV adopts a random process \(N_{rp}\) to achieve sufficient exploration and collects experiences \(e_{t}^{i}=\left(s_{t}^{i},a_{t}^{i},r_{t}^{i},s_{t+1}^{i}\right)\) that are stored in the replay buffer \(D_{s}\) (lines 8-9) and sampled randomly to update the policies. The value of each state-action pair is estimated by the cumulative discount reward \(\text{R}_{i}\) (lines 10 and 12), which is used to calculate the advantage of action. A random minibatch of samples in the replay buffer is sampled by each USV to improve the formation generation policies (lines 13-16). The \"soft\" updates are used to make the target function change more slowly and improve the stability of learning (lines 18-19).
## 5 Industry Applications and Manufacturers
### Performance Benchmarks and Empirical Insights into Autonomous Forklifts  
As per a new study [119], autonomous forklifts and intralogistics systems have significantly enhanced operational efficiency and reduced costs across various industries. A focused investigation into small- and medium-sized enterprises (SMEs) within Romania's forklift industry highlights the transformative impact of artificial intelligence (AI) integration. Table 13 summarizes the influence of AI implementation on key business metrics, showcasing substantial benefits in operational performance and resource optimization.
# Reputation-Based Trust Approaches in Named Data Networking
## 2 Background
### NDN Trust and Security
This implies that content providers are responsible for designing and implementing related access control methods in order to protect their content from unauthorized consumers. To this end, utilizing credential-based schemes, and particularly encryption-based techniques [17], is a common approach. For instance, the main concept of the mechanism proposed in Reference [18] is to generate two data packets for each content object--a first one including the encrypted content and a second includinga related access policy. An access policy is defined as a set of attributes which are used to infer if a user is allowed to access specific content. In this model, in order for a user to obtain a desired content object, the user must not only maintain a secret key to decrypt the content but the user's attributes should also match the attributes included in the associated access policy (specified in the second data packet), as well. A centralized proxy server (e.g., a selected router) is delegated with the responsibility to check if users' attributes match those specified by the corresponding access policy and thus, inspect access to protected content.
# Automated Detection of Misinformation: A Hybrid Approach for Fake News Detection
## 2 Related Works
### Works on Comparing ML Models  
[PERSON] et al. [4] conducted a comprehensive benchmark study on fake news detection, evaluating various machine learning approaches on diverse datasets. They explore traditional machine learning and deep learning models, including advanced pre-trained language models like BERT, RoBERTa, and ELECTRA. Their study addresses the issue of dataset bias by assessing models on different types of news articles. In comparison, our work focuses on the automated detection of misinformation within written content through a novel hybrid word embedding model. We integrate traditional TF-IDF methods with advanced feature extraction techniques, considering the text's linguistic, psychological, morphological, and grammatical aspects.  
[PERSON] et al. [8] proposed a machine learning ensemble approach for the automated classification of news articles. They conducted extensive experiments on four real-world datasets, including the ISOT fake news dataset, two datasets from Kaggle, and a combined dataset. The proposed framework involved training various machine learning algorithms, including logistic regression, support vector machine, multilayer perceptron, and k-nearest neighbors. Ensemble techniques such as bagging, boosting, and voting classifiers were employed to enhance classification accuracy further.  
While both works explored a wide range of machine learning models on multiple datasets, our study emphasizes the effectiveness of the hybrid model in surpassing benchmarks and outperforming alternative experimental setups. Additionally, our work provides a systematic and replicable approach for text classification, emphasizing dataset alignment and balance and the utility of transfer and incremental learning techniques.  
[PERSON] et al. [5] compared four machine learning algorithms using NLP techniques on a public dataset. Their study, while insightful, does not leverage a dedicated framework for comparing the performance of the models. This difference in approaches underscores the unique contribution of our work in offering a more structured and comprehensive assessment of machine learning models for fake news detection.  
[PERSON] et al. [9] employed an ensemble of advanced, recurrent neural networks--specifically, long short-term memory (LSTM) and gated recurrent unit (GRU)--to discern the veracity of news articles. To enhance accessibility and real-world applicability, the researchers have implemented an Android application that can be used to authenticate news articles from various sources on the internet. Our work differs in several key aspects. We adopted a broader range of machine learning models, including some innovative techniques not explored in this study. Also, while their research relied on a single custom dataset and some standard ones, our work engaged with multiple datasets to mitigate potential biases and enhance the generalizability of our findings.
# Opportunistic Network Algorithms for Internet Traffic Offloading in Music Festival Scenarios
## 5 Evaluation
### Results
#### 5.3.3 Discussion  
The best-performing algorithms, as stated previously, were SWb, SSW1, SSW2v1, and SSW2v2. In order to study their scalability and have a better understanding of their behavior, we further tested them using a larger simulation area of 40 \(\times\) 40 m\({}^{2}\). The results are thus shown in Figures 12 and 13.  
Figure 11: Delivery delay comparison between the Bluetooth and Wi-Fi Direct traces.  
Figure 12: Results for the larger Bluetooth trace.  
Figure 10: Delivery rate and overhead for the Wi-Fi Direct trace.  
Furthermore, we also analyzed ML-SOR [27], which is a more advanced opportunistic routing and dissemination solution which assumes that there are multiple layers for device interaction, such as social connections, interests, etc. Based on these layers, ML-SOR nodes apply a utility function to each message upon a contact, in order to decide which message is forwarded to which encountered node. The solution thus uses multi-layer context information in order to improve the routing process. We chose this algorithm because it is a recent one that has been proven to have good results for various use cases, but was not tested in a festival scenario.  
Thus, Figures 12 and 13 show that all Spray-and-Wait-based algorithms scale relatively well, except for SSW1, which has a more dramatic overhead increase, from 1300 (on the larger Bluetooth trace) to 1527 (on the larger Wi-Fi Direct trace), but which is compensated by a decreased hop count. Moreover, while the hop count stays almost constant for SSWb, SSW2v1, and SSW2v2, SSW1 performs better when Wi-Fi Direct is used, as the hop count drops from 260 to 168. Furthermore, it can be observed that ML-SOR does not behave well at all in terms of overhead, which is far larger than that of any of the other algorithms.  
Aside from this, ML-SOR does not manage to deliver a single message in this scenario, having a delivery rate of 0 (which is why the delivery delay and hop count are not depicted in Figures 12 and 13, since they are only computed when messages reach their destinations). This shows that a solution such as ML-SOR might not be suitable for a festival scenario out of the box, mainly because of the density of the network and the large number of contacts in a short period of time. From our analysis, it appears that messages tend to bounce around between nodes in small areas, and thus are not able to move towards their destinations, which might be located farther. We plan to further extend this analysis and even modify ML-SOR to adapt it to a festival scenario.  
Looking at the results, it can be observed that smaller delivery delay values can be accomplished at the price of higher overheads. Even so, there is a small difference in terms of overheads between SWb and SSW2, while the latter achieves better delivery delay values by far.
## 4 Performance Evaluation
### Simulation Results
#### 4.2.2 Traffic Bit Rate Variation  
Figure 4 presents the performance metrics of PDR, End-to-End Delay, and Average Energy Consumption against varying traffic bit rates for QLR, DQR, and our proposed protocol under a fixed node speed of 15 km/h. As shown in this figure, our protocol consistently outperforms QLR and DQR across all traffic bit rates.  
As shown in Figure 4a, our proposed protocol maintains a higher PDR compared to QLR and DQR across all traffic bit rates. Notably, at the highest traffic bit rate of 5000 bps, our protocol exhibits a PDR improvement of approximately 18.7% over QLR and 13.2% over DQR. This improvement is due to our protocol's effective clustering and AHP-based weighting mechanisms, which prioritize reliable nodes and optimize routing paths using DQN learning. Even under high traffic bit rates, our protocol efficiently manages routing decisions to ensure high packet delivery rates without significant drops in performance. End-to-End Delay for each protocol under varying traffic bit rates is illustrated in Figure 4b. Our proposed protocol consistently achieves the lowest delay across all traffic bit rates. At 5000 bps, the delay is reduced by approximately 21.2% compared to QLR and 16.6% compared to DQR. This reduction is achieved through our protocol's ability to swiftly identify optimal routing paths via clustering and DQN, thereby minimizing the time packets spend traversing the network. The efficient handling of high traffic bit rates ensures that packets are routed through less congested paths, resulting in lower delays.  
Additionally, Figure 4c compares the Average Energy Consumption of our proposed protocol with QLR and DQR across different traffic bit rates. Our protocol maintains relatively stable energy consumption, while QLR and DQR exhibit increasing energy consumption as the traffic bit rate increases. At 5000 bps, our protocol consumes approximately 35.2% less energy than QLR and 22.7% less than DQR. This energy efficiency is facilitated by AHP-based clustering, ensuring balanced energy utilization among nodes, and DQN optimization that minimizes unnecessary transmissions. Additionally, our protocol effectively manages the queue length, preventing congestion and reducing the need for retransmissions, which further contributes to energy savings even under high traffic loads.
# Usability Analysis of Smart Speakers from a Learnability Perspective for Novel Users
## 3 Results  
### Task-Completion Time  
The average task-completion times for the voice-only task and the task with the remote control are shown in Figures 2 and 3 below. The two-way ANOVA for the voice-only condition showed that only the main effect of the number of tasks was significant (\(F\)(9, 162) = 21.488, \(p\) < 0.01), and there was no interaction effect. The Tukey-Kramer multiple comparisons revealed significant differences in task-completion time between the first and each time (\(p\) < 0.01), between the second and the fourth to tenth times (\(p\) < 0.01), and between the third and the tenth times (\(p\) < 0.05).  
The two-way ANOVA in the condition with remote-control operation showed that the main effect of the number of tasks (\(F\)(9, 135) = 11.102, \(p\) < 0.01) and the interaction between the presence of a screen and the number of tasks (\(F\)(9, 135) = 2.279, \(p\) < 0.01) were significant. Since there was an interaction, a simple main-effect test was conducted, which confirmed the significant difference in the presence or absence of a screen in the first session (\(F\) (1, 100) = 16.294, \(p\) < 0.01). This indicates that the smart speaker with a screen allowed the task to be completed faster than the smart speaker without a screen, in terms of the task-completion time for the task with remote-control operation for the first time. Regarding the number of tasks, significant differences were found in both conditions (without screen: \(F\)(9, 135) = 11.816, \(p\) < 0.01; with screen: \(F\)(9, 135) = 2.135, \(p\) < 0.05). The Tukey-Kramer multiple comparisons showed significant differences in task-completion time between the first and second (\(p\) < 0.01) and second and third (\(p\) < 0.01) trials in the no-screen condition. On the other hand, in the condition with the screen, significant differences were observed between the first trial and the fourth and sixth trials (\(p\) < 0.05) and between the first trial and the seventh and tenth trials (\(p\) < 0.01).  
\begin{table}
\begin{tabular}{p{28.5 pt} p{28.5 pt}} \hline \hline Impact & \\ \hline High & Problems that prevent the completion of the task once encountered and directly lead to task failure \\ Medium & Problems that contribute to task failure but do not directly interfere with it \\ Low & Problems that do not contribute to task failure but annoy or frustrate the experimental participants \\ \hline Frequency & \\ \hline High & Problems that most users are likely to face even outside of the tasks in this study \\ Medium & Problems specific to the tasks in this study or problems that some users may face outside of the tasks in this study \\ Low & Problems that rarely occur in the tasks in this study \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of level of impact and frequency of usability problems.