esa-sceva/satcom-chunk-collection huggingface dataset:
# Image Analysis in Autonomous Vehicles: A Review of the Latest AI Solutions and Their Comparison
## 5 Discussion, Limitations, and Future Research Trends  
### Discussion  
The integration of advanced AI-based image analysis into autonomous vehicles (AVs) has witnessed tremendous progress, driven by sophisticated datasets and cutting-edge AI methodologies. This review outlined several datasets--Cityscapes, NuScenes, CARLA, and Talk2 Car--each serving a distinct role in evaluating and training AI models for various AV tasks, including semantic segmentation, 3D object detection, and vehicle control. Advanced  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **AP50** & **Year** & **Paper** \\ \hline Udeer\_HuBo-VLM & 76.74 & 2023 & [18] \\ Deformerable-MDETR & 74.4 & 2021 & [162] \\ FA & 73.51 & 2022 & [163] \\ Stacked VLBert & 71.0 & 2020 & [164] \\ \hline \hline \end{tabular}
\end{table}
Table 8: Talk2 Car model rankings.  
AI models such as Convolutional Neural Networks (CNNs), Transformer models, Generative Adversarial Networks (GANs), and Vision Language Models (VLMs) have been instrumental in pushing the boundaries of autonomous driving technologies.  
AI techniques have significantly improved the precision and robustness of AV perception systems. For instance, semantic segmentation models show high mean Intersection over Union (mIoU) scores while maintaining fast processing speeds, evident from the performances of models like YOLOv8-DSAF and RT-DETR on the Cityscapes dataset. In 3D object detection, models such as EA-LSS and BEVfusion leverage data from multiple sensors, enhancing the detection accuracy in complex environments. For vehicle control in virtual environments, frameworks like ReasonNet and CarLLaVA show high driving scores and route completion rates, further solidifying their roles in AV navigation and decision making.
## 5 Proposed Dynamic Cluster Head Election Algorithm
### Proposed Dynamic Re-Clustering and Self-Organization Method
#### 5.2.1 Mitigation of Energy Consumption during Cluster Reconfiguration  
Occasionally, the CH consumes more energy due to the cluster preparation stage and many data transfers. Re-clustering is proposed to distribute the CH role among the sensor nodes equally and extend the network lifetime. However, re-clustering increases power consumption, due to additional control messages, and delays real-time data transmission [35; 36]. The proposed model provides a solution that involves deleting dead or failed nodes from clusters, which saves wasted energy in sending messages to a dead or failed node. It also saves wasted time to get out of the false election of the failed nodes (black holes). As a consequence, it will not consider dead or failed nodes in the upcoming cluster reconfiguration process.
## 3 Results and Discussion
### Links with Maximum of Heat Release Rate First Derivative  
Figure 9 presents the correlations between the maximum value of the first derivative of heat release rate and the centre of combustion (Figure 9a), the combustion noise indicator (Figure 9b) and the second maximum of pressure derivative (Figure 9c).  
Referring to Figure 9a, despite the observed trends can be considered a consequence of the link between the two quantities and \(\mathrm{ROHR}_{\mathrm{MAX}}\) reported in Figure 8a,c, the linear relationship is confirmed also between them. The inverse link is related to the fact that an increase in (\(\mathrm{dRoHR}/\mathrm{d}\theta\))\({}_{\mathrm{MAX}}\) is due to a higher level of rail pressure for Sets 3 and 4, moving \(\theta_{50}\) closer to TDC, reducing its value because faster combustion occurred. For Set 2, this behaviour is related to a higher contribution from the low-pressure loop, as already outlined in the previous paragraphs.  
As far as combustion noise indicator is concerned (Figure 9b), its rise is related to the faster combustion (at least in the premixed phase) achieved with rail pressure control (Sets 3 and 4), for a lower level of the EGR rate from the HP circuit (Set 1) or higher LP proportion (Set 2).  
The trends of the second maximum of pressure derivative as a function of (\(\mathrm{dRoHR}/\mathrm{d}\theta\))\({}_{\mathrm{MAX}}\) shown in Figure 9c are expected, due to the influence of \(\mathrm{RoHR}_{\mathrm{MAX}}\) on the maximum levels of heat release rate first derivative (Figure 7a) and on (\(\mathrm{dp}/\mathrm{d}\theta\))\({}_{\mathrm{MAX}}\) (not shown). However, it is interesting to  
Figure 9: Relationship between (\(\mathrm{dRoHR}/\mathrm{d}\theta\))\({}_{\mathrm{MAX}}\) and \(\theta_{50}\) (a), \(\mathrm{I}_{\mathrm{n}}\) (b), (\(\mathrm{dp}/\mathrm{d}\theta\))\({}_{\mathrm{MAX}}\) (c) in different test sets.  
note the large number of data sets showing high levels of determination coefficient for this relationship (which is not true for \(\mathrm{I_{n}}\), as shown in Figure 9b) and the fact that it would be possible to identify a single correlation among all the available data. The first result is related to the definition of \(\mathrm{I_{n}}\) (Equation (8)) where the first maximum of pressure derivative also appears. This quantity is associated with the combustion of fuel injected during pilot and pre-injections (Figure 2). Taking into account that slight or no variations of \((\mathrm{dp}/\mathrm{d}\theta)_{\mathrm{MAX\,1}}\) values were observed when modifying the different control variables (as can be deduced from Figure 6 and discussed in Reference [1]), it can be concluded that the description of combustion noise issues may be developed referring only to the second maximum \((\mathrm{dp}/\mathrm{d}\theta)_{\mathrm{MAX\,2}}\). Further details will be added discussing the direct link between combustion noise indicator and \((\mathrm{dp}/\mathrm{d}\theta)_{\mathrm{MAX\,2}}\), reported in Figure 11.
# Detecting Selected Instruments in the Sound Signal
## 4 Research Methodology  
The initial process enabling conducting the research was the loading and appropriate processing of _wav_ files and their gradual transformation until obtaining the _MFCC_ coefficients, which serve as the model's input data.  
The type of model, the optimal configuration of its parameters, and the degree of complexity were the essence of the tests performed. These elements are described in more detail in Section 5. Then, the model's decisions (musical instrument recognition results obtained on a given model) were subject to effectiveness assessment.  
The model evaluation was based on several metrics:  
* _Accuracy_, defining the ratio of the number of test samples that were correctly assigned to a given instrument to the number of all test samples.
* _Sensitivity_--_true positive rate_, that is a value showing what percentage of recordings of a specific instrument was correctly recognized in relation to the number of all recordings of only this instrument in the test set. This value is averaged for all instruments.
* _Precision_--_positive predictive value_, which expresses how many of the examples marked as a specific instrument turned out actually to be that instrument. This value is averaged for all instruments.
* _F1_-_score_, a measure of the harmonic mean (Equation (3)) of precision and sensitivity. The closer this value is to one, the better it indicates the model's performance. In the ideal case, when it takes the value 1, the analyzed model shows perfect sensitivity and precision. \[F1\text{-}score=\frac{2\cdot precision\cdot sensitivity}{precision+sensitivity}\] (3)
* The value of the _loss function_ of the neural network for each learning epoch, which should decrease throughout the entire learning process, after which this value should be appropriately low.
* A _confusion matrix_, which is very suitable for analyzing results in the context of multi-class problems such as the one discussed here. The matrix shows how many of the solutions proposed by the network turned out to coincide with real values, how many were misclassified, and how. Values represent the number of samples or the share (sometimes percentage) in a given row compared to all samples in a given class.  
The neural network base models used in the work are rather light. This is due to the current choice of path to improve the results obtained. On the one hand, it is possible to expand the base model with the expectation of improving the results. On the other hand, it is possible to use an ensemble containing relatively simpler base models. If the computational load is not taken into account, it would also be possible to build ensembles containing complex neural network structures. The current research focuses on creating an ensemble containing relatively light base models. Nevertheless, the basic model will be optimized through changes to its architecture.
This is the first study, to our knowledge, in which the levels of nine VOCs (acetone, isoprene, toluene, p/m-xylene, o-xylene, styrene, benzaldehyde, naphthalene, and 2-ethyl-1-hexanol) in exhaled breath have been analysed in women and their infants for this purpose. Acetone and isoprene would originate in human metabolism (they are two of the most frequent VOCs in exhaled breath), whereas the other seven VOCs are derived from air pollutants. Among them, aromatic compounds (toluene, styrene, etc.) could derive from several sources, such as fuel combustion, smoking, household renovations, etc. [18; 33; 42].
## 2 Modelling  
This section discusses the numerical models used to simulate the propagation of electromagnetic waves in a plasma. The numerical model has been implemented in COMSOL Multiphysics, a numerical simulation software which allows an easy interface of different physics. The model has been built over a standard COMSOL physics, the ray optics, and it has been implemented to take into account the propagation of electromagnetic properties in a thermonuclear plasma.
## 3 Packet Detection Algorithms  
Generally, packet detection can be described as a binary classification problem [14; 15]. We regard the detected results as two hypotheses, \(H_{0}\) and \(H_{1}\). \(H_{0}\) means that the packet is absent and there is only noise. \(H_{1}\) means that the packet is present.  
\(H_{0}\): Packet is absent.  
\(H_{1}\): Packet is present.  
For most detection algorithms, there are two main values, the decision variable \(m_{n}\) and the threshold \(Th\). When \(m_{n}\) is equal to or greater than \(Th\), it is considered that a data packet has been detected, and vice versa. Thus, \(H_{0}\) and \(H_{1}\) can also be expressed as follows.  
\(H_{0}\): \(m_{n}<Th\Rightarrow\) Packet is absent.  
\(H_{1}\): \(m_{n}\geq Th\Rightarrow\) Packet is present.  
In this section, four packet detection algorithms are introduced, as shown in Figure 4. They are divided into two categories according to the signal characteristics used: energy-based and preamble-based. The energy-based algorithms use the change of signal energy. The preamble-based ones take advantage of the preamble structure. Simulation experiments  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**802.11 Version** & **Modulation Mode** & **Frequency Band (GHz)** \\ \hline
802.11n & OFDM & 2.4/5 \\ \hline
802.11 ac & OFDM & 5 \\ \hline
802.11 ah & OFDM & Sub-1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 3: PPDU of OFDM signals. The STF consists of 10 identical short symbols.  
were carried out on MATLAB to verify each algorithm's principle. The experiments were conducted on IEEE 802.11 ah with a 10 dB signal-to-noise ratio (SNR). The sampling rate is 20 MHz and the length of one symbol is 16 samples.
# Performance Prediction of Underwater Acoustic Communications Based on Channel Impulse Responses
###### Abstract  
Predicting the channel quality for an underwater acoustic communication link is not a straightforward task. Previous approaches have focused on either physical observations of weather or engineered signal features, some of which require substantial processing to obtain. This work applies a convolutional neural network to the channel impulse responses, allowing the network to learn the features that are useful in predicting the channel quality. Results obtained are comparable or better than conventional supervised learning models, depending on the dataset. The universality of the learned features is also demonstrated by strong prediction performance when transferring from a more complex underwater acoustic channel to a simpler one.  
underwater acoustic communications; regression; convolutional neural networks; deep learning; channel impulse response +
Footnote †: journal: 2022, 12, 1086. [[https://doi.org/10.3390/app12031086](https://doi.org/10.3390/app12031086)]([https://doi.org/10.3390/app12031086](https://doi.org/10.3390/app12031086))
# Evaluation of Biodentine Tricalcium Silicate-Based Cement after Chlorhexidine Irrigation
## 4 Discussion
In order to obtain more accurate results, it would be necessary to increase the number of measurement points and the number of tested specimens, which may be a direction for further research. It has been reported that chlorhexidine can enhance the antimicrobial activity of mineral trioxide aggregate (MTA), a golden-standard biocement, while concurrently increasing its cytotoxicity [38; 39]. No similar studies were found regarding Biodentine. In the light of the results of this research, it could be advisable to investigate the toxicity of the residues found on the tested material surface. All of the proposed research methods were used to evaluate and analyze the surface of the tested material. While the obtained data can provide a reliable representative image of the modifications that occur in the biocement specimens, it would be of scientific interest to investigate the phenomena occurring in the deeper layers of the set cement. Another limitation of the study is its in vitro form. Any future research could involve the observation and analysis of Biodentine clinical behavior after perforation closure performed prior to completing endodontic procedures, thus exposed to chemical action of chlorhexidine. In the era of biocompatible bioceramic materials, it is essential to understand their specific indications, proper handling, and modes of application. This knowledge is particularly important for ensuring long-term success in complex cases, such as chamber or root perforations, that arise just prior to cleaning, shaping, and irrigation protocols. The null hypothesis of this study was confirmed--chlorhexidine, either ultrasonically activated or not, does not alter the structure of the tested biocement as it targets only the organic matter. The surface morphology and chemical composition is altered by the sediments resulting from material irrigation with chlorhexidine.
## 2 Context: SIMSSA Project  
Within the context of cultural preservation and dissemination, the Single Interface for Music Score Searching and Analysis (SIMSSA) project [20] aims at not only making the digital images of ancient scores available to the public but, also, analyzing their structure to make them accessible. Thus, the interpretation of the meaning of musical symbols is of great importance and so is the correct identification of their location with respect to their corresponding staff. Among others, the project deals with the music scores in the Salzinnes Antiphonal manuscript dated to the middle of the 16 th century; this is the data that will be dealt with within this work. Different schemes have been used to process and analyze these data: convolutional neural networks have been employed for binarization [5], categorization of score elements [14; 21] and layering into their constituent elements [22]. An example of the application of this analysis process is shown in Figure 1, where the original image (a), staves (b), background (c) and symbols extracted (d) are shown for a sample score. In these approaches, the staff lines are still considered to make the detection, segmentation, and classification of symbols more complicated, which encourages their removal.  
The detection and complete tracking and interpolation of the staff lines remains unconsidered in spite of its importance for the interpretation of the symbols [14]. The reconstruction of staff lines is the main focus in this work.  
Figure 1: _Cont._
## 3 Results (First Collection)  
The sample characterization data were stratified by high training score and low training score groups and are expressed as mean and standard deviation (SD) and presented in Table 1. The sample normality was duly tested by the Shapiro-Wilk test, presenting a normal distribution pattern for all dependent variables (\(p<0.05\)). Flowchart 1 presents the results of the recruitment, entry, eligibility and exclusion of the participants in the experimental collection. The Student \(t\)-test for independent groups designed to determine the differences in characterization between groups showed equality in the variables of age (\(p\) = 0.575), BM (\(p\) = 0.673), height (\(p\) = 0.523) and BMI (\(p\) = 0.981).  
The independent Student \(t\)-test compared participants with high vs. low strength scores, indicating differences between groups for maximum DL strength (\(p\) < 0.0001), relative DL/BM strength (\(p\) < 0.0001), maximum SQ strength (\(p\) < 0.0001) and relative SQ/BM strength (\(p\) < 0.0001). The baseline variables are presented in Table 2.  
The performance results of vMART and the maximum incremental test, as well as the estimated metabolic demand values for both, are presented in Table 3. There were no differences for any baseline dependent variables related to the running tests (V\({}_{\text{Peak}}\): \(p\) = 0.228; vMART: \(p\) = 0.145).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{6}{c}{**High Strength Score**} & \multicolumn{6}{c}{**Low Strength Score**} \\ \hline  & **Age** & **BM** & **Height** & **BMI** & **Age** & **BM** & **Height** & **BMI** \\ \hline  & **(Years)** & **(kg)** & **(m)** & **(kg\(\cdot\)m\({}^{-2}\))** & **(Years)** & **(kg)** & **(m)** & **(kg\(\cdot\)m\({}^{-2}\))** \\ \hline Mean & 28.0 & 73.9 & 1.71 & 25.1 & 26.6 & 75.6 & 1.75 & 24.5 \\ SD & 8.1 & 11.4 & 0.08 & 2.7 & 6.3 & 11.9 & 0.09 & 3.2 \\ \hline \multicolumn{6}{l}{SD = standard deviation; BMI = body mass index; BM = body mass.} \\ \hline  & & & & & & & & \\ \cline{2-7}  & & & &
## 6 Simulation Results and Discussion
### Resource Allocation  
Here, we separate the resources into non-human and human. For the vehicles, the actual results indicate that during a working day, the cars and the motorbikes approacha utilization of almost 100% by about 11.00-12.00. The lorries seem to reach a maximum of about 99% utilization by almost the end of the working day, something that indicates that they are usually used for very long trips and they start later during the working day. Notice that their utilization is much lower (about 88%) by the first working hours, while the motorbikes' utilization approach 97% by early in the morning. Our simulation results indicate a similar behavior, however, the utilization results appear to be 3-4% lower. Probably, this can be explained by two facts: (1) the breaks provided by our model are longer than the actual breaks the employees take, (2) our model considers each shipment as a trip between a starting and a terminal point. Usually, a shipment may include traveling to some intermediate destinations as well, and (3) the actual duration of a trip by either vehicle may differ from the one given by our model, as our model fails to consider factors such as traffic or accidents, etc. Figure 11 shows a comparison between the actual data and our simulation results.  
For the human resources, we averaged the actual data for all the drivers and compared against the average values given by our model. The results are shown in Figure 12. Our simulation result indicate that the employees' utilization is, on the average, about 12% less than the actual one. The peak utilization values were obtained between 12:00 and 14:00 as expected. However, apart from the longer breaks that our model provides (which normally are not taken), our model does not provide equal probability of all drivers of the same class to be used in a shipment, if they are available. However, if one driver has returned from a shipment, even if he has taken a break, his probability of taking over another shipment is reduced compared to a worker that has taken over no shipment or has taken over shorter shipments. This fact somehow reduces the utilization of some drivers, thus reducing the average human resource utilization.  
Figure 11: Comparing the actual and simulated non-human resource utilization values.
## 3 Results and Discussion
### Failure Modes after Exposure to High Temperatures
#### 3.3.2 Failure Modes  
The failure appearance of MPC and epoxy resin specimens after exposure to high temperatures is shown in Figures 8 and 9, respectively. For the MPC specimens at 105 \({}^{\circ}\)C, they failed through the falling-off of small lumps of concrete, perhaps due to minor eccentricities of loading or nonhomogeneity of adhesive matrices. The MPC specimens failed in the adhesive layer for all of the treatments under high temperatures ranging from 200 \({}^{\circ}\)C to 400 \({}^{\circ}\)C, perhaps due to the adhesive layer cracking. The MPC specimens failed through rupture of fiber strips, perhaps mainly due to the strength degradation of MPC or the adhesive cracking at high temperature. The complete debonding failure between the adhesive and the concrete substrate happened for the epoxy resin specimens under temperatures from room temperature to 200 \({}^{\circ}\)C, perhaps due to the good adhesive performance of the epoxy resin. However, peeling off of the carbon fiber sheets happened for the epoxy resin specimens at 300 \({}^{\circ}\)C due to the serious deterioration of epoxy resin.  
Figure 8: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Compressive Strength of** & \multirow{2}{*}{**Curing Age (Days)**} & \multirow{2}{*}{**Bond Strength (MPa)**} \\
**Concrete /\({}_{\text{c.m.}}\) (MPa)** & & \\ \hline  & 3 & 1.50 \\
30.95 & 7 & 1.71 \\  & 28 & 1.82 \\ \hline  & 3 & 1.61 \\
42.67 & 7 & 1.75 \\  & 28 & 1.88 \\ \hline  & 3 & 1.68 \\
49.89 & 7 & 1.87 \\  & 28 & 2.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Influence of concrete strength and curing age on bond strength of magnesia-phosphate cement (MPC).  
Figure 8: Failure modes of MPC specimens after exposure to high temperatures: (**a**) 105 \({}^{\circ}\)C; (**b**) 200 \({}^{\circ}\)C; (**c**) 300 \({}^{\circ}\)C; (**d**) 400 \({}^{\circ}\)C; (**e**) 500 \({}^{\circ}\)C.  
Figure 9: Failure modes of epoxy resin specimens after exposure to high temperatures: (**a**) 105 \({}^{\circ}\)C; (**b**) 200 \({}^{\circ}\)C; (**c**) 300 \({}^{\circ}\)C.
# DPtracer: Integrating Log-Driven Accountability into Data Provision Networks
## 2 Background
### Multi-Agent Data Provisioning
#### 2.1.1 Price Oracle Aggregation  
Price oracles use multi-level aggregation, as depicted in Figure 0(a), to consolidate price data from multiple data sources to a single result. For instance, Chainlink, a prominent price oracle service, delivers reliable asset prices to blockchain systems through three-level aggregations [13]. Price data aggregators gather prices from multiple raw sources and perform the first-level aggregation, such as calculating a weighted average based on volume and liquidity. Node operators filter outliers and compute the median value as the second-level aggregation. These results may be delivered individually or collectively by aggregating them [9]. Finally, the multiple values from node operators are merged into a single value through the oracle network aggregation, which may occur in a blockchain system. Other oracle services, like Band Protocol [10] and Pyth Network [14], also use multi-level aggregation and refinement strategies to provide reliable, correct data to blockchain systems.