esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection
## 5 Results
### Performance of YOLOv5s  
The training process of the YOLOv5s model took about 1 h 43 min to run 1200 epochs. The training result converged and stabilized after about 800 epochs, which were shown in the curves as given in Figure 10. Given the values of mAP_0.5, loss, precision, and recall were 0.748, 0.026, 0.82, and 0.68 at the 1200 th epoch as shown in Figure 10a-d, respectively.  
After the training process, YOLOv5s trains the model using the testing dataset images which were not employed during the training. Each pothole is highlighted using a bounding box having a label presenting the value of its corresponding confidence as shown in Figure 11. The YOLOv5s model identifies defects on road surface with confidence values ranging from 0.56 to 0.93. It is noted that this model gets the error detection of pothole in some cases as a red circle marked in the images.  
Table 3 shows the mAP_0.5 value of YOLOv4, YOLOv4-tiny, and YOLOv5s. Given the parameter of mAP as 0.5, the mAP_0.5 values of YOLOv4, YOLOv4-tiny, and YOLOv5s are 77.7%, 78.7%, and 74.8%, respectively. It is confirmed that the YOLOv4-tiny model of which the mAP_0.5 value is the highest manifests the best performance, hence, being the best fittest for practical applications involved in pothole detection. Object detection algorithms sample many regions from the input image, determine if these regions contain objects of interest, and adjust the boundaries of the regions so as to predict the ground-truth bounding boxes of the objects accurately. Once the anchor shapes are determined, the sizes will be fixed during the training process. This might be sub-optimal since it disregards the augmented data distribution in training, the characteristics of the neural network structure and the task (loss function) itself. Improper design of the anchor shapes could lead to inferior performance in specific domains. In this study, the size of the box is fixed for all versions of YOLO to make controlled experiments.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Model** & **mAP (\%)** \\ \hline YOLOv4 & 77.7 \\ \hline YOLOv4-tiny & 78.7 \\ \hline YOLOv5s & 74.8 \\ \hline \hline \end{tabular}
\end{table}
Table 3: mAP comparison.  
Figure 10: Performance and loss function of YOLOv5s. (**a**) mAP_0.5, (**b**) loss, (**c**) precision, (**d**) recall.  
However, all 3 models show low confidence values for small potholes located at long distances. In addition, under bad weather conditions, lack of light has not been studied. These are the limitations of this study and will need to be studied further.
## 2 Materials and Methods
### Quantitative HPLC-PDA Assay  
#### 2.4.1 HPLC-PDA Equipment and Methodology  
Qualitative HPLC analyses were carried out using Waters 600E Multisolvent Delivery System (Waters, Milford, MA, USA) with a PDA detector (Waters 2998) scanning in the wavelength range of 220-450 nm, a model 7725 sample injection valve (Rheodyne, Pittsburgh, PA, USA) with a 20 \(\mu\)L injection loop, and an LC workstation equipped with Waters Empower 2 software for data collection and acquisition. A Nucleodur C18 HPLC column (250 \(\times\) 4.6 mm i.d., 5 \(\upmu\)m; Macherey-Nagel, Bethlehem, PA, USA) guarded by a C18 Hypersil ODS pre-column (5 mm, 4 mm \(\times\) 4 mm, i.d.; Agilent Technologies, Palo Alto, CA, USA) was used. Constant temperature of the column was maintained at 25 \({}^{\circ}\)C using a Jetstream Plus 5480 thermostat (Peltier, Langenzersdorf, Austria). The phenolic compounds were separated using the mobile phase consisting of solvent A (0.5% water solution of orthophosphoric acid, \(w/v\)) and solvent B (methanol), with the elution profile as follows: 0-1 min, 20% (\(v/v\)) B; 1-25 min, 20-70% (\(v/v\)) B; 25-28 min, 70% (\(v/v\)) B; 28-29 min, 70-20% (\(v/v\)) B; 29-35 min, 20% (\(v/v\)) B (equilibration). All gradients were linear. Flow rate: 1.0 mL/min. Detection wavelength: 360 nm. All chemicals used in HPLC analyses including methanol (Avantor Performance Materials, Gliwice, Poland), water, and 85% (\(w/w\)) orthophosphoric acid (Merck, Darmstadt, Germany) were of HPLC grade.
# NFT-Based Framework for Digital Twin Management in Aviation Component Lifecycle Tracking
## 4 Discussion
### Future Directions of Research for NFT-Based Aviation Component Lifecycle Management  
The NFT-based approach for aviation-component lifecycle management opens several promising avenues for future research:  
* Blockchain optimization with improved scalability, energy efficiency, and transaction speed.
* AI and machine learning integration with NFT-based systems to enhance predictive maintenance and optimize supply chain management.
* Interoperability and standardization for NFT metadata, smart contract protocols, and data formats specific to aviation components.
* Smart components directly linked to their digital NFT twins for real-time data updates.
* Enhanced security and privacy, tailored for aviation industry requirements.
* Legal and regulatory frameworks to accommodate blockchain and NFT technologies, including the legal status of NFTs as digital representations of physical assets.
* Expanded NFT applications for other aspects of aviation operations.
* Examination of how permanent, immutable records of component lifecycles might impact aircraft resale values, insurance practices, and future design processes.  
These research directions aim to address current limitations, enhance system capabilities, and explore new applications of NFT-based technologies in aviation.
## Appendix B  
With regard to the performance evaluation, the performance of our proposed noise reduction approach according to the size of the analysis window was investigated. As discussed in Section 3.2.2, the frames in an analysis window participate in estimating spectral energy variation. We are confident the duration of the window, that means the number of frames, affects the correctness of the hard-decision of speech-present and absent bins. Here, we observed the SD in the same manner as Figure 10 while varying the number of frames in the analysis window from five to 11. Figure A8 shows the results. The proposed approach demonstrated significant differences in noise reduction performance according to the number of frames in the analysis window. The result of five frames showed the worst performance. Thus, five frames is not long enough to provide sufficient information about spectral energy variation to determine speech-present and absent bins. In contrast, results obtained with longer durations provided better performance. In particular, higher SNR values yielded significant performance differences among duration sets, and the results were similar with low SNR values. This indicates that the duration does not affect performance significantly under severe noise conditions. The best performance was observed with nine frames. However, the result obtained with 11 frames was degraded. Thus, we consider nine frames to be the optimal analysis window size.
# Research Progress on Modulation Format Recognition Technology for Visible Light Communication
## 3 Feature-Based Modulation Recognition Methods
### Higher-Order Statistics (HOS) Features  
Higher-order statistics (HOS) address the limitations of traditional second-order statistics in VLC systems, which are impacted by LED nonlinearity and channel noise, and exhibit insufficient discrimination for complex modulation schemes such as 16-QAM and 16-PAM.  
In 2017, [PERSON] et al. [19] applied fourth-order cumulants to ACO-OFDM VLC systems. Since the theoretical value of the fourth-order cumulant of Gaussian noise is zero, while the cumulants of non-Gaussian modulated signals are unique, this method can effectively suppress noise and distinguish different modulation formats. The mathematical essence of higher-order cumulants can be derived through the moment-generating function. For a zero-mean random process \(y[n]\), the definition of the fourth-order cumulant is given by:  
\[C_{4k}=E\left[y^{4}[n]\right]-3\Big{(}E\left[y^{2}[n]\right]\Big{)}^{2}(k=0), \tag{10}\]  
where \(E[\cdot]\) denotes the expectation operation. For discrete signals, the practical estimation form is:  
\[\widehat{C_{40}}=\frac{1}{N}{\sum}_{n=0}^{N-1}y^{4}[n]-3\bigg{(}\frac{1}{N}{ \sum}_{n=0}^{N-1}y^{2}[n]\bigg{)}^{2}. \tag{11}\]  
Similarly, the definitions of the one-lag and two-lag fourth-order cumulants are:  
\[\widehat{C_{41}}=\frac{1}{N}{\sum}_{n=0}^{N-1}y^{3}[n]y^{*}[n]-3\widehat{C_{20 }C_{21}}, \tag{12}\]  
\[\widehat{C_{42}}=\frac{1}{N}{\sum}_{n=0}^{N-1}[y[n]]^{4}-\left|\widehat{C_{20 }}\right|^{2}-2\widehat{C_{21}^{2}}, \tag{13}\]  
where \(\widehat{C_{20}}\) and \(\widehat{C_{21}}\) are the estimates of the second-order moments. The authors collected four modulation formats, namely 4/16-PAM and 4/16-QAM, in an ACO-OFDM VLC system. By leveraging the asymmetric clipping characteristic of the ACO-OFDM signal, there is an essential difference in the cumulants of QAM signals compared to PAM signals, which stems from the original signal's real/imaginary parts. The normalized cumulants \(\widehat{C_{4i}}\) of different-order PAM signals exhibit separable statistical boundaries. By testing classification thresholds through Monte Carlo simulations, a correct recognition rate of 88.9% can be achieved with 2000 samples when the SNR exceeds 15 dB. However, performance drops sharply when the SNR falls below 5 dB due to noise dominance (as shown in Figure 4). This study confirms that HOS possess both noise robustness and modulation sensitivity.  
Figure 3: Basic structure of feature-based modulation recognition methods.  
However, the high computational complexity of HOS limits its feasibility in dynamic real-time applications.
# Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences
### Characteristics of EMG Signals  
As mentioned above, a typically measured electromyography signal originates from numerous sources, not only from the muscle's activity. Among them, there are biological, environmental, electronic, and numerical interferences. Some of them are listed below.  
* Low-frequency drift due to the input impedance of the analog system
* Power line interference containing 50/60 Hz and the higher spectral components
* Electro-cardiac heartbeat
* Electrical contact between skin and electrodes (changeable with the movement of a patient)
* Aliasing in high-frequency spectral range depending on the sampling clock
* Noise of the analog electronic circuit
* Quantization noise
* High-frequency noise generated by the digital part of electronics
* Numerical noise due to the representation of the number of the recorded data and rounding
* Signal distortion due to the specific digital signal processing methods applied (spectrum leakage, group delays, nonlinear phase characteristics, etc.)  
The interferences listed above were considered in order to develop a new effective algorithm that can be implemented in a portable EMG signal processing device.
# Towards Detection of Interest Using Physiological Sensors
## 3 Physiological Patterns and Features of Interest
### Correlation between Self-Report and Physiological Sensors  
The correlation between self-report and physiological measures has been discussed widely from different perspectives (for a review, see [93; 94; 95]). In this section, this correlation will be addressed with regard to interest.  
The reviewed studies showed great potential for detecting interest using physiological sensors. However, subjective measures, especially the self-report method, was employed in the majority of these studies as presented in the measurement column of Tables 1-3. The self-report method is accepted as the gold standard for determining one's status and is often used with physiological measures to enhance and support their result. This is because of the long history and the solid basic research that used the self-report method and because of the lack of adequate physiological equipment at earlier times. The question would be to what extent can we trust the self-report method, especially if it conflicted with the result of physiological measures? On the one hand, there have been serious concerns raised about the credibility of the self-report method in certain conditions such as classroom sessions or for certain groups such as children. On the other hand, concerns have been raised as well about the result of physiological measures that could be affected by the individual's health condition or by faulty equipment. This implies that each method will have its pros and cons and that combining methods or modalities could be a good way to avoid such conflict in the first place. Recent studies employed different methods such as cameras [48], interviews [38], mouse behavior [31], etc. This was applicable for two reasons: (1) interest can be reported subjectively and/ or objectively unlike pain, for example, which is considered a subjective phenomenon [96] and (2) the advances in technology that made it easy to incorporate two or more modalities.  
Hence, it is recommended to combine subjective and objective measures when applicable to measure interest in all of its richness. Subjective measures report how one's feel and think consciously about certain condition while the objective measures are necessary to tell about the unconscious mechanisms underlying this feeling or thought. This will offer complementary information about the association between interest, environmental condition, behavior, and brain and will have a valuable contribution to the understanding of the nature of interest. it is suggested to combine both measures until finding a credible and reliable physiological index that will overcome self-report limitations.
# Efficacy of High-Dose Polyclonal Intravenous Immunoglobulin in COVID-19: A Systematic Review
## 3 Results  
Electronic and manual searches yielded 674 potentially relevant studies (Figure 1). Six hundred and seven articles were excluded after preliminary screening. The remaining 67 articles were deemed potentially eligible, and their full text was assessed. Fifty-five articles were then excluded because of case series or case reports, protocols or because they did not meet the inclusion criteria. Two additional studies were excluded because, although they focused on IVIG use in COVID-19 patients, they were not controlled trials [21; 22]. Thus, for this systematic review we considered 10 studies fulfilling our pre-specified criteria, including 2401 COVID-19 patients [23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. Four studies were RCT [25; 29; 30; 32], and six studies were non-RCT (all retrospective) [24; 26; 27; 28; 31; 32]. Five studies were conducted in China, two in Iran and one each in India, the USA and Turkey. In two studies, IVIG use was restricted to non-severe COVID-19 cases [27; 29], while in the other eight trials IVIGs were used in severe or critical patients. Table 1 summarizes the main characteristics and results of these studies. The dosage and timing of IVIG administration was greatly variable between studies. The comparator group included standard therapy (nine studies) or placebo (one study) [25].
## 3 EKF-GAPF-Based Method for Multipath Parameter Estimation
### Accurate Identification Based on PF
#### 3.2.2 Enhancing the Optimization Capability of PF with AGA  
A common problem with PF is that after several iterations, the variance of particles increases due to the presence of process noise, meaning that the weights of many particles are close to zero and have little contribution to the posterior estimation. To address this issue, the GAPF [16] introduced GA to improve the resampling process of PF, which mitigates particle degradation to some extent. However, GA is prone to fall into local optimal solutions [28], resulting in limited performance of multipath estimation.  
The three crucial steps of GA are selection, crossover, and mutation [29]. In the selection phase, elite individuals are preserved and directly passed to the next generation, ensuring that optimal solutions are not lost. Then, selection is performed, and individuals with high fitness are more likely to be selected and spread their genes. During crossover, genetic recombination swaps partial genes between two individuals, generating new offspring that explore a broader solution space. A higher crossover probability aids in increasing population diversity, but an excessively high probability may affect the convergence of the algorithm. Mutation increases the diversity of the solution space by randomly changing thegenes of individuals, preventing early convergence to the local optimum [30]. A mutation rate that is too high will destroy the existing excellent solutions, while a mutation rate that is too low may limit the global search ability of the algorithm.  
Therefore, reasonable settings of crossover and mutation factors are crucial to ensure the effectiveness of GA and to balance global search and local exploitation. An adaptive adjustment strategy is proposed in this paper to dynamically set these factors, and combined with an elite retention strategy to ensure that the best individuals are preserved and passed to the next generation. This dual strategy not only enhances the algorithm's search efficiency but also improves the stability and quality of the final solution. The above process can be expressed as follows:  
\[P_{c}=CR_{1}-CR_{2}\cdot(I/M) \tag{18}\]  
\[P_{m}=\left\{\begin{array}{l}G\times\left(1-\frac{I}{M}\right)\times\left( \frac{g}{S_{i}}\right),\ g_{i}\leq g\\ G\times\left(1-\frac{I}{M}\right)\times\left(1-\frac{g}{S_{i}}\right),\ g_{i}>g \end{array}\right. \tag{19}\]  
where \(CR_{1}\) is the maximum crossover rate, \(CR_{1}-CR_{2}\) is the minimum crossover rate, \(I\) is the current number of iterations, \(M\) is the maximum number of iterations, G is the initial crossover probability, g is the average fitness, and \(g_{i}\) is the fitness of the current particle. \(P_{c}\) gradually decreases with the increase in the number of iterations to enhance the stability of the population. Meanwhile, \(P_{m}\) is flexibly adjusted based on the number of iterations and particle fitness to avoid falling into local optimal solutions.
###### Abstract  
The great majority of infant formula (FM) for neonate's nutrition are produced using ingredients from cow milk. Recently, some countries, such as China and New Zealand, are turning their attention to the use of ovine milk ingredients for FM production. In this study, a pilot plant process has been set up to produce infant formula ingredients from Sarda sheep milk. To meet the nutritional needs of neonates (0-6 and 6-12 months of age) two different liquid milk-derived formulations (IF1 and IF2, respectively) obtained mixing whole milk, skimmed milk, and whey milk ultrafiltration concentrate (retentate) were produced. Compositional analysis of milk, retenate, and the final IFs showed that the two formulations contain elements of nutritional interest, such as well-balanced content of high biological value proteins (casein:whey proteins ratio of 30:70 and 60:40 for IF1 and IF2, respectively), vitamin A, E and B5, cholesterol, minerals, nucleotides, free amino acids and essential fatty acids (n-6:n-3 \(\sim\)1), compatible with the growth and development needs of neonates. Therefore, the obtained IF1 and IF2 can be proposed as valuable ovine dairy ingredients for FM manufacturing. Further studies will be necessary to verify the adaptability of the developed process from laboratory to industrial scale application.  
infant milk formula; breast milk; sheep milk; chemico-physical properties; NMR metabolites; ultrafiltration; retenate +
Footnote †: journal: selected topics  
sources of protein, especially to prevent cow's milk allergy. Recently, FMs based on ovine milk are marketed in some countries, such as China and New Zealand [3]. Sheep milk exhibits, compared to other ruminant's milk, interesting peculiarities [6; 7; 8]. Compared to cow's milk, sheep's milk shows a higher content of short and medium chain fatty acids, and a reduced size of fat globules, a characteristic that makes it easier to digest [6; 9], and a higher content of conjugated linoleic acid (CLA), which has beneficial effects on health demonstrated by numerous studies [7; 10]. Sheep's milk shows other nutritional characteristics, such as a higher content of nucleotides [11], vitamins A, and E, calcium and other minerals [9; 10]. Caseins and whey proteins from ovine milk show health promoting properties and are important source of bioactive peptides [7; 8]. Upon digestion, sheep milk beta-casein does not release the beta-casomorphin-7 (BCM7). Although still a matter of debate, adverse effects of this peptide on human health, particularly in babies, have been suggested [12]. To the best of our knowledge, there are no studies that have deeply investigated the composition of sheep's milk and its derivates to be used in the production of infant formulae. In this work, a technological process was developed, on a pilot scale, for manufacturing liquid milk-derived formulations (IF) from sheep's milk to be used in the preparation of FM intended for feeding infants from 0-6 months and from 6-12 months (IF1 and IF2, respectively). Chemico-physical analyses of dairy ingredients used and of IF samples were carried out by different analytical techniques.
# Satellite-Assisted Disrupted Communications: IoT Case Study
## 4 Experiments
### Experimentation Scenarios
#### 4.2.1 Scenario 1: IoT Sensors for EU-EU and EU-U.S. Communication  
In this subsection we investigated a scenario where sensors located in the EU transmit to ground stations in different regions (in EU and U.S.), exploring the impact of varying satellite numbers in a constellation on the mean RTT and ping loss. Two examples were implemented, deviating only the number of satellites in the constellation: the first with 360 satellites (i.e., 6 satellites per plane, 60 satellites per plane), and the second with 600 satellites (i.e., 10 satellites per plane, 60 satellites per plane). Initially, we examined the scenario with 150 satellites, identifying the absence of established communication links and consequently, we doubled the number of satellites in the constellation. Within the topology, 16 sensors were positioned, with 5 in the U.S. transmitting pings to an MCC in the EU, 5 in the EU following the reverse path, and 6 engaged in EU-to-EU communications. Additionally, 11 MCCs were incorporated, with 5 of them functioning as ships to facilitate alternative routes among the satellites of different planes through the _intersatelliteLink_ parameter. The _sendInterval_ was set to 300 s (i.e., 5 min) and the _updateInterval_ at 100 s to achieve a balance between suitability and reduced execution time. Finally, the simulation time limit was set to 24 h to demonstrate a complete day of transmissions. The average distance between sensors and ground stations in EU and U.S. is 5320.82 km, while the EU-to-EU distance is 2881.52 km. The simulation parameters of Scenario 1 are detailed in Table 3.