esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Aging Resistance of Graphene Oxide Modified Asphalt
## 2 Materials and Methods
### Evaluation of Anti-Aging Performance
#### 2.4.3 Characteristic Functional Group Test  
A Fourier transform infrared spectroscopy (FTIR) instrument (Columbus, OH, USA) was used to test the characteristic functional group changes of the asphalts after UV aging, the preparation procedures of asphalt samples for FTIR are described briefly: First, the asphalt CS\({}_{2}\) solution with 5 wt % concentration of asphalt was prepared, then two drops of asphalt CS\({}_{2}\) solution were placed on the KBr chip by the glue dropper, so the thin film asphalt sample could be obtained after the CS\({}_{2}\) was fully volatilized. The scan wave number range was from 4000 to 400 cm\({}^{-1}\), and scan times were 64 times.
# Flow Control, Active and Passive Applications
###### Abstract  
The boundary layer (BL) dynamic performance greatly affects the forces acting on any Bluff body. Ideally, the boundary layer should be attached to the surface but when separation occurs, the vortical structures and the dynamic forces' amplitude rapidly increase. In many aerodynamic applications, the Bluff bodies are shaped in such a way that the boundary separation is delayed as much as possible. Nevertheless, using novel technologies, it is possible to reattach the previously separated BL, or at least further delay its separation. One of the novel techniques which allows for the modification of the separation point of the BL is Active Flow Control (AFC). This consists of injecting/sucking fluid in pre-defined locations. In the vast majority of AFC applications, it is essential to perform an energy assessment in order to make sure that the energy saved by the reduction/increase of the forces due to the modification of the BL separation point is much larger than the energy employed for the actuation. In order to achieve this goal, it is essential to properly tune the five parameters associated to any AFC implementation, groove position, groove width, momentum coefficient, jet inclination angle and jet frequency. Such tuning can be carried out via a parametric optimization or using any optimizer. In other words, AFC is always associated with optimization methodologies; otherwise, the energy assessment cannot be successfully accomplished. The present book is based on a set of published articles that highlight some novel applications of flow control.
# Dual Cancelled Channel STAP for Target Detection and DOA Estimation in Passive Radar
## 2 Passive Radar STAP Scheme
Once a target is detected, an asymptotically efficient estimate of its DOA is given by the MLE [33], which takes the maximum of the log-likelihood function with respect to \(\phi\):  
\[\hat{\phi}_{t}=\arg\max_{\phi}\left\{\frac{\left|\mathbf{s}^{H}(\phi)\hat{\mathbf{Q}} ^{-1}\mathbf{x}\right|^{2}}{\mathbf{s}^{H}(\phi)\hat{\mathbf{Q}}^{-1}\mathbf{s}(\phi)}\right\}. \tag{2}\]  
The above STAP scheme proved effective for passive radar GMTI, but it involves a non-negligible level of complexity. It requires the estimation and inversion of a \((NL\times NL)\) disturbance covariance matrix, potentially for each range-Doppler bin. Moreover, target DOA estimation requires finding the maximum of a non-linear function, either by fast converging algorithms or by evaluating it for a set of values and selecting the one yielding the maximum. This might be computationally expensive, the cost being proportional to the desired estimator accuracy. Such cost may not be in line with the usual low complexity of passive radar, especially in the case of mobile systems offering onboard processing. In addition, the availability of a sufficient number of uniform training data to limit the adaptivity loss may be difficult to be guaranteed in a passive bistatic radar scenario. In fact, the typical ground-based transmitters of opportunity are likely to produce a non-uniform illumination of the ground due to the masking effects of terrain. Moreover, due to its silent nature and potentially limited coverage, passive radar can be operated also at short range where non-uniform clutter distributions occur, especially for airborne acquisition geometry. To mitigate the above limitations, we propose an alternative STAP approach for target detection and DOA estimation in an operational mobile passive radar.
Identifying Coastal Wetlands Changes Using a High-Resolution Optical Images Feature Hierarchical Selection Method  
[PERSON]  
1 Key Laboratory for Digital Land and Resources of Jiangqi Province, East China University of Technology, Nanchang 330013, China 12 School of Geography and Resource Science, Neijiang Normal University, Neijiang 641102, China 23 State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China 34 College of Water Sciences, Beijing Normal University, Beijing 100875, China 4  
[PERSON]  
4 Cousstal wetlands are dynamic and fragile ecosystems where complex changes have taken place. As they are affected by environmental changes and human activities, it is of great practical significance to monitor coastal wetlands changes regularly. High-resolution optical data can observe changes in coastal wetlands, however, the impact of different optical features on the identification of changes in coastal wetlands is not clear. Simultaneously, the combination of many features could cause the \"dimension disaster\" problem. In addition, only small amounts of training samples are accessible at pre- or post-changed time. In order to solve the above problems, the feature hierarchical selection method is proposed, taking into account the jumping degree of different image features. The influence of different optical features on wetland classification was analyzed. In addition, a training samples transfer learning strategy was designed for wetland classification, and the classification result at pre- and post-changed times were compared to identify the \"from-to\" coastal wetlands changes. The southeastern coastal wetlands located in Jiangsu Province were used as a study area, and ZY-3 images in 2013 and 2018 were used to verify the proposed methods. The results show that the feature hierarchical selection method can provide a quantitative reference for optimal subset feature selection. A training samples transfer learning strategy was used to classify post-changed optical data, the overall accuracy of transferred training samples was 91.16%, and it ensures the accuracy requirements for change identification. In the study area, the salt marsh increased mainly from the sea area, because salt marshes expand rapidly throughout coastal areas, and aquaculture ponds increased from the sea area and salt marshes, because of the considerable economic benefits of the aquacultural industry.  
change detection; feature selection; jumping degree; transfer learning; coastal wetlands
## 3 Obstacle Avoidance Motion Planning Framework
### Violation Cost Potential Field
#### 4.3.1 Collision Risk  
The predicted collision risk is determined based on TTC. The vehicle trajectory intersections are obtained by considering the current and predicted motion states of the ego vehicle and other traffic participants. The predicted collision risk \(R\) in PVCI is defined as:  
\[R=\frac{\text{TTC}_{\text{std}}}{\text{TTC}_{i}} \tag{33}\]  
where \(\text{TTC}_{i}\) is the collision time between the ego vehicle and the \(i^{\text{th}}\) obstacle vehicle. \(\text{TTC}_{\text{std}}\) is the standard collision time. The \(\text{TTC}_{\text{std}}\) values are defined as 2.5 s, 3 s, and 3.5 s when the relative speed falls below 10 m/s, between 10-15 m/s, and exceeds 15 m/s, respectively [18].
## Materials and Methods
Perceived exertion was estimated using the Borg RPE scale (6-20) [30], and its correlation with objective measurements was assessed. The Physical Activity Enjoyment Scale (short form) (PACES) was used to collect data about the participants' subjective perceptions of satisfaction with PA in VR under lower limb loading and no additional loading conditions [31]. This research tool consists of 8 statements, each indicating a different aspect of the enjoyment of the PA undertaken in a broad sense: \"I find it pleasurable\", \"It's a lot of fun\", \"It's very pleasant\", \"It's very invigorating\", \"It's very gratifying\", \"It's very exhilarating\", \"It's very stimulating\", and \"It's very refreshing\". A seven-point Likert scale was assigned for each statement, with one meaning \"strongly disagree\" and seven meaning \"strongly agree\". The study participants' task was to mark, for each statement, the value that best reflected their perceptions of the activity considered in a given situation. The score was the mean of the points scored for all questions. Furthermore, a proprietary survey questionnaire, which consisted of 7 items, was conducted to assess students' attitudes toward PA in VR and their opinions on the potential of VR to support PA (Table 1). The Likert (1-7) scale was used in the questionnaire. Statistical calculations were performed using Jamovi (v. 2.2.3.0) and Statistica (v.13) software. Arithmetic means and standard deviations were calculated. The Shapiro-Wilk test was used to examine the normality of distribution. The significance of differences was evaluated using Student's \(t\)-test or Wilcoxon test, depending on data distribution. The effect size was estimated using Cohen's d for the Student's \(t\)-test and the rank-biserial correlation coefficient (r\({}_{\text{rb}}\)) for the Wilcoxon test. Spearman correlation analysis was employed to assess the correlations. Figure 2: TRAVR Training Ops active video game (screenshot).
## 4 Experiment and Analysis
### Basic Settings  
To enable a fair comparison, the hyperparameters used in each benchmark are provided in Table 2. The performance of CDML and the compared models is evaluated based on five runs, and the average performance was reported. All experiments were carried out on an RTX-2080 Ti GPU.  
To evaluate the efficacy of the models, a range of metrics including 7-class accuracy (Acc7), binary accuracy (Acc2), F1 score, Mean Absolute Error (MAE), and correlation (Corr) are employed. Higher values for Acc7, Acc2, F1 score, and Corr signify better performance, indicating that the models have achieved more accurate and reliable results.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Setting** & **CMU-MOSEI** & **CMU-MOSEI** \\ \hline Batch Size & 16 & 32 \\ Initial Learning Rate & 1 10 3 & 1 10 3 \\ Optimizer & Adam & Adam \\ Cross-modal Layers & 4 & 4 \\ Multihead Attention Heads & 8 & 8 \\ Dropout & 0.1 & 0.1 \\ Epochs & 50 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The hyperparameter settings adopted in each benchmark.  
In contrast, for MAE, lower values showcase better performance as they indicate smaller errors in predictions.
## 1 Introduction
Then, angular spectrum estimation errors and the far-field noise reduction performance with the near-field error strategy are investigated based on the numerical simulations. Finally, the impact of the location of the secondary source is verified by experiments.
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 5 Adversarial Ant Colony Optimization Algorithm for Solving Non-TT Routing Problem
### Definition of the Optimization Problem in Non-TT Routing
#### 5.3.1 Ant Colony Initialization  
For the route assigning task of \(m\) non-TT flows, we initialized \(m\) ant colonies with \(Z\) ants per colony. All ants from the \(i\)th colony were located at the corresponding source node \(src_{i}\). We also initialized \(m\) different pheromone concentration values for each edge in \(E\), denoted as \(\tau_{ij}^{k}\):  
\[\tau_{ij}^{k}=C_{0},\forall i,j\in\{1,\ldots,n\},\forall k\in\{1,\ldots,m\}. \tag{13}\]
## 3 Results and Discussions
### Summary of the Evaluation  
Table 2 showed the MAE and SD of the three schemes. The color coding gave a good overview of the model's performance. The white color meant lower errors than the pink color. The average of MAE and SD were calculated in the last row in Table 2, where there are the results of MAE1 to MAE5 and SD1 to SD5 for scheme I, the results of MAE6 and SD6 for scheme II, the results of MAE7 to MAE8, and SD7 to SD8 for scheme III. The average of MAE and SD were 1.85 mmHg and 1.82 mmHg for scheme I, and 4.37 mmHg and 2.49 mmHg for scheme II. The MAE and SD became a little great in \"scheme III Bi-LSTM\", because different subjects had individual differences. However, the corrective factors were added after the \"scheme III Bi-LSTM\". Both of the MAE and SD dropped in \"scheme III corrections\". The MAE \(\pm\) SD from 12.46 \(\pm\) 6.56 mmHg downed to 6.37 \(\pm\) 4.90 mmHg. Figure 9a-c showed the box plots of the three schemes, where the high MAE and SD values were shown by red crosses \"+\". We can see the errors were controlled below 3 mmHg in the scheme I, 5 mmHg in the scheme II, and 7 mmHg after adding the corrective factors in the scheme III. Generally speaking, the three schemes showed that the proposed method was a convenient method to estimate RVSBP non-invasively. The model was suitable for estimating RVSBP even in the situation of very high and very low RVSBP values. The method proved that the model had stable generalization ability within trials, across trials, and across subjects, with the addition of correction factors. Overall, continuous RVSBP tracking based on HS signals, no matter short or long sequences, using the proposed method could get good results.  
Figure 8: Performance of scheme III.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multirow{2}{*}{**No. Record**} & \multicolumn{8}{c}{**Scheme I**} & \multicolumn{8}{c}{**Scheme II**} & \multicolumn{8}{c}{**Scheme III**} \\ \cline{2-13}  & & \multicolumn{2}{c}{**Indices = 1**} & \multicolumn{2}{c}{**Indices = 2**} & \multicolumn{2}{c}{**Indices = 3**} & \multicolumn{2}{c}{**Indices = 4**} & \multicolumn{2}{c}{**Indices = 5**} & \multicolumn{2}{c}{**LSTM**} & \multicolumn{2}{c}{**Bi-LSTM**} & \multicolumn{2}{c}{**Corrections**} \\ \cline{3-13}  & & **MAE 1** & **SD1** & **MAE** & **SD2** & **MAE** & **SD3** & **MAE** & **SD4** & **MAE** & **SD4** & **MAE** & **SD5** & **MAE** & **SD6** & **MAE7** & **SD7** & **MAE** & **SD8** \\ \hline No. 1 & 61 & 0.21 & 0.21 & 0.24 & 0.19 & 0.25 & 0.16 & 0.19 & 0.13 & 0.28 & 0.15 & 2.15 & 1.68 &
## 1 Introduction
Additionally, the possibility to collect photons only at a given portion of the DTOF (by using a classical time-correlated single photon counting board or with simpler devices [14]) allows for the ability to increase the number of late photons, thus improving the visibility of possible deep inhomogeneities in the medium which affect the DTOF shape. Different works based on FG-SPADs have been published, demonstrating experimentally that an improved spatial resolution, contrast and even chromophore quantification can be achieved using a gated detector [15; 16; 17]. However, to the best of our knowledge, the FG-SPAD detectors have never been adopted to demonstrate the improvement given by the use of short-SDD in fNIRS measurements. Indeed, the higher spatial resolution allows for the ability to retrieve more precisely the area wherein the activation occurs and the capability to collect photons in a given portion of the DTOF, improving the robustness of the detectability of a brain activation. In this paper, we propose an instrument [18] based on a prototypal dual-channel (710 and 820 nm) high power (around 100 mW output for each channel) laser which allows for the ability to exploit at its best capacity the high-dynamic range acquisition technique. The laser light was spread over a sufficiently large area in order to guarantee skin safety by means of a spacer, while eye safety was guaranteed by means of the use of proper protection. Two state-of-the-art fast-gated SPAD modules (featuring a diffusion tail of about 90 ps [19]) were adopted to monitor both wavelengths, thus allowing for the recovery of the concentration of both O\({}_{2}\)Hb and HHB simultaneously. The proposed system was firstly characterized using some tests from two internationally-agreed protocols for performance assessment of TR-instrument/oximeter (such as the Instrument Response Function/IRF) from the \"Basic Instrumental Protocol\" (BIP) protocol [20], and contrast and contrast-to-noise ratio (CNR) from \"nEUROPt\" ones [21]). We then exploited the system for fNIRS measurements with the aim to demonstrate the improvement in terms of detectability of brain activation with respect to the classical oximetry instrument [22; 23], simulating a free-running (i.e., not-gated) acquisition.
## 4 Improvements to Reduce Power Consumption
it can be observed, we were able to greatly reduce the download time of the compressed slices from camera (up to 700 ms in the largest image size), and also to reduce the time spent to establish camera connection (190 ms in average). These improvements have reduced the running mode execution time in all image sizes, as shown in Figure 10(b), being up to 0.9 s for the highest image size. This would also imply a reduction in power consumption and as consequence an enlargement of the image sensor operational life. After adjusting the new power consumptions, the resulting autonomy of our image sensor with the new SPI-Camera is extended in a 25% for all image sizes. So, configuring the image sensor node to take pictures at 640 \(\times\) 480 size every 30 minutes and the same batteries than the ones described in Section 3.3, the image sensor autonomy increases from 7.8 to 9.8 months.
## 3 Potential Applications in Biological Studies
### Other Compounds with Biological Activity  
Several reactions have been explored for the synthesis of glycerol carbonate derivatives with interesting biological activities. In this sense, [PERSON] and co-workers [143] investigated the condensation of ferrocenemethanol **139** with glycerol derivatives and related amines in the absence of solvent and catalyst. The reactions were performed under neat homogenous conditions and eleven novel compounds were described. Among the tested alcohols, GC **1a** was reacted with **139** in the presence of catalytic CO2 or without any catalyst, leading to the corresponding ferrocenylmethyl ether **140** with 92% and 97% yields, respectively (Scheme 67). Ferrocenylmethyl ether **140** three other ferrocenyl analogues were tested in vitro as fungicides and were shown to inhibit fungi growth against _Botrytis cinerea_ and _Penicillium_ spp.  
Recently, [PERSON] and co-workers [144] described the copper-catalyzed enantioselective synthesis of highly substituted \(\beta\)-amino alcohols **141** starting from ethynylated carbonates **6** and arylamines **63**. The reaction was performed at \(-20\)\({}^{\circ}\)C in toluene and in the presence of triethylamine and **(S,S)-L** as a copper ligand (Scheme 68). Using this procedure, fourteen novel \(\beta\)-amino-\(\beta\)-ethynyl alcohols **141** were obtained with 65-99% yields. When reacting ethynylated carbonates bearing naphthyl or heterocyclic groups under the optimal conditions, the efficacy of the method was maintained, giving the desired products with 93% and 98% yields, respectively. In contrast, the only aliphatic amine tested, 'butylamine, yielded the respective \(\beta\)-amino alcohol with only 75% yield. The reaction enantioselectivity (determined by HPLC) presented _er_ ratios varying from 77:23 to 94:6.
## 2 Results
### Detailed Analysis of Putative ORFs  
#### 2.5.1 Structural Genes, DNA Packaging and Lysis  
The XaC1 an XbC2 phages encode several structural proteins, including major head protein, tail, neck, tail sheath, head completion, long tail fibers and assembly proteins, baseplate, tail tube and other structural proteins. Moreover, some structural proteins showed enzymatic activity, such as tail lysozymes (ORF315 for XaC1; ORF236 and ORF237 for XbC2), prohead core scaffold proteases (ORF332) and head maturation protease (ORF515) for XaC1. Both phages encode one tail assembly chaperone and a cochaperonin type GroES, which have participated in promoting protein folding and morphogenesis. In the case of DNA packaging, both phages harbor two large terminase homologs that were found in tandem (ORF353 and ORF354 for XaC1; ORF270 and ORF271 for XbC2) and the portal protein (ORF248, XbC2; ORF329 XaC1), but no small terminase subunit was found. In addition to tail-associated lysozymes, phage XaC1 encodes for a rz-like spanin (ORF226) and an EPS depolymerase (ORF298) with a pectin_lysae fold (IPR011050), making the latter possibly important for biofilm disruption and polysaccharide degradation during adhesion [24]. Meanwhile, XbC2 encodes an O-spanin (ORF1159) and a lysozyme (ORF184) (Tables S9 and S10).
## 4 Discussion and Conclusions
High ionization levels may cause severe malfunctions in semiconductor parts, decreasing the performance and the reliability of the electronic systems. An accurate estimation of such levels would help prevent hardware failures and software errors. A detailed study of extreme events may contribute to the updating of safety measures and regulations, as well as to the updating of air traffic flow and capacity management, by taking into consideration the respective occupational exposure conditions. An extension of this work is planned in order to include more scenarios, i.e., different input parameters regarding the spectrum of incoming particles based on experimental data (such as the ones provided in [5; 6]), more FLs, actual flight plans and extreme events such as SEPs and GLEs. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12115297/s1](https://www.mdpi.com/article/10.3390/app12115297/s1)]([https://www.mdpi.com/article/10.3390/app12115297/s1](https://www.mdpi.com/article/10.3390/app12115297/s1)). Table S1. CRII and dH*(10)/dt values for cut-off rigidities 0.1 GV, 3.1 GV, 8.5 GV and 14.9 GV over the years 1996-2019. Conceptualization, P.M. and A.T.; data curation, P.M., A.T. and A.N.S. ; formal analysis, A.N.S. ; investigation, P.M. and A.T.; methodology, M.G. ; resources, P.M., A.T. and A.N.S. ; software, P.P. and I.G.U. ; supervision, H.M.; project administration, P.M. and A.T.; validation, H.M. and P.K. ; writing--original draft preparation, P.M., A.T. and A.N.S. ; writing--review and editing, H.M., N.C., M.D. and A.G.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. This work is supported by the ESA Space Safety Programme's network of space weather service development and pre-operational activities and supported under ESA Contract 4000134036/21/D/MRP, in the context of the Space Radiation Expert Service Centre. The European Neutron Monitor Service's research is funded by the ESA SSA SN IV-3 Tender: RFQ/3-13556/12/D/MRP. A.Ne.Mo.S is supported by the Special Research Account of Athens University (70/4/5803). I.G.U. acknowledges partial support from the Academy of Finland (projects ESPERA No. 321882). Thanks are due to the Special Research Account of the University of Athens for supporting the Cosmic Ray research. Thanks are also due to the Oulu Cosmic ray colleagues for kindly providing cosmic ray data as well as the cosmic-ray-induced ionization model. The authors declare no conflict of interest.