esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Universal GNSS Channel Design Decisions
#### 2.1.2 Sub-Carriers  
As seen in Table 1, signal modulations involve up to two sub-carriers combined in different phase relations. In fact, a phase-controlled sub-carriers generation module based on a single NCO makes up a universal channel. This NCO is used to derive up to two slower periodic signals from a third one (_i.e._, SC2); the slowest signal being used to dictate the chipping rate of the primary spreading code. By doing so, an NCO phase ambiguity issue arose, which was overcome with the introduction of a SC2 period counter used in the navigation solution algorithm.  
To properly deal with signals characterized by a quarter of a cycle phase shift between chip transition and carrier rising edge (_i.e._, cBOC), a minimalistic approach requires a source clock with twice the required rate and a dual-edge register, as depicted in Figure 2. This approach would equally apply to Galileo E1A signal with cBOC \((15,2.5)\), where a sub-carrier six times that of the spreading code rate, both clock signals being in phase quadrature.  
Another requirement brought up by the sub-carriers is their respective weight in time. Indeed, the TMBOC pilot component requires the ability to null (_i.e._, switch off) sub-carriers in time. To be future-compliant with any periodicity length, applied on any sub-carrier, a single 16 kbit RAM block is used, achieving a maximum periodicity of 512 addresses \(\times\) 32 bits/(2 components \(\times\) 2 sub-carriers) = 4096. To use it efficiently, the RAM block is configured as a dual port RAM, written from the 32-bit data bus until the RAM is filled up, but only four bits are read per address to accommodate data and pilot components at once.  
Furthermore, CBOC and TMBOC impose different sub-carrier amplitudes. Pursuing a matched filter approach, the replica should mimic the targeted signal as much as possible. The resulting weighing factors \(\alpha\) for sub-carrier SC1 and \(\beta\) for SC2 must carry the following values: \(\alpha\in[1,\ 0.95,\ 0]\) and \(\beta\in[1,\ \pm 0.30,\ 0]\). The signed resolution requires a total of 6 signed bits to induce a representative ratio between one another: \(\frac{\beta}{\alpha}=\frac{0.30}{0.95}=\frac{6/32}{19/32}\) with \(\alpha+\beta=25/32\), introducing a potential scaling loss. These 6-bit coefficients may be updated at every chip in this simple TMBOC implementation, as depicted in Figure 3. For example, during the sequential acquisition process, four steps are followed:For each component, both 1-bit square sub-carriers are delayed to obtain Early (E), Prompt (P) and Late (L) replicas; the correlator spacing is set to \(\pm\)\(T_{\mathrm{s}}\)/4 with the fastest sub-carrier period \(T_{\mathrm{s}}\).
2. Prompt and Differential (D = E - L) are obtained on 2 bits for each sub-carrier.
3. P & D replicas are scaled to their pre-defined constant weight through a mapping function or Look-Up Table (LUT).
4. For each component, the two scaled sub-carriers are summed.
## 3 Experiments and Discussion  
To demonstrate the effectiveness and limitation of the proposed method, a series of simulated and real measurements with a sinusoidal surface and an f-theta lens surface was carried out. The weakness of the Gaussian process modelling was also demonstrated in a series of evaluation experiments. The real measurement experiments were conducted on a Werth VideoCheck UA multi-sensor CMM (Werth Messtechnik GmbH, Giessen, Germany) (see Figure 4) and the results were analysed and discussed. The designed and machined sinusoidal and f-theta lens freeform surface are shown in Figure 5. Two sensors were used in the measurement experiment: one is the Werth opto-electronic non-contact laser distance sensor (Werth Messtechnik GmbH, Giessen, Germany) and the other is the trigger probe Renishaw TP200 (Renishaw plc, Gloucestershire, UK) with a 1.997-mm diameter tip. The probing error of the trigger probe is \(\pm\)0.65 \(\upmu\)m while the probing error of the laser sensor is \(\pm\)1.0 \(\upmu\)m. The Maximum Permissible Measuring Error (MPE) of the CMM is (0.75 + \(L/300\)) \(\upmu\)m. The experiment was conducted in a clean room and the environmental temperature was 20 \(\pm\) 1 \({}^{\circ}\)C.  
Figure 4: Werth VideoCheck UA multi-sensor CMM (coordinate measuring machine) used for the experiments.
## 6 Security Analysis
### Informal Security Analysis
#### 6.1.9. Resistance to Privileged Insider Attacks  
Adversary \(\mathcal{A}\) captures the user's registration request message \(ID_{i}\) as a privileged insider and attempts to derive the user's secret \(UI_{i}\) using the intercepted \(ID_{i}\). However, in the proposed scheme, \(UI_{i}\) is masked using a random number \(r_{k}\) and the master key \(S_{k}\) of the \(CS\). Hence, \(\mathcal{A}\) cannot recover \(UI_{i}\) or compute the session key \(SK=h(UID_{i}||SI_{j})\oplus h(n_{i1}||n_{i2}||n_{i3})\), even with knowledge of \(ID_{i}\). Therefore, our scheme is resilient to privileged insider attacks.
## 2 SPME-GC/MS Methods
### Aroma Compounds and Wine Aging  
Aging in wooden barrels is a process used to stabilize the color and to improve limpidity and the sensorial characteristics of wines. During aging many compounds are transferred from the wood to the wine: polyphenols, lactones, coumarins, polysaccharides, hydrocarbons and fatty acids, terpen, C\({}_{13}\)-norisoprenoids, steroids, carotenoids, and furan compounds. Wood volatiles such as _cis_- and _trans_-\(\beta\)-methyl-\(\gamma\)-octalactones (oak lactones), syringaldehyde, vanillin, coniferaldehyde, sinapaldehyde and their alcohols, propiosyringone and propiovanillone, hydroxy-megastigmen-2-one and hydroxy-megastigmen-3-one [51], furfural, 5-methyl furfural, guaiacol, eugenol, 4-ethylphenol and 4-ethyl guaiacol [52, 53], furfuryl alcohol, \(\beta\)-ionone, \(\gamma\)-noanalactone, and acetovanillone [54] confer the typical organoleptic characteristics of aged wines.  
In making barrels for wine aging, oak (_Quercus sessilis_, _Q. petraea_, _Q. robur_, _Q. peduncolata_, _Q. alba_) is the wood more often used but other species, such as acacia (_Robinia pseudoacacia_), chestnut (_Castanea sativa_), cherry (_Prunus avium_), and mulberry (_Morus alba_ and _Morus nigra_) are also being considered [55].  
SPME-GC/MS was used to study the evolution of wine aroma during aging in 225-L barrels (barriques) made with these wood types. Experimental conditions used are reported in Table 3; main compounds identified are reported in Table 4[55].  
Wines aged in acacia, chestnut and oak wood showed higher contents of vanillin and eugenol and the acacia-aged sample showed an increase of 4-ethylguaiacol. Mulberry-aged wine had a significant decrease of 4-ethylguaiacol and increase of 4-ethylphenol; the wine aged in cherry barrel already showed high levels of 4-ethylguaiacol after three months of aging.  
\begin{table}
\begin{tabular}{l l} \hline SPME fiber & 65-\(\mu\)m carbowax/divinylbenzene (CAR/DVB) \\ Sample volume & 10 mL \\ Vial volume & 20 mL \\ Addition to the sample & 3 g NaCl \\ Sample heating & 70 \({}^{\circ}\)C for 10 min \\ Extraction temperature and time & 70 \({}^{\circ}\)C for 30 min \\ Desorption temperature and time & 230 \({}^{\circ}\)C fo 5 min \\ Fiber cleaning & 10 min \\ GC column & HP-INNowax (30 m \(\times\) 0.25 mm i.d; 0.25 \(\mu\)m film thickness) \\ Injection & Splitless \\ Oven program & 40 \({}^{\circ}\)C for 5 min, 3 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C, held 10 min \\ MS conditions & ionization energy 70 eV, acquisition SIM mode \\ \hline \end{tabular}
\end{table}
Table 3: SPME-GC/MS conditions used to study the evolution of volatile compounds of Raboso Piave wine during aging in five different types of wood barrels [55].
# TA-RRT*: Adaptive Sampling-Based Path Planning Using Terrain Analysis
## 3 Proposed Method
### Terrain-Based Step Size Adaptation
#### 3.3.2 Adaptive Step Size  
To enhance the efficiency and precision of path planning, we apply an adaptive step size based on terrain density. In more complex terrain, shorter step sizes enable precise path exploration, while longer step sizes are used in simpler terrain to reduce exploration time. A logistic function was used to adjust the step size according to the terrain density,  
Figure 4: Example of (**a**) the search result for corners within the radius of the reference point using a KD tree. (**b**) The tree structure of the KD tree.  
decreasing the step size nonlinearly as density increases. This approach ensures that the step size is appropriately scaled, shorter in complex terrain and longer in simpler terrain, maximizing the efficiency of path exploration. The adaptive step size using the logistic function is as follows:  
\[\delta=\frac{(\beta_{1}-\beta_{2})}{1+\exp\{(\rho-\beta_{3})/\|\beta_{4}\|\}}+ \beta_{2}. \tag{3}\]  
Here, \(\beta_{1}\) and \(\beta_{2}\) represent the maximum and minimum step sizes, while \(\beta_{3}\) and \(\beta_{4}\) correspond to the midpoint (offset) and slope of the logistic function. The values \(\beta_{1}=0.7\), \(\beta_{2}=0.5\), \(\beta_{3}=4\), and \(\beta_{4}=0.5\) are empirically used in this paper. Figure 5 illustrates the variation in step size according to terrain density.
# Research on Human Exposure to Transport-Induced Vibration in Buildings
## 7 Conclusions
The study highlights the crucial role of weighting functions in the accurate evaluation of human exposure to vibrations. Different weighting functions can significantly impact the analysis results. Weighting functions such as \(W_{\text{b}}\), \(W_{\text{k}}\), and \(W_{\text{m}}\) offer different sensitivities to vibration frequencies. The study finds that while \(W_{\text{b}}\) and \(W_{\text{k}}\) functions provide similar results in the frequency range of 5 to 25 Hz, the combined \(W_{\text{m}}\) function yields substantially lower values. The choice of weighting function directly influences the calculated VDVs. Using the \(W_{\text{m}}\) function results in approximately half the VDVs compared to \(W_{\text{b}}\) and \(W_{\text{k}}\) functions. Measurement points should be located according to the relevant standards, which may vary. For external vibrations, measurement points should typically be in the central part of the floor within one-third of the length/width, while for internal vibrations, modal analysis of the structure should guide placement. It is recommended to place the measurement point in the structural center of the floor whenever possible to minimize discrepancies and ensure consistency in data collection. The study compares the RMS (root mean square) and VDV (vibration dose value) methods, noting that each has specific applications and advantages. The RMS is beneficial for assessing continuous vibration exposure by providing a measure of vibration energy, while the VDV is more sensitive to peak values, making it suitable for evaluating potential discomfort and complaints. The selection of the evaluation method should be based on the nature of the vibration exposure and the specific requirements of the study. For instance, the RMS is preferable for general assessments, whereas the VDV is better for scenarios with significant peak vibrations. The study also highlights the need to consider the crest factor when choosing the evaluation method, as different standards provide specific guidelines based on this value. Conceptualization, A.K.-K., F.P. and R.B. ; methodology, A.K.-K. and F.P. ; software, F.P. ; validation, A.K.-K. and R.B. ; formal analysis, A.K.-K. and R.B. ; investigation, A.K.-K. and F.P. ; resources, A.K.-K. and F.P. ; data curation, A.K.-K., F.P. and R.B. ; writing--original draft preparation, A.K.-K., F.P. and R.B. ; writing--A.K.-K., F.P. and R.B. ; visualization, A.K.-K. and F.P. ; supervision, R.B. ; project administration, A.K.-K.; funding acquisition, R.B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available upon request from the corresponding author. The authors declare no conflicts of interest.
# Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles
## 3 Non-Cooperative Relative Positioning
### Vision-Based Solutions
By using the laws of perspective, they state range errors of 5% at 44 m and 10% at 90 m. Furthermore, [PERSON] et al. worked on a monocular vision system for a robotic follower vehicle obtaining a mean distance error of 0.62 m for distances of 10 m to 23 m [47]. With an approach based on fusing a priori knowledge of the vehicle width using a vehicle type classifier with geometric constraints of the street, [PERSON] et al. achieved an accuracy of 6.5% for ranges of 5 m to 80 m with a monocular vision system [48]. Cameras work usually at frame rates between 15 fps and 25 fps and, thus, have measuring rates comparable to radar sensors and laser scanners. However, relative velocity cannot be directly measured, but has to be differenced from successive images. Camera sensors are, like human visual perception, sensitive to adverse lighting conditions, as for instance, fog and rain or low sun and blinded by the head lights of approaching vehicles.
## 3 Results
### The Spatial Distribution of Differences Between BDS and GPS Positioning Results
Thus, due to the non-uniform distribution of GEO and IGSO satellites, the accuracy of BDS positioning results shows significant spatial latitude-dependent feature, having a declining pattern with the increasing latitude of the station [36]. We analyzed the spatial pattern of the mean vertical differences in CMONOC. The largest mean vertical difference, with its quantity being 30 mm, comes from the westernmost station XJBL, and they show opposite directions at nearby station XJBY and station XJKE. In other districts, they also show random patterns. Stations with infinitesimal value of the mean vertical differences are spatially randomly distributed: Station DLHA on the northeast margin of Tibetan plateau, station NMBT on the Mongolian plateau, station SNMX and station HBZG in the south China block. As towards the absolute quantities of the mean vertical differences, they are \(\sim\)8-12 mm in southwest China district,  
Figure 5: The spatial distribution of mean values on differences between BDS and GPS positioning results. \(\sim\)10-12 mm in north China district, \(\sim\)10-15 mm in northeast China district, indicating no spatial latitude-dependent pattern, \(\sim\)8-30 mm in Xinjiang district, \(\sim\)10 mm in Gansu district, indicating also no spatial longitude-dependent pattern.
## 4 Discussion  
This research study shed light on the accuracy of CFD simulations. Previous research showed that the SKE model had some shortcomings in reproducing the low-speed-wind regions behind buildings, but modified k-\(\epsilon\) models could improve these. In this study, various modified SKE models and RKE models (applied with the wall function, the damping function, etc.) were used to compare their efficiency, and the results were validated by comparing them to wind tunnel experiment results. It was found from this research study that: (a) There was a difference in wind speed between the experiment results and CFD predictions in the contour plots of the Y-velocity (\(V\)) at \(\mathrm{z/b}=0.125\) (Figure 10). (b) There was also a difference in the turbulent kinetic energy between the experiment results and CFD predictions in the contour plots of the turbulent kinetic energy at \(\mathrm{z/b}=1.25\). The study showed that using polyhedral meshes, the modified k-\(\epsilon\) models could improve the accuracy of the simulations but could still not perfectly match the wind tunnel experiment results. The approach to optimize the closure coefficients of the RANS turbulence model or the method of coupling multiple models could improve the accuracy of CFD simulations, but they are difficult to be applied in engineering design because this approach requires engineers to master more professional knowledge of fluid dynamics. It is hoped that the existing models can be further modified based on these research findings and finally lead to a more accurate and more convenient toolkit for engineers.  
Having said the above, the modified k-\(\epsilon\) models are still widely used for design and analysis purposes in many engineering applications due to their low computational cost and data storage requirement. It was found from this study that:  
1. In the low-wind-speed area, the STLKE model and the RTLKE model could predict turbulence kinetic energy in a more accurate way. The RTLKE model also performed well in predicting the wind speed on the vertical and horizontal planes. The RTLKE model could be used for simulations in research studies of low wind-speed areas to predict the size of the static-wind areas around high-rise buildings, the diffusion time of pollutants around buildings, etc.
2. In the high-wind-speed area, the STLKE model and the RTLKE model could accurately predict wind speed. Between them, the STLKE model performed better in simulating the turbulence kinetic energy. Therefore, the STLKE model is recommended to be used for simulations in research studies of high wind-speed areas (e.g., windward side) of high-rise buildings to predict the damage of high wind speed to the area around buildings during a typhoon, the maximum wind-speed area around high-rise buildings, etc.  
Wind speed is considered important in current high-rise building designs. However, there is a lack of relevant guidelines or tools that could help designers predict outdoor wind environment. It is, therefore, hoped that findings from this research study supplement some existing building design guidance.
## 3 Results
### Velocity and Pressure Distribution
#### 3.2.1 Relationship Between Flow Velocity and Velocity Distribution  
Increasing the inlet velocity from 0.01 m/s to 2 m/s resulted in a noticeable reduction in the wake region, as observed in the streamline and velocity magnitude contour plots (Figures 7-10 and 13-28). This phenomenon aligns with established fluid dynamics principles [13; 17; 18], attributing the wake reduction to a downstream shift in the boundary layer separation points, consistent with prior discussion. However, no apparent variations in the wake structure were observed between inlet velocities of 0.01 m/s and 0.02 m/s (Figures 13-28), likely due to the minimal difference between these low-magnitude velocities, rendering the flow behaviour effectively indistinguishable within the simulation's resolution [13; 17; 18].  
All Setups also showed a localised region of accelerated flow through the O-ring's central aperture (Figures 7-25). This velocity increase is attributed to the streamline convergence necessitated by the reduced cross-sectional area, a phenomenon governed by the continuity equation for incompressible, steady flow (Equation (6) [17]). Theoretically, this acceleration can be calculated using the continuity equation, with a 2 m/s inlet velocity, predicts a higher velocity than the simulated 2.242 m/s. As depicted in velocity magnitude contours (Figures 13-28), the magnitude of this acceleration was directly proportional to the inlet flow velocity. Specifically, Setups 4 and 8, employing an inlet velocity of 2 m/s, resulted in a simulated maximum flow speed of 2.242 m/s through the O-ring's centre. The divergence between the theoretical calculation and the simulated result may be due to factors not accounted for in the simplified continuity equation, such as viscous effects or the specific geometry of the O-ring's aperture (Table 4).  
\[\rho_{1}A_{1}V_{1}=\rho_{2}A_{2}V_{2} \tag{6}\]  
Including incompressible conditions, as follows:  
\[A_{1}V_{1}=A_{2}V_{2} \tag{7}\]
# Lane following Learning Based on Semantic Segmentation with Chroma Key and Image Superposition
## 4 Experimental Results
### Line Extraction Task  
This task serves to train an Autoencoder to focus on the relevant elements in the image and to ignore other elements--that is, to extract only the lines from the road coding their form and orientation in the latent vector of this network. The schema already described in Section 3.2 and represented in Figure 5 will be used, enabling the system to take images of the circuit in the target scenario and to extract the lines from the road.  
Initial experiments were performed to determine the adequate parameter settings. It was found that the reconstruction capacity of the Autoencoder was notably improved by including a certain amount of input noise in the training process. The lighting changes in the different scenarios employed to build the datasets can be compensated for with this input noise addition, which delivers a more tolerant model. By testing different input noise distributions with an increasing standard deviation, a progressive improvement was obtained. The best reconstruction capacity is achieved with a standard deviation of 0.4, and this degrades progressively from the value of 0.5 onwards. Therefore, a standard deviation of 0.4 was chosen for training a more robust Autoencoder (improvements shown in Figure 8 and video 4 [39]).  
Another experiment deals with downsized images. Based on the dataset with images sized at 320 \(\times\) 240 pixels, a new set is generated with a resolution of 160 \(\times\) 120 pixels, intended to accelerate the inference process in the SBC. Figure 9 shows that both the training and the validation errors decrease slowly after a rapid descent at the beginning of the training. Although the training lasts up to 100 epochs, a good convergence is achieved in the first 40-50 epochs. The learning process and the reached errors are similar for both image sizes.  
There are no relevant differences in the values of the loss function when comparing both resolution sizes. The results obtained with the network generated in the previous experiments are quite good (for example, see Figure 10).  
Figure 8: (**a**) Input Image from the target scenario. (**b**) Reconstructed output for the Autoencoder trained with input noise std_dev = 0.0. (**c**) Output for std_dev = 0.4.  
Figure 9: Descent of the loss functions for the same dataset resized to different resolutions. (**a**) Image size 320 \(\times\) 240. (**b**) Image size 160 \(\times\) 120.
# Management of the First Feeding of Dorada _Brycon sinensis_ with Two Species of Cladocerans
###### Abstract  
The management of the first feeding is a critical stage in the viability of the larvae and fingering rearing. So far, the first feeding of the bryconoids record the best results when fed with forage larvae; thus, the aim was to evaluate two species of cladocerans as live prey in the first feeding of dorada _Brycon sinensis_ and to evaluate their effects on the control of cannibalism. Larvae (\(1.2\pm 0.15\) mg and \(5.9\pm 0.4\) mm initial weight and total length) were fed _Moina minuta_ (Mm), _Macrothrix elegans_ (Me) or a mixture (50:50) of cladocerans (Mix) at a rate of 20 prey mL\({}^{-1}\), once for 24 h. Another dorada larvae group were fed newly hatched larvae of _Piarcatus brachypomus_ (\(4.5\pm 0.9\) mm) as forage larvae (F\({}_{\rm L}\)) in a ratio of 2:1 (prey:predator). The larvae were stocked to 50 L\({}^{-1}\) in aquaria with 5 L of useful volume (12 per treatment). The growth, survival, stress resistance, cannibalism mortality, and the number of prey in the gut contents were analyzed. Dorada larvae fed F\({}_{\rm L}\) showed higher growth, but those fed Mm showed the highest survival rate (76.1 \(\pm\) 6.6%) and the lowest cannibalism mortality (16.8 \(\pm\) 3.7%) (\(p<\) 0.05). The use of the cladocerans allowed high survival and stress resistance (95.3 \(\pm\) 2.4%), and _M. minuta_ proved to be a suitable prey for cannibalism control in the management of the first feeding of dorada larvae.  
cannibalism; live food; larval feeding; fish larvae +
Footnote †: journal: selected topics