esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Bioprocess for Xanthophylls Production by Microalgae
### Cultivation Systems
#### 7.1.2 Closed Systems
For the production of ASX from the green microalgae _Haematococcus pluvialis_, Cyanotech corporation [[https://www.cyanotech.com/is](https://www.cyanotech.com/is)]([https://www.cyanotech.com/is](https://www.cyanotech.com/is)), (accessed on 15 April 2021) an example of industries that use PBRs for the green phase (vegetative growth) and ponds as open systems for the red phase (production of xanthophylls). Mera Pharmaceuticals Inc. ([[http://www.merapharma.com/](http://www.merapharma.com/)]([http://www.merapharma.com/](http://www.merapharma.com/)), accessed on 15 April 2021) was among the first companies which established large scale ASX production facilities globally. The company was located where weather conditions are extremely suitable for outdoor _Haematococcus_ cultivation [138]. Based on the same system, the company employed a two-step autotrophic cultivation approach. The green phase of microalgae growth was conducted in 25,000 L computerized outdoor PBRs, and the red phase of ASX accumulation in raceway ponds. Mixotrophic culture of _Haematococcus_ has long been sought as an alternative approach for the traditional two-step ASX production process [139; 140]. In fact, the world's first commercial ASX production facility from the microalga _Haematococcus_ appears to have been based on mixotrophic culture technologies in 1995 [138]. However, since then, phototrophic culture approaches have been amply developed and have become the main strategy for the production of ASX from microalgae. The advantage of mixotrophic cultivation is that the production can be carried out indoors and under optimal controlled conditions. However, the cost of materials and energy consumption might be too high to compete with phototrophic culture using sunlight. Recently, a novel ASX production process based on a mixotrophic mode has been developed with a heuristic multilevel LED light regime and the highest content of 3.3% was achieved at the white-blue regime [141]. Polyol alcohols (glycerol and mannitol) have also been shown to be more efficient carbon sources than acetate for the efficient and cost-effective ASX production from _Haematococcus_[142].
###### Abstract  
Oceanographic remote sensing, which is based on the sensitivity of reflected signals from the Global Navigation Satellite Systems (GNSS), so-called GNSS-Reflectometry (GNSS-R), is very useful for the observation of ocean wind speed. Wind speed estimation over the ocean is the core factor in maritime transportation management and the study of climate change. The main concept of the GNSS-R technique is using the different times between the reflected and the direct signals to measure the wind speed and wind direction. Accordingly, this research proposes a novel technique for wind speed estimation involving the integration of an artificial neural network and the particle filter based on a theoretical model. Moreover, particle swarm optimization was applied to find the optimal weight and bias of the artificial neural network, in order to improve the accuracy of the estimation result. The observation dataset of the reflected signal information from BeiDou Geostationary Earth Orbit (GEO) satellite number 4 was used as an input for the estimation model. The data consisted of two phases with \(I\) and \(Q\) components. Two periods of BeiDou data were selected, the first period was from 3 to 8 August 2013 and the second period was from 12 to 14 August 2013, which corresponded to events from the typhoon Utor. The in situ wind speed measurement collected from the buoy station was used to validate the results. A coastal experiment was conducted at the Yangjiang site located in the South China Sea. The results show the ability of the proposed technique to estimate wind speed with a root mean square error of approximately 1.9 m/s.  
wind speed estimation; GNSS-reflectometry; artificial neural network; particle swarm optimization; particle filter; BeiDou GEO satellite +
Footnote †: journal: Sensors
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 4 Live Misalignment Offset Prediction
The results showed good agreement, confirming the initial hypothesis and that machine learning methods may be used to estimate the offset successfully. When comparing these results with the MALO calculations in Figure 10, we may conclude that an error of 5-6 mm on the lateral offset was sufficiently accurate to provide relevant information to the transmitter so that an optimum divergence was chosen, hence leading to better performance for the optical link.
# Experimental Study on Chromaticity Control in Visible Light Communication Systems
## 3 Research on RGB LEDs Dimming
### Analysis of Experimental Results  
Due to the integration of lighting and communication in this system, it is necessary to analyze the communication performance of optical communication systems. The light source is vertically illuminated onto the receiving surface, and the color temperature of the light source is adjusted according to the mixing ratios obtained from the dimming experiment, which are 0.6:0.28:0.12, 0.5:0.32:0.19, 0.47:0.33:0.2, and 0.45:0.34:0.21. Ensure that the distance between the transmitter and receiver remains fixed, change the modulation bandwidth, and calculate the error rate under different modulation bandwidths to obtain the maximum modulation bandwidth for reliable transmission of the system. In the experiment, the communication distance was 250 cm and the modulation bandwidth of the three channels was changed. The total modulation bandwidth under different color  
Figure 19: Experimental setup diagram.  
Figure 18: RGB LEDs visible light communication system.  
temperatures was recorded and synthesized, and the error rate was calculated. Figure 20 shows the error rate curve with modulation bandwidth at five color temperatures of 3594 K, 4564 K, 5497 K, 6570 K, and 7550 K is shown. As shown in Figure 20, as the modulation bandwidth increases, the bit error rate also increases. When the error rate is below \(10^{-3}\), the maximum modulation bandwidth for the five color temperatures can reach 9.2 MHz, 10 MHz, 11 MHz, 11.5 MHz, and 11.7 MHz, respectively. The modulation bandwidth for high color temperatures is higher than that for low color temperatures. When the modulation bandwidth is 9 MHz, the error rate at all color temperatures is less than \(3.8\times 10^{-3}\), achieving reliable transmission of visible light communication systems.  
When the modulation bandwidth is 8 MHz, Figure 21 shows the error rate curve of the five color temperatures of 3594 K, 4564 K, 5497 K, 6570 K, and 7550 K with the distance shown. As shown in Figure 21, in the actual test, as the distance increases, the error rate of the system also increases, and the speed of change becomes faster and faster. When the transmission distance is 350 cm, the error rate at each color temperature is lower than the limit error rate value of 3.8 \(\times\) 10\({}^{-3}\) for forward error correction. The bit error rate of the cold white temperature is significantly lower than that of the warm white temperature. The conclusion is that as the color temperature increases, the communication performance of the system improves. However, when the color temperature exceeds 6500 K, the increase in color temperature has a smaller impact on the communication system performance.  
Figure 21: Change in error rate with distance for different color temperatures.  
Figure 20: Changes in bit error rate with modulation bandwidth for different color temperatures.
# Multilabel Classification of Radiology Image Concepts Using Deep Learning
## 2 Research Methodology
### Complete Pipeline of the Proposed Methodology
#### 2.1.5 Deep Learning Model Implementation  
The training of all models was performed using transfer learning, employing pre-trained weights from ImageNet for DenseNet121, ResNet101, and VGG19. Fine-tuning was carried out by unfreezing the last convolutional block of each network while keeping the earlier layers frozen. This approach aligns with the methodology used in skin cancer classification, where fine-tuned DenseNet121 achieved notable accuracy improvements [33].  
The models were optimized using the Adam optimizer, with an initial learning rate of 0.0001. A learning rate scheduler (ReduceLROnPlateau) was applied, reducing the learning rate by a factor of 0.1 if the validation loss did not improve for five consecutive epochs.  
We trained the models for a maximum of 50 epochs, using a batch size of 32, and employed binary cross-entropy as the loss function, suitable for multilabel classification tasks. Early stopping with a patience of 10 epochs based on the validation loss was applied to prevent overfitting.  
The dataset was divided into 80% for training and 20% for validation. Data augmentation techniques, including random rotations, horizontal flips, and scaling, were utilized during training to enhance model generalization. Such augmentation strategies are commonly employed in brain tumor analysis using MRI data to improve model robustness [34].  
All experiments were conducted using the PyTorch framework on a single NVIDIA GPU. This configuration was selected after preliminary tuning to achieve a stable balance between training time and model performance. Similar setups have been effectively used in end-to-end deep learning architectures for early Alzheimer's diagnosis using 3D neuroimaging biomarkers [35]. Specifically, all experiments were conducted on a workstation equipped with an NVIDIA RTX 3090 GPU, 64 GB RAM, and an Intel Core i9 processor, running Ubuntu 20.04 and using PyTorch version 1.12.1.  
We implemented multiple prototypes, including DenseNet121, ResNet101, and VGG19, both with and without transfer learning, to assess their effectiveness on our dataset. We have also used both GPU and CPU-founded techniques. Regarding training times, DenseNet121 required approximately 45 min, ResNet101 about 50 min, and VGG19 approximately 60 min for full training over 50 epochs. These times reflect the usage of transfer learning and early stopping criteria.
# Non-Hertzian Elastohydrodynamic Contact Stress Calculation of High-Speed Ball Screws
## 4 Results Analysis and Discussion
### Analysis of the Elastohydrodynamic Contact Stress with Different Helix Angles  
The elastohydrodynamic characteristics of point \(A\) under the preload are 1500 N and the entrainment velocity is 1005.3 mm/s, which are shown in Figure 10. When the lubricant film is entrained into the contact region, the secondary peak of the film contact stress in the exit coordinates of the contact region is generated, as shown in Figure 10a. Figure 10b shows the film thickness distribution of point \(A\).  
Figure 8: The variation of the non-Hertzian contact stress with different helix angles. (**a**) Contour map of non-Hertzian contact pressure distribution \(P_{A}\) (Mpa). (**b**) Distribution difference between non-Hertzian contact pressure and Hertzian contact pressure \(P_{A}\)-\(P_{AH}\) (Mpa).  
Figure 11a,b show that the variations of central elastohydrodynamic contact stress calculated by the Hertzian solution and non-Hertzian solution with different helix angles, respectively. The variation trends of the central elastohydrodynamic contact stress of point  
Figure 10: Elastohydrodynamic characteristics using the non-Hertzian contact stress calculation method. (**a**) Elastohydrodynamic contact stress distribution. (**b**) Film thickness distribution.  
Figure 9: Comparison of the Hertzian and non-Hertzian contact stress. (**a**) The central contact stress of the point \(A\). (**b**) The central contact stress of the point \(B\). (**c**) The contact stress error. (**d**) The contact region deflection angle.  
\(A\) and point \(B\) are the same. However, the values of the central elastohydrodynamic contact stress are greater than that of the central contact stress of point \(A\) and point \(B\).  
Figure 11c,d show that the variations of central elastohydrodynamic film thickness calculated by the Hertzian solution and non-Hertzian solution with different helix angles, respectively. The elastohydrodynamic film thickness of non-Hertzian solution at point \(A\) decreases as the helix angle, else the one at point \(B\) increases as the helix angle, which indicates that the lubrication state of the nut contact point \(B\) is good than the screw contact point \(A\).
# Joint Image Compression and Encryption Using IWT with SPIHT, Kd-Tree and Chaotic Maps
## 4 Proposed Image Compression and Encryption Algorithm
### Chaotic-Map-Based Encryption
\[X_{n+1}=\left(r+(1-ax_{n})^{2}\right)\text{mod}1 \tag{11}\]  
\[X_{n+1}=\left(r+(1-8x_{n})^{2}\right)\text{mod}1 \tag{12}\]  
The authors replace \((X_{n})^{2}\) in Equation (11) with the term \((1-aX_{n})^{2}\) and modulo 1, with \(a=2\), \(4,8\). A larger Lyapunov exponent indicates more-chaotic behavior. The above-modified quadratic chaotic map has Lyapunov exponent 3.4709, which is a very good value compared to other chaotic maps; thus, this map yields a chaotic sequence with desired properties.
## 4 Results of the Study
### Analysis of the Amount of Settlement on the Reinforced Surface  
In order to investigate the control effect of grouting reinforcement on surface settlement, this study selected a reinforced section with similar topography to the unreinforced section for surface settlement monitoring. Three monitoring points (mileage 4330, 4342, and 4352) were arranged above the centerline of the shield tunnel to analyze the variation rule of surface settlement. The horizontal coordinate is the position of the cutter plate incision during shield tunnel excavation, the vertical coordinate is the amount of surface settlement, and the change in settlement with shield tunneling is shown in Figure 10.  
As can be seen from Figure 10, the rule of change in surface settlement along the shield axis in the reinforced section is as follows: when the shield cutout approaches the location of the measurement point from a distance, the measurement point appears to be slightly elevated, which may be due to the joint action of the soil chamber pressure and grouting pressure; when the shield cutout approaches the measurement point, the surface begins to settle and tends to converge, and the settlement curve shows a period of gentle change; after the gentle section, the surface settlement again increases and a period of gentle change then appears. After the gentle section, the amount of surface settlement increases again, followed by a period of gentle changes. When the shield tail is close to the measurement point, the surface settlement reaches a very small value; at this time, the shield tail may cause a large settlement, but under the effect of synchronized grouting, the surface settlement partly rebounds and finally tends to converge. The alternation of  
Figure 10: Cumulative settlement curve of measurement points in the reinforced section.  
Figure 9: Surface settlement values of shield tunnel under the influence of different grouting pressures.  
gentle and steep sections in the settlement curve is mainly due to the combined effect of the taper of the shield machine itself and the grouting reinforcement. In addition, due to the influence of the measurement time interval, the gentle section of the cumulative displacement curve at some measurement points is not obvious.
# SpaceSheep: Satellite Communications for Ovine Smart Grazing
## 3 Results
### On-Site Tests  
To test the performance of the system in a real environment, we simulated sending events recorded by the gateway during the monitoring period for a flock of 18 sheep between November 18 th and 29 th 2021; Figure 5 shows the number of alarms that were generated for the days observed. Since most alarms are associated with the behavior of living animals, the number of alarms generated daily does not follow any regular pattern. Furthermore, the number of alarms reached its maximum value on the 24 th of November, when 192 alarms were detected. Approximately 44% of the daily values were in the range of 100 and 140, while 22% of the days generated fewer than 10 alarms. The system registered an average of approximately 100 alarms per day.  
Table 3 displays the number of alarms and their sizes produced during those days, which are grouped by alarm type. Some alarm types have greater values than expected (in the realistic scenario), which are explained by the characteristics of the system in question: (i) the infraction alarms were produced despite the conditioning being disabled since the tests were carried out in November after the vines were harvested; thus, the animals were not warned when committing an undesirable behavior, thereby continued to perform the same actions and triggered their alarms repeatedly; (ii) the beacon did not cover the entire pasture area, which meant that some of the animals were not detected, even though they were inside the designated area.  
Figure 4: Comparison between the original JSON format and MessagePack encoding efficiencies.  
To validate the possibility that the hardware being used in the gateway did not compromise performance, the alarm detection and sending times were measured, both for the gateway implemented on the Raspberry Pi and on the Core i7 4500U (1.8 GHz), 16 GB RAM PC.  
As for the temporal analysis results illustrated in Figure 6, the computer presented a better performance than the Raspberry Pi in every test, which was expected due to its higher processing capacity.  
Figure 5: Variation in the number of alarms throughout the 12 days.  
Figure 6: Impact of the number of collars on the time spent publishing the information received by the WSN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Device** & **Alarm Type** & \multicolumn{2}{c}{**Size (Bytes)**} \\ \hline Collar & Panic & 6 & 1159 \\  & Absence & 75 & 1147 \\  & Battery & 2 & 1137 \\  & Infraction & 689 & 1126 \\  & Lost & 0 & 1134 \\  & Equipment & 141 & 1157 \\ Beacon & Absence & 3 & 1159 \\  & Battery & 4 & 1134 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of alarms generated for alarm type.
## 6 Simulation Results
### Algorithm Comparison Experiment  
This section compares four distinct algorithms' performance across eight different scene settings. The algorithms under consideration are MOEA/D-MFAR, MOEA/D, NSGA-II, and MOPSO. To ensure a fair comparison, algorithm parameters such as population size, number of iterations, etc., are uniformly configured. Initially, the PFs under the eight scenarios are illustrated in Figure 9. Red scatter points represent MOEA/D-MFAR results, green scatter points denote MOEA/D, blue scatter points indicate NSGA-II outcomes, and purple scatter points signify MOPSO results. Evidently, regardless of the scenarios, MOEA/D-MFAR consistently outperforms other algorithms in obtaining superior PFs. Intuitively, MOEA/D-MFAR exhibits the best domination ability, followed by MOEA/D, NSGA-II, and MOPSO. Under the dual-objective optimization problems (refer to Figure 8a-d), MOEA/D-MFAR yields elongated PFs, while under the three-objective optimization problems (refer to Figure 8e-h), it manifests complete surfaces. This under-scores the exceptional search capability of MOEA/D-MFAR within the target space. For a more objective analysis of algorithm performance, Table 5 displays the final HV values of PFs and the number of feasible solutions. The best values are bolded. On one hand, MOEA/D-MFAR exhibits an absolute advantage in HV value, indicating superior results in addressing this type of multi-objective optimization problem. This outcome is attributed to a more rational resource allocation. On the other hand, MOEA/D-MFAR attains the highest number of feasible solutions, showcasing its ability to address a broader range of dynamic demand scenarios. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**ASDR (kb/s)** & **Scenario 1** & **Scenario 2** & **Scenario 3** & **Scenario 4** & **Scenario 5** & **Scenario 6** & **Scenario 7** & **Scenario 8** \\ \hline MOEA/D-MFAR & **0.00472** & **4.88970** & **0.00638** & **6.06020** & **0.78157** & **3.81755** & **0.55242** & **4.39425** \\ MOEA/D-MFAR-ndr & 6.71880 & 21.21205 & 15.34665 & 24.34125 & 24.09956 & 32.22496 & 32.87763 & 53.12022 \\ MOEA/D & 37.91355 & 48.36469 & 27.09743 & 45.89225 & 63.04678 & 74.17693 & 54.01430 & 78.72201 \\ NSGA-II & 36.54003 & 105.26025 & 33.97831 & 89.76500 & 94.81778 & 112.85205 & 144.71659 & 154.59454 \\ MOPSO & 164.07773 & 140.23824 & 169.52251 & 189.42786 & 89.40870 & 163.86959 & 148.69868 & 173.26047 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: ASDR of different algorithms.
# A Review on Mixed Reality: Current Trends, Challenges and Prospects
## 3 Concept of Mixed Reality
### Data Management  
The data management strategy and the data itself are significant parts of any MR application. The former includes the presentation of data related to both the real and virtual objects. This also comprises certain relevant data sections such as a standard markup language, enabling it for use across different platforms, a search schema, providing meaningful information, a heterogeneous and big volume data management section known as Big Data, a proper filtering strategy, and the extraction of user-based information (context such as proximity or user view), as described in the following studies.  
Ref. [64] developed XML-based data markup representation based on the 4 WIH structure containing real and virtual world information, as well as a link between them. The use of this structure increases the flexibility of MR content for use across various platforms to enable interoperability. Ref. [65] proposed the use of ontology in this regard. This concept generates standard metadata that could be used during an object search on an MR platform. Ref. [66] proposed an appropriate strategy for big data handling in the MR space using a graph and manifold learning. This strategy handles heterogeneous structures such as temporal, geospatial, network graph, and high-dimensional structures. Ref. [67] employed filtering in low-resolution devices such as mobile devices. In that study, three methods of filtering were used: spatial, visibility, and context filtering. Spatial filtering was based on assessing the proximity of the object to the user. Visibility filtering evaluates the visibility of an object to the user. Context filtering aims to extract useful context-related user and user environment data via the computer vision method for feature extraction.  
Despite the importance of the proposed methods, various different challenges remain. In big data management with graph visualization, the synchronization concept is an important problem, particularly in collaborative applications. This concept enables all users to see the same graph in the same place and interpret it. Furthermore, a new concept such as graph movement can be considered to improve the algorithm [66]. Using the content filtering strategy produces problems in accessing content when the receiver is not near the information [67].