esa-sceva/satcom-chunk-collection huggingface dataset:
# Marine Microalgae for Potential Lutein Production
## 7 Extraction of Lutein from Microalgae and Challenges
Optimal extraction conditions were determined as temperature at 27 \({}^{\circ}\)C, pressure at 210 bar, 1.5 mL g\({}^{-1}\) ethanol, and ultrasound power at 1000 W. Under these conditions, a maximum extraction yield of 1.24 mg lutein g\({}^{-1}\) crude material was obtained and when compared to other methods, it was found that USCCE could result in increasing lutein extraction yield significantly at much lower extraction pressure and temperature. [PERSON] et al. [73] also applied supercritical fluid extraction followed by HPLC and LCMS analysis to lutein extraction from _Chlorella pyrenoidosa_. Under optimum conditions, temperature at 50 \({}^{\circ}\)C, pressure at 250 bar, and 50% ethanol as the co-solvent, the extraction yield recovery of lutein was 87.0%. From the results, it was concluded that SFE yielded in high purity lutein recovery and the process developed during this study might be suitable for the commercial production of lutein. In conclusion, in regard to the large-scale production of lutein, only solvent extraction has, to date, achieved high degrees of efficiency and purity. However, new advances in methods and techniques such as selective precipitation with supercritical CO\({}_{2}\) and new advantageous solvents, such as ethyl lactate, which have been proposed for the extraction of other plant matter [74], may also be applied to microalgae and prove beneficial.
## 3 Results and Discussion  
### Spectral Interpretation  
The Raman scattered spectra shown in Figure 1a were obtained for both the pure Grignard reagent and the Grignard reagent spiked with toluene. The corresponding spectral data obtained were preprocessed using the MSC preprocessing method. During model development, only the spectral region from 400 to 1650 cm\({}^{-1}\) was selected for developing the model owing to the presence of the peaks related to the toluene concentration in the Grignard reagent, while the rest of the spectral region (1800-2500 cm\({}^{-1}\)) was not considered in this study, as there was no relevant information in this spectral region. The MSC pre-processed spectra show changes in the spectral pattern and intensities at around 600 cm\({}^{-1}\) and 1200 cm\({}^{-1}\), which is further clearly mentioned in the expanded spectral regions indicated as (a1) and (a2). These variations in the intensity of the spectra may have occurred owing to increased concentrations of toluene present in the Grignard reagent samples. It is difficult to differentiate the pure and adulterated Grignard reagent spectra by merely looking at the specific peaks obtained. Thus, there is a need for the multivariate analysis method to obtain better analytical performance for the prediction of possible adulteration.
## 2 Related Work  
Applying artificial intelligence (AI) algorithms and deep learning (DL) techniques to automate medical image analysis is an active area of research. Medical image analysis using deep convolutional neural networks (DCNNs) [36; 37; 38; 39; 40; 41] relies heavily on annotated 3D medical images, which are difficult to create, owing to the labor-intensive process and necessary powers that is often unavailable [19; 20; 21]. Recent studies have used LDCT-based radiomic signatures for lung cancer screening [42] to predict the survival in patients based on the risk score generated by radiomic models [43].  
**Semi-Supervised learning (SSL)** is a learning approach that can utilize a limited amount of labeled data along with a significant amount of unlabeled data to improve model performance [44]. Typical SSL methods include entropy minimization [45; 46; 47], self learning or pseudo labeling [48; 49; 50; 51], and consistency regularization [52; 53; 54; 55]. SSL is extensively employed in medical image processing tasks such as image detection, segmentation, and classification [30; 56; 57; 58; 59; 60; 61].  
**Generative models** are another category of techniques commonly utilized in SSL and have been proven to generate additional realistic samples [62]. In [63; 64], generative adversarial networks (GANs) utilized labeled and unlabeled data with a localization classifier to extract information from unlabeled data, which was otherwise insufficient. One of the most influential among the generative techniques is expectation maximization (EM), which was introduced by [PERSON] et al. [65]. Although EM assumes an underlying generative model, it has been shown to be compatible with CNNs [66], in which EM can be applied to improve the semantic segmentation of natural images.  
**Semi-Supervised Active Learning:** Several recent studies have explored the combination of SSL and AL, resulting in semisupervised active Learning (SSAL). For example, in [67; 68], SSL was employed to learn the latent representation of both labeled and unlabeled samples, with AL performed on the learned semantic distribution. One popular AL method is uncertainty sampling [69], which selects the least certain data point for acquisition of its ground truth label. Additionally, an AL-based approach [70] was used to overcome data scarcity issues by iteratively selecting the most informative unlabeled samples for labeling and include them in the labeled dataset.  
Various techniques have been utilized in prior research to assess the degree of uncertainty in active learning, including Monte Carlo dropout [71], ensemble models [72], and data augmentation for classification tasks [73]. In a recent study, [PERSON] et al. [74] proposed a loss function, a generalized version of binary cross entropy, that accounts for label uncertainty.  
**Reversed active learning (RAL) [75]** is an approach that remove samples found to be uninformative based on computed confidence intervals. While active learning adds ground truth labels to the dataset, the reverse active learning approach removes samples from the dataset, thereby reducing the number of samples in the overall training set.
## 6 Discussion
We find that point-based networks, among all the mainstream methods, look the most promising for PCSS and support this statement by the leaderboard in Table 11 and additional public benchmarks [30, 62]. Pooling-based MLPs, attention-based transform ers, and point-based CNNs perform on par, with small deviations depending on which benchmark is investigated. Yet, multiple sources lately comment on transformers being the most versatile general-purpose architecture for different types of data and computer vision tasks [168, 187, 188]. Even though, many of the new transformer models still incorporate the best parts of convolutions. That means future models are more likely to use both than to abandon CNNs entirely. There are, of course, some drawbacks associated with the topic of deep learning in general and PCSS for the construction industry, which we want to discuss. We find in Section 3.2 that PCSS results are sensitive to geometric similar shapes and models easily get confused in the presence of class imbalance. This problem can be illustrated with the practical example of bridge's part segmentation. Even though most bridges have similar components with the same function, these components look very different across several bridge types. If models can not generalize geometries, the industry faces the risk that the needed number of specialized datasets adds up fast, eventually becoming impossible to cover. Further, the costs associated with deep learning are the high demand for computing power, particularly in the training phase, and also for inference. This is true for all deep learning, but transformers are particularly affected [189, 190]. Training the models requires expensive GPU server clusters, which are not common in conventional civil engineering offices. Cloud services can be a workaround for the training phase, but real-time application on-site will require equipping robots with powerful hardware. Finally, point cloud semantic segmentation is only one first step of machine learning applications in civil engineering. Instance segmentation is becoming increasingly important, as the construction industry continues to integrate digital technologies into the building process and construction surveillance [191, 192, 193]. Instance segmentation can be considered a refined version of semantic segmentation. Where semantic segmentation assigns all segments (points) of the same recognized class into one global group, instance segmentation can differentiate between different objects of the same group. With the ability to automatically identify and label objects in a 3D point cloud, instance segmentation can provide valuable information for various construction-related tasks, including fully autonomous robot operation, as-built reconstruction, automated building information modeling (BIM), progress tracking, and quality control.
## 4 Results and Discussion
### Influence of Outdoor Weather Conditions  
Based on the simulation results in Figures 3-5 and the preliminary research literature [16], the outdoor PM\({}_{2.5}\) concentration significantly influences the concentration of PM\({}_{2.5}\) particles in the stadium. Although the fresh air system somewhat filters the air entering the stadium, the outdoor air easily enters the stadium through various gaps and entrances, thereby affecting the indoor air quality. The outdoor PM\({}_{2.5}\) concentration was uniformly valued between 30 and 150 \(\upmu\)g/m\({}^{3}\) (30, 50, 70, 90, 110, 130, and 150 \(\upmu\)g/m\({}^{3}\)) based on the actual measured situation during simulation to clarify the influence of outdoor PM\({}_{2.5}\) concentration on indoor PM\({}_{2.5}\) concentration. Variations in the indoor PM\({}_{2.5}\) concentration under different values were observed. The values for the fresh air system of the venue were based on the air conditioning system parameters specified by the ASHRAE Handbook of the United States (2019) [47].  
Figure 8 shows the average PM\({}_{2.5}\) concentration in each venue area under different outdoor air conditions. As the outdoor PM\({}_{2.5}\) concentration increased, the indoor PM\({}_{2.5}\) concentration showed a gradual upward trend. For every 20 \(\upmu\)g/m\({}^{3}\) increase in outdoor PM\({}_{2.5}\) concentration, the indoor PM\({}_{2.5}\) concentration increased by approximately 6 \(\upmu\)g/m\({}^{3}\). When the outdoor PM\({}_{2.5}\) concentration reached 90 \(\upmu\)g/m\({}^{3}\), the average indoor PM\({}_{2.5}\) concentration in the main area of the venue began to exceed the specified value. When the outdoor PM\({}_{2.5}\) concentration was 150 \(\upmu\)g/m\({}^{3}\), the indoor PM\({}_{2.5}\) concentration was 5.5 times that of the outdoor PM\({}_{2.5}\) concentration at 30 \(\upmu\)g/m\({}^{3}\).  
Table 4 lists the distribution values of the PM\({}_{2.5}\) concentration in the stadium when the outdoor PM\({}_{2.5}\) concentrations were 30, 70, and 110 \(\upmu\)g/m\({}^{3}\), respectively. The areas with higher PM\({}_{2.5}\) concentrations were concentrated in the competition area, east and west sides of the spectator area, and areas with high concentrations of air supply under the seats. The PM\({}_{2.5}\) concentration was lower in the corridor and evacuation gate areas. In the referee and temporary areas, PM\({}_{2.5}\) was mainly concentrated on the side of the area. Table 4 also presents the overall increases in the outdoor and indoor PM\({}_{2.5}\) concentration. The spatial distribution characteristics remained basically unchanged.  
Figure 7: Comparison of measured data and simulated data at different collection points in different scenes: (**a**) comparison between simulated and measured values in Scene 1 and Scene 2; (**b**) comparison between simulated and measured values in Scene 3 and Scene 4.
## 3 Equipment and Scenario Description
### Multi-RAT Robotic Platform  
Here, the multi-RAT robotic platform used to evaluate our LHO scheme is presented. It can carry up to four kilograms of payload and reach 7 km/h speed. It is currently capable of performing remote operations in indoor environments for approximately two hours without recharging. The installed hardware allows us to detect obstacles located half-a-meter away from the vehicle, which in combination with the data obtained from the on-board camera makes it possible to implement capabilities for autonomous driving.  
Our vehicular platform is equipped with cmWave (WiFi) and mmWave (WiGig) interfaces in order to provide multi-connectivity features. Wi-Fi interface is SL-1506 dongle working on RT5370N chip and implementing the standard 802.11n. The module uses closed-loop power control with an output power range between 2 and 18 dBm conveyed via a single 2 dBi omnidirectional antenna. As we used a Linux-based operating system, the communication rate adaptation was driven by the Minstrel algorithm, which is part of the mac80211 kernel subsystem [45]. In the case of mmWave connection, the vehicular platform is equipped with a Mikrotik wAP 60G client station based on the Qualcomm QCA6320 WiGig module with maximum Tx power of 21.67 dBm. The module is further provided with a 6 \(\times\) 6 phased array antenna with a maximum gain of 13.5 dBi.  
Figure 1: A conceptual figure of the principle of the location-aware handover (LHO) scheme in an industrial multi-radio environment.  
At the core of the platform is an UDOO x86 single-board computer. In particular, the two motors of the vehicular platform are controlled via a RoboClaw motor controller connected to the UDOO board. Further, the RoboClaw has a power connection to external battery packs and a voltage regulator. A photo detailing the components of the platform is shown in Figure 2 and the technical details and features of the platform are summarized in Table 2. The platform can also operate in manual mode, where the vehicle is controlled by the operator remotely. Hence, the operator is monitoring the video stream provided by the camera installed on the front of the vehicle.  
The platform allows us to collect various types of statistics. First, we monitor the communication performance by analyzing signal strength, end-to-end packet latency, and throughput data. For the installed WiGig transceiver, we also collect antenna array configuration statistics provided by the application programming interface (API) of the device. On top of that, UDOO board has an integrated six-axis sensor, which allows us to monitor the heading of the vehicle. Further, the motors of the driving wheels have magnetic encoders, which make it possible to estimate the location of the vehicle in relation to the starting position.
###### Abstract  
The present work envisages the development of a novel and low-cost self-mixing interferometry (SMI) technology-based single particle sensing system in a microchannel chip for real time single micro-scale particle sizing. We proposed a novel theoretical framework to describe the impulse SMI signal expression in the time domain induced by a flowing particle. Using Hilbert transform, the interferometric fringe number of the impulse SMI signal was retrieved precisely for particle size discrimination. For the ease of particle sensing, a hydrodynamic focusing microfluidic channel was employed by varying the flow rate ratio between the sample stream and the sheath liquid, and the particle stream of a controllable width was formed very easily. The experimental results presented good agreement with the theoretical values, providing a 300 nm resolution for the particle sizing measurement.  
Keywords:self-mixing interferometry; microfluidics; hydrodynamic focusing; particle sizing; sensing mechanisms; Hilbert transform +
Footnote †: journal: Applied
## 4 Discussion
### Numerical Analysis
#### 4.3.3 Numerical Results and Analysis  
Figure 15 presents the stress distribution of S11 (horizontal, \(sigma_{xx}\)) and S22 (vertical, \(sigma_{yy}\)) from the simulation in the Brazilian disc. The distribution of the horizontal tensile stress (Figure 15a,b) revealed that the maximum value was acquired at the centre of the discs and gradually decreased from the centre to two loading points. The maximum of teh vertical compressive stress was found at the loading point as shown in Figure 15c.d. With the same loading condition, the tensile stress in the BTD was larger than that in the BTC at the centre of the discs as shown in Figure 15a,b.  
Figure 14: Two plane-stress models, BTC (**a**) and BTD (**b**), in FEM.  
Figure 13: The stress–strain curve of concrete used in the brittle cracking model.  
Figures 16 and 17 show the horizontal \(sigma_{xx}\) and vertical \(sigma_{yy}\) normal stress distribution from the analytical, numerical solution, and experimental results in the BTC and BTD. Though both analytical and numerical solutions closely matched at the centre of the discs, there was an evident difference observed near the boundary, which was likely caused by the stress singularity occurring at the loading points. In this study, the experimental stress distribution is also described in Figures 16 and 17 by calculating the strain from the DIC results as follows:  
\[\left\{\begin{array}{l}\sigma_{xx}=E\varepsilon_{xx}\\ \sigma_{yy}=E\varepsilon_{yy}\end{array}\right. \tag{6}\]  
In this equation, E represents the elastic modulus of concrete. Here, E was assumed to be the same for the tensile and the compression load, that is, E = 30 GPa.  
As shown in Figure 16, the experimental normal compressive stress \(sigma_{yy}\) was quite close to the analytical and numerical solutions, while there was a larger tensile stress \(sigma_{xx}\) in the experiment than in the analytical and numerical solutions in Figure 17. The difference of \(sigma_{xx}\) could be expected because of the discrepancy in the E displayed in the tensile and compressive states. [PERSON] [8] reported an asymmetric behaviour of concrete in the [PERSON]'s modulus during compressive and tensile stages. In this article, the [PERSON]'s modulus was about 10.2 GPa and 27.2 GPa during tensile and compressive stages, respectively, which was obviously different. Actually, a similar asymmetric behaviour of E during these two stages was also investigated and reported by [PERSON] and [PERSON] [47].  
Figure 15: The Horizontal stress (S11) and vertical stress (S22) stress distribution in simulations, here, (**a**,**c**) from results of BTC tests, (**b**,**d**) from BTD tests.
## 3 Typical Weight Models
### SNR-Elevation Model  
The weight models described in Sections 3.1-3.3 are given as functions of only the SNR or elevation angle. However, in the SNR-elevation model, these two factors were considered simultaneously. This approach was first suggested by [7], and its typical formula is given by Equation (4). [PERSON] and [PERSON] [7] recommended choosing the value of parameter \(k\) differently depending on the signal LOS analysis results. They used \(k=1\) when the signal was received directly from the satellite and assigned values other than \(1\) to the multipath-impacted signal. Thus, a larger variance must be allocated to the multipath signals.  
\[\sigma_{i}^{2}=k\frac{10^{-0.1\cdot SNR_{i}}}{\sin^{2}(\varepsilon_{i})} \tag{4}\]
# DynamiChain: Development of Medical Blockchain Ecosystem Based on Dynamic Consent System
## 4 Implementation
### Dynamic Consent System  
Dynamic consent algorithm is implemented in the Chaincode1, and blocks are generated throughout the following process (Figure 5). In this process, the participant called Data Provider1 in Org1 tries to send their data to the hospital in Org3 according to the dynamic consent system through the Dapp1. First, Data Provider1 requests transaction generation to send the data to the hospital via Dapp1 (Figure 5a). After receiving the transaction generation request, Dapp1 generates a transaction that contains the message of sending data and sends the transaction to the endorsing peer, Peer3. Peer3 can be connected to Dapp1 after passing the certification process using the certificate of Data Provider1. After connection, Dapp1 calls up the update function of the chaincode installed in Peer3 and requests the chaincode execution (Figure 5b). Upon receipt of the transaction from Dapp1, Peer3 simulates the chaincode by referring to the World State DB and checks the results of read/write set to determine whether it is endorsed or not (Figure 5c). If the transaction execution result is valid and the endorsement of Peer3 is passed, Peer3 sends the results and the digital certificate of Peer3 to Dapp1 (Figure 5d). Receiving the simulation results from Peer3, Dapp1 checks to see if the value of the read/write set is the same as its expected value and checks whether the digital certificate of Peer3 has been received. After verification, Dapp1 broadcasts the transaction containing the digital certificate of Peer3 and the results of read/write set to the Orderer1 node to create the block (Figure 5e). The Orderer1 identifies the time stamp fields, which is required to order the transactions, and then orders the transactions to be included in the block to create the latest blocks (Figure 5f). After that, Orderer1 delivers the created blocks to all the peers on the network (Figure 5g). Every peer who receives the latest block performs verification by running a Validation System ChainCode (VSCC) system chain code to verify the results and certificates of all transactions contained in the block. If the validation process is passed, all the peers update the distributed ledger stored in their local repository (Figure 5h). Peers complete the distributed ledger update process by sending the results of the updated distributed ledger to Dapp1 (Figure 5i).  
Figure 5: Blockchain generation flowchart of DynamiChain. (**a**) Request transaction creation. (**b**) Connect endorsing peer (peer3), and request chaincode execution. (**c**) Execute chaincode (**d**) Return endorsed transactions and certificate of endorsing peer. (**e**) Forward endorsed transactions and certificate of endorsing peer. (**f**) Order transactions and create a block, (**g**) send a new block, (**h**) commit transaction and update distributed ledger, and (**i**) send the result of the updated distributed ledger.