esa-sceva/satcom-chunk-collection huggingface dataset:
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 2 Materials and Methods  
As stated above, the concept and framework of different numerical models for modeling microplastics' fate and transport are reviewed in this study in detail, considering  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{tabular}{c} **Particle-** \\ **Tracking** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Hydrodynamic** \\ **Model** \\ \end{tabular} & \begin{tabular}{c} **Dimensionality** \\ \end{tabular} & \
\begin{tabular}{c} **Method** \\ **Trasport** \\ \end{tabular} &
\begin{tabular}{c} **Ref.** \\ **Transport** \\ \end{tabular} \\ \hline D-WAQ PART & Delft3D-FLOW & 2D/3D & Lagrangian & Transport & [75; 77] \\ Ichthyop & ROMS & 2D/3D & Lagrangian & Transport & [76; 97] \\ TrackMPD & GCGM 1  & 3D & Lagrangian & Both & [61] \\ CaMPSim-3D & TELEMAC & 3D & Lagrangian & Both & [98] \\ \hline \hline \end{tabular}
\end{table}
Table 1: The most popular numerical 2D and 3D models used for simulating microplastics’ fate and transport.  
various ranges of microplastics' behavior characteristics and key physical processes affecting their variations. Lagrangian particle-tracking models are investigated in terms of considering physical properties (and hydrodynamic aspects), physical transformational processes, physical transport processes, and numerical schemes (Figure 1). In other words, the reviewed models have been compared based on capability in considering the physical properties of microplastics (mainly size, shape, and density), and in simulating the physical transformational and transport processes that microplastics undergo. Moreover, the numerical methods that have been employed by each of them in terms of considering the type of mesh they can read, their computational schemes, and the methods they employ for implementing interpolation have been addressed. Finally, limitations and gaps in the reviewed models and needed areas of improvement are determined and discussed in full.
## 7 Conclusions  
This article has explored WSN platforms and energy harvesting techniques in the context of the SHM process for bridges using an SLR process. As a result, 46 research articles have been selected. The selected articles are classified into three main categories. These categories are WSN platforms, energy harvesting techniques, and a combination of both. Consequently, various WSN platforms have been explored in terms of certain performance parameters. Furthermore, energy harvesting techniques are classified into vibration energy (PZT as well as electromagnetic), solar energy, wind energy, RF energy, and thermal energy. Moreover, the selected studies have been analyzed in terms of some additional design considerations such as the inspection scale (local as well as global) and the response type (static as well as dynamic).  
According to the obtained results in this SLR, it can be claimed that vibration-based and solar energy are the most frequently used harvesting techniques for the SHM of bridges. Similarly, accelerometers, strain gauges, and temperature sensors are commonly deployed sensors. In addition, the global inspection scale techniques and the dynamic response analysis method are commonly practiced. Furthermore, it can be argued that the accuracy of WSN platforms in measuring different quantities is comparable with that of the wired systems. Even though there are some limitations (such as the search process, selection/rejection criterion, and the selected databases), it facilitates the selection of appropriate WSN platforms and energy harvesting techniques according to the SHM system requirements.  
The energy harvesting techniques considered in this SLR present an alternative power supply for WSN nodes. Nevertheless, they are not sufficient to power the WSN nodes. Therefore, further investigations of novel combined energy harvesting techniques should be considered in future research studies to overcome these limitations. Furthermore, most of the recent studies in the field focus only on the investigations of global damage detection scale methods. Consequently, future research studies should further investigate the implementation of local scale damage detection techniques to localize and assess the impact of damage on bridge structures. This may increase the efficiency of SHM systems in damage detection and increase the lifetime of the bridges.  
Conceptualization, O.S.S. and M.R.; investigation, O.S.S.; writing original draft preparation, O.S.S. and M.R.; writing review and editing, M.R.; supervision, M.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Deanship for Research and Innovation, Ministry of Education in Saudi Arabia under the project number: IFP22 UQU4250001 DSR193.  
Data Availability Statement: Not applicable.  
The authors extend their appreciation to the Deanship for Research and Innovation, Ministry of Education in Saudi Arabia for funding this research work under the project number: IFP22 UQU4250001 DSR193.  
The authors declare that they have no conflict of interest.
# Rapid Green Extractions of C-Phycocyanin from _Arthrospira maxima_ for Functional Applications
###### Abstract  
Cyanobacteria are a rich source of bioactive compounds, mainly in the _Arthospira_ sp., and one of the most interesting components in recent years has been C-phycocyanin (C-PC). There have been several conventional methods for their extraction, among which stand out: chemical products, freezing-thawing (FT); enzymatic, and maceration (M); which have come to be replaced by more environmentally friendly methods, such as those assisted by microwaves (MW) and high-pressure homogenization (HPH). The aim of the research was to use these two \"green extraction processes\" to obtain C-PC from cyanobacteria _Arthrospira maxima_ because they improve functionality and are fast. Extractions of C-PC were studied by means of two experimental designs for MW and HPH, based on a response surface methodology (RSM) employing, firstly, a factorial design [3]: power (100, 200, and 300 W), time (15, 30, and 60 s), and types of solvents (distiller water, Na-phosphate buffer and, distiller water: Na-phosphate buffer (Ph 7.0; 1:1, _v/v_); and secondly, two factors with different levels: Pressure (800, 1000, 1200, 1400, and 1600 bar) and, types of solvents (distilled water, Na-phosphate buffer (pH 7.0) 100 mM and, Na-phosphate buffer:water 1:1, (_v/v_)). Optimum C-PC content was achieved with the HPH process under Na-phosphate solvent at 1400 bar (\(291.9\pm 6.7\) mg/g) and the MW method showed improved results using distilled water as a solvent at 100 W for 30 s (\(215.0\pm 5.5\) mg/g). In the case of conventional methods, the freeze-thawing procedure reached better results than maceration using the buffer (\(225.6\pm 2.6\) mg/g). This last one also did not show a significant difference between solvents (a range of 147.7-162.0 mg/g). Finally, the main advantage of using green extractions are the high C-PC yield achieved, effectively reducing both processing times, costs, and increasing the economic and functional applications of the bioactive compound.  
cyanobacteria; phycobiliproteins; microwave; high-pressure homogenization; functional ingredient
# Secure and Privacy-Aware Blockchain Design: Requirements, Challenges and Solutions
## 4 Attacks and Countermeasures
### Blockchain Attacks and Countermeasures
[112] proposed to use the Trust zone mechanism present on ARM processors that creates a secure execution environment (SEE) to guarantee a safe and reliable environment for the execution of sensitive processes. This is realized with non-maskable interrupts (NMI) and guarantees complete isolation from the operating system, using a secure memory zone and storage to protect user's private key, wallet's address, and transaction verification process. * **Blockchain Poisoning:** It is an attempt to store illegal files (e.g., malware, malicious content, and privacy information, etc.) in the free space for smart contracts of blockchain. A malicious user can force blockchain nodes to download malicious files which leads to other attacks in the blockchain such as DoS (see Section 4.1). Another kind of poisoning consists of spamming many transactions and indexing the outputs so when those outputs are used as decoys on other transactions, one can consider them decoys and increase the odds of tracing the real outputs, leading to the statistical modeling attack [113]. * **Countermeasures:** blockchain poisoning attack can be prevented by introducing a minimal fee determined by its resemblance with existing contracts in which the minimal fee reduces if the number of similar contracts increases. The idea is to calculate the fee of all contracts which are used on blockchain and it becomes higher if there is no similar contract. In this approach, the cost to send a transaction relies on the number of similar contracts. Because the number of malicious smart contracts related to falsified transactions is small in the blockchain, then, a malicious user would need to pay a higher fee, which reduces the likelihood of such an attack to occur [113]. In summary, due to its public nature, blockchain is vulnerable to several attacks. In order to tackle these attacks and improve blockchain security and performance, we have discussed possible countermeasures. Among these, smart contract attack is a growing concern due to bugs in code, which can lead to loss of cryptocurrency and privacy leakage. Writing secure smart contracts can be difficult due to various rules, as well as platform vulnerabilities and limitations. Research towards securing the execution of smart contracts on blockchain-based systems is still an open question.
# Towards Efficient Sink Mobility in Underwater Wireless Sensor Networks
## 4 Proposed Scheme
### Data Transmission  
After network configuration, data transmission phase begins. In the proposed scheme, the sensor nodes in sparse region send data to MS if it is in the transmission range of sensor nodes. In case the sensor nodes in a sparse region do not find MS in its transmission range, then sensor nodes send data to the adjacent region nodes that have MS in their transmission range. In the dense regions, sensor nodes send the sensed data to their respective CH. CH forwards the received data to either of the sink in its transmission range. If CH does not find any of the sink in its transmission range, then the node in the adjacent region who has sink in its range is traced and data is forwarded to it. The complete working of proposed scheme is presented in Figure 2.  
Figure 2: Flow chart of the proposed scheme.
# Textural Analysis Supports Prostate MR Diagnosis in PIRADS Protocol
## 2 Materials and Methods
### Prostate MRI Dataset  
The MRI scans of 125 patients (24-87 years old) were acquired during standard diagnostic procedures, which met the PIRADS protocol. However, for this research, only 92 patients (24-85 years old) were chosen free of artefacts. These patients did not have hip prostheses or artefacts from previous pelvic surgeries and were properly prepared for the examination (e.g., with excessive bowel content). The mean age of the patients was 60.3 \(\pm\) 12.29.  
For each patient, two data sequences were recorded: TW2-MRI axial sequences with 2 mm slices and a distance factor equal to 0, and DWI with the use of a single shot echo planar sequencer (EPI) mode with a value b equal to 0-800-1500 and a distance factor of 0. Additionally, T1-weighted sequences were prepared for post-contrast evaluation. Table 1 summarises the parameters used for the preparation of the TW2-MRI sequence. The 1.5 T scanner was used.  
The data were anonymised, and procedures applied during data preparation complied with the Declaration of Helsinki and the Declaration of Good Clinical Practice [42]. The Local Ethics Committee supported written consent to conduct this study (No. 1072.6120.21.22, 23 February 2022). The next step was to involve the 7 radiologists with proven experience in prostate imaging in the process of PCa evaluation during the standard diagnostic process using the PIRADS protocol. The data obtained were collected as a reference for the proposed PIRADS staging. The distribution of the PIRADS values in the cohort was as follows: 6% for PIRADS = 1, 30% for PIRADS = 2, 3, 20% for PIRADS = 4, and 15% for PIRADS = 5.  
Images from MRI scans were converted linearly from 12-bit into 8-bit data, where signals reflected pixel illumination values in the range of 0 to 255. Since each prostate MRI scan had around 10 projections, the total number of images used in the research consisted of 751 MR images of prostate gland lesions. Figure 1 presents the variation of the projection number on an MRI scan. Figure 2 shows a prostate MR image with a manual annotation depicting the peripheral and central prostate zone.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Time echo & 105 \\ Relaxation time & 3320 \\ Flip angle & 160 \\ Imaging matrix & 256 \(\times\) 320 \\ Voxel & 0.6 \(\times\) 0.6 \(\times\) 2 \\ Field of view & 200 \\ Concentrations & 2 \\ Averages & 4 \\ Parallel acquisition & 2 \\ Distance factor & 0 \\ Overall sequence time & 300 \\ \hline \end{tabular}
\end{table}
Table 1: Values of parameters following the PIRADS guidance to perform MRI scans.  
Figure 1: The histogram presents the number of patients having the same number of images in MRI scans. The number of analysed images per patient differs due to gland size and zone margins.
# A Real-Time GNSS/PDR Navigation System for Mobile Devices
## Abbreviations  
The following abbreviations are used in this manuscript:  
AHRS - Attitude and Heading Reference System  
DOF - Degrees Of Freedom  
DR - Dead Reckoning  
DFT - Discrete Fourier Transformation  
GNSS - Global Navigation Satellite System  
IMU - Inertial Measurement Unit  
INS - Inertial Navigation System  
KF - Kalman Filter  
MARG - Magnetic, Angular Rate, Gravity  
NED - North East Down  
PLL - Phase-Locked Loop  
PDR - Pedestrian Dead Reckoning  
PNS - Pedestrian Navigation System
# A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
## 3 System and Network Model
### Network Infrastructure  
As the defined architecture must be able to support mobile nodes and a large coverage area, we assume a WSN-based communication infrastructure with a cluster-tree topology for the Inter-BAN layer. These assumptions require a specific look to, at least, the following technical challenges: the possibility of the collision of beacons; the integration of mobile nodes in a pre-established network (association and re-association); the guarantee of connectivity without packet losses (handover, buffers); and the guarantee of time requirements through the efficient allocation of communication resources (superframe duration).
## 5 Experiments and Results
### Experiment III: Multilingual Shared Features Ranking and Analysis
It can be found that emotion-related features of languages differ from one another, and Spectral Slope ranks high in all three languages, which can be easily found in MSFs named _slopv0-500_sma3 nz_amean_ and ranked first. In particular, the first features in each monolingual feature named _HNRdBACF_sma3 nz_amean_, _spectralFlux_sma3_sddevNorm_, and _FosemitotherFrom27.5 Hz_sma3 nz_pcltrange0-2_ are ranked the fourth, the fifth, and the second, respectively. In other words, except for the third feature, the top five features in MSFs cover both the common features of the three languages and the first feature of each language. It is worth mentioning that the third feature concerns the Spectral Slope, which also coincides with the fact that Spectral Slope plays a vital role in emotional expression in SER [74]. Besides, we find it interesting that the HSFs related to the F0 Pitch account for almost half of the top 10 features of SFs, which shows that the F0 Pitch is an essential LLD in SAVEE. However, this vital LLD only appears in the top 10 features of EFs, while not in the top 10 features of CFs. This is probably because SAVEE (English) and EMO-DB (German) belong to the same Germanic language family, so there is a certain similarity in the acoustic features when expressing emotions. To provide a macro view of the similarities and differences in the top 30 monolingual and multilingual feature rankings, the features are divided into four feature groups: Equivalent Soundlevel, Temporal Features, Frequency, Energy, and Spectral (Figure 8). It is easy to see that Spectral accounts for the most significant portion among all features, probably because it has a large amount in eGeMpas features and contains strong emotional information, which is widely used in the end-to-end model. Apart from the Spectral,  
Figure 7: The feature ranking and comparison procedure based on the attention mechanism. there are many distinguishing features in the different languages. As for EMO-DB, the Temporal Features and Equivalent Soundlevel are essential to recognize the emotion, while the energy-related features are more critical in CASIA, and frequency-related features are more important in SAVEE. The MSF balances the portion of each feature group, which may convey that it can maximize the learning of features shared by all languages and discard some language-sensitive features to achieve generalization.
## 3 Mathematical Model
### Center Plane Offset
#### 3.2.2 T/T Rotating Shaft Internal Diameter  
The three check points at the T/T rotating shaft can be used to calculate the internal diameter, as shown in Figure 6.  
Figure 4: Center plane offset.  
Figure 5: Check points of H/T rotating shaft length.  
The coordinates of the three check points are expressed as:  
\[d1(d1_{x},d1_{y},d1_{z}) d2(d2_{x},d2_{y},d2_{z}) d3(d3_{x},d3_{y},d3_{z})\]  
The vectors \(\overrightarrow{d_{3}d_{1}}\) and \(\overrightarrow{d_{3}d_{2}}\) are:  
\[\overrightarrow{d_{3}d_{1}}=\left((d1_{x}-d3_{x}),\left(d1_{y}-d3_{y}\right),(d 1_{z}-d3_{z})\right) \tag{4}\] \[\overrightarrow{d_{3}d_{2}}=\left((d2_{x}-d3_{x}),\left(d2_{y}-d3_ {y}\right),(d2_{z}-d3_{z})\right) \tag{5}\]  
The use cross product for the vectors \(\overrightarrow{d_{3}d_{1}}\) and \(\overrightarrow{d_{3}d_{2}}\), and their normal vector \(\overrightarrow{n}\) can be obtained as:  
\[\overrightarrow{n}=\overrightarrow{d_{3}d_{2}}\times\overrightarrow{d_{3}d_{ 1}}=\left(n_{x},n_{y},n_{z}\right) \tag{6}\]  
where  
\[n_{x}=\left|\begin{array}{cc}\left(d2_{y}-d3_{y}\right)&(d2_{z}-d3_{z})\\ (d1_{z}-d3_{z})&\left(d1_{y}-d3_{y}\right)\end{array}\right|\]  
\[n_{y}=-\left|\begin{array}{cc}\left(d2_{x}-d3_{x}\right)&(d2_{z}-d3_{z})\\ (d1_{x}-d3_{x})&(d1_{z}-d3_{z})\end{array}\right|\]  
\[n_{z}=\left|\begin{array}{cc}\left(d2_{x}-d3_{x}\right)&\left(d2_{y}-d3_{y} \right)\\ (d1_{x}-d3_{x})&\left(d1_{y}-d3_{y}\right)\end{array}\right|\]  
The spherical general shown in Equation (7) is used to find the axis point coordinate and axial bore radius in Figure 6:  
\[x^{2}+y^{2}+z^{2}+dx+ey+fz+g=0 \tag{7}\]  
where the spherical axis point coordinate (\(d4\)) in Figure 6 is \(\left(\dfrac{-d}{2},\dfrac{-e}{2},\dfrac{-f}{2}\right)\), and \(d\), \(e\), \(f\), and \(g\) are real numbers.  
The vector \(\overrightarrow{d_{4}d_{3}}\) can be obtained as:  
\[\overrightarrow{d_{4}d_{3}}=\left((d3_{x})-\left(\dfrac{-d}{2}\right),\left( d3_{y}\right)-\left(\dfrac{-e}{2}\right),\left(d3_{z}\right)-\left(\dfrac{-f}{2} \right)\right) \tag{8}\]  
The vectors \(\overrightarrow{d_{4}d_{3}}\) and \(\overrightarrow{n}\) are perpendicular to each other so that their inner product is zero.  
\[\overrightarrow{d_{4}d_{3}}\cdot\overrightarrow{n}=0 \tag{9}\]  
Figure 6: The three check points at the T/T rotating shaft.  
Following this, we substitute the coordinates of three check points \(d1(d1_{x},d1_{y},d1_{z})\), \(d2(d2_{x},d2_{y},d2_{z})\), \(d3(d3_{x},d3_{y},d3_{z})\) into Equation (7) to form Equations (10)-(12). Additionally, Equation (13) is obtained based on Equation (9).  
\[(d1_{x})^{2}+\big{(}d1_{y}\big{)}^{2}+(d1_{z})^{2}+d(d1_{x})+e\big{(}d1_{y} \big{)}+f(d1_{z})+g=0 \tag{10}\]  
\[(d2_{x})^{2}+\big{(}d2_{y}\big{)}^{2}+(d2_{z})^{2
## 4 Results and Discussion
### Comparison of Product Part Forming under Different Moisture Content  
Figure 12--B1 shows the PC material molded sample image that has passed the material drying time of 4 h, as recommended by the material supplier, and the measured moisture content is 100 ppm which belongs to the B1 group. As expected, the product was transparent, and no defects were found in the sample under these parameters. The measured gloss of the bottom molded product was 98.4 GU. Similarly, Figure 12--B2 represents the molded sample image for the parameter group B2, in which the material passed through the 2 h material drying time, and the measured moisture content was 600 ppm. The image of the molded samples shows that the material had obvious silver mark defects and the measured gloss value of the product part sample was 68.43 GU.  
Figure 11: Measurement data of product: (**a**) Surface gloss under different barrel temperature (measurement angle 60\({}^{\circ}\), 85\({}^{\circ}\)); (**b**) Standard deviation of product molding weight under different barrel temperature.  
Figure 10: Product part appearance under different barrel temperatures.  
Figure 12--B3 represents the molded samples produced through the optimization of plasticization parameters (i.e., barrel temperature, back pressure, screw speed). When the product was at the barrel temperature of 280 \({}^{\circ}\)C, the back pressure was 15 MPa, and the screw speed was 50 rpm under the plasticization parameter B3 group, the measured gloss value was 98.1 GU. This experiment reveals that through reasonable plasticization parameter correction, it is possible to reduce the moisture defect and, comparing the gloss of the product samples (Figure 12), shows that the optimized plasticization parameter (B3 group) can reduce drying time to obtain a product surface quality that is the same as the fully dried material (B1 group) and conforms to the standard. The standard deviation of the gloss data in the B3 group was 0.6, which meets the gloss requirements of qualified products. The proposed value can be used as a basis for product quality judgment, and the surface quality of the molded samples can be controlled through parameter correction. Furthermore, this method may help molders to reduce drying time, improve production efficiency and achieve cost savings.