esa-sceva/satcom-chunk-collection huggingface dataset:
# Challenges and Potential in Increasing Lutein Content in Microalgae
## 4 Strategies to Overcome These Barriers
### Future Perspectives  
The development of a cell factory for lutein production in microalgae demands further exploration on the regulation of lutein biosynthesis, degradation and sequestration in microalgal species. The regulation of lutein biosynthesis has garnered considerable attention in recent years. However, the sequestration of lutein plays a critical role in the accumulation of lutein. Future research on this topic is warranted.  
Several critical barriers block lutein sequestration in lipid droplets in microalgal species. The exploration of lipid droplet biogenesis in the chloroplast, carotenoid cross membrane mechanisms, and carotenoid esterification mechanisms rely on an optimized genetic engineering toolbox for different microalgal species. Unfortunately, the toolbox is still in the formative stage for most microalgae.  
Investigating similar mechanisms by using a model yeast strain such as _Saccharomyces cerevisiae_ and _Yarrowia lipolytica_, with their relatively simple cell structure, and with a more mature genetic engineering toolbox, could help understand the carotenoid sequestration mechanisms.  
The nonconventional oleaginous yeast _Y. lipolytica_ has been engineered as a cell factory for production of different carotenoids due to several unique features [104; 105; 106]. On one hand, _Y. lipolytica_ can accumulate a massive amount of acetyl-CoA as a precursor of the mevalonate pathway, which provides sufficient geranyl diphosphate (GPP), far-nesyl diphosphate (FPP) and geranylgeranyl diphosphate (GGPP) as the intermediates for the accumulation of carotenoids [107; 108]. On the other hand, large amounts of oil are accumulated in the lipid droplets of _Y. lipolytica_ that provide a desired lipophilic container for carotenoid storage [104; 107]. Although lutein biosynthesis has not been achieved in _Y. lipolytica_, due to the similarity of chemical structure its isomer zeaxanthin could be used to investigate carotenoid esterification mechanisms and other mechanisms of carotenoid sequestration.
# Deducing Energy Consumer Behavior from Smart Meter Data +
## 4 Proposed Framework
### Data Quality Service
#### 4.1.1 Sample availability  
It is defined as the amount of samples observed in a time slot over the expected amount of sample. To calculate the amount of samples expected to be in a specific period of time \(N_{max}^{(m,T_{n})}\), where \(T_{n}\) is a specific period of time for a specific meter \(m\), the sample phase \(\phi_{start}^{(m,T_{n})}\) for the given period needs to be known. Figure 3 shows an illustration of a signal, the black dots are the samples and the big red dots are the ones that are missing. The sample phase is the time from the beginning of the period \(T_{n}\) to the first expected sample. This is needed since for a timeslot \(T_{n}\) of a length of \(T_{P}\) can the maximum expected sample amount vary with 1. This is shown in Figure 3 where the period \(T_{n}\) has a potential of 11 samples, whereas period \(T_{n+1}\) only can have 10.  
Figure 3: Illustration of availability analysis.  
Figure 2: Framework structure.  
\[N_{max}^{(m,T_{n})}=\lfloor\frac{T_{P}-\phi_{start}^{(m,T_{n})}}{T_{s}^{(m)}} \rfloor+1 \tag{1}\]  
\[q^{(m,T_{n})}=\frac{N_{observed}^{(m,T_{n})}}{N_{max}^{(m,T_{n})}} \tag{2}\]  
As shown in Equation (1), the maximum number of samples for a meter \(m\) in the period \(T_{n}\) calculated by taking the period time \(T_{P}\), corrected with the sample phase \(\phi_{start}^{(m,T_{n})}\) for the given period, and dividing it with the sample time \(T_{s}\). The sample availability quality of the meter is calculated as the ratio of observed samples in the timeslot \(T_{n}\) to the maximum samples, shown in Equation (2).  
To inspect the quality of the collected data in a given period \(T_{n}\), that have a set of meters \(\mathcal{M}\) with a cardinality of \(M\), we took the mean value of all the meter quality's, as shown in Equation (3).  
\[\mu_{q(\textbf{M},T_{n})}=\frac{1}{M}\sum_{m=1}^{m=\textbf{M}}q^{(m,T_{n})} \tag{3}\]  
A quality vector \(Q\) is constructed for each house. The quality vector contains the house quality found with a period \(T_{P}\) in one hour.  
\[Q^{(\textbf{M},\textbf{T})}=\{\mu_{q^{(M,T)}}|T\in\{T_{1},\ T_{2},\...,\ T_{n},\...,\ T_{N}\}\} \tag{4}\]
# Inverse Optimal Design for Position Control of a Quadrotor
## 3 Design of Inverse Optimal Position Controller  
In this section, we design an inverse optimal position controller to converge the position states of a quadrotor to the reference values.  
For the position control, inverse optimal based altitude controller is designed. Next, the reference roll and pitch angles are calculated through altitude control based position control. Finally, the position control of the quadrotor is performed through attitude tracking control. The overall control scheme is expressed by the block diagram as Figure 2.  
In (5), the \((x,\ y)\) positions dynamics of a quadrotor can be represented, using the states \(P=[X\,Y]^{T}\) and \(V=\dot{P}\), as follows:  
\[\left\{\begin{array}{l}\dot{P}=V\\ \dot{V}=u_{1}\left[\begin{array}{l}u_{x}\\ u_{y}\end{array}\right]/m\end{array}\right. \tag{7}\]  
where \(u_{x}=cos\phi sin\theta cos\psi+sin\phi sin\psi\), and \(u_{y}=cos\phi sin\theta sin\psi-sin\phi cos\psi\).  
Using the redefined control input \(U\) and the input-coupling transformation matrix \(T^{*}\), which is invertible, (7) can be rewritten as follows:  
\[\left\{\begin{array}{l}\dot{P}=V\\ \dot{V}=T^{*}U\end{array}\right. \tag{8}\]  
Figure 2: The overall control scheme for the quadrotor position control.  
We analyze the position control problems of a quadrotor from two perspectives. The first one is the exponential stability, while the other one is the inverse optimality.  
The position of a quadrotor converges to the reference position. In other words, the position state \(P\) of a quadrotor satisfies the following equation related to the position control of a quadrotor:  
\[\|P(t)-P_{d}\|\leq\alpha e^{-\beta t}\|P_{0}-P_{d}\|, \tag{9}\]  
where \(P_{0}\) and \(P_{d}\) are the initial and reference positions, respectively.  
In this paper, we also show the inverse optimality of the proposed control input. The control input \(U\), which makes the position error zero, minimizes the following objective functional:  
\[J(\delta(0),U):=\int_{0}^{\infty}(\delta^{T}\Theta\delta+U^{T} \Gamma U)dt, \tag{10}\]  
for a positive definite matrix \(\Theta\) and \(\Gamma\). Here, the position error is defined as \(\delta:=P-P_{d}\).  
Using the position dynamics of a quadrotor, as expressed in (5), we can find the altitude control input \(u_{1}\) of a quadrotor, which is presented as the dynamics of its \((x,\ y)\) position. In this paper, using the characteristics of the position dynamics, as explained above, we design the \((x,\ y)\) position control input using the altitude control input \(u_{1}\).  
In this paper, to design an inverse optimal position controller, we use the following assumption:  
**Assumption 1**: _In the control system, the position and attitude values of the quadrotor can be perfectly estimated._
## 4 SRB Activities in Alkaline Environment
### Response of SRB to Alkaline Environment
#### 4.1.4 Other Changes in Bacteria and Environment  
Besides, the motility of the _D. vulgaris_ cell was shown to be restrained as the expression of flagellar genes was decreased. This indicated that under alkaline stress, bacteria tend to be involved in response of the cell to environmental pH rather than motility. This response of _D. vulgaris_ was similar to _E. coli_[42].  
Furthermore, several researchers reported the change in initial elevated environmental pH to around neutral pH during the survival and growth of neutrophil bacteria. The decrease in environmental pH to around neutral can be beneficial for bacteria to growth. An anaerobic bacteria _Lactobacillus plantarum_ produces lactic acid and, hence, lowers the environmental pH to a preferable pH condition for their growth [89]. _Vibrio tapetis_ which cause brown ring disease was reported to decrease the environmental pH to around 7.5 for their metabolism [90]. The decrease in environmental pH was also observed in SRB. _D. vulgaris_ was reported to change from alkaline pH to around 7.4 after 28 days of immersion [18]. The possible explanation is that _D. vulgaris_ can modulate environmental pH by pH homeostasis--neutralizing activities during their survival and growth. Additionally, the metabolic products of sulphate reduction H2S is more dissolved in the alkaline environment which can release proton to environment and contribute to the decrease in environmental pH [18]. In fact, the growth of _D. vulgaris_ was reported to be arrested at a pH of 10 [18,73], however they resume growth after few days when environmental pH decreases [18]. The drop of environmental pH can be partly caused by SRB metabolic activities in high pH environment which were mentioned in Sections 4.1.1-4.1.3. Additionally, the reaction of bacterial metabolites/cation (such as cation released from corroded materials) with hydroxide ion OH\({}^{-}\) from the environment, which caused a reduction in the concentration of hydroxide ions [18]. Finally, the metabolic activities of other bacteria shared the same environment can cause the fall in environmental pH. The resumption of bacterial growth suggests that there is a phase lag of SRB for a few days when they develop the strategy to combat high pH environment.  
Overall, the response of neutrophil SRB to alkaline environment showed different SRB species might have different specific strategy to alkaline stress. For example, gene expression of _DVU3110_ related to oxidoreductase was not found in _Desulfovibrio desulfuricans G20_. More studies in different SRB species response to alkaline stress need to be conducted to improve the evolution of SRB in alkaline environment.
# In-Band Full-Duplex Relaying for SWIPT-Enabled Cognitive Radio Networks
## 2 System Model and Problem Formulation
### Information and Power Transfer Model
Furthermore, PT can transmit data directly to PR during the first phase, and thus, the received signal at PR via the direct link during the time \(\alpha T\) is given as:  
\[y_{\mathbf{p}}^{dir}[k]=\sqrt{P_{\mathbf{p}}f_{pp}x_{\mathbf{p}}[k]}+n_{ \mathbf{p}}^{\prime}[k]. \tag{6}\]  
It is assumed that the processing delay is negligibly small compared to the transmission time. Moreover, the time slot \(k\) is an arbitrary slot at which PT sends the signals to PR. For simplicity, we omit \(\tau\) and \(k\), hereafter.
# Helmholtz Coils Based WPT Coupling Analysis of Temporal Interference Electrical Stimulation System
## 3 Analysis of Calculation and Experimental Results  
Modeling and calculations were carried out to verify the feasibility of a temporal interference electrical stimulation system based on Helmholtz coils. Firstly, with the transmitting coil parallel to the receiving coil, excitations passed through the two transmitting coils according to the conditions for generating temporal interference. The magnetic field distribution of this system in free space was obtained to verify the uniformity of the magnetic field generated by the Helmholtz coils. On this basis, a human tissue model with realistic electromagnetic parameters was constructed to simulate the distribution of the superimposed magnetic field generated by temporal interference in the human body. The possibility of an efficient and stable wireless power transfer between the system and the receiver at a depth of the human body was further validated. Finally, an experimental platform with the same parameters as the calculation was built to investigate the current characteristics of the coil at the receiving coil. This demonstrated the feasibility of the proposed system for the therapeutic electrical stimulation of the sciatic nerve through temporal interference.
###### Abstract  
Power converters (PCs) with their control techniques help regulate voltages of nodes in microgrids with different types of loads such as resistive, inductive, nonlinear, constant power, or critical loads. However, constant power loads (CPLs) affect the stability of the voltage in the output of PCs and are usually difficult to regulate with traditional control techniques. The sliding-mode control (SMC) with the washout filter technique has been recently proposed to address this issue, but studies that consider the phenomenon and parameters present in real systems are required. Therefore, this paper focuses on evaluating the dynamic behavior of an SMC based on a washout filter using three different loads: A constant impedance load (CIL), a nonlinear CPL, and a combination of CIL and CPL. The CIL considered a resistance connected to the circuit, whereas the nonlinear CPL was designed by using a buck converter with zero average dynamics and fixed-point induction control techniques (ZAD-FPIC). The tests consisted of creating some variations in the reference signals to identify the output voltage and the error that the control brings according to the different loads. Besides, this study focuses on representing the dynamic behavior of signals when loads change, considering quantization effects, system discretization, delay effects, and parasitic resistors. Additionally, bifurcation diagrams are created by changing the control parameter \(k\) and plotting the regulated voltage and the error produced in the output signals. To illustrate the advantages of the SMC with the washout filter technique, a comparison was made with other techniques such as the proportional-integral-derivative (PID) and conventional SMC by varying the load. The results showed that SMC with the washout filter technique was superior to the PID and the conventional SMC because it stabilizes the signal faster and has a low steady-state error. Additionally, the control system regulates well the output voltage with the three types of load and the system remains stable when changing the parameter \(k\) for values greater than 1, with a low error in the steady-state operation.  
DC-DC power converters; constant impedance load; constant power load; sliding-mode control; washout filter; ZAD-FPIC
## 3 System Design Results
### Infrared Subsystem Design  
The infrared band subsystem uses a detector with a pixel size of 25 \(\upmu\)m, the focal length of the system is 1250 mm, and the field of view is the same as the visible light band, 1\({}^{\circ}\)\(\times\) 0.2\({}^{\circ}\). The system design parameters are shown in Table 2.  
The infrared band subsystem is composed of five mirrors, the primary and secondary mirrors are shared with the visible light band, the third mirror and the fifth mirror are ellipsoidal, and the fourth mirror is hyperboloid. The angle between the incident light in the central field of view and the horizontal direction is also 0.9\({}^{\circ}\), and the direction is opposite to the visible light band. The infrared system structure diagram is shown as in Figure 5.  
We select nine points A (\(-\)0.5\({}^{\circ}\), \(-\)1.1\({}^{\circ}\)), B (\(-\)0.5\({}^{\circ}\), \(-\)1\({}^{\circ}\)), C (\(-\)0.5\({}^{\circ}\), \(-\)0.9\({}^{\circ}\)), D (0\({}^{\circ}\), \(-\)1.1\({}^{\circ}\)), E (0\({}^{\circ}\), \(-\)1 \({}^{\circ}\)), F (0\({}^{\circ}\), \(-\)0.9\({}^{\circ}\)), G (0.5\({}^{\circ}\), \(-\)1.1\({}^{\circ}\)), H (0.5\({}^{\circ}\), \(-\)1\({}^{\circ}\)), I (0.5\({}^{\circ}\), \(-\)0.9\({}^{\circ}\)) on the image plane to evaluate the long-wave infrared imaging performance of the system [14]. The image quality evaluation of the system is shown in Figure 6. It can be seen from the figure that the MTF curve of each field of view of the system in the infrared band is close to the diffraction limit, and the average MTF value at 20 lp/mm reaches 0.36. It can be seen from the image point diagram that the energy of the image points is concentrated, and the image points in each field of view are much smaller than the Airy disk. In addition, due to the asymmetrical field of view of the off-axis system, the distortion curve in the Code V software cannot truly reflect the system distortion. We can see from the text data of the distortion grid that the maximum value of the system Rad Dist in Code V is 0.4%, and the maximum value of Tan Dist is 0.12%. The image quality of the system is good and meets the expected requirements.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Parameter** & **Focal Length** & **Pupil Diameter** & **Pixel Size** & **FOV (\({}^{\circ}\))** & **System F\#** & **Wavelength** (nm) \\  & **(mm)** & **(mm)** & **(mm)** & **FOV (\({}^{\circ}\))** & **System F\#** & **(mm)** \\ \hline Value & 1250 & 500 & 25 \(\times\) 25 & 1 \(\times\) 0.2 & 2.5 & 7700–10,500 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Optical system design parameters.  
Figure 5: Schematic diagram of infrared band subsystem design.
# Intelligent Intrusion Detection of Grey Hole and Rushing Attacks in Self-Driving Vehicular Networks
## 3 The Proposed Intrusion Detection System
### Simulation of Traffic and Mobility Scenarios  
We utilised two software tools to generate a real-world traffic of abnormal/normal behaviour for self-driving and semi self-driving vehicles: Simulation of Urban Mobility Model (SUMO) and MObilty VEhicles (MOVE) [27]. The output files of these tools are used as input to ns-2 [28]. The reasons for employing SUMO to generate mobility scenarios are that it is open source, widely used in VANETs, microscopic and facilitates multi-model traffic simulation [29]. In addition, it is computationally efficient and straightforward to adapt with various numbers of vehicles as well as the MOVE model designed on SUMO [30; 31].  
The mobility models are divided into three types: urban, highway and rural models [32]. The urban mobility model includes many types of models, such as the Random Way Point (RWM) model, the Manhattan mobility model and the Rice University Model (RUM) [32]. In this paper, we use the Manhattan mobility model because it is widely used in the research field and it allows vehiclesto move in different directions [32]. The Simulation of Urban Mobility Model (SUMO) and MObilty VEhicles (MOVE) are shown in Figure 2.  
The self-driving communication simulation is carried out using ns-2 and employs four abnormal vehicles: two rushing attacks and two grey hole attacks on the system. Figure 3 shows the system simulation designed in ns-2.  
A vital problem in simulation systems are the parameters employed during the initial stages because they have an important role in identifying the mobility, behaviour and traffic type of vehicles. Table 1 details initial parameters like the Radio Propagation Model (Two Ray Ground), Type of Traffic (CBR), and Routing protocol (greyholeaodv) [33].  
Figure 3: Screenshot of network simulator.  
Figure 2: Screenshot of Simulation of Urban Mobility Model (SUMO) and MObilty VEhicles (MOVE).
# Examining the Link between Isokinetic Strength Metrics and Ball Speed in Women's Soccer
## 2 Materials and Methods
### Data Collection
#### 2.3.2 Isokinetic Strength Evaluation of Muscular Performance  
Players' isokinetic performances were measured using an isokinetic dynamometer of Humac NORM type (CSMI, Stoughton, MA, USA) [35; 36; 37]. Isokinetic measurements of both the dominant (D) and non-dominant (ND) extremities were used to assess peak and average torque for each joint. Data were obtained for absolute torque (Abs), torque normalized to body weight (%BW), and the ratio of ipsilateral torque of agonist-antagonist muscles (%IPS). Calculations were made when the tested limb was parallel to the ground, accounting for the effect of gravity. The study utilized the Con/Con, Ecc/Ecc, and Iso protocols. All tests were conducted by the same laboratory assistant, who received training in the Humac NORM test.  
For each joint, a warm-up set consisting of five repetitions at 50% perceived effort was performed prior to the test [38]. The purpose of the warm-up set was to familiarize the subjects with the isokinetic testing sensation and provide a specific warm-up for the muscles to be tested [39]. Following the warm-up and between sets, players were given a 90 s rest period. The laboratory assistant verbally encouraged the tested athlete throughout each test session. Concentric measurements were conducted at angular velocities of 60-180-300\({}^{\circ}\)/s, as previously recommended for young soccer players [40; 41; 42]. Eccentric measurements were performed at an angular velocity of 60\({}^{\circ}\)/s. As this test speed has predominantly been adopted in the scientific literature and is considered the optimal testing method for assessing peak torque and maximum power, there is consensus on performing a 5-repetition test at 60\({}^{\circ}\)/s isokinetic speed [42]. Finally, there is agreement in the literature that an isokinetic protocol should encompass three speeds, as the protocol should provide information on maximum strength, muscle endurance capacity, and the speed-force relationship [42]. Hence, in our study, 5 repetitions were conducted for both concentric and eccentric contractions.  
The isokinetic knee EXT-FLX test (con/con and ecc/ecc) was conducted with the athlete seated. Prior to the test, the isokinetic device's seat back angle was adjusted to 85 degrees, and the seat rotation angle was set at 40 degrees. The dynamometer's rotation angle was also set to 40 degrees, while the tilt angle remained at 0 degrees and the height at eight units. The monorail, back translation, and fore-aft values were fine-tuned to precisely align the athlete's knee cap and the dynamometer shaft. The athlete's stationary leg and torso were securely fastened using the dynamometer straps. The athlete's leg was set to a zero-degree angle at full extension, and a 90-degree flexion angle was designated, resulting in a movement range spanning from 0 to 90 degrees. For the isometric measurements, the leg angle was 45 degrees, and the contraction duration was 5 s. Only EXT strength was measured in isometric contractions [43].