esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### The Effects of Vegetation Growth Carryover on Vegetation Growth  
Most people believe that vegetation growth depends mainly on external climate factors and human activity [1; 45; 46]. Our findings suggest that vegetation carryover has a significant positive impact on vegetation growth (Figures 3 and 4). That is to say, the VGC effect is the primary positive factor controlling the seasonal vegetation growth, also shown by others [17; 18]. The positive effect of VGC in the three active growth seasons makes it easier to carry over the vegetation state in each season, and then affects vegetation growth in subsequent seasons. For instance, although sunshine duration had a negative effect on seasonal vegetation growth by increasing evapotranspiration [41; 47], the positive and strong VGC effect can override this adverse effect. The same phenomenon was observed previously for high temperature [17; 18]. In addition, the VGC effect in the central and northern part of our study area was stronger than in other regions, confirming that vegetation in drought regions may have a stronger VGC effect [10; 18]. This strong vegetation carryover effect in drought regions implies that disturbances might have severe effects on vegetation in these areas. Initial ecological restoration and sustainable conservation would promote vegetation growth and increase vegetation productivity, which is of great significance to arid areas. Our results also exhibited that the persistence of the vegetation carryover can be sustained to the next year. So, a year with better vegetation growth is often followed by another year with better vegetation growth. Prior work has reported that drought legacies mostly last for one year, with significantly fewer legacies in the second and third years [48], as did our study of the VGC effect (Figure 7). Correspondingly, if vegetation does not overcome the influence of negative disturbances (e.g., wildfires, insect pests, and extreme climate) in time, the vegetation will slow growth in the coming years [18]. All the above evidence confirms the importance of the VGC effect on seasonal vegetation growth. Vegetation carryover is a critical pathway that should not be ignored.
# Photoacoustic Imaging for Management of Breast Cancer: A Literature Review and Future Perspectives
## 4 Discussion
### Questions Raised by [PERSON]'s Research Group and Answers
However, these clinical studies did not give any specific PA image marker to differentiate between the contralateral and healthy breast. [PERSON] et al. [128] performed a three-dimensional PA imaging with a hemispherical detector array on 22 patients with unilateral breast cancer to identify a potential biomarker for breast cancer. They concluded that an increased average count of vessel branching points at a superficial depth below breast skin might indicate the presence of the primary breast cancer as compared with that of the contralateral unaffected breasts. _Question 4: How much beneficial PA imaging will be for screening, detection, and diagnosis of breast cancer. Whether PA imaging can be utilized for dense breast imaging and neoadjuvant chemotherapy monitoring?_  
From the results of clinical studies conducted so far by different research groups, it can be said that PA imaging can be utilized for the detection of breast cancer. However, there is no clear experimental evidence to support the utility of PA imaging in breast cancer screening. Very few clinical studies [25; 37; 39; 35] have been done so far, and the results of these studies indicate that PA imaging can potentially be used for breast cancer diagnosis. However, for gathering conclusive evidence in support of the capability of PA imaging for breast cancer diagnosis, a significant number of clinical studies with large population must be performed. In the clinical study performed by [PERSON] et al. [35] they observed that there was no noteworthy difference in the average PA contrast among the X-ray low-dense and high-dense breasts. [PERSON] et al. [39] observed that, following chemotherapy, intramural blood flow was increased by which the blood vessels inside the tumor became visible. This shows that PA imaging may potentially be utilized for monitoring chemotherapy.
# Improved Temporal Fuzzy Reasoning Spiking Neural P Systems for Power System Fault Diagnosis
## 2 ITFRSNP System
### Comparison Analysis with Other Methods
#### 4.3.1 Case Result Comparisons
When the alarm message is missing, or the timestamp is incorrect, there is a problem of missed diagnosis, and the fault tolerance must be improved [20, 21]. In reference [20], alarm messages are categorized as valid or invalid based on their relationship and temporal constraints with PRs and CBs, with valid alarms being assigned confidence degrees. However, the method in [20] classifies the alarm message of CB\({}_{0318}\) as invalid (with a confidence degree of 0.2) in Case 9, because CB\({}_{0318}\) has no corresponding PR alarm message. The confidence degree of L\({}_{0318}\) is 0.3836 (lower than the threshold in [20]), and L\({}_{0318}\) is diagnosed as a non-faulty component. The method in [21] corrects the confidence degrees of PRs and CBs that do not meet the temporal constraint in the sequence of \"Fault time--PR operation time--CB trip time\". If the timestamp of a PR alarm message is incorrect, this method determines that the PR and the corresponding CB do not meet their temporal constraints, resulting in a missed diagnosis. For example, in Case 8, because the timestamp of Lp\({}_{1803}\)P is incorrect, Lp\({}_{1803}\)P and CB\({}_{1803}\) do not meet the temporal constraints, their confidence degrees are corrected to 0, and L\({}_{0318}\) is diagnosed as a non-faulty component. In addition, the temporal reasoning in [21] defaults the fault time to zero, but the alarm message does not include the fault time; thus, determining the fault time is a problem that this method must solve. Combining the above case comparisons, the accuracy and fault tolerance comparison of the proposed method with other methods is shown in Table 4. The proposed method deduces the fault time by the operation times of PRs and CBs, adjusting the confidence degrees of the suspected faulty components by using a measure function and fault time. Furthermore, this study addresses the misdiagnosis problem in the case of neighboring components configured with the same backup protection and incorrect operations of the PRs and CBs. It also maintains diagnostic precision even when alarm messages of the boundary CB or main PRs of the bus and transformer are absent. Temporal errors do not affect the temporal reasoning of PRs and CBs. In contrast, the temporal results of different CB subnets are cross-verified, enhancing the reliability of temporal reasoning and adjustments made by the measure function. The comparison of fault results demonstrates that this study makes full use of temporal features and reasoning in complex fault scenarios, effectively enhancing the accuracy and fault tolerance of fault diagnosis.
###### Abstract  
The scope of our study was to analyze the impact of implant prosthetic rehabilitation, in bilateral terminal partial eedentulism with mandibular bone atrophy, and potential benefits of mandibular bone augmentation through finite element analysis. A 3D mandible model was made using patient-derived cone-beam computed tomography (CBCT) images, presenting a bilateral terminal edentation and mandibular atrophy. A virtual simulation of bone augmentation was then made. Implant-supported restorations were modeled for each edentulous area. Forces corresponding to the pterygoid and the masseter muscles, as well as mastication conditions for each quadrant, were applied. The resorbed mandible presented high values of strain and stress. A considerable variation between strain values among the two implant sites in each quadrant was found. In the augmented model, values of strain and stress showed a uniformization in both quadrants. Virtually increasing bone mass in the resorbed areas of the mandible showed that enabling larger implants drastically reduces strain and stress values in the implant sites. Also, although ridge height difference between the two quadrants was kept even after bone augmentation, there is a uniformization of the strain values between the two implant sites in each of the augmented mandible quadrants.  
finite element analysis; implant-supported restorations; bone resorption; bone augmentation; strain +
Footnote †: journal: Applied Sciences
## 3 CLIK Algorithms for UAM Control
### Acceleration-Level CIK Algorithm for UAMs
The linear and angular velocities of the base, \(\dot{\mathbf{x}}_{b}\), can be measured or computed from the following momentum constraint (6):  
\[\dot{\mathbf{x}}_{b}=\left[\mathbf{H}_{b}\right]^{-1}\left(\mathbf{h}-\left[\mathbf{H}_{bm} \right]\mathbf{\dot{q}}\right) \tag{26}\]  
For calculating \(\left(\left[\dot{\mathbf{J}}_{b}\right]\dot{\mathbf{x}}_{b}+\left[\dot{\mathbf{J}}\right] \dot{\mathbf{q}}\right)\), we notice from Equation (22) that this term is equal to the acceleration of the end-effector when the generalized accelerations (\(\ddot{\mathbf{x}}_{b}\), \(\ddot{\mathbf{q}}\)) are null:  
\[\left(\left[\dot{\mathbf{J}}_{b}\right]\dot{\mathbf{x}}_{b}+\left[\dot{\mathbf{J}}\right] \dot{\mathbf{q}}\right)=\left.\ddot{\mathbf{x}}_{e}\right|_{\ddot{\mathbf{x}}_{b},\dot{ \mathbf{q}}=0}=\ddot{\mathbf{x}}_{e,0} \tag{27}\]  
This acceleration is calculated through the forward recursion of the Newton-Euler recursive scheme for solving free-base manipulators inverse dynamics [22]. On the other hand, the term \(\left(\left[\dot{\mathbf{H}}_{b}\right]\dot{\mathbf{x}}_{b}+\left[\dot{\mathbf{H}}_{bm} \right]\dot{\mathbf{q}}\right)\) can be computed from Equation (21):  
\[\left(\left[\dot{\mathbf{H}}_{b}\right]\dot{\mathbf{x}}_{b}+\left[\dot{\mathbf{H}}_{bm} \right]\dot{\mathbf{q}}\right)=\left.\dot{\mathbf{h}}\right|_{\ddot{\mathbf{x}}_{b},\ddot {\mathbf{q}}=0}=\dot{\mathbf{h}}_{0} \tag{28}\]  
where \(\dot{\mathbf{h}}_{0}\) corresponds to the total momentum of the system when the generalized accelerations are null. As for the algorithm presented in Section 3.1, this inverse kinematics algorithm considers the dynamics of the UAM, now through the constraint described by Equation (21). Other CLIK algorithms presented in the literature (such as the one in [13]) only consider the geometry and state of the UAM.
# A Microscopic Traffic Flow Model Characterization for Weather Conditions
###### Abstract  
Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length 800 m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather.  
microscopic traffic flow; adverse weather; acceleration exponent; intelligent driver model; vehicle trajectories; traffic stability +
Footnote †: journal: 2022, 12, 12981. [[https://doi.org/10](https://doi.org/10)]([https://doi.org/10](https://doi.org/10)).  
40% and 11%, respectively, and the time headway increases by 1 s [11]. Furthermore, fuel consumption increases during adverse weather due to the greater delay.  
The number of vehicles worldwide is estimated to reach 3 billion by 2040 [12]. This will increase congestion and accident probability, which will be worse during adverse weather [13]. According to the National Center for Statistics and Analysis, \(22,697\) people died in 2020 and 2.43 million people were injured in 2018 due to accidents on United States roads [8]. Automated driving technology is being developed to reduce the number of traffic accidents [14; 15]. However, automated driving is challenging in adverse weather [15].  
Three types of models are used to characterize traffic flow: microscopic, macroscopic, and mesoscopic. Microscopic models consider individual vehicles and are often based on driver physical and psychological response [16]. These models consider parameters such as vehicle position, velocity, distance, and time headway [17]. Macroscopic models are used to characterize collective traffic flow behavior, while mesoscopic models consider both individual and collective behavior [18].
## 2 Materials and Methods
### Instrumental Analysis  
LC-quadrupole-time-of-flight (QTOF) analysis was performed with an HPLC system (Agilent Series 1200; Agilent Technologies, Palo Alto, CA, USA), consisting of vacuum degasser, autosampler, binary pump, and column oven coupled to both a DAD and QTOF mass analyzer (Agilent Series 6520). The analytical column was a Kinetex pentafluorophenyl(100 \(\times\) 2.1 mm, 2.6 \(\mu\)m particle size, Phenomenex, Italy) thermostated at 30 \({}^{\circ}\)C. The sample injected volume was 5 \(\mu\)L. The mobile phase components A and B were water and methanol, respectively, both acidified with 1 mM formic acid. The eluent flow rate was 0.25 mL/min. The mobile phase gradient profile was as follows: 0-16 min, 20%-100% B; 17-19 min, 100% B; 19-20 min, 20% B; 21-27 min, 20% B. DAD signals were recorded in the 210-600 nm range. The DAD was directly connected to a QTOF mass analyzer, giving a time shift of ca. 0.15 min in the MS chromatogram. The QTOF system was equipped with an electrospray ionization interface (ESI), operating in dual ESI mode and negative ESI acquisition, with the following operating parameters: capillary voltage, 3500 V; nebulizer pressure, 35 psi; drying gas, 10 L/min; gas temperature, 350 \({}^{\circ}\)C; fragmentor voltage, 120 V; skimmer 65 V. The QTOF calibration was performed daily with the manufacturer's solution. In all chromatographic runs the signal at _m_/2 112.9856 was set as the reference mass. The instrument provided a typical resolving power (FWHM) of about 18,000 at _m_/2 311.0805. Mass spectra at MS and MS/MS level were recorded over the range 80-1000 _m_/2 with a scan rate of 4 spectra/s, and 50-1000 _m_/2 with a scan rate of 6 spectra/s, respectively. Collision energies were set within the range 15-30 eV. Bioactive compounds previously identified in _Z. punctata_[21] and caffe acid derivatives classified by [PERSON] et al. [10; 11; 12; 13] were used as a list of preferred precursor ions for the auto MS analysis with a tolerance of 20 ppm (total cycle of 1.3 s, 6 compounds per cycle, isolation width of 4 u, active mass exclusion enabled after 30 spectra and 0.3 min, absolute and relative precursor threshold 1000 counts and 0.001%, respectively). For the post-run analysis, the [M-H]\({}^{-}\) signals were processed with a tolerance of 10 ppm in HRMS full scan mode and their identification was then confirmed mainly by comparing the obtained fragmentation patterns and UV spectra with data reported in the literature or specific MS databases such as Massbank ([[http://www.massbank.jp/?lang=en](http://www.massbank.jp/?lang=en)]([http://www.massbank.jp/?lang=en](http://www.massbank.jp/?lang=en))) and mzCloud(tm) ([[https://www.mzcloud.org/](https://www.mzcloud.org/)]([https://www.mzcloud.org/](https://www.mzcloud.org/))). Mass spectra acquisition and data analysis were processed with MassHunter Workstation B 06.00 software (Agilent Technologies, Palo Alto, CA, USA).
## 1 Introduction  
In 1957, [PERSON] suggested the concept of an \"epigenetic landscape\" which is now involved in reprogramming [1]. In 1962, [PERSON] replaced the UV-treated nucleus of a frog egg with a differentiated nucleus which proved that cells could be reprogrammed [2]. Nearly 45 years after [PERSON]'s report, [PERSON] first generated induced pluripotent stem cells (iPSCs) from mouse and human somatic cells using Oct4, Sox2, Klf4, and c-Myc transgenes (OSKM) [3,4]. Developing iPSCs was a ground-breaking event in cancer therapeutic research and the field of regenerative medicine because of the recapitulated pluripotency with potential to differentiate into various cells or tissues [5,6]. However, low efficiency of iPSC generation and safety issues involved with the use of viruses or transgenes poses limitations for clinical translation [6]. Thus, many researchers have worked to improve the efficiency and to develop low-risk methods for generating iPSCs,such as using specific proteins, small molecules, and virus-free carriers [7; 8; 9; 10; 11; 12; 13; 14]. Some researchers have applied external stimuli to induce iPSCs. Low-frequency electromagnetic fields could support the generation of iPSCs from somatic cells [15]. A graphene-based substrate resulted in increasing the efficiency of iPSCs by promoting the mesenchymal to epithelial transition (MET) process [16]. It also has been reported that shear stress maintains and strengthens the stemness of stem cells [17]. As a small molecule, vitamin C improved iPSC generation from mouse and human somatic cells with transgenes [18]. Vitamin C treatment downregulated the genes involved in differentiation while upregulating the ones related to pluripotency. It was indicated that vitamin C inhibited mESC differentiation and maintained pluripotency [19].  
In this study, we induced stem cell-like cells from mouse embryonic fibroblasts (MEF) using external stimuli such as short-term shear stress (STSS) or a combination of short-term shear stress and vitamin C (SSVC) under suspension culture without using any transgene. Figure 1 showed the schematic diagram of the methods. For short-term shear stress, we chose pipetting of culture media. To induce pluripotent stem cells, this method has never been used before. After generating shear stress and treatment with vitamin C to MEF, we checked pluripotency by ALP (alkaline phosphatase) assay, immunocytochemistry, and Western blotting. Finally, we checked the differentiation ability of spheroids with induced pluripotency.
## 3 Wireless Body Area Network (WBAN)
### Sensors for pulse rate
In the context of pulse rate measurement, a piezoelectric sensor can be placed on the skin or a blood vessel, and the mechanical vibrations caused by each heartbeat can be converted into an electrical signal to determine the pulse rate. 3. Capacitive sensor: It measures changes in capacitance between two electrodes caused by the pulsating blood flow. These sensors typically involve placing the electrodes on the skin or blood vessels, and as the blood flow varies with each heartbeat, the capacitance also changes. 4. ECG sensor: It can provide information about the heart's electrical activity. ECG sensor detects the electrical signals generated by the heart's contractions and relaxations. Various parameters, including heart rate, can be derived from these signals. Selecting pulse rate sensors, for different medical applications, should consider several factors that are summarized in Table 3[40; 41; 42].
# Holographic Silicon Metasurfaces for Total Angular Momentum Demultiplexing Applications in Telecom
## 2 Materials and Methods
### Subwavelength Grating Design  
The metasurface version of the computed optical elements has been realized in the form of spatially-variant subwavelength gratings, whose ridges orientation is rotated pixel-by-pixel introducing a spatially-dependent form birefringence. The key element of the metasurface is represented by the subwavelength linear grating cell, whose local orientation \(\theta\)(\(u\),\(v\)) is fixed in order to transfer the desired geometric-phase \(\Omega\)(\(u\),\(v\)) to the input wavefront, according to Reference [36]:  
\[\theta(u,v)=\frac{\Omega(u,v)}{2} \tag{16}\]  
being (\(u\),\(v\)) the coordinates of the reference frame on the optical element plane. The phase-patterns of the designed optical elements have been calculated as 4-bit grayscale images (16 phase levels) and converted into subwavelength grating metasurfaces with custom MATLAB(r) codes. The gray level \(j\), in the range from 0 to 15, has been associated to the rotation angle \(j\)2\(\pi\)/32 of the corresponding subwavelength grating vector. For a given grating thickness, numerical simulations must be performed in order to identify the optimal profile, in terms of duty-cycle and period, providing the maximum conversion efficiency, i.e., \(\pi\)-delay between ordinary and extraordinary axes. In Reference [36], a numerical study was performed implementing Rigorous Coupled-Wave Analysis (RCWA) [37,38] for a binary silicon grating in air at 1310 nm, in order to extract the optimal configurations of period and duty-cycle which provide \(\pi\)-retardation. For a thickness of 535 nm with a duty-cycle around 0.5, the grating period providing a \(\pi\)-delay is around 290 nm. This configuration was chosen for the design and fabrication of the silicon PBOEs presented in this study.
## 4 Validation of General ESS-Based VSM Control
### Simulation Test of a Single ESS-Based VSM under Step Load Change
#### 4.1.2 Case 2: Add VSM without Energy Recovery, for Different Frequency Integral Gains  
When VSM control without energy recovery is applied to ESS, the frequency nadir becomes 59.35 Hz at t = 10.39 s for the same load change, as shown in Figure 15. The transient power released from ESS in virtual inertia, damping and governor control exhibits HPF and LPF behaviors, in consistent with the transfer functions in Equations (3) and (4). The oscillation in frequency is alleviated due to the damping from ESS. However, the SoC of ESS keeps falling, as with conventional droop-controlled converter.  
For frequency integral gain \(k_{i,SG}\) = 10, the SoC variation (12.31%), corresponding to both virtual damping and damping, is close to the analytical estimation (Equation (6)), namely  
\[\left(\frac{D_{\text{VSM}}+k_{p,\text{VSM}}}{k_{i,\text{SG}}}\Delta P_{L} \right)\bigg{/}E_{nom}\,=\,\left(\frac{10+15}{10}\cdot 0.375\right)\bigg{/}6.8\,= \,13.79\%\]  
For a smaller frequency integral gain \(k_{i,SG}\)= 5, the steady-state energy variation of ESS increases consequently. The SoC variation (24.63%) also approximates the analytical estimation (27.57%) based on Equation (6), namely  
\[\left(\frac{D_{\text{VSM}}+k_{p,\text{VSM}}}{k_{i,\text{SG}}}\Delta P_{L} \right)\bigg{/}E_{nom}\,=\,\left(\frac{10+15}{10}\cdot 0.375\right)\bigg{/}6.8\,= \,27.57\%\]  
Such deep discharging will accelerate ESS aging for long-time operation.  
Figure 14: Simulation results for synchronous generator solely serving the loads. The waveforms show that the power of synchronous generator (\(P_{\text{SG}}\)) balances the total load, and the frequency nadir is low and there is oscillation.  
Figure 13: Power network to test the VSM based on a single ESS.