esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems
## 4 Analysis
### Frame Rate Superposition of the TMISs  
The noise power spectrum (\(s(f)=1\)) of white noise, and \(\Delta f_{n}\) at this time is called the noise standard bandwidth \(\Delta f_{0}\). Because the system's electronic filter transfer function is generally a low-pass filter, it can be seen that the relationship between the system's equivalent noise bandwidth and the \(3 dB\) frequency \(f_{0}\) is \(\Delta f_{n}=f_{0}\tau/2\). Generally, to maintain the maximum value of the light pulse signal waveform, let \(f_{0}=1/(2\tau_{d})\). Therefore, the relationship between the noise equivalent bandwidth \(\Delta f_{n}\) and \(\tau_{d}\) of the system is  
\[\Delta f_{0}=\frac{\pi}{4\tau_{d}} \tag{36}\]  
Figure 10: Relationship between background temperature \(T_{B}\) and MRTD (ITD).  
where \(\tau_{d}\) is the dwell time of the detector. For a staring focal plane detector component, the dwell time of the detector is \(\tau_{d}=1/f_{p}\), where \(f_{p}\) is the frame rate, so the noise equivalent bandwidth \(\Delta f_{n}\) of the system can be expressed as  
\[\Delta f_{n}=\frac{\pi f_{p}}{4} \tag{37}\]  
Combining Equations (18) and (20) shows that reducing the imaging frame rate can reduce the MRTD and the MDTD of the system. Therefore, when designing TMISs with a certain magnification, the temperature resolution of the system can be improved by frame rate superposition.
## 2 Materials and Methods
### Resting State Preprocessing and Analysis  
A customised data cleaning pipeline optimised for the preprocessing of stroke data was constructed [22]. The pipeline used functions from DCMstack [[https://github.com/moloney/dcmstack](https://github.com/moloney/dcmstack)]([https://github.com/moloney/dcmstack](https://github.com/moloney/dcmstack)) (accessed on 6 April 2017), Analysis of Functional NeuroImages (AFNI) [33], SPM12 v6685 [[http://www.fil.ion.ucl.ac.uk/spm/software/spm12/](http://www.fil.ion.ucl.ac.uk/spm/software/spm12/)]([http://www.fil.ion.ucl.ac.uk/spm/software/spm12/](http://www.fil.ion.ucl.ac.uk/spm/software/spm12/)) (accessed on 6 April 2017), Advanced Normalization Tools (ANTs) [34], Numpy [35], Scipy [36], and Nibabel [[https://github.com/nipy/nibabel](https://github.com/nipy/nibabel)]([https://github.com/nipy/nibabel](https://github.com/nipy/nibabel)) (accessed on 6 April 2017), combined under the NiPype framework [37]. Anatomical image preprocessing consisted of segmentation using the new segmentation method and coregistration to the mean EPI image [22]. White matter and cerebrospinal fluid (CSF) masks were created by thresholding the segmented white matter and CSF images at 0.99 and eroding two times using a \(3\times 3\times 3\) mm structure element to minimise partial volume effects. Normalisation to Montreal Neurological Institute (MNI) space was achieved by transforming an MNI space \(3\times 3\times 3\) mm template image to subject space and then using the inverse transformation matrix to warp the T1 image from subject space to MNI space. Stroke participants had their FLAIR and lesion mask included in the pipeline, which were coregistered to the T1 image and coregistered to the EPI image [22].  
Prior to preprocessing, we conducted a systematic, visual quality inspection of each participant's resting state data. Participants were excluded if their data were shown to have consistent, excessive motion or noticeable distortions. No participants were excluded on this basis. The preprocessing of EPI data included despiking, slice timing correction to the central slice, and realignment to the first volume. Motion- and physiological-related artefacts were regressed from the data using the Friston 24 parameter model [38] and aCompCor [39], taking the top five components each for white matter and CSF mask extracted signals. The global signal from within the brain mask was also regressed. This can help attenuate residual motion and physiological effects not removed by prior cleaning [40].
## 2 The Rubisco Reactivity  
The Rubisco enzyme (Ribulose-1,5-bisphosphate carboxylase/oxygenase, EC 4.1.1.39) is a promiscuous enzyme catalyzing both the carboxylation and oxygenation reactions of the ribulose-1,5-bisphosphate-enediolate (Scheme 1).  
The carboxylase activity of the enzyme (Scheme 1a) allows for CO\({}_{2}\) fixation in photosynthetic organisms by converting D-ribulose-1,5-bisphosphate (RuBP) into two molecules of 3-phospho-D-glycerate (3 PGA). The latter is subsequently reduced to D-glyceraldehyde 3-phosphate (G3P) within the Calvin-Benson-Bassham (CCB) cycle (see Scheme S1) [10]. Part of the G3P produced is further converted into hexose sugars. An analysis of the stoichiometry of the Calvin cycle shows that it takes six turns of the cycle (plus 12 moles of NADPH and 18 moles of ATP) to fix six moles of CO\({}_{2}\) into 1 mol of hexose sugar. As shown in Scheme 1a, the RuBP conversion proceeds stepwise encompassing enolization of RuBP followed by a carboxylation and hydration reactions to give, ultimately, two molecules of 3-phospho-D-glycerate. A detailed analysis of the reaction mechanism as well as a discussion of the energy profile of the overall reaction is reported in Section 8.  
The oxygenase activity of the enzyme (Scheme 1b) produces 3-phospho-D-glycerate (3 PGA) and 2-phosphoglyolate (2 PG) by reaction of the 2,3-enedi(ate) intermediate with O\({}_{2}\). Due to the competition of CO\({}_{2}\) and O\({}_{2}\) for the Rubisco active site, the oxygenation reaction has been considered, for a long time, as a deleterious process with an overallnegative impact on CO\({}_{2}\)-fixation [11]. However, very recently, this view has been criticized in the light of recent research progresses (see Section 10) [12].
## 6 Conclusions  
In this paper, a unique model was developed which combined the global path planning with the local path planning in order to meet the requirements for the large-scale complex environment. The global path planning only performs the homogenization operation on the task sequence route, so that the subsequent local path planning solution scale is in a relatively stable range. The local path planning was modeled as a nonlinear optimization with multi-constraints, including the time, the danger area, curvature, and ocean currents constraints. The online learning SHH was proposed to attack the complexity of the path planning model. This paper analyzed the results in three scenarios. Moreover, our rigorous comparison of the three algorithms, including PSO, FFA, and SHH algorithms, showed that the online learning SHH algorithm had significant advantages in solving the proposed path planning model in terms of computing time, optimization ability and stability.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 3 Objective Function Formulation  
The logic diagram of the model is shown in Figure 3. The problem in this paper can be divided into two levels. The first level defines the inject power of MGs and the states of remote-controlled switches to minimize the power loss and the voltage offset. The model in the second level is performed to gain the minimum cost of the MGs. Each level has its objective functions and constraints. The decisions of the first level would determine the behavior of the second level while the second level would impact the decision of the first level. Thus the relation of two levels is established, the mathematical model of which can be expressed as follows.  
\[\text{Level 1}:\left\{\begin{array}{l}\underset{u}{\min}F(u,v)\\ g(u,v)\leq 0\\ h(u,v)=0\end{array}\right.\quad\text{Level 2}:\left\{\begin{array}{l} \underset{v}{\min}f(u,v)\\ J(u,v)\leq 0\\ K(u,v)=0\end{array}\right. \tag{19}\]  
where \(F(u\),\(v)\) and \(f(u\),\(v)\) are the objective function of the first and second level, respectively. \(u\), \(v\) are decision variables of the first and second level, respectively. \(g(u\),\(v)\), \(J(u\),\(v)\) are inequality constraints respectively. \(h(u\),\(v)\) and \(K(u\),\(v)\) are equality constraints respectively.
# The Perpetual Vector Mosquito Threat and Its Eco-Friendly Nemeses
## 4 Effective \"Green\" Technologies
### Sterile Insect Technique (SIT)  
The application of the SIT has its origin in the 1950s when it was used to control insect pests in agriculture [128; 129]. For mosquito control, specific vector species are reared and mated in insectaries. The resultant male and female pupae are separated, and, subsequently, males that emerge are exposed to ionizing X-ray or gamma radiation that induces deleterious dominant mutations in germ cells. Sterile males are released on a mass scale to compete with wild competent males in the field. Following mating with sterile males, females lay sterile eggs that do not develop into progeny, leading to a reduction in species-specific vector populations and the desired effect of lowering disease burdens on human populations. The SIT has been used since the 1960s and more recently with measurable success to control _Anopheles_, _Aedes_, and _Culex_ species in the United States, Asia, Central America, and Cuba, and plans are underway for its application on the African continent [130; 131; 132; 133; 134; 135]. Nevertheless, the widespread use of the SIT is hampered by a number of formidable factors, including mass production, sex separation, and the continuous release of sterilized males for effective suppression of robust populations in the field [122; 136].
# Surface Texture Measurement on Complex Geometry Using Dual-Scan Positioning Strategy
###### Abstract  
In this paper, a surface measurement method based on dual-scan positioning strategy is presented to address the challenges of irregular surface patterns and complex geometries. A confocal sensor with an internal scanning mechanism was used in this study. By synchronizing the local scan, enabled by the internal actuator in the confocal sensor, and the global scans, enabled by external positioners, the developed system was able to perform noncontact line scan and area scan. Thus, this system was able to measure both surface roughness and surface uniformity. Unlike laboratory surface measurement equipment, the proposed system is reconfigurable for in situ measurement and able to scan free-form surfaces with a proper stand-off distance and approaching angle. For long-travel line scan, which is needed for rough surfaces, a surface form tracing algorithm was developed to ensure that the data were always captured within the sensing range of the confocal sensor. It was experimentally verified that in a scanning length of 100 mm, where the surface fluctuation in vertical direction is around 10 mm, the system was able to perform accurate surface measurement. For area scan, XY coordinates provided by the lateral positioning system and the Z coordinate captured by the confocal sensor were plotted into one coordinate system for 3D reconstruction. A coherence scanning interferometer and a confocal microscope were employed as the reference measurement systems to verify the performance of the proposed system in a scanning area of 1 mm by 1 mm. Experimental data showed that the proposed system was able to achieve comparable accuracy with laboratory systems. The measurement deviation was within 0.1 \(\upmu\)m. Because line scan mechanisms are widely used in sensor design, the presented work can be generalized to expand the applications of line scan sensors.  
surface texture measurement; confocal sensing; surface form tracing; 3D reconstruction; roughness +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion
### Varying Temperature  
In this scenario, the cold-side temperature is held constant while the hot-side temperature quickly changes, causing a simultaneous NTD situation as shown in Figure 9, thus testing the durability of the MPPT approaches. Every two seconds, the temperature changes. The comparison is presented in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Tech.** & \begin{tabular}{c} **Power** \\ **Achieved** \\ **(W)** \\ \end{tabular} & \begin{tabular}{c} **Actual** \\ **Global** \\ **Power (W)** \\ \end{tabular} & \
\begin{tabular}{c} **Efficiency** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Energy (W.s)** \\ **Time (s)** \\ \end{tabular} \\ \hline MRA & 691.8 & 692 & 99.97 & 673.7 & 0.22 \\ \hline WOA & 691.1 & 692 & 99.86 & 669.1 & 0.34 \\ \hline GWO & 688.9 & 692 & 99.55 & 622.3 & 0.49 \\ \hline PSO & 686.1 & 692 & 99.14 & 608.3 & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative analysis for Case 1.  
Figure 8: Energy comparison Case 1.  
Due to temperature variations, every meta-heuristic approach exhibits re-initialization, exploratory, and exploitative behavior after every two seconds Figure 10a. The MRA monitors the GM more quickly than the previous MPPT approaches and exhibits extremely low oscillations. The MRA, WOA, GWO, and PSO monitor the average power at 404.35 W, 404.0 W, 403.45 W, and 403.35 W, respectively. This demonstrates that the MRA tracks the biggest power loss due to significant oscillations at the GM. In this instance, the tracking efficiency is 99.96%, 99.87%, 99.74%, and 99.71%, respectively.  
The track and settle times following the temperature change may be used to test the robustness of the MRA-based MPPT approach. According to Figure 10b, the average tracking times for the GM using the MPPT approaches are 240.1 ms, 360.3 ms, 500.2 ms, and 610.8 ms (b). Due to the dynamic population grouping that must be chosen each time, the WOA also requires a lot of time to track the GM. Due to this impact, the exploration is more time consuming. Figures 10c and 11 depict voltage transients and energy harvesting, respectively.  
Figure 10: Comparative analysis of all techniques for Case 2 (**a**) Duty Cycle Variation (**b**) Power Tracking (**c**) Current Variation.  
Figure 9: Temperature variation in Case 2.
## 3 Results and Discussions
### Physical-Chemical Monitoring
Error bars denote standard deviation calculated on three replicates, while lines are the best fit natural logarithmic curves. Different letters indicate statistically different means at 0.05 confidence. Figure 5: ORP values variations in SSE batches experiments with CS (**left panels**) and SS (**right panels**) for different L/S ratios. Error bars denoted standard deviation calculated on three replicas. Figure 6 shows the effect of different L/S ratios on ORP. In the Control, ORP decreased due to the dilution effect, departing from ORP values of approximately 210 mV for CS and 220 for SS, to reach an asymptotic value around 150 mV and 170 mV, respectively. In Graphene's batches ORP values were less affected by the influence of the liquid phase and tended to remain more stable than the Control ones. Both CS and SS batches showed statistically different means respect to Control except for CS L/S = 1 and for SS L/S = 5. This was probably due to the antioxidant properties of this material [38]. In Compost batches, ORP values were not affected by the change in L/S ratios remaining substantially close to 200 mV for both soils except for L/S = 5, with statistically different means respect to Control except for CS L/S = 1. Biochar batches showed lower values of ORP than the Control in both soils, with statistically different means respect to Control. This was due to the effect of aromatic compounds on Biochar surfaces that provided a source of electron donors that could create a reducing environment [37]. In Zeolites batches ORP mimicked the trend of the Control for both CS and SS batches, with ORP values slightly higher than the Control, with statistically different means respect to Control except for SS L/S = 5.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### OFDM Generation
#### 5.8.2 The IFFT  
In the IFFT module, the inverse fast Fourier transform is performed simply over the obtained resource grid, involving the context of the slots and subcarriers as well. Depending on the current subcarrier spacing, 3.75 kHz or 15 kHz, the IFFT size equals 512 or 128, respectively. It should be noted that the size of the IFFT processing directly influences the computational cost of the module. However, considering the fact that, due to 12 or 48 subcarrier-based symbol allocation, the input vector contains a lot of zero data (due to the zero-padding operation). It is also a good example in the context of an optimization task by reducing the number of the IFFT calculations regarding unused transformation elements.
## 2 IMC with Synchronized Decoupled Controller
### Modulation Process of PWM Rectifier and SPWM Inverter  
The modulation technique is operated by comparing two signals; sinusoidal modulating signal which is considered as a low-frequency signal and triangular carrier signal which is considered as a high-frequency signal. The intersection of these signals generates pulses with varying duty cycles. In this work, three balanced-sinusoidal controlled voltages are compared with their respective triangular voltage waveforms, where the output of these intersections is applied to control the switching function of the switching components in each leg of the converter. The switching frequency applied for the SPWM technique is set to 12 kHz. The value of switching frequency is set to 12 kHz. The switching devices are functioning in a complementary manner in each phase: When the upper leg is in the open position, the lower leg will be in the closed position, and vice versa.
# Incentive Price-Based Demand Response in Active Distribution Grids
## 6 Conclusions  
This paper proposes a demand response method in which the incentive prices are computed and communicated to each DER close to real-time by which the voltage regulation objective of the DSO and electricity cost minimization of the customer are both fulfilled. The proposed method is verified through simulation studies in which it is applied for controlling the charging/discharging pattern of a group of BESS connected to the simplified model of a real-life LV grid with PV systems. In this paper, the optimization problems of the LVGC and BESS controllers are formulated over a receding horizon using the forecasted powers. The distributed implementation of the above-mentioned optimization problems is achieved by employing an iterative distributed algorithm that computes the incentive prices iteratively. Simulation results show that the costs of electricity power consumption for the customers with BESS is less if the proposed DR method is used. The studies in this paper have not considered the other objectives of the LVGC such as power loss minimization. However, they can be included in the proposed method after the inclusion of the corresponding objective functions and constraints. Future work includes extending the proposed incentive price based DR for EVs, electric boilers and refrigeration systems.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Over-Voltage Events** & **Under-Voltage Events** & **Costs Computed as per (\(14\)) [pu]** \\ \hline Case 1: No BESS & yes & yes & - \\ Case 2: BESS control w/o proposed & slightly above limit & yes & 143.91 \\  & & & \\ Case 3: BESS control with proposed & no & no & 110.32 \\  & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the Simulation Results.  
Figure 7: Plot of case 3: (**a**) setpoints of BESS, (**b**) SOC of BESS, and (**c**) node voltages at end of the feeder.  
**Author Contributions:** [PERSON], the main author of this article, prepared the draft. [PERSON] and [PERSON] the co-authors, have provided technical inputs, reviewed the draft and supervised the work. [PERSON] was also responsible for the administration and funding acquisition of the project \"DECODE\". All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research work was funded by Energinet, the Danish transmission system operator, under PSO-ForskEL programme, grant no. 12414, for the project \"DECODE\".  
**Acknowledgments:** The authors would like to thank Norlys A/S, ABB A/S, and Kenergy A/S for their support and valuable inputs during various project meetings. The main author would like to thank [PERSON], University of Toronto, Canada for his invaluable inputs and guidance while doing this research work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Precoding for RIS-Assisted Multi-User MIMO-DQSM Transmission Systems
## 4 Interference Cancellation
### Strategy I. ZF-BD Precoding
The matrix \(\mathbf{V}_{k}^{(0)}\) contains the last \(N_{r}\) columns of \(\mathbf{V}_{k}\), which form an orthogonal basis that is in the null space of \(\overline{\mathbf{H}}_{k}\). Then, \(\mathbf{V}_{k}^{(0)}\) can be used as the precoding matrix \(\mathbf{W}_{k}\) in Precoder 1. Note that the BD technique is applied for \(N_{t}=jN_{r}\), where \(j\) is the number of users under MUI. Considering (20), the received signal is reduced to  
\[\mathbf{y}_{k}=\mathbf{H}_{k}\mathbf{W}_{k}\tilde{\mathbf{x}}_{k}+\mathbf{n}_ {k}, \tag{23}\]  
which is an interference-free signal. Finally, the complete system is reduced to  
\[ \begin{bmatrix}\mathbf{y}_{1}\\ \mathbf{y}_{2}\\ \vdots\\ \mathbf{y}_{j}\end{bmatrix} = \begin{bmatrix}\mathbf{H}_{1}\mathbf{W}_{1}&\cdots& \mathbf{0}\\ \mathbf{0}&\cdots&\mathbf{0}\\ \vdots&\ddots&\vdots\\ \mathbf{0}&\cdots&\mathbf{H}_{j}\mathbf{W}_{j}\end{bmatrix}\begin{bmatrix} \tilde{\mathbf{x}}_{1}^{1}\\ \tilde{\mathbf{x}}_{1}^{2}\\ \vdots\\ \tilde{\mathbf{x}}_{1}^{i}\end{bmatrix} + \begin{bmatrix}\mathbf{n}_{1}\\ \mathbf{n}_{2}\\ \vdots\\ \mathbf{n}_{j}\end{bmatrix} , \tag{24}\]  
which shows the cancellation of the undesired components in the destination.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 6 Analysis of SLR Results
### Golden Papers' Synopses
To solve the COVID-19 diagnosis problem effectively while maintaining patient privacy, this paper [58] offers insights into how edge computing and machine learning technologies might be used. By enabling remote healthcare units to take advantage of the collaborative learning paradigm without revealing local data, this paper advances the field of research. To analyze visual input intelligently at the edge, a multi-modal ML model that can recognize COVID-19 in both X-ray and ultrasound imagery is trained using a collaborative learning framework based on clustered federated learning (CFL). In comparison to conventional FL (such as X-ray and ultrasound images), it has been discovered that CFL can handle the divergence in data distribution from numerous sources better while still being able to meet strict security, privacy, and quality of service criteria (low latency). To reduce network congestion, the suggested approach employed [59] a priority-based data routing strategy. In terms of the percentage of correctly received packets, average throughput, and average hop-by-hop delay, the suggested congestion management mechanism for healthcare-focused IoT networks is assessed and contrasted with the current renowned TARA methods in the literature. These findings suggest that in real IoT-based healthcare studies, our proposed strategy performs better than the current congestion control approaches. The presented method reduces the network's overall energy use and increases the QoS. The study papers mentioned above were carefully examined, and the answers to the research questions are given below.