esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper presents an improved maximum power point tracking (MPPT) algorithm using a fuzzy logic controller (FLC) in order to extract potential maximum power from photovoltaic cells. The objectives of the proposed algorithm are to improve the tracking speed, and to simultaneously solve the inherent drawbacks such as slow tracking in the conventional perturb and observe (P and O) algorithm. The performances of the conventional P and O algorithm and the proposed algorithm are compared by using MATLAB/Simulink in terms of the tracking speed and steady-state oscillations. Additionally, both algorithms were experimentally validated through a digital signal processor (DSP)-based controlled-boost DC-DC converter. The experimental results show that the proposed algorithm performs with a shorter tracking time, smaller output power oscillation, and higher efficiency, compared with the conventional P and O algorithm.  
PV; P and O; MPPT; fuzzy logic control; DC-DC converter Article
## 3 Challenges in FSO Communication  
Following the introduction of fundamental concepts in FSO communication systems, this section explores the challenges primarily arising from atmospheric channel effects. Subsequently, we discuss various FSO systems and channel models that can be utilized to assess performance.
## 2 Problem Formulation
### Reformulated Model Based on Chance-Constrained Method
Based on the above method, Equations (4)-(7) can be reformulated into the following form of robust constraints:  
\[S_{jk}+M\cdot\Big{(}3-z_{ik}-z_{jk}-x_{ijk}\Big{)}\geq S_{ik}+P_{ik}+\xi_{ik} \hat{P}_{ik}^{max},\forall i,j;k\in K_{i}\cap K_{j};\xi_{ik}\in[-\sigma_{ik}, \sigma_{ik}] \tag{18}\]  
\[S_{jk}+M\cdot\left(3-z_{ik}-z_{jk}-x_{ijk}\right)\geq S_{ik}+F_{\widetilde{P} _{ik}}^{-1}(1-a),\quad\forall i,j;k\in K_{i}\cap K_{j} \tag{12}\]0  
\[S_{jk}+M\cdot\left(3-z_{ik}-z_{jk}-x_{ijk}\right)\geq S_{ik}+F_{\widetilde{P} _{ik}}^{-1}(1-a),\quad\forall i,j;k\in K_{i}\cap K_{j} \tag{12}\]1  
\[S_{jk}+M\cdot\left(3-z_{ik}-z_{jk}-x_{ijk}\right)\geq S_{ik}+F_{\widetilde{P} _{ik}}^{-1}(1-a),\quad\forall i,j;k\in K_{i}\cap K_{j} \tag{12}\]2  
Finally, by integrating Equations (1)-(3), Equations (17)-(21), and Equations (8)-(10), we can obtain the reformulated model based on the chance-constrained method (Model_2). Under the condition of having historical data support, this model is deterministic and can therefore be solved using an optimization solver. Meanwhile, since Model_2 establishes an equivalence relationship between the constraint feasibility probability and the confidence interval of the processing time parameters, schedulers can assess the feasibility probability of the model's constraints based on the range of processing times. It is essential to note that chance-constrained programming also presents certain potential risks. These include sensitivity to extreme values or outliers, which can increase decision-making risks; the significant impact of the chosen risk level on the model's outcomes, where selecting a high-risk level may result in excessive conservatism, while choosing a low-risk level may elevate risk, and there is a lack of clear standards or criteria for determining an appropriate risk level. Consequently, after collecting actual production data, we first preprocess these data to mitigate the effects of outliers. In setting the risk levels, we also test the solving results under different risk levels to provide scheduling decision-makers with options based on their specific needs.
## 2 Literature Review  
### 4D BIM and Construction Management  
The 4D BIM construction technology increases the dimension of time based on the 3D model [16] so that the entire construction process can be expressed dynamically and thus be more intuitive. The 4D theory was first proposed by Stanford University in 1996, and the CIFE 4D-CAD system was developed on this basis [17]. [PERSON]'s team proposed an extended 4D construction management model [18]. This model takes WBS as the core and combines construction management elements such as schedule planning and resource management to visualize the construction process. In 2012, a 4D Site Management Model (4 DSMM) [19] was further proposed by this team. 4 DSMM links 3D models with specific schedules to generate 4D models of site management. Additionally, [PERSON] [20] used the 4D BIM model coupled with the quality information model to implement the project's construction quality control. [PERSON] [21] also used a tabu-search algorithm and 4D models to improve the construction project schedule.  
Although using 4D BIM in the management of prefabricated buildings is beneficial, 4D BIM cannot be cultivated with incomplete, untimely data exchange and lacking real-time visibility [22]. Firstly, forming the 4D BIM models relies on specific BIM software [23]. It means information is inaccessible once being separated from this particular software. Additionally, the 4D BIM is mainly focused on on-site construction management. Prefabricated buildings have a lot of off-site construction information, which has difficulties in interoperating among different stakeholders, phases, and BIM software [24]. Once specific details of one model are modified, other models cannot update the information timely. This is disadvantageous for the highly dynamic construction process of prefabricated buildings. In summary, the lack of information interoperation between BIM software keeps the management of prefabricated buildings inefficient and locked-in to tool vendors [25]. Unlike the methods of 4D BIM and construction management in this literature, this paper adopts the standard for sharing data in the whole process of engineering projects--IFC standards--as the basis for modeling the 4D management information model of prefabricated building. By establishing IFC-based information model and by combining it with the innovative graph database technology proposed in this paper, the problem of ineffective data interoperability can be solved.
# The PIV Measurements of Time-Averaged Parameters of the Synthetic Jet for Different Orifice Shapes
## 2 Methods
### Data Reduction  
The SJ parameters during investigations were measured by the same Reynolds number, which may be defined as follows:  
\[Re=\frac{d\cdot U_{e}}{\
u} \tag{2}\]  
where \(d\) is the equivalent diameter of the actuator orifice (mm), \(\
u\) is a kinematic viscosity of operation fluid (m\({}^{2}\)/s), and \(U_{e}\) is a characteristic velocity of SJ. The characteristic velocity is defined as follows:  
\[U_{e}=\frac{1}{TA}\int\limits_{A}^{T/2}\
u\text{d}\tau\text{d}A \tag{3}\]  
where \(T\) is an oscillation period of loudspeaker membrane (s), \(A\) is an orifice cross-section area (mm\({}^{2}\)), \(u\) is the instantaneous velocity in the \(x\)-direction (m/s), and \(\tau\) is the time (s).  
The equivalent diameter of the actuator orifice was calculated as follows [26]:  
\[d=\sqrt{4\frac{A}{\pi}} \tag{4}\]  
The Strouhal number based upon the equivalent diameter; oscillation frequency, \(f\), (Hz); and characteristic velocity is given by the following equation [15; 31]:  
\[St=\frac{f\cdot d}{U_{e}} \tag{5}\]  
The dimensionless stroke length was calculated as follows [11; 32]:  
\[L_{0}=\frac{U_{e}}{df} \tag{6}\]  
The Strouhal number and the dimensionless stroke length are the variables that are commonly used to describe the SJ. The Strouhal number is the reverse of the dimensionless stroke length; therefore, they can be used alternately.  
The characteristic velocity, \(U_{e}\), was calculated from the reaction force measurement on the precision balance. The force measurement technique is described in detail in [32], while the relationship between the characteristic velocity (\(U_{e}\)) and reaction force was adapted from [33] in the following form:  
\[U_{e}=F\frac{2}{\pi}\sqrt{\frac{4 mg}{\rho\tau d^{2}}} \tag{7}\]where \(m\) is a mass measured by balance (kg), \(g\) is the gravitation acceleration (m/s\({}^{2}\)), \(\rho\) is the air density (kg/m\({}^{3}\)), and \(F\) is a correction factor equal to the following [33]:  
\[F=\left\{\begin{array}{c}0.5\leq L_{0}<5\Rightarrow 1.22L_{0}^{-0.1}\\ 5\leq L_{0}\leq 68\Rightarrow 1.02\end{array}\right. \tag{8}\]  
The other parameters used in the paper are the time-averaged velocity in the \(x\)- and \(r\)-direction and their variances, which were calculated as follows:  
\[U=\frac{1}{N}\sum u \tag{9}\]  
\[V=\frac{1}{N}\sum v \tag{10}\]  
\[\sigma_{u}^{2}=\frac{1}{N-1}\sum(u-U)^{2} \tag{11}\]  
\[\sigma_{v}^{2}=\frac{1}{N-1}\sum(v-V)^{2} \tag{12}\]  
where \(N\) is the number of measurements, and \(v\) is instantaneous velocity in the \(r\)-direction (m/s). Based on these variables, the turbulent kinetic energy (TKE) and the local vorticity in a plane (\(x\), \(r\)) were computed as follows:  
\[TKE=\frac{3}{4}\left(\sigma_{u}^{2}+\sigma_{v}^{2}\right) \tag{13}\]  
\[\omega=\frac{\partial V}{\partial x}-\frac{\partial U}{\partial r} \tag{14}\]
## 3 Design and Implementation
### Real-Time Measurement  
After the functionality of the proposed method was verified, we decided to perform mapping and identification of dynamic objects simultaneously. When testing such a case, the whole process was run in a loop, alternating part of SLAM and dynamic object identification. This procedure can be seen in Figure 16. In the first step, it was necessary to process the measured data from the 2D lidar and locate it on the map. Based on the  
Figure 14: Identification at low object speed.  
Figure 13: Dynamic objects in global coordinates.  
Figure 15: Identified objects at low-speed values.  
accuracy of this step, a further direction of the calculations took place. In the second step, the individual measurements were transformed into a point cloud. The point cloud was segmented and point clusters were formed. These clusters were then compared and the presence of a dynamic object was evaluated.  
As already mentioned, the finding of dynamic objects is based on a comparison of three measurements. Therefore, at least three measurements are needed before the first comparison is made. After performing three initialization measurements, the identification of objects with mapping alternates regularly.  
During the first measurement, we realized that the main limiting element would be the computing power of the evaluation unit. A laptop with 8 GB RAM and a 2.2 GHz dual-core processor was used for our measurements. A total of 13 measurements were made in this test. The execution of one cycle, which included SLAM and identification, took an average of 4.53 s. The identified dynamic object passed a 19 m track at a speed of 1.5 km/h. Our mapping device passed the 4.5 m track with a speed of 0.35 km/h. Again, the problem of glass reflectance and incorrect segmentation occurred during this measurement. The results can be seen in Figure 17.
## 1 Introduction
It can convert an oil-wet sandstone reservoir to a water-wet one by changing the wetting characteristics of the reservoir rock. One of the most crucial factors to take into account is the ionic concentration and composition of low-salinity water. In our research, we demonstrated that oil molecules are bonded to clay particles on the surface of rock with the help of divalent cations. The electrical force of highly ionically concentrated saline water will force divalent cations to the clay surface [15]. If the salinity of water is reduced, divalent cations can expand, and mono-valent ions can access and replace their places [16]. LSW flooding is a new technology that has been successfully employed in various areas and affects the reservoir rock's wetting properties, thus increasing oil recovery; it can turn an oil-wet sandstone reservoir into a water-wet one [17]. The ionic composition of injected water is one of the main parameters to be considered. A study revealed deionized water has more potential to be used as an EOR technique than regular seawater. By injecting the right concentration of LSW, we can change the wettability of the reservoir [18]. To address the limitations of low-salinity water flooding, we propose a hybrid technique that combines low-salinity water flooding with nanofluids. This approach in EOR can improve the disadvantages of LSW flooding such as water channeling and improve sweep efficiency during flooding operations [19]. The nanofluids can modify fluid-rock interactions, while LSW can alter the wettability and promote spontaneous imbibition, leading to higher oil recovery [20]. LSW flooding can change the wettability of the rock surface by making it more favorable for displacement [21]. Nanofluids, on the other hand, can further enhance wettability alteration by providing additional surface activity through nanoparticle adsorption and interaction with the rock surface [22]. This combined effect can lead to improved oil mobilization and displacement [23]. In this study, we used sandstone rocks as the research object, and the treated thin slices were immersed in formation crude oil to form an oil film. The wettability angle of the thin slices' surface in both LSW solution and pure water at different times was measured. The formation of an oil film on the sandstone rock surface and the mechanism of wettability alternation were explained. We concluded that LSW-based NF flooding hasmany advantages for use as an EOR technique. Below, we present all the needed data and the method of our investigation.
## 2 Materials and Methods
### Participants  
A tool to compute statistical power analyses [35] was used to estimate a minimum sample size for the experiment of the current study. This was determined by referring to previous similar research reported in [29], which obtained large effect sizes, as in Experiment 1b ([PERSON]'s d = 0.62). Setting the alpha level at 0.05 (two-tails) and a power of 0.80 to detect the effects of Experiment 1b [29], the suggested sample size was 23 participants. Overall, 36 volunteers naive to the objective of the experiment participated. Of the initial sample, one volunteer was removed due to an incomplete questionnaire and two were removed due to an unusually low percentage of correct trials (51% and 53%) in performing the delayed match-to-sample task. Of the remaining 33 participants whose scores on the \"Depression\" subscale of the Depression Anxiety Stress Scales-21 (DASS-21; [36; 37]) were below 14 (i.e., normal or mild), they were classified as low-state-depression (LSD) individuals (\(N\) = 19). Those scoring 14 or above (i.e., moderate, severe, or extremely severe) were classified as high-state-depression (HSD) individuals (\(N\) = 14). Event-related potential (ERP) and behavioral data from these two groups were analyzed. ERP studies concentrating on early P\({}_{\text{D}}\) and N2 pc generally evaluate 12 to 20 participants per subgroup, as demonstrated by an earlier investigation involving two groups with varying levels of trait anxiety [38]. Consequently, our sample size was adequate for detecting variations in early P\({}_{\text{D}}\) or N2 pc between the LSD and HSD groups. The specific characteristics of the subjects in both groups are detailed in Table 1. Participants affirmed that they had no medication use, history of chemical dependency, neurological issues, psychiatric/psychological disorders, or closed-head injuries. There were no significant differences in age or gender between the two groups. However, the HSD group scored higher on all three subscales of the DASS-21 questionnaire compared to the LSD group.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Group** & **HSD (\(N\) = 14)** & **LSD (\(N\) = 19)** & **Statistics** \\ \hline Age (years) & 22.00 (3.35), 21.00 (18–27) & 24.05 (8.10), 21.00 (19–45) & \(t\) (31) = \(-\)8.89, \(p\) = n.s. * \\ Gender (male, female) & 4, 10 & 9, 10 & \(\chi^{2}\) (1) = 1.19, \(p\) = n.s. * \\ Depression score & 21.86 (6.35), 24.00 (14–32) & 4.74 (4.18), 4.00 (0–12) & \(t\) (31) = \(-\)4.87, \(p\) \(<\) 0.001. \\  & & & \(r\) = 0.85 * \\ Anxiety score & 15.00 (9.54), 15.00 (4–30) & 4.21 (4.89), 2.00 (0–14) & \(t\) (31) = \(-\)3.42, \(p\) \(<\) 0.001. \\ Stress score & 19.57 (6.98), 17.00 (8–30) & 8.32 (6.26), 8.00 (0–20) & \(t\) (31) = \(-\)3.63, \(p\) \(<\) 0.001. \\  & & & \(r\) = 0.63 * \\ \hline \end{tabular} Values are mean (SD) and median (minimum-maximum). * n.s.: non-significant; \(t\)-test, chi-square test, and Mann–Whitney U standardized test, accepted at the 0.05 level of significance (2-tailed).  
\end{table}
Table 1: Demographic information of subjects with varying levels of state depression.
## 3 Results and Discussion
### Composites Structure  
The structural analysis results of two selected composites (CF2.5 and CF20) by scanning electron microscopy (SEM) are shown in Figure 3.  
Based on the images of the structures shown in Figure 3, it can be concluded that all fractures are brittle fractures. Moreover, the nature of these breakthroughs does not change  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **M (Nm)** & **E (kW)** & **T (\({}^{\circ}\)C)** & **P (MPa)** \\ \hline C & 16–17 & 0.95–0.97 & 165 & 10–11 \\ CF2.5 & 18–20 & 0.98–1.02 & 166 & 12–14 \\ CF5 & 20–21 & 1.01–1.05 & 168 & 14–17 \\ CF10 & 21–22 & 1.04–1.08 & 170 & 16–19 \\ CF20 & 22–23 & 1.06–1.11 & 171 & 20–24 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The extrusion process parameters for PCL and its composites with HF.  
Figure 3: Structure of selected fracture surfaces of bar-shaped specimens obtained from non-irradiated and irradiated CF2.5 (left) and CF20 (right) composites.  
with an increasing radiation dose. In general, all images obtained for composites with the same degree of fiber filling, i.e., 2.5% or 20%, have comparable structures. This proves that an increase in the radiation dose does not cause significant changes in the fracture structure of composites with a specific HF content. Subtle changes can only be found when assessing the adhesion at the polymer-fiber interface. With an increasing radiation dose, the distance between the phases slightly decreases, which is especially visible in the case of sample CF2.5. On one hand, it may be caused by an increase in thermal expansion of the irradiated material, mainly of the polymer phase, as a result of the temperature increase. On the other hand, it may be related to lower secondary shrinkage due to the changes in the structure of macromolecules after irradiation. Figure 3 also shows that the fibers in composites with a higher percentage filling are finer, which is consistent with the data shown in Figure 2. The craters seen in some photos may come from the extraction of the fibers from the matrix during the fractures. These craters do not disadvantage the material, but are merely a replica of the other half of the sample. The surface structures of the other composites (CF5 and CF10) are very similar and therefore they are not shown here.
# An Accurate Probabilistic Model for TVWS Identification
## 5 Experimental Results and Discussion  
In this section, results obtained with the M-SSS scanning the UHF-TV spectrum are presented and discussed. It also explains the different parametric considerations that are needed to accurately distinguish between occupied and idle channels.
## 3 Results
### Estimated Demand for Maritime  
The maritime sector is generally defined as domestic shipping and inland waterways. However, a more detailed definition can be found in the Monitoring, Reporting and Verification (MRV) of CO\({}_{2}\) emissions for maritime sector [29]. In the technical report  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **2025** & **2030** & **2035** \\ \hline SAF min. share \% & 2 & 4.3 & 15 \\ Synthetic aviation fuels & 0 & 0.7 & 5 \\ Total (Mtoe) & 2 & 5 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Proposed mandates as per the ReFuelEU aviation [10].  
\"A Clean Planet for All\", issued by the EC in 2019 [30], the expected demand for the maritime sector in 2030, under the baseline scenario, accounted for about 60 Mtoe.  
In contrast to the other sectors, the definition of a clear target in terms of alternative fuels is more complex for the EU maritime. Inland waterways might benefit from the expected extension of the infrastructures needed for the electrification of transports, while other more energy-dense solutions currently seem more suitable for bulk freight vessels. [PERSON] et al. [4] investigated the main elements needed to foster a higher penetration of alternative fuels. According to the study, this would depend on an array of technical and non-technical factors, not necessarily limited to the cost and GHG savings; the authors listed enablers such as technical maturity, safety regulations, operators' expertise, etc. According to the evaluation, bio LNG and biodiesel appear to be the most technically mature and commercially ready alternatives to reduce the GHG intensity of the sector.  
In terms of expected volumes, the FuelEU maritime proposal [12] does not provide specific figures for possible shares of alternative fuels in 2030. The proposal aimed to define a common EU regulatory framework to increase the share of alternative fuels in the mix of international maritime transport without creating barriers to the single market. The FuelEU proposal was expected to stimulate the European shipping sector, sending a clear signal and providing appropriate support. Given the dimension of the demand, and the lack of a specific certification process required for the fuel use, the authors proposed to assume a target similar to the aviation's proposal for the use of alternative fuels in 2030.  
With an expected demand of 60 Mtoe, a 5% share would account for 3 Mtoe. This figure is close to the results of a meta-analysis carried out by [PERSON] et al. [14]. To derive the share of bioLNG and biodiesel, and assuming a similar substitution rate, we used the current LNG to heavy fuel oil (HFO) ratio, which valued 3% of LNG over the total fuel consumed in 2018 [17]. Consequently, the expected biodiesel demand would account for 2.91 Mtoe and the bioLNG for 90 ktoe.