esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of an Intelligent Solution for the Optimization of Hybrid Energy Systems
## 4 Conclusions  
This work focused on the problem of optimizing HES. In this context, it was proposed to develop an optimal solution based on ontology.  
The HES are systems that combine multiple energy sources to meet electricity needs. They allow improvements in terms of energy efficiency and increasing integration of RE sources. However, the process of optimizing these systems is complex, especially with its distributed and interactive nature with the external environment.  
In an HES, energy sources and storage tools are combined to meet consumer demand for electricity while ensuring the lowest cost. These tasks are the main goals for HES optimization. However, because of the intermittent nature of RE, the optimization of the HES is difficult, which depends principally on the data of energy sources and climate. Responding to the optimization problem is precisely the objective of this work.  
There is a set of tools for HES optimization, such as algorithms and software, where each is based on one or hybridization between several approaches (probability, linear programming, fuzzy logic, neural networks, etc.). There is also some software for HES optimization, the most popular being Hybrid, TRNSYS [38], Hybrid2, Homer [39], and HOGA [40]. However, the choice between these tools is considered a real problem. First, a generic optimization solution for HES was developed. This contribution shows its reliability following an explanatory scenario and based on real data from Algerian sites. The results indicate the importance of the proposed solution for real uses, where the user can benefit from many advantages, such as saving time, accuracy, and ease of use.  
For future works, this solution needs collaborative work with experts in many fields, which will allow the development of the existing solution in many points, as well as the introduction of some other technologies, such as multiagent systems, especially the mobile agent, which can navigate to the source of information to take it instead of staying in a fixed address and communicate with the rest of the agents based on network protocols, which makes the solution more effective. Finally, extensive testing must be performed for other case studies to find the limits of the use of this solution.  
Formal analysis, D.S.; writing--original draft, D.S.; software, D.S. and H.H.; methodology, D.S. and O.C.; validation, D.S. and F.H.; funding acquisition, O.C., F.H. and H.H.; supervision, D.S., O.C. and Y.S.; visualization, F.H. and H.H.; writing--review and editing, Y.S., A.H. and H.H.; project administration, D.S. All authors have read and agreed to the published version of the manuscript.  
Froncess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia, Project Number PNURSP2022R236.  
Not applicable.  
This project was supported by Princess Nourah bint Abdulrahman University researchers supporting Project Number (PNURSP2022R236), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.  
The authors declare no conflict of interest.
## 3 Results
### Trends
#### 3.3.2 Trends of the Aerosol, Tropospheric NO\({}_{2}\) and Clouds Radiative Effect  
The contribution of the aerosol, tropospheric NO\({}_{2}\), and cloud radiative effect on the SSR trends is reflected in the RE\({}_{\text{aer}}\), RE\({}_{\text{NO2}}\), RE\({}_{\text{Ic}}\) and RE\({}_{\text{iic}}\) magnitude trends, which are shown in Figure 9 and in Table 3. The corresponding timeseries, along with the trends, the relative trends, and their uncertainties for each station, are shown in Figures S13-S16, correspondingly. As the radiative effect takes negative values, it is underlined once more here (see also Section 2.2.3) that the trends refer to the magnitude of the radiative effect rather than its algebraic values. Figure 8: SSR trend for the period 2005–2019 over 16 locations in Greece based on (**a**) data produced here with SBDART, (**b**) CM SAF SARAH-2.1 SSR data and (**c**) CERES EBAF edition 4.1 SSR data. (**d**) SSR trend based on ground-based observations from the stations of Athens and Thessaloniki. Statistically significant trends at the 95% confidence level are indicated with a cross. Negative RE\({}_{\rm aer}\) magnitude trends are found over all the locations, except for HCMR. The strongest statistically significant RE\({}_{\rm aer}\) magnitude trends to appear over the urban locations of Patras (\(-0.35\) W/m\({}^{2}\)/year or \(-1.74\) %/year) and Thessaloniki (\(-0.31\) W/m\({}^{2}\)/year or \(-2.05\) %/year), with Xanthi, Argos, Volos, Orestiada and Antikythera following (see Table 3). The negative RE\({}_{\rm aer}\) magnitude trends are driven by the negative AOD\({}_{550}\) trends shown in Figure 10a and Figure S17, and in Table 4. The strongest statistically significant AOD\({}_{550}\) trends are seen over Patras, Thessaloniki, and Xanthi (\(-0.005\)/year or \(-2\) to \(-2.5\)%/year). The AOD\({}_{550}\) trend values for all the 16 locations of interest shown in Table 4 (ranging from \(-0.005\)/year to zero) are in accordance to the average trend value of \(-0.0022\)/year for the Eastern Mediterranean, given in a previous study using C6 MODIS/Aqua data for the period 2002-2015 [90]. The RE\({}_{\rm NO2}\) magnitude, despite being negligible as shown in Section 3.2, exhibits a negative trend over all the locations examined here, except for one (Antikythera) (see Figure 9b and Figure S14, and Table 3). The strongest statistically significant trends appear over the urban locations of Kozani (\(-0.008\) W/m\({}^{2}\)/year or \(-3.91\)%/year), Athens (\(-0.008\) W/m\({}^{2}\)/year or \(-3.44\)%/year) and Thessaloniki (\(-0.004\) W/m\({}^{2}\)/year or \(-2.47\)%/year). The negative RE\({}_{\rm NO2}\) magnitude trends reflect the negative trends of the tropospheric NO\({}_{2}\) VCD appearing over all the locations, except for Antikythera (Figure 10b and Figure S18, and Table 4).
## 3 Optimization of the Hybrid Last-Stage Launch Vehicle
Figure 6 provides the comparison of trajectory, velocity, and flight path during the flight for the optimization results of Design ID 20245 for the hybrid last stage, along with the reference points of the solid last stage, for the Minotaur-I launch vehicle [56]. When Figure 6 is examined, it is observed that the values obtained because of the optimization study led to reaching the target orbit in a shorter time than the reference, and in the cruise flight phase, the final stage experiences less speed loss due to being lighter than the reference. The flight path angle gradually decreases and approaches zero after the steep climb, as expected. Tables 5 and 6 provide a comparison between the trajectory and propulsion characteristics of Design ID 20245 and the reference values. It is noted that the altitude reached as a result of the optimization is 747.6 km, which is 0.863% different from the target orbit altitude of 741.2 km. Furthermore, the velocity required for the payload to reach the target orbit is 7482.2 m/s. The velocity achieved as a result of optimization is 7425 m/s, with a 0.763% difference from the reference value. The change in propellant type, transitioning from HTPB to HTPB/LOX, indicates the use of an oxygen-enhanced hybrid propulsion system. This alteration is particularly noteworthy in the \(m_{p}\) parameter; while Minotaur-I has a mass of 770.2 kg, this value has decreased to 416.2 kg in the optimized Minotaur-I-like configuration. Additionally, significant differences are observed in the stage length and thrust values. In the optimized configuration, Minotaur-I-like has increased the \(L^{*}\) value from 1.34 m to 2.93 m, but this has led to a decrease in \(T\) (from 36.9 kN to 34.48 kN). As a result of these changes, the \(l_{sp}\) value has substantially increased from 287 s to 308.95 s,  
Figure 6: The trajectory of Design ID 20245 and reference points. indicating that the rocket operates more efficiently. Moreover, \(t_{\text{bum}}\) has significantly decreased in the Minotaur-I-like configuration, going from 66.8 s to 36.65 s. This implies that the rocket can complete its mission in a shorter time frame. Finally, \(I_{t}/w_{0}\) has increased in the Minotaur-I-like configuration, from 253.3 to 257.7. This signifies that the launch vehicle, relative to its total mass, provides more thrust, optimizing its performance. These changes demonstrate that Minotaur-I-like, compared to the Minotaur-I configuration, is an optimized design that achieves significant improvements in specific performance metrics.
# A Photon Imaging Detector Model with High Resolution and High Counting Rate
###### Abstract  
A cross strip (XS) anode detector is a photon-counting imaging detector with high spatial resolution. However, due to the Poisson distribution characteristics of the photons emitted by the target, photons with a small time interval will cause signal superposition and resolution degradation. This is particularly significant at a high photon count rate. The key link that restricts the counting rate of the XS detector is the electronic system. In this paper, we propose a new electronic signal processing system scheme using a digital trapezoidal shaping filter instead of a traditional Gaussian shaping filter, which enables the detector to maintain a high resolution at high count rates. In order to verify the feasibility of the scheme, the relationship between shaping errors and shaping parameters is studied. Furthermore, the relationship between spatial resolution and photon-counting rate at different noise levels is revealed by numerical simulation. The results show that the detector can achieve a spatial resolution of \(<\)50 \(\upmu\)m at a photon count rate of \(>\)6 MHz for 1000 e RMS noise.  
cross strip anode; trapezoidal shaping filter; spatial resolution; high photon count rate +
Footnote †: journal: Physics Letters A  
1
Footnote 1: email: [EMAIL_ADDRESS]  
Footnote †: journal: Physics Letters A
# Smart Internet of Things Power Meter for Industrial and Domestic Applications
## 3 Proposed Solution
### Hardware Architecture
#### 3.1.2 Power Supply: Main Power Supply, Battery Backup, and Overvoltage Protection  
The power meter was designed to offer maximum functional availability. From the power supply perspective, this was achieved using a primary power source connected to the grid and a backup power supply represented by a rechargeable battery. The main power supply provides power to the device during normal operational conditions (grid voltage is present in the nominal range), and the battery is used only in emergency conditions (grid voltage absence, under or overvoltage).  
To withstand the voltage variation of the grid, a wide input voltage range AC/DC converter was chosen: Multicomp MP-LD05-23B05R2. It accepts an input voltage range between 85 and 305 V AC, allowing the device to operate even in grid undervoltage and overvoltage scenarios. The 1 A output current (at 5 V) is sufficient for powering the internal circuits. A slow-acting, 2 A fuse and a varistor were added to protect the input side of the circuit, and an AC line filter reduces the unwanted grid noise. The power backup circuit was based on a 3.7 V, 620 mAh Li-Po battery connected to a specialized battery charging IC: Analog Devices MAX8903 AETI+T. This circuit is connected to the 5 V output of the main power supply and charges the battery when the grid voltage is present.  
A switching circuit based on PNP and P-MOS transistors was implemented to allow the device to be powered from the battery if the main supply becomes unavailable. The output generated by the energy source selecting circuit is then fed to the input of a buck-boost power supply, implemented with the ISL91127 from Renesas Electronics, that stabilizes the voltage at 3.5 V. To minimize the noise in the circuits, we used decoupling capacitors and ferrite beads.  
The Op-Amps and ADC used in the signal conditioning and acquisition circuits require both a positive and a negative power supply. For the positive supply, a 3 V, 300 mA output current LDO voltage regulator was used: Texas Instruments TLV74330 PDBVR.  
The negative voltage was generated using a Texas Instruments LM2660 charge-pump voltage converter and then passed through a Microchip MIC5271 negative LDO regulator. The output voltage was set to \(-1.2\) V and the maximum output current is 100 mA. Both circuits are powered from the main power supply.
## 6 Discussion
### Strengths and Limitations of the DFDLOF Framework
#### 6.1.2 Limitations  
Despite these strengths, DFDLOF has limitations that need addressing. One of the primary challenges is the complexity of integrating such a framework into existing educational infrastructures. The requirement for substantial data collection and processing, along with the need for advanced machine learning expertise, might pose challenges for some educational institutions.  
Another limitation is the potential risk of data privacy and security concerns. With DFDLOF relying heavily on learner data, ensuring the security and ethical use of these data is paramount. There is also a risk of over-reliance on technology, potentially overshadowing critical human elements of teaching and learning.  
Finally, while DFDLOF is designed to be adaptable to various learning contexts, its effectiveness in diverse cultural and socio-economic settings remains an area for further exploration. Ensuring the framework is equally effective and accessible across different demographics is crucial for its widespread applicability.  
To provide a clearer overview, Table 5 summarizes the strengths and limitations of the DFDLOF framework, along with specific examples and analyses:
## 3 Applications
### Light Modulators  
Another very useful property of GST material is the large complex refractive index contrast between their amorphous and crystalline states and low loss in the infrared region [51; 55; 85]. This enables the writing of GST-based structures designed for telecom and mid-IR wavelengths where the losses are sufficiently low for highly versatile applications. [PERSON] et al. demonstrated a path towards electrically actuated phase-change pixels for transmissive and reflective spatial light modulators in the near and mid-infrared region.  
More generally, since the optical properties of these two states in the devices differ dramatically, and this difference leads to a variety of important applications, such as, plasmonic devices [86], phase modulators [87], waveguide devices [88], chiral metamaterials[89; 90], and low epsilon media [91].
## 4 Discussion
A reliable _in-line_ quantification of CDW, RCDW, and subsequently PHA concentration was reached until a CDW of 84 g\(\cdot\)L\({}^{-1}\). To increase the robustness of the method further at higher cell densities, the calculated gas-hold up in the bioreactor [45] could be integrated in the correlation of the PDW spectroscopy signals. In order to quantify the direct impact of the PHA concentration on the optical coefficients, further studies referencing cell counts and sizes by flow cytometry and microscopy need to be conducted. A wavelength of 638 nm was used to evaluate the PDW spectroscopy signals during this study, which did not show any correlations with the oil addition or emulsification (Figure 3). The emulsification process is very important for an efficient growth. It was previously shown that an overexpression of lipases results in a reduced lag phase and subsequently a more efficient process [43]. In previous studies, it was shown that PDW spectroscopy was used to measure emulsions [46; 47]. An _in-line_ determination of the oil content or the emulsion formation would be a very valuable additional information for bioprocess development and process control. This information might result from integration of additional wavelengths into the PDW spectroscopy set-up. To summarize, PDW spectroscopy allows _in-line_ estimation of the CDW, RCDW, and PHB content in real-time. In contrast, _off-line_ analysis is typically carried out to determine the PHB content, which includes drying the cells and polymer derivatization before time-consuming HPLC or GC analysis. By using PDW spectroscopy, process development and scale-up could be accelerated. In addition, this technology could be used at a large scale for process monitoring and control of _R. eutropha_ cultivations. Specifically, the real-time adjustment of feeding strategies according to the PHA production rates--determined by PDW spectroscopy signals--holds great potential.
# Plasma-Sprayed Flexible Strain Sensor and Its Applications in Boxing Glove
## 3 Materials, Manufacturing, and Testing
### Manufacturing  
#### 3.2.1 Fabrication of Flexible Sensors  
Flexible sensors with different patterns were fabricated using the proposed controllable plasma spraying method for conductive nanoparticles (Figure 6). For comparison, flexible sensors were also fabricated using a spraying method without the plasma treatment. The air pressure during the spraying process was 3 L/min, the amplitude of the plasma power supply voltage was 8.20 V, and the frequency was 89.7 kHz.
###### Abstract  
Generally, the results of imaging the limited view data in the inverse scattering problem are relatively poor, compared to those of imaging the full view data. It is known that solving this problem mathematically is very difficult. Therefore, the main purpose of this study is to solve the inverse scattering problem in the limited view situation for some cases by using artificial intelligence. Thus, we attempted to develop an artificial intelligence suitable for problem-solving for the cases where the number of scatterers was 2 and 3, respectively, based on CNN (Convolutional Neural Networks) and ANN (Artificial Neural Network) models. As a result, when the ReLU function was used as the activation function and ANN consisted of four hidden layers, a learning model with a small mean square error of the output data through the ground truth data and this learning model could be developed. In order to verify the performance and overfitting of the developed learning model, limited view data that were not used for learning were newly created. The mean square error between output data obtained from this and ground truth data was also small, and the data distributions between the two data were similar. In addition, the locations of scatterers by imaging the out data with the subspace migration algorithm could be accurately found. To support this, data related to artificial neural network learning and imaging results using the subspace migration algorithm are attached.  
artificial neural network; inverse scattering problem; limited view; subspace migration algorithm +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON] Construction of Full-View Data from Limited-View Data Using Artificial Neural Network in the Inverse Scattering Problem. _Appl. Sci._**2022**, 12, 9801. [[https://doi.org/10.3390/app1299801](https://doi.org/10.3390/app1299801)]([https://doi.org/10.3390/app1299801](https://doi.org/10.3390/app1299801))
# Strategic Development of Piezoelectric Nanogenerator and Biomedical Applications
## 2 Piezoelectric Materials
### Single-Crystal Materials  
The first class of piezoelectric materials contains single-crystal materials; ZnO, GaN, InN, and ZnS fall in this category and primarily exhibit a wurtzite structure. Among these, ZnO has been extensively explored for PENG applications owing to its availability and biocompatibility.  
The first PENG was fabricated using ZnO nanowire arrays [11], which raised the problem of low output efficiency. Since then, numerous attempts have been made to enhance the output; for example, a PENG was built using a single nanowire laterally placed on a flexible substrate with both ends connected to the electrode. The nanowire was stretched by bending the flexible substrate to generate piezoelectric potential along the nanowire. The piezoelectric response was produced via periodic bending and relaxation of the nanowire in the form of an electric signal. Such a PENG based on a laterally grown single-ZnO nanowire generates an output voltage of 20-50 mV and a short-circuit current of 400-750 pA. A PENG based on laterally grown ZnO nanowires was employed to harvest energy from human motions, such as finger tapping and heartbeats [23].  
Researchers introduced the VING and LING structures because of the low output performance of single-nanowire-based nanogenerators. The fabrication of VING and LING was achieved by integrating ZnO nanowire arrays vertically and laterally, respectively; thus, they are also referred to as integrated nanowire nanogenerators. Integrated nanowire PENGs produced a maximum open-circuit voltage and short-circuit current of 1.26 V and 28.8 nA, respectively [24]. Although the PENG with an array of ZnO nanowires was able to deliver a decent output, it was relatively low due to the poor piezoelectric characteristics of ZnO. This led to further research aiming to discover materials with good piezoelectric properties, namely, the piezoelectric coefficient and dielectric constant.
# Lower-Body Positive Pressure Treadmill Training for Pediatric Gait Disorders: A Scoping Review
## 3 Results
### Effect of Anti-Gravity Treadmill Training on Muscle Strength, Spasticity and Gross Motor Function  
Some studies investigated the effect of anti-gravity training on lower-limb muscle strength [18; 27; 28] and spasticity [26], as well as gross motor function [20]. Few studies assessed muscle strength in isometric [27; 28] or concentric mode [18] or the rate of force development [28]. They highlighted an increase in ankle and knee isometric muscle strength, as well as in the rate of force development after anti-gravity training. Regarding spasticity, [PERSON] et al. [26] reported a decrease in intrinsic and reflex stiffness following anti-gravity training. The changes were greater than those observed in the control group. Finally, [PERSON] et al. [20] reported a significant improvement in the gross motor function measure (GMFM-66) after the different training modalities (AlterG, BWS and Lokomat)  
Figure 1: Prisma flow chart of the selection process.  
in both GMFM-D (standing section) and GMFM-E (walking-running section). However, there was no statistically significant difference between the groups in terms of GFMF-D and GMFM-E.