esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Resources for the Hybrid System
### Performance Level  
To evaluate the system's main parameters and corresponding control, the performance values were defined in an annual profile. The surplus energy produced by the solar panel is determined by Equation (6). This case arises when there is high electricity production due to fairly high levels of solar radiation that exceed the energy needs of the demand. The power consumption of the units considered is related to the heating system \(W_{el,sys}\), presented in Equation (7) by calculating the HP consumption where the fan, the control system, and the pumps are involved. Energy was absorbed from the public electricity grid and sized in relation to the heating levels \(W_{el,sys,grid}\) using Equation (8). The electrical demand of the building was also evaluated using Equation (9), which corresponds to the electricity for the medical center. The stored energy available for feedback to the energy system is calculated using Equation (10). The spatial performance factor (SPF) of the heat pump is determined using Equation (11). All heating of the system was considered with \(SPF_{sys}\), Equation (12), where the useful energy for heating the different compartments within the building is delivered, as well as the demand of the system. The spatial performance factor relative to the electrical grid is determined using Equation (13). The self-sufficiency ratio, Equation (14), is a proportion of energy consumption that can be supplied with photovoltaics and is determined in relation to the total building. The fraction of electricity from the photovoltaic solar panels that are consumed in the center is evaluated using the self-consumption ratio SCR, Equation (15).  
\[P_{el,PV,exc}=\max[(P_{el,PV}-P_{el,hh}),O] \tag{6}\]  
\[W_{el,sys}=\int\left(P_{el,HP}+P_{el,pumps}\right)d \tag{7}\]  
\[W_{el,sys,grid}=\int\max[\left(P_{el,sys}-P_{el,PV,exc}\right),O]d \tag{8}\]  
\[W_{el,grid}=\int\max[\left(P_{el,sys}+P_{el,hh}-P_{el,PV}\right),O]d \tag{9}\]  
\[W_{el,feedin}=\int\max[\left(P_{el,PV,exc}-P_{el,sys}\right),O]d \tag{10}\]  
\[SPF_{HP}=\frac{\int\dot{Q}_{cond}dt}{W_{el,HP}} \tag{11}\]  
\[SPF_{sys}=\frac{\int\left(\dot{Q}_{SH}+\dot{Q}_{DHW}\right)dt}{W_{el,sys}} \tag{12}\]  
\[SPF_{sys,grid}=\frac{\int\left(\dot{Q}_{SH}+\dot{Q}_{DHW}\right)dt}{W_{el,sys, grid}} \tag{13}\]  
\[SSR=1-\frac{W_{el,grid}}{W_{el,sys}+W_{el,hh}} \tag{14}\]  
\[SCR=1-\frac{W_{el,feedin}}{W_{el,PV}} \tag{15}\]
## 4 Simulation Results
### Scenario Five  
We also considered a future scenario for the university building with a PV system of 50 kWh, an ESS of 80 kWh and a smart parking lot to support 150 EVs. The simulation results showed a great reduction in building power consumption during the period from 11:00 a.m. to 6:00 p.m., as shown in Figure 10. However, there was a small reduction in the power consumption during the period between 9:00 a.m. and 11:00 a.m.  
Table 7 shows a comparison between different scenarios with respect to peak power consumption, charging/discharging costs and the number of EVs. The initial peak demand of the educational building was about 152 kW. Considering an uncontrolled charging scenario, there was a significant increase in the peak power consumption of about 26.92% (208 kW). However, scheduling EV charging with the help of the PV system and the ESS gave an opportunity to decrease the building's peak power. The simulation results showed that peak load decreased by about 14.47% and the charging cost of EVs reduced from 202,294 KRW in scenario one to 74,319 KRW in scenario five.  
Figure 10: Results of scenario five with 150 EVs, a PV system of 50 kWh and an ESS of 80 kWh.  
Figure 9: Results of scenario four with 100 EVs, a PV system of 30 kWh and an ESS of 50 kWh.
## 2 Methodology
### L-Band Observation Model  
Both carrier phase measurements and the code measurements are adopted in the POD processing. Ionospheric-free (IF) combination is used to remove the first-order ionospheric effect. The L-band observation equations about IF phase measurements \(P_{k,IF}^{j}\) and code measurements \(\Phi_{k,IF}^{j}\) are expressed as  
\[P_{k,IF}^{j}=\rho_{k}^{j}-C\cdot\left(dt^{j}-dt_{k}\right)+m_{k}^{j}\cdot T\tau _{k}+\varepsilon_{k,p^{\prime}}^{j} \tag{1}\]  
\[\Phi_{k,IF}^{j}=\rho_{k}^{j}-C\cdot\left(dt^{j}-dt_{k}\right)+m_{k}^{j}\cdot T \tau_{k}-\lambda_{IF}N_{k,IF}^{j}+\varepsilon_{k,L}^{j}, \tag{2}\]  
where \(\rho_{k}^{j}\) is the distance from the satellite \(j\) to the L-band antenna phase centers of the receiver \(k\), \(dt^{j}\) and \(dt_{k}\) are satellite and receiver clock offset, respectively; \(T\tau_{k}\) is the non-dispersive troposphericdelay in zenith direction, which can be mapped to satellite-to-receiver direction by multiply the mapping function \(m_{k}^{j}\); C is the light speed; \(\lambda_{IF}\) is the wavelength of the IF observation and \(N_{k,IF}^{j}\) is the corresponding ambiguity; and \(\varepsilon_{k,p}^{j}\) and \(\varepsilon_{k,L}^{j}\) represent the receiver noise of code and phase measurement, respectively.
## 2 Materials and Methods
### Participants and Sampling  
An online survey was conducted to evaluate pedestrians' perceived risk for the sidewalks depicted in the 61 collected images--47 illustrating sidewalks adjacent to construction sites and 14 representing normal conditions. Participants were recruited through a poster that was publicly displayed on a university campus for approximately six weeks (between 1 July 2023, and 10 August 2023). The poster included information about the study's purpose, the estimated time commitment, and compensation for participation. Each participant received a mobile gift voucher equivalent to approximately USD 10 upon completion of the survey. To distribute rewards and prevent duplicate participation, mobile phone numbers were collected solely for one-time verification. These numbers were not linked to survey responses and were discarded after reward distribution.  
The survey itself was administered through an online platform, and all responses were submitted anonymously. Prior to proceeding with the survey, participants were again informed about the general purpose of the study, the voluntary nature of their involvement, and their right to withdraw at any time without penalty. Informed consent was obtained from all participants before they began the survey. They were also assured that their responses would remain confidential. The research protocol was reviewed and approved by the Institutional Review Board of Incheon National University (Approval No.: 7007971-202305-010A).  
Table 1 summarizes the demographic characteristics of the participants. A total of 42 individuals (33 males and 9 females) voluntarily participated in and completed the survey. This sample size is comparable to those used in similar image-based studies, which have typically recruited between 36 and 40 participants [17; 31]. All participants were at least 20 years old, had experience walking along sidewalks adjacent to construction sites in urban areas, and reported no significant physical or mobility limitations. The majority of respondents (88.1%) were between 20 and 29 years old, which may reflect the university campus setting in which recruitment took place. In terms of the perceived sidewalk constraint, 64.3% of participants agreed or strongly agreed that the sidewalk became narrow due to construction work.  
Figure 2: Street images: (**a**) a sidewalk adjacent to a construction site and (**b**) a normal sidewalk.
# Prospects for the Development of High Energy Density Dielectric Capacitors
## 2 A Review of the Literature on Energy Storage Using Dielectric Materials
### Composites of Mixtures of Different Polymers  
There have been many studies [16; 17; 18; 19] of combining two or more polymers with the objective of attaining a composite with properties more optimum for energy storage. That is a higher dielectric constant, lower loss tangent, and more linear behavior at high voltages. As an example [16], PDVF, which has a high dielectric constant, but relatively high losses and limited voltage linearity, is usually combined with other polymers to obtain a composite polymer with improved loss and voltage linearity than PDVF. Mixing/blending of polymers is not as simple a process as the term would imply as the mixing takes place on a nanoscale with the configuration/spacing of the different polymer molecules being difficult to predict and measure [17; 18; 19]. Most host polymers are likely to be mixes of several polymers [15; 16]. Both of these papers involve the mixing of PVDF with several other polymers to increase the dielectric constant and reduce the conduction losses. Examples of mixing polymers are shown in Figure 1, indicating significant increases in dielectric constant in the composites.  
In order to meet the requirements for high dielectric constant, low losses, high voltage, multilayer systems in which the layers have every distinct and different properties and only one of the layers has a high dielectric constant. Reviews of multilayer, high energy density systems are given in [20; 21; 22]. The multilayer arrangement combines one or two layers having a high dielectric constant with or two layers with very high resistance, high breakdown strength, and relatively low dielectric constant. The layer(s) with very high resistance are intended to block leakage current and increase the effective breakdown voltage of the multilayer system. Research has been done on both the sandwich-type in which the high energy density layer is in the middle with two outside blocking layers and the reverse sandwich-type with the blocking layer in the middle and two high \(\varepsilon\) layers on the outside. There has been a large variation in the thickness of the layers in the multilayer systems analyzed. The effective dielectric constant of the layered system is less than that of the high \(\varepsilon\) layers, but the increased breakdown voltage results in an increased energy density of the layered system.
## 3 Results and Discussion
### UV-Vis Analysis  
The optical properties were studied by performing UV-Vis absorption spectroscopy (220 to 1400 nm), and a broad spectrum lying between 400 and 550 nm was obtained for \(\alpha\)-MnO\({}_{2}\) with a peak at 416 nm, as shown in Figure 3c. For \(\beta\)-MnO\({}_{2}\), this broad spectrum lay between 300 and 600 nm, and a peak at 576 nm, as shown in Figure 3d, with maximum absorbance was detected. A strong absorbance peak at 407 nm, as shown in Figure 3e, was obtained for \(\gamma\)-MnO\({}_{2}\), with a broad spectrum lying between 300 and 600 nm. Further, the band gap was calculated using [PERSON]'s equation, and the direct band gap of \(\alpha\)-, \(\beta\)-, and \(\gamma\)- was found to be 1.86 eV, 1.08 eV, and 1.68 eV, respectively. All the synthesized nanostructures fall under the categories of semiconducting materials. The band gap of \(\alpha\)-MnO\({}_{2}\) was higher than that of \(\beta\)-MnO\({}_{2}\) due to its smaller crystalline size [18], and \(\gamma\)-MnO\({}_{2}\) had the lowest band gap compared to \(\alpha\)-MnO\({}_{2}\), despite having the smallest crystalline size; this might be due to its semi-crystalline nature [19].
## 5 Discussion
### Computational Limitations of SHAP in Real-Time Forensic Applications  
While SHAP has demonstrated strong capabilities in producing consistent and high-fidelity explanations, one of its main limitations lies in its computational cost. This becomes particularly critical when applied to complex models or high-dimensional datasets, where explanation generation requires multiple model evaluations to estimate Shapley values. As a result, SHAP is less suitable for real-time forensic applications or environments that demand low-latency decision-making, such as critical infrastructure, online detection systems, or industrial platforms with constrained processing capabilities. Consequently, SHAP is better suited for post-incident forensic analysis, digital auditing, or retrospective investigations, where interpretability and explanatory robustness are prioritized over operational immediacy. This limitation should be carefully considered when deploying XAI techniques in cybersecurity systems with forensic requirements.
# A Review of Posture Detection Methods for Pigs Using Deep Learning
## 3 Application of Deep Learning Methods to Pig Posture Detection
### Other Deep Learning Detection Methods  
Segmentation and extraction of pig contours from binary images by segmentation algorithms (such as threshold segmentation algorithm and watershed transformation) were once the dominant approaches for detecting pigs in 2D images or depth images. On this basis, the incorporation of deep learning technologies can greatly improve performance. [PERSON] et al. [35] replaced traditional feature extraction methods with a CNN to deeply extract features from various parameters related to depth distance and pig contours. Their method could achieve a classification accuracy of 94.63% over five posture types, which outperforms earlier methods relying on manual feature extraction. The study of [PERSON] et al. [37] is another example of the efficacy of deep learning in pig contour extraction. In their work, binary segmentation was first executed by a neural network instead of traditional threshold segmentation algorithms, and then the panoptic segmentation was obtained by combining instance segmentation. This compound segmentation method enables the extraction of individual pigs at pixel-level accuracy, thereby providing valuable information for future pig posture recognition. [PERSON] et al. [83] used Mask R-CNN [84] to segment the body of pigs and determine whether their postures were curved or straight. In addition, a YOLOv4 [85] model was used to improve the detection of tails, with an average precision of about 90% to serve as an alternative to Mask R-CNN.  
In contrast to common anchor-based techniques, such as typical two-stage and one-stage models, anchor-free pig posture detection models have the great ability to independently learn the anchor scale and thereby obviate the necessity of pre-establishment of anchors, which can notably reduce the computation complexity [52]. Moreover, [PERSON] et al. [40] have developed a method that omits the use of bounding boxes and directly detects the precise posture of animals through key points located on specific body parts, including the shoulder and back. [PERSON] et al. [86] designed a CNN-based key point detector, which excels at identifying two key points in piglets, namely the snout and hip. In the study of [PERSON] et al. [39], an anchor-free CenterNet model was deployed to discriminate the standing  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Posture Classification** & **Data Source** & **Detection Model** & **Evaluation Index** & **Results** & **Advantage** & **References** \\ \hline \multirow{6}{*}{Standing, lying, and sitting} & \multirow{6}{*}{Improved} & \multirow{2}{*}{Improved} & AP\({}_{0.5}\) for & \multirow{2}{*}{99.5\%} & \multirow{6}{*}{[56]} \\  & & & localization & & & \\ \cline{3-5 and lying posture with an AP of 86.5%, and then the HRNet with Swin Transformer block (HRST) was designed for detecting the joint points of pigs in standing posture, which reached 40 frames per second in speed.
## 3 Optimization Algorithm  
### Data Preprocessing  
Before the optimization, an operation is carried out to place each \(v_{ijk}\) for \(V_{ij}\) within the interval \(T_{j}\) for each satellite j. Based on the Equations (6)-(8), \(v_{ijk}\) is placed on each interval \(t_{jl}\). Figure 6 describes the process of placing a target in each interval \(t_{jl}\) of the total interval \(T_{j}\) and creating connections between targets in neighboring intervals. The process starts by  
Figure 5: A simplified illustration of the constraints.  
placing a target in each interval. If it traverses each interval \(t_{jl}\) and the target's observation start and end time satisfy the interval inclusion condition, the target is defined to belong to \(t_{jl}\). To reduce unnecessary computation, if a target that does not belong to \(t_{jl}\) is encountered consecutively, the definition is terminated, and the same operation is performed in the next interval \(t_{jl+1}\). After the placement of targets for all intervals \(T_{j}\) is finished, connection information between each target in neighboring intervals \(t_{jl}\) and \(t_{jl+1}\) is created based on the azimuth of the targets. The pseudo code of the interval data preprocessor is described in Appendix A.1, Algorithm A1.
## 3 Electrodeposition
### Data-Driven Design of HEAs  
Computational calculations and machine learning [69] are increasingly used to predict the composition and properties of HEAs, significantly accelerating their design by processing large datasets and enabling targeted optimization. Key advances include the use of high-quality databases, domain-specific descriptors, and techniques like text mining, transfer learning, and inverse design to improve prediction accuracy and guide experiments. Computational simulations are still relatively rarely used in the context of the electrochemical deposition of HEA coatings, but they are worth mentioning as a promising tool [20; 38; 39].  
[PERSON] et al. [20] used machine learning to analyze Ni-Co anomalous codeposition as a foundational model for the synthesis of multicomponent alloys. Based on data from the literature, they found that higher concentrations of cobalt ions and the presence of agitation favor an increased Co-to-Ni ratio in the deposits, while higher current density reduces it. Interactions between bath composition, temperature, and current density determine whether the process is activation- or mass-transport-controlled, with agitation enhancing cobalt deposition at high current densities by reducing concentration gradients near the electrode.  
Other reports [38; 39] present computational simulations aimed at predicting the structure and properties of corrosion-resistant multicomponent alloys with potential applications in marine engineering. [PERSON] et al. [39] investigated the effect of metal concentration on phase evolution in the AlCrCuFeNi system by analyzing the redistribution of solid solutions during solidification. Through optimization calculations based on selected elemental concentrations and boundary conditions, they proposed an alloy composition of Al10 Cr35 Cu15 Fe12 Ni28. The simulated phase diagram for this composition revealed predominant BCC and FCC phases and indicated enhanced thermal stability. These predictions were experimentally validated by electrodepositing the alloy from a chloride-organicelectrolyte at a constant potential of \(-3\) V, resulting in only a 2% average deviation between nominal and actual compositions. The deposited alloy exhibited a uniform structure and demonstrated improved corrosion resistance in artificial seawater compared to pure copper. In turn, [PERSON] et al. [38] conducted similar studies on the CoCrFeMnNi alloy. They analyzed the domains of solid solution formation by varying criteria (e.g., Allen electronegavity, atomic radius difference, density, intermetallic formation enthalpy to mixing enthalpy ratios) and evaluated the corrosion resistance of the coating deposited on an aluminum substrate. However, the substrate itself exhibited better corrosion resistance than the deposited coating.
# Spin-Related Micro-Photoluminescence in Fe\({}^{3+}\) Doped ZnSe Nanoribbons
## 2 Experimental Section  
Fe doped ZnSe nanoribbons were synthesized by the chemical vapor deposition (CVD) method with Au used as the catalyst. In addition, 0.8 g mixture of ZnSe (Alfa Aesar, Haver Hill, MA, USA, 99.99% purity) and Fe\({}_{2}\)O\({}_{3}\) (Aladdin, 99.9%, Shanghai, China) powders, with a mass ratio of 20:1, were loaded in ceramic boat and inserted into the central region of the quartz tube mounted in the tube furnace. The cleaned mica pieces coated with about 10 nm thick Au layer were placed downstream of the quartz tube at a distance of 10 cm away from precursor. The high purity mixed gas with 10% hydrogen and 90% argon flowed through the quartz tube for one hour in order to purify the growth environment of nanoribbons. Then, the precursor was heated to 1150 \({}^{\circ}\)C for 90 min. After the reaction finished, the furnace was allowed to cool down to room temperature. Yellow colored Fe doped ZnSe nanoribbons were obtained on mica pieces.  
The structural morphology and elemental composition of Fe doped ZnSe nanoribbons were characterized by scanning electron microscopy (SEM, Zeiss SUPRA 55, Carl Zeiss, Jena, Germany) equipped with energy dispersive spectroscopy (EDS) (Carl Zeiss, Jena, Germany). The phase purity of Fe doped ZnSe nanoribbons was measured by XRD with a Bruker D8-advance X-ray diffractometer (Bruker, Karlsruhe, Germany), using a Cu K\(\alpha\) radiation source (wavelength at 1.5418 A). The 20 range is from 20\({}^{\circ}\) to 70\({}^{\circ}\) with a step of 0.02\({}^{\circ}\) and a count time of 2 s. The micro-Raman and photoluminescence (PL) spectra at room temperature were carried out by a laser cofocal photoluminescence microscope system (Acton SP2500, Olympus BX51M, Princeton Instruments, Trenton, NJ, USA) with continuous wave (CW) laser 532 nm and 405 nm as the excitation sources, respectively. Temperature-dependent PL spectra and PL lifetime measurements (5-300 K) were carried out using a liquid-helium-cooled crystal and a frequency-doubled Ti:Sapphire mode locked femtosecond pulsed laser (Mira 900, Coherent,Santa Clara, CA, USA) with emission wavelength of 405 nm as the excitation source. The duration of the laser pulse is 130 fs and the repetition rate is 76 MHz. PL lifetime decay profiles were recorded by a time correlated single photon counting system (PicoHarp 300, PicoQuant, Berlin, Germany).
## 4 Conclusions  
A comparison of LAI estimates from Sentinel-2 MSI and Landsat-8 OLI satellite data over croplands and other vegetation cover types, using different biophysical processor, was presented. The results from the linear regression analysis demonstrate a general underestimation of the LAI biophysical processor calibrated on S2 spectral bands at a 10 m spatial resolution, compared with the biophysical processor calibrated on the S2 and L8 full spectrum. The biophysical processor calibrated on the full spectrum provides stronger linear relationships between LAI estimated from S2 MSI and L8 OLI satellite sensors, encouraging the use of estimated biophysical parameters time series from virtual satellite constellation, to derive important cropland use information over large areas.  
The poster presentation is available online at [[https://www.mdpi.com/article/10.3390/IECAG2021-09683/s1](https://www.mdpi.com/article/10.3390/IECAG2021-09683/s1)]([https://www.mdpi.com/article/10.3390/IECAG2021-09683/s1](https://www.mdpi.com/article/10.3390/IECAG2021-09683/s1)).  
Not applicable.  
This work contains modified Copernicus Sentinel data Copernicus Service information (2021). Landsat8 OLI image was obtained courtesy of the U.S. Geological Survey. The author is grateful to the many individuals working on the development of free and open-source software for supporting the sharing of knowledge.  
The author declares no conflict of interest.