esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal and Performance Analysis of a Photovoltaic Module with an Integrated Energy Storage System
## 5 Conclusions  
The thermal analysis of a commercial PV module with an integrated energy storage system has been carried out in this paper. Energy storage was implemented by a set of thin flat batteries placed on the back side of the module, without exceeding the original thickness of the aluminum frame. An airgap was intentionally introduced between the batteries and backsheet, for the sake of air cooling, with a thermal radiation shield to prevent overheating of the batteries. The thermal analysis was carried out by introducing a simplified 1D thermal model for the PV module, with and without the batteries.  
Thermal models were numerically implemented by Simulink/Matlab, and different verification tests were carried out to validate the model results. Both PV cells and batteries temperature were extrapolated by the proposed thermal models, considering the different environmental conditions within the 12 months of the year (using midday values). In general, it has been proved that the batteries' temperature does not exceed 40 \({}^{\circ}\)C, which leaves a safety margin as compared to the maximum operating temperature given by the manufacturer (50-55 \({}^{\circ}\)C).  
As expected, despite the cooling airgap, there is a remarkable PV cell temperature increase introduced by the back side batteries, estimated at 20-25 \({}^{\circ}\)C for the PV cells on the battery area.  
Figure 11: Average PV cells temperature profile over the day (April 15) with and without integrated storage batteries, and corresponding air temperature (\(T_{a}\)) and sun irradiance (\(G_{n}\)).  
Figure 10: Estimation of the power from the PV module with integrated storage batteries in % compared to the original PV module without batteries, corresponding to Table 4.  
Considering the average PV cell temperature over the whole PV module, this increment is reduced to 10-15 \({}^{\circ}\)C, representing a reasonable over-temperature in terms of the restriction of the PV conversion efficiency. A first estimation of the electric power decrease in real operating conditions is in the order of 6%, considering the worst case scenario for each month of the year (solar midday on a sunny day).  
By the proposed thermal model, the analysis can be readily extended to the average PV module temperature over the whole year in order to precisely estimate the reduction in annual production of electric energy and to evaluate the overall performance of the integrated PV generation-storage system. A more detailed investigation of the electrical performance over the whole year will be the subject of a future paper.  
[PERSON] was the corresponding author and, with [PERSON], contributed to the manuscript composition, model developments, and thermal measurements. [PERSON] also implemented and simulated the system by Simulink. [PERSON] and [PERSON] supervised the manuscript composition and the experimental tests, carried out at the Department of Electrical, Electronic and Information Engineering, University of Bologna (Italy).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Permeability vs. Mean Pore Radius  
We also computed the mean pore radius \(r_{p}\) for each sub-volume extracted from heterogeneous and fractured samples to study its relationship with absolute permeability. Along with other properties, the pore radius plays a key role in the evaluation of the permeability of porous media. For example, if we assume that a porous medium consists of many capillary tubes (pores) with a radius \(r\), then the permeability of this porous medium can be calculated by following the relationship \(k=\phi r^{2}/8\). Obviously, the permeability is sensitive to the pore radius. In the literature, the equation \(k=c\phi^{7}l^{\delta}\) is used to describe the relationship between \(k\) and \(r_{p}\) for various porous media, where \(l\) is the characteristic length of a porous medium [22; 23; 24; 25; 26]. [PERSON] and [PERSON] presented the equation \(k=8.5\big{(}\phi r_{cr}^{2}\big{)}^{1.3}\) to describe the relationship between the permeability, porosity, and critical pore radius of various artificial and natural porous materials [25]. It should be stated that below we use the mean radius of interconnected pores, \(r_{pc}\), and all pores, \(r_{p}\) (interconnected + unconnected).  
Figure 12 illustrates the permeability of sub-volumes from all samples before and after their dissolution versus \(\phi r_{p}^{2}\) (Figure 12a,c) and \(\phi_{con}r_{pc}^{2}\) (Figure 12b,d). Figure 12a,c show that the relationship between permeability and \(\phi r_{p}^{2}\) can be described well using the power law \(k=c\left(\phi r_{p}^{2}\right)^{\varepsilon}\), especially for heterogeneous samples, where \(\varepsilon\) and \(c\) are the power exponent and coefficient of proportionality, respectively. As can be seen from Figure 12, \(\varepsilon\) varies from 1.87 to 4.77 before and from 2.94 to 4.29 after rock dissolution, respectively. As  
Figure 11: Permeability vs. \(\tau\) (**a,c**) and \(S\) (**b,d**).  
it turns out, the relationship \(k\sim\phi_{cont}r_{pc}^{2}\) is stronger compared to the relationship \(k\sim\phi r_{p}^{2}\) (Figure 12a) for heterogeneous samples, which can be seen from the higher coefficients of correlation \(R^{2}\) of the equation \(k\sim\phi_{cont}r_{pc}^{2}\) (Figure 12b). Figure 6 demonstrates that the mean pore radius is also one of the important factors that significantly affect the permeability of a porous medium.
## 2 Literature Review
### Preliminary Feasibility Assessments  
During the construction planning process, designers conduct a preliminary feasibility study to ensure consistent compliance with various regulatory conditions and determine the extent of the fulfillment of requirements. This assessment must be repeated for every design modification, requiring significant time and effort from the designers. The goal is the optimal execution of an architectural plan that satisfies the maximum number of requirements while complying with various regulatory requirements. Thus, by automating labor-intensive tasks and exploring design alternatives with optimal performance, unnecessary time and financial expenditures can be avoided.  
[PERSON] and [PERSON] [28] have presented a process for identifying design alternatives that excel in daylight and energy performance. This method relies on building simulations and the application of a genetic algorithm to design variations stemming from a parametric design. This emphasizes improving the design solution by automatically identifying building design alternatives and optimizing daylight and energy efficiency for these choices.  
[PERSON] et al. [44] developed a generative design tool geared towards curtailing embodied emissions (EE) during the conceptual phase of high-rise residential structures. This tool takes into account the industry-standard design objectives and constraints. In summary, a new geometric system is introduced. The proposed GenGHG system is groundbreaking in the creation and evaluation of design alternatives. Remarkably, the design alternatives generated using this system manifested a 7% reduction in EE, in contrast to the non-optimal alternative solutions.  
[PERSON] et al. [37] put forth a semantic method to automate the automatic compliance checking (ACC) process for building projects. The ACC procedure retrieves rule terms and logical associations from regulatory documents via natural language processing (NLP), checks semantic similarities, and matches the extracted terms with BIM data keywords (either concepts or attributes). Subsequent SPARQL queries were autonomously constructed, based on the logical relationships between the BIM keywords, to ultimately verify regulation compliance.
## 4 Data Preparation for Neural Networks
### Static and Dynamic Offset Detection  
On-board instrumentation in vehicles is often installed with misalignments between its own reference system and that of the vehicle. This can lead to miscalculations during  
Figure 7: Filtered vs. no-filtered channels.  
certain procedures. To prevent this, it is important to detect and correct these misalignments or offsets. There are two types of offsets that can be identified: static and dynamic. To fix these offsets, the authors followed the subsequent procedures:  
* In order to detect the static offset, it is required to select a range of data acquisitions with the vehicle in stationary conditions. Once the range is selected, the average values of longitudinal and lateral accelerations, as well as longitudinal velocity channels are calculated within the selected range. These average values are then subtracted from the original signals;
* To detect the dynamic offset, a specific range of acquisitions is chosen in which the vehicle is moving on a straight line with a constant speed, usually in the pit lane. Once the range is selected, the average steering angle, yaw rate, and sideslip angle values are calculated within that range. These average values are then subtracted from the original signals to obtain the dynamic offset.  
The non-contact optical sensor, which was used to acquire longitudinal velocity and sideslip angle, can have also an inclination in the \(xy\) plane. This inclination can be calculated with Equation (4).  
\[\beta_{0}=\arctan(\frac{V_{y_{0}}}{V_{x_{0}}}) \tag{4}\]  
where \(V_{y_{0}}\) and \(V_{x_{0}}\) are, respectively, the mean of the longitudinal and lateral velocity measured in straight stationary maneuvers. This angle enables the correction of optical sensor measurements.
## 9 Real-World Applications, Case Studies, and Tools
### Content Moderation and Policy Compliance  
AI agents have revolutionized content moderation workflows across digital platforms through sophisticated annotation capabilities. Content moderation exemplifies this transformation, where platform-specific policies encoded into agent prompts enable the consistent, high-speed labeling of user-generated content across categories including hate speech, harassment, and inappropriate material [125]. On the other hand, agent-based solution significantly reduce the psychological burden on human moderation teams while maintaining annotation quality [126]. Research studies have demonstrated that LLM-based annotation agents can match crowd worker accuracy for hate speech detection while processing content exponentially faster [107]. Industries have further advanced this paradigm by developing systems where LLMs serve dual roles as both content generators and safety annotators, creating a closed-loop system that ensures policy compliance throughout the content lifecycle [127; 128]. These agent-based moderation systems represent a paradigm shift in how platforms handle the ever-increasing volume of user-generated content requiring supervision. Community-aware friend-suggestion engines now fuse text, interaction graphs and topic interest vectors, providing rich multimodal labels that autonomous annotators can exploit for continual learning [129].
## 4 Experiments
### Measurement of Relative Nonlinearity Parameter  
The received surface acoustic wave signals, their frequency spectra, and the linear fitting plots of \(A_{1}\)\({}^{\prime}\)\({}^{2}\) and \(A_{2}\)\({}^{\prime}\) are shown in Figure 3 for aluminum and copper specimens. Figure 3a-c shows the results of the aluminum specimen and Figure 3d-f shows the results of the copper specimen. The surface acoustic wave signals shown in Figure 3a-d were obtained at the highest laser intensity, and Figure 3b-e shows their frequency spectra. The magnitudes of the fundamental and second-order harmonic frequency components are indicated by red dots. The fundamental frequency in aluminum was detected at 1.0 MHz and the second harmonic frequency was detected at 2.0 MHz, as intended. In copper, the fundamental frequency and second harmonic frequency components were detected at 0.74 MHz and 1.47 MHz, as expected.  
Figure 2: Experimental setup to generate and receive surface acoustic waves using the fully non-contact laser-ultrasonic technique.  
Note that a relatively large third harmonic is generated, because the surface acoustic wave close to the square wave form is generated by the line-arrayed slit mask. In this case, even-order harmonics are suppressed; however, the occurrence of odd-order harmonics is unavoidable [20]. When the third harmonic frequency is put in together with the fundamental frequency, the magnitude of the second harmonic frequency may change owing to the mixing effect. Nevertheless, as the magnitude of odd-order harmonics depends on the fundamental frequency, only the constant '8' in Equation (1) will vary. However, because this constant will be canceled in the calculation of the relative ratio in Equation (6), Equation (8) is valid as it is and does not affect the proposed measurement technique based on the relative ratio.  
The linear relationship between \(A_{2}\)' and \(A_{1}\)'2 is shown in Figure 3c,f. In both specimens, the R-squared values are approximately 0.98, which confirms the linearity between \(A_{2}\)' and \(A_{1}\)'2. The measurements were repeated four times at each laser power, the deviation was so small that it cannot be seen in the figure.  
Table 1 shows the relative nonlinearity parameter measurement results of each specimen and their ratio.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material** & **Relative Nonlinearity Parameter, \(\beta^{\prime}\)\({}_{SAW}\)** & \(\beta^{\prime}\)\({}_{SAW}\), _Copper/\(\beta^{\prime}\)\({}_{SAW}\), _AI2024_ \\ \hline Al2024 & \(25.71\pm 0.60\) & \(1.333\pm 0.119\) \\ Copper & \(34.27\pm 2.20\) & \(1.333\pm 0.119\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement values for the relative nonlinearity parameter of Al2024 and copper.  
Figure 3: Experimental results: (**a**–**c**) represent Al2024, and (**d**–**f**) represent copper. (**a**,**d**): Raw signals, (**b**,**e**): frequency spectra of raw signals, (**c**,**f**): linear fitting of \(A_{1}\)’2 and \(A_{2}\)’.
## 4 Discussion
### Pulp Response to Dental Bleaching  
The pulpal response to dental bleaching has undergone extensive investigation [18; 19; 98; 99; 100]. Indeed, in a systematic review conducted by [PERSON] [13], the influence of different light sources on pulp tissue response during dental bleaching procedures was examined. The review findings suggested that the application of different light parameters during dental bleaching can yield distinct effects on pulp tissue. Remarkably, it was observed that PBM, when implemented with specific parameters (10 J/cm\({}^{2}\) on cells [19]), offers a promising potential for mitigating the detrimental impact on pulp tissue caused by bleaching. However, these authors noted that the establishment of a safe and effective protocol for the clinical application of PBM in dental practice remains an ongoing pursuit.  
Clinically, pulp health can be examined through its sensitivity [53; 55; 56; 58; 59; 60; 61; 62; 63; 67; 69; 70], but also through the analysis of DNA-damage biomarkers, or proteolytic and/or ROS activities within the dentin-pulp complex [57; 66]. In the current study, the laser-activated groups did not demonstrate significantly higher sensitivity. Similarly, other investigation methods (e.g., analysis of biomarkers and ROS activities) did not yield negative results for the test groups. To be specific, out of the 14 investigations related to sensitivity, five reported positive effects [53; 55; 63; 65; 70], eight showed no significant difference [58; 59; 60; 62; 64; 67; 69], and only one had a negative outcome [56]. Both studies examining DNA damage biomarkers and proteolytic activities within the dentin-pulp complex found no significant difference between the laser-activated and control groups [57; 66]. While these outcomes appear encouraging, we were unable to identify any statistically significant impact of contact time, wavelength, fluence, handpiece type, or gel thickness on tooth sensitivity in the analysis conducted on the studies included in this systematic review.  
Presently, there is a growing trend towards performing minimally invasive procedures, and hence efforts have been made to decrease the concentrations of HP in bleaching gels, including the incorporation of catalytic nanoparticles, as well as adjusting the duration and frequency of treatment sessions [101; 102; 103]. Although these modified approaches do not achieve the same level of effectiveness as traditionally applied higher concentrations [104], there is a consensus that prioritizing biological safety is preferable over the speed of the procedure [71]. As noted herein, our statistical analysis showed that the laser-activated groups required lower HP levels to achieve results similar to those of the control groups.
## 3 Results and Discussion
### Determination of Effective Moisture Diffusivity and Activation Energy  
Effective moisture diffusivity (D\({}_{\text{eff}}\)) describes the conductive aspect of every possible mechanism of moisture transfer. It depends mainly on the temperature, the sample's water content, and the product texture temperature. Therefore, it is a key element to describe drying kinetics. In addition, the activation energy is also a fundamental drying parameter that defines the energy required for both moisture evaporation and diffusion, it is derived from experimental drying curves.  
The variation of the effective diffusivity coefficient with drying time at the three temperatures is shown in Figure 7. The values of D\({}_{\text{eff}}\) for _Arbutus unde_ L. were \(1.51\times 10^{-9}\), \(2.68\times 10^{-9}\) and \(4.70\times 10^{-9}\) m\({}^{2}\)/s at 60, 70, and 80 \({}^{\circ}\)C, respectively (Table 4).  
The effective diffusion coefficient increased as the drying air temperature increased. This can be due to the increase in the rate of the air heat supply to the fruit samples, and the agitation and accelerated movement of water molecules inside the fruit matrix, which ended up increasing the drying rate. The increased temperature augmented the energy needed for the rotational, transitional, and vibrational motion of water vapor. This induced a higher gradient of moisture, thus increased the mass transfer rate, which results in higher moisture diffusivity [66]. These moisture diffusivity values obtained for the _Arbutus unde_ L. were within the range given for food materials moisture diffusion (\(10^{-11}\) to \(10^{-6}\) m\({}^{2}\)s\({}^{-1}\)) [67; 68].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Temperature of Drying (\({}^{\circ}\)C)** & **D\({}_{\text{eff}}\) (m\({}^{2}\)/s)** & **r** & **E\({}_{\text{a}}\) (kJ/kg)** & **R** \\ \hline
60 & 1.5147 \(\times\) 10\({}^{-9}\) & 0.9750 & 2203.62 & 0.9486 \\
70 & 2.6863 \(\times\) 10\({}^{-9}\) & 0.9651 & & \\
80 & 4.7046 \(\times\) 10\({}^{-9}\) & 0.9563 & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Values of the effective diffusivity and activation energy of the _Arbutus unde_ L.  
Figure 7: Effects of both air flow and temperature on the effective diffusion coefficient.  
The activation energy is defined as the amount of energy needed to remove moisture of a solid sample. Higher value of the activation energy means higher temperature sensibility of the Deff coefficient [62]. The average activation energy recorded for _Arbutus unedo_ L. is 2203.46 kJ/kg with an \(R^{2}\) of 0.9486 (Figure 8). Similar results were reported for white mulberry [69], and for black mulberry fruits [57].  
\[D_{eff}=1.0588\times 10^{-3}\exp\left(-\frac{265.0493}{T}\right) \tag{18}\]
###### Abstract  
Future Geosynchronous Synthetic Aperture Radar (GEOSAR) missions will provide permanent monitoring of continental areas of the planet with revisit times of less than 24 h. Several GEOSAR missions have been studied in the USA, Europe, and China with different applications, including water cycle monitoring and early warning of disasters. GEOSAR missions require unprecedented orbit determination precision in order to form focused Synthetic Aperture Radar (SAR) images from Geosynchronous Orbit (GEO). A precise orbit determination technique based on interferometry is proposed, including a proof of concept based on an experimental interferometer using three antennas separated 10-15 m. They provide continuous orbit observations of present communication satellites operating at GEO as illuminators of opportunity. The relative phases measured between the receivers are used to estimate the satellite position. The experimental results prove the interferometer is able to track GEOSAR satellites based on the transmitted signals. This communication demonstrates the consistency and feasibility of the technique in order to foster further research with longer interferometric baselines that provide observables delivering higher orbital precision.  
interferometry; orbit determination; GEOSAR +
Footnote †: journal: Remote Sensing
## 2 Method  
This paper aims to review the existing models. A survey of the advantages and disadvantages of each model might lead to recommendations for developments in future models. This paper differentiates the models into three categories: statistics of collision rates, statistical models, and simulation models. The advantages and disadvantages of the models are compared in order to gain a full overview of the risk elements for more advanced models.  
Risk assessment for the probability of collision between ship traffic and structures has been evolving over time. Statistical probability results can be derived from historical accident databases. Nevertheless, when the accident databases are not available, statistical models are used for probabilistic assessments. A successful probabilistic assessment depends on proper consideration of the elements that significantly contribute to the risk.  
The scope of this review consists of two dimensions. First, this research yields insights into the historical background, procedures, and advantages and disadvantages of each statistical model for ship collisions with structures. Afterwards, comparisons of risk elements in the models are considered. Second, this paper examines the existing simulation models designed for probabilistic risk assessment. The simulation models mimic the autonomous individual ship collision avoidances and uncertainties that are not so evident in statistical models, as [PERSON] [10] found that few waterway risk analysis applications reflect uncertainty. The advantages and disadvantages are discussed in relation to typical existing simulation models. This review will shed light on the way to retain the advantages in the next generation of probabilistic risk assessment models. The methods for model improvement are also discussed.  
This literature review was conducted by searching the digital library of Wuhan University of Technology, including ScienceDirect(r), Scopus(r), Google Scholar, other databases, and relevant journals. There are some important materials that are not presented as electronic versions, e.g., three random models [11] are also included to gain a full picture of the development of models.  
The search of the databases consisted of combinations of key words such as \"risk\", \"probabilistic\", \"safety\", \"collision\", \"assessment\", \"ship\", \"bridge\", \"structure\", \"maritime\", and \"marine\". The references in the relevant literature reviews [12; 13] were also included in the examination of the existing research. The last resources were the relevant standards (e.g., ASSHTO model) and models introduced by experienced experts [14]. As a result, there were a total of 245 references considered; however, this overview only selected representative or innovative models, as some of the papers were applications or minor discussions.