esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Study Limitations and Further Research  
The GPP obtained from the eddy covariance method has been considered a ground-truth measurement. This method might have some limitations due to assumptions, physical phenomena, instrument problems, and specifics of terrain and setup [43]. Anyway, eddy covariance estimations are generally considered in the literature as the best data to validate models [81].  
Although Sentinel-2 models showed good performance over almost three years, future research is needed to obtain a longer time series of GPP to make these site-specific models more robust due to the inter-annual variations of the Mediterranean climate. This fact is especially important to obtain accurate models under different climate change scenarios. In addition, adjustments in the model are necessary to better capture maxima values in spring and minima values in summer and winter, which could be crucial for ecological and economic grassland management. In this sense, it could be very useful to obtain a GPP model based only on remote sensing data to avoid dependence on site field variables. Further research is needed to try new complex models, such as machine learning techniques, to achieve a better adjustment. In addition, with longer time series, statistical methods such as time series analysis could be applied for also assessing the temporal evolution of variables through years, i.e., inter-annual variation and their dynamics [82; 83], and this fact could enhance GPP forecasting.  
The models studied here are valid for a single study area. In order to test the models, they must be applied to other grasslands with similar climatic conditions. Secondly, to upscale the model, they must be applied to other Mediterranean grasslands with different ecological and climatic characteristics. In this sense, the main limitation will be the existence of numerous flux towers to make a robust validation. Therefore, the Fluxnet [84] and ICOS [85] communities offer an excellent opportunity to try this model in other grassland sites with an Eddy Covariance flux tower.  
Finally, it is essential to transfer these models to farmers and policymakers. It would be important to make an application for mobile devices that could indicate the status of the grassland in real time by remote sensing images and the development of robust models that could estimate and forecast the Net Primary Production and the yield of the pasture. However, this fact should require training in aspects such as remote sensing techniques and information management by these stakeholders.
## 1 Introduction
The existing challenges regarding the hybrid microgrid, with a comparison of feasible solutions and the degree of research focused on each solution, are highlighted by this paper. It will not only help to identify the references for any focused topic quickly but also present the latest trends and the research gaps left regarding the hybrid microgrid. It is incumbent to have this meticulous knowledge to serve as a light for the pathway leading to future research. This paper can be divided into six sections. Section 1 gives a brief introduction regarding the survey of the hybrid microgrid. Section 2 discusses the structure of a hybrid AC/DC microgrid. In Section 3, the key issues and challenges in the integration of microgrids are observed. Section 4 covers the classification and details of optimization techniques being employed for the hybrid microgrids. Section 5 presents the coordination control techniques. Section 6 compares this review paper with other recent surveys regarding the hybrid microgrid, and Section 7 concludes the study. The main contributions of this paper are as follows:  
1. The paper discusses the integration of AC and DC subgrids and the use of interlinking converters. It unites the recent studies regarding the interlinking converters and aims to summarize the findings of the most researched section of integration and the existing research gap and latest trend in this field. * It emphasizes the protection of the hybrid microgrid and sums up the latest techniques used for protection and evaluates their performance in different operational scenarios of the hybrid microgrid. * The paper presents a critical analysis of all the optimization techniques employed in hybrid microgrids regarding power flow, power generation, minimizing the uncertainty issues, and the design and topology of the hybrid microgrid. It also finds the recent trends in optimization techniques, finds the efficiency of each area of techniques used, and suggests the latest areas of research where significant progress can be achieved regarding hybrid microgrids. * The control methods for power flow and generation, as an important aspect of hybrid microgrid, are taken into understudy for this review. It encompasses all the recent control methodologies and proposes novel strategies which are being researched and found more cost-effective. * The paper presents a comprehensive comparative analysis with the existing surveys in all the relevant fields and establishes a novel framework regarding the review of all the major research studies in one paper.
# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 2 From Vehicular Ad Hoc Networks to Internet of Vehicles
### Vehicle Ad Hoc Networks (VANETs)
It is possible to predict the link state by combining the driving direction, speed and road information of the vehicle. Nevertheless, VANET has the characteristics of openness, high-speed movement and dynamic topology change, which cause some transmission problems:  
* The stability of wireless channels is poor, which is influenced by many factors, including the current road condition information, the relative speed and direction between vehicles, the types of vehicles, the buildings along the road, etc. ;
* Rapid network topology change and short link life;
* Limited network capacity. The distribution of nodes in VANET is restricted by roads, and it presents a \"tubular\" shape. According to the calculation method of the network capacity of a random plane, it can be found that its network capacity is more limited than that of the general wireless mobile network;
* As the traffic density changes, the network load will also change greatly, so the nodes must have strong adaptability to this rapid change;
* VANET and driving safety are closely related, while OBU's operating environment is relatively harsh, which requires more stringent requirements for the reliability and safety of these devices.
## 6 Numerical Validation
### Isolated Operation Scenario  
One of the most important applications of distributed generation is the possibility of providing electrical service to remote (i.e., rural areas), where conventional power systems are non-existent [30]. To demonstrate the ability of the proposed MI-SDP model to be applicable to isolated DC distribution networks, we considered the following: (i) the slack source was removed from the 69-node test feeder, (ii) the operative voltage bounds were assigned between 0.90 and 1.00 pu, (iii) three distributed generators could be located in the DC network, and _iv)_ the capability of each distributed generator was 20 pu.  
Once that simulation scenario was evaluated, the proposed MI-SDP approach found that the best locations of these distributed generators were nodes 19, 50, and 61, with power injections of about 5.1695, 15.0132, and 18.7636 pu, respectively. The final power loss in this isolated operation scenario was about 0.0538 pu, while the low-voltage profile was about 0.9952 pu at node 11. These results confirm the possibility of extending the proposed MI-SDP model to rural applications, especially for providing service to non-interconnected zones.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Solver** & **Nodes** & **Sizes [pu]** & \(p_{\text{loss}}\) [pu]** & **Proc. Times [s]** \\ \hline DICOPT & \(\{21,61,64,69\}\) & \(\{1.4985,10.2452,2.7200,1.0931\}\) & 0.1557 & 12.4270 \\ CONOPT & \(\{21,61,64,69\}\) & \(\{1.4985,10.2452,2.7200,1.0931\}\) & 0.1557 & 3.0360 \\ MINOS & \(\{21,27,61,66\}\) & \(\{1.1471,0.3497,11.6025,2.4577\}\) & 0.1575 & 4.8520 \\ SBB & \(\{21,22,61,64\}\) & \(\{0.8870,0.6103,10.2434,3.8163\}\) & 0.1573 & 5.4250 \\ BONMINH & \(\{21,61,64,65\}\) & \(\{1.4985,10.2452,2.3027,1.5105\}\) & 0.1557 & 4.5550 \\ \hline MI-SDP & \(\{21,61,64,67\}\) & \(\{1.4986,10.2452,2.4010,1.4122\}\) & 0.1556 & 14.4688 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of the MI-SDP with the MINLP model solved in GAMS for the 69-node test feeder with four distributed generators.
## 3 Results of Field Tests  
In this study, thermal imaging analysis of solar photovoltaic modules was performed by infrared cameras (FLIR Duo R) mounted on the UAV, and IR images were immediately transmitted back to the ground station for analysis. After receiving the IR image, the ground station performs grayscale processing and Gaussian filtering. At the same time, 3-D images or color images are presented to increase the identification of defects. Finally, the IR image is analyzed through the image processing techniques introduced above. The location of the defect area is identified and the ratio of the fault area to the total area of the solar module is obtained.  
Figure 5: The image analysis of a healthy solar module in the first stage experiment 1. (**a**) IR origin image. (**b**) Gaussian filtered grayscale image. (**c**) 3-D Gaussian filtered image. (**d**) Histogram of thermal temperature image. (**e**) Cumulative chart of thermal temperature image.  
Experiments were conducted in two stages. In the first stage, modules with known conditions were applied to locate the characteristics of IR images. Four modules were used in this stage, including one in good condition, and three with defects that are already known. In the second stage, unknown modules were applied for testing the validation of the process to improve the reliability and validity of this study. A total of six modules were applied in this stage.
# Trustworthiness of Situational Awareness: Significance and Quantification
## 4 Quantifying Trustworthiness of SA
### Model for Quantifying the Trustworthiness of COP
The availability of nodes is also included in Equation (3) for the trustworthiness calculation because higher availability reinforces higher trustworthiness. Similarly, Equation (4) also includes confidentiality, integrity, and availability. Along with the three security metrics, Equation (4) includes the quality of links, which pertains to the error rates in the links. The higher the error rate of a link is, the lower its trustworthiness score. The trustworthiness evaluation of COP can be performed by using a weighted system where weights are assigned to sources based on their relative importance or effect on the operation. For instance, an operation that uses aircraft will put a lot of emphasis on the air picture, not only to safeguard airspace but also to understand what is taking place in the airspace. Thus, a higher weight will be assigned to the sources of air pictures in determining the trustworthiness of a system with air operations as compared to a solely land operation that does not involve any aircraft for the operation. Showing a commander the trustworthiness value of the COP as well as the trustworthiness value of sources making up the COP provides him/her the liberty to determine whether he/she would like to trust the COP or not. A seasoned commander can set his/her preferred minimum trustworthiness value of a source and/or the overall COP. The information sources that do not meet his/her minimum criterion can be discarded from the COP without compromising the trustworthiness of the entire COP.
# Research on Seismic Wave Quality Factor of Marble Jointed Rock Mass under SHPB Impact
## 4 Rock Mass Seismic Wave Quality Factor
### Quality Factor Calculation Method
Because of the inelastic characteristics of jointed rock mass samples, \(W_{l}\) is consumed in the loading-unloading process and will no longer transfer the elastic rod. The other part of the deformation energy is removed, which is transferred back to the elastic rod in the form of stress wave with the deformation recovery of the sample. This part of energy is called the releasable deformation energy \(W_{e}\). According to the dynamic loading process of jointed rock mass sample, when the stress wave duration ends, that is, \(t=t_{0}\), the loading stress of the sample decreases to 0 and the sample just goes through a dynamic loading and unloading cycle. At this point, the releasable deformation energy \(W_{e}\) of the sample deformation energy has been transferred to the elastic rod in the form of stress wave, and the rest is dissipated energy \(W_{l}\) so \(W_{l}=W_{s}(t_{0})\). Equation (11) can be obtained from Equation (10). \[W_{l}=W_{s}(t_{0})=W_{i}(t_{0})-W_{r}(t_{0})-W_{t}(t_{0}) \tag{11}\]  
When the stress wave interacts with the jointed rock mass sample, that is, when \(t<t_{0}\), the deformation energy \(W_{s}(t)\) of the jointed rock mass sample contains two parts of energy, namely, the released deformation energy \(W_{e}\) and the dissipated energy \(W_{l}\). Its maximum value \(W_{s}max\) is the maximum deformation energy of the deformation energy during the DYNAMIC test of SHPB, which can be expressed as Equation (12). \[W_{s}max=\max_{0\ \leq\ t\ \leq\ 10}W_{s}\ (t)=\max_{0\ \leq\ t\ \leq\ 10}\ (ACE\int_{0}^{t_{0}}\epsilon_{t}^{2}(t)- \epsilon_{r}^{2}(t)dt-\epsilon_{t}^{2}(t)) \tag{12}\]  
According to Equations (10) and (11), the dissipated energy of jointed rock mass sample can be calculated, as shown in Equations (13) and (14). \[W_{l}=W_{s}(t_{0})=Ac_{0}E\int_{0}^{t_{0}}\epsilon_{t}^{2}(t)-\epsilon_{r}^{2 }(t)dt-\epsilon_{t}^{2}(t) \tag{13}\]  
\[Q_{s}=2\pi\frac{Wsmax}{W_{l}} \tag{14}\]  
The time-history curves of three-wave strain in Figure 8 are substituted into Equations (12)-(14), and the seismic wave quality factors of jointed rock mass can be obtained with the aid of data processing software.
## 3 EMI NO\({}_{2}\) Retrieval Algorithm
### Spectral Fitting
#### 3.1.3 De-Stripe Correction  
The fitted NO2 SCD from EMI exhibits non-physical \"stripes\" (Figure 3a) like OMI [26; 31], which could result from the calibration error in the irradiance data [24]. These biases, which appeared in the initial SCD results, need to be removed by spatial filtering before subsequent procedures. The de-striping method used in this study is based on the method proposed by [PERSON] et al. (2007) for the early phase of OMI data. Some adjustments are as follows: Firstly, owing to the presence of cloud pixels, the variance for the whole window cannot be calculated. Therefore, variances of pixels in the along-track direction of each row were calculated to find a window with the least total variance. Secondly, only the first Fourier term was considered as the low frequency to be removed.  
Figure 2: The slit function of the EMI VIS1 band before and after the launch.  
Taking the data of a swath overpass China region on November 3, 2018 as an example (Figure 3), the steps of de-stripe correction are as follows:  
(1) Find the potential windows with the size of 111 across-track pixels by 100 along-track pixels in the whole orbit with SZA \(<\) 80\({}^{\circ}\), and then compute the total variance of the 111 rows in each potential window and pick the window with the smallest total variance (red window in Figure 3a);  
(2) For the selected window, use the pixels in the along-track direction to obtain 111 values of the averaged SCD (blue line in Figure 3c);  
(3) Convert the 111 mean values into the frequency domain by Fourier analysis and remove the first term with the lowest frequency, and then convert the remaining frequencies into 111 correction values (red line in Figure 3c) by an inverse Fourier transform.  
The selected window of this case has the minimum variance summation of \(6.35\times 10^{15}\) molec cm\({}^{-2}\), representing the minimum SCD variation in the along-track direction. The total SCD after de-stripe correction is shown in Figure 3b, and the averaged value of each row in the selected window is \(5.86\times 10^{15}\) molec cm\({}^{-2}\) (yellow line in Figure 3c).  
Figure 3: EMI NO\({}_{2}\) slant column density (SCD) (**a**) before and (**b**) after de-stripe correction (**c**). The data shown here is a swath overpass China region on November 3, 2018. Pixels with cloud cover have been removed.
# Investigation of Open Air Stability of CsPbBr3 Thin-Film Growth on Different Substrates
## 3 Results and Discussion
### Morphological Analysis  
To verify the effect of the aging process on the sample morphologies, SEM images were acquired for the \"as-prepared\" (Figure 3) and aged samples (Figure 4).  
Figure 3: SEM images of the “as-prepared” samples: (**a**) quartz glass; (**b**) ITO; (**c**) soda-lime glass; (**d**) (111) gold. Insets display zoomed views of the same images.  
It can be noted that, even though the deposition process was carried out in the same run and, therefore, the films were grown in exactly the same conditions, the obtained films displayed evident morphological differences. On quartz glass (Figure 3a), the deposit results constituted nanosized grains uniformly distributed all over the surface, seen at a high magnification level. A similar morphology, but with slightly larger granular features, was observed on ITO and soda-line glass (Figure 3b,c), while, on (111)-gold, the film was unevenly distributed on the substrate (Figure 3d).  
The morphology of the films after six days of aging is depicted in Figure 4.  
On quartz glass and ITO, there was an evident recrystallization of the coating, promoting the formation of a large crystalline patchwork (Figure 4a,b). On soda-line glass, there were apparently no significant morphological differences, while the film deposited on gold presented the most irregular morphology, constituting large barely interconnected granular structures which compromised the uniformity of the film (Figure 4d).
## 3 Results
### Characterization of CMSCs  
In vitro cultured CMSCs were characterized by flow cytometry. The panel of markers was adapted to the criteria of the International Society for Cell Therapy (IST) [10]. CMSCs isolated from human chorion, after passage 1, showed expression of characteristic mesenchyme expression and absence of hematopoietic features. The cell-surface markers analyzed using FACS revealed that CMSCs after passage 1 had positive expression of CD105, CD90, CD73, CD29, CD44, and CD54 and negative expression of CD45 and CD34. CMSCs cells were positive for many markers common to MSCs. The graph in Figure 3a,b shows the expression of the markers observed. Multilineage CMSCs potential was tested for the three following types--osteogenic, chondrogenic, and adipogenic differentiation. After 2-3 weeks, the presence of calcium deposits, chondrogenic matrix, and lipid vacuoles was observed. The differentiating ability of CMSCs under suitable stimulatory culture conditions was confirmed (data not shown).  
Figure 3: (a) Phenotype expression of mesenchymal and haematopoietic markers of CMSCs at passage 1, CD105+, CD90+, CD73+, CD29+, CD44+, CD54+, CD45–, and CD34–. Histograms represent mean \(\pm\) S.E of seven independent experiments and (b) representative flow cytometry analysis for CMSCs at passage 1.  
Figure 2: (a,b) Colony-forming unit fibroblast (CFU-F) colonies formation from CMSCs after passage 1 with Romanowsky–Giemsa staining (10\(\times\) magnification of objective).
# Real-Time Detection of Insulator Defects with Channel Pruning and Channel Distillation
###### Abstract  
Insulators are essential for electrical insulation and structural support in transmission lines. With the advancement of deep learning, object detection algorithms have become primary tools for detecting insulator defects. However, challenges such as low detection accuracy for small targets, weak feature map representation, the insufficient extraction of key information, and a lack of comprehensive datasets persist. This paper introduces OD (Omni-dimensional dynamic)-YOLOV7-tiny, an enhanced insulator defect detection method. We replace the YOLOv7-tiny backbone with FasterNet and optimize the convolution structure using PConv, improving spatial feature extraction efficiency and operational speed. Additionally, we incorporate the OD (Omni-dimensional dynamic)-SlimNeck feature fusion module and a decoupled detection head to enhance accuracy. For deployment on edge devices, channel pruning and channel-wise distillation are applied, significantly reducing model parameters while maintaining high accuracy. Experimental results show that the improved model reduces parameters by 53% and increases accuracy and mean average precision (mAP) by 3.9% and 2.2%, respectively. These enhancements confirm the effectiveness of our lightweight model for insulator defect detection on edge devices.  
object detection; YOLOv7-tiny; insulator defect; channel pruning; channel-wise distillation +
Footnote †: journal: Applied Sciences
## 2 Background and Literature Review
### Window Analysis Based on Energy, Environmental and Industrial Ecological Efficiency  
Many studies have considered the Malmquist Productivity Index (MPI) to examine panel data, but [PERSON] and [PERSON] [10] presented that MPI does not appropriately reflect the features of technical growth, and the efficiency progress index attained might not be fair. Additionally, desirable and undesirable outputs have diverse technical structures, including technical heterogeneity. Consequently, window analysis is considered more appropriate approach. Based on the window analysis context, the efficiency of a DMU in one period can be compared with its efficiency in another period, and the efficiency of the other DMUs can also be compared, to reflect the heterogeneity between DMUs over a sequence of covering windows [11; 12]. Based on the serious resource consumption and ecological pollution, the expansion of China's green economy will greatly influence the nation's future global economic development. Initially, ecological DEA method is used to analyze Green Economic Efficiency (GEE) at the regional level in China. Then, according to panel data, the window analysis method is applied to examine the regional differences of China's regional GEE. The total GEE of China is gradually growing, and the local variances are still noteworthy, and the development of GEE can support to decrease regional changes [13]. [PERSON] and [PERSON] [14] considered the DEA window method to examine the environment efficiency of 17 countries over 1980-2002. They evaluated the presence of a Kuznets type connection among countries' environmental efficiency and national income. Permitting for active effects they found that the modification to the target ratio is instantaneous. They also find that augmented economic activity does not continuously guarantee environmental safety and accordingly the path of development is significant in addition to the progress itself. [PERSON], [PERSON], [PERSON] and [PERSON] [11] considered a total-factor framework to examine energy efficiency in 23 developing countries over 1980-2005. They discovered the total-factor energy efficiency and change trends by using DEA window analysis. Seven countries show little change in energy efficiency over time. Eleven countries had nonstop reductions in energy efficiency. Among five countries perceiving incessant progress in total-factor energy efficiency, China had the highest growth.