esa-sceva/satcom-chunk-collection huggingface dataset:
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 4 Results and Discussion
### Agrovoltaico
#### 4.7.3 The Planning Process  
The project was realized in four phases between 2010 and 2012 and addresses landscape and architectural planning scales. The scope was to overcome legislative and public acceptance barriers for the installation of PV systems in agricultural areas through an innovative solution allowing food and energy production with the regional regulation on land occupation. The private investor REM initially invested 2.5 million euros in the pilot project before replicating it in two other Italian locations for a total of 30 million euros in value including the satellite activities and nearly 700 people involved.
# Personalized Video Summarization: A Comprehensive Survey of Methods and Datasets
## 4 Classification of Personalized Video Summarization Techniques
### Video Domain  
The techniques can be divided into two categories: those whose analysis is domain specific and those whose analysis is not domain specific.  
* In techniques that refer to _domain-specific_ analysis, video summarization is performed in that domain. Common content areas include home videos, news, music, and sports. When performing an analysis of video content, blur levels are reduced when focusing on a specific domain [11]. The video summary must be unique to the domain. To produce a good summary in different domains, the criteria vary dramatically [54].
* In contrast to domain-specific techniques, _non-domain-specific_ techniques perform video summarization for any domain, so there is no restriction on the choice of video to produce the summary. The system proposed by [PERSON] et al. [67] can generate a video summarization without relying on any specific domain. The summaries presented in [12; 13; 14; 17; 20; 22; 25; 27; 30; 31; 33; 35; 46; 48; 52; 55; 56; 58; 59; 60; 61; 64; 66; 68; 69; 70; 71; 74] are not domain specific. The types of domains found in the literature are personal  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Paper** & \multicolumn{2}{c}{**Type of Summary**} & \multicolumn{2}{c}{**Features**} & \multicolumn{2}{c}{**Domain**} & \multicolumn{2}{c}{**Time**} & \multicolumn{2}{c}{**Method**} \\ \hline [PERSON] et al., 2019 [71] & & x & & x & x & x & x \\ \hline [PERSON] et al., 2019 [61] & x & & & x & x & x & x & x \\ \hline [PERSON] et al., 2019 [28] & x & & x & x & x & x & x & \\ \hline [PERSON] et al., 2019 [65] & x & & x & & & x & x & x \\ \hline [PERSON] et al., 2020 [52] & x & & x & & & x & x & x \\ \hline [PERSON] et al., 2020 [55] & x & x & x & & & x & x & x \\ \hline [PERSON] et al., 2021 [30] & x & & x & & & x & x & x \\ \hline [PERSON] et al., 2021 [25] & x & x & x & x & x & x & x & x \\ \hline [PERSON] et al., 2021 [58] & x & & & x & x & x & x & x \\ \hline [PERSON] et al., 2022 [31] & x & & x & x & x & x & x & \\ \hline [PERSON] et al., 2022 [33] & x & & x & & x & x & x & \\ \hline [PERSON] et al., 2022 [70] & x & & x & & x & x & x & x \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
Figure 6: Distribution of the papersreported in Table 1 according to the type of features of the personalized summary.  
videos [53; 54], movies/serial clips [19; 28; 29; 36; 50], sports videos [18; 36; 38; 39; 40; 41; 42; 43; 44; 45], cultural heritage [62], zoo videos [8], and office videos [26].  
Table 2 provides a classification of works by domain type. Figure 7 presents the distribution of papers in the domain of personalized summary. In Figure 7, it can be seen that the large number of works produce personalized non-domain-specific summaries at a percentage of around 67%, in contrast to the works that produce personalized domain-specific summaries at a percentage of around 33%.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 3 Results and Analysis  
### Effect of Kirigami Structure on Crawling Efficiency  
#### 3.1.1 Experimental Conditions  
To investigate the influence of kirigami pattern geometry on locomotion performance, five distinct cut configurations--linear, triangular, trapezoidal, hybrid linear-triangular, and hybrid linear-trapezoidal--were fabricated on 100 \(\upmu\)m transparent PET sheets ([PERSON]'s modulus E = 4.33 GPa, [PERSON]'s ratio \(\upnu\) = 0.4). Each pattern was designed with a cut density of \(3\times 14\) units, uniformly distributed across a 51 mm \(\times\) 140 mm area and aligned longitudinally with the direction of actuation. The crawling performance of each kirigami configuration was evaluated on a compliant polyurethane (PU) foam substrate, chosen to replicate soft, friction-sensitive terrain. A precision PM linear actuator with a 30 mm stroke and a constant extension-retraction speed of 15 mm/s was employed to drive locomotion. For each test, the crawler underwent five full actuation cycles, and the average crawling distance was recorded to quantify the effect of pattern geometry on traction, deformation behavior, and net displacement.
## 4 Case Study  
### Data and Parameter Setting  
The validity of the proposed optimal control and operation strategy was demonstrated by applying it to a simulated power system, as illustrated in Figure 10. This system is composed of thermal units, hydro units, and wind farms. The thermal units are combined with a gas turbine G2 (50 MW) and steam turbine G1 (100 MW). The hydro units have two hydro turbines G3 and G4, rated at 70 MW and 60 MW, respectively. The load is based on modeling to represent a linear frequency dependency on the active power characteristics. Wind farms WF1-WF5 (each 16 MW) were linked to the small power system and an individual wind farm consists of eight PMSG wind turbines (each 2 MW). To solve the KE optimization problem, the maximum iteration number in the PSO is set to 100, and the population size is set to 50. \(r_{1}\) and \(r_{2}\) are uniform random factors with assigned values between 0 and 1 in each  
Figure 9: Proposed optimal strategy scheme.  
step and for each particle component to add randomness to the velocity update. The inertial weight \(w\) is set to reduce from 0.9 to 0.4 in different iterations. Each of the acceleration coefficients, \(c_{1}\) and \(c_{2}\), is set to between 0.5 and 2.5 to achieve a mean value of unity for the stochastic factor [30]. Based on the characteristics of the wind turbine, Figure 11 depicts the correlated wind speed profiles for individual wind farms in the small power system. These results are simulated using the PSCAD/EMTDC tool by adopting the parameters described in the Appendix A.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Parameter Settings
#### 5.1.2 Parameter Settings for CPS-MOEA  
In applying the CPS-MOEA to the optimization of a mobile photovoltaic-diesel-storage microgrid system, a systematic and methodical approach was employed. The process began with the formation of an initial population of 50 candidate solutions. This size was chosen to ensure a broad and diverse range of starting points for the optimization, which is crucial for the success of evolutionary algorithms. This diversity allows the algorithm to explore a wide variety of solutions, increasing the chance of finding optimal solutions.  
The next step involved the genetic operations phase, where crossover and mutation operations were applied to the candidate solutions. These operations are fundamental to the evolutionary algorithm framework, as they introduce variation and enable the exploration of new solution spaces. By simulating natural evolutionary processes, these genetic operations help in discovering potentially optimal solutions by combining and altering existing solutions. Over 100 generations, these genetic operations were repeatedly performed, with each cycle involving the selection of the best solutions, applying genetic operations, and evaluating the new solutions. This iterative process is crucial for gradually enhancing the solution quality, aiming to improve upon the objectives defined for the mobile photovoltaic-diesel-storage microgrid system.  
After conducting 5000 function evaluations--a significant achievement in the context of evolutionary algorithms--a set of solutions representing the Pareto front was obtained. The Pareto front is a collection of non-dominated solutions, each representing an optimal trade-off between the multiple objectives pursued in the study.  
This outcome is a testament to the capability of evolutionary algorithms, like CPS-MOEA, to achieve well-converged and diverse sets of solutions, especially for complex multi-objective optimization problems.
## 4 Experiment 1
### Results
The significant interaction between background type and \(L_{\text{Aeq,B}}\) in predicting annoyance (\(p<0.05\)) can be observed in Figure 5. While, with increasing \(L_{\text{Aeq,B}}\), annoyance from stimuli with eventful background sounds slightly increased, it decreased for the stimuli with less eventful background sounds. Furthermore, practically no difference could be observed between annoyance from the stimuli with less eventful and those with eventful background sounds at a background level of 34 dB(A). That is, when the level of the background sound was sufficiently low, it did not matter which type of background sound was used; at this background sound pressure level, annoyance was not different on average from that for the stimuli without background sound. Figure 5: Interaction (experiment 1) background type and \(L_{\text{Aeq,B}}\): Mean acoustic comfort ratings across subjects and their 95% confidence intervals are depicted. Circle (\(\blacktriangledown\)), square (\(\blacksquare\)) and cross (\(\bigtimes\)) stand for no, less eventful, and eventful background, respectively. Note that only the annoyance range from 4–7 is shown here, although the scale ranged between 0 to 10. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Parameter** & **Symbol** & **Coeff.** & **95\% CI** & \(\mathbf{p}\) \\ \hline Intercept & \(\mu\) & \(-25.056\) & \([-26.754;-23.359]\) & 0.00 \\ Background type & \(\tau_{\text{B},i}=\text{No}\) & 0 \({}^{a}\) & \\  & \(\tau_{\text{B},i}=\text{L}\) & 0.642 & \([-0.083;1.366]\) & 0.08 \\  & \(\tau_{\text{B},i}=\text{E}\) & \(-0.353\) & \([-1.191;0.485]\) & 0.41 \\ \(L_{\text{AE,F}}\) & \(\beta\) & 0.413 & \([0.392;0.435]\) & 0.00 \\ \(L_{\text{Aeq,B}}\) & \(\gamma\) & \(-0.017\) & \([-0.036;0.002]\) & 0.09 \\ Playback no. & \(\theta\) & 0.017 & \([0.011;0.024]\) & 0.00 \\ Type\(\times L_{\text{Aeq,B}}\) & \(\tau_{\text{B},i}=\text{No}\) & 0 \({}^{a}\) & \\  & \(\tau_{\text{B},i}=\text{L}\) & 0 \({}^{a}\) & \\  & \(\tau_{\text{B},i}=\text{E}\) & 0.030 & \([0.003;0.056]\) & 0.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiment 1, model 2: model coefficients (Coeff. ), their 95% CI, and probabilities (\(p\)) of the linear mixed effects model for annoyance. The parameters and symbols are explained in Equation (2).
## 5 Conclusions  
This study focuses on the prediction of RF-EMF levels at a university campus. First, band-selective measurements were performed to determine the contribution of 2G/3G/4G services to the total RF-EMF level. Contributions for each frequency band were extracted from these measurements, and it was proven that 99% of the total EMF in the environment originated from base stations. Then, the long-term RF-EMF measurements were conducted for 35 days in a location where the population density is high. The measurement results showed that weekly averaged RF-EMF levels varied between 1.1968 V/m and 1.3880 V/m, which are well below the ICTA's limit. Additionally, the RF-RMF levels measured on Wednesday and  
Figure 16: Fit plot for (**a**) Monday, (**b**) Sunday.  
Thursday showed a higher correlation compared to the other days. These measurement data were processed and utilized to create an ANN model with LM and BR learning algorithms to predict the RF-EMF levels. The average MSE performances of the proposed LM and BR models were evaluated, and there were no significant performance differences between the models. The regression performances of the models were also similar, and the best prediction performance was obtained for Monday with 99.6% accuracy, whereas Sunday's performance was with an accuracy of 93.8%. The proposed models help to predict future RF-EMF levels accurately in the medium using existing measurement results.  
This research received no external funding.  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The author declares no conflict of interest.
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 5 Conclusions  
In this paper, we analyze the RCS performance and deformation monitoring accuracy of a novel dihedral CR array under multi-resource SAR data. A simplified model for estimating the RCS reduction caused by the deviation angle between the SAR incident angle and the CR's axis is presented. Through verification using multi-resource SAR data and various types of CR on the Huangtupo slope, it was found that the proposed model introduces a systematic overestimation bias of approximately 2 dBsm with the neglecting of panel dielectric constants and related parameters. Through a comparative analysis of RCS time series derived from the proposed model and the actual measurements, it was identified that certain dihedral CRs exhibit environmental obstructions and installation defects. The methodology presented in this paper could be used to assess the CRs' product quality and the environment condition around CRs.  
The analysis of the RCS time series results from overlapping Sentinel data indicates that for the horizontally placed dihedral CR, the deviation angle difference is approximately 12\({}^{\circ}\). This leads to a reduction of about 3 dBsm in the RCS of the horizontally placed dihedral CR with a side length of 0.4, which may result in maximum 0.5 mm deformation accuracy  
Figure 11: The theoretical deformation monitoring accuracy of the CR array in the Sentinel (**a**) and TSX (**b**) data.  
loss in the Sentinel data. This characteristic will serve as a vital reference for extracting homogenous strong scatterers, such as building walls, in the overlapping regions under Sentinel data.  
Based on the observed result of the theoretical deformation monitoring accuracy of the CR array on the Huangtupo slope, it is evident that the compactly designed CRs in this study were capable both with ascending and descending multi-resource SAR data. Our results show that for Sentinel data, a square panel with a side length of 0.4 m can achieve 1 mm deformation monitoring accuracy, and for X-band spotlight mode data, the size can be reduced to 0.2 m.  
In the future, establishing CR arrays with larger scales could provide more accurate models for the RCS reduction by deviation angles through empirical measurement results.  
Conceptualization, T.L.; Methodology, J.L. and S.M.; Validation, Y.X.; Formal analysis, S.M. and Y.X.; Resources, T.L.; Writing--original draft, J.L.; Writing--review & editing, T.L., Y.W. and and G.N.; Supervision, and G.N.; Funding acquisition, T.L. All authors have read and agreed to the published version of the manuscript.  
This research is supported by the National Natural Science Foundation of China (NSFC), grant number 42074031, U2340228, 41674032.  
The TSX datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Results
### Analytical Results  
Table 1 presents the analytical results of the critical Euler and yield load analysis for each component of the MFAU cell, based on the linear [PERSON] and [PERSON] theory. In this section, the critical slenderness ratio of the TPU material, \(\alpha_{cri}\), is compared with that of each column component, \(\alpha_{col}\). As mentioned, if the slenderness ratio of a material is less than that of each column component, the [PERSON] theory is valid for the analysis, while if the value of \(\alpha_{cri}\) is higher than the value of \(\alpha_{col}\), the [PERSON] theory is employed instead. As can be seen in Table 1, the two column components of the MFAU cell model, which are set for a free-fixed boundary condition, fail in buckling before yielding under uniaxial compression. It is important to mention that this boundary condition was chosen because it can potentially predict the critical buckling load in a worst-case design, and because it is difficult to identify the boundary conditions of both columns in the MFAU cell model due to its complex geometry.  
Nevertheless, the value of the critical buckling load of the vertical column is close to the load at the yield point of the TPU material. This means that the failure behaviour of the vertical column can alternately fail in buckling and yielding if the higher load level is  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Vertical Column** & **Diagonal Column** \\ \hline \(\alpha_{cri}\)\(<\)\(\alpha_{col}\) & 47.60 \(>\) 20 & 47.60 \(<\) 67.04 \\ Theory & Johnson & Euler \\ \(F_{cri}\) (N) & 34.00 & 9.68 \\ \(P_{y}\) (N) & 37.17 & 37.17 \\ \(\sigma_{cri}\) (MPa) & 19.21 & 5.47 \\ \(\sigma_{y}\) (MPa) & 21.00 & 21.00 \\ Mode of failure & Buckling & Buckling \\ \hline \hline \end{tabular}
\end{table}
Table 1: Analytical buckling results for both column components.  
Figure 6: Stress-slenderness ratio construction of an MFAU cell failure line.  
applied to the MFAU cell model. While the critical load of the diagonal column is 0.26\(\times\) of the yield load of the material, the column first shows buckling behaviour in the linear elastic region before yielding as the compressive loading is applied. It is worth noting that the independent buckling behaviour of each component will be provided in the following section through the numerical results.
# A Progressive Semantic-Aware Fusion Network for Remote Sensing Object Detection
## 4 Experimental Results and Discussion
### Experimental Results and Discussion
#### 4.4.2 Results on the NWPU VHR-10 Dataset  
In Table 2, we compare our ProSAF-Net with several advanced methods, including YOLOv3 [12], Faster R-CNN [10], FMSSD [54], CAD-Net [29], EGAT [55], Cascade R-CNN [49], YOLOv7 [14], CANet [56], ABNet [16], and TBNet [53]. Among these approaches, our ProSAF-Net achieves the highest mAP of 96.57%. Specifically, it achieves the best AP in three out of ten categories: ship, baseball field, and bridge. The ship category consists of relatively small objects in the images, empirically validating the operational efficacy of ProSAF-Net in sub-pixel target recognition. The outstanding capability in small object detection stems not only from efficient feature fusion but also from the integration of contextual information, which is essential. This is primarily achieved through SCAM, which leverages both global and local contextual information to effectively distinguish foreground objects from the background while suppressing background noise. For baseball field and bridge categories, the resemblance between their appearances and surrounding environments poses a significant challenge. The fact that ProSAF-Net performs well in these categories indicates its robustness in distinguishing objects from complex backgrounds. While our method may not yield the highest performance in every category, it ranks second in storage tank and vehicle categories, further demonstrating its competitive performance. To better illustrate the effectiveness of  
Figure 6: Representative examples of detection outcomes from ProSAF-Net on the RSDD dataset.  
our approach, Figure 7 visualizes the detection results produced by ProSAF-Net. Some of the images have relatively complex backgrounds. For example, in the first row's third image, small boats on the sea can be confused with ocean waves, which poses a challenge for object detection. However, our method effectively detects and localizes these targets. Additionally, objects such as basketball and tennis courts exhibit colors similar to their surrounding environments, further demonstrating the robustness of our approach in distinguishing objects from complex backgrounds.  
Figure 7: Representative examples of detection outcomes from ProSAF-Net on the NWPU VHR-10 dataset. These images cover a variety of complex environments, such as mountainous areas, urban streets, and ocean surfaces. Additionally, they include objects of various scales, ranging from small objects like airplanes and ships to large objects such as ground track fields and basketball courts. Our method demonstrates high detection accuracy across all scenarios.
# Human Activity Recognition Using CSI Information with Nexmon
## 7 CSI Data Preprocessing and Feature Extraction
### Feature Extraction for Experiment 1.1  
After applying PCA, the CSI matrix \(CSI_{pca}\) is used in two different ways for the machine learning algorithms. First, by extracting seven features, namely:  
1. Standard Deviation
2. Mean Absolute Deviation (MAD)
3. Median
4. MeanSecond Central Moment (Variance)  
Third Central Moment (Skewness)  
Fourth Central Moment (Kurtosis)  
Thus for two PCA components, it provides a total of fourteen features for every \(CSI_{pca}\) data matrix as \(CSI_{features}\) which is used in Experiment 1.1 and further used for SVM, see Algorithm 5.  
\(\chi\) = 1800 number of files for activities  
**Input:** reduced dimension matrix \(\chi\) with \(CSI_{pca}\) [\(150\times 2\)] ;  
**Output:** The features as \(CSI_{features}\)  
1. \(CSI_{out}\) [\(150\times 14\)] \(\leftarrow\) Feature extraction on \(\chi\) with (\(CSI_{pca}\) [\(150\times 2\)])
2. \(CSI_{features}\) \(\leftarrow\)\(CSI_{out}\) [\(150\times 14\)]  
**Algorithm 5** Feature Extraction  
In the second method, the extracted \(CSI_{pca}\) is used for the LSTM network without extracting any feature matrix for Experiment 1.2.  
The block diagram representation of the whole preprocessing is shown in Figure 7.  
Figure 7: CSI-denoised using Hampel, DW00, and then PCA.