esa-sceva/satcom-chunk-collection huggingface dataset:
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 4 Experimental Results and Discussion
### Risk Map  
This study, based on the InSAR time-series data of Guangzhou from October to December 2022, employs a trained LSTM risk assessment model to calculate and map the road collapse risk distribution in five urban districts of Guangzhou. The specific risk distribution is illustrated in Figure 6, where the IDW interpolation method is utilized for risk value visualization. From an overall perspective, the high-risk road sections for road collapse in Guangzhou city in 2022 exhibit evident spatial heterogeneity and clustering characteristics. In the western and southern regions, high-risk areas present a contiguous distribution pattern, while the northern region demonstrates a generally low-risk situation. The overall map reveals significant differences in the road collapse risk assessment results among various urban districts in Guangzhou, displaying a distinct spatial differentiation pattern. These disparities may be closely related to multiple factors such as the urbanization process, infrastructure conditions, and geological environmental conditions in each region. Consequently, it is necessary to formulate differentiated risk prevention and control strategies based on the actual circumstances of each region.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Recall** & **F1** & **AUC** \\ \hline LSTM & 0.764 & 0.643 & 0.722 & 0.766 \\ GRU & 0.713 & 0.545 & 0.672 & 0.632 \\ SVM & 0.722 & 0.521 & 0.633 & 0.581 \\ KNN & 0.748 & 0.631 & 0.711 & 0.739 \\ RF & 0.687 & 0.619 & 0.670 & 0.693 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of model performance.  
Figure 6: Risk map.
###### Abstract  
This paper presents a UAV-swarm-communication model using a machine-learning approach for search-and-rescue applications. Firstly, regarding the communication of UAVs, the receive signal strength (RSS) and power loss have been modeled using random forest regression, and the mathematical representation of the channel matrix has also been discussed. The second part consisted of swarm control modeling of UAVs; however, a dataset for five types of triangular swarm formations was generated, and K-means clustering was applied to predict the cluster. In order to obtain the correct swarm formation, the dendrogram of all types was investigated. Finally, the heat map and contour were plotted for all kinds of swarm clusters. Furthermore, it was observed that the RSS of proposed swarms had good agreement with swarm distances.  
Keywords:path-loss; artificial intelligence/machine learning for UAV communications; clustering; UAV swarm control; integration of UAV communications with space and terrestrial networks; emergency services +
Footnote †: journal:
## 2 Materials and Methods
### Whole-Network Adjustment Method  
The Whole-network adjustment method uses multi-source clock offset data to obtain the clock offset parameters of satellite-ground and inter-satellite of all satellites. Assuming there is \(m\) satellites and \(n\) inter-satellite links, the \(m\) satellite-ground clock offsets and \(n\) inter-satellite clock offsets can be performed by whole-network adjustment method, and the equation can be abbreviated as:  
\[\mathbf{L}=\mathbf{C}\mathbf{x}-\mathbf{v} \tag{5}\]  
where \(\mathbf{L}=\begin{pmatrix}\Delta T_{i}(t)\\ \Delta T_{ij}(t)\end{pmatrix}_{(m+n)\ast 1}\) represents the discrete point of \(m\) satellite-ground and \(n\) inter-satellite clock offset; \(\mathbf{C}=\begin{bmatrix}\mathbf{C}_{i}\\ \mathbf{C}_{ij}\end{pmatrix}_{(m+n)\ast 3m}\) refers to the coefficient matrix of observation time; \(\mathbf{x}=\begin{pmatrix}x^{1}\\ \vdots\\ x^{i}\\ \vdots\\ x^{m}\end{pmatrix}_{3m\ast 1}\) indicates the column vector of clock offset parameters; \(x^{i}=\begin{pmatrix}a_{0}^{i}\\ a_{1}^{i}\\ a_{2}^{i}\end{pmatrix}\) denotes the clock offset parameter of each satellite; \(\mathbf{v}=\begin{pmatrix}0\\ v_{ij}\end{pmatrix}_{(m+n)\ast 1}\) refers to the link delay error [22]. The coefficient matrix is expressed as follows:\[\mathbf{C}_{i}=\left[\begin{array}{cccccccccc}1&t-t_{0}&\left(t-t_{0}\right)^{2}&0&0& 0&\cdots&0&0&0\\ 0&0&0&1&t-t_{0}&\left(t-t_{0}\right)^{2}&\cdots&0&0&0\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ 0&0&0&0&0&0&\cdots&1&t-t_{0}&\left(t-t_{0}\right)^{2}\\ \end{array}\right]_{m\times 3m}\]  
\[\mathbf{C}_{ij}=\left[\begin{array}{cccccccccc}0&\cdots&-1&-\left(t-t_{0}\right) &-\left(t-t_{0}\right)^{2}&0&\cdots&1&\left(t-t_{0}\right)&\left(t-t_{0}\right) ^{2}&0&\cdots\\ 0&\cdots&-1&-\left(t-t_{0}\right)&-\left(t-t_{0}\right)^{2}&0&\cdots&1&\left(t -t_{0}\right)&\left(t-t_{0}\right)^{2}&0&\cdots\\ \cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots&\cdots\\ 0&\cdots&-1&-\left(t-t_{0}\right)&-\left(t-t_{0}\right)^{2}&0&\cdots&1&\left( t-t_{0}\right)&\left(t-t_{0}\right)^{2}&0&\cdots\end{array}\right]_{n\times 3m}\]  
The principle of least squares is used to obtain the normal equation corresponding to the above equation:  
\[\mathbf{C}^{\mathrm{T}}\mathbf{P}(\mathbf{L}+\mathbf{v})=\mathbf{C}^{\mathrm{T}}\mathbf{P}\mathbf{C}\mathbf{x} \tag{6}\]  
where \(\mathbf{P}\) refers to the weight matrix of the observed column vector, and it can be assigned as the identity matrix when all satellites and atomic clocks are stable according to the classical least square method [23]. The normal equation is solved to obtain the clock offset parameter \(\mathbf{x}\) of satellite-ground and inter-satellite multi-source data of all satellites.
# Fine-Tuned Visual Transformer Masked Autoencoder Applied for Anomaly Detection in Satellite Images
## 4 Conclusions  
The results presented in this paper can be viewed as a demonstration of a general approach to the anomaly detection task. Model evaluation shows that, in general, this approach may be successfully applied to different models that are assembled into an encoder-decoder architecture. However, it is also crucial to note that the anomaly detection accuracy results are considerably lower for the custom dataset. It is also worth mentioning that the results may be influenced by the low resolution and the level of detail present in each image from the custom dataset. This paper addresses the first use of the fine-tuned Masked Vision Transformer Autoencoder for a custom-built satellite image dataset. The classifier was trained in a self-supervised manner. The experiment described in Section 3.3 could be treated as a proof of concept for the method to be used in different datasets and in different but more uniform environments. Inferring from the experiment, it can be stated that the model designed for the experiment was not capable of discriminating between different and diverse environment samples and one should limit the area of interest to a single type of landscape or scenery. However, the model was not sufficiently effective to be applied to the general task of anomaly detection for all possible types of satellite images, and it was shown that fine-tuned models were capable of detecting novelties for a custom dataset. Additionally, with an established image resolution, the problem can be transferred to aerial images and applied in various related fields.  
Conceptualization, J.G. and J.K.; methodology, J.G. and J.K.; software, J.G.; validation, J.G.; formal analysis, [PERSON]; investigation, J.G. and J.K.; resources, [PERSON]; data curation, [PERSON]; writing--original draft preparation, [PERSON]; writing--review and editing, J.G. and J.K.; visualization, [PERSON]; supervision, J.K.; project administration, [PERSON]; funding acquisition, J.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a research subsidy from AGH University of Krakow 16.16.120.773.  
Not applicable.  
The dataset used is the ImageNet-1K dataset (openly available on [[https://www.image-net.org/download.php](https://www.image-net.org/download.php)]([https://www.image-net.org/download.php](https://www.image-net.org/download.php)) (accessed on 12 June 2024)).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AE & Autoencoder \\ Grad-CAM & Gradient-based Class Activation Mapping \\ MAE & Masked Autoencoder \\ ViT & Vision Transformer \\ ViTMAE & Vision Transformer with Masked Autoencoder \\ \end{tabular}
## 5 Conclusions  
The existing adaptability assessment frameworks often address changes within the same building typology and function, relying on methods and indicators mostly subjective. In addition, the effectiveness and validity of those are not sufficiently tested or verified. Therefore, more empirical data prioritizing the most valuable criteria that facilitate adaptations are required for all building typologies.  
The wide variety of existing CE frameworks' objectives and scopes can be attributed to the open nature of the CE definition and conceptualization, which has led to controversial understanding by the different stakeholders.  
Integrating DFA assessment in building circularity frameworks helps objectify its role as a key enabler to circularity strategies. It also informs more accurate and inclusive decision-making as well as performance assessment.  
One of the major shortcomings of most circularity frameworks developed so far is the lack of integration of the triple bottom line of sustainable development, which is supposed to be the ultimate goal for the circular economy. However, it is not the case for sustainability assessment frameworks that recognize circularity as the contemporary enabler to sustainability which upgrades its requirements over time.  
An integrated approach to benchmark the new generation of sustainable buildings, therefore, requires measures that address the complexity of a building system consisting of different hierarchal levels with their adaptability and circularity respective requirements. Furthermore, the technical integration of circularity must be coupled with value chain management and reconciliation with the other sustainability factors.  
From the review and analysis presented in this study, the following gaps are identified:  
1. There is a lack of quantitative models for adaptability assessment as well as evidence on the effectiveness of their indicators and criteria on the different typologies of buildings.
2. There is a lack of multi-criteria approaches supporting design that allow to deal with conflicting situations, as well as assess design options and advise better circularity scenarios at whatever point throughout a building's lifecycle. More automated indicators are essential to evaluate design options and facilitate necessary trade-offs for enhanced circularity.
3. Future research should tackle the practicality questions of some of the application of the prominent tools on real-life case studies and identify further obstacles and rooms for innovation that on-desk research methods could not identify.  
Conceptualization, R.A.; methodology, R.A.; validation, R.A., L.B. and H.G.; formal analysis, R.A., L.B. and H.G.; investigation, R.A.; data curation, R.A.; writing--original draft preparation, R.A.; writing--review and editing, R.A., L.B. and H.G.; visualization, R.A.; supervision, L.B. and H.G.; funding acquisition, R.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Portuguese Foundation for Science and Technology (FCT), grant number PD/BD/150400/2019.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Descriptive Analysis
#### 3.1.2 Main Source Publications  
The nine most prolific journals account for 40% of the total records of the sample principally considered. It is possible to conclude that the most common aspect of the journals considered is related to energy efficiency and buildings, as well as to the science and technology of solar energy applications. In fact, _Energy and Buildings_ has the dominant share, with 16.53% of the published records, followed by _Solar Energy_ (6.19%), and _Solar Energy Materials and Solar Cells_ (4.12%).  
Concerning the authorship of the publications, [PERSON], who is affiliated with the United States and Canada, has made the most contributions to the field, with 36 publications, followed by [PERSON] (33), and [PERSON] (28). Most of the publications are affiliated with Italian universities (97 papers), followed by the United States and Canada, with 74 and 36 publications, respectively.  
During the last decade, with the increases in the UHI effect and climate change, it became necessary to draw attention to the development of geoengineering-based solutions, such as cool surfaces. This strategy has been implemented for roof applications, building facades, and pavements. Nevertheless, the academic research placed an emphasis on cool roofs, with a much higher number of articles published on this application than for any of the others (Figure 4). Thus, the number of publications regarding cool roofs in the third period reached 251 documents, while, in the case of pavements and facades, only 58 and 22 papers were identified, respectively.
## 3 Results
### Statistical Analysis
#### 3.4.1 Analysis of Parameters in the I Measurement
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & & **Mean Value** & **std.d** & **Min** & **Max** & **c.var** & **conf.inter.** & **sk** & **ku** & \(p\) \\ \hline \multirow{4}{*}{Control Group} & VOL (I) & 430.97 & 92.88 & 251.5 & 608.7 & 21.55 & 396.28 & 465.66 & \(-\)0.05 & \(-\)0.67 & 0.823 \\ \cline{2-11}  & density & \(-\)89.23 & 71.86 & \(-\)297.8 & 44.9 & 80.53 & \(-\)116.07 & \(-\)62.39 & \(-\)0.48 & 0.87 & 0.977 \\ \cline{2-11}  & pocket (I) & 6.27 & 2.21 & 3.0 & 11.0 & 35.29 & 5.44 & 7.09 & 0.49 & \(-\)0.88 & 0.510 \\ \hline \multirow{4}{*}{Experimental Group} & VOL (I) & 417.57 & 93.77 & 197.8 & 574.5 & 22.45 & 382.55 & 452.59 & \(-\)0.38 & \(-\)0.43 & 1.000 \\ \cline{2-11}  & density & \(-\)94.00 & 71.36 & \(-\)251.2 & 76.0 & 75.92 & \(-\)120.65 & \(-\)67.34 & 0.25 & \(-\)0.10 & 0.783 \\ \cline{1-1}  & pocket (I) & 6.67 & 2.52 & 3.0 & 13.0 & 37.85 & 5.72 & 7.61 & 0.58 & \(-\)0.32 & 0.622 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of I measurement in the control and experimental groups. \begin{table}
\begin{tabular}{c c c} \hline
**Analysis** & **F** & \(p\) \\ \hline MANOVA & 0.299 & 0.826 \\ \hline VOL (I) & 0.309 & 0.580 \\ \hline Density (I) & 0.066 & 0.798 \\ \hline Periodontal pocket (I) & 0.426 & 0.516 \\ \hline \end{tabular}
\end{table} 
Table 3: Significance of the difference between the groups, in regard to the monitored parameters in the I measurement.
# Enhancing Brioche Bread with Emulsified Seed and Nut Oils: Nutritional and Sustainable Benefits
## 3 Results and Discussion
### Physical Determinations of Bricoche Bread
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**ID** & **Hardness** & **Elasticity** & **Cohesiveness** & **Chewiness** \\ \hline Control & 74.50 \(\pm\) 4.98 a & 0.82 \(\pm\) 0.02 b & 0.36 \(\pm\) 0.02 b & 30.13 \(\pm\) 4.07 a \\ Almond 50\% & 50.66 \(\pm\) 3.54 b & 0.87 \(\pm\) 0.01 ab & 0.45 \(\pm\) 0.02 ab & 19.73 \(\pm\) 0.01 b \\ Pistachio 50\% & 49.52 \(\pm\) 0.36 b & 0.88 \(\pm\) 0.01 ab & 0.52 \(\pm\) 0.01 a & 18.21 \(\pm\) 1.88 b \\ Walnut 50\% & 47.90 \(\pm\) 2.25 b & 0.91 \(\pm\) 0.03 a & 0.53 \(\pm\) 0.01 a & 17.31 \(\pm\) 1.16 b \\ Poppy seed 50\% & 46.80 \(\pm\) 4.38 b & 0.87 \(\pm\) 0.01 ab & 0.49 \(\pm\) 0.01 a & 19.84 \(\pm\) 1.73 b \\ Chia seed 50\% & 42.00 \(\pm\) 1.31 b & 0.87 \(\pm\) 0.05 ab & 0.53 \(\pm\) 0.02 a & 17.12 \(\pm\) 1.15 b \\ Melon seed 50\% & 46.10 \(\pm\) 3.01 b & 0.90 \(\pm\) 0.01 a & 0.54 \(\pm\) 0.02 a & 18.52 \(\pm\) 1.32 b \\ Pumpkin seed 50\% & 42.23 \(\pm\) 1.17 b & 0.89 \(\pm\) 0.02 ab & 0.52 \(\pm\) 0.01 a & 17.48 \(\pm\) 0.87 b \\ Almond 100\% & 20.49 \(\pm\) 1.15 cd & 0.89 \(\pm\) 0.03 ab & 0.53 \(\pm\) 0.03 a & 9.73 \(\pm\) 1.60 c \\ Pistachio 100\% & 19.33 \(\pm\) 1.45 d & 0.88 \(\pm\) 0.03 a & 0.51 \(\pm\) 0.02 a & 8.78 \(\pm\)1.05 c \\ Walnut 100\% & 18.33 \(\pm\) 1.34 d & 0.89 \(\pm\) 0.03 a & 0.54 \(\pm\) 0.02 a & 7.43 \(\pm\) 1.05 c \\ Poppy seed 100\% & 23.80 \(\pm\) 1.83 cd & 0.86 \(\pm\) 0.01 ab & 0.51 \(\pm\) 0.01 a & 10.01 \(\pm\) 49.51 b \\ Chia seed 100\% & 19.98 \(\pm\) 0.99 d & 0.88 \(\pm\) 0.01 ab & 0.48 \(\pm\) 0.03 a & 8.89 \(\pm\) 125.69 c \\ Melon seed 100\% & 20.92 \(\pm\) 1.28 d & 0.85 \(\pm\) 0.01 ab & 0.47 \(\pm\) 0.01 a & 8.39 \(\pm\) 1.32 c \\ Pumpkin seed 100\% & 25.41 \(\pm\) 2.73 c & 0.85 \(\pm\) 0.02 ab & 0.49 \(\pm\) 0.03 a & 10.56 \(\pm\) 1.84 c \\ \hline \hline \end{tabular} Hardness and chewiness are presented in N. The numbers represent the means \(\pm\) standard deviations. Different letters in the same column for each attribute indicate significant differences (\(p<0.05\)).  
\end{table} 
Table 3: Texture parameters in biroche breads reformulated with partial (50%) and total substitution (100%) of butter by seed and nut oils. should be considered, such as the pigments in the oils, which could affect the final color of the bread and potentially negatively influence consumer perception.
# Efficient Control of a Non-Linear System Using a Modified Sliding Mode Control
## 3 Simulation and Experimental Results
### Experimental Results  
For the experiment, the third link of the hydraulic robot manipulator in Figure 4 was considered. It was known that the hydraulic system was highly non-linear in nature and had many uncertainties. As a result, the controller without an observer like SMC and PDSMC, did not have a good performance. Therefore, only two controllers with perturbation compensation have been implemented on the real system. An input sine wave was given to the real system, and the experiment was done with different values of lambda (20 and 30). The desired trajectory was a sine wave with a frequency of 1 (rad/s).  
The experimental result of the error trajectory between SMCSPO and PDSMCSPO is shown in Figure 16 with lambda = 20. The parameters of both algorithms are the same as those used in the simulation. The solid red line shows the error trajectory of SMCSPO. As we can see, SMCSPO had more error than PDSMCSPO. The reason was that PDSMCSPO had more attenuation towards the perturbation signal during the sliding phase as discussed before (Figure 14). The dotted blue line is the error trajectory of PDSMCSPO which has a smaller error.  
In the next experiment, the value of lambda was increased from 20 to 30. As it is shown in Figure 17, with an increase in the value of lambda, the attenuation of the perturbation signal increases. As a result, error decreases, which can be seen in Figure 17. The solid red line is the error trajectory of SMCSPO, and the blue line is the error trajectory of PDSMCSPO. The trajectory error was reduced in both cases because of the increme  
Figure 16: Error trajectories of both control schemes (lambda = 20).  
Figure 15: Sliding surface over perturbation of both schemes.
## 5 Simulations and Experimental Results
Figure 15: Time plot showing the delay between the instant when data is sent from the controller-node and the time when the corresponding feedback is received. The maximum value is 0.0210 s, the minimum value is 0.0100 s, and the estimated average delay is 0.0129 s.  
An additional simulation was performed to highlight the behaviour of the proposed SEA of _Serpens_. As shown in Figure 16, a scenario of a terrain cluttered with cylindrical objects is simulated. The entire body of _Serpens_ is constrained by obstacles. The same input signal as outlined in Equation (1) for the desired position (\(\theta_{d}\)) is adopted to control the joint module close to the head, as highlighted in Figure 16. The oscillatory motion of the joint module determines collisions of the corresponding link with the adjacent obstacles. These collisions are accommodated through the high level of compliance offered by the SEA of _Serpens_. The motor position \(\theta_{m}\) is allowed movement through passive-compliance despite the load position \(\theta_{l}\) being blocked by external obstacles. The consequential deviation over time between the motor gear position and the spring reference position is shown in Figure 17.  
Figure 16: The body of _Serpens_ is constrained by cylindrical obstacles. (**a**) The same input signal as outlined in Equation (1) for the desired position (\(\theta_{d}\)) is adopted to control the highlighted joint module close to the head. (**b**) A zoomed view of one of the joint modules while colliding with obstacles. The motor position \(\theta_{m}\) is allowed movement through passive-compliance despite of the load position \(\theta_{l}\) being blocked by external obstacles.
## 2 Materials and Methods
### NLS (Nonlinear Least Squares) Algorithm  
The Nonlinear Least Squares (NLS) principle is based on a statistical Maximum Likelihood (ML) candidate research [38; 39]. This class of algorithms can theoretically achieve the highest degree of accuracy, especially on discretized pitches, at the cost of greater computational complexity.  
The algorithm proceeds by iteratively minimizing the cost function for the estimation error.  
The algorithm starts by selecting a range of pitch candidates that cover the expected range of the fundamental frequency: this operation requires a prior setup of the algorithm,  
Figure 5: Processing steps within the OBP pipeline. The orange line shows the period, which is correctly detected as the center of the section below the threshold. The “Thresholding” signal is 1 when the “Modified Autocorrelation” signal goes below a fixed threshold (minimum search).  
which adds latency before being usable. For each candidate, a synthesized signal is generated with harmonics or sinusoids at that pitch frequency. The objective is to find the pitch candidate that best matches the observed signal by minimizing the sum of squared differences between the observed and synthesized signals. Optimization techniques like gradient descent are used to update the pitch candidates iteratively until convergence. Finally, the pitch candidate with the minimum objective function value is selected as the estimated fundamental frequency or pitch of the signal.  
The NLS algorithm is known for its ability to handle complex harmonic structures, variable pitch signals, and noisy environments. It is particularly effective when the signal contains multiple overlapping or interfering harmonic components.  
For the purpose of this study, a fast implementation of an NLS-based algorithm by [PERSON] et al. [40] is employed, shown to reduce the complexity by solving two Toeplitz-plus-Hankel systems of equations and using the recursive-in-order matrix structures.