esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Approaches to the reconstruction problem from protein contact maps
### Methods based on the gradient minimization of a cost function
The distance between two residues \(i,j\) in the protein structure can be upper bounded by just considering the successive powers (\(M^{2}=M\cdot M,M^{3}=M\cdot M\cdot M,...\)) of the protein contact map. In fact, if for some power \(n\) of the contact map \(M\) the entry \(M_{ij}^{n}\) is greater than \(0\) then we have that in the protein structure there must exist at least one path of length at least \(n\) connecting the \(i\)-th and the \(j\)-th residue which imposes the constraint  
\[|c_{i}-c_{j}|\leq nt.\]  
The authors describe a method to obtain more accurate measures. We can partition the set of distances on the protein structure into \(N\) disjoint sets. For \(1\leq n\leq N\), we denote  
\[d_{ij}\in D_{n}\ \mbox{if}\ M_{ij}^{1}=0,...,M_{ij}^{n-1}=0\ \mbox{and}\ M_{ij}^{n}>0.\]  
If a couple of residues \(i,j\) has expected distance \(d_{ij}\in D_{n}\) then there are exactly \(M_{ij}^{n}\) shortest paths of length \(n\) connecting \(i\) and \(j\). By experimental tests, the authors report the existence of a linear dependence between the values \(M_{ij}^{n}\) and the \(d_{ij}\in D_{n}\) distances which can be approximated by the polynomials  
\[d_{ij}=g_{0}^{(n)}+\sum_{k}g_{k}^{(n)}(M_{ij}^{n})^{k},d_{ij}\in D_{n} \tag{3}\]  
where the coefficients \(g_{k}^{(n)}\) are those which assure the best fitting to equation 3 and have to be determined experimentally. The energy cost function becomes  
\[E=\sum\limits_{i=1}^{N}\sum\limits_{j=i}^{N}\gamma_{ij}\left[|c_{i}-c_{j}|-d_{ ij}|^{2}+\sum\limits_{i=1}^{N-1}E_{bond}^{i}\right. \tag{4}\]where the coefficients \(\gamma_{ij}\) depends on the class of the \(d_{ij}\) distance, i.e. if \(d_{ij},d_{kl}\in D_{n}\) then \(\gamma_{ij}=\gamma_{kl}\). The backbone constraint term \(\sum\limits_{i=1}^{N-1}E_{bond}^{i}\) is defined by  
\[E_{bond}^{i}=\gamma_{0}(|c_{i}-c_{i+1}|-3.8)^{2}.\]  
The constants \(\gamma_{0},\gamma_{ij}\) represent the weights of each relative constraint in the energy function. Also in this case the initial solution is computed at random.
# Edge AI for Real-Time Anomaly Detection in Smart Homes
## 3 Proposed System Architecture
### Anomaly Detection Approach
#### 3.2.2 Model Selection Rationale  
The Isolation Forest model was chosen over one-class SVM due to its higher efficiency in handling high-dimensional IoT data and its ability to detect outliers without requiring a labelled dataset. Although effective in some cases, one-class SVM is computationally expensive because it relies on complex kernel functions, making it impractical for real-time execution on low-power edge devices [18].  
Similarly, the LSTM Autoencoder was selected over Gated Recurrent Units (GRUs) due to its superior ability to retain long-term dependencies in sequential data. While GRUs offer computational efficiency, they may struggle to capture extended behavioral trends, which are essential for accurate anomaly detection in smart home IoT environments [19].  
Although transformer-based models, such as the Temporal Fusion Transformer (TFT) and Time Series Transformer (TST), have demonstrated promising results in sequential anomaly detection, they were not implemented in this study due to their high computational demands. These architectures rely on extensive matrix multiplications and self-attention mechanisms, significantly increasing inference time and memory consumption, making them unsuitable for resource-limited edge devices [21].
## 2 Materials and Methods
### Remote Sensing Data
#### 2.3.3 LIDAR Data  
The LIDAR (Light Detection and Ranging) sensor was used in the study to measure variables related to the height and degree of canopy coverage within the field plots. LIDAR data were acquired from two aerial survey missions that took place between 2015 and 2019. The average point density was 4 points/m\({}^{2}\) with a relative height accuracy of 10 cm. The accuracy of the point clouds was verified using previously surveyed ground control points. For each aerial survey mission, the point clouds were filtered and classified into ground and non-ground (building and vegetation) using TerraScan software ([[https://terrasolid.com/products/terrascan/](https://terrasolid.com/products/terrascan/)]([https://terrasolid.com/products/terrascan/](https://terrasolid.com/products/terrascan/)), accessed on 25 February 2021). Following the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sub-Regions** & **Number of Relative Orbits** & **Orbit** & **Number of Images** & **Polarization Band** \\ \hline Gargano Promontory & 146 & Ascending & 25 & VV, VH \\ Daunian Subappenines & 44 & Ascending & 26 & VV, VH \\ Murge & 146 & Ascending & 50 & VV, VH \\ Arco Jonico Tarantino & 146 & Ascending & 38 & VV, VH \\ Salentinian peninsula & 146 & Ascending & 25 & VV, VH \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Sentinel-1 images acquired for the field plots.  
method by [PERSON] et al. [49], a Digital Terrain Model (DTM) (1 \(\times\) 1 m\({}^{2}\)) was generated by interpolating the ground points through Inverse Distance Weighting interpolation. The Digital Surface Model (DSM) was obtained by selecting the maximum height of vegetation points for each 1 \(\times\) 1 m\({}^{2}\) pixel after filtering for outliers. Subsequently, the Canopy Height Model (CHM) was calculated as follows:  
\[\text{CHM}=\text{DSM}-\text{DTM} \tag{5}\]  
where CHM is the distance between the ground and the top of the vegetation, DSM is the Digital Surface Model and DTM is the Digital Terrain Model. Moreover, canopy and vegetation cover were subsequently extracted from CHM. We defined canopy cover as the percentage of vegetation cover with a height greater than 2 m, and vegetation cover as the percentage of the entire vegetation. Within each plot, the average and 0.95 th percentile of CHM, canopy cover and vegetation cover were extracted.
# Empirical Performance Models of MAC Protocols for Cooperative Platooning Applications
## 4 Empirical Models
### Number of Lanes and Transmit Power of Followers  
We observe that increasing the number of lanes used by the platoons side-by-side and the transmit power of followers increases monotonically the number of neighbours, as shown in Figure 9a,b, respectively. In the case of the increasing number of lanes, the different spatial arrangement of the  
Figure 8: Network metrics for RF neighbours varying homogeneous platoon size. Figure 7: Network metrics for RF neighbours varying heterogeneous platoon size. platoons as more lanes are introduced leads to more nodes effectively entering the range of each individual node. A similar effect, even more pronounced, happens when increasing the transmit power of the followers. Higher transmit power means larger communication range and thus more neighbours are observed. Consequently, there are less hidden nodes and the CSMA/CA becomes more effective in avoiding collisions. This occurs among all kinds of nodes, both followers and leaders. Therefore, the number of neighbours is also representative of the platoons layout in lanes as well as the power the followers use to transmit. The results for the number of collisions as a function of the number of neighbours, when varying the number of lanes, are shown in Figure 9(a). Figure 9(a) shows the same relationship but when varying the followers transmit power. We consider the observed points to follow a bell shaped curve, that we describe analytically using the Gaussian function:  
\[y=\alpha\cdot e^{\left(\frac{x-\beta}{2\gamma^{2}}\right)}\]  
with a maximum when the transmit power is 1 mW (or 0 dBm). Below that value, the communication range is small, so as the number of observed neighbours and thus collisions. Above that value, nodes start progressively listening to more nodes, making the underlying carrier sense MAC mechanism more effective, particularly reducing the occurrence of hidden nodes. Thus, collisions also decrease as, in this case, they result mainly from the occurrence of two (or more nodes) trying to access the medium simultaneously (i.e., their backoff counter hits zero). The busy time still presents a log-linear relationship for both cases, as depicted in Figures 9(b) and 9(b). Figure 9: Number of RF neighbours vs. number of used lanes and Tx power. (**a**) Number of RF neighbours vs. number of lanes (default Tx power and platoon size); (**b**) number of RF neighbours vs. Tx power (default number of lanes and platoon size). Figure 11: Network metrics for RF neighbours varying the power followers transmit.
# Beneficial Effects of _Pistacia__terebinthus_ Resin on Wine Making
## 3 Results and Discussion
### Volatile by Products Detected by HS-SPME GS-MS in Wines  
The technique of headspace solid phase microextraction/gas chromatography-mass spectrometry (HS-SPME/GC-MS) was used for the qualitative determination of the volatile by-products in wines produced by fermentation of must by immobilized and free cells at 28, 21, 14 and 7 \({}^{\circ}\)C (Table 5). A total of 125 compounds was detected in the wines, mainly alcohols, esters, ketones, aldehydes, and acids, many of which belong to terpen (Table 6). Their detection depends on the presence or not of the resin, fermentation time and the fermentation temperature. The detected terpen are monoterepenes (\(\alpha\)-pinene, ocimene, \(\beta\)-pinene, limonene, career, etc.) monoterepenoids (1,\(\delta\) cineole, linalool oxide, linalool, borryl acetate, etc. ), and sesquiterpenoids (spathulenol and farnesol). \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**No**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**R.I**} & \multirow{2}{*}{**R.I.B**} & \multicolumn{2}{c}{**28 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**21 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**14 \({}^{\circ}\)C**} & \multicolumn{2}{c}{**7 \({}^{\circ}\)C**} & \multirow{2}{*}{**Identification**} \\  & & & & **I/W** & & **F/W** & & **I/W** & & **F/W** & & **F/W** & **I/W** & **F/W** & **Method** \\ \hline
1 & Ethyl Acetate & 879 & 885 & + & + & + & + & + & + & + & + & + & a,b \\ \hline
2 & Ethanol & 885 & 883 & + & + & + & + & + & + & + & + & a,b \\ \hline
3 & 2,5-Hexanediol & 969 & 907 & + & - & + & - & - & - & - & - & b \\ \hline
4 & \(\alpha\)-Pinene & 994 & 1017 & + & - & + & - & - & - & - & - & b \\ \hline
5 & Toluene & 1015 & 1043 & + & + & - & - & - & - & - & - & b \\ \hline
6 & \(Z\)-\(\beta\)-Ocimene & 1019 & 1035 & + & - & + & - & - & - & - & - & b \\ \hline
7 & 2-Fluoro-1-propene & 1063 & 1079 & + & + & + & + & + & - & - & - & b \\ \hline \end{tabular}
\end{table} 
Table 5: Volatile by-products detected by SPME/GC-MS analysis in must fermentation batches (12.5 \({}^{\circ}\)Be) using immobilized and free cells at 28, 21, 14 and 7 \({}^{\circ}\)C.
# Heuristic Global Optimization for Thermal Model Reduction and Correlation in Aerospace Applications
## 5 Discussion
### Strengths and Limitations  
Strengths
* The methods demonstrate high accuracy and effective convergence, making them well suited for handling non-linearity in thermal models.
* The approach offers a structured methodology for automating and improving parameter tuning, reducing reliance on manual adjustments of the methods of tuning parameters.  
Limitations:
* Selecting the optimal tuning parameters for each method requires extra computational cost, which can lead to increased computational time.  
* These findings are based on a simplified thermal model; further validation is needed for larger and more complex aerospace systems.
## 4 Discussion
### Uncertainties  
Some uncertainties exist in this study. First, the SM measurements provided by the flux network are a single point of data and may not represent the soil water status of the flux footprint of the flux tower well. Second, the values obtained using the physicochemical properties of the site soil when calculating the field water holding capacity and permanent wilting point of the site are obtained directly from off-the-shelf products because not all sites provide soil data, which may lead to some uncertainties. These uncertainties could be alleviated when more in situ observations are available in the future.
## 5 Security Analysis
### Speed Analysis  
The speed of the proposed scheme was tested using a computer with an Intel(R) Core(TM) i7-6700 HQ 2.60 GHz CPU, 16.00 GB memory and a 500 G solid state drive. The analysis was conducted using Matlab2018a on a Windows 10 operating system. The average time required for a single simulation was 0.04 s. The comparison with digital encryption methods [35; 36] are summarized in Table 3, which clearly predicts that the average encryption rate of the proposed scheme is faster than that of [35; 36], thus making this communication scheme useful in WSNs.  
Figure 11: The CMPDFS process with \(D=0.5+0.1j\) and \(D^{\prime}=0.5+0.11j\).  
Figure 12: The error between message signal \(H\) and \(H_{g}\).
## 5 Tomographic Detection of Carotid Plaque and Artificial Intelligence  
The use of CT scans for carotid plaque detection included several different methods. The choice of method generally depends on the resources available and the specific needs of the clinician. Manual segmentation is the most widely used method for carotid plaque [112]. It involves a radiologist or a trained technician manually outlining the plaque in each slice of the CT scan. While this method is considered the gold standard, it is time-consumingand can be prone to inter-observer variability. Semi-automatic segmentation involves software to automatically segment the carotid plaque and then having a radiologist or a technician verify and correct the results. This method is faster than manual segmentation and is less prone to inter-observer variability but is still time-consuming and can be affected by the quality of the CT scan [113]. Deep learning-based segmentation involves using AI algorithms to automatically segment the carotid plaque in CT scans. This method has shown promising results in recent studies and is becoming increasingly popular. It is faster and more accurate than manual and semi-automatic segmentation methods, but unfortunately, it requires a large dataset for training and can be also affected by the quality of the CT scan [114]. Finally, hybrid methods involve combining multiple methods to achieve the best results. For example, a semi-automatic segmentation method can be used to obtain the initial results, and subsequently, a deep learning-based segmentation can be applied to correct and improve the results. This method allows the best results to be achieved by combining the strengths of multiple methods [115].  
The incorporation of AI algorithms in vascular imaging is an evolving area that has the potential to have a significant influence on clinical practice, helping the applicability, accuracy, and safety of tomographic acquisitions.  
Multiple AI algorithms have been proposed to address a wide range of CTA pre-procedural problems [116, 117], including noise reduction [117], contrast dose and radiation reduction [118, 119], and image quality control [120].  
It should be underlined that the vast majority of research taking advantage of AI to detect and classify atherosclerotic plaques, including their composition, was conducted on coronary atherosclerotic plaque with the purpose of developing a non-invasive coronary computed tomographic angiography, with encouraging results [121].
## 2 Background  
### Local Binary Pattern  
Local Binary Pattern [28] is one of the most common methods in image processing. The LBP operator assigns binary values as a result of comparing them to each other by scrolling floating windows and selecting the center pixel value in the middle of these windows as the threshold level. The generated binary number sequence is called the LBP code, which is used to specify different properties in the image, such as edges, corners, light or dark areas, line regions. The size of the window can be changed according to the problems in order to capture different properties. The length of the LBP code is circularly defined by two parameters, such as the number of sample pixels \(p\) and the radius \(r\) of the symmetric circular neighborhood as shown in Figure 1. \(LBP_{p,r}\) defines the appropriate \(2^{p}\) different output value from the neighboring pixel set.  
The histogram of the image is obtained by Equation (1) using LBP. (\(x_{c}\), \(y_{c}\)) is the location of the given pixel and \(i_{p}\) and \(i_{c}\) are the gray level of the center pixel.  
\[LBP_{p,r}(x_{c},y_{c})=\sum_{p=0}^{p-1}s(i_{p},i_{c})2^{p},\;s(x)=\left\{ \begin{array}{cl}1,&if\;x\geq 0\\ 0,&if\;x<0\end{array}\right. \tag{1}\]  
The process of extracting the feature vector (histogram) using the sliding windows of the LBP operator is shown in Figure 2. The center pixel of the window is set as the threshold value using sliding windows. The neighboring pixel values greater than the threshold value assigned to 1 and the small values assigned to 0. The obtained values multiplied by the mask and collected.  
Figure 1: LBP parameter samples.  
Figure 2: Example of converting LBP code to decimal.  
[PERSON] et al. [22; 29] divide LBP codes into two different classes: uniform and non-uniform. A code with a uniform distribution has maximum of 2 times different bits transitions. Examples of uniform and non-uniform LBP code are shown in Figure 3. Figure 3a has 2 transitions; uniform LBP code; Figure 3b is the non-uniform LBP code because it has 4 transitions.
## 3 Modeling and Control Strategy Design
### Optimization with the PSO Algorithm
The Simulink model will be called and the driving cycle will be run from the beginning to the end with the given EF. The inner loop PSO is executed to solve the ECMS at each time step. The fitness function of a particle is defined in Equation (19), with constraints defined in Equation (21). 4. After the simulation of the whole cycle is ended, the fitness function of each particle in the outer loop PSO will be evaluated and the iteration continues until the stopping criteria are satisfied. Figure 8: Dual-loop PSO algorithm: **(a)** the outer loop of the PSO algorithm searches for the optimal EF; **(b)** the inner loop of the PSO algorithm solves the ECMS problem at each instant. For online applications, since the driving cycle information is unknown, the EF will be fixed after offline optimization and only a single-loop PSO is applied to solve the ECMS problem.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 4 Experimental Setup
### Metrics  
We adopted three groups of metrics to evaluate the proposed method. The maximum F-measure (\(F_{\beta}\)) [54] and mean absolute error (\(MAE\)) were used for evaluating the proposed saliency detection method, as seen in Section 2. The centroid error (\(CE\)) was adopted to evaluate the pixel error of the computed centroid coordination based on the saliency detection results. The detection probability \(P_{d}\) and false alarm rate \(P_{f}\) were introduced to compare them with different space debris detection methods. The F-measure is defined as \(F_{\beta}=\frac{(1+\beta^{2})>precision\times recall}{\beta^{2}\times precision+ recall}\), which denotes a harmonic average of precision and recall. Precision and recall were obtained by comparing the predicted saliency maps (binarized at every integer threshold in the range of [0, 255]) with their ground truth. \(\beta^{2}\) is empirically set to 0.3 as suggested in [55]. The mean absolute error is computed by \(MAE=\frac{1}{N}\sum_{k=1}^{N}\lvert s_{k}-g_{k}\rvert\), where \(s_{k}\in S\) and \(g_{k}\in G\) denote the predicted saliency maps and the corresponding ground truth, respectively. \(k\) and \(N\) refer to the pixel position and pixel number of a saliency map. \(MAE\) represents the averaging pixel-wise error between the predicted saliency map and ground truth. S-measure [56] is another popular saliency detection metric that reflects the structural similarity between the predicted salient objects and the ground truth. However, since space debris appears as blobs without distinct structures, S-measure is not utilized in this work. The centroid error is calculated by comparing the ground truth and the computed pixel coordinate obtained by the centroid algorithm. The detection probability \(P_{d}\) and false alarm rate \(P_{f}\) are defined by \(P_{d}=\frac{N_{d}}{N_{all}}\) and \(P_{f}=\frac{N_{f}}{N_{all}+N_{f}}\), respectively. \(N_{d}\) represents the number of correctly identified space debris. The object would be considered as space debris when the computed centroid pixel coordinate falls within the radius of 5 pixels from the center coordinate of ground truth. \(N_{f}\) represents the number of background stars and noise that are incorrectly identified as space debris, while \(N_{all}\) represents the total number of space debris. A large assessment score indicates good performance for \(F_{\beta}\) and \(P_{d}\), and the reverse for \(MAE\), \(CE\), and \(P_{f}\).
# Mean Shift Segmentation Assessment for Individual Forest Tree Delineation from Airborne Lidar Data
## 4 Experiments and Results
### Computing Costs  
The computing time of a single plot is not significant, but for a larger study area with higher lidar point density, the computing time can be a factor to consider when choosing the segmentation method. The Aberfoyle plot is taken to test the speed of segmentation, as it has the largest coverage (106 m by 88 m), with more trees and higher point density. The computing costs of the mean shift with fixed and adaptive kernels for iterating, both over each point and random seed points, are recorded. In addition, the computing times of the watershed and region growing methods are also recorded for comparison.  
Table 9 shows the computing times of mean shift and watershed running with an Intel Core i7-6700 HQ CPU on a 64 bit laptop system in MATLAB R2018a, and of region growing in R. The time doubled when the kernel was set to be adaptive. Also the cost is about eight times higher when the shift is computed for each individual point. There are also slight differences when using different kernel shapes and weightings, but they can be neglected compared to the reported settings. When choosing random seed points, the segmentation results will vary for each run. On average, the matching rate is 5% to 10% lower than that computed on every point. The watershed method, although producing the lowest matching results, is significantly faster than mean shift. The region growing method performed consistently better than watershed, and took slightly longer time, but it was still much faster than mean shift.