esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Anomaly Detection for In-Vehicle Monitoring--An Application-Oriented Review
## 3 Publicly Available Datasets
### Synthetic Alternatives  
UBnormal [55] is a novel supervised open-set benchmark composed of multiple virtual scenes for video anomaly detection. The artificial generation of the scenes of this dataset using Cinema4D is essential to provide pixel-level annotations for abnormal events in the training set, allowing for the use of fully supervised methods for video anomaly detection. The dataset consists of 29 virtual scenes with 660 anomalies and nine different types of abnormal behaviour. The videos were generated at 30 FPS and are composed of photorealistic frames as far as both background and actors are concerned. Normal and abnormal frames of some scenes are illustrated in Figure 5a,b, respectively. Additionally, the work of [PERSON] et al. [55] introduces an interesting concept of expanding real-world datasets. The translation of simulated objects from UBnormal to Avenue [11] or ShanghaiTech [4] was proposed using a CycleGAN [56], producing enhanced results when used to train state-of-the-art methods. Similar hybrid strategies could be studied as a solution to the lack of available public datasets for anomaly detection inside vehicles.  
SVIRO-Uncertainty [63] is a high-quality synthetic dataset that is not directly related to the task of anomaly detection, which is understood as abnormal actions perpetrated by people. Nonetheless, it has the potential to be adapted to study a subset of this problem: the detection of abandoned or dangerous objects. SVIRO-Uncertainty is made up of sequences of the rear bench of a vehicle, in which each of the three seats might contain a passenger or an object, as displayed in Figure 5c,d. The original goal of this dataset was to train models capable of classifying the object that is occupying each position. However, it could be applied to abandoned object detection based on the context of its presence in the sequence. The dataset is quite large, containing two separate training sets, 4384 scenes with adult passengers only and 3515 using adults, child seats and infant seats. The adaptation of the dataset relies on providing context clues about the objects present in the frame, defining the anomaly as an object that should not be present in the sequence. An object could have been abandoned (e.g., a phone left in a seat) if its presence is not expected without a person present in the frame. Moreover, a relevant type of anomaly is linked to dangerous objects, such as firearms or knives. Due to the size of SVIRO-Uncertainty, providing such labels to a significant amount of sequences might be a virtually impossible task. A brief summary of the analysed datasets is available in Table 5.
###### Abstract  
With the increase in the complexity of communication systems, order statistics have become increasingly significant in performance evaluation, especially for advanced diversity techniques over fading channels. However, existing analytical approaches are not useful owing to their high complexity. In this paper, we present novel and insightful statistical results of the partial sums of ordered Gamma-distributed random variables. By adopting a unified analytical framework to obtain the desired joint probability density function from the joint moment-generating function, we redefine and derive the common key functions specialized for the Gamma distribution. The derived formula is presented in the form of either general closed-form expressions or at least a single integral form instead of the originally complicated multiple-integral form. In terms of a feasible application of our results, we present a complete and general closed-form analysis of the statistics of the combined received signal-to-noise ratio of the distributed cyclic delay diversity with the cyclic prefix single-carrier scheme. We also show that our analytical results can provide potential mathematical solutions for other wireless communication systems. Selected numerical examples are presented and discussed to illustrate the effectiveness of the applied approach and related results. All the derived analytical results were compared and verified by using Monte Carlo simulations to verify the accuracy of our analysis.  
order statistics; transmit diversity; combined SNR; distributed cyclic delay diversity; cyclic prefix single carrier +
Footnote †: journal: Applied  
11 November 2022  
Accepted: 5 December 2022  
Published: 6 December 2022
## 2 MT-HVdc Transmission Systems and Necessities  
Offshore platforms are required for each offshore wind farm to install a VSC converter and a number of connections to connect the HVdc links depending upon the MT-HVdc application. The fundamental design of an MT-HVdc system relies both on economic and technical factors imposed by both society and utility. Further economic factors include geological position, length of dc circuits, and its rating, ultra-fast mechanical actuator (UFMA), and the number of HVdc circuit breakers, VSC converters, and its ratings, need of communication between converters and additional offshore substations. Moreover, technical perspectives can be successful usage of dc circuits, availability of security to the MT-HVdc system under abnormal conditions, dc grid flexibility, and inertia sharing with mainland ac grid and redundancy.  
Great Britain's security and quality of supply standards have proposed principles for offshore wind farms' connection with onshore ac networks [31]. Hence, an MT-HVdc grid needs to ensure the following:  
1. Direct voltage must be regulated during both faulty and normal operating conditions.
2. In the event of fault occurrence, the MT-HVdc system should provide support to the mainland ac grid.
3. In case of any VSC station failure, an MT-HVdc system needs to guarantee that power transferred to the ac network will not be reduced more than the maximum power failure (\(P_{max\text{-}fail}\) e.g., for Great Britain its 1320 MW [32]).  
The performance of MT-HVdc greatly depends on the employed control strategy, while control mainly relies on a kind of ac grid connection and the dc network topology [30]. This paper does not investigate control strategies in detail; however, operation and control of the MT-HVdc grid are discussed in our published research [33; 34; 35]. Accordingly, \(P\)-\(V_{ac}\) control is deployed to regulate the ac voltages of WFs at a precise level [33]. Proportional integral (PI) control is used for constant voltage generation at 50 Hz. PI controller diminishes the voltage error (\(\text{e}=V_{\text{WF}}\ ^{*}-V_{\text{WF}}\)), which was then employed as a performance index [33; 34]. MT-HVdc system specifications and parameters for PI controllers are enlisted in Table 1.  
For modeling of the MT-HVdc grid, an average VSC value model is used, while offshore WFs are demonstrated as fixed power sources. The dc transmission lines are modeled via the \(\pi\) model.
## 3 Algorithms  
### Single-Air-Route Planning Based on A* Algorithm  
#### 3.1.1 Valuation Function  
1. Cost function  
Based on the layered route strategy, it is assumed that the starting point \(S\) coordinate of the UAV is \((X_{0},Y_{0})\) and the ending point \(E\) coordinate is \((X_{n},Y_{n})\). The coordinates of a point \(P_{x}\) in the planned route are \((X_{x},Y_{x})\), and the cost function is set according to Function (7) as follows:  
\[g(x)=\sum_{i=1}^{x}{(1+D_{i}+S_{i}+L_{i})\sqrt{\left(X_{x}-X_{x-1}\right)^{2}+ \left(Y_{x}-Y_{x-1}\right)^{2}}} \tag{21}\]  
2. Heuristic function  
Euclidean distance is a widely used heuristic function form in the A* algorithm. We believe that UAV transportation should be mainly point-to-point flight, and only fly around in obstacle avoidance or high-risk areas. Therefore, using Euclidean distance as the heuristic function is suitable for the research scenario of this paper. The Euclidean distance between the current point \(P_{x}\) and the endpoint \(E\) can expressed as:  
\[h(x)=\sqrt{\left(X_{n}-X_{x}\right)^{2}+\left(Y_{n}-Y_{x}\right)^{2}} \tag{22}\]  
The A* algorithm judges the merits and demerits of the track points through the valuation function \(f(x)\), expressed as:  
\[f(x)=g(x)+h(x) \tag{23}\]
## 1 Introduction
At present, most researches use neural network, decision tree model, and mathematical model to build DDM, but these methods need a large sample size or workload, and their prediction accuracy needs to be improved [5; 6; 7; 8]. Support Vector Machine (SVM) is a widely accepted machine learning method with strong generalization ability; it can use nonlinear methods to map the variables to be classified (SVC, support vector machine classification) or regressed (SVR, support vector machine regression) into higher or more infinite dimensional feature spaces. But, in the classification problem, SVR adopts the same principle as SVC, and SVR can predict the value of infinite possible output. In addition, the tolerance margin is set up in SVR to approximate the most accurate classification results [16]. Although SVR is more complex than SVC, it has better flexibility in solving the multi-classification problem. Therefore, in this paper, the SVR algorithm is used to predict the multi-driving decision, and the output threshold range is set for each driving decision. In the research of driving decision, SVR is mainly used to car following behaviors [17; 18], driving risk assessment [19], and so on. At present, most researches use SVR to solve the problems by specifying the kernel functions directly [17; 18; 19], but sometimes it may be found that the kernel function does not match the target problem. Therefore, in order to make the objective problem automatically choose the optimal kernel function, a new kernel function is proposed to optimize the SVR model. So, in this paper, by simultaneously referring vehicle states and road conditions, an optimized SVR model is developed to obtain the inherent complexity of driving decisions, including car following, lane changing, and free driving. Specifically, this study makes the following contributions:  
1. A detailed analysis of DDM for autonomous vehicles is conducted, which suggests that the control maneuvers of autonomous vehicle depend on the extracted traffic environment feature, not only including vehicle states, but also road conditions. 2. A SVR model, optimized by a weighted hybrid kernel function and particle swarm optimization (PSO) algorithm, is developed to establish DDM for autonomous vehicle. In order to validate the effectiveness of the optimized SVR model, the SVR model with a single RBF kernel function and BP neural network (BPNN) model are tested to compare with it. 3. By comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis, the effect of road conditions on driving decisions is quantitatively evaluated.
## 4 Discussion  
### Site Effect Differences  
Comparing the results for the HVSR and TRSR estimations, it can be noticed that the resonance frequencies were differential, and the rotational resonance frequencies were higher than translational. In the case of the IMI station, the resonance frequencies corresponding to the first maximum of the average H/V ratio reached 1.6 Hz and 1.8 Hz (Table 3) for translation and rotation, respectively. The PLA station also showed higher values of the rotational resonance frequency (for average H/V) of 5.6 Hz than the translational resonance frequency of 4.2 Hz (Table 3). Similar differences were observed for the EW and NS directions (Table 3). A question arises as to what influences the change in the resonance frequency. The resonance frequency can be approximated as follows [40]:  
\[f_{0}=\frac{V_{s}}{4H} \tag{13}\]  
where \(V_{S}\) is the shear wave velocity, \(H\) is the thickness, and \(f_{0}\) denotes the resonance frequency. Considering the dependence on rotational resonance frequency similar to the translational motion, the rotational records were probably influenced by the amplification effect produced by shallow structures according to Equation (13). The difference in the resonance frequency for the IMI station is insignificant. Thus, the thickness of the resonance layer is similar for both types of motion. Nevertheless, the result from the PLA station showed a shift between the resonance frequencies, which can be related to the varied values of H (Equation (13)). Taking into account that both sensors were mounted directly in the same place and geological settings were unchanged, other factors might affect the resonance shift. As mentioned before, in the case of the PLA station, the 20 m thick resonance layer corresponded to the shallow Quaternary sediments (up to 5 m thick) and strongly weathered Carboniferous layer, mainly eroded or fractured. One of the factors that might affect the resonance frequency shift was related to the intense fractures occurring in the shallow layers. The geological structures were less compacted in the PLA station than in the IMI station, which could have caused the higher value of the rotational resonance frequency for the lower H-value (Figure 7) [22,24].
## 3 Proposed Approach
### Tracking the Mobile Platform
#### 3.3.1 Height-Adaptive, Non-Predictive PID Controller
** The descent speed during landing remains at a constant value of 0.3 m s\({}^{-1}\) when flying 0.7 m above the landing platform, i.e., when the sonar indicates more than 0.7 m. Below 0.7 m, the UAV increases its downward speed notably to 2.0 m s\({}^{-1}\). The intuition behind this design choice is that, when too close to the landing platform, the latter is not viewed completely by the UAV's downward-looking camera, and in turn the computed centroid might not represent the real center position of the platform. It is therefore a better option to rely on the correct measurements taken at altitudes higher than 0.7 m and then perform the final approach stages faster. To determine whether the drone has successfully landed on the landing platform, we use the sonar measurements and the linear acceleration provided by the IMU. If there exists some linear acceleration, either in the x or y direction, that means the drone is moving. Moreover, if the sonar indicates a value smaller than a threshold for a certain period of time, the drone is assumed to have landed; this is not, however, a sufficient condition, since it might as well have landed on the ground. Therefore, to be certain that the UAV has actually landed on the moving platform and not on the ground, both conditions must be met, namely (1) the sonar measurement must be smaller than a threshold persistently and (2) the IMU must indicate a non-zero linear acceleration. Note that these assumptions are valid because the landing platform is assumed to be moving constantly. In practice, the UAV mostly lands smoothly without major rebounds after the first contact with the landing platform, as we could verify in a set of preliminary experiments. After all, given that the height-adaptive PID controller ensures that the landing platform's centroid and the UAV's body frame are aligned along the vertical axis at all times, landing mostly occurs very close to the center of the \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**PID Gains**} & \multicolumn{2}{c}{**Altitude Range**} \\ \cline{2-3}  & **4 m** & **2 m** \\ \hline \(K_{p}\) & 0.694 & 0.697 \\ \hline \(K_{i}\) & 0.198 & 0.199 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Height-dependent PI gains. platform. Therefore, in all experiments (Section 4) we can safely use the initial touch point between the landing platform and the UAV as stop signal for data recording.
## 2 Thermal Energy Storage Systems in Buildings
### Passive Storage Systems Incorporating PCMs
#### 2.2.2 Conventional Building Envelopes  
The building can be considered as a thermodynamic system with the building envelope as its boundary. The balance between energy production, consumption, and storage in buildings is influenced by internal and external loads, as well as the thermal resistance and thermal capacity of the envelope [20]. Therefore, improving the thermal performance of the building envelope is crucial to reduce energy consumption for heating and cooling.  
Conventionally, the building structure itself is a means of energy storage. Thus, many passive storage systems rely on structural elements of the building such as walls, roofs, windows, and floors. This type of system can be accomplished on the surface of the structure, which is called surface activation. In this configuration, no specific storage system is used; rather, the intrinsic storage capacity of the building is exploited. It is possible to improve the surface activation system by inserting ducts or pipes in the materials composing the structure to allow a more efficient circulation of the heat transfer fluid [11].  
It is also possible to improve the thermal performance of conventional buildings through suitable insulation. The purpose of this insulation layer is to limit heat loss. Building insulation materials can be classified into three categories: conventional, state-of-the-art, and natural. Conventional insulation materials like mineral wool, polystyrene, cellulose, and cork are produced by both chemical and mechanical processes and are commercially available. State-of-the-art insulators are mostly in the research and development stage, such as aerogel, vacuum insulation board, etc. Natural insulators are obtained from agriculture and forest residues, sheep woods [21], and biochar that is obtained from the pyrolysis of agricultural residues.  
In most cases, the replacement of part of the conventional building materials by waste materials, such as coffee grounds, natural fibers, wood aggregates, and rubber waste particles, enable to decrease the thermal conductivity and density of the building composites leading to better insulators. Building materials and cement composites have thermal conductivities in the range of 0.120-0.975 W m\({}^{-1}\) K\({}^{-1}\)[22].  
However, the main disadvantage of insulation materials is the reduction of mechanical properties, such as compressive and tensile strength, which limits their percentage in building materials (between 2.5% and 10%) [23; 24].  
Lastly, the thermal capacity, energy efficiency, and comfort impact of the thermal storage unit (building, structure or storage unit) can be increased by using PCMs.
# Semantic Segmentation with High-Resolution Sentinel-1 SAR Data
## 5 Experimental Evaluation and Discussion
### Segmentation Performance  
In this section, we evaluate the results of the UNet, PSPNet, and HRNet models given in detail above. For our models, the training times took from 1 to 2 days. Using multi-GPU systems could offer significant improvements, and more images for the dataset could then be made available. All models shown on the newly-created dataset achieved an overall pixel accuracy above 88%. We reached respective overall PAs of 92.23%, 90.74%, and 88.59% and mIoUs of 70.60%, 65.99%, and 62.87% with the HRNet, PSPNet and UNet models. These results can be seen in Table 3. The overall segmentation results for all studied models are given in Table 4. There we present MA and mIoU values for five classes and compare the results with the new dataset. As seen in the table, preprocessing steps increased success by approximately 3% in every class and proved that preprocessing is essential for SAR segmentation. The purpose of choosing the three biggest cities of Turkey was that the urban areas were more prevalent in terms of the surface area in the dataset. However, urban areas' scores were the lowest ranked in this study, as they represented the smallest part of the dataset. The highest success obtained in these areas was water, as the water areas were readily apparent.  
In Table 5, a summary of all segmentation evaluation results is shown. Here, PA values of all validation images are presented in order to compare each model with the others. When the images with the lowest PA values against the validation images are examined, it is seen that the urban areas are more often in the images. The PA values of two of the trained deep neural network models are higher than 90%, and the best-performing one among these models was the HRNet model, which achieved an overall PA of 92.23%.  
We obtained different results in our study with images of the same three cities in our dataset taken at different times. It can be concluded that the results of images taken from identical regions at different times may differ within identical models because the backscattering of SAR diversifies the dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **HRNet** & **PSPNet** & **U-Net** \\ \hline HRNet & X & \(\leftarrow\) 3536 & \(\leftarrow\) 18,685 \\ \hline PSPNet & X & X & \(\leftarrow\) 15,423 \\ \hline U-Net & X & X & X \\ \hline \hline \end{tabular}
\end{table}
Table 4: McNemar test results for deep learning models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **U-Net** & **PSPNet** & **HRNet** \\ \hline mIoU (\%) & 62.87 & 65.99 & **70.6** \\ \hline MA Value (\%) & 73.41 & 75.33 & **80.47** \\ \hline Overall PA (\%) & 88.59 & 90.74 & **92.23** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of the segmentation performance of deep learning models.
## 3 Overview of Hyperspectral Imaging Spectroscopy (HSI)  
HSI is a modern method which incorporates the critical concept of imaging and spectroscopy and can concurrently obtain spectral and image information from a sample [48; 49; 50; 51; 52; 53; 54]. NIR-HIS was initially used in remote sensing studies but now serves as an emerging technology in various quantitative applications in the food [55; 56; 57; 31], medicine, and agriculture industries [58; 59; 60]. HSI is a promising method for the rapid and nondestructive sorting and prediction of quality parameters in various root and tuber crop categories, including yam and cassava [21]. NIR-HSI systems can capture a broad range of spectra data from visible to near-infrared and far-infrared regions of electromagnetic radiation. The pixel in the NIR-HSI image has a continuous spectrum of about a hundred bands [61; 62; 63].  
Additionally, the image contains valuable information on the intrinsic chemical compositions and their spatial distributions within the target. HSI has shown the potential to characterize the biochemical and biophysical constituents, including their spatial distribution, simultaneously. Spectral imaging technology is classified as multispectral, hyperspectral, or ultraspectral [19; 64].  
Hyperspectral images can be generated in various ways, which include a tunable filter, push broom, and whiskbroom, respectively [64]; this depends on the hardware used for the data acquisition. A tunable filter keeps the target fixed and obtains images subsequently from one wavelength to another; this is used when the number of wavelengths needed is limited. [PERSON] and [PERSON], [19] stated that a push broom and whiskbroom rely on scanning the target in the spatial domain by moving the target either line-by-line (push broom) or point-by-point (whiskbroom). Additionally, HSI can be operated in different optical modes, such as reflectance, transmittance, absorbance, or fluorescence, depending on the optical properties of the samples. Most of the published work was performed in the reflectance mode [65; 66; 67; 68]. A \"hypercube\" is a three-dimensional (3-D) structure obtained with HSI that consists of two spatial and one spectral dimension [69; 70]. Becauseof their ability to combine conventional imaging and spectroscopy, HSI systems can provide physical and geometrical features of the target (i.e., color and appearance) and the chemical composition. As a result, hyperspectral imaging technology has distinct advantages in detecting plant materials' outward and intrinsic quality. It has numerous advantages over traditional analytical methods, including the nondestructive nature of samples and the unrivalled prediction accuracy. It can quickly determine the chemical composition of foods and the spatial distribution of the quality attributes (69).