esa-sceva/satcom-chunk-collection huggingface dataset:
# Static and Moving Target Imaging Using Harmonic Radar
## 3 Imaging of Moving Targets
### Moving Target Imaging Approach  
In this development, a range-Doppler plot is created for the fundamental frequency (\(n=1\)) response as well as for the harmonic response (\(n=2\)). Data were collected as a nonlinear target was moved to different ranges. At each target position, a full frequency sweep was performed. The frequency data collected at each target position spans the columns of a complexes data matrix. Each subsequent data acquisition fills the next row, which is called the slow time axis. An Inverse Fast Fourier Transform (IFFT) operation is performed across the rows to transform the frequency domain data into the fast time domain, which can then be transformed into the distance (or range) domain, as shown in Figure 3.  
A Fast Fourier Transform (FFT) is then applied to each column transforming the slow time domain data into the Doppler frequency domain, as shown in Figure 4. In order to convert the Doppler frequency domain into the speed domain, scaling by the appropriate wavelength is needed.  
From Fourier analysis, the maximum unambiguous Doppler frequency shift is based on the time pulse repetition interval (PRI). The PRI, \(\tau_{b}\), is defined as the time it takes to collect data from all \(N\) frequency samples (\(N=81\)) to create one range profile. The maximum Doppler frequency, \(f_{D\text{max}}\), is given by [61]  
\[f_{D\text{max}}=\frac{1}{2\tau_{b}} \tag{6}\]  
Dividing both sides by the number of complete data collections taken, \(M\), yields the Doppler frequency step size, \(\Delta f_{D}\), given by [61]  
\[\Delta f_{D}=\frac{1}{2M\tau_{b}} \tag{7}\]  
where the product \(M\tau_{b}\) represents the total amount of time it takes to collect data for one range-Doppler plot. This is called the coherent processing interval (CPI). Multiplying Equations (6) and (7) by the average wavelength yields the maximum unambiguous target speed, \(S_{D\text{max}}\), and the step size or speed resolution, \(\Delta S_{D}\), for the speed axis, respectively, given by [61]  
\[S_{D\text{max}}=\frac{\lambda}{2\tau_{b}} \tag{8}\]  
Figure 4: Transforming the slow time domain to the Doppler speed domain.  
Figure 3: Transforming the raw frequency domain data to the range domain.  
\[\Delta S_{D}=\frac{\lambda}{2M\tau_{b}} \tag{9}\]  
Applying these equations to the linear and non-linear responses results in an \(n\)-fold reduction in the unambiguous target speed for the \(n\)-th harmonic and an \(n\)-fold reduction in speed step size.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 2 Description of Satellite Data Transmission
### Conflict in Ground Resource Utilization
#### 2.2.3 Rlc  
RLC occurs when the allocation of equipment for a current task necessitates traversing the IFSM, but the ground station's receiving system has reached its maximum allowed crossings. As shown in Figure 1, assigning SDC 1 through \(i\) and demodulators \(j\) through \(n\) to a task requires the use of a switching path that spans across two IFSMs. If the maximum allowed number of crossings through an IFSM has been reached, the task cannot be accommodated with the intended converters and demodulators. This necessitates either abandoning the task or reassigning it to equipment that does not require matrix crossings.
# Smoothness of Graph Energy in Chemical Graphs
## 2 Preliminaries  
The concept of smoothness of a molecular descriptor (or topological index) can be viewed as an answer to the question of whether a slight change in the structure of a molecule leads to a slight change in the molecular descriptor. [PERSON] et al. approached this problem in [12] and introduced the structure sensitivity and the abruptness of a topological index. A sensitive topological index should have a high structure sensitivity, but needs to keep the abruptness as low as possible. The graph edit distance GED can be used to compare graphs with a small structural change. Graph edit distance is defined as the cost of the least expensive sequence of edit operations required to transform one graph into another; for a survey on GED, see [13]. Our goal is to compare molecules of the same size and order. More precisely, for a graph \(G\), let \(\mathcal{S}(G)\) be a set of graphs obtained from \(G\) by adding an edge together with its endpoint. Consequently, the graph edit distance between \(G\) and any graph from \(\mathcal{S}(G)\) is equal to two. For a molecular descriptor \(TI\) of the graph \(G\), the _structure sensitivity_\(SS_{G}(TI)\) and the _abruptness_\(\operatorname{Abr}_{G}(TI)\) are defined as follows:  
\[SS_{G}(TI)=\frac{1}{|\mathcal{S}(G)|}\sum_{H\in\mathcal{S}(G)}\Bigl{|}\frac{ TI(G)-TI(H)}{TI(G)}\Bigr{|}\]  
and  
\[\operatorname{Abr}_{G}(TI)=\max_{H\in\mathcal{S}(G)}\Bigl{|}\frac{TI(G)-TI(H )}{TI(G)}\Bigr{|}\,.\]  
In [14], a new method for quantifying the structure sensitivity and abruptness of a molecular descriptor is presented that outperforms the first method. Instead of comparing graphs based on a fixed graph edit distance, we chose a molecule \(M\) represented by a molecular graph \(G\) and then formed a set of all molecules in our dataset that are similar to \(M\). The set of molecular graphs of these molecules is denoted by \(\mathcal{M}(G)\). Then, a _modified structure sensitivity_\(SS_{G}^{*}(TI)\) and a _modified abruptness_\(\operatorname{Abr}_{G}^{*}(TI)\) of a molecular descriptor \(TI\) for a molecular graph \(G\) were assessed with the following formulas:  
\[SS_{G}^{*}(TI)=\sqrt{\frac{1}{|\mathcal{M}(G)|}\sum_{H\in\mathcal{M}(G)}(TI(G )-TI(H))^{2}}\]  
and  
\[\operatorname{Abr}_{G}^{*}(TI)=\max_{H\in\mathcal{M}(G)}\bigl{|}TI(G)-TI(H) \bigr{|}\,.\]  
The average values of a modified structure sensitivity and a modified abruptness of a molecular descriptor \(TI\) over molecular graphs in the set \(\mathcal{M}\) were calculated as follows:  
\[\overline{SS^{*}}(TI)=\frac{\sum_{G\in\mathcal{M}}\operatorname{SS}_{G}^{*}( TI)}{|\mathcal{M}|\Bigl{(}\max_{G\in\mathcal{M}}TI(G)-\min_{G\in\mathcal{M}} TI(G)\Bigr{)}}\]  
and  
\[\overline{\operatorname{Abr}^{*}}(TI)=\frac{\sum_{G\in\mathcal{M}} \operatorname{Abr}_{G}^{*}(TI)}{|\mathcal{M}|\Bigl{(}\max_{G\in\mathcal{M}} TI(G)-\min_{G\in\mathcal{M}}TI(G)\Bigr{)}}\,.\]
# Behavioural Realism and Its Impact on Virtual Reality Social Interactions Involving Self-Disclosure
## 1 Introduction
### Some Avatars May Appear Unnatural Due to Insufficient Behavioural Realism  
Researchers have argued that as avatars appear more human, they may unintentionally provoke a sense of discomfort among viewers if their behaviour deviates from what is expected [30; 61; 62; 63; 64; 65]. This discomfort is often attributed to a disconnect between the avatar's visual appearance and behaviour. For instance, an avatar that visually resembles a real human but moves rigidly can seem off-putting. Advancements in motion capture technology can enhance the behavioural realism of an avatar, particularly for those who appear as real humans, which can reduce unsettling feelings [23; 25; 62; 64]. [PERSON] et al. [30] suggest that including realistic motion capture can avoid a possible \"uncanny valley\" phenomenon. Supporting this, their study compared participants' reactions to a cartoon avatar with a lower resolution and a detailed, human-like avatar with a higher resolution. Both avatars received positive reactions; however, the human-like avatar with high behavioural realism was rated higher in appeal, relatability, and trust compared to the cartoon avatar with less behavioural realism. Thus, the right combination of an avatar's visual appearance and behavioural realism using motion capture technology may help to avoid the uncanny valley.  
A discrepancy between an avatar's appearance and behaviour can create unpleasant experiences for some users. As mentioned previously, avatars that resemble real humans but exhibit unnatural movements may reduce the quality of VR social interactions [30; 61; 62; 63; 64; 65]. Similarly, avatars with less realistic appearances, such as cartoon avatars, may elicit discomfort if their movements are too realistic [18; 46; 66; 59]. This may result in some people preferring less behavioural realism to match the avatar's appearance. Although previous studies have primarily focused on the benefits of higher levels of behavioural realism for human-like avatars, a high level of motion capture may be detrimental for less realistic avatars by making them appear unnatural [1; 46; 67; 68; 1]. The current study focussed on an avatar's behavioural realism, achieved via two different hardware/software platforms. Given that motion capture can sometimes appear unnatural, it is possible that some people may prefer avatars with less behavioural realism.
## 6 Discussion
### LSTM Performance  
The LSTM-based model underperformed compared to traditional machine-learning models.The poor performance of the LSTM model observed in our experiments is attributed to a combination of factors. While suboptimal hyperparameter settings such as the number of layers, units per layer, and learning rate may have contributed, the primary limitations appear to be inherent to the problem context. Specifically, the datasets used (CIC-DDoS2019 and N-BaIoT) lack strongly structured temporal sequences, which limits the advantages of using LSTM architectures designed to capture long-term dependencies. Furthermore, LSTM models require extensive training data with rich sequential patterns and high computational resources, which may not align well with the flow-based features commonly found in these datasets. Therefore, both the suboptimal tuning and structural limitations of the data explain the reduced effectiveness of LSTM in this scenario. With an accuracy of 89.81%, recall of 37.60%, and F1-score of 53.04%, the LSTM model showed difficulties in correctly detecting anomalous patterns in network traffic. This is reflected in the high false negative and false positive rate in the confusion matrix.  
Some of the reasons that could explain this inferior performance include the following:  
* Model complexity: LSTM neural networks are more complex models that require a larger amount of data and training time to achieve optimal performance. In this study, the LSTM model may not have had sufficient depth or ability to capture the complex temporal patterns present in the data.
* Data quantity and quality: Although large datasets were used, the amount of data may not have been sufficient to adequately train the LSTM model. In addition, while the data were preprocessed, temporal sequences may not have been adequately represented or the model may not have taken advantage of these sequences effectively.
* Hyperparameter optimization: The optimization of LSTM model hyperparameters, such as the number of layers, the number of units in each layer, and the learning rate, among others, was not further explored. A more thorough optimization could have significantly improved the model performance.
* Architecture and configuration: The configuration used for the LSTM model may not have been the most appropriate for the type of data and the problem addressed.  
Hyperparameter tuning in the LSTM model was deliberately limited to maintain comparability with ensemble models and due to computational limitations. While configurations based on values widely accepted in the literature were used, the search space was not exhaustively explored. This limitation, along with the lack of sequential structure in the data used, partly explains the observed poor performance. In this context, it is recognized that the application of more advanced techniques such as Bayesian optimization could significantly improve the model's tuning capacity.
# Horizontal PGA Estimates for Varying Deep Geological Conditions--A Case Study of Banja Luka
## 1 Introduction
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Type 1 Spectrum:** & **Type 2 Spectrum:** \\
**Eurocode 8 [9] Ground Types** & **“Most Contributing”** & **“Most Contributing”** \\  & **Earthquakes with** & **Earthquakes with** \\  & \(M_{\text{S}}\) **> 5.5** & \(M_{\text{S}}\) \(\leq\) 5.5** \\ \hline Ground type A, V\({}_{\text{S},30}\) > 800 m/s & & \\ “Rock, at the surface up to 5 m of weaker material.” & \(a_{\text{g}}\) & \(a_{\text{g}}\) \\ Ground type B, V\({}_{\text{S},30}\) = 360–800 m/s & & \\ “At least several tens of meters thick deposits. Very & \(a_{\text{g}}\)\(\times\) 1.2 & \(a_{\text{g}}\)\(\times\) 1.35 \\ dense sand, gravel, or very stiff clay.” & & \\ Ground type C, V\({}_{\text{S},30}\) = 180–360 m/s & & \\ “Deep deposits, several tens of meters up to & \(a_{\text{g}}\)\(\times\) 1.15 & \(a_{\text{g}}\)\(\times\) 1.5 \\ hundreds of meters thick. Dense or medium dense & & \\ sand, gravel, or stiff clay.” & & \\ Ground type D, V\({}_{\text{S},30}\) \(<\) 180 m/s & & \\ “Deposits. Loose-to-medium cohesionless soil, or & \(a_{\text{g}}\)\(\times\) 1.35 & \(a_{\text{g}}\)\(\times\) 1.8 \\ predominantly soft-to-firm cohesive soil.” & & \\ Ground type E & & \\ “Alluvium layer at the surface, between 5 and 20 m & \(a_{\text{g}}\)\(\times\) 1.4 & \(a_{\text{g}}\)\(\times\) 1.6 \\ thick, above stiffer material. V\({}_{\text{S},30}\) \(<\) 360 m/s.” & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: PGA for different ground types according to Eurocode 8 [9], where \(a_{\text{g}}\) represents the PGA value indicated on the accompanying seismic hazard map for the investigated site. a thickness of more than 100 m. For classifying the deep geology conditions, we will use the classification of [PERSON] and [PERSON] [10], who divided deep geology into three types: the geological rock, sediments, and complex or intermediate geological site surroundings.
# Detection and Quantification of Damage in Metallic Structures by Laser-Generated Ultrasonics
## 6 Conclusions  
This paper presents a non-destructive damage detection, visualization, and quantification technique based on laser-generated ultrasonics and state-space predictive models. Different types of damage to aluminum alloy plates and a twin-screw compressor body were detected and quantified by using NNPE. This feature is extracted from the attractors reconstructed by the ultrasonic responses in the time domain recorded by a transducer mounted at the surface of the structure. By capturing the differences in the NNPE values between the undamaged and damaged structures, the nonlinearity of the structures, which are usually caused by damage, can be detected. Furthermore, the damage can be visualized and even quantified by observing the spatial distribution of NNPE values calculated by the ultrasonic responses from the area excited by a scanning laser pulse. Artificial damage to aluminum  
Figure 16: The damage visualization by the NNPE values (the pitches between the two adjacent points are: (**a**) 0.2 mm; (**b**) 0.1 mm.  
Figure 17: The damage visualization by the linear RMS values.  
alloy plates, as well as the actual sand inclusions and fatigue cracks in a twin-screw compressor body, have been successfully detected and quantified by using the proposed technique.  
Some important issues that are not considered in this paper are the capability of the technique in detecting the depth of the damage, the internal damage, and the damage on curved surface. A follow-up study with the proposed feature NNPE will focus on those issues.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON] wrote the paper.  
This research was funded by National Natural Science Foundation of China under Grant Number 51375434.  
The authors declare no conflict of interest.
# Solving the Fuzzy Transportation Problem by a Novel Particle Swarm Optimization Approach
## 5 Particle Swarm Optimization (PSO)
Furthermore, inertia weight plays a key role in the process of providing balance between exploration and exploitation. Periodically, several variations have been developed and introduced, successfully attaining a dynamic approach to the optimal solution by striking a balance between algorithm's exploration and exploitation searches. In our previous research [24], we introduced two new variations. They succeeded in finding the optimal solution under extensive experimental analysis and testing of different combinations of values for variables \(c_{1}\), \(c_{2}\) and \(w\). Trigonometric Acceleration Coefficient-Particle Swarm Optimization (TrigAC-PSO)Trigonometric acceleration coefficient-particle swarm optimization (TrigAC-PSO), has been proven to be the variant yielding the most favorable outcomes in solving the transportation problem [24]. In fact, the TrigAC-PSO variation underwent testing across a set of 32 instances, and the outcomes were compared with widely recognized exact methods and some preceding variations of the PSO algorithm. In comparison to all other methods, the TrigAC-PSO variation achieved the best results [24]. In TrigAC-PSO, initially, each particle is guided by the collective knowledge and experience acquired by the swarm, were the value of \(c_{2}\) significantly exceeds the value of \(c_{1}\). Subsequently, leveraging the learning mechanism, each particle formulates its unique strategy and accumulates individual experience, with the value of \(c_{2}\) decreasing while the value of \(c_{1}\) increases (refer to Equations (11) and (12)). The adjustment of \(c_{1}\) and \(c_{2}\) continues until both parameters are equalized to 2 in the algorithm's last generation. The subsequent equations are employed to compute the cognitive and social acceleration parameters [24]:  
\[c_{1}=\frac{c_{1f}}{2}+sin\frac{2c_{1i}\cdot t}{t_{max}}\cdot\frac{\pi}{2} \tag{11}\]\[c_{2}=c_{2i}+cos\frac{c_{2f}\cdot\pi\cdot t}{2\cdot t_{max}}-\frac{1}{2} \tag{12}\]
* In the initial iteration, the personal acceleration value \(c_{1i}\), is set to 0.5, while the social acceleration value \(c_{2i}\), is set to 3.5;
* In the last iteration of the algorithm, both the personal \(c_{1f}\) and social \(c_{2f}\) acceleration values are adjusted to 2;
* The inertia weight (\(w\)) is dynamic and depends on the current iteration \(t\) and the maximum number of iterations \(t_{max}\) and is defined by the following equation: \[w=\frac{t_{max}-t}{t_{max}}\] (13)
## 1 Introduction
The properties demonstrated by tensegrity structures have received increased attention, especially from civil engineers in applications such as bridges, domes and towers [19; 20; 21; 22], or in deployable structures and smart structures with monitoring, self-control, self-repair, and active control [9; 23; 24]. The potential of the tensegrity concept has inspired new metamaterials. Metamaterials are artificially invented materials that have properties which are not observed in nature. These are composite structures with extreme unusual mechanical properties. In recent years increased interest in the research of metamaterials has been observed. The different types of metamaterials, their unique properties and advantages are presented in [25]. Some researchers are considering the development of new complex tensegrity cells and the application of the tensegrity concept to metamaterials [26; 27; 28; 29; 30]. The arbitrary use or misuse of the word tensegrity is particularly noticeable in descriptions of cable-strut structures, for example in bridge engineering [20]. A precise understanding of this unusual structure is essential. In the absence of externally applied loads, tensegrity, as a type of form-active structure, needs self-equilibrium configuration as determined in the process called form-finding. Over the past decades, many research efforts have been devoted to searching for effective form-finding methods [4; 6; 11; 12; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. A thorough review of the existing methods for tensegrity structures is included in [42; 43; 44; 45] and new form-finding approaches continue to be invented [46; 47; 48]. Previously used methods, usually complex and time-consuming, are well-suited for searching for equilibrium configurations but often fail to address all characteristic features of tensegrity structures. In this paper, a qualitative analysis of truss matrices (compatibility matrix and stiffness matrix with the effect of self-equilibrated forces) is used to search for a tensegrity form. The matrices are established using the finite element method. This simple approach allows researchers to define the characteristic features of tensegrity structures. Both plane and three-dimensional trusses can be analyzed with this method. Engineering design principles allow the use of two-dimensional computational models, which, compared to the spatial structures, far more intuitively understandable and have limited diversity. The aim of this paper is to analyze plane (2D) tensegrity structures found in the literature. The authors' own precise definition proposed and implemented here, is a result of detailed analysis of the descriptions of tensegrity characteristics. The concept of tensegrity systems is misused in reference to both mathematical models and completed engineering structures where the terms \"tensegrity bridges\" or \"tensegrity domes\" are applied to the cable-strut models. The proposed evaluation method will allow proper understanding of these unique structures and ensure the correct use of the term in all applications.
# Whitening CNN-Based Rotor System Fault Diagnosis Model Features
## 3 Proposed Improvement for Fault Diagnosis Models  
This study seeks to improve the current state-of-the-art CNN-based techniques for diagnosing faults from raw vibration data. The experiments in this study adapt three reportedly highly performing 1D-CNN models, remove all BN functions and normalise the layer inputs with ND instead. The study then evaluates the original models employing BN in parallel with the corresponding models employing ND. The baseline models are known as [PERSON]'s model [32], the stacked residual dilated convolutional neural network (SRDCNN) [18] and the Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) [15]. Although numerous other fault diagnosis models exist, these were chosen due to their simple and effective architectures. For example, WDCNN has been shown to produce the best bearing fault diagnosis results with slight modifications as an ensemble [16] and with an additional RNN-path [17]. However, the contrast in the results between models employing ND and BN can be sufficiently shown with these models without additional modifications.  
This section is divided into three parts. Section 3.1 visits briefly the baseline model architectures. Then, Section 3.2 describes the ND practical implementation details. Finally, some general training algorithmic design choices are presented in Section 3.3.