esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology of Time-Frequency Separation Neural Network
### Enhanced Defect Detection with a Time-Frequency Separation Neural Network
#### 2.3.2 Separation Weight Estimator and Loss Function  
After extracting the high-level time-frequency features, the separation weights of the features can be acquired from a dense or fully-connected layer regressor. The dense layers can be divided into n modules, with each module estimating the separation weight for an individual source. The output of the \(i\)th module is a 1 \(\times\) m vector, \(\mathbf{w}_{i}\). When multiplied with the original observation signals, the separated sources can be attained and used to calculate the objective function with the separation weights as follows:  
\[\begin{split}&\ell(\mathbf{W})=-\frac{1}{L}\sum\limits_{i=1}^{n} \sum\limits_{t=1}^{L}\left[G(\mathbf{y}_{Gauss})-G(\mathbf{y}_{i}(t))\right]^ {2}+\lambda\ell_{2}(\mathbf{W})\\ &=-\frac{1}{L}\sum\limits_{i=1}^{n}\sum\limits_{t=1}^{L}\left[g_ {Gauss}^{i}-G(\mathbf{w}_{i}\mathbf{x}(t))\right]^{2}+\frac{\lambda}{L}\| \mathbf{W}\|_{2}^{2}\end{split} \tag{9}\]  
where \(n\), \(L\) indicates the number of sources and sampling points in the signal, respectively. \(\lambda\) is the adjustment coefficient to balance the two losses. \(g_{Gauss}\) is a constant related to the Gaussian distribution, which is determined only if the observation signals remain invariant. The objective or loss function of the network comprises two parts in Equation (9), namely the non-Gaussianity and L2 regularization part. The negative non-Gaussianity loss was calculated with the third approximate function for negentropy, generated from the CDF of the super Gaussian distribution in Section 2.1, of which the local minimum revealed a potential candidate of an independent source. The L2 regularization term is added as a penalty term to improve the sparsity and further prevent overfitting in the optimization. Moreover, since the objective and optimization mechanism in networks are quite independent from each other, the proposed method can be combined with lots of superior optimizers to obtain a more accurate separation performance, such as with stochastic gradient descent, Levenberg, RMSprop, Adagrad, and adaptive moment estimation (Adam) [28]. The Adam optimizing criterion was selected in this study in view of its advantages in adaptive learning rates, fast convergence, and highly efficient computation.  
In summary, the estimation of separation weights is essentially a regression task. Unlike traditional regression networks, the target in this self-organizing task is not a preset sequence but is determined by the extreme value in the non-negentropy criterion. In light of the proposed structure, the separation weights could be estimated accurately with sufficient training of the observation signals.
# Detail-Preserving Shape Unfolding
## 5 Experimental Details
### Comparisons  
In this section, we compare the results generated by our approach with the state-of-the-art methods: the least squares multidimensional scaling method [11] (LSMDS), skeleton based canonical form [28] (SCF), and detail-preserving mesh unfolding [24] (DPMU). Note that, the method proposed by [PERSON] et al. [25] is not included in our comparisons because of its expensive time consuming, which is 20 times slower than DPMU, and its retrieval accuracy is not the best among the above three methods in the similar dataset [50] from the recent survey paper [29]. To conduct fair comparisons, LSMDS [11] is modified to accept tetrahedral mesh as input and the weights for all vertex pairs are set as 1. For the other methods, all parameters are the same as described in their articles.  
In Figure 5, we illustrate the comparison results among our method, LSMDS, SCF and DPMU. We can observe from this figure that LSMDS always loses some local details of the original meshes, especially for the models with limbs and end-points. While SCF can preserve more details than LSMDS, its efficacy highly depends on the the quality of the skeletal structure which is sensitive to geometry and topology of the mesh. For instance, it is always difficult to extract the accurate skeleton for hand and bird models. As a result, SCF cannot obtain satisfactory results for these meshes. DPMU and our method generate satisfactory results for almost all models. However, our method explicitly preserve the rigidity of the original mesh.  
Figure 6 shows the CDF curves of \(E_{rig}\), \(E_{str}\) and \(E_{geo}\) for different methods on the dataset [47]. For local rigidity (a), less then 50% samples locate in [0, 2] for LSMDF and SCF methods, and about 95% samples locate in that interval for DPMU, while almost all samples locate in that interval for our method. From (b), we can see that all methods can achieve very similar performances on mesh stretching. In (c), LSMDS can achieve the best performance on geodesic distance preservation, as they consider geodesic distance as hard constraints. However, it is hard to keep the local details. DPMU and our approach have the similar accuracy on this metric and SCF is the worst one. In conclusion, our method is the best for rigid transformation while keeping good performance on \(E_{str}\) and \(E_{geo}\). Similar results also can be found in Table 1. The difference is that they are calculated on the same category.  
Figure 5: Illustration of mesh unfolding results of different methods. (**a**) original meshes, (**b**) the results of least squares multidimensional scaling method (LSMDS) [11], (**c**) the results of skeleton based canonical form (SCF) [28], (**d**) the results of detail-preserving mesh unfolding (DPMU) [24] and (**e**) our results.  
Figure 6: The cumulative probability distributions of \(E_{rig}\) (**a**), \(E_{str}\) (**b**) and \(E_{geo}\) (**c**) for different methods.
# Application of Genetic Algorithms for Periodicity Recognition and Finite Sequences Sorting
## 1 Introduction
This work specifically focuses on the application of the control law obtained for synchronizing an irregular network consisting of six different chaotic systems. The paper underscores the utility and advantages of the proposed synchronization scheme through numerical simulations of complex chaotic networks. In another work [7], the possibility of creating a secure optoelectronic communication system using chaotic Rossler oscillators and semiconductor lasers is investigated. The results confirm that the system is viable. In the paper [3], a numerical analysis of the Bernoulli process with periodic realizations is carried out. Overall, the study of periodic sequences allows for a deeper understanding and analysis of the structure and characteristics of unpredictable sequences, as well as the identification of regular patterns and other key aspects [8]. This paper represents the first step in exploring unpredictability through deep learning methods. The research focuses on the periodicity of random symbolic sequences, which is one of the elements of chaos [9]. Therefore, the investigation of this problem serves as a prerequisite for further delving into the subject matter. In this paper, the object of the study was a binary sequence. The study of periodic sequence is due to a wide range of applications in science and practice. For example, in signal processing, periodic sequences are important. The study of their properties will allow you to gain a good understanding in the generation and control of signals, as well as in solving problems related to filtering and signal processing. It can also be useful in the field of data encoding and decoding. They can be used as part of a code system that monitors the need to transmit information [10]. The advantages and disadvantages of the aforementioned papers in this section are outlined in the following Table 1.
## 4 Application of Blockchain Technology in Knowledge-Based Networks
### Traffic Optimization
#### 4.3.3 QoS Offering  
Applications can confirm when particular QoS demands are fulfilled by directing traffic in accordance with those criteria [279]. Machine Learning-based Blockchain QoS Routing (MLBQR) inside a vehicular KDN is applied to transmit many forms of traffic, including footage, tele-medicine, text, and others. QoS adherence in the realms is delivered and confirmed via the communication of trust data utilizing blockchain and self-executing contracts, while Q learning is utilized to improve the overall QoS by optimizing blockchain parameters [280]. Likewise, MLSMBQS, a machine learning-enabled blockchain framework for QoS-driven blockchain sharding optimization, which reduces privacy concerns in QoS-based OpenFlow routing with the aid of blockchain, has been studied for an IoT-based KDN [281]. Additionally, for blockchain-powered IoT KDN systems, which are regarded as autonomous systems, a QoS improvement framework has been proposed that utilizes the power of blockchain and machine learning to create secure side chains [282]. Furthermore, ATQMB is a framework that leverages blockchain and machine learning to create a QoS-aware media access control model with the aid of encryption, a distributed agreement method, and self-executing contracts with the goal of providing QoS at a reduced cost and adjusting to QoS's unpredictable character for knowledge-defined wireless networks [283]. In contrast, meta-heuristics have been used for intelligent task scheduling in a framework known as QoS-ledger, where permissionless blockchain is leveraged to preserve medical data while QoS computation is performed using an algorithm in healthcare networks [284].
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AWGN & Additive White Gaussian Noise \\ BER & Bit-Error-Rate \\ BPSK & Binary-Phase-Shift-keying \\ BCJR & Bahl-Cocke-Jelinek-Raviv \\ BP & Belief Propagation \\ CN & Check Node \\ DP-LDPC & Double Protograph Low-Density Parity-Check \\ ECC & Error Correction Code \\ EPR4 & Extended Class IV PR \\ i.u.d & independent and uniformly distributed \\ ISI & Inter-Symbol-Interference \\ JSCC & Joint Source-Channel Coding \\ JSC & Joint Source-Channel Decoder \\ LLR & Log-Likelihood Ratio \\ LDPC & Low-Density Parity-Check \\ M-JPEXIT & Modified Joint Protograph Extrinsic Information Transfer \\ MR & Magnetic Recording \\ MI & Mutual Information \\ OD-ISI & One-Dimensional Inter-Symbol-Interference \\ PLDPC & Protograph Low-Density Parity-Check \\ PR & Partial Response \\ PEG & Progressive Edge Growth \\ SNR & Signal-to-Noise Ratio \\ TD-ISI & Two-Dimensional Inter-Symbol-Interference \\ VN & Variable Node \\ \end{tabular}
# Application of the KINEROS 2 Model to Natural Basin for Estimation of Erosion
## 3 Erosion Estimation Models Used  
Soil erosion has been an important topic of study since the second quarter of the 20 th century. Investigators have created different models throughout the years to determine the quantity of solid material eroded and/or deposited in a plot or in a basin. There are two main groups of erosion models: empirical models (USLE, RUSLE and MUSLE) and physically based models. In this study the physically based model used was KINEROS 2.
## 3 Methods  
### Overview of the Proposed Method  
Figure 4 illustrates the overall process of the proposed method. In a typical training procedure, a single model is generated from the original dataset. By varying the training and validation combinations, we can produce multiple redundant models based on the same dataset. Although these models share similar properties and performance, they exhibit differences due to variations in data exposure during training. These discrepancies reflect the confidence level of each pixel in the GT, and this information is incorporated to create supplemented masks. Furthermore, modified masks are created by substituting the GT class of confusing pixels with the dominant class determined through the voting of redundant models. The process for creating the datasets is explained in detail in Section 3.3.  
Figure 4: Three types of models trained from the original dataset: the raw model (e.g., Model-1), the enhanced model, and the consensus model.  
Figure 3: Four samples from the CITY-20 dataset. On the left are the images, and on the right are the corresponding class masks.  
As a result, we derived three models for comparison: the raw model from the original dataset, the enhanced model trained using the supplemented dataset, and the consensus model trained on the modified dataset.  
There are numerous projects and datasets available for land-cover segmentation of aerial images. However, various constraints--such as project duration, image capture location, timing, and environmental conditions--can make it challenging to achieve consistent performance from the models trained on them. Heterogeneous dataset fusion is a vital strategy for enhancing performance in real-world applications. Nonetheless, inconsistencies often arise between these distinct datasets concerning class composition, imaging sensors, annotation styles, and quality. Similar to the case of a single dataset, redundant models can help reduce discrepancies in heterogeneous datasets. Figure 5 illustrates a dataset fusion configuration based on redundant models utilizing two distinct datasets.  
The synthetic dataset (SD) is created by merging two distinct datasets. To generate the SD, we oversampled the host dataset to ensure the number of training samples matched that of the guest dataset. The redundant models trained on the host dataset assess confidence levels for both datasets to produce the supplemented SD. This process aligns the two datasets from the perspective of the host dataset. The modified SD is then created from the supplemented SD using the same method that is applied to a single dataset. As a result, we now have three additional models: the raw fusion model (FM), the enhanced FM, and the consensus FM.
# Microwave Liquid Crystal Technology
## 2 How to Use LC at Microwave Frequencies?
This is different for both other cases, where the electric field is used to reach at least one state. The only difference in RF signal and bias E-field is the frequency. The minimization of overall system's energy according to Equation (8) must hold also in this case. The temporal behavior of the LC volume is encapsulated in the elastic term as this includes the mechanical materials properties. Due to the viscosity, the director cannot follow an electric field with fast changing amplitude. Additionally, the dielectric contrast required for LC steering is much higher at DC than at RF frequencies. Hence, the influence of the RF field can be neglected. This is also relevant for the linearity of the components. The electric tuning of the material leads automatically to a non-linear behavior of the components, as the RF field will also change the state of LC. There are just few measurements of LC's linearity published. In 2006, IP3 measurements of two different nematic liquid crystals have been published in [7]. Measurements of a phase shifter showed that the IP3 of this device is in the range of 60 dBm. Since then, no other measurements have been published. The excellent large signal characteristics obtained in this measurement already fulfill the requirements of many RF applications. Furthermore, the achieved performance makes linearity measurements of LC very difficult, as standard measurement equipment is at its boundaries. Hence principles for measuring passive intermodulation (PIM) must be adapted for LC non-linear characterizations. This specific topic will gain more interest in future as commercialization advances. Up to now, no devices showed significant non-linearity. After the fundamental device concepts have been discussed, the material must be characterized before we will show implementation advances around 100 GHz. For material characterization different realizations have been discussed recently. In the following we will show the most important ones.
## 2 Results and Discussion
### Effect of PAW Treatment on the Main VOCs in Rocket Leaves
#### 2.1.3. Ketones  
Ketones are reported to have an important role in plant defense. They were previously reported to contribute to the sensory characters of rocket, as they are correlated with the pleasant odours [16]. Among the identified ketones, 3,5-Octadien-2-one and (3\(E\),5_E_)-3,5-Octadiene-2-one were described to impart a pungent green aroma of medium intensity in Brassicaceae species [17]. In addition, 6-Methyl-5-hepten-2-one was previously identified in rocket leaves by [15; 17], and hexahydrofranesyl acetone (phytone), a very common ketone in Brassicaceae plants that resulted from the oxidative degradation of the diterpene alcohol (_E_)-phytol that occurs as a side chain of chlorophyll a [26], were detected. Among the identified ketones, another group, called volatile norisoprenoids was also identified  
Figure 3: (**A**): Relative abundance of erucin nitrile in control and PAW-treated samples, (**B**): Relative abundance of erucin and erucin nitrile in control and PAW-5-treated sample.  
in rocket samples, such as (_E_)-\(\beta\)-ionone, \(\beta\)-ionone-5,6-epoxide, (_E_)-geranylacetone, and dihydroactinidiolide, that were detected in all rocket samples. [PERSON], [PERSON], [PERSON], and [PERSON] [17] have reported the presence of geranylacetone (0.1 %) in rocket leaves. The major ketone in all rocket samples was \(\beta\)-ionone, also previously detected in rocket leaves by [5; 15]. Moreover, dihydroactinidiolide was detected in Brassicacea by [PERSON] et al. [27].  
Except for PAW-5, which showed a significant increase in the total relative abundance of ketones, other PAW treatments showed non-significant changes compared to the untreated sample. Our results are in good agreement with some previous results, such as those of [PERSON] et al. [28], who reported non-significant changes in the total composition of ketones in milk following cold plasma treatment. Regarding the individual compounds, PAW-20 and PAW-10 did not induce any significant changes except for the \(\beta\)-ionone-5,6-epoxide, which increased significantly in PAW-10 samples. Moreover, a significant increase was observed in the relative abundance of 6-methyl-5-hepten-2-one and \(\beta\)-ionone-5,6-epoxide in PAW-2 samples, however, 2,5-dimethyl-3-hexanone could not be identified. PAW-5 showed a significant increase in 2,5-dimethyl-3-hexanone, 3-octen-2-one, 3,5-octalation-2-one, (_E_)-\(\beta\)-ionone, and \(\beta\)-ionone-5,6-epoxide, which consequently led to the significant increase in the total ketone content of this sample. Furthermore, [PERSON] et al. [29] detected higher contents of geranylacetone in brown rice upon processing with cold plasma. A reason for the increase in total content of ketones in PAW-5 could be the lipid oxidation that may have occurred in this sample, which can lead to further formation of the secondary lipid oxidation products epoxides, aldehydes, dimers, or ketones [30].
## 2 ISAR Imaging Model of Maneuvering Targets
Assuming that the target has completed envelope migration correction, the target scattering point is corrected in the corresponding range unit, and the range unit where the \(P\) point is located has \(N\) scattering points. The azimuth echo signal of the range unit can be expressed as:  
\[s_{p}(t)=\sum_{i=1}^{N}\sigma_{i}\exp\left(-j2\pi\bigg{(}f_{i}t+\frac{1}{2}k_{ i}t^{2}\bigg{)}\right) \tag{9}\]  
where \(\sigma_{i}=AT_{p}\sin c\Big{[}T_{p}\Big{(}f+2\frac{k_{r}}{c}[(\omega_{l}\cdot \mathbf{\gamma})t_{m}+\frac{1}{2}(\Omega_{l}\cdot\mathbf{\gamma})t_{m}^{2}]\Big{)} \Big{]}\),\(f_{i}=2\omega_{l}\cdot\mathbf{\gamma}/\lambda\),\(k_{i}=2\mathbf{\Omega}_{l}\cdot\mathbf{\gamma}/\lambda\). \(\sigma_{i}f_{i}\), and \(k_{i}\) represent the amplitude, central frequency, and chirp-rate of the azimuth echo of the \(i\)th scattering point, respectively. It can be seen from Equation (9) that the target azimuth echo signal is a multi-component LFM signal with different central frequencies and chirp rates due to the distribution of a plurality of scattering points having different lateral distances in the same range cell. Figure 2 is a time-frequency diagram of a certain range cell. Figure 2 depicts a pulse-range cell diagram after translation compensation of range compression; Figure 2 demonstrates the time-frequency transformation of the azimuth signal of the range cell where the black arrow is located. From the time-frequency diagram, it can be seen that the range cell signal is approximately a multi-component LFM signal. As shown in Figure 1b, suppose \(P(x_{i},y_{i})\) is the \(i\)th scattering point on the range cell, \(x_{i}\) is the lateral distance of the imaging plane, and the effective rotational component \(\omega_{\rm e}\) can be expressed as angular velocity \(\omega\) and angular acceleration \(\Omega\) in the imaging plane. Then, \(\omega_{l}\cdot\boldsymbol{\gamma}=x_{i}\omega\) and \(\boldsymbol{\Omega}_{l}\cdot\boldsymbol{\gamma}=x_{i}\Omega\). Therefore, the ratio of chirp rate \(k_{i}\) to central frequency \(f_{i}\) can be obtained:  
\[K_{\Omega\omega}=\frac{k_{i}}{f_{i}}=\frac{2\boldsymbol{\Omega}_{l}\cdot \boldsymbol{\gamma}/\lambda}{2\omega_{l}\cdot\boldsymbol{\gamma}/\lambda}= \frac{\Omega}{\omega} \tag{10}\]  
It can be seen from the rotation ratio \(K_{\Omega\omega}\) that the coefficient is only related to the rotation ratio of the target and is independent of the coordinates of the scattering point. Therefore, for the rigid body target, rotation ratios remain unchanged, and the rotation ratios \(K_{\Omega\omega}\) of the sub-echo LFM signals of all scattering points are also the same. In turn, Equation (9) can be resolved as:  
\[s_{p}(t_{m})=\sum_{i=1}^{N}\sigma_{i}\exp\left(-j2\pi f_{i}\left(1+\frac{1}{2}K _{\Omega\omega}t_{m}\right)t_{m}\right) \tag{11}\]
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
## 4 Growth of GaN Nanorods Using MSE
### Selective-Area Growth of Nanorods Using MSE
This results in the formation of irregular and nonuniform nanostructures. Therefore, the minimization of mask and substrate damage through proper beam control is vital in growing uniform and well-defined nanorod arrays using MSE.
## 4 Experiments
### Datasets and Evaluation Metrics  
#### 4.1.1 Datasets  
CrowdHuman [22], a challenging pedestrian benchmark, is widely used to evaluate the performance of pedestrian detectors in crowded scenes. In this paper, we choose CrowdHuman to evaluate the performance of our proposed method in crowded scenes. CrowdHuman consists of images from over 40 different cities worldwide, which are crawled by Google. These images depict dense and diverse crowds, posing significant challenges for pedestrian detection. CrowdHuman comprises a total of 24,370 images, with 15,000 images allocated for training, 4370 images for validation, and 5000 images for testing.  
CityPersons [23], a subset of Cityscapes [43], is a commonly used benchmark dataset for pedestrian detection. It is selected in the paper to evaluate the performance of MA-NMS in slightly crowded scenes. CityPersons exhibits a high level of diversity and depicts numerous cities and countries in Europe. CityPersons comprises 5000 images, with 2975 images allocated for training, 500 images for validation, and 1525 images for testing. Table 1 presents the statistics of the training sets for CrowdHuman and CityPersons.