esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Fast Hyperbolic Hough Transform
### Global Convergence  
In this subsection, Theorem 1 will be proven:  
**Theorem 1**.: _Let the global maximum of the HHT, calculated over search area \(\mathcal{S}\) on fine grid \(\mathcal{G}_{f}\) of grid size \(D_{\mathcal{G}_{f}}\), be \(A_{\mathcal{G}_{f}}\), such that the maximum is at grid point(s) \(P_{0}\), i.e., \(A_{\mathcal{G}_{f}}=A(P_{0})=\underset{P\in\mathcal{G}_{f}}{\max}A(P)\). Let us create a coarse grid \(\mathcal{G}_{c}\) by omitting grid lines from \(\mathcal{G}_{f}\), such that the grid size of \(\mathcal{G}_{c}\) is \(D_{1}=2^{k-1}D_{\mathcal{G}_{f}}\), \(k\in N\). If the output of the F-HHT algorithm, executed over \(\mathcal{S}\) with initial grid \(\mathcal{G}_{c}\), using \(k\) iterations, is \(P_{0,FHHT}\), then \(P_{0,\ FHHT}=P_{0}\)._  
The theorem ensures that the global maximum of the HHT, calculated on a fine grid with size \(D_{\mathcal{G}_{f}}\), can be found with the F-HHT in \(k\) iteration steps, starting from a much coarser grid of size \(2^{k-1}D_{\mathcal{G}_{f}}\). Note that \(P_{0}\) may contain one or more grid points of \(\mathcal{G}_{f}\).  
**Lemma 1**.: _If there is a promising tile \(\mathcal{T}\) with center \(P\) that contains one or more points of \(P_{0}\), then this tile is preserved (not removed) during the pruning step._  
Proof of Lemma 1.: For tile \(\mathcal{T},\overline{A}(P)\geq A(P_{0})=A_{\mathcal{G}_{f}}\). Since \(A_{\mathcal{G}_{f}}>A_{maxmin}\), \(\overline{A}(P)>A_{maxmin}\), therefore \(\mathcal{T}\) is not removed in step 12. Notice that each coarser grid during the iteration is part of the fine grid \(\mathcal{G}_{f}\). Thus, for the actual \(bestA\) calculated in step 10, it holds that \(bestA\leq A_{\mathcal{G}_{f}}\). This ensures that \(\mathcal{T}\) is not removed in step 13, either. Thus \(\mathcal{T}\) is preserved. \(\square\)  
Proof of Theorem 1.: At the start of the first iteration step, all tiles are marked promising, thus the tiles containing one or more points of \(P_{0}\) are also marked as promising. According to Lemma 1, these tiles are preserved in the pruning step and then they are replaced by four smaller promising tiles. These smaller tiles also contain the points of \(P_{0}\). Thus, in the beginning of the second iteration step, promising tiles still contain all points of \(P_{0}\). This is true for all iteration steps, thus at the beginning of the \(k\)th (last) iteration step, promising tiles contain all points of \(P_{0}\).  
Notice that in the last iteration step the set of search lists PromisingList is a (small) subset of the fine grid \(\mathcal{G}_{f}\) (with grid size of \(D_{1}/2^{k-1}=D_{\mathcal{G}_{f}}\)), where the HHT was evaluated. Thus, points of \(P_{0}\) are the center points of some promising tiles. Since all points in \(P_{0}\) are preserved, the maximum points \(bestP\), computed in the last iteration step, are necessarily the same as the maximum points calculated by the HHT on the whole grid \(\mathcal{G}_{f}\). Therefore \(P_{0,FHHT}=P_{0}\). \(\square\)
## 2 Visions, Applications and Challenges of the IoT
### The IoT Challenges
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Focused Area** & **Challenges** & **References** \\ \hline \multirow{8}{*}{Constrained Resources} & \(\bullet\) & Limited manufacturing techniques for small size and low-cost device resources \\  & \(\bullet\) & Spectrum resources scarcity for IoT \\  & \(\bullet\) & enabling technologies \\  & \(\bullet\) & Smart antenna \\ \hline \multirow{8}{*}{Scalability, Reliability and Interoperability} & \(\bullet\) & Self-addressing, discovering and classification \\  & \(\bullet\) & Host identification and address mapping \\  & \(\bullet\) & Interoperability and availability \\  & \(\bullet\) & Lack of efficient and reliable communication by using TCP \\  & \(\bullet\) & (transmission control protocol)/ UDP (user datagram protocol) protocol \\  & \(\bullet\) & Unreliable packet delivery \\  & \(\bullet\) & Lack of interoperability between different protocols \\ \hline \multirow{8}{*}{Privacy and Security} & \(\bullet\) & Integrity, validation, authentication and trust \\  & \(\bullet\) & Data and physical device security \\  & \(\bullet\) & Confidentiality \\  & \(\bullet\) & Cyclic redundancy check (CRC) \\  & \(\bullet\) & Message authentication code (MAC) \\  & \(\bullet\) & Limitations of symmetric \\  & \(\bullet\) & cryptography and public–key \\  & \(\bullet\) & cryptography operation \\  & \(\bullet\) & Different IoT threats such as fragmentation attack \\  & \(\bullet\) & Poor encryption \\ \hline \multirow{8}{*}{Big Data and Cloud Computing} & \(\bullet\) & Lack of computational resources \\  & \(\bullet\) & Low data storage \\  & \(\bullet\) & Loss of data packets \\  & \(\bullet\) & Optimization of multi-objective functions \\  & \(\bullet\) & Edge computing \\  & \(\bullet\) & Liability sensitization toward \\  & \(\bullet\) & redundant tasks \\  & \(\bullet\) & Centralized data acquisition system \\  & \(\bullet\) & Must support domain-specific programming \\ \hline \multirow{8}{*}{Universal Standardization} & \(\bullet\) & For technology and other regulatory \\  & \(\bullet\) & For communication among \\  & \(\bullet\) & heterogeneous devices \\ \cline{1-1}  & \(\bullet\) & Protocol standardization \\ \cline{1-1}  & \(\bullet\) & Spectrum harmonization \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Challenges of the IoT.
## 3 Proposed Methodology
### Feature Extraction  
To appreciate the visual substance in a video, an elevated level component preparing stage is imperative for separating film attributes. These characteristics are discovered using CNN on various gages that are steady to helper varieties. The previous studies [42], [57] discussed different pre-trained neural networks utilized to catch the highlights through the video frames.  
The video is the input of our proposed model, and the video is differentiated into a sequence of visual frames. Every frame is fed into CNN for extracting the features. We have implemented 2D CNN for 2D feature extraction. The 2D CNNs recognize the segmentation map for a solo frame using 2D convolutional kernels. Predictions for segmentation maps for a complete volume are made by one frame at a time. To create predictions, the 2D convolutional kernels can use context throughout the frame's height as well as width (\(256\times 256\)). We have used the pre-trained models VGG-16 [42], InceptionV3 [47], and Xception [48] for mining the 2D features from each visual frame. All those models pre-trained on ImageNet [57] dataset contain approximately 1.2 million images of over 1000 classes. The VGG-16 [42] model consists of 16 layers; the first 13 layers are convolutional layers, and then the three dense layers are for classifying images. The InceptionV3 model consists of 48 layers, and the extended Inception or Xception contains 71 layers. For transfer learning, the last fully connected layer is removed from the model. The 2D CNN models are chosen for reducing the training time for the large MSVD [41] and MST-VTT [35] dataset. Inspired by [11], for the extraction of action features, we have used an Inflated 3D network (I3D) [50] pre-trained in kinetics dataset. The 3D CNNs predict the segmentation maps for the adjacent frames using 3D convolutional kernels. The prediction of the 3D convolutional kernels can use the 2D convolutional kernels incorporated with an additional dimension (256 \(\times\) 256 \(\times\) 8). Another factor of 3D CNN is to consider the training pace. It consumes more time and requires expensive equipment. Therefore, we have used UCF-101 [49], a small dataset for motion feature extraction, and used Faster R-CNN [51] for facial recognition in some cases. At that time, the features are concatenated and fed into an attention-based bi-LSTM encoder.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 5 Discussion  
The simulation results show that UWALOHA works well with low network loads and small network sizes but performance worsens quickly as the network load or size increase. This occurs because without control packets, UWALOHA can reduce the transmission overhead but produces more collisions and retransmissions when the packet traffic increases. In UW-CSMA/CA and SFAMA, handshaking effectively avoids collisions, so these protocols outperform UWALOHA under high network loads and large network sizes. However, the RTS/CTS control packets introduce transmission overhead, which reduces the performance under low network load and small network size. Handshaking also increases the delay in transmission. ROPA allows additional concurrent transmission to the source node after the source node transmits its data, so its performance is better than UW-CSMA/CA and SFAMA under different network loads and network sizes. Compared with ROPA, the proposed method supports arbitrary multi-pair concurrent transmission, which increases transmission opportunity in a transmission cycle. Therefore, it produced the best performance in normalized throughput, delivery rate, average end-to-end delay, and average energy consumption under different network loads and network sizes in the simulations. The simulation results also show that the proposed method, with sending sequence optimization, outperforms that without sending sequence optimization.  
Although the proposed method works well in the one-hop cluster, several aspects of its performance can be further improved. First, in Equations (1) and (2), we assumed that if \(s_{i}\) sends out its data packet before \(s_{j}\), then the data packets from \(s_{i}\) will arrive at \(d_{i}\) and \(d_{j}\) earlier than the data packets from \(s_{j}\). This assumption sets a constraint on the packet arrival sequence at the destination node, which simplifies the question but leads to a sub-optimal solution. Further improvement can be expected by removing this constraint. Second, in the proposed method, collisions of data packets still occur if a node loses one or more CTS packets. Any method that can reduce or eliminate the collision of CTS packets will improve the performance of the proposed method. Third, the proposed method is developed for one-hop clusters. Although it can be extended to multi-hop situations, the performance will worsen when nodes belonging to multiple clusters join the communication. Therefore, inter-cluster optimization should be considered for multi-hop networks.
## 2 Materials and Methods
### Field Data  
At each sampling site (Figure 1C), a 2500 m\({}^{2}\) plot (250 m \(\times\) 10 m) was established to collect ecological variables relevant to forest composition and disturbance analysis [17; 58]. A central straight-line transect of 250 m intersected the plot, with an additional perpendicular transect of 20 m placed at the beginning of the plot [63].  
Vegetation and bare soil cover were recorded using the point-intercept method [64] at 1-m intervals along the 250-m transect [63; 64]. The ratio of vegetation cover to bare soil is a key indicator of Chaco vegetation integrity, where mature forests exhibit high vegetation cover, while disturbed areas are characterized by an increase in bare soil [20; 49].  
Additionally, we recorded vegetation structure parameters related to forest composition and degradation [17; 50]. Vertical space was divided into four layers: one herbaceous layer and three woody layers (low \(<\) 2 m, medium 2-8 m, and high \(>\) 8 m). Every 5 m, we measured the maximum height of species recorded in each vegetation layer and counted the number of seedlings (0-30 cm height) within a 2 m\({}^{2}\) subplot [58; 63].  
On the perpendicular 20-m transect, we collected a composite litter sample, consisting of 10 sub-samples (each 40 cm\({}^{2}\)) taken every 2 m. Finally, within the 2500 m\({}^{2}\) plot (250 m \(\times\) 10 m), we recorded tree species density (for individuals \(>\) 5 cm in diameter) and measured their diameter at breast height (DBH) (Table 2).  
The structural and compositional variables of woody vegetation (e.g., cover and height by species, litter characteristics) are commonly used descriptors of the main vegetation types in the Chaco region [20; 53], as well as for assessing their structural integrity and degree of alteration [17; 29; 58; 65].  
The structural and compositional variables measured during fieldwork (2017-2019), such as tree height, basal area, and woody species cover, remain relatively stable over short timescales in arid ecosystems like the Arid Chaco [62]. This stability ensures a reliable temporal alignment between the field data and the selected RS images from 2020. Moreover, these variables typically do not undergo significant changes within a single year, unless extreme events such as wildfires or severe deforestation occur--none of which were recorded during the study period.
## 4 Analysis and Discussion  
### Image Quality  
I, Q, U, DOP, AOP images of the observed polycrystalline and monocrystalline silicon solar panels were resolved by Equations (7) and (8), as shown in Figures 6 and 7, where I (0\({}^{\circ}\)), I (60\({}^{\circ}\)), I (120\({}^{\circ}\)), I, Q, U, DOP and AOP were listed from left to right, respectively.  
From Figures 6 and 7, the edges of the defects are blurry, and several small defects are difficult to distinguish from image I, which is equivalent to traditional electroluminescence. However, the geometric information of defects can be noticed from I (0\({}^{\circ}\)), I (60\({}^{\circ}\)), I (120\({}^{\circ}\)). Q and U images are relatively sensitive to defect edge details, DOP and AOP images were significantly enhanced with textural details, where the internal grid structure can be clearly recognized and the defect area has a higher contrast.  
To evaluate the quality of each image objectively, five indicators of information, entropy (IE), average gradient (AG), standard deviation (SD), space frequency (SF), and edge intensity (EI) were adopted, and the values of them were calculated and shown in Tables 1 and 2 as follows:  
From Tables 1 and 2, the standard deviation, average gradient, spatial frequency, and edge intensity of DOP and AOP images have been greatly improved compared to image I, indicating that DOP and AOP images are rich in more texture details, which is consistent with the subjective observation of human eyes.
## 5 Conclusions and Remarks  
The need for deeper insights into the characterization of bituminous materials used in industrial and infrastructural applications cast the basis for the presented research. Beyond the traditional rheo-mechanical analysis of bitumens, a deeper characterization is needed for today's multi-functional products introduced in both industry and road infrastructures. In this research, it was shown that the combined use of TLC technique and \({}^{1}\)H-NMR spectroscopy is a feasible approach to distinguish between different bitumens even with similar physical and rheological properties.  
In light of the presented achievements, the following conclusions can be made:  
* Asphaltene and maltene fractions of bitumens can be successfully separated by means of TLC techniques and be used for further characterization tests in the laboratory.
* The choice of solvents determination, mobile phases and support material are crucial to obtain a consistent separation of the binder components.
* \({}^{1}\)H-NMR provides a wider perspective on the chemical characteristics of the various binders than traditional SARA analysis.
* Although within the proposed technique unconventional laboratory work is required, the obtained data provide a comprehensive understanding on the chemo-mechanical nature of the binders.  
Figure 9: The \({}^{1}\)H-NMR spectra of zone A obtained through a preparative TLC for the three bitumens: (**a**) paving 160/220; (**b**) paving 70/100; and (**c**) industrial 160/220.  
* The acquired knowledge will be at the basis of any prospective alteration/modification of binders for commercial purposes.  
The described general findings are based on the laboratory study reported in this paper. Any other investigation into the use of coupled TLC and \({}^{1}\)H-NMR methodologies may differ with changes in the bituminous material' characteristics. A consistent database should be created and disseminated for further scientific upgrades.  
All authors contributed equally to this work.  
The authors declare no conflict of interest.
# Action Recognition via Adaptive Semi-Supervised Feature Analysis
## 5 Experiments
### Performance on Action Recognition  
A linear SVM was utilized as the baseline. Based on the comparisons, we observe the following: (1) KGMA achieved the best performance, and our semi-supervised algorithm was better than linear SVM, which is a widely used supervised classifier (2) all methods achieved better performances using more labeled training data, as shown in Figure 3, or enlarging the semi-supervised parameter (i.e., \(\eta,\beta,\mu\)) range such as Figure 5; (3) we averaged an accuracy of \(3\times c,5\times c,10\times c\), and \(15\times c\) cases, and the recognition of KGMA on JHMDB, HMDB51, and UCF101 improved by 2.97%, 2.59%, and 2.40%, respectively. When using TSN features, the recognition of our KGMA on the above-mentioned datasets improved by 2.21%, 3.77%, and 2.23%, respectively. Evidently, our semi-supervised method can improve recognition by leveraging unlabeled data compared to linear SVM with labeled data merely. Figure 3 illustrates that our algorithm benefits from the multiple-feature analysis, kernelized Grassman space, and iterative skills of the PBB method.  
These results can be attributed to several factors. First, our method not only leverages semi-supervised approaches, but also leverages intra-class action variation and inter  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **Features (dim \(\times\) nSample)** & **Parameters** & **Times (s)** & **Iter.** & **Error** & **Relative Error** & **Obj-Val** \\ \hline ALS & TSN (2048 \(\times\) 660) & \(\eta=0.001\), \(\beta=0.01\), \(\mu=0.001\) & 0.4880 & 4 & 0.5972 & \(2.0691\times 10^{-4}\) & 2.0137 \\ \hline SPG & TSN (2048 \(\times\) 660) & \(\eta=0.001\), \(\beta=0.01\), \(\mu=0.001\) & 6.1992 & 49 & 0.4706 & \(8.1024\times 10^{-4}\) & 32.0130 \\ \hline PBB & TSN (2048 \(\times\) 660) & \(\eta=0.001\), \(\beta=0.01\), \(\mu=0.001\) & 23.5855 & 56 & 0.6146 & \(7.1873\times 10^{-4}\) & 10.0185 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mathematical results on JHMDB using \(15\times c\)-labeled training samples. “Obj-Val” means objective function value.  
Figure 4: The convergence curves of the three optimization methods on the JHMDB dataset, with the final convergence results shown in Table 2. Due to the larger oscillations of PBB, the data for the first 29 iterations of SPG and PBB have been omitted here in order to better illustrate the comparative convergence of ALS, SPG and PBB.  
class action ambiguity simultaneously. Therefore, ours gain more significant performance than other approaches when there are few labeled samples. Second, we uncover the action feature subspace on the Grassmannian manifold by incorporating Grassmannian kernels, and solve the objective function optimization by the adaptive line-search strategy and the PBB method mathematically. Hence, the proposed algorithm works well in few labeled cases.
# Advanced DSP for Coherent Optical Fiber Communication
###### Abstract  
In this paper, we provide an overview of recent progress on advanced digital signal processing (DSP) techniques for high-capacity long-haul coherent optical fiber transmission systems. Not only the linear impairments existing in optical transmission links need to be compensated, but also, the nonlinear impairments require proper algorithms for mitigation because they become major limiting factors for long-haul large-capacity optical transmission systems. Besides the time domain equalization (TDE), the frequency domain equalization (FDE) DSP also provides a similar performance, with a much-reduced computational complexity. Advanced DSP also plays an important role for the realization of space division multiplexing (SDM). SDM techniques have been developed recently to enhance the system capacity by at least one order of magnitude. Some impressive results have been reported and have outperformed the nonlinear Shannon limit of the single-mode fiber (SMF). SDM introduces the space dimension to the optical fiber communication. The few-mode fiber (FMF) and multi-core fiber (MCF) have been manufactured for novel multiplexing techniques such as mode-division multiplexing (MDM) and multi-core multiplexing (MCM). Each mode or core can be considered as an independent degree of freedom, but unfortunately, signals will suffer serious coupling during the propagation. Multi-input-multi-output (MIMO) DSP can equalize the signal coupling and makes SDM transmission feasible. The machine learning (ML) technique has attracted worldwide attention and has been explored for advanced DSP. In this paper, we firstly introduce the principle and scheme of coherent detection to explain why the DSP techniques can compensate for transmission impairments. Then corresponding technologies related to the DSP, such as nonlinearity compensation, FDE, SDM and ML will be discussed. Relevant techniques will be analyzed, and representational results and experimental verifications will be demonstrated. In the end, a brief conclusion and perspective will be provided.  
optical fiber communication; digital signal processing; coherent detection; equalization; nonlinearity compensation; space division multiplexing; machine learning; neural network +
Footnote †: journal: Applied Soft. 2019, 9, 4192; doi:10.3390/app9194192
# Intelligent Decision Forest Models for Customer Churn Prediction
## 4 Results and Discussion
### Answers to Research Questions  
Based on the investigations, the following findings were obtained to answer the RQs posed in the introduction section.  
* How efficient are the investigated DF models (LMT, FT, and RF) in CCP as compared with prominent ML classifiers  
The experimental findings showed that the investigated DF models (LMT, FT, and RF) outperformed the prominent ML classifiers in terms of CCP performance. This higher CCP performance was demonstrated on both Dataset 1 and Dataset 2.  
* How efficient are the DF models' enhanced ensemble variations in CCP?  
The WSVEDFM and WSEDFM outperformed the individual DF models and the Bagged and Boosted individual DF models on both the original and balanced (SMOTE) CCP datasets. Furthermore, the SMOTE methodology we used addressed the intrinsic class imbalance issue seen in the CCP datasets and improved the CCP performances of the suggested DF models, notably the WSVEDFM and WSEDFM techniques.  
* How do the suggested DF models and their ensemble variations compare to existing state-of-the-art CCP solutions?  
Furthermore, observable findings indicated that the suggested DF models (LMT, FT, and RF) and enhanced ensemble variants (WSVEDFM and WSEDFM) outperformed current CCP solutions on the studied CCP datasets in most cases.