esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion and Future Directions
The swelling capabilities and tuneable mechanical characteristics of HA hydrogels popularise them for the tissue-engineering approach for direct repair of the disc or as a vehicle for cell therapy of small-molecule drug delivery. HA also acts as a vehicle for GF regulating protein synthesis and cell proliferation [162]. HA and its derivatives effectively bind GF through electrostatic interactions to induce signalling and modulate ligand-receptor activity [162]. The degradation of the HA macromolecule by native tissue hyaluronases can enhance small-molecule release [163]. In general, the majority of the HA applications were investigated in small or large animal models which are limited in their translation of IVD degeneration in humans [93]. These models were not spontaneous disc degeneration models but induced acute inflammatory models through puncture [164; 165] or enzymatic digestion [166]. Therefore, these models might not be ideal for testing therapeutics because they induce rapid degeneration of the disc, rather than a natural onset of disease. Hence, a model of IVD degeneration which replicates the human IVD degeneration pathology is necessary. Next, what is expected in the prospective studies is the assessment of the effect of potentially identified HA-based therapeutics on a larger scale and in the context of the cross-talk of multiple inflammatory signalling pathways to address the complex environment of the degenerative disc disease and support achieving the ultimate goal which is finding a universal HA-based treatment for intervertebral disc degeneration. HMW HA which was widely used by numerous studies as an anti-inflammatory and matrix enhancing molecule [167; 168] must be further investigated in a spontaneous model of disc degeneration in a large animal model to address the anti-inflammatory effect on combined key identified inflammatory signalling pathways such as TNF\(\alpha\), IFN\(\alpha\) and IL1-\(\beta\) as illustrated in Figure 3. Another critical factor to focus on is the development of a robust minimally invasive technique for HA administration without requiring open surgery. Although there are challenging obstacles ahead, HA-based injectable hydrogels are strong candidates as effective treatments for disc degeneration.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## Appendix A  
This appendix contains the detailed accuracy assessment results as tables. For each table, the upper part is a plot based confusion matrix; the lower part is a confusion matrix with the respective summed weights of the plots. Map categories are the rows, reference categories are the columns.  
**FNF maps:**  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **F** & **D** & **NF** & **Total** &
\begin{tabular}{c} **Map Area** \\ **[ha]** \\ \end{tabular} & **Weights** \\ \hline F & 329 & 5 & 52 & 386 & 6693.80 & 0.758 \\ D & 3 & 48 & 24 & 75 & 331.79 & 0.038 \\ NF & 18 & 0 & 96 & 114 & 1807.39 & 0.205 \\ Total & 350 & 53 & 172 & 575 & 8832.98 & 1.000 \\ \hline Class & F & D & NF & Total & User & Producer & Overall \\ \hline F & 0.586 & 0.048 & 0.124 & 0.758 & 0.773 & 0.946 & 0.788 \\ D & 0.001 & 0.030 & 0.007 & 0.038 & 0.788 & 0.382 \\ NF & 0.032 & 0.000 & 0.172 & 0.205 & 0.842 & 0.569 \\ Total & 0.620 & 0.077 & 0.303 & 1.000 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: “Union” Peru disturbance map validation.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Class** & **F** & **Dlarge** & **Dsmall** & **Total** & **Map Area** & **Weights** \\ \hline F & 516 & 7 & 29 & 552 & 42,399.24 & 0.896 \\ Dlarge & 4 & 99 & 0 & 103 & 2169.83 & 0.046 \\ Dsmall & 17 & 0 & 19 & 36 & 2763.15 & 0.058 \\ Total & 537 & 106 & 48 & 691 & 47,332.23 & 1.000 \\ \hline Class & F & Dlarge & Dsmall & Total & User & Producer & Overall \\ \hline F & 0.837 & 0.011 & 0.047 & 0.896 & 0.935 & 0.966 & 0.912 \\ Dlarge & 0.002 & 0.044 & 0.000 & 0.046 & 0.961 & 0.795 & \\ Dsmall & 0.028 & 0.000 & 0.031 & 0.058 & 0.528 & 0.396 & \\ Total & 0.867 & 0.055 & 0.078 & 1.000 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 13: “Optical Only” disturbance method validation Gabon (only forest area).
## 2 Materials and Methods
### AdaBoost
\tag{3}\]  
where m and n are the total number of 0 (negative samples) and that of 1 (positive samples), respectively. The weight is a scale of how much each training sample affects the classification performance of a weak classifier. It selects the weak classifier that has the smallest sum of weights of misclassified training samples. Step 3 is the most important part of the AdaBoost algorithm. To select T weak classifiers, steps 1 and 2 are repeated T times. (1) is the step of determining weak classifiers. The error (\(\varepsilon=\sum_{i=1}^{\mathrm{N}}\mathrm{w_{t,i}}|\mathrm{h_{j}}(\mathrm{x_ {i}})-\mathrm{y_{i}}|\)) is estimated for each weak classifier (\(\left\{\mathrm{g_{j}}\right\}_{\mathrm{j=1...k}}\)), where, the weak classifier (\(\mathrm{g_{j}}\)) with the smallest error (\(\varepsilon_{\mathrm{t}}\)) becomes a weak classifier (\(\mathrm{h_{t}}\)) in the step. (2) is the process of updating and normalizing weights. A weight is normalized (\(\mathrm{w_{t+1,i}}\leftarrow\frac{\mathrm{w_{t+1,i}}}{\sum_{i=1}^{\mathrm{w_{t +1,i}}}}\)) after calculating a coefficient (\(\alpha_{t}=\log\left(\frac{1-\varepsilon_{\mathrm{t}}}{\varepsilon_{\mathrm{t} }}\right)\)) and updating a weight (\(\mathrm{w_{t+1,i}}=\mathrm{w_{t,i}}\times\mathrm{e}^{\alpha_{t}}|\mathrm{h_{ \mathrm{t}}}(\mathrm{x_{i}})-\mathrm{y_{i}}|\)). Step 4. The strong classifier H is constructed by combining T weak classifiers (ht), obtained by repeating the (1) and (2) of Step 2 T times, and \(\mathrm{\alpha t}\). \(\mathrm{\alpha t}\) is the weight for the weak classifier selected at each step. In other words, since the weight of the t-th weak classifier (ht) is inversely proportional to the error (\(\mathrm{\epsilon t}\)), it improved the performance of the final strong classifier that is made by combining weak classifiers by giving a larger weight to a weak classifier with better classification performance. The formula for estimating the strong classifier is as illustrated in Equation (4). \[\mathrm{H(x)}=\left\{ \begin{array}{rl}1\ if\ \mathrm{\Sigma_{t=1}^{T} \alpha_{t}h_{t}(x)>0.5}\\ 0\ \mathrm{otherwise}\end{array} \right.. \tag{4}\]  
The process of AdaBoost in this study is presented in Figure 1.  
Figure 1: The process of AdaBoost. In this study, the learning rate for each AdaBoost = 0.3, subsample ratio of columns = 0.8, regularization term on weights = 0.1, and subsample ratio of the training instances = 0.8 were used as hyperparameters of AdaBoost.
###### Abstract  
In this work, deep learning (DL)-based transmit antenna selection (TAS) strategies are employed to enhance the average bit error rate (ABER) and energy efficiency (EE) performance of a spectrally efficient fully generalized spatial modulation (FGSM) scheme. The Euclidean distance-based antenna selection (EDAS), a frequently employed TAS technique, has a high search complexity but offers optimal ABER performance. To address TAS with minimal complexity, we present DL-based approaches that reframe the traditional TAS problem as a classification learning problem. To reduce the energy consumption and latency of the system, we presented three DL architectures in this study, namely a feed-forward neural network (FNN), a recurrent neural network (RNN), and a 1D convolutional neural network (CNN). The proposed system can efficiently process and make predictions based on the new data with minimal latency, as DL-based modeling is a one-time procedure. In addition, the performance of the proposed DL strategies is compared to two other popular machine learning methods: support vector machine (SVM) and \(K\)-nearest neighbor (KNN). While comparing DL architectures with SVM on the same dataset, it is seen that the proposed FNN architecture offers a ~3.15% accuracy boost. The proposed FNN architecture achieves an improved signal-to-noise ratio (SNR) gain of ~2.2 dB over FGSM without TAS (FGSM-WTAS). All proposed DL techniques outperform FGSM-WTAS.  
deep learning; euclidean distance-based antenna selection (EDAS); feed-forward neural network (FNN); fully generalized spatial modulation (FGSM); support vector machine (SVM); transmit antenna selection (TAS)
# Uncovering Hidden Community Structure in Multi-Layer Networks
## 4 Hidden Community Detection Method
### Technique to Weaken the Layers  
Most of the community detection algorithms easily identify the dominant communities with strong relationships among them; however, there can be some nodes inside the network with weak relationships that, together with the dominant communities, can represent some useful information. Hence, there is need to identify those weak communities first, which can be achieved by weakening the structure of the strong communities. Secondly, as is the case for the multiple-layer graphs, there is a possibility among the weak communities to have some hidden overlaps as well. We have identified a method to reduce a single layer in the community structure by removing an edge. This method is inspired by algorithms that find communities by removing edges, such as the classic Girvan-Newman algorithm, which removes edges with high betweenness [1] and follows the scenario that if any network contains communities that are generally associated with a few intergroup edges, then all shortest paths between the different communities must go along one of these few edges. Thus, the edges connecting communities will have high edge betweenness. By removing these edges, we disparate groups from one another and reveal the underlying community structure of the graph. Another more recent work of [PERSON] and [PERSON] removes edges based on local Fiedler vector centrality [16]. One important point to consider is that, if there is a layer with overlapping community structure, then to avoid duplication in the weakening process, one can categorize communities by their sizes and perform the weakening process first with the larger set of communities, then with the smaller and avoid weakening the overlapping sections again. The remove edge function performs well when there are only a small number of layers. This technique weakens a detected community by removing all intra-community edges, considering it to be a simplified graph. This method also works well when there are small overlaps among several layers.
# Two-Step Multiuser Equalization for Hybrid mmWave Massive MIMO GFDM Systems
## 4 Receiver Design
### Hybrid Analog-Digital Equalizer
#### 4.3.4 Complexity Computation  
In this section, we perform the complexity analysis of the proposed hybrid equalizer. This evaluation is divided into two parts, the computation of the analog equalizer (Algorithm 1) and the computation of the digital equalizer.  
As we can see in Algorithm 1, metric (33) is computed, whose complexity results from product of a matrix of size \(U\times N_{rx}\) by another matrix of size \(N_{rx}\times N_{cl}N_{ray}U\). Therefore, the complexity of (33) is \(O(N_{cl}N_{ray}N_{rx}U^{2})\). For each RF chain, the metric is computed once, and then the complexity of Algorithm 1 is \(O(N_{cl}N_{ray}N_{rx}N_{rx}^{RF}U^{2})\).  
Additionally, the digital equalizer is computed based on the inversion of a \(U\times U\) matrix, whose complexity is \(O(U^{3})\). Therefore, we conclude that \(O(U^{3}+N_{cl}N_{ray}N_{rx}N_{rx}^{RF}U^{2})\) is the total complexity computation of the proposed hybrid equalizer. The complexity of the analog precoder is \(O(N_{tx}^{2})\).  
```
1:\(W_{a,0}=\) Empty Matrix
2:\(W_{res,l,0}=\mathbf{H}_{l}^{H}\)
3:for\(p=1,\ \ldots\,N_{rx}^{RF}\)do
4:\(n_{opt,p}=\) argmax\({}_{r=1,\ldots,N_{cl}N_{nr}}\bigcup\limits_{l=0}^{NM-1}\left[ \mathbf{A}_{rx}^{H}\mathbf{W}_{res,l,p-1}^{H}\mathbf{W}_{res,l,p-1}\mathbf{A} _{rx}\right]_{r,r}\)
5:\(W_{a,p}=\left[\mathbf{W}_{a,p-1}\middle|\mathbf{A}_{rx}^{(n_{opt,p})}\right]\)
6: To compute \(\overline{\mathbf{W}}_{d,l,p}\left[\mathbf{W}_{a,p}\right]\) using Equation (34)
7:\(\Omega_{p}=NM\mathrm{diag}\left(\sum\limits_{l=0}^{NM-1}\left[\overline{\mathbf{ W}}_{d,l,p}\left(\mathbf{W}_{a,p}\right)^{H}\mathbf{H}_{l}\right]\right)^{-1}\)
8:\(W_{res,l,p}=\Omega_{d}\overline{\mathbf{W}}_{d,l}(\mathbf{W}_{a})^{H}\mathbb{ E}[\mathbf{y}_{l}\mathbf{y}_{l}^{H}]-\Omega_{d}\mathbf{H}_{l}^{H}\)
9:endfor
10:return\(\mathbf{W}_{a}=\mathbf{W}_{a,N_{rx}^{RF}}\)
```  
**Algorithm 1** Analog Equalizer
## Abbreviations  
The following are the important abbreviations used in this manuscript:  
M-MIMO Massive multiple Input and Multiple Output RIS Reconfigurable Intelligent Surfaces SE Spectral Efficiency EE Energy Efficiency InnH Indoor Hotspot UMi Urban Microcellular 5G Fifth-Generation 6G Sixth-Generation ABER Average Bit Error Rate mmWave Millimeter Wave UE User Equipment BS Base Station LoS Line-of-Sight NLoS Non Line-of-Sight SNR Signal-to-Noise Ratio B5G Beyond 5G
## 3 Theoretical Background
### Dynamic Time Warping  
Dynamic time warping ([PERSON], 2016) is an algorithm that measures the similarity between two time series. Initially, the algorithm was created to be used in speech recognition, but also, it is a good solution for time series problems in other areas. It is a good technique for finding the optimum path between two sequences. Furthermore, it allows one to map the similar parts between two time series, regardless of the phase difference, and it is well defined even for time series of different lengths.  
Additionally, DTW can be seen as a distance measure that can match a point in a time series \(S\) with points in a time series \(Q\). The DTW distance is well defined even for time series of different lengths. Finally, any warping path is a way of matching the S and Q time series, so that all points match at least a point of another time series.  
In particular, the \(\delta\) distance measures the distance between two points in time series:  
\[\delta(i,j)=x_{i}-y_{j}.\]  
\(\gamma\) is the cumulative distance for each point (also denoted as \"cost\") (Figure 2). The closer to the diagonal the warping path is located, the more similar the two sequences are:  
\[\gamma(i,j)=\delta(i,j)+min[\gamma(i-1,j),\gamma(i-1,j-1),\gamma(i,j-1)].\]  
Figure 1: Example Symbolic Aggregate Approximation (SAX) method to take a symbolic representation of a time series. Dimensionality reduction via Piecewise Aggregate Approximation (PAA). The symbolic representation is: baabccbc ([PERSON], 2016).  
Figure 2: Example of the similarity comparison of two sequences using DTW. The \(\delta\) distance measures the distance between two points in the time series. \(\gamma\) is the cumulative distance for each point. The closer to the diagonal the warping path is located, the more similar the two sequences are.
# Security Establishment in ADS-B by Format-Preserving Encryption and Blockchain Schemes
## 4 Experimental Results
As expected, the SADS-B processing time is higher than the standard time. However, this does not lead to any drawback in SADS-B Out, since the processing time (0.084 milliseconds) is far less than the minimum transmission rate (0.5 s). The SADS-B In performance does not show any degradation during this test, and its processing time is less than 120 microseconds. However, executing SADS-B SDAM along with other SDAMs leads to longer processing times, and its performance might be affected by a high volume of ADS-B traffic. This performance issue must be addressed. Spectrum verification was done to compare the time domain and frequency domain signals simultaneously. As Figure 11 illustrates, there is no harmony or strong signal out of the ADS-B band. Thus, SADS-B has not produced interference. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Software** & \multicolumn{2}{c}{**MATLAB Simulink**} & \multicolumn{2}{c}{**C++**} \\ \hline System type & ADS-B & SADS-B & ADS-B & SADS-B \\ \hline Time (millisecond) & 1.107 & 3.104 & 0.049 & 0.084 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Mean value of computational time of standard ADS-B and SADS-B in Simulink and C++. Figure 10: (**a**) Sensitivity test bench block diagram, (**b**) Measured accuracy of standard ADS-B and SADS-B signals. Figure 9: Correlation and serial correlation of plain and cipher data. To sum up, the result of entropy and serial correlation proves that the proposed algorithm offers a sufficient level for both factors. The higher entropy and the lower serial correlation, the more security of the data. Therefore, the ADS-B message integrity and confidentiality will be guaranteed. The proposed blockchain scheme is not only used for exchanging the key, but is also utilized as a database to store ADS-B Out data. The ADS-B In after receiving the data can verify and validate with the stored database. Therefore, the authentication of each node can be performed indirectly without adding an extra payload to ADS-B messages.
# Future Scenarios for Software-Defined Metro and Access Networks and Software-Defined Photonics
## 1 Introduction
### SDN-Induced Market Place Evolution  
It can be easily predicted that the revolution introduced by the SDN reconfigurable approach will have a strong impact on the business models of network operators and on the whole telecommunication ecosystem. On the other hand, the market place evolution will drive SDN technological development as well. For instance, since bandwidth demand is growing and providing more bandwidth is not free, the actual flat rate charging regimes, where revenues and usage of network resources are decoupled (the users buy a bundle of capacity and services and their use of the network does not affect how much they pay), will no longer be economically sustainable. Bandwidth on demand (BoD) business models will become a necessity for network operators. Furthermore, network operators will need to transform their production chain in order to be competitive with Google, Amazon, Facebook, and the other over-the-top (OTT) players which can deliver high flexibility, agility, and highest availability. Deutsche Telekom, for instance, will find its strategy also on SDN-inspired real-time network and service management to become a software-defined operator [7]. A new global capacity marketplace, where capacity can be bought and sold in near-real-time [6], can be envisioned. In this scenario,network operators, which are typically global in scope, will open their NOS interfaces to third parties (developers, smaller companies, etc.) in a logic of competitive collaboration. Therefore, in order to reduce time-to-market and to satisfy the predominant upfront demand, a new global social-economic order will born, where digital services will be offered globally and tailored and delivered locally [6].
# Deep Learning-Based Automatic Safety Helmet Detection System for Construction Safety
## 3 Methodology
### Evaluation Criteria
#### 3.4.2 Training the Algorithm  
For training, the different neural network Yolo architectures are used, and all other implementations are done using PyTorch [43]. As mentioned before, model training was performed using 3000 randomly sampled images, while for the validation and testing of the model, 1000 images each were utilized with a batch size of 32. Furthermore, stochastic gradient descent (SGD) was utilized for training the network using a learning rate of 0.001 and momentum of 0.8. The GloU-based loss function was minimized using an early stopping condition based on the validation dataset.
# UY-NET: A Two-Stage Network to Improve the Result of Detection in Colonoscopy Images
## 2 Literature Review
It is worth noting that medical images typically exhibit relatively simple semantic features, fixed structures, and less irrelevant information. In other words, most features extracted from these images convey plain yet sufficient information, making the skip connections in the U-shaped structure relatively effective. Since U-Net utilizes a U-shaped structure, its application for segmenting medical images holds great promise. As discussed earlier, both U-Net and YOLOv4 are highly suitable for detecting abnormalities from medical images, including those obtained by endoscopes. U-Net is also known for its relatively simple structure, while YOLOv4 is renowned for its widespread usage. However, no study has ever combined the two algorithms to establish a new two-stage network model, let alone explore its accuracy in detecting polyps from colonoscopy images. To gather evidence to answer the unknown question, the present study combined U-Net and YOLOv4 to create UY-Net. The accuracy of UY-Net was estimated and compared to the performance of the four individual object detection algorithms (i.e., YOLO3-spp, YOLOv4, RetinaNet, and Faster R-CNN). To be specific, two hypotheses formulated for testing were presented as follows:  
Figure 1: Architecture of U-Net. 1. Performing U-Net first would result in precise segmentation of abnormalities from the colonoscopy images; after abnormalities were precisely segmented, the subsequent application of YOLOv4 would result in accurate detection of colorectal polyps;
2. UY-Net would achieve higher accuracy of polyp detection than the four detectors.
## 3 Proposed Wi-Fi Backscatter System Using Multiple Antennas
The symbol data transmitted from the tag sensor are detected by the reader comparing the received CSI and the prestored CSI data set. Prestored CSI data can be acquired in training sequences prior to tag sensor data communications [11]. This method can transmit two bits per one Wi-Fi packet from symbols 0 to 3, unlike an existing scheme only transmitting one bit for each Wi-Fi packet. In the second method, the channel diversity effect can be obtained by using multiple antennas of a tag sensor, which can improve the performance, although there is no gain in the data rate. The tag sensor turns all switches of the antennas on and off depending on the data zero (0) and one (1), respectively. In this method, the channel can be defined by  
\[\mathbb{H}_{TAG_{off}}=\left[ \begin{array}{ccc}h_{11_{HR}}\\ h_{21_{HR}}\\ \vdots\\ h_{N1_{HR}}\end{array} \right] \tag{9}\]  
\[\mathbb{H}_{TAG_{off}}=\left[\begin{array}{ccc} \begin{array}{ccc}\hat{h} _{11}&\hat{h}_{12}\\ \hat{h}_{21}&\hat{h}_{22}\\ \vdots\\ \hat{h}_{N1}&\hat{h}_{N2}\end{array} \right]\left[ \begin{array}{c}\overline {h}_{11}\\ \overline{h}_{21}\end{array} \right]+\left[ \begin{array}{c}h_{11_{HR}}\\ h_{21_{HR}}\\ \vdots\\ h_{N1_{HR}}\end{array} \right]=\left[ \begin{array}{c}\overline{h}_{11}\ast \hat{h}_{11}+\overline{h}_{21}\ast\hat{h}_{12}\\ \overline{h}_{11}\ast\hat{h}_{21}+\overline{h}_{21}\ast\hat{h}_{22}\\ \vdots\\ \overline{h}_{11}\ast\hat{h}_{N1}+\overline{h}_{21}\ast\hat{h}_{N2}\end{array}  \right]+\left[ \begin{array}{c}h_{11_{HR}}\\ h_{21_{HR}}\\ \vdots\\ h_{N1_{HR}}\end{array} \right] \tag{10}\]  
where \(\mathbb{H}_{TAG_{off}}\) and \(\mathbb{H}_{TAG_{in}}\) denote channels when the data zero and one are transmitted, respectively. In the proposed algorithm, the variation of a channel can be more severe compared to the method employing a single antenna because of antenna diversity. Since the BER performance of a Wi-Fi backscatter system highly relies on the amount of channel variation, the proposed backscatter method can achieve higher BER performance.
# Towards Automatic License Plate Detection
## 4 Results and Discussion
### License Plate Localization Module (LPLM) Accuracy
* Our findings suggest that illumination variation poses a major threat to any object detection and recognition algorithm, such as face detection, pedestrian detection, face recognition, and even a license plate recognition system. A license plate recognition system is highly dependent upon accurate and precise features being fed by the detector module. Therefore, we feel it is suitable to investigate the non-uniform illumination variations in the PKU G5 category. * To further test the robustness of the LPLM, we manually reduced the input image resolution up to 320 \(\times\) 240 pixels. On the aforesaid image resolution, the LPLM accurately detected the license plate that had the resolution of 28 \(\times\) 19 pixels.