esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Reducing Transmission Complexity in MIMO-WCDMA Networks Using PCA
Notation ab indicates all elements from a to b with unity step. Hence, matrix \(\mathbf{C}_{\mathbf{Z}_{k,d}}\) can now be sorted, where the indexes of each sorted value are stored in the two-dimensional matrix \(\mathbf{B}\). Therefore, the modified matrix \(\mathbf{A}\) can be alternately written as:  
\[\mathbf{A}\leftarrow\sum_{c=1}^{C}\left(\mathbf{r}_{k,\mathbf{B}(c,1)}\mathbf{ H}_{k,\mathbf{B}(c,1),T_{k}}\right)^{\mathrm{H}}\mathbf{r}_{k,\mathbf{B}(c,2)} \mathbf{H}_{k,\mathbf{B}(c,2),T_{k}} \tag{24}\]  
In this case, the \(C\) (\(1\leq C\leq L^{2}\)) most important terms can be taken into account during matrix \(\mathbf{A}\) formulation. In the results that will be presented in the following section, \(C\) is chosen to be an even number, since \(\mathbf{C}_{\mathbf{Z}_{k,d}}(l,l^{\prime})=\overline{\mathbf{C}_{\mathbf{Z}_{ k,d}}(l^{\prime},l)}\). This technique, which will be referred as MSNR-PCA algorithm throughout the rest of the paper, is described in Algorithm 2. Note that \(\mathbf{O}_{M_{t}\times M_{t}}\) is a zero matrix of dimensions \(M_{t}\times M_{t}\), while the output of \(\mathbf{B}_{k}\leftarrow\mathrm{sort}(\mathbf{C}_{\mathbf{Z}_{k}})\) is an \(L^{2}\times 2\) two dimensional matrix, where for example the \(k\)th pair of rows corresponds to the (\(i\),\(j\)) indexes of the \(k\)th maximum value of \(\mathbf{C}_{\mathbf{Z}_{k}}\). ```
Step 1:\(k\gets 0\) Step 2:\(k\gets k+1\), \(\mathbf{B}_{k}\leftarrow\mathrm{sort}(\mathbf{C}_{\mathbf{Z}_{k}}^{\mathrm{U}})\), \(\mathbf{B}_{k}\leftarrow\mathbf{B}_{k}(1:C)\) Step 3: Set \(n\gets 0\), \(\mathbf{t}_{k,n}\leftarrow\left(1/\sqrt{dim}\right)\mathbf{I}_{dim}\), \(P_{k,n}\leftarrow\mathbf{t}_{n,l}^{\mathrm{H}}\mathbf{t}_{n,j}\), \(\epsilon=10^{-3}\) and \(\mathbf{r}_{k,l,n}=\left(\mathbf{H}_{k,l,T_{k}}\mathbf{t}_{k,n}\right)^{ \mathrm{H}},1\leq l\leq L\) Step 4:\(n\gets n+1\), \(\mathbf{A}_{n}\leftarrow\mathbf{O}_{M_{t}\times M_{t}}\) for\(j=1:1:C\) \(\mathbf{A}_{n}\leftarrow\mathbf{A}_{n}+\mathbf{H}_{k,n(1),T_{k}}^{\mathrm{H}}\mathbf{r }_{k,\mathbf{B}_{k}(j,1),n}\mathbf{r}_{k,\mathbf{B}_{k}(i,j),n}\mathbf{r}_{k,\mathbf{B}_{k}(i,j),n}\mathbf{H}_{k,\mathbf{B}_{k}(i,j),T_{k}}\) end Step 5:\(\mathbf{t}_{k,n+1}\leftarrow\mathbf{V}_{n}\mathbf{D}^{1/2}\), \(\mathbf{r}_{k,l,n}=\left(\mathbf{H}_{k,l,T_{k}}\mathbf{t}_{k,n}\right)^{ \mathrm{H}},1\leq l\leq L\)\(P_{k,n+1}\leftarrow\mathbf{t}_{k,n+1}^{\mathrm{H}}\mathbf{A}_{n} \mathbf{t}_{k,n+1}\) Step 6: If \(\left|\mathrm{tr}\left(P_{k,n+1}\right)-\mathrm{tr}\left(P_{k,n}\right)\right| \leq\epsilon\cdot\mathrm{tr}\left(P_{k,n}\right)\) algorithm terminates, else go to Step 4. Then go to Step 2. ```  
**Algorithm 2** Maximization of SNR Algorithm based on Principal Component Analysis (PCA)
## 3 Cell Manipulation Control  
### Holographic Optical Tweezers System  
The holographic optical tweezers system controls the hologram generated by the computer loaded on the spatial light modulator (SLM) by programming, then controls the SLM to carry out phase modulation, divides a single beam of incident light into multiple outgoing beams, and finally produces a large array of light traps. The dynamic manipulation of multi-biological cells can be realized by refreshing the hologram according to a certain frame rate. At the same time, the holographic optical tweezers system can obtain the real-time position of cells, which can not only generate optical traps to capture multiple biological cells according to specific formation patterns, but also manipulate each of them independently. As such, it has great application potential in biological research. In the past decade, many cell manipulation systems based on holographic optical tweezers have been developed [36; 37; 38].
## Appendix A  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Dataset**} & \multicolumn{8}{c}{**Accuracy/\%**} \\ \cline{3-11}  & & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline \multirow{3}{*}{k-NN} & Abalone & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 & 42.87 \\  & Heart Disease & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 & 33.33 \\  & Bank Marketing & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 & 60.02 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Peak \\ Clustering \\ \end{tabular} } & Abalone & 59.28 & 60.12 & 61.08 & 58.32 & 61.08 & 58.20 & 56.53 & 58.20 & 57.60 & 59.28 \\  & Heart Disease & 43.33 & 39.93 & 43.33 & 48.33 & 43.33 & 41.67 & 41.67 & 44.88 & 44.88 \\  & Bank Marketing & 69.54 & 67.63 & 69.48 & 70.02 & 69.48 & 63.64 & 67.96 & 63.64 & 67.41 & 69.59 \\ \hline \multirow{3}{*}{BP} & Abalone & 60.72 & 60.00 & 67.78 & 63.83 & 67.78 & 59.40 & 63.95 & 59.40 & 55.33 & 68.86 \\  & Heart Disease & 28.33 & 34.98 & 36.67 & 65.02 & 36.67 & 60.07 & 63.33 & 60.07 & 43.33 & 65.02 \\  & Bank Marketing & 66.47 & 59.32 & 67.72 & 67.49 & 67.72 & 60.01 & 61.81 & 60.01 & 67.74 & 59.80 \\ \hline \multirow{3}{*}{GANs-HDI} & Abalone & 92.22 & 91.26 & 94.01 & 95.21 & 94.01 & 96.05 & 95.21 & 96.05 & 93.65 & 94.49 \\  & Heart Disease & 68.33 & 66.67 & 66.67 & 71.67 & 66.67 & 71.67 & 68.33 & 71.67 & 69.97 & 68.33 \\ \cline{1-1}  & Bank Marketing & 89.69 & 89.66 & 89.79 & 89.77 & 89.79 & 89.96 & 89.59 & 89.96 & 89.71 & 90.65 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison detail information of four algorithms on UCI datasets.
# Extending the Performance of Hybrid NoCs beyond the Limitations of Network Heterogeneity
## 2 Related Work
### Millimetre-Wave vs. Surface Wave-Enabled WiNoCs  
RF interconnect has low area and low power consumption due to its CMOS compatibility. However, RF interconnect relies on long transmission lines for guided data transmission, which requires alignment between transmission pairs. Alternatively, mm-wave has emerged as a more feasible wireless solution with promising CMOS components that can scale with transistor technology (Figure 3b). However, the on-chip antennas and transceivers have non-negligible area and power overheads. Conventional wireline-based NoCs, on the other hand, are highly efficient for short distances despite their limitations over long distance. Consequently, WiNoCs have been proposed to exploit both the global performance benefits of mm-wave, as well as the short range low power and area benefits of the wireline communication fabric in NoCs. However, the wireless communication fabric is lossy and hence lowers the overall reliability of WiNoCs [45; 46].  
Surface wave communication has been recently demonstrated as a feasible on-chip wireless solution with improved long-range communication, low-power and high bandwidth [6; 47]. Here, the wireless communication layer of WiNoCs is replaced with a carefully-designed dielectric-coated metal layer as the waveguide medium in the form of surface wave communication fabric for global communication, which generates an NoC architecture as shown in Figure 3c. The surface wave communication fabric facilitates low power and low latency remote communication with a reasonably high performance to area ratio compared to mm-wave-based WiNoCs [47].  
In both mm-wave and surface wave-enabled WiNoCs, the routers at the wireless nodes are equipped with a wireless transmission interface, which serves as a bridge between the wireless and the wireline communication layers. The wireless transmission interface, responsible for transmitting and receiving wireless signals, works closely with the routing logic, virtual channel allocator, arbiter and crossbar switch for efficient wireless signal transmission. Routers without the wireless transmission interfaces must forward packets to the nearest wireless nodes in a multi-hop manner before they can finally exploit the single-hop wireless links to remote destinations. Moreover, if the destination node is not a wireless node, the packet is transmitted to the nearest wireless node and then transmitted through the multi-hop wireless layer. Therefore, the performance of WiNoCs is reduced due to the extra timing overhead and multi-hop transmission of packets in the network. Hence, novel router architectures that offer long-range minimal-hop communication with low area and power overheads are required at the non-wireless node to exploit the full potential of emerging WiNoCs.
# Multiscenario Simulation and Prediction of Land Use in Huaibei City Based on CLUE-S and PLUS Models
## 3 Results
### Simulation and Validation
#### 3.2.2 Simulation Results of CLUE-S Model  
The LU in 2015 was adopted as the base map to simulate the LU in 2020 to validate the accuracy of the CLUE-S model simulation. Firstly, the auto-logistic regression results were calculated in SPSS and saved as the alloc1.reg parameter file as requested by the CLUE-S model. Secondly, we processed the driving factors, calculated the LU demand, prepared the transfer matrix, and set the main parameters of the model. Then, we ran the CLUE-S model to obtain the simulated LU map in 2020, as shown in Figure 4a.  
The CLUE-S simulated map in 2020 was compared with the status map in 2020 for verification. A kappa coefficient of 0.727 can be calculated in ArcGIS based on Equation (4), indicating that the simulation is generally consistent.
# Vision, Enabling Technologies, and Scenarios for a 6G-Enabled Internet of Verticals (6G-IoV)
## 2 Technology Enablers for the Internet of Verticals
### Network Slicing  
Network slicing [8; 9] is an innovative architectural solution in 5G networks which, thanks to virtualization, allows an operator to provide multiple services with different performance characteristics. It is defined in the 3 GPP Release 15 specifications. Further enhancements to network slicing are occurring in successive releases. Each network slice  
\begin{table}
\begin{tabular}{c c} \hline
**Year** & **Research Trends** \\ \hline
2018 & 5G evolution \\ \hline
2020 & 6G structure and framing, 6G technology components, 6G requirements \\ \hline
2022 & 6G systemization of research \\ \hline
2026 & 6G technical standardisation \\ \hline
2030 & 6G evolution, 6G commercialisation \\ \hline \end{tabular}
\end{table}
Table 1: The 5G and 6G research trends from 2018 to 2030.  
operates as an independent, virtualized version of the network designed to serve a defined business purpose or customer. Thus, each slice consists of all the network resources required to address the specific need. For a given application, the network slice is the only network that the application sees. The other slices, to which the application is not subscribed, are invisible and inaccessible. GSMA [9] has identified the following vertical sectors as ones that will benefit from network slicing: Augmented Reality and Virtual Reality, Automotive, Energy, Healthcare Manufacturing, Internet of Things, Public Safety, and Smart Cities.  
The standardization for statically provisioned network slices has already been completed in 3 GPP. However, more work is being carried out [8] to enable dynamically provisioned network slices in an SDN-type approach. A combination of specialized applications plus 5G Network Slicing and Virtual Private Network (VPN)/Virtual Routing and Forwarding (VRF) technologies could be deployed to create large extranets that would connect and federate multiple vertical slices, hence paving the way for the IoV.  
Towards the 6G era, the parallel streams of Open RAN (O-RAN) and Centralized RAN (C-RAN) will be fully realized, as well as the Cell Free architecture, hence allowing RAN resources to also be fully virtualized and software-controlled. Consequently, in the 6G era, we will see full (core, transport, and RAN) slicing of mobile networks, hence enabling end-to-end slicing of the network per (vertical) service. There is a need for orchestration solutions for the networking resources associated to each slice, so they can be dynamically coordinated to meet the performance requirements of services. Several AI approaches, including Deep Reinforcement Learning (DRL), are used for automated resource orchestration. Moreover, federated learning-based DRL solutions for 6G RAN slice orchestration are proposed in the literature [10]. Network slicing is also expected to complement digital twins-based 6G systems along with other enabling technologies, such as blockchain [11].
# An Optimization Routing Algorithm Based on Segment Routing in Software-Defined Networks
## 4 Performance Evaluation  
In this section, we compare the performance of our proposed algorithms with the Lee algorithms in [16], SPF [12], WSP [14], SWP [13], MIRA [15] and in terms of link cost, load balance, maximum link utilization and the value of path evaluation. In this paper, we use MATLAB to simulate and randomly generate the Waxman network topology. The probability of having an edge between node \(u\) and node \(v\) is given by formula \(\beta e^{-\frac{d}{\alpha sin}}\), where \(\alpha\) and \(\beta\) are the network characteristic parameters. The larger the \(\alpha\), the greater the proportion of the short side relative to the long side. The larger the \(\beta\), the greater the density of the edge. \(d\) is the Euclidean distance between any two nodes in the graph, and \(len\) is the length of a side of the square area. The generated Waxman topology is 80 nodes with 602 links, 100 nodes with 943 links, and 150 nodes with 2190 links. In the simulation, each source host sends the flow packets to the destination host and each flow packet is in the range of 10 MB to 100 MB. Routing requests are sent 10 times per second [34]. The link capacity is set to 1 GB/s. We take \(k=5\) when calculating the first \(k\) shortest paths.
## 2 Basic Principle of Cross-Well Elastic Wave CT  
### System Composition and Operation Process  
The cross-well elastic wave CT system is mainly composed of three parts: host, source, and geophone, as shown in Figure 1. When working, the transmitting probe and receiving probe (a bunch of geophones) are placed in two detection channels (PVC pipes) and lowered to the bottom of the hole. Keeping the geophone immobile, the transmitting probe is spaced 1 m apart to excite the elastic wave (P-wave) from bottom to top. Due to the difference in density, modulus, and other aspects of the pile, slurry consolidation, and the soil around the pile after grouting, the propagation velocity of the P-wave in different media is different; thus, the geophone receives different elastic wave-transmission travel times. According to different elastic-wave travel times, the velocity field distribution map of the media between wells can be reconstructed, and then the media distribution in the stratum can be inferred according to the velocity field.  
Figure 1: Schematic diagram of elastic wave CT system.
## 1 Introduction
Reference [13] put forward a real-time conveyor belt detection algorithm based on a multi-scale feature fusion network, in particular, a new weighted loss function was designed to optimize the network and to improve the detection effect on the conveyor belt edge. Laboratory experiments proved that it more reliable than the Canny algorithm, FCNs, UNet, and Deeplab v3 networks. A multi-scale Retinex method was proposed in Reference [14] to enhance multi-view images of mine conveyor belt adaptively, which can remove the influence of uneven illumination and harsh underground environments on image quality, and make the details of dark areas in the images more obvious. A method based on machine vision and image processing techniques was proposed in Reference [15] for the estimation of volume and size distribution of aggregate transported on conveyor belts. Reference [16] proposed an on-line belt state detection method based on the line laser technology, to accomplish conveyor belt longitudinal rip detection rapidly and accurately, and also the monitoring system was designed. A new non-contact belt conveyor speed measurement system was proposed in Reference [17], which can meet the measurement speed requirements in the long time operation of conveying. Motivated by the above observations, most scholars focus their attention on the effective extraction of the edge of the conveyor belt but do not analyze the specific relationship between edge line feature and deviation behavior. In this paper, while analyzing the relationship between edge line characteristics and deviation, two deviation detection algorithms are proposed, verified and compared, which provide powerful technical assistance for the deviation detection of the conveyor. Section 2 explains the experimental setup, the methodology is explained in Section 3, results are presented in Section 4 and discussion in Section 5, then the conclusion is highlighted in Section 6.
# Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
## 4 Experimental Results and Analysis  
### Experimental Data Set  
The data was acquired in May of 2018 by MCS in the LTE network of Shanghai, the most well-known city of China which located at the east coast of the country. The dataset includes a total of 729,294 samples contributed by 88,594 users. The target URLs of web browsing involved are 10 top websites in China such as Sina, Sohu, Weibo and Taobao.  
Since the KQI prediction discussed in this paper is a classification problem, the original numerical labels {\(D_{dns}\), \(D_{tcp}\), \(D_{req}\), \(D_{res}\)} of the dataset are converted into Boolean ones according to predefined thresholds. In a real network, in most cases the KQIs in the samples are positive, only a very few of them are negative. The label imbalance in general will impact on the prediction seriously. To mitigate this problem, it is better to restrict the ratio of positive and negative samples within 3:1. Therefore, here we employ the boxplot method to determine appropriate thresholds of the labels. Specifically, for each label we take Q3, namely, the upper quartile in the whole dataset as the threshold. Label values higher than the threshold are marked as negative, and vice versa.  
The boxplots of the labels are shown in Figure 1. Since the label values for negative samples are always very high, common logarithm is taken for all the samples for ease of illustration.  
In Figure 2 we show the spatial distribution of the samples (down-sampled for illustration purpose). The samples with null or invalid feature values, or the outliers whose label values are beyond Q3+1.5 IQR and Q1-1.5 IQR are eliminated. The samples cover almost the whole metropolitan area of about 6340 km\({}^{2}\), including the Chongming Island.  
Figure 1: Boxplot of key quality indicators (KQI) labels (unit: ms).
# AI-Based Analysis of Policies and Images for Privacy-Conscious Content Sharing
## 3 Information Retrieval for Privacy Policies
### Feature Engineering
#### 3.1.1 Using Bi-Grams and Feature Selection  
In order to consider more significant terms, for building feature vectors, we consider not only single terms but also _bi-grams_. Bi-grams are couples of adjacent words appearing in a text. They can help obtain a more effective classification model, because some entities or concepts are naturally represented by two, such as 'personal information', 'email address', or 'collect data' [10]. Obviously, considering the single terms (i.e., _uni-grams_)in the previous examples would result in a complete loss of the concept semantics.  
However, using bi-grams together with uni-grams increases the number of features extracted from text. Most importantly, not all of the bi-grams are useful for text classification.  
**Example 2**.: _Consider the paragraph: 'In some circumstances, Google also collects information about you from publicly accessible sources'. The uni-grams and bi-grams in the paragraph are: 'In','some', 'circumstances', 'Google', 'also', 'collects', 'information', 'about', 'you', 'from', 'publicly', 'accessible','sources' and 'In some','some circumstances', 'circumstances Google', 'Google also', 'also collects', 'collects information', 'information about', 'about you', 'you from', 'from publicly', 'publicly accessible', 'accessible sources'._  
In the above example, bi-grams like 'you from' and 'circumstances Google' are not significant and should be discarded because they do not represent any entity or concept. Indeed, they are meaningless when read out of the paragraph context. Moreover, the construction of a feature vector with uni-grams and bi-grams extracted from an entire set of privacy policies would generate a very large number of features. This fact may generate different problems, described below.  
* Overfitting: the classification model only learns to correctly classify training instances (labeled samples) but fails to classify new unknown instances;
* The text is noisy: frequently, texts obtained from the Web contain special characters, links, dates, numbers, or terms that do not add meaningful information to the classification task;
* Stopwords: many words in the document do not add useful information for classification, and unnecessarily increase the size of the feature vectors. These words are called'stopwords'. Examples of stopwords are: 'the', 'at', 'that'.  
To remove unneeded features and reduce the set of features, we applied the following text preprocessing steps.  
* Using regular expressions, we removed numbers, urls, and dates from the original text;
* We removed stopwords using the nltk standard stopword list [24];
* We applied _stemming_: i.e., the process of reducing words to their root. For instance, _collected_ is transformed to _collect_, _computation_ is transformed to _comput_.
# An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR
###### Abstract  
This paper studies the imaging of a ground moving target with airborne circular stripmap synthetic aperture radar (CSSAR). First, the range equation of a target moving with accelerations is developed. Then, a new range model of high accuracy is proposed, since the commonly used second-order Taylor-approximated range model is inaccurate when the azimuth resolution is relatively high or the target moves with accelerations. The proposed range model also makes it easy to derive an accurate analytical expression for the target's 2-D spectrum. Third, based on the proposed range model, the target's 2-D spectrum is derived and an efficient imaging method is proposed. The proposed imaging method implements focusing via a phase multiplication in the 2-D frequency domain and utilizes the genetic algorithm to accomplish an efficient search of the parameters of the proposed range model. Finally, numerical experiments are conducted to validate the proposed range model and the proposed imaging method.  
circular stripmap synthetic aperture radar; imaging method; range equation model; ground moving target imaging; generic algorithm +
Footnote †: journal: remote sensing  
Article  
An Efficient Ground Moving Target Imaging Method for Airborne Circular Stripmap SAR. _Remote Sens._**2022**, _14_, _210. [[https://doi.org/10.3390/rs14010210](https://doi.org/10.3390/rs14010210)]([https://doi.org/10.3390/rs14010210](https://doi.org/10.3390/rs14010210))