esa-sceva/satcom-chunk-collection huggingface dataset:
# Covert Communications in Active-RIS-Aided NOMA Systems: Element Grouping or Not?
## 4 Simulation Results
Moreover, the covert communication rates achieved by the AO algorithm with element grouping increased with increasing \(N\), until reaching about \(N=12\), when they began to decrease. However, the covert communication rates achieved by the randomized \(\mathbf{\Theta}\) and \(\{\mathbf{\Theta},\mathbf{W}\}\) continued to decrease. Similarly to Figure 5a, the curves in Figure 5b show that the w/o-EG scheme achieved [PERSON]'s highest achievable rate under the considered \(N\) range. Figure 4: (**a**) Covert communication rate versus \(\epsilon\). (**b**) [PERSON]’s achievable rate of versus \(\epsilon\). Figure 3: (**a**) Covert communication rate versus location of ARIS. (**b**) [PERSON]’s achievable rate versus location of ARIS. The trade-off between the covert communication rate performance of the w/o-EG and w-EG schemes was investigated in Figure 6a, where we set \(P_{a}^{\max}=P_{c}^{\max}=35\) dBm, \(P_{r}^{\max}=30\) dBm, \(M=4\), \(N=16\), and \(\epsilon=0.1\). The results in Figure 6a show that the covert communication rate decreased with the increasing number of elements in the grouping. When the number of elements in the grouping was zero, which corresponded to the w/o-EG scheme, the maximum covert communication rate was achieved. If all the elements worked in the grouping, i.e., the w-EG scheme, the minimum covert communication rate was obtained. Furthermore, the trade-off between [PERSON]'s achievable rate performance of the w/o-EG and w-EG schemes is plotted in Figure 6b. Similar to the results in Figure 6a, [PERSON]'s maximum and minimum achievable rates were obtained by the w/o-EG and w-EG schemes, respectively. With the increasing number of elements in the grouping, the achievable rates obtained by all the considered schemes decreased monotonically. Figure 5: (a) Covert communication rate versus \(N\). (b) [PERSON]’s achievable rate versus \(N\). Figure 6: (a) Covert communication rate versus number of elements in the grouping. (b) Achievable rate of [PERSON] versus number of elements in the grouping.
# Activity Prediction Based on Deep Learning Techniques
## 3 Design of Network Models
### MLP Network Model Structure  
The MLP is a multi-layered feedforward artificial neural network that maps input data sets to a set of appropriate outputs. It consists of multiple hidden layers, and nodes in the hidden layers are fully connected to the nodes in the following layer. The nodes of the layers are neurons with nonlinear activation functions, such as the sigmoid function, except for the nodes of the input layer. An MLP with one hidden layer can have the following structure, as shown in Figure 5. It consists of input nodes, output nodes, and hidden nodes, and the hidden nodes are linked to each of the input nodes and also to the output nodes with certain weights. This simple model can be extended to models with more hidden layers.
## 3 Sensors Configuration Analysis
### Ring-Shaped Sensor  
The last sensor was the ring-shaped sensor (Figure 8). Its dimensions were: sensor diameter \(d\) = 1 m, the height of the sensor \(H\) = 0.3 m, and depth of the ground hole \(h\) = 0.3 m. Excitation for simulations was defined as a static electric field with a strength equal to \(E\) = 100 V/m.  
Figure 8: The ring-shaped sensor model was taken into consideration.  
The sensor was located equal to ground level and \(A=1\) m above them. Figure 9 shows the electric field distribution for given boundary conditions in three dimensions \(E(x,y,z)\) for flat plate sensors equal to ground level and \(A=1\) m above them.  
The electric charge accumulated on the sensor at height \(A=0\) m above ground level had a value of \(Q_{3,0}=-1.27\times 10^{-9}\) [C]. The electric charge accumulated on the sensor at height \(A=1\) m above ground level had a value of \(Q_{3,1}=-3.92\times 10^{-10}\) [C].  
For a sensor in the form of a ring and a grater, the surface density of the electric charge can be heterogeneous. Applying [PERSON]'s law to these shapes to determine the spatial charge based on the E-field is inappropriate since the surfaces in question are not so-called Gaussian surfaces. The distribution of the vector \(E(x,y,z)\) shows the space around the sensor representing the measured current in the external circuit. The \(E_{y}\) component is presented for vertically oriented sensors since the E-field is perpendicular to the conductor surface (Figure 9).  
Figure 9: The electric field distribution near the ring-shaped sensor placed equal to ground level (**a**) and \(A=1\) m above ground level (**b**).
# Integrated Microwave Photonics for Wideband Signal Processing
## 5 Integrated MWP for Optical Sensing Applications
As the temperature increases, the signal is shifted to a higher frequency where a linear relationship is formed between the temperature change and RF frequency shift, as shown in the linear fit in Figure 4d. The linear dependence of the frequency shift on temperature is estimated to be around 7.7 GHz/\({}^{\circ}\)C, thus demonstrating its high sensitivity. Figure 4: _Cont._
# Surface Correlation-Based Fingerprinting Method Using LTE Signal for Localization in Urban Canyon
## 2 Algorithm Overview  
Generally, a fingerprinting method involves an offline phase and an online phase. In the offline phase, a survey is performed to construct a fingerprinting database as displayed in Figure 1. This is achieved by storing the RSS of signals received from surrounding signal sources at a specified Reference Point (RP). In the online phase, a terminal device measures the RSS received from surrounding signal sources and estimates location by searching the RP with similar fingerprint. As shown in Figure 1, the RSS measurement in the conventional fingerprinting method is through instantaneous RSS data, which can increase ambiguity due to noise and distortion by the surrounding environment. While the effects of noise and distortion are low in areas with high RSS, it is impossible for stable and accurate localization due to the increased ambiguity in areas with very low RSS such as deep urban areas.  
Figure 1: Overview of conventional and proposed fingerprinting methods.  
The SC method compares RSS patterns accumulated during movement with the fingerprinting database to estimate the location. The SC method can provide high performance (availability) regardless of the high and low areas of the LTE signal strength because it compares the spatial RSS variation pattern, not instantaneous data, with the reference pattern from the database. In this paper, we define the RSS pattern accumulated during movement, i.e. the spatial RSS pattern, as the _Surface_. We have already shown that the performance of the conventional fingerprinting method improves by comparing a one-dimensional fingerprinting database with spatial RSS patterns [22; 23]. An urban area is a two-dimensional space with multiple roads combined, not just a simple one-dimensional space. Therefore, the SC method requires the generation of reference RSS patterns that match with a pedestrian's route from the fingerprinting database for comparison between the measured spatial RSS pattern (_Surface_) and the database. To do this, it is required to construct a link-centered database rather than an RP-oriented database as seen in the conventional fingerprinting method. The SC method consists of four processes for localization: (1) domain conversion of RSS measurements; (2) generation of reference trajectory from a fingerprinting database; (3) correlation; (4) localization.
## 2 Applications of Nanotechnology in the Petroleum Industry
### Drilling and Completion
#### 2.2.1 Mud Additives  
Nanomaterials such as silica and graphene have been suggested as drilling fluid additives [52]. Nano-based mud contains at least one additive using nanomaterial with a size range between 1-100 nm [51; 53]. [PERSON] et al. [26] observed that silica NPs reduced shale invasion by 10-100 times, leading to improved stability of the mud at elevated pressures and temperatures at different rheology values. [PERSON] et al. [54] performed experiments on Marcellus and Mancos shales with 3 wt % of silica NPs as additives in water-based mud and found that silica NPs could physically plug the shale and significantly reduce the fluid invasion in the shale zone at a lower loading level in the water-based mud. Similarly, [PERSON] et al. [27] performed experimental tests with 10 wt % of six types of silica NPs inthe Atoka shale and found a drastic reduction in shale permeability impairment. They also observed that the mud had a higher plastic viscosity, lower yield point, and reduced fluid loss. [PERSON] et al. [55] conducted an experimental study using a combination of biopolymer, surfactant, and nanoparticle for mud additives. They concluded that nanoparticles combined with biopolymers and surfactants could be a solution to solid free fluids in horizontal drilling.  
Metallic and metal oxide-based NPs for enhancing the properties of drilling fluids have also been investigated [30; 56; 57], specifically iron-based and calcium-based NPs [57], and MgO and ZnO NPs [30]. Adding NPs was found to improve the thermal stability of drilling fluids and control the filtration loss from the reservoir. Recently, [PERSON] et al. [58] investigated the effect of a graphene nano-sheet with a concentration range from 25 ppm to 100 ppm and found that graphene in oil-based fluid exhibited a higher viscosity with shear rate ranges from 0-140 s\({}^{-1}\) and behaved like a Newtonian fluid although it is classified as a Bingham fluid.
# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 4 Experimental Verification and Result Analysis
### Comparison of Speed and Precision
When small objects are included in complicated background, the mAP of the TF-YOLO method is 31.5%, which is higher than that of the YOLOv3-tiny method by 27.2%, and SPP-net by 30.3%. When compared with YOLOv3, RCNN, and Faster RCNN, the precision of the \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Precision** & **Classes** & **YOLOv3-tiny** & **YOLO\_k** & **TF-YOLO** \\ \hline \multirow{8}{*}{**AP(\%)**} & airplane & 0.81806 & 0.81926 & **0.85771** \\  & ship & 0.89595 & 0.92718 & **0.99084** \\  & storage tank & 0.74237 & 0.76223 & **0.77361** \\  & baseball diamond & 0.73624 & 0.82734 & **0.84513** \\  & tennis court & 0.88762 & 0.89091 & **0.89542** \\  & basketball court & 0.91849 & 0.93965 & **0.95219** \\  & ground track field & 1.0 & 1.0 & **1.0** \\  & harbor & 0.92718 & 0.94183 & **1.0** \\  & bridge & 0.75806 & 0.79562 & **0.81266** \\  & vehicle & 0.68493 & 0.73731 & **0.81731** \\
**mAP(\%)** & & 0.83689 & 0.86413 & **0.89449** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the accuracy of the three networks. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**mAP (\%)**} & \multicolumn{8}{c}{**AP (\%)**} \\ \cline{3-11}  & & **Aero** & **Bird** & **Boat** & **Car** & **Chair** & **Dog** & **Person** & **Plant** & **Sheep** & **Cow** \\ \hline SPP-net & 30.3 & 42.7 & 33.9 & 27.5 & 24.8 & 25.3 & 11.2 & 34.2 & 15.1 & 41.6 & 43.7 \\ RCNN & 36.2 & 44.9 & 38.2 & 23.4 & 38.6 & 29.3 & 15.2 & 37.6 & 19.7 & 46.5 & 68.6 \\ Faster RCNN & 67.9 & 74.0 & 58.7 & 66.3 & 72.5 & 45.7 & 69.5 & 73.6 & 56.7 & 86.4 & 75.7 \\ YOLOv3 & 55.9 & 68.5 & 41.2 & 50.4 & 80.3 & 57.9 & 68.5 & 36.7 & 32.6 & 51.6 & 71.4 \\ YOLOv3-tiny & 27.2 & 39.9 & 20.5 & 12.9 & 33.6 & 18.7 & 11.4 & 23.4 & 15.3 & 41.7 & 54.7 \\
**TF-YOLO** & **31.5** & **42.6** & **35.4** & **19.7** & **35.4** & **22.1** & **12.7** & **29.7** & **15.7** & **42.1** & **59.2** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Small objects detection results for region-based proposal methods on VOC 2007 dataset. TF-YOLO method is lower than these methods. Nevertheless, the detection speed of the TF-YOLO method is much faster than these classical methods, which is shown in Table 4. Taking both accuracy and detection speed into consideration, TF-YOLO method exhibits the best performance in small object detection among the above methods in an embedded system.
## 3 Results  
### Results Based on Laboratory Methods  
As described in Section 2.3, the first step was to synchronize the data to the same clock. The length of the examined line for both Line 1 and Line 2 was 5.3 km, between km 505.2 and 510.5. The time interval between two zigzag length samples was 0.1 s, and the zigzag length variation can be observed in Figure 14a for Line 1 and Figure 14b for Line 2. Since the medium speed of the inspection car varied between the two lines, the number of determined values for the two graphics differed. As a result, Figure 14a has a total of 3900 red points connected by a blue line and a measurement time of 390 s. As a result, Figure 14a contains a total of 3900 points and a measurement duration of 390 s. Figure 14b contains 4200 points and has a duration of 420 s.  
The second step consists of synchronizing the data with the rail's length by representing the zigzag length variation over the same distance at 1 m intervals. Consequently, the number of values for both Line 1 and Line 2 is the same: 5300. Figure 15a,b represent the zigzag length variation for Lines 1 and 2, respectively. Additionally, the pillar positions are illustrated, with Line 1 at \(+350\) units and Line 2 at \(-350\) units. We used the term \"units\" since they depict the pillars' positions on the graphics without any relation to the rail axis.  
Figure 14: Zigzag length variation at time intervals of 0.1 s: (a) Line 1, (b) Line 2.
###### Abstract
In structure, this paper is roughly divided into several parts, (1) design of the device, (2) implementation of the device (hardware assembling and software programming), (3) validation of digitalised EMG signals, (4) testing of the device, and (5) discussion on limitation and future studies. The outcomes showed that (1) this proposed device can digitalise EMG and the processed EMG signals were validated in comparison with the output from a commercial system; (2) the digital EMG can be displayed, saved, and analysed in a PC; and (3) using the device, the EMG signal can be employed to drive a CPM movement.
## 3 RF Generator Requirements  
Several requirements are imposed on the RF generator, all related to the optical wavelength domain and the used AOTF. In Table 1, an overview is given of the driving parameters in the design of an RF generator setup from a general point-of-view. Additionally, in Table 2 specific requirements are listed related to the selected optical domain A distinction has been made between general requirements, which apply to all optical ranges (VIS, NIR, and UV) and specific requirements related to certain optical domains.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**General Requirements** & **Value** & **Unit** \\ \hline Unwanted spectral component suppression & \(<\)–30 & dB \\ Accuracy & 1 & kHz \\ Resolution & 5 & kHz \\ Long term stability (10 s) & 5 & kHz \\ Stabilization time & \(<\)5 & ms \\ Output power uncertainty & 1 & mW \\ Nominal load & 50 & Ohm \\ Output power level & \(>0\) & dBm \\ \hline \hline \end{tabular}
\end{table}
Table 1: RF generator general requirements.  
This list of requirements flows down from scientific requirements in the frame of ground- and space-based imaging and polarization instruments in which AOTFs are used [3; 4; 8; 21; 22; 23; 24].  
The two key parameters that drive AOTFs are the applied frequency and power level. E.g., the driving of a Tellurium-Dioxide (TeO\({}_{2}\)) AOTF in the VIS and NIR range is achieved by applying an RF power of approximately 300 mW and a frequency between 45 and 120 MHz, while a potassium-dihydrogen-phosphate (KDP) AOTF active in the UV needs around 3 W of power in the frequency range 125 to 250 MHz [12].  
These requirements serve as the baseline approach against which the RF generation techniques, listed in the next paragraph, are designed and tested.
## 3 Results
### Electrochemical Tests  
#### 3.5.1 Polarizations  
Current-potential plots resulting from cathodic and anodic polarization curves of steel in 1 M HCl in the presence of the studied bio-copolymer at various concentrations were recorded. The Tafel plots, recorded with a cathodic-to-anodic polarization of the system are shown in Figure 9.  
The electrochemical parameters and the inhibition efficiencies (\(E_{I}\)), determined by the following Equation (5), are presented in Table 3:  
\[E_{I}\%=\left(1-\frac{I_{corr}}{I_{corr}^{o}}\right)\cdot 100, \tag{5}\]  
where \(i_{corr}\) and \(i_{corr}^{o}\) are the corrosion current density values with and without St63 Gly37 bio-copolymer inhibitor, respectively, determined by extrapolation of the cathodicbranch of the Tafel plot.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Inhibitor Concentration (mg L\({}^{-1}\))** & \(E_{corr}\) **(mV vs. SCE)** & \(\beta_{c}\) **(Vdec\({}^{-1}\))** & \(i_{corr}\) **(μAcm\({}^{-2}\))** & \(E_{I}\) **(\%)** \\ \hline
0 & \(-439\) & 0.155 & 304 & - \\
5 & \(-440\) & 0.180 & 223 & 27 \\
10 & \(-444\) & 0.185 & 192 & 37 \\
50 & \(-455\) & 0.190 & 157 & 48 \\
100 & \(-458\) & 0.191 & 113 & 63 \\
200 & \(-459\) & 0.187 & 56 & 82 \\
300 & \(-460\) & 0.190 & 27 & 91 \\ \hline \hline \end{tabular} \(E_{corr}\)—corrosion potential, \(i_{corr}\)—corrosion current density, \(E_{I}\)—inhibition efficiencies, \(\beta_{c}\)—Tafel slope constant.  
\end{table}
Table 3: Polarization parameters (\(E_{corr}\) and \(I_{corr}\)) values and inhibition efficiencies of C-Mn steel corrosion in 1 M HCl at different concentrations of St63 Gly37 bio-copolymer at 298 K.  
Figure 9: Cathodic and anodic polarization curves of C-Mn steel in 1 M HCl at different concentrations of St63 Gly37. Scan rate 1 V min\({}^{-1}\).
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 3 Proposed Method
### Improved TF-IDF Based Method  
The traditional collaborative filtering algorithm calculates the user similarity matrix based on the user's rating data of items, which is easily affected by popular items. For example, \"Shawshank Redemption\" is a very good movie. If the user \(A\) and user \(B\) both gave the movie \"Shawshank Redemption\" 5 points, the traditional collaborative filtering algorithm will come to the conclusion that the user \(A\) and user \(B\) have high similarity. However, the fact is not necessarily the case. As we know, the same behavior of users on popular items does not mean that they have similar interests. On the contrary, if two users have taken the same behavior on unpopular items, it is more likely that their interests are similar. For example, if both users \(A\) and \(B\) have watched a relatively small number of movies, such as musicals, then they can be considered to have similar interests. Therefore, in order to eliminate the impact of popular items on the user similarity, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to the traditional collaborative filtering algorithm in this paper, which is used to punish the popular items in the user behavior list. The main reason to use the TF-IDF method is that it is suitable for the problem of weight extraction. In addition, the TF-IDF method is simple and easy to calculate [28].  
TF-IDF is a statistical method, which is often used to evaluate the importance of a word to a file. The importance of a word is directly proportional to the number of times it appears in the file, but at the same time, it is inversely proportional to the frequency it appears in the file library [28]. Based on the principle of TF-IDF, an improved user similarity calculation method is proposed to reduce the weight of the impact of popular items on the user similarity. If an item appears in the user's behavior list, but it also appears many times in other users behavior list, this item is regarded as a popular item, and its impact on the user similarity should be punished. The weight of the \(i\)-th item in this paper is calculated as:  
\[W_{i}=TF_{i}\times IDF_{i}=\frac{freq(i,u)}{\mid u\mid}\times\lg\frac{\mid U \mid}{(1+popular(i))} \tag{4}\]  
where \(freq(i,u)\) represents the number of times that the \(i\)-th item appears in the behavior list of user \(u\); \(\mid u\mid\) represents the length of behavior list of user \(u\); \(\mid U\mid\) represents the total number of users; and \(popular(i)\) represents the number of times that the \(i\)-th item appears in all of the user behavior lists.  
Then the weight of the item is introduced into the equation of Pearson similarity (see Equation (2)), and an improved similarity calculation method is obtained as:  
\[Sim_{\text{User}}(u,v)=\frac{\sum_{i\in I}\left(R_{u,i}\times W_{i}-\bar{R}_{ u}\right)\times(R_{v,i}\times W_{i}-\bar{R}_{v})}{\sqrt{\sum_{i\in I}\left(R_{u,i} \times W_{i}-\bar{R}_{u}\right)^{2}\sum_{i\in I}\left(R_{v,i}\times W_{i}- \bar{R}_{v}\right)^{2}}} \tag{5}\]
## 5 Results and Discussion
### Empirical Results  
Two sets of experiments were carried out. In the first set of experiments, the performance of the BERT and LSTM models was assessed using only Rule 1 as proof of concept. In the second set of experiments, the relative performance of the two algorithms was evaluated. The data in the three email datasets were divided in a 70/30 ratio, with 70% data for training and 30% for testing. Furthermore, binary cross entropy [15] was used as the loss function. Note that the purpose of this study is to show the applicability of the proposed fuzzy measure, and therefore only necessary details are provided without going into in-depth comparative analysis of the algorithms.