esa-sceva/satcom-chunk-collection huggingface dataset:
# Functional and Antimicrobial Properties of Propolis from Different Areas of Romania
## 3 Results
### Antimicrobial Activity and MIC
For the extract from Alba (A), the mean MIC was 3.12 mg/mL, while for S it was 5.63 mg/mL; the extract from Harghita County (H) had the highest observed value (11.25 mg/mL), which represents the lowest level of activity against this particular strain. _P. aeruginosa_ ATCC 27853 demonstrated a high degree of sensitivity to C2 extract, with a MIC value of 2.56 mg/mL. In contrast, the efficacy of the C1, A, and S extracts was less pronounced, with values ranging from 5.63 mg/mL to 12.5 mg/mL. The lowest efficacy was recorded for extract H, with an MIC value of 18.75 mg/mL. In the case of _S. enterica_ ATCC 25928, the most effective extract was C2, with a minimum inhibitory concentration (MIC) of 1.28 mg/mL, followed by C1 with 2.56 mg/mL. Extract A demonstrated a relatively good response with 3.12 mg/mL. The least effective extracts for this strain were those for S and H, with 5.63 mg/mL. With regard to _A. baumannii_ ATCC 19606, the extract exhibiting the greatest efficacy was C1, with a value of 6.25 mg/mL, followed by C2, with 8.13 mg/mL. The extracts from H and S demonstrated comparatively reduced efficacy, with values of 11.25 mg/mL and 12.5 mg/mL, respectively. In the case of _C. albicans_ ATCC 10231, a notable sensitivity was observed in response to all five propolisis extracts. The yeast strain demonstrated a particularly high sensitivity to extracts C1 and C2 (0.39 mg/mL), followed by extract A with 1.03 mg/mL. The extracts with the highest minimum inhibitory concentration (MIC) values were S and H, with values of 1.28 mg/mL and 1.5 mg/mL, respectively. The positive control used in the MIC test varied from 1.56 mg/mL to 12.5 mg/mL depending on the strain tested.
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 4 Experimental Results  
### Experimental Setup  
In this section, the experimental setup used for the proposed method is described. We evaluated the proposed scheme based on standard criteria from IHC [7]. Figure 4 illustrates the general process of our evaluation for the benchmarking of our watermarking system. The evaluation included the processes of watermark pattern embedding, pattern extraction, video frame compression (MPEG-4 part 10), and digital/analog-analog/digital (DA-AD)conversion. Finally, the average bit error rate (BER) of the extracted bit information was calculated. The proposed algorithm was implemented in a MATLAB environment (ver 2012b), and on a computer platform consisting of an Intel i7-3770 (3.40 GHz) CPU with a 16-GB main memory employed to assess the performance.
# Research on the Performance of an End-to-End Intelligent Receiver with Reduced Transmitter Data
## 3 Transmitter Data Clipping
Assuming that the length of the data \(Y_{2}\) at the transmitting end is eight and the data is trimmed at equal intervals of four bits, the trimmed data is \(S\). The trimming process can be realized as shown in Figure 9. The trimmed data s is expressed as follows by the formula:  
\[S=g_{1}(Y_{2}) \tag{8}\]  
\(g_{1}()\) in the above equation indicates that \(Y_{2}\) is cropped at equal intervals through the matrix shown in Figure 9. Previous research work, such as in the literature of [18], has shown that intelligent receivers have the ability to extract the original bit information from the transmitter-side data that is interfered by the channel. For equidistant clipping, combining Formula (8) with Figure 9, we find that the process can be realized by relevant formulas, and that the data after the equidistant clipping still retains a certain regularity. Moreover, the data at the transmitter side is reduced in an orderly manner, i.e., for the intelligent receiver the inputs are changed in an orderly manner and the equal-interval cropping retains a large amount of information. Therefore, when the data at the transmitting end decreases in order, we believe that under the condition of strong nonlinear fitting of the receiving neural network, it can recover the original data by finding the relationship between the clipped data and the original binary bit data.
## Materials and Methods
### Rock Mass Quality and Tunnel Support
#### 2.6.2 Back Calculation of Rock Mass Quality Based on Supported Tunnel  
Because the two systems have been developed and updated for support design, the installed supports in a tunnel can be used for determining rock mass quality. Equation (18) and the \(Q\)-system support chart reveal that rock bolt spacing is a function of rock mass quality only, and this spacing can be directly used for rock mass quality (both \(RMR\) and \(Q\) value) determination. The use of fiber-reinforced shotcrete, which is a function of the rock mass quality (\(Q\) or \(RMR\)) and tunnel size (span or D\({}_{\rm e}\)), is a mandatory part of the current tunneling practice. The thickness of this shotcrete along with the tunnel size can also be used to identify the rock mass quality through the back analysis of an already supported tunnel section based on support charts of \(RMR_{89}\) and \(Q\) system. The rock mass quality obtained from the rock bolt spacing is an entirely single value (100%). The shotcrete thickness is a function of the rock mass quality and tunnel size, and the contribution of the rock mass quality to the shotcrete thickness depends on the inclination of a tangent in the shotcrete support line as found in the support charts of both \(Q\) and \(RMR\) systems. The final rock mass quality for both systems can be evaluated from two different rock mass quality values (rock mass quality from rock bolt spacing and rock mass quality from shotcrete thickness) through their weightage. The procedure for the back calculation of the rock mass quality is described in Figure 1 and illustrated in Example 1.  
Example 1:  
Tunnel span or D\({}_{\rm e}\) = 11.17 m  
Rock bolt spacing = 1.6 m  
Shotcrete thickness = 9 cm  
\(RMR_{1}\)* = 48.6 (contribution of \(RMR\) to rock bolt spacing is 100%)  
\(RMR_{2}\)* = 55.6 (contribution of \(RMR\) to shotcrete thickness is 52.09%)  
\(RMR\)* = 51 (\(RMR\)* is calculated from \(RMR_{1}\)* and \(RMR_{2}\)* based on their contribution (%))  
\(Q_{1}\) = 0.7 (contribution of \(Q\) to rock bolt spacing is 100%)  
\(Q_{2}\) = 1.7 (contribution of \(Q\) to shotcrete thickness is 49.25%)  
\(Q\) or \(Q_{c}\) = 1.03 (\(Q\) or \(Q_{c}\) is calculated from \(Q_{1}\) and \(Q_{2}\) based on their contribution (%))
###### Abstract  
In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.  
deep neural network; Internet of Things; linear discriminant analysis; Wi-Fi based indoor localization 2018 8 1062 26 June 2018 29 June 2018
# An Empirical Recommendation Framework to Support Location-Based Services +
## 2 Related Work and Comparative Analysis
### Collaborative Filtering  
[PERSON] et al. (2018) proposed an approach to overcome cold start and data sparsity problems. They included user attributes and ratings of the items to calculate the similarity score by a weighted coefficient value. [PERSON] (2017) introduced a system to predict the user pattern using their previous navigational activities. Here collaborative filtering is used to find the user with similar interests. [PERSON] et al. (2018) used collaborative filtering on the similar user and user's check-in spots. Similarly, [PERSON] et al. (2019) worked on a model that not only considered the user's previous history but also some other features such as location and ratings given by similar users. They also introduced two modules: offline clustering and online CF method. [PERSON] et al. (2019) performed collaborative filtering to generate a service recommendation system. First they create a model for each user's information. Then they calculate the similarity using the Pearson correlation on the user's service information. [PERSON] et al. (2019) introduced a hierarchical-graph-based similarity measurement (HGSM) module on the user's individual check-in history and check-in similarities between users. This approach contains three sections for calculating the content-based filtering: (i) the individual's data on outdoor activities, (ii) data at a certain geographical region, and (iii) the sequenced data of geographic spaces. [PERSON] et al. (2019) worked on the collaborative filtering where the primary data of users are from the adaptive web. They developed the system on user ratings using the nearest neighbor algorithm. [PERSON] et al. (2019) combined three algorithms to provide a better recommendation system. First, they used a memory-based method to generate the score model. Then, matrix factorization is introduced for the training process and finally the CF method to combine these two models. A similar approach of matrix factorization was used by [PERSON] et al. (2019) on the Netflix dataset. [PERSON] et al. (2019) also discussed various collaborative filtering methods on their work.
# Prediction of Ocean Weather Based on Denoising AutoEncoder and Convolutional LSTM
## 4 Applications
### Case Studies and Discussion
Actually, it is hard to directly compare the accuracy of the model in this study with that of the general prediction model provided by weather centers. To compare the accuracy between them, the ocean weather data up to now should be obtained, and the proposed model should be generated using those data. However, ECMWF and HYCOM provide only data from several months ago; hence, it is difficult to obtain the data for generating the model at a specific point in time. In addition, even for predictions at a specific point in the past, they only provide actual data from the past, not data of how they predicted the weather at that time (i.e., past forecast data). However, for indirect comparison, three parameters of verification data for the ocean weather (significant wave height (SWH), peak wave period (PWP), and wind speed (WS)) provided by the Weather Meteorological Organization (WMO) and ECMWF (2020) were additionally investigated. According to the verification data, it can be seen that the error rate accumulates and increases as the forecast period increases in the prediction models of weather centers. A total of ten organizations surveyed by the WMO compared the result of error between predicted ocean weather after three days and the actual value. The error of SWH is about 0.3-0.5 m, PWP is about 2.0-2.6 s, and WS is about 2.7-3.4 m/s, respectively. Figure 13 shows the RMSE of SWH presented in WMO and ECMWF [23]. In this study, the results of predicting the ocean weather after three days using the proposed model were presented in Section 4.4 (Case 5). As a result of predicting the ocean weather after three days using the proposed method in Case 5, the error was 0.22 m for MWH, 0.48 s for MWP, 2.46 m/s for WU, and 2.24 m/s for WV, respectively. It can be seen that it shows a similar error rate presented by WMO and ECMWF (2020). Therefore, if there is no experience to make a numerical prediction model for the ocean weather, the proposed method is expected to be helpful.
# An Image-Based Fall Detection System for the Elderly
## 4 Methodology
### Person Positioning and Posture Recognition
On the other hand, since the center of \(P_{i}\) is the waist, if the center of \(P_{i}\) is in \(C_{n}\), it means that the person is trying to sit on the chair. Even if the person falls, he/she can still sit on the chair. If the distance from the center of \(P_{i}\) to the center of \(C_{n}\) is more than half the height of \(C_{n}\), it means that the person is not sitting on the chair. Finally, if the person is sitting correctly, the person should keep his/her back straight and bend their knees about 90 degrees. That height is about 7/8 times the height of the person when he/she is standing. If the height of the person's bounding box in the current frame \(P_{i}\) is less than or equal to 7/8 times the height of the person's bounding box \(P_{i}^{N}\), then we consider that the person is sitting. Figure 4 shows the flow of the state when the person is sitting, in progress, or in danger. If the person is in danger or if IFADS cannot detect the person, it executes fall detection. ```
Input: \(P_{i}\), \(S_{i,fid}\), \(P_{i}^{B}\) Output: \(S_{i}\)
1 Execute posture recognition pre-progress (\(fid\))
2 if(\(S_{i,fid-1}\) = In progress) or (\(S_{i,fid-1}\) = Sitting) or (\(S_{i,fid-1}\) = In danger) then
3 if(\(P_{i}\cdot top\) higher than \(C_{n}\cdot top\)) then
4 if(\(P_{i}\cdot center\) in \(C_{n}\)) and (\(C_{n}\cdot center\) in \(P_{i}\)) then
5 if(\(D\cdot C_{y}\ \leq 1/2*C_{n}\cdot height\)) then
6 if(\(P_{i}\cdot height\leq 7/8*P_{i}^{B}\cdot height\)) then
7\(S_{i}\leftarrow\) Sitting
8 else
9\(S_{i}\leftarrow\) In progress
10 else
11\(S_{i}\leftarrow\) In progress
12 else
13\(S_{i}\leftarrow\) In progress
14 else
15\(S_{i}\leftarrow\) In danger
16 Execute fall detection
```  
**Algorithm 6** Posture recognition (between in progress, sitting, and in danger)Since the person may be obscured by other objects, we recognize whether the person is obscured by other objects. Algorithm 7 shows the steps of posture recognition (obscured). The current surveillance camera can record at least five frames per second, and the person's height cannot change significantly within 0.2 s. Thus, if the height of the person is less than or equal to half the height in the last frame, we consider that the person is obscured. Conversely, if the state of the person is obscured in the last frame, but the height of \(P_{i}\) is higher than the height of \(P_{i,fit}c_{-1}\) that is not obscured by other objects, we consider that the person is not obscured by objects. ```
Input: \(P_{i}\), \(S_{i,fit}\) Output: \(S_{i}\)
1. if (\(S_{i,fit-1}\)!= Observed) and (\(S_{i,fit-1}\)!= Falling) then
2. if (\(P_{i}\):\(height\ \leq\ 1/2*P_{i,fit-1}\):\(height\)) then
3. \(S_{i}\leftarrow\) Obscured
4. \(fid^{C}\gets fid\)
5. else
6. if (\(P_{i}\):\(height\ \geq\ P_{i,fit}c_{-1}\):\(height\)) then
7. \(S_{i}\gets S_{i,fit}c_{-1}\)
```  
**Algorithm 7** Posture recognition (obscured)
## 5 Conclusions  
For the rapid calculation of the visible time window (VTW) for Earth observation payloads of the space station for regional targets, this paper proposes a triple time interval hybridization strategy (TTIHS). It integrates orbit filtering, large-step angle calculation, and short interval binary search strategies, employing different strategies in different time intervals to improve computational speed. The algorithm adopts an orbit filtering strategy based on the concept of expanded rectangles to exclude large-scale invisible time intervals, shortening the calculation time interval of the time-consuming coarse search phase from the global interval to a smaller one, thus significantly reducing the computational workload and improving the calculation speed. Additionally, the algorithm's applicability and parameter  
Figure 11: Relationship graph between TTIHS accuracy and time step.  
effects are analyzed. An optimized analysis of the calculation time step for the time interval is conducted, providing a reference for selecting the time step length. Proven by mathematical analysis of time complexity and simulation results using real-world data, the algorithm significantly improves computational speed, with a speed increase of 2.73 times compared to the existing rapid algorithm, while maintaining computational accuracy.  
This paper proposes a new computational framework that includes orbit filtering based on improvements to the existing rapid algorithm, providing a rapid and accurate method for calculating the visible time window of Earth observation payloads on the space station. The characteristic of this algorithm is its rapid computation and simple structure, providing support for visible time window calculation for large-scale Earth payload missions outside the space station, especially for emergency and real-time mission planning. The principles of this method can also be applied to coverage analysis of Earth observation payloads. Adaptive discretization of polygon boundaries will further enhance the effectiveness of the proposed algorithm.  
Conceptualization, Y.S. and C.D.; methodology, Y.S. and X.J.; software, Y.S. and Y.L.; validation, Y.S., X.J. and Z.L.; formal analysis, Z.L.; investigation, Y.S.; resources, H.Z.; data curation, Y.S.; writing--original draft preparation, Y.S.; writing--review and editing, Y.S., Z.L., C.D. and H.Z.; visualization, Y.S.; supervision, H.Z.; project administration, C.D. and H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Publicly available datasets were analyzed in this study. This data can be found here: [[http://celestrak.com/](http://celestrak.com/)]([http://celestrak.com/](http://celestrak.com/)) (accessed on 23 December 2023).  
The authors declare no conflicts of interest.
## 3 S-Transform  
There are many methods for time-frequency analysis and the effects on ISAIL image imaging are also different. The Figure 3 shows the result of the short-time Fourier transform and S-transform for the same range cell.  
Figure 2: The process of ISAIL time-frequency imaging.  
The rigid body and micro-Doppler components of the radar can be approximated as single-frequency and sinusoidal modulated components. It can be seen from the figure that, due to the limitation of the window function, the distribution of the features of the rigid body and the micro-Doppler is not obvious in STFT. However, the S-transformation makes the target and micro-Doppler clearer, due to its enhanced diffusion into the high-frequency region.  
The ST is proposed by American geophysicist [PERSON] et al. in 1996. It is a method between the short-time Fourier transform and the wavelet transform. It uniquely combines the frequency dependence of time-frequency resolution and the localization of real and imaginary parts. The definition of the S transform:  
\[S_{x}(t,f)=\int_{-\infty}^{+\infty}x(\tau)\omega^{*}(\tau-t)e^{-j\pi f\tau}d\tau \tag{5}\]  
\(\omega(t)\) is a window function. According to the Heisenberg uncertainty principle, the time-frequency area of the Gaussian window function can be minimized. So the window function is selected as a Gaussian window:  
\[\omega(t)= \frac{1}{\sigma\sqrt{2\pi}}\exp[-\frac{t^{2}}{2\sigma^{2}}] \tag{6}\] \[\sigma(f)=\frac{1}{|f|} \tag{7}\]  
\(\sigma\) is the scale factor that determines the width of the window function. It can be seen from the scale factor that at the high frequency, the signal changes drastically, the time period is relatively small, and a narrow time window is selected. While at the low-frequency, the signal change is relatively stable, the time period is relatively large and the time window is wider. At the same time, it is equivalent to adding a larger amplitude at the high frequency. It can be seen from Figure 3(a) that the ST has a large amplitude at high frequencies (micro-Doppler information) and a small amplitude at low frequencies (rigid body portions), which is disadvantageous for the removal of the rigid body portion. Therefore, the data needs to be pre-processed first. This paper introduces the variable trend window S transform [20].The scale factor in the window function has been improved.  
\[\sigma(f)=\frac{\frac{\theta}{3}[\lg 10(f+0.01)+1]}{|f|} \tag{8}\]  
Figure 3: STFT and S-transform result of the same range cell.  
Through the improved window function, the frequency resolution of the S-transform at high frequencies is improved and high-frequency distortion is suppressed. At the same time, the weight of the amplitude at high frequencies is reduced. The effectiveness of the micro-Doppler component is weakened.  
After pre-processing, the amplitude of the high-frequency part becomes lower, highlighting the low-frequency part, which can better avoid the interference of the micro-Doppler information and more effectively take out the rigid body. Then the rigid body portion can be extracted by the RANSAC algorithm.
# Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid +
## 4 Cybersecurity Threats in Energy System OT Networks
### Confidentiality Threats  
Cyber attacks that affect the confidentiality of users in the power grid are mainly focused on the customer, distribution, and service provider domains of the NIST Smart Grid Conceptual Model [27]. AMIs enable more precise, real-time monitoring of customer energy consumption for more precise billing and to provide feedback to customers about their energy consumption habits. This level of customer feedback necessitates communications of potentially sensitive customer information in the AMI communication networks, presenting a potential threat to customer confidentiality. In addition to customers, intrusions within almost all domains in the conceptual model may reveal sensitive user information (e.g., employees). In this section, we present a survey of threats to confidentiality in energy grid OT networks.  
As explained in [49], the primary challenges concerning confidentiality in AMI are customer privacy and operations integrity and availability. Since the latter concerns have been discussed in previous sections, the primary focus in this section concerning confidentiality in AMI is regarding customer privacy. As shown in [50], smart devices may be identified by an attacker by analyzing their energy consumption, and sensitive customer information may be revealed by analyzing meter readings. The main attacks targeting confidentiality are packet capturing for traffic scanning, port scanning directed at specific protocols such as DNP3, and social engineering or password phishing attacks.
# Responses of Medicinal and Aromatic Plants to Engineered Nanoparticles +
## 6 Effects of Carbon-Based NPs on Medicinal and Aromatic Plants
### SWCNTs  
Attenuation of Abiotic Stress  
Exposure of _Hyoscyanus niger_ seeds under drought stress (0.5-1.5 MPa) to low concentrations of SWCNTs for 14 days resulted in improved water uptake and upregulation of mechanisms involved in starch hydrolysis, while oxidative stress and electrolyte leakage were suppressed. In the presence of SWCNTs, plant defense system was activated, resulting in increased activities of antioxidant enzymes, such as APX, CAT, POD, and SOD as well as improved biosynthesis of phenolics and proline, which finally contributed to the mitigation of drought stress [308].