esa-sceva/satcom-chunk-collection huggingface dataset:
# Passive RFID-Based Inventory of Traffic Signs on Roads and Urban Environments
## 5 Discussion: Comparison with Related Works
Regarding distance range, we can observe how the proposed work achieves a satisfactory performance comparing with the referenced works, mainly because they are focused on placing multiple tags on the floor to locate accurately the vehicle passing over them [19; 22], or to identify vehicles placing the reader just on the floor to identify the tag attached to the vehicle passing over or very close to the antenna [20; 21; 23]. In other words, they do not have the purpose of identifying a tag relatively far from the car. Only [21], which performs an analysis at simulation level, achieves up to 10 m, with the vehicle running up to 90 Km/h. Also from theoretical calculations, [23] achieves a distance range of 6 m, which is similar to that obtained in the present work. Regarding the speed of the car, it is possible to observe how [22] achieves a very low value because this work is focused on minimizing the positioning error (<1.35 cm) of a mobile vehicle in an indoor environment, whereas [19; 20; 21; 23] achieve higher speeds at theoretical and simulation levels, as described in Table 8. Note that the present work is able to detect the tags also running at 120 Km/h (see Figures 8 and 10) at simulation level, in the same order of some of these approaches. Therefore, comparing with the previously presented works using the same technology and frequency band, we can conclude that the proposed system presents, as main novelties, its use for inventory of traffic sings applications and the experimentally achieved values of distance range and speed of the car.
## 2 Finite Element Model of FSW
### Material Model  
The proper selection of a material model is very important for the accurate calculation in the simulation process. The material changes from a solid state to a viscous state, and the accuracy can be improved by defining a wide range of strains. The stress-strain curves at different temperatures in the material library are used as input to the simulation, and the isotropic strengthening principle is considered. Flow stress is defined as a function of strain, strain rate, and temperature, as shown in Equation (6).  
\[\overline{\sigma}=\overline{\sigma}(\epsilon,\dot{\tilde{\epsilon}},T) \tag{6}\]  
where \(\overline{\sigma}\) is the flow stress, \(\epsilon\) is the strain, \(\dot{\tilde{\epsilon}}\) is the strain rate, and \(T\) is the temperature.
## 3 Proposed Design
### Application Traffic Patterns Identification
#### 3.1.6 Respiration  
A patient's respiratory status is helpful to measure a few important health indicators like short of breath, changes in skin color, general pallor and partial or complete loss of consciousness. Therefore, respiratory status monitoring of a patient is an essential aspect in the healthcare domain. From Table 1, considering sampling rate 25 Hz and the sampling resolution 8 bits we have SIT = 40 ms; MPDS = 25 bytes/s; DNS after 40 ms = 1 byte; DNS after 40 ms including minimum header size = 128 bits; Total data rate required by respiration packet = 3.2 Kbps.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### Proposed System
#### 2.3.2 Remote Access Network  
The remote access network is the network server that developed into a middleware layer and application layer. LoRa Gateway forwarded packets to LoRa network server through an IP network. The network server then processed the packet and forwarded it to the application layer. In the middleware layer, the LoRa network server processed and saved the data about RSSI, sound sensor, accelerometer sensor, latitude, longitude, and altitude into the database. The application layer then took the information from the database of the LoRa network server. Then, web applications in several conditions (such as the occurrence of illegal logging) sent the data, location and environment condition to the telegram channel that utilized application program interfaces (APIs). When the illegal cutting of logs occurred, the system informed the GPS position of the moving log to the forest police through the web application and telegram. The detailed architecture of the LoRa network is described in Figure 5.  
The data parameters that determine the networks involve the RSSI value, sound sensor parameter, and accelerometer sensor parameter. The RSSI value is usually used to measure the strength of the radio signal. So, it can be used as a distance threshold value between two LoRas. The sound parameter is used to detect the chainsaw sound, and the threshold is used to classify the chainsaw sound from other sounds. The accelerometer sensor parameter is determined by the log movement value as a classification threshold whether there is any difference in the slope of the trees caused by the chainsaw or not. The latitude and longitude parameter determined as a GPS parameter to indicate the location  
Figure 5: Architecture of LoRa Network in the Proposed System.  
of the moving sensor nodes. The position of latitude and longitude value then translated into the map position in the google map application.
## 4 Conclusions
### Limitations and Future Works  
While this research has provided an impact assessment model to understand the sustainability mechanism of river water and its effects on asset formation, some limitations have been identified. Firstly, this DO modeling by Streeter Phelps equation is the most popular method to determine the water quality of a river, where the variation of only two parameters (DO and BOD) are considered [49; 50] and, to determine the variation of these two parameters, several factors such as temperature, velocity, water depth, river coefficients (deoxygenation and reoxygenation), oxygen solubility time, DO-saturation and riverbed coefficient, are also considered [50]. However, some important parameters such as COD, NO\({}_{3}\), PO\({}_{4}\), and NH\({}_{3}\), can be considered to develop better water quality model to explore more insight into the system [11]. Secondly, this study has faced data limitations. For example, we do not have updated data for many variables such as water supply, navigation, dust, NOx, Sox, HF, flood, water, and soil nutrition regulation value. We have adjusted these values to get updated data. Therefore, such data generation would be a significant future research work. Finally, we did not consider investment costs in waste control, decentralization for maintaining sustainable river water, and the trade-off between IW and the cost, which will be an important aspect of future research.  
Conceptualization, B.K. and T.M.; methodology, B.K.; software, B.K.; validation, B.K. and T.M.; formal analysis, B.K.; writing--original draft preparation, B.K.; writing--review and editing, B.K.; visualization, B.K.; supervision, T.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by The Project for Human Resource Development Scholarship by Japanese Grant Aid (JDS).  
Not applicable.  
The data are not publicly available due to privacy restrictions of related ministries.  
The authors would like to thank the editor and [PERSON] for their constructive comments and suggestions, which have improved the quality of the paper significantly.  
The authors declare no conflict of interest.
## 3 Building Elements
### Encryption Procedure  
A high-level description of the encryption procedure is presented, and later the generated elements in the encryption: boxes, permutations, and schedule keys.  
AICLDH has a fourteen-round symmetric cryptosystem [49]. Each round uses a different box of type \(S-8\times 8\)_box_[50], which is dynamic since it changes in each process. Before starting the first round, a permutation with a number of elements equal to the image size is executed. It is also dynamic and works over all the image pixels, altering the pixel positions in the entire image. The schedule keys involved have a size equal to the image size. It is important to note that boxes, permutation, and schedule keys change in each image encryption, even if it is the same image but at a different time.  
Below, the steps in every round of the encryption algorithm are described.  
1. First, the permutation \(P\) is applied to the original image. Afterward, the xor operation is applied over the permuted image and the first round key. Later, the resulting chain is divided into blocks of one byte. Immediately, the substitution operation is carried out with the first box. The substitution operation is carried out in the same way as in AES [50].
2. From rounds 2 to 13, the xor operation is applied between the output of the previous round and the corresponding schedule key. Subsequently, the substitution is operated with the corresponding \(S-8\times 8\)_box_.
3. In the last round, the xor operation is executed using the output string of round 13 and the schedule key 14. Then, this previous result is divided into blocks of one byte, and the substitution operation is applied to the last box. Finally, a xor is performed between the string resulting from the substitution operation and the schedule key 15. This final result is the encrypted image.  
Once the high-level description has been made, a detailed description of the elements involved in the encryption process is explained. Before this, we will see the pre-processing that must be performed.
## 4 Calculating the Actual Leakage Rate and Location  
The above estimated state vectors \(\mathbf{X}_{j}\) include the pressure at each segment point \(P_{i,j}(i=1,\ldots,N)\), the flow to the left of each segment point \(Q^{-}_{i,j}(i=1,\ldots,N)\), and the virtual leakage rate \(K_{i,j}(i=2,\ldots,N-1)\) at each segment point. To obtain the real leakage rate and the location, the idea of an equivalent system is used. That is, the virtual multi-point leakage pipeline should have the same initial conditions and boundary conditions as the actual pipeline. The pipeline flow loss and pressure drop caused by both of them should be the same. The computation formula of the actual leakage rate and leakage location of the actual pipeline [73] can be defined as follows.  
\[\left\{\begin{array}{c}\hat{K}_{j}=\sum\limits_{i=2}^{N-1}\hat{K}_{i,j}\\ \hat{\mathbf{x}}_{k}=\frac{\sum\limits_{i=2}^{N-1}\hat{K}_{i,j}\cdot x_{ki}}{\hat{K }_{j}}\end{array}\right. \tag{13}\]  
Using the virtual leakage rate at each segment point \(\hat{K}_{i,j}\) and the known \(x_{ki}\), combined with Equation (13), the actual leakage rate and leakage location of the actual pipeline can be obtained.
# Performance Analysis of Troposphere Scattering Communication Channel with Chirp-BOK Modulation
## 3 Theoretical Analysis for Bit Error Rate
### Frequency-Selective Fading  
The impulse response of baseband signals on frequency-selective channels can be expressed as  
\[h(t)=\sum\limits_{l=1}^{L}\alpha_{l}e^{-\beta_{l}}\delta(t-\tau_{l}), \tag{17}\]  
where \(l\) is the channel index, \(L\) is the number of resolvable paths, \(\delta(\cdot)\) is the delta function, and \(\{\alpha_{l}\}_{l=1}^{L}\), \(\{\theta_{l}\}_{l=1}^{L}\), \(\{\tau_{l}\}_{l=1}^{L}\) are the random channel amplitudes, phases and delays, respectively. The received signal can be expressed as  
\[\begin{split} y(t)&=h(t)*s(t)+n(t)+i_{ISI}(t)\\ &=\sum\limits_{l=1}^{L}\alpha_{l}e^{-\beta_{l}}x(t-\tau_{l})+n(t )+i_{ISI}(t),\end{split} \tag{18}\]  
where \(n(t)\) denotes Additive White Gaussian Noise (AWGN) and \(i_{ISI}(t)\) is the inter symbol interference (ISI) component. Assuming that the fading amplitude is a random variable with a mean-square value \(\Omega=\overline{a^{2}}\). The instantaneous SNR per bit of the Lth channel is given as \(\gamma_{l}=\Omega_{l}E_{b}\big{(}1-\rho_{ij}\big{)}/N_{0}\) and the average SNR per bit of the Lth channel can be expressed as \(\tilde{\gamma}_{l}=\Omega_{l}E_{b}\big{(}1-\rho_{ij}\big{)}/N_{0}\).  
The mean-square value \(\Omega_{l}\) is related to power delay profile of the channel, which is considered as an exponentially decaying power delay profile (PDP).  
\[\Omega_{l}=\Omega_{1}e^{-(l-1)\delta},\delta\geq 0,l=1,2,\ldots,L, \tag{19}\]  
where \(\Omega_{1}\) is the average fading power of the first path, and the parameter \(\delta\) is the power decay factor. The variance of the ISI component is [32]\(\sigma_{ISI}^{2}=(\Omega_{T}-1)\Omega_{1}E_{b}/2P_{g}\), where \(\Omega_{T}=\sum\limits_{l=1}^{L}e^{-(l-1)\delta}\) is the normalized total average fading power, and \(p_{g}\) is the processing gain. Therefore, the equivalent interference plus noise power spectral density can be written as  
\[\frac{N_{e}}{2}=\frac{\Omega_{T}-1}{2P_{g}}\Omega_{1}E_{b}+\frac{N_{0}}{2} \tag{20}\]  
The average signal-to-interference plus noise ratio is  
\[\tilde{\gamma}_{e}=\bigg{(}\frac{2}{\tilde{\gamma}_{1}}+\frac{\Omega_{T}-1}{ 2P_{g}}\bigg{)}^{-1}. \tag{21}\]Following the same procedure as in Section 3, the average BER over frequency-selective Rayleigh fading in a closed form is obtained as  
\[\bar{p}_{e}=\frac{1}{2}\left[1-\sqrt{\frac{\bar{\gamma}_{e}}{1+\bar{\gamma}_{e}}} \right]. \tag{22}\]
## 3 Results and Discussion
### Cytotoxicity Tests  
The EpiDerm model faithfully replicates the three-dimensional structure of human skin. In vitro studies provide not only precise information regarding the impact of substances on skin cells but also obviate the necessity of animal testing. Models like EpiDerm become crucial tools in refining safety standards within the cosmetic, pharmaceutical, and other industries related to skincare, facilitating more ethical and precise in vitro research [34]. The results shown in a graph (Figure 4) present the percentage of relative viability with standard deviations (\(\pm\)SD).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{3}{c}{**Inhibition collagenase value of _A. reptans_ extracts (\%)} \\ \hline L\_L & R\_L \\ \hline
49.37 \(\pm\) 0.03 & 66.96 \(\pm\) 0.02 \\ \hline
**Inhibition tyrosinase value of _A. reptans_ extracts (\%)} \\ \hline L\_L & R\_L \\ \hline
41.33 \(\pm\) 0.04 & 47.52 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Inhibition activity of collagenase and tyrosinase for extracts obtained from _A. reptans_ (L_D—dried leaves, R_D—dried roots, L_L—freeze-dried leaves, R_L—freeze-dried roots).  
Summarizing the results of the cytotoxicity analysis, the obtained percentages of relative cell viability are as follows: negative control (NC) achieved 100% cell viability, indicating the absence of toxicity for this substance. Positive control (PC) led to a decrease in cell viability to 5.93%, confirming its toxicity. The extract from dried leaves showed a moderate impact with 86.05% cell viability, while freeze-dried leaves achieved 96.70% viability. The dried roots extract presented 97.26% viability, and freeze-dried roots obtained a result of 96.95%. Conclusions drawn from the analysis suggest that the tested substances from two samples of leaves (L_D, L_L) and two samples of roots (R_D, R_L) seem to exhibit a low level of cytotoxicity compared to the positive control, indicating potential safety for cells. The reliability of the conducted test was confirmed by the negative control, which showed the absence of toxicity for the control substance. These findings contribute valuable insights into the safety profile of the investigated substances, supporting their potential application with minimal adverse effects on cell viability.
## 3 Problem Statement
### Barge-Based FOWTs' Motions Analysis by Response Amplitude Operators
As environmental conditions, it is assumed that the wave direction is zero degree and the air is still. Hence, it is expected that the states excited along sway direction have very slight oscillations. This fact can be proven in Figure 4b,d,e,g representing the sway, side-to-side, roll and yaw RAOs, respectively. With analyzing the heave RAO shown in Figure 4c, it is noticeable that three platforms have almost the same RAO curves. It means that three platforms almost oscillate similarly in heave. Additionally, after the period of 11.5 s for long-wave periods, the heave RAOs  
Figure 4: RAOs for (**a**) Surge. (**b**) Sway. (**c**) Heave. (**d**) Side-to-side. (**e**) Roll. (**f**) Pitch. (**g**) Yaw. (**h**) Fore-aft. curves are close to 1 m/m which means that the platforms' heave follows the wave input oscillation. In this article, analysis of two figures are very important, represented in Figure 4f,h. Analysing of the Pitch RAO (PRAO) and Fore-aft RAO (FRAO) in the figures gives the idea of proposed control strategy in Section 3.2. The PRAO and FRAO curves for standard barge and closed-OWCs-based platforms are almost identical with very small differences. Also, in the figures, when there is a higher RAO, oscillation in that mode is more. It is noticeable in Figure 4f that PRAOs increase to reach the natural periods at 11.9 s, 12.02 s and 12.58 s for the standard barge, open-OWCs-based barge and closed-OWCs-based barge platforms, respectively. After that natural period, the platforms' PRAO declines to reach near zero. It is observable that for the period ranges of 2-12.25 s and 2-12.35 s, the open-OWCs-based barge platform's PRAO is considerably less than that of the standard barge and the closed-OWCs-based barge platforms, respectively. Also, for the periods longer than 12.25 s and 12.35 s, the standard barge and closed-OWCs-based barge platforms' PRAO respectively less than the PRAO for the open-OWCs-based barge platform. The FRAOs for the platforms have the same behaviour as the PRAOs. It may be noticed in Figure 4h that the FRAOs increases to reach its natural periods at 11.79 s, 11.88 s and 12.58 s for the standard barge, open-OWCs-based barge and closed-OWCs-based barge platforms, respectively. After that platforms' FRAO declines to reach near zero. Like the PRAOs, the FRAO for the open-OWCs-based barge platform is lower than the standard barge and closed-OWCs-based barge platforms for the periods shorter than 12.25 s and 12.35 s respectively. Besides, for the periods of longer than 12.25 s and 12.35 s, the open-OWCs-based barge platform's FRAO is higher than the standard barge and closed-OWCs-based barge platforms' FRAOs, respectively.
## Appendix A  
Table 1 summarizes the results of the scenario simulation conducted in this study. All PWS values were measured in the AAA sac.  
\begin{table}
\begin{tabular}{c c c c} \hline
**DdHhNAnIA** & **Adventitia (kPa)** & **Media (kPa)** & **Intima (kPa)** \\ \hline D40H110 NA01A20 & 357.856 & 357.064 & 444.306 \\ D40H110 NA01A80 & 336.68 & 350.636 & 440.584 \\ D40H110 NA01A140 & 344.756 & 359.684 & 441.998 \\ D40H190 NA01A20 & 366.868 & 376.335 & 464.93 \\ D40H190 NA01A80 & 341.025 & 358.598 & 449.266 \\ D40H190 NA01A140 & 370.371 & 381.259 & 398.85 \\ D40H110 NA01A20 & 408.545 & 366.859 & 458.022 \\ D40H110 NA01A20 & 327.661 & 353.739 & 448.409 \\ D40H110 NA01A140 & 321.392 & 362.292 & 453.608 \\ D40H190 NA01A20 & 384.403 & 401.012 & 468.754 \\ D40H190 NA01A80 & 369.512 & 389.926 & 475.351 \\ D40H190 NA01A140 & 344.925 & 377.789 & 467.515 \\ D40H110 NA1401A20 & 428.71 & 420.968 & 484.413 \\ D40H110 NA1401A80 & 279.652 & 343.531 & 427.682 \\ D40H110 NA1401A40 & 358.445 & 365.581 & 451.361 \\ D40H190 NA1401A20 & 442.726 & 400.205 & 509.011 \\ D40H190 NA01A401A20 & 365.698 & 388.783 & 481.649 \\ D40H190 NA1401A140 & 393.411 & 398.98 & 491.017 \\ D80H110 NA01A20 & 525.382 & 502.761 & 531.946 \\ D80H110 NA01A80 & 595.655 & 505.547 & 549.219 \\ D80H110 NA01A140 & 514.826 & 501.778 & 548.356 \\ D80H190 NA01A20 & 721.917 & 683.943 & 730.629 \\ D80H190 NA01A80 & 685.424 & 672.681 & 721.127 \\ D80H190 NA1401A40 & 714.781 & 679.127 & 718.698 \\ D80H110 NA01A20 & 535.609 & 503.137 & 579.57 \\ D80H110 NA01A80 & 529.957 & 495.819 & 551.566 \\ D80H110 NA01A140 & 524.229 & 495.498 & 542.27 \\ D80H190 NA01A20 & 624.29 & 596.221 & 710.945 \\ D80H190 NA01A80 & 690.938 & 674.51 & 712.007 \\ D80H190 NA01A140 & 677.435 & 671.997 & 711.378 \\ D80H110 NA1401A20 & 634.462 & 504.617 & 560.634 \\ D80H110 NA1401A80 & 550.968 & 496.806 & 577.645 \\ \hline \end{tabular}
\end{table}
Table 1: PWS measurements in the simulated scenario.
## 2 Literature Review and Theoretical Foundation
### Antecedents to Information Disclosure
#### 2.3.2 Nudge Theory and Sensitivity Signal  
The Nudge Theory was first introduced by [PERSON] and [PERSON] [31] which stated that people's behaviors can be influenced by small suggestions and positive reinforcements. Nudging is founded on the assumption that people's behaviors are not always rational due to cognitive limitations, and that said behavior is affected by the display of possibilities in a choice context [86; 87; 88]. Hence, nudging is used in the design of an environment within which a choice is made to make people lean a certain way versus another. However, nudging also respects freedom of choice [31; 89]. Nudges have been used in the digital world, by changing certain user-interface design elements to guide users' behaviors [90; 91; 92; 93].  
Based on Nudge Theory, there are different ways that users could be notified about the information they share online, specifically when they interact with chatbots. An example of a nudge can be as simple as increasing the salience of the desired option. For example, labeling menu items with their respective calorie count or nutritional facts have been used in the food industry for decades as a strategy to help people make informed and healthy choices [94]. Being informed of the calories, for instance, in each menu item has been shown to improve transparency to customers about what they put in their bodies and, in some cases, change order behavior [95]. Another example is the disclosure of ads on social media and websites. The United States Federal Trade Commission promoted back in 2013 the use of labels and visual cues to help consumers recognize and distinguish ads from the regular content on different interfaces [96].  
The above reasoning could also be applied to information disclosure to chatbots. Based on the literature, it is known that information sensitivity is a determining factor in disclosure behaviors. Thus, explicitly signaling the sensitivity level of the question being asked by the chatbot could have an effect on user disclosure. Hence, we posit that the relationship between question sensitivity and users' information disclosure is moderated by the question sensitivity signal, such that:  
**H2a.**_When a low sensitivity signal is present (vs. absent) for less sensitive questions, disclosure increases._  
**H2b.**_When a high sensitivity signal is present (vs. absent) for more sensitive questions, disclosure decreases._