esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Asymptotic Secrecy Outage Performance Analysis
### Validation of Analysis and Performance Comparison  
In Figure 2, we compare the secrecy outage performance of three secondary users with various TAS schemes, numbers of antennas, and secrecy communication rates in the CR-NOMA network. The analytical results are based on Sections 3 and 4, and the asymptotic results are based on Section 5. From Figure 2, we can obtain the following views: (1) The results of the theoretical analysis and Monte Carlo simulation are very consistent, which verifies the correctness of theoretical analysis. (2) With the ST and MCC schemes for all secondary users in Figure 2d,e, we increase the secrecy communication rate of each secondary user, i.e., \(R_{1}^{s}\), \(R_{2}^{s}\), \(R_{3}^{s}\), the overall SOP of the secondary network will increase, and the secrecy outage performance will deteriorate. This is due to the fact that when the \(R_{1}^{s}\), \(R_{2}^{s}\), and \(R_{3}^{s}\) increase, the outage threshold of \(U_{1}\), \(U_{2}\), and \(U_{3}\) also increase correspondingly. (3) For the overall SOP of the secondary network, the max-\(U_{1}\) of the MCC scheme is far superior to other schemes. The intuitive reason is that in the signal transmission process, \(U_{1}\) is the farthest user, and its SOP is relatively high, which greatly impacts the overall SOP. (4) When the BS is equipped with multiple-antenna, the secrecy outage performance of secondary users is significantly better than that of the SISO-CR-NOMA network.  
Figure 2: _Cont._In Figure 3, we compare the SOP of the MCC and ST schemes in different interference temperatures. From Figure 3a,b, as can be seen, with the increase of \(P_{t}\), the SOPs of \(U_{1}\) and \(U_{2}\) present a curve trend that first decreases, then increases. It can be explained by features of SIC technology and QoS constraints of PD. As with Formulas (4), (5) and (7), when signal \(x_{2}\) is used as interference, \(U_{1}\) uses SIC to decode its own signal. With the increase of \(P_{t}\) to a certain value, the influence of \(P_{t}\) on user channel capacity begins to exceed other parameters such as the power distribution coefficient and channel gain, so the interference effect of \(x_{2}\) becomes stronger, the user channel capacity and eavesdropping channel capacity decrease synchronously, the security capacity decreases, the SOP increases, and the secrecy performance drops sharply. Meanwhile, with the increase of \(P_{t}\), SOP is finally constant. This result verifies the correctness of Section 5. Because the transmission power of BS is constrained by the QoS of PD. By comparing Figures 2d and 3d, we found that reducing \(Q\) can reduce the interference between signals simultaneously, improving the network's secrecy outage performance.  
Figure 3: _Cont._  
Figure 2: The secrecy outage performance of secondary users with various TAS schemes, numbers of antennas, and secrecy communication rates. (**a**) SOP of \(U_{1}\); (**b**) SOP of \(U_{2}\); (**c**) SOP of \(U_{3}\); (**d,e**) overall SOP with different secrecy communication rates.
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
## 4 Results and Evaluation
### Current Passenger Safety Mechanism
Case 2: The passenger is traveling on a long-distance highway, and an accident occurs, there is no security camera nearby, but there are other vehicles on the road. In the second scenario as shown in Figure 4b, the probability of obtaining timely aid depends on whether a vehicle traveling on the same route gives timely assistance or not. Because of legal concerns, some people are afraid to help in such instances. However, in the majority of situations where passers-by are unwilling to help until legal authorities arrive, they at least notify legal authorities promptly. The overall performance of the system, in this case, will be evaluated based on the six parameter values as shown in Table 5.  
Substituting the weight values of Table 5 in Equation (2), we get the probability of success as shown in Equation (4):  
\[Prob(S)=\frac{1}{6}(0.5+0.5+0.5+0+0.5+0.5)=\frac{2.5}{6}=0.42 \tag{4}\]  
Case 3: The passenger is traveling on a long-distance highway, and an accident occurs. There is no security camera or a vehicle on the road nearby. The worst-case scenario is the third scenario as shown in Figure 4c; in most situations, this scenario results in mortality since the victim is left on the road for an extended period without aid, and in most cases, massive blood loss is the cause of death. The only way of getting assistance, in this case, is if another local or legal vehicle passes along that route. Hence, the probability of first aid availability is almost less than 0.20 in this case.
## 3 Numerical Experiment and Discussion
### Experimental Results  
Moreover, due to the current and future electronic systems demand, the distance between elements in an array becomes smaller, leading to a stronger mutual coupling effect causing unacceptable or bad impedance-matching and radiation behavior [45]. Adding the impact of mutual coupling in this study, measured experiment samples, which is the trusted way of testing all approaches, were further employed by subjecting the PV, SPV, PWD-SRP, and DPD-MUSIC to experimental tests. The SAA was situated in the middle of an anechoic chamber, and sources were positioned at 74 DoAs, obtained from various combinations of different 18 azimuths and elevations. The selected azimuths ranged from 5\({}^{0}\) to 365\({}^{0}\) using a 20\({}^{0}\) step size. Readers are referred to [6], where the data were originally published, for more details about the experimental setup or architecture using the SAA. A gross error (GE) analysis of performance was used to judge all algorithms. Figure 6 shows a performance comparison between the PV, SPV, PWD-SRP, and DPD-MUSIC. The SPV and PV have an improved performance compared to the DPD-MUSIC and PWD-SRP, even with the mutual coupling (MC) effect. Antenna arrays generally have different advantages, such as beamforming capacity and a high gain, which are used for various mobile communications, including controlled radiation pattern antenna for EM immunity to interference and military radar applications. However, the current trend in technology means that recent systems are smaller, meaning that there is a smaller space between elements in the array. This leads to a higher MC, poor radiation features, and impedance mismatch. This has a severe effect on antenna array signal processing [4; 6]. Therefore, when estimating the DoA of signals vis-a-vis antenna array, PV and SPV are recommended as they are more robust to MC. Therefore, the PV and SPV are good candidates for DoA estimation, particularly in practical situations.  
Figure 5: RMSEs estimate versus SNR with four sources.
## 2 Theoretical Background and Mathematical Models
### Electric Field Change in the Initially Charged Channel of the LEMP Sprout  
The electric field expression (\(\mathrm{e_{x}}\)) is divided into two stages. In the first stage, the static electric field (\(\mathrm{e_{x}}^{\mathrm{s}}\)) is expressed, and in the other stage the dynamic electric field (\(\mathrm{e_{x}}^{\mathrm{d}}\)) is expressed. These two electric field expressions occur in the range of \(\mathrm{ct}>\sqrt{\mathrm{d}^{2}+\mathrm{x}^{2}+\mathrm{h}^{2}}\) and are shown in Equation (31a,b):  
\[\mathrm{e_{X}^{g}}=\frac{Z_{0}I_{0}}{4\pi\beta}\cdot\frac{\mathrm{x}}{ \mathrm{d}^{2}+\mathrm{x}^{2}}\cdot\frac{2\mathrm{h}}{\sqrt{\mathrm{d}^{2}+ \mathrm{x}^{2}+\mathrm{h}^{2}}} \tag{31a}\] \[\mathrm{e_{X}^{d}}=\frac{Z_{0}I_{0}}{4\pi\beta}\cdot\frac{\mathrm{x}}{ \mathrm{d}^{2}+\mathrm{x}^{2}}\left(\frac{\beta ct-\mathrm{h}}{\sqrt{\left( \beta ct-\mathrm{h}\right)^{2}+\delta^{2}}}-\frac{\beta ct+\mathrm{h}}{\sqrt{ \left(\beta ct+\mathrm{h}\right)^{2}+\delta^{2}}}\right) \tag{31b}\]The initial loading condition of the LEMP sprout is depicted in Figure 9a,b [33; 34; 35]. The equations used to calculate the change in the electric field for this state are expressed in Equations (32)-(34):  
\[\mathrm{x}_{1}=\frac{1}{2}\cdot\frac{(\mathrm{ct}+\mathrm{x})^{2}-\mathrm{h}^{2 }-\mathrm{d}^{2}}{\mathrm{ct}+\mathrm{x}} \tag{32}\]  
\[\tau_{\mathrm{m}}=\beta(\mathrm{ct}-\mathrm{x})-\mathrm{h},\ \tau_{\mathrm{p}}=\beta( \mathrm{ct}-\mathrm{x})+\mathrm{h} \tag{33}\]  
If the LEMP channel is initially loaded, Equations (32) and (33) are combined to obtain the main equation, as given in Equation (34):  
\[\int\limits_{\mathrm{x}}^{\mathrm{x}_{1}}\mathrm{e}_{\mathrm{x}}^{2}\mathrm{ dx}=\frac{Z_{0}\mathrm{l}_{0}}{4\pi\beta}\left[\begin{array}{c}\ln\left(\frac{ \tau_{\mathrm{p}}-\beta\mathrm{x}_{1}+\sqrt{\left(\beta\mathrm{x}_{1}-\tau_{ \mathrm{p}}\right)^{2}+\delta_{1}{}^{2}}}{\tau_{\mathrm{m}}-\beta\mathrm{x}_{ 1}+\sqrt{\left(\beta\mathrm{x}_{1}-\tau_{\mathrm{m}}\right)^{2}+\delta_{1}{}^{ 2}}}\right)\\ +\,\ln\left(\frac{\tau_{\mathrm{m}}-\beta\mathrm{x}+\sqrt{\left(\beta\mathrm{x }-\tau_{\mathrm{m}}\right)^{2}+\delta{}^{2}}}{\tau_{\mathrm{p}}-\beta\mathrm{x }+\sqrt{\left(\beta\mathrm{x}-\tau_{\mathrm{p}}\right)^{2}+\delta{}^{2}}} \right)\\ +\,\ln\left(\frac{-\mathrm{h}+\sqrt{\mathrm{d}^{2}+\mathrm{h}^{2}+\mathrm{x}_{ 1}^{2}}}{\mathrm{h}+\sqrt{\mathrm{d}^{2}+\mathrm{h}^{2}+\mathrm{x}_{ 1}^{2}}}\right)\\ -\,\beta\ln\left(\frac{\mathrm{x}_{1}-\beta\tau_{\mathrm{m}}+\sqrt{\left(\beta \mathrm{x}_{1}-\tau_{\mathrm{m}}\right)^{2}+\delta_{1}^{2}}}{\mathrm{x}_{1}- \beta\tau_{\mathrm{p}}+\sqrt{\left(\beta\mathrm{x}_{1}-\tau_{\mathrm{p}} \right)^{2}+\delta_{1}^{2}}}\right)\\ +\,\ln\left(\frac{\mathrm{x}-\,\beta\tau_{\mathrm{m}}+\sqrt{\left(\beta \mathrm{x}-\tau_{\mathrm{m}}\right)^{2}+\delta{}^{2}}}{\mathrm{x}-\beta\tau_{ \mathrm{p}}+\sqrt{\left(\beta\mathrm{x}-\tau_{\mathrm{p}}\right)^{2}+\delta{}^ {2}}}\right)\end{array}\right] \tag{34}\]
## Materials and Methods
### Burnt Areas Detection
The S1 temporal aggregation is performed on bi-monthly basis calculating the average of the SAR stack. The 2-month time span is necessary to further reduce the noise of SAR data records. The calculation of backscatter differences between consecutive periods allow to detect fire scars that exhibit large differences in backscatter. The difference in the signal between different periods is simply computed by subtracting the backscatter of the second period to the backscatter of the first one. Pixels that display a difference greater than a specific threshold are labeled as fire scars. The threshold is land cover dependent. The variation of backscatter in evergreen forest is relatively stable due to the homogeneous forest canopy. Conversely, croplands and other land uses exhibit important variation in backscatter signal over time. In order to quantify the S1 forest-fires backscatter threshold, artificial test cases have been designed over the study area. The test was carried out using different threshold differences between S1 backscatter from different periods ranging from \(-0.1\) to \(-2.0\) dB with a step of \(-0.1\) dB. A backscatter difference of \(-1.0\) dB provided the optimal threshold value for the assessment of fire scars in the evergreen forests of the study area. A lower threshold value would have increased the data noise, whereas, at higher values, the fire scars effects would have been less evident. A simple threshold difference approach can however introduce commission errors. S1 data, although filtered through the Map Gamma, still suffer from speckle noise and the backscatter strongly depends on the soil moisture content of the ground. A difference between two consecutive periods, e.g., due to soil moisture across intact forests may thus be detected as fire scar. To mitigate the negative effect of these commission errors a \"coherency test\" between three consecutive periods (i.e., the S1 bi-monthly aggregations described above) is introduced. The test is carried out by detecting fire scars between: (1) the first and the second period (i.e., March-April aggregation vs. January-February one); and (2) the first and the third period (i.e., March-April aggregation vs. December-January one). Fire scars found for the first pair that are not subsequently confirmed by the second pair are rejected. Therefore, pixels classified as burned that do not display a time-consistent change in backscatter signals are considered as outliers. This procedure is applied to ensure that changes in backscatter signal are due to the vegetation structure rather than to temporary differences in soil moisture. To remove possible errors caused by topography, SAR data are usually first corrected using a maximum slope threshold. As the terrain is flat in the study area, such correction is neither needed nor applied. The procedure has been applied with both VV and VH polarizations, but only VV shows results coherent with S2 results.
# Intelligent Reflective Surfaces for Wireless Networks:
## 3 IRS-Assisted Wireless Networks
### Signal Model
#### 3.1.2 Signal Model of Downlink IRS-Assisted Systems  
In this system model shown in Figure 4, we consider a typical downlink scenario with LOS propagation and multi-stream transmission such that the BS with \(N_{\mathrm{t}}\) antennas allocates \(N_{\mathrm{s},k}\) streams to be transmitted to each user equipped with \(N_{\mathrm{r}}\) antennas. The vector of the \(N_{\mathrm{s},k}\) symbols transmitted by the BS to the \(k\)-th user is \(\mathbf{s}_{k}=\left[s_{k1},s_{k2},\ldots,s_{kN_{k}}\right]^{T}\), \(\mathbf{H}_{\mathrm{BU}}\in\mathbb{C}^{N_{\mathrm{r}}\times N_{\mathrm{t}}}\) represents the channel response from the BS to the \(k\)-th user, \(\mathbf{H}_{\mathrm{IU}\,k}^{(\ell)}\in\mathbb{C}^{N_{\mathrm{r}}\times N}\) stands for the channel response from the \(\ell\)-th IRS to the \(k\)-th user, and \(\mathbf{H}_{\mathrm{BI}}^{(\ell)}\in\mathbb{C}^{N\times N_{\mathrm{t}}}\) is the channel response from the BS to the \(\ell\)-th IRS. According to this system model, the received signal by the \(k\)-th user reads as  
\[\mathbf{y}_{k}=\left(\underbrace{\mathbf{H}_{\mathrm{BU}_{k}}}_{\mathrm{LOS \,term}}+\sum_{\ell=1}^{L}\mathbf{H}_{\mathrm{IU}\,k}^{(\ell)}\mathbf{\Theta} ^{(\ell)}\mathbf{H}_{\mathrm{BI}}^{(\ell)}\right)\sum_{u=1}^{K}\mathbf{P}_{u} \mathbf{s}_{u}+\mathbf{n}_{k}, \tag{2}\]  
where \(\mathbf{n}=\left[n_{1},n_{2},\ldots,n_{N_{\mathrm{t}}}\right]^{T}\) represents the complex-valued AWGN and \(\mathbf{P}_{k}\in\mathbb{C}^{N_{\mathrm{r}}\times N_{\mathrm{s},k}}\) represents the precoder employed by the BS to communicate with the \(k\)-th user. At reception, each user obtains its symbols by applying a linear filtering process with \(\mathbf{W}_{k}\in\mathbb{C}^{N_{\mathrm{r}}\times N_{\mathrm{s},k}}\), that is, \(\mathbf{s}_{k}=\mathbf{W}_{k}\mathbf{y}_{k}\).  
Note that the mean square error (MSE)-duality to transform the receive filters in the downlink into the precoders in the uplink can be invoked to describe an uplink scenario through a downlink scenario and vice versa [33; 34].
###### Abstract  
The Inertial Measurement Unit (IMU) is widely used in the monitoring of mining assets. A good example is the Polish underground copper ore mines of KGHM, where research work with the use of the IMU has been carried out for several years. The potential of inertial sensors was ensured by the development of advanced analytics using machine learning methods to support the maintenance management of an extensive machine park and machine manufacturer in adapting various construction elements to mining conditions. The key algorithms developed in the field of inertial data concern: identification of cycles and components of the haulage process operations, identification of dynamic overloads, technical diagnostics of rotating elements, assessment of road conditions (bumps, slopes, damages), assessment of the technical condition of the pavement, assessment of the operator's driving style, and finally the machine location in the mining excavation. One of the key operational contexts, necessary in the development of analytics for underground mining vehicles, is the identification of the turning moment of the machine at the intersection together with the determination of the driving direction and the turn angle. In the case of a mine with a room-and-pillar system, where the excavation system has the Manhattan structure, it is possible to use many simplifications to correctly estimate the machine motion path. The identification of the spatial context and the turning maneuver is of key importance both in the development of the machine location system, but also in multi-dimensional analyzes, including the analysis of dynamic overloads or the assessment of the operator's driving style and work safety. The article presents a comparison of several mathematical models used for the machine turn detection problem, which were trained and tested on the real-life industrial data recorded using IMU during a single working shift of the self-propelled machine.  
mining vehicles; automation; inertial sensors; underground mining; localization techniques +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Sensorial Analysis: Consumer Acceptance of Oven-Dried S. ramossissima  
Aimed at evaluating the acceptance of _S. ramossissima_ as a natural salt substitute, a sensory test with a ketchup formulated with dried _S. ramossissima_ at 2.2% (sample 2.2% DS) and 3.0% _w/w_ (sample 3.0% DS) was carried out with 102 volunteers (69% women and 31% men, aged between 29 and 59 years old). The information obtained in the questionnaires about each consumer is shown in Supplementary Materials Table S1. For this experiment, the oven-dried plant was selected as it presents several advantages over the freeze-dried  
Figure 3: Fresh, oven-dried, and freeze-dried extracts from _S. ramossissima_ exerted antiproliferative effects in a dose-dependent manner after exposition for 24 h. Dose-response curves of the antiproliferative effect induced by the (**a**) freeze-dried extract, (**b**) oven-dried extract, and (**c**) fresh extract. (**d**) EC\({}_{50}\) values of the antiproliferative response induced by the fresh (EC\({}_{50}\) =15.82 \(\pm\) 2.65 mg/mL), oven-dried (EC\({}_{50}\) = 17.56 \(\pm\) 1.05 mg/mL), and freeze-dried (EC\({}_{50}\) = 17.24 \(\pm\) 1.36 mg/mL) extracts in HT29 cells (cell model of CRC) did not show significant differences. Results shown are means of at least three independent experiments performed in triplicates \(\pm\) SD. (*) represent the existence of statistical difference between that dose and the previous one, using one-way ANOVA for multiple comparisons by [PERSON]’s test (_p_ < 0.05).  
sample, including (i) the reduction of alcohols, which could impact the consumer acceptability due to the association with marine odor, and (ii) low processing costs, which is more attractive to food industry applications. Additionally, this sample also showed similar bioactive effects as the freeze-dried plant, indicating that the lower phenolic composition did not impact its antiproliferative and antihypertensive potential.  
Consumers evaluated the sensory characteristics of the two ketchups, including appearance, aroma, flavor, and overall liking. The mean scores for the ketchup sensory attributes are presented in Figure 4.  
Consumer acceptability scores for overall liking, aroma, and flavor were significantly different (\(p>0.05\)) across the two ketchup formulations as the sample 2.2% DS showed higher consumer acceptability compared to sample 3.0% DS. In contrast, the consumer acceptability scores for appearance were not significantly different between the two ketchups. The results of the evaluation indicate a good consumer acceptance for the ketchup with the lower addition of the oven-dried \(S\). _ramossissima_ (sample 2.2% DS) and, consequently, a product with less salt content (0.91% of salt) (see Supplementary Materials Table S2). Although the consumption of ketchup is mostly associated with unhealthy eating, the addition of the dried \(S\). _ramossissima_ as a table salt substitute in ketchup becomes a potential ingredient to be considered, which can be applied for the development of low sodium products.
# Improved Defect Detection Using Adaptive Leaky NLMS Filter in Guided-Wave Testing of Pipelines
## 4 Test Setup
### Experimental Trials  
In the laboratory trials, a Teletest MK4 device [44] was used in order to inspect an 8-inch schedule 40 steel pipe with a length of 6 m. The test tool included a collar of 3 ring transducers with 30 mm ring spacing, located 1.5 m away from the back end of the pipe. Each ring included 24 linearly spaced thickness-shear (d15) piezoelectric transducers. The system provided both excitation and reception of signals in pulse-echo mode with the operating frequency of 20 to 100 kHz. The device can capture the transducers' signals with a sampling frequency of 1 MHz. On the reception side, an internal bandpass filter was applied to filter any frequencies outside the main bandwidth of the excitation bandwidth. The device outputs the time-domain signal received from each ring, as well as the processed signal with the general propagation routine. In this paper, the signals from the rings are extracted in order to apply the algorithm.  
The defects in these trials are located 4.5 m from back end of the pipe (3 m away from the test tool). The defects were introduced by removing the pipe's surface material with a saw. The defect sizes were varying from 3% to 8% CSA with steps of 1% CSA (total of 6 defect sizes). The signal routes of the laboratory trials are shown in Figure 10. As opposed to the FEM test setup, the Defect-2 signal, which was generated by reflection of backward leakage signal from the defect, is now overlapped with the front-end signal and is no longer detectable.  
Figure 10: Torsional mode reception route for laboratory trials.  
Figure 9: Torsional mode reception route in the FEM test cases.