esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
## 2 Methodology, Materials and Methods
### Performance Analysis  
In order to evaluate the performance of the proposed method, the performance of the classifiers are expressed in terms of classification accuracy (Acc), defined as follows [41]:  
\[Accuracy(\%)=\frac{TP+TN}{TP+FN+TN+FP}\times 100 \tag{6}\]  
where \(TP\), \(TN\), \(FP\) and \(FN\) denote true positives, true negatives, false positives and false negatives, respectively.
# A Wideband Eight-Element Antenna with High Isolation for 5G New-Radio Applications
###### Abstract  
A wideband multiple-input multiple-output (MIMO) antenna with high isolation for fifth-generation (5G) new-radio (NR) smartphone employment operating at a range from 3.3-5 GHz is presented in the paper. The wideband antenna is comprised of eight identical radiation units and applies defected ground structure (DGS) to offer higher isolation between antenna units. Each of the antenna elements is formed of a C-shaped microstrip line, two triangle-shaped strips and a rectangular strip. The measurement results present that the transmission coefficients of the presented antenna are smaller than \(-18\) dB in the required wideband, and the envelope correlation coefficient (ECC) values are better than 0.005. The realized gain and efficiency of the designed antenna are about 3.5 dBi and higher than 75%, respectively. The volume of this total antenna is \(150\times 75\times 7\) mm\({}^{3}\), and the measurement of each antenna unit is merely \(6.8\) mm \(\times\) 7 mm \(\times\) 0.8 mm (0.077 \(\lambda\times 0.08\)\(\lambda\times 0.09\)\(\lambda\), \(\lambda\) is the wavelength of 3.4 GHz). The presented MIMO antenna is suitable for 5G new-radio (NR) smartphone applications.  
MIMO; wideband; 5G NR; DGS; high isolation +
Footnote †: journal: _Sci.  
Adopting the technique of self-neutralization between the monopole antenna and the co-located loop antenna to contribute to satisfying improving isolation of larger than 16.5 dB is presented in [9]. In [17], the introduction of the DGS can change the ground current to adjust the resonance and reduce the current coupling between antenna ports. In addition, open-ended slots in [18] act as DGS to obtain a compact size and improve the isolation between antenna elements.  
As an important development technology of mobile communication, 5G is applied to two spectrum scopes of sub-6 GHz and MMW bands [21; 22; 23]. It is well known that the 5G NR ranges contain N77, N78, and N79. In addition, N78 is assigned to European Union (EU); China selects N77 and N79. In [15; 16], the MIMO antenna with eight elements can operate in a wideband that covers 5G NR N77 and N79.  
In this work, the designed eight-element antenna includes the formation of eight same elements and DGS to acquire good performance. Each antenna unit is formed of a C-shaped strip, two triangle-shaped strips and a rectangular strip to attain a wideband of 3.3-5 GHz. Furthermore, the volume of the antenna unit is merely 6.8 \(\times\) 7 \(\times\) 0.8 mm\({}^{3}\), so the wideband with a compact antenna element is realized innovatively. The results of the measurement show that the presented 5G antenna can contain 5G NR N77 (3.3-4.2 GHz) and N79 (4.4-5 GHz). The mutual coupling and values of ECC are smaller than \(-\)18 dB and 0.005 in the desired wideband, respectively. The existence of DGS can skillfully decrease the interaction between antenna units and increase the efficiency of the designed antenna, which is at least 75%.
## 3 Theoretical Foundation and Numerical Model Construction
### Theoretical Foundation
#### 3.1.2 Damage Constitutive Equation  
The deterioration of materials or structures caused by microstructural defects--such as microcracks and voids--under the influence of mechanical loads and environmental factors is referred to as damage [21]. Based on [PERSON]'s strain equivalence principle [22], the damage constitutive relationship for an isotropic continuous medium can be established by introducing a damage variable D and using the nominal stress \(\sigma\):  
\[\varepsilon=\frac{\widetilde{\sigma}}{E}=\frac{\sigma}{(1-D)E} \tag{2}\]  
In the equation, \(\widetilde{\sigma}\) and \(E\) represent the effective stress and the elastic modulus of the damaged material, respectively. The damage variable \(D\) ranges from 0 to 1, where \(D\) = 0 indicates an undamaged state and \(D\) = 1, corresponds to complete damage. Furthermore, the degree of damage can be characterized by relating the elastic modulus of the damaged element, which is expressed as follows:  
\[E=E_{0}(1-D) \tag{3}\]  
Figure 2: Green mudstone and brown shale interbedding details.  
This study refers to the elastic-softening damage model implemented in the RFPA 3D v5.0 software, as illustrated in Figure 3. Tensile stress (or strain) is considered positive, while compressive stress (or strain) is negative. Before the damage threshold is reached, each mesoscopic element is assumed to behave in a purely elastic manner, exhibiting a linear stress-strain relationship. At the macroscopic level, this corresponds to linear elastic behavior. Once the damage threshold is exceeded, the material enters a nonlinear deformation stage, characterized by strain softening--where the stress level decreases with increasing strain until it reaches a constant residual strength [23].  
The Mohr-Coulomb strength criterion can be expressed as follows:  
\[-\sigma_{3}+\sigma_{1}\frac{1+sin\phi}{1-sin\phi}-\sigma_{c}=0 \tag{4}\]  
In the equation, \(\sigma_{c}\) is the uniaxial compressive strength of the rock, \(\phi\) is the internal friction angle, \(\sigma_{1}\) and \(\sigma_{3}\) represent the maximum and minimum principal stresses, respectively.
###### Abstract  
Transpedicular screw instrumentation systems have been increasingly utilized during the fusion of lumbar spine procedures. The superior segment facet joint violation of the pedicle screw is thought to have potential for accelerating symptomatic adjacent-segment pathology (ASP). The purpose of this study was to investigate the relationship between the superior segment facet joint violation by transpedicular screws and the development of ASP. Among all patients who underwent operations involving one- or two-level posterior lumbar arthrodis at the Chonnam National University Hospital from 1992 to 2012, 87 patients were selected for this study. Fifty-six patients were included in the ASP group, and 31 were included in the non-ASP group. We used lumbar three-dimensional computed tomography (CT) to assess the violation of the superior facet joint by a transpedicular screw. The assessment is presented in scores ranging from zero to two, with zero indicating no violation (type I); one point indicating suspected violation (type II); and two points indicating definitely facet joint violation (type III). Facet violation was reported in 31 patients in the ASP group (\(n=56\)), and in 13 patients in the non-ASP group (\(n=31\)). The types of facet joint violation according to our scoring system were as follows: type I, 59 screws (52.7%); type II, 26 screws (23.2%); and type III, 27 screws (24.1%) in the ASP group; and type I, 43 screws (69.4%), type II, 14 screws (22.6 %); and type III, 5 screws (8.0%) in the non-ASP group. The score of facet joint violation in each patient according to our scoring system were as follows: 0 points, 25 patients (44.6%); 1 point, 8 patients (14.3%); 2 points, 4 patients (7.1%); 3 points, 11 patients (19.7%); 4 points, 8 patients (14.3%) in the ASP group; and 0 points, 18 patients (58.1%); 1 point, 4 patients (12.9%); 2 points, 7 patients (22.6%); 3 points, 2 patients (6.4%); 4 points, 0 patients (0%) in the non-ASP group. The mean scores were 1.4 points in the ASP group and 0.8 points in the non-ASP group (\(p<0.05\)). We conclude that the position of the pedicle screw farther away from the facet joint surface can reduce the degeneration of the superior adjacent segment. Therefore, close attention to the screw position during surgery may reduce the rate of superior adjacent-segment pathologies.  
a +
Footnote †: journal: Clinical Medicine  
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# A Cross-Layer Media Access Control Protocol for WBANs
## 2 Related Works
### Route Protocol  
An Energy-Efficient Multihop routing protocol (ESTEEM) is proposed in [19], which aims to improve the network performance based on a multihop routing scheme. Proper relays can be selected to forward data generated by remote sensors to the coordinator. Using the least energy and choosing the shortest distance are desired when selecting the routing path in ESTEEM. A temperature-aware clustering routing method is presented in [20], which adopts a fuzzy logic scheme. Similar sensors can form a cluster to aggregate their data. The clustering method considers multiple factors such as the temperature of the cluster head, the number of similar neighbors, the number of neighbors, energy distribution, and the wireless channel state. A blockchain-based Adaptive Thermal-/Energy-Aware Routing (ATEAR) protocol for WBAN is proposed in [21], where both energy limitation and temperature value are considered when choosing the routing path for remote sensors. An Adaptive Thermal-Aware Routing algorithm for WBAN is proposed in [22] to prevent overheating of implanted biomedical sensor nodes. The proposed scheme can switch routing paths according to the temperature situation of nodes in the network. The AMERP routing protocol [23] is designed based on the Adaptive Moment Estimation (Adam) optimizer-trained deep learning network. The performances of both homogeneous and heterogeneous network configurations can be effectively enhanced. The proposed protocol performs well under various network environments. A two-tier cooperation-based high-reliable and lightweight forwarding (TTCF) scheme is presented in [24], which aims to improve reliability and prolong the network lifetime. Transmitted data are minimized, and relay selection is optimized to reduce energy consumption.
# Influence of Macrophyte and Gut Microbiota on Mercury Contamination in Fish: A Microcosms Study
## 3 Results
### Distribution and Transformation of Isotopic Tracer \({}^{199}\)Hg in Contaminated Units  
\({}^{199}\)IHg and \({}^{199}\)MeHg concentrations were determined in different aquatic compartments at Ti and Tf, and in addition in fish skeletal muscle at Tf by GC-ICP-MS (Table 2). At Ti (after 7 days of spike equilibration and just before fish introduction), the majority of \({}^{199}\)Hg was under its inorganic form in all compartments. However, methylation potentials were measured because of the formation of \({}^{199}\)MeHg (maximum in the \"plant-Hg\" condition: 18% in water and 13% in plant). The highest [\({}^{199}\)IHg] and [\({}^{199}\)MeHg] were observed in plants (maximum in \"plant-Hg\": 2573 ng \({}^{199}\)IHg:g\({}^{-1}\) and 311 ng \({}^{199}\)MeHg:g\({}^{-1}\)), followed by water and sediment. At Tf, after fish introduction, [\({}^{199}\)IHg] and [\({}^{199}\)MeHg] decreased in all compartments (water, sediment, plant) in all conditions compared to results at Ti. However, methylation potentials increased in the plant and sediment compartments only in conditions with sediment (\"plant+sediment\" and \"sediment\"), with the maximum value observed in plant compartment from the \"plant+sediment\" condition (27%), whereas no change was observed in the \"plant\" condition. In fish skeletal muscle, similar [\({}^{199}\)MeHg] levels were measured in three experimental conditions (154 \(\pm\) 40 to 181 \(\pm\) 70 ng \({}^{199}\)MeHg:g\({}^{-1}\)) and higher [\({}^{199}\)IHg] levels were observed in the \"plant\" condition (64 \(\pm\) 25 ng \({}^{199}\)IHg:g\({}^{-1}\)) than in the two others. Methylation potentials in fish were the highest of all aquatic compartments (77%-90%). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Condition** & **Matrix** & **[IHg]** & **[MeHg]** & **\% MeHg** & **n** \\ \hline \multirow{4}{*}{Plant-C} & Water & \(1.1\pm 0.3\) & \(0.1\pm 0.1\) & \(7.7\pm 3.8\) & 4 \\  & Plant & \(38.9\pm 3.7\) & \(6.2\pm 3.6\) & \(11.3\pm 5.1\) & 4 \\  & Fish & \(345.2\pm 127.4\) & \(1152.6\pm 136.4\) & \(77.3\pm 7.9\) & 6 \\ \hline \multirow{4}{*}{Plant+sediment-C} & Water & \(0.8\pm 0.1\) & \(0.1\pm 0\) & \(13.4\pm 1.3\) & 6 \\  & Sediment & \(5.7\pm 1.2\) & \(0.1\pm 0\) & \(1.6\pm 0.8\) & 4 \\  & Plant & \(24.7\pm 7.3\) & \(1.4\pm 0.4\) & \(5.6\pm 0.5\) & 6 \\  & Fish & \(220.3\pm 24.4\) & \(10832.2\pm 68.8\) & \(83.2\pm 1.3\) & 9 \\ \hline \multirow{4}{*}{Sediment-C} & Water & \(0.9\pm 0.2\) & \(0.1\pm 0\) & \(21.3\pm 10\) & 6 \\  & Sediment & \(5\pm 0.4\) & \(0.1\pm 0\) & \(1.1\pm 0.2\) & 5 \\  & Fish & \(174.5\pm 15\) & \(948.1\pm 63.9\) & \(84.3\pm 1.2\) & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Mercury speciation (ng:g\({}^{-1}\) dry weight _dw_) and methylmercury proportion (%) in different aquatic compartments from control units (-C; background concentrations from field). Measurements were performed at the Ti and Tf of the experiment and were pooled in this table.
## 4 Discussion  
It was supposed that it would not to be possible to distinguish between two locations while performing the identification process from a maximum possible reading distance, which should be up to 8 m while using the maximum allowed operating power of 2 W ERP according to ETSI EN 302,208 direction.  
The experiment proved that even at maximum reading distance, the two tags can be distinguished, although according to the trends showed in Figure 12, there could be quite a close position behind the maximum reading distance where the measured data sets would probably merge. For this reason, we plan to use battery assisted passive tags (BAP) with higher reading distances to find that position in the follow-up research.  
Figure 11: Box-and-whisker plot for reading distance 8 m.  
Figure 10: Histogram for reading distance 8 m.  
Maximum possible reading distance of the desired BAP RFID tag Confide Survivor B, which should be up to 60 m while using the maximum allowed operating power, is considered to be fully sufficient to find the merge distance.
# Micro-Location Temperature Prediction Leveraging Deep Learning Approaches
## 2 Material and Methods
### Weather Station
#### 2.1.2 Rain Gauge  
It is recommended that the rainfall sensor is placed in a location where there are no obstructions that would prevent rain from falling into the sensor container. Wind is a major problem for the accuracy of the rainfall measurement, because it can simply carry a large proportion of the rainfall past the opening, as it carries the rain at a more oblique angle. This can lead to a large error in the rainfall measurement. However, a heated rain gauge can be used to measure the height of the snow cover, where snowflakes turn into water droplets upon contact with the container. It is also important that the rain gauge is very well fixed and that, in the event of a strong wind, there is no vibration to move the balance in the container and thus contribute to erroneous precipitation measurements. The height of the installation in our case is two meters above the ground, and the sensor is placed on a well-fixed pole with individual additional reinforcements [13] (Figure 3). The accuracy of the sensor can be seen in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Measuring Characteristics** & **Values** \\ \hline Manufacturer & HOBO rain gauge \\ Model & RG3 \\ Resolution & 0.10 \({}^{\circ}\)C at 25 \({}^{\circ}\)C (0.18 \({}^{\circ}\)F at 77 \({}^{\circ}\)F) \\ Calibration & Requires annual calibration \\ Measurement range & \(-20\)\({}^{\circ}\)C to 70 \({}^{\circ}\)C (\(-4\)\({}^{\circ}\)F to 158 \({}^{\circ}\)F) \\ Accuracy & \(\pm 0.47\)\({}^{\circ}\)C at 25 \({}^{\circ}\)C (\(\pm 0.85\)\({}^{\circ}\)F at 77 \({}^{\circ}\)F) \\ Max. 4000 tipping points/logging interval & 1\% error up to 127 mm/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Characteristics of rainfall measurements with tipping points [14].  
Figure 3: Rain gauge sensor.
# An Efficient Multistage Approach for Blind Source Separation of Noisy Convolutive Speech Mixture
## 4 Results and Discussion
### Energy Distribution of Observed Mixtures  
In a noisy real-time environment, the non-stationary nature of the observed convolutive mixture contains high- as well as low-energy components, as shown in Figures 7 and 8.  
Figure 4: Change in SDR for convolutive pink noisy mixture with variable SNR for the 2 nd proposed model.  
Figure 5: Change in SDR for convolutive pink noisy mixture with variable RT for the 1 st proposed model.  
Figure 6: Change in SDR for convolutive pink noisy mixture with variable RT in the 2 nd proposed model.  
Figure 7 shows the normalized energy of different frequency bins contain in the observed convolutive mixture 1. Figure 8 reflects the energy distribution of convolutive mixture 2 for various frequency blocks.
## 6 Results and Discussion
### Case 3. Partial Shading Condition  
Case three also represents another PS condition and their irradiance pattern is illustrated in Table 2. The value of GM is tracked in this case of PS. The Value of GM tracked is 781 W. In case 2 of partial shading, a \(4\times 1\) PV array is used. The power of the proposed hybrid technique is compared with other DFO, PSO, CS, FA ACO, and InC techniques. Among all the techniques PSO has the highest oscillation and ACO has no oscillation but they cannot find the GM and are stuck on LM. Power loss occurs due to oscillation in the PV system.  
The Power of DFO, PSO, CS, FA, InC, ACO, and hybrid techniques are 780 W, 779 W, 770.2 W, 769 W, 712 W, 768 W, and 780.4 W, respectively. InC algorithm shows the lowest efficiency of 91.17 among all the techniques and it has the 2 nd lowest convergence time of 0.22 s. The drawback of the InC technique is that it is stuck on LM which results in loss of  
Figure 11: Power, Current and Voltage comparison of all techniques for Case 1 and Case 2.  
power. InC technique with variable step size has the lowest settling and convergence time that's why the proposed hybrid technique uses two stages.  
The convergence time of PSO, DFO, InC, InC-DFO, CS, FA and ACO techniques are 1.10 s, 0.53 s, 0.22 s, 0.20 s, 1.41 s, 1.01 s, and 0.31 s, respectively. When comparing all the techniques it is observed that InC has the least convergence time. The settling time of ACO, FA, CS, hybrid, InC, DFO, and PSO are 0.35 s, 1.42 s, 1.93 s, 0.89 s, 0.22 s, 0.73 s, and 1.65 s, respectively. It is noted that the proposed technique presents the lowest settling time that decreases the power losses and enhances the efficiency of the PV energy systems. CS has the highest settling time that may result in loss of power.
# Cooperative Downlink Listening for Low-Power Long-Range Wide-Area Network
## 5 Performance Evaluations
### Traffic Analysis  
One of the critical features of the CDL is that by using a shared downlink slot, the network server is able to process broadcast messages transmitted to each ED. In particular, the time synchronization message of this broadcast message must be used periodically to synchronize all the nodes. Thus, in this experiment, each node is synchronized from the network server once a day, and the total amount of packets (i.e., traffic) generated in the network is measured when the network is maintained for 30 days. For more extended experiments, we measured the number of packets generated by increasing the number of nodes from 1 to 5, and based on this data, we calculated the number of packets when the number of nodes was increased from 10 to 100. In addition to the synchronization message once a day, each node sends its sensing data to the network server once an hour and uses a model that receives ACK for it. Figure 11 shows the total number of packets generated during a month in LoRa and CDL. The results show that when the number of nodes is small, the amount of traffic of LoRa standard and CDL does not show much influence on broadcasting, so there is no great difference in the total amount of traffic, but the amount of traffic is remarkable as the number of nodes increases. This is because the CDL broadcast function has been effective in reducing the traffic. In particular, in LoRa, a large number of nodes are accommodated in a single gateway due to a long communication distance. However, the larger the network, the more the traffic reduction effect becomes.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline _Current on Tx_ & 36 mA \\ _Current on Rx_ & 11 mA \\ _Current on sleep_ & 2 uA \\ _RX1 window duration_ & 1 s \\ _RX2 window duration_ & 1 s \\ _TX duration_ & 1 s \\ _Supply Voltage_ & 3.3 V \\ _CDL period_ & 1 h \\ \hline \end{tabular}
\end{table}
Table 2: Experimental environments.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
## 3 Failure Type Prediction Method Using Physical Indices and Data Features
### Data Generation  
As mentioned in Section 2, the main failure causes of the high-speed solenoid valve in the actual work process are coil degradation and drying up of lubricant. To collect the driven current signal of high-speed solenoid valve under the influence of these two failure factors in different life stages, we built an accelerated aging test platform.  
The physical figure of the test platform is shown in Figure 4. The braking control unit outputs the Pulse Width Modulation (PWM) control signal to make the high-speed solenoid valve operate periodically in 30 ms. The data acquisition card will store the current signal measured by the current transmitter in the industrial computer. The data acquisition period is 5 min, the acquisition time is 1 s, and the sampling frequency is 100,000 Hz  
The current data of the high-speed solenoid valve is collected every five minutes. Regarding every five minutes as a cycle, the generated data set consists of 60 solenoid valves, each with more than 3000 cycles.  
The driven current curve of the high-speed solenoid valve at the 500 th, 1500 th, and 2500 th cycles are shown in Figure 5. From Figure 5, we can see that as the cycle increases, the stable current increases. In contrast, the action time and the current drop reduce. It is obvious that, as the number of operations increases, the coil has a certain degree of aging and the lubricant has dried up to some extent while we cannot directly judge the change trend of trigger current and reaction time because it is the result of combined action of these two failure factors.  
Figure 3: Scheme of proposed failure type prediction using physical indices and data features.  
Figure 4: Accelerated aging test platform: (**a**) the accelerated aging test platform of the high-speed solenoid valve and (**b**) the generalized representation of the test platform, where the green lines are the signal flow and the blue lines are the air flow.  
Figure 5: Driven current curves of the high-speed solenoid valve at different life cycles.