esa-sceva/satcom-chunk-collection huggingface dataset:
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 3 Results  
This research aimed to evaluate the similarity ratio of three different rocking systems by predicting their finite rocking rotations and rocking overturning under natural ground motions. This goal was accomplished by employing two regression techniques: fuzzy linear regression and a hybrid method with combined logistic regression and fuzzy logic.  
Three typical rocking structural systems were subjected to 5000 ground motion records [81]. The ground motion records database encompasses a comprehensive collection of seismic data, capturing a diverse range of scenarios. With \(\mathrm{V}_{s,30}\) values spanning from 100 m/s to 2000 m/s, corresponding to soil classes ranging from A to D, the database accounts for various soil conditions. Moreover, \(\mathrm{R}_{\mathrm{rupt}}\) ranges from 0.07 km to 185 km, while the magnitude \(\mathrm{M}_{\mathrm{w}}\) ranges from 3.2 to 7.9. The database includes records for all faulting types, providing a holistic view of seismic activity. A comprehensive collection of 18 seismic parameters was carefully chosen to characterize the seismic intensity of an earthquake event. These parameters were extracted from the recorded seismic accelerograms. Among the examined IMs, the acceleration-based ones determine the uplift of the rocking blocks, while the velocity and frequency-based IMs are highly correlated with rocking overturn [23; 24; 25]. It should be mentioned that elastic-response-spectrum-based IMs are not included since rocking motion cannot be described by equivalent elastic oscillators [45]. As long as rocking overturning implies an infinite rocking rotation, the response data has to be treated separately. In the absence of a rocking overturn, the finite damage index was predicted using fuzzy linear regression models. However, the rocking overturn prediction has to be treated as a classification problem due to the discontinuity of the damage index. To this end, logistic regression combined with fuzzy logic was employed. It has to be mentioned that both the IMs and response values are given on a logarithmic scale.
## 5 In-Band Outage Probability Factor  
If large-bandwidth (wideband) radio-links are designed, such those in satellite communications with spread spectrum or similar methods, we should know how much the outage probability changes within the large bandwidth, for a fixed power margin \(A\), to assess possible signal linear distortions.  
We can estimate the change expected in the bandwidth by studying the in-band probability factor \(I_{P}\) (GHz\({}^{-1}\)) defined as:  
\[I_{P}=[(d\rho_{m}(f))/df)]/(\rho_{m}(f)) \tag{5}\]  
In Equation (5), \(d\rho_{m}(f)/df\) is the frequency derivative of Equation (3) and \(\rho_{m}(f)\) is given by Equation (3). To estimate from Equation (5) the value of \(I_{p}\) in a bandwidth \(B_{w}\) (GHz) centered in \(f=f_{c}\) it is sufficient to multiply the right-hand side approximately for \(B_{w}\); therefore, Equation (4) numerically refers to \(B_{w}=1\) GHz, hence, in finite terms it gives the ratio \(I_{p}=\Delta\rho_{m}(f_{c})/\rho_{m}(f_{c})\) in the 1-GHz bandwidth around \(f_{c}\). Therefore, at the frequency \(f=f_{c}+0.5\) GHz the outage is greater because the same rain attenuation is exceeded with higher probability than at the lower frequency \(f=f_{c}-0.5\) GHz. In other words, to avoid outage in all bandwidth, the power margin must be equal to the rain attenuation measured at \(f=f_{c}+0.5\) GHz, not at \(f=f_{c}\).  
Figure 8 shows \(I_{P}\) for the indicated sites. It is evident that the distortion due to the different in-band outage probability--because of a fixed power margin in the AWGN channel calculated at \(f=f_{c}\) --can be significantly different from site to site, and also at a site from frequency to frequency. For Spino d'Adda, at \(f=40\) GHz, continuing the example of Section 3, \(I_{p}=0.044\); therefore, the outage time difference between the extreme frequencies in \(B_{w}=1\) GHz bandwidth is (0.044) \(\times 70=3.1\) hours in an average year. To a first approximation, this time difference is proportional to \(B_{w}\).  
In general, higher frequencies show lower values because a fixed rain attenuation is exceeded with very near probabilities than at lower frequencies. This is evident for Tampa (see Figures 1-4), which gives the lowest curve in Figure 8. The opposite behavior is found when the fixed rain attenuation at higher frequencies is exceeded with different probability than at lower frequencies, as it occurs for Madrid (see Figures 1-4), which gives the highest values in Figure 8. In other words, the slopes of the probability distribution, as those shown in Figures 1-4--i.e., the probability density function of rain attenuation and, in the zenith paths here considered, also of rain rate--affect this kind of distortion more than other parameters.
# Integrated Module Antenna for Automotive UWB Application
## 2 Proposed Antenna Design
### Pattern Improvements with Modified Ground Stubs and Parasitic Radiators
Additionally, the patch array shows 5.11 dBi gain and 91.2\({}^{\circ}\) beamwdith in xz-plane. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline \(W_{1}\) & 17.5 mm & \(L_{1}\) & 0.75 mm \\ \(W_{2}\) & 17.25 mm & \(L_{2}\) & 7.88 mm \\ \(W_{3}\) & 17.5 mm & \(L_{3}\) & 1.37 mm \\ \(W_{4}\) & 16.55 mm & \(L_{4}\) & 0.65 mm \\ \(W_{5}\) & 16.8 mm & \(L_{5}\) & 0.74 mm \\ \(W_{6}\) & 17.5 mm & \(L_{6}\) & 0.74 mm \\ \(W_{7}\) & 0.74 mm & \(L_{7}\) & 0.57 mm \\ \(W_{8}\) & 15 mm & \(L_{8}\) & 1 mm \\ \(W_{9}\) & 2 mm & \(L_{9}\) & 1 mm \\ \(W_{10}\) & 2.5 mm & \(L_{10}\) & 0.5 mm \\ \(W_{11}\) & 3 mm & \(L_{11}\) & 1 mm \\ \(W_{12}\) & 1.5 mm & \(L_{12}\) & 8 mm \\ \(W_{13}\) & 0.4 mm & \(L_{13}\) & 15.3 mm \\ \(W_{14}\) & 5.35 mm & \(L_{14}\) & 1.07 mm \\ \(W_{15}\) & 2.5 mm & \(L_{15}\) & 5 mm \\ \(W_{16}\) & 0.5 mm & \(L_{16}\) & 6 mm \\ \(W_{17}\) & 0.4 mm & \(g\) & 0.4 mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Design parameters of the proposed antenna. Figure 11: Radiation pattern of the proposed antenna: monopole (**a**) yz-plane at 6.5 GHz, (**b**) yz-plane at 8.0 GHz, patch array (**c**) xz-plane at 8.0 GHz. Figure 10: Return loss and isolation of the proposed antenna.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods  
### Study Area  
Our general study region covers coal-fired power plants in the United States, with two testing scenarios set up for this study. As our benchmark site, we used a single power plant (first scenario) in rural Alabama (Figure 1). It is about 66 miles south of Mobile, AL. The plant was chosen as it is far away from cities and other emission sources that could introduce noise to the remote sensing observations. Additionally, studying isolated power plants gives us a clear idea of the impact to the population or natural environments.  
The second scenario set of experiments focused on combining all coal-fired power plants throughout the U.S. This was set as a goal to study the performance of machine learning models in this context and their ability to generalize prediction accuracy for NO\({}_{2}\). Obtaining a large-scale emissions prediction for all the power plants gives an additional perspective on the impact of pollutant emissions on the climate. A machine learning model that predicts regardless of location is as beneficial as location-based predictions.  
temperature at 2 and 10 meters, wind components at 2, 10, and 50 meters, surface pressure, and total precipitable water. This is coarser data, so only one grid point was used for each power plant. This data was utilized as input for the study's first scenario experiments to test if meteorological features influenced the performance of the ML models.  
MERRA-2 has several different types of data that were used, specifically surface temperature, bias-corrected precipitation, cloud fraction, and surface wind speed. The data collected from MERRA-2 were provided on a horizontal grid. The grid has 576 points in the longitudinal direction and 361 points in the latitudinal direction corresponding to a resolution of 0.625\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). MERRA-2 validation was compared to ground measurements; strong correlation and low errors were found for air temperatures with a 0.99 correlation coefficient observed and 0.81 and 0.99 for wind speed ([PERSON] et al. [30], 2021).
## 1 Introduction
We consider a single UAV to serve ground users taking advantage of the mobility of the UAV. In order to maximize network capacity with the consideration of fairness, trajectory and scheduling of the mobile UAV aerial base station are jointly optimized. In this paper, we propose a novel approach for optimizing the trajectory and scheduling of the UAV to provide wireless to service ground users while maximizing network capacity with the consideration of fairness. We consider the business requirements for delay tolerance, so the number of slots divided over a hovering period is equal to the number of users. So that all users in the area can be served, we considered a UAV-BS hovering with a circular trajectory. We divided the period of the UAV-BS into time slots according to the number of users, and each user only uses one slot. We take the square of the difference between the average signal-to-noise ratio of each user within a time slot and the maximal signal-to-noise ratio available to the user as the metric to measure fairness. Our goal is to maximize fairness by jointly optimizing the hovering radius of the UAV-BS and the users schedule. The formulated problem is a mixed-integer non-convex optimization problem that is challenging to solve. In addition, we adopted the ant colony algorithm and the genetic algorithm to optimize the above two parameters. The main contribution of this paper as follows: Firstly, we considered a UAV-BS hovering with a circular trajectory so that all users in the area can be served. Joint trajectory and scheduling optimization are considered to maximize the network capacity with the consideration of fairness. Secondly, an alternate iteration approach that contains ant colony algorithm and genetic algorithm are proposed to solve this problem. Finally, the simulation results evaluate our algorithm.
## 2 Study Area and Data  
### Study Area  
Our study area was located in the southeastern Tibetan Plateau, which includes parts of two sub-basins: Salween Basin and Brahmaputra Basin (corresponding to the area of one ascending and one descending Sentinel-1A/1B frame; see Figure 1). The climate of this region is mainly influenced by two atmospheric circulation systems [22]. The dry season period is from November to April, with little precipitation and cold climate provided by the southern branch of the mid-latitude westerlies [23]. May to October is the wet season; during this time, the westerlies become weak and the Indian monsoon's penetrate the region, indicating the beginning of rainy season. The heavy rainfall in the wet season accounts for 60-90% of the annual total [24]. The terrain and geomorphology of southeastern Tibet are rather complex and diverse, and form a landscape of alpine canyons. Monsoonal temperate glaciers have developed over a large range across the region. These monsoonal temperate glaciers are more sensitive to climate warming and abundant precipitation, and are thinning and melting faster than continental glaciers. Affected by the combined interactions from local climate conditions and higher ice-layer temperatures, glacial lakes in this region are much larger and deeper, and generally shown as navy blue in color [9; 20].
# A Deep Learning-Based Approach for the Identification of a Multi-Parameter BWBN Model
## 4 Results and Discussion
### Discussion  
Since the training dataset uses estimated parameters with NSGA-II, the effect of errors introduced by this genetic algorithm will be present in the neural network predictions in addition to the network's own errors. To eliminate the superposition effect of the two errors, the method of parallel connection of multiple evaluation functions is also used in the construction of a neural network in this paper. It can be seen from the identification example results in Figure 5 that for the specimens with ideal results, such as S1, S5 and S11, good identification accuracy is achieved, whereas for the specimens with unsatisfactory test results, these cause a large noise due to improper test process or operation, such as S6, S12 and S13, and the predicted results are within the acceptable error range. For reducing error propagation, the R\({}^{2}\) and adjR\({}^{2}\) values of 12 parameters of the BWBN model are given in Figure 7. The abscissa of Figure 7 is the prediction value, and the ordinate is the test value. The closer the prediction point is to the blue line, the better the prediction effect will be. The x-axis is the predicted value of the present model, and the y-axis is the predicted value of the traditional NSGA-II algorithm. It can be seen that the correlation coefficient R\({}^{2}\) and adjR\({}^{2}\) are higher than 0.8, which indicates that the deep learning model in this paper is feasible. The corresponding training loss and verification loss are shown in the Figure 8. The abscissa of Figure 8 is the epochs of iterations, and the ordinate is the loss function value. It can be seen that the decline of the loss function tends to be gentle after about 20 generations, which means that the recognition results basically meet the error conditions.  
Figure 6: Comparison of the BWBN result with experimental result.  
Figure 8: Loss of train set and test set.  
Figure 7: Regression index of test set.
###### Abstract  
Recently, Internet of Things (IoT) deployments have shown their potential for aiding the realisation of the Sustainable Development Goals (SDGs). Concerns regarding how the IoT can specifically drive SDGs 6, 11 and 9 in developing countries have been raised with respect to the challenges of deploying licensed and unlicensed low-power wide area network (LPWAN) IoT technologies and their opportunities for IoT consumers and service providers. With IoT infrastructure and protocols being ubiquitous and each being proposed for different SDGs, we review and compare the various performance characteristics of LoRaWAN and NB-IoT networks. From the performance analysis of our networks, NB-IoT, one of the standardised promising cellular IoT solutions for developing countries, is more expensive and less energy-efficient than LoRaWAN. Utilising the same user equipment (UE), NB-IoT consumed an excess of 2 mAh of power for joining the network and 1.7 mAh more for a 44-byte uplink message compared to LoRaWAN. However, NB-IoT has the advantage of reliably and securely delivering higher network connection capacity in IoT use cases, leveraging existing cellular infrastructure. With a maximum throughput of 264 pps at 837 ms measured latency, NB-IoT outperformed LoRaWAN and proved robust for machine-type communications. These findings will help IoT consumers and service providers understand the performance differences and deployment challenges of NB-IoT and LoRaWAN and establish new research directions to tackle IoT issues in developing countries. With Nigeria as a case study, for consumers and organisations at a crossroads in their long-term deployment decisions, the proposed LPWAN integrated architecture is an example of the deployment opportunities for consumer and industrial IoT applications in developing countries.  
Keywords:cellular networks; developing countries; Internet of Things; licensed; Narrowband Internet of Things; rural areas; test network; unlicensed +
Footnote †: journal: 2021
# MUXER--A New Equipment for Energy Saving in Ethernet
## Materials and Methods
### MUXER's Communication
#### 3.4.2 MUXER and Switches
Figure 9 provides an example of this mechanism, (I1, E1) is the ports that is connected to switch S1, and (I2, E2), (I3, E3) are also belong to another switch S2. In Figure 9a, I1 is connected with E1, I2 is connected with E2, and I3 is connected with E3. After that, if MUXER turned off switch S1, it interrupted the main circuit of I1 to E1, and it opens the connected of another circuit from I1 to E2, which is described in Figure 9b. In case of I2, I3, if MUXER wants to change their connections to S1, its actions which are performed on I2, I3 are also same with I1. Figure 7: Flow of MUXER to turn off switches. Figure 8: How MUXER can switch data signal from switch 1 to switch 2. (**a**) Before MUXER switches some circuits; (**b**) After MUXER switches some circuits. Figure 9: Structure of electricity circuits to switch data signals from an egress port to another egress port. (**a**) Switch S1 (I1 and E1) is in ON state; (**b**) MUXER turn of switch S1 (I1 and E1) and S1 goes in OFF state to open the connection of another circuit from I1 to E2 (no use of E1 now). In case of 4.B4, MUXER controls the power functions of the switches in a group that it is handling, hence MUXER must have a function to turn on the turned-off switch when the network environment return to normal status or high loading traffic. There is one condition to turn on the switches that MUXER is controlling. That is the number of data signals that come to MUXER must be larger than a defined threshold. In here, it means the number of ingress ports are receiving data signals. After MUXER recognizes that the above condition is satisfied, MUXER generates turn-on control signals to its correspondence power ports. In the similar way with the flow that turns off switches, power control port transmits power control signals to power functions of switches, and wakes up them.
# Simulation Research on the Effects of Air Gaps and Ambient Temperature on a 27.5 kV Power Cable
## 1 Introduction
By the finite element method, scholars [30; 31] analyzed changes in cables and surrounding soil temperature increases under different laying conditions, evaluated the electromagnetic environment near power cables and quantitatively evaluated the effect of magnetic shielding. The problem of electric field distortion and temperature increases in power cables can be well studied with the finite element method. However, a cable runs in the complex environment of the laying site and will inevitably face the influence of electrical stress and thermal stress, resulting in air gap and insulation deterioration, which poses a threat to the safety of a high-speed railway traction power supply system. At present, studies on the internal damage process of power cables under different air gap defects and different ambient temperatures are still lacking. However, this study evaluated the impact of air gap and ambient temperature on power cables by analyzing the results of a electrothermal coupling model under normal and different cable damage conditions, improved the damage mechanism of power cables and studied how to reduce the damage to power cables. Improving the life of power cables is of great help. In order to ensure the safe operation of an electrified high-speed railway, it is urgent to research the influence of air gap defects of a 27.5 kV power cable with more special and harsh working conditions. In this paper, an electric-thermal field coupling model was built and simulated under different working conditions, including normal operation and cable damage. The distributions of the electric field and thermal field under different conditions of cables were explored. It was found that the cable has poor heat resistance and easily produces air gaps. The influence of various temperature differences on a damaged air gap inside a cable was studied. Finally, the damage mechanism of partial discharge of an air gap, leading to a further increase in the cable air gap, is proven.
# Bearing Behavior of Cold-Formed Thick-Walled Steel Plates with a Single Bolt
## 1 Introduction
There are very few reported research studies in the open literature on the bearing behavior of cold-formed thick-walled steel bolted connections. For example, [PERSON] et al. [9] conducted experimental research and design calculations on cold-formed thick-walled steel with square and rectangular hollow sections. Their results indicate that both of the strengths predicted by the Australian/New Zealand standard using the material properties obtained from the flat coupon tests and the strengths forecasted by the Chinese standard, based on the mean design yield stress of the full section, are quite conservative. [PERSON] et al. [17] investigated the true stress-strain curves of the flat and corner regions of the cold-formed channel section. Their study showed that the tensile secondary-hardening phenomenon in the plastic strain stage was observed in the true stress-strain curves of the flat coupons, which exhibited a significant improvement in true ultimate strength. This paper reports the results of an experimental and analytical study on the behavior of cold-formed and thick-walled plates connected by a single bolt under double shear. The effects of the end/edge distance on the bolt bearing behavior were investigated. The bearing resistance values predicted by the (modified) ANSI/AISC 360 [6] and the EN1993-1-8 [7] were compared with that of the measurements.