esa-sceva/satcom-chunk-collection huggingface dataset:
# Networked Unmanned Aerial Vehicles for Surveillance and Monitoring: A Survey
## 3 Traffic Monitoring
### UAV Video-Processing Techniques  
Regarding traffic information processing, some research focuses on processing the videos taken by UAVs when they hover at some fixed positions for purposes of road detection [41; 42], vehicle detection and tracking [43; 44], and traffic-parameter extraction [45; 46]. In particular, the convolutional neural network (CNN) has been used to estimate traffic parameters from videos captured by a UAV [47], and Reference [48] investigated vehicle speed estimation based on two consecutive frames of a UAV's video. Though these approaches achieve good performance, they are only suitable for the case where the UAV can maintain its attitude. When there is some disturbance such as wind gust, the aforementioned methods may not perform well. To this end, researchers have studied the ego-motion issue, which may be generated by an intentional effect from a remote pilot or an external factor such as wind. Two main approaches have been seen in the literature. The first one is an intuitive method called image registration, which targets turning the moving background into a fixed background [49; 50]. Then, many traditional methods on background subtraction can be applied. The second one is the optical flow-based ego-motion estimation [51; 52]. Optical flow is an effective method to extract motion patterns from videos, and it is often combined with unsupervised learning to estimate the UAV ego-motion. We refer readers to recent surveys for more information about video-processing techniques [53; 54].
## 4 Model Development
### Model Validation  
Model validation is the process of applying the developed model to new datasets to assess its performance and accuracy with new data. Eight new datasets that represented different school models from different cities were used. As can be seen from Table 13 and Figure 12, the developed model had excellent accuracy for these cases. The third column in the table shows the error percentage. It is clear that the model provided an excellent prediction and the percentage of error ranged from \(-3.10\%\) to less than \(+11\%\). It was noted that \(87.5\%\) of the predicted values had less than \(10\%\) error, showing the developed model could predict the energy consumption well. The achieved accuracy is in accordance with the fifth-class estimate issued by \"Association for the Advancement of Cost Engineering\" (ACCE) [32].  
Moreover, a sensitivity analysis was applied to identify the change of the model output as a function of a single input variable, while all the other variables were unchanged. Figure 13 depicts how the input variables varied with the energy consumption (output), while the other input variables were fixed. As can be seen, the total roof area increased exponentially for high values of energy consumption, while the total built area for all floors decreased for high values of energy consumption. Here, it seems that the number of AC units was a function of the number of classrooms rather than a function of the school area. Furthermore, in Figure 13, the number of floors and the number of classes had an inverse relationship with energy consumption. This was as a result of the roof area is the most important  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Predicted Energy Consumption (kWh/Year)** & **Actual Energy Consumption (kWh/Year)** & **Error \%** \\ \hline
480,623.86 & 481,799.00 & \(-0.24\) \\
406,777.85 & 412,194.00 & \(-1.31\) \\
399,430.36 & 412,194.00 & \(-3.10\) \\
362,324.47 & 343,587.00 & \(5.45\) \\
197,452.39 & 198,800.00 & \(-0.68\) \\
370,902.12 & 343,587.00 & \(7.95\) \\
255,557.65 & 230,180.00 & \(11.03\) \\
673,892.46 & 671,537.00 & \(0.35\) \\ \hline \hline \end{tabular}
\end{table}
Table 13: Model validation.  
Figure 11: Two-line comparison for the model testing.  
factor for energy consumption. For example, a total school area of 3000 sqm that is divided into three floors (1000 sqm each) is much better than if it is divided into two floors (1500 sqm each); therefore, a multi-story school building is better than a one-floor school. Furthermore, the optimal total roof area was 1000 sqm, while the mean floor area of the existing schools was 1524.47 sqm. Note that the point in gray color represents the reference point.
# Broadband Millimeter-Wave Front-End Module Design Considerations in FD-SOI CMOS vs. GaN HEMTs
###### Abstract  
Millimeter-wave (mm-Wave) phased array systems need to meet the transmitter (Tx) equivalent isotropic radiated power (EIRP) requirement, and that depends mainly on the design of two key sub-components: (1) the antenna array and (2) the Tx power amplifier (PA) in the front-end-modules (FEMs). Simulations using an electromagnetic (EM) solver carried out in Cadence AWR with AXIEM suggest that for two uniform square patch antenna arrays at 24 GHz, the 4 element array has ~6 dB lower antenna gain and twice the half power beam width (HPBW) compared to the 16 element array. We also present measurements and post-layout parasitic-extracted (PEX) EM simulation data taken on two broadband mm-Wave PAs designed in our lab that cover the key portions of the fifth-generation (5G) FR2-band (i.e., 24.25-52.6 GHz) that lies between the super-high-frequency (SHF, i.e., 3-30 GHz) band and the extremely-high-frequency (EHF, i.e., 30-300 GHz) band: one designed in a 22 nm fully depleted silicon on insulator (FD-SOI) CMOS process, and the other in an advanced 40 nm Gallium Nitride (GaN) high-electron-mobility transistor (HEMT) process. The FD-SOI PA achieves saturated output power (\(P_{OUT,SAT}\)) of ~14 dBm and peak power-added efficiency (PAE) of ~20% with ~14 dB of gain and 3 dB bandwidth (BW) from ~19.1 to 46.5 GHz in measurement, while the GaN PA achieves measured \(P_{OUT,SAT}\) of ~24 dBm and peak PAE of ~20% with ~20 dB gain and 3 dB BW from ~19.9 to 35.2 GHz. The PAs' measured data are in good agreement with the PEX EM simulated data, and 3 rd Watt-level GaN PA design data are also presented, but with simulated PEX EM data only. Assuming each antenna element will be driven by one FEM and each phased array targets the same 65 dBm EIRP, millimeter wave (mm-Wave) antenna arrays using the Watt-level GaN PAs and FEMs are expected to achieve roughly 2\(\times\) wider HPBW with 4\(\times\) reduction in the array size compared with the arrays using Si FEMs, which shall alleviate the thorny mm-Wave line-of-sight (LOS)-blocking problems significantly.  
Keywords:fifth generation (5G); 6G; front-end module (FEM); high-electron-mobility-transistor (HEMT); half power beam width (HPBW); millimeter wave (mm-Wave); power amplifier (PA) +
Footnote †: journal: applied sciences
# On-Demand Maintenance Method Using Fault Prediction to Reduce Elevator Entrapment
## 3 Prediction of Elevator Entrapment Incidents
### Data Sources and Description
In the elevator risk assessment framework, the influence weight of each input parameter on the input layer is automatically determined through data-driven model training, generating a parameter weight distribution without manual intervention. These weighted parameters undergo transformations through complex neural network computations to derive the final entrapment probability values for individual elevators. It should be emphasized that a single parameter's weight cannot be directly equated with its proportional impact on the output probability, owing to the sophisticated nonlinear transformations inherent in the neural network architecture. However, parameters with higher weight contributions generally demonstrate greater influence on the probability output. By selecting parameters exceeding the 0.5% weight threshold (while excluding those with lower weights that demonstrate negligible impact), the retained parameter subset accounts for 87.454% of the total predictive weight.
## 5 Discussion
### GCPs Limitation and Co-Georeferencing  
A limitation of our method is the time and manpower required to use GCPs for georeferencing the reconstructed 3D models. In addition, our survey sites benefitted from the availability of nearby reference stations whose presence is not guaranteed in the majority of remote Arctic locations. Nevertheless, other positioning techniques could be employed such as real-time kinematic (RTK) surveys with a local master station, for example. RTK is switter compared to static GNSS surveys, and the rapid advancement of the technology is achieving comparable accuracies with less effort involved.  
Another interesting option is the use of on-board dual frequency GNSS receivers on the UAS sensor load that are capable of providing sensor position and orientation for each frame for direct sensor orientation (DSO) [46; 47; 48]. In DSO, the exterior orientation parameters for each image are computed by processing the on-board GNSS and the Inertial Measurement Unit (IMU) data, thus bypassing the deployment and acquisition of GCPs. Such techniques are not yet capable of providing the same accuracy as GNSS positioning in static, ground-based surveys (centimeter to millimeter scale), but it is expected that these technological challenges will be surpassed in the future and GCPs will not be required anymore.  
Finally, our results confirm that co-georeferencing can provide improved snow depth estimates. Consequently, common visible points can be acquired only once and then repeatedly used for georeferencing the time-variable snow surfaces. GCP surveys would only need to be performed once for the area of interest. These points could be natural features or artificially placed markers (e.g., poles that stand above the snow surface) with precisely known positions distributed around the monitored area of interest.
# Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area
## 3 Effects of Different Parameters on Sound Pressure Level
### Analysing the Influence of Track Geometry on Sound Pressure Levels  
In the following section, the aim is to analyze the influence of the track geometry in terms of noise emissions. For that purpose, the data concerning the passage of _Eurotram_ vehicles on the Type IV track will be analyzed. In both sections of the track (straight and curved), the vehicle circulates at a reduced speed of about 20 km/h. It should be noted that the curve has a very small radius, about 50 m, as illustrated in the Figure 13.  
The presence of the curve leads to a substantial increase in noise levels in the whole frequency range. This is due to the contact between the wheel flange and the rail. The wheel is restricted in its movement by the presence of the rail, generating an increase in noise levels. Despite this phenomenon, there is no squa  
Figure 12: Comparison of sound pressure levels for three different types of the track (I, II, III) for a _Eurotram_ vehicle operating at 40 km/h: (**a**) Microphone 1; (**b**) Microphone 2.  
Figure 13: Site illustration with curved track geometry and the corresponding signalling (+) of the measurement location.  
mechanism is common in situations such as this, but, in this case, this phenomenon is not recorded due to the reduced speed of circulation, one of the mechanisms used to avoid the appearance of this localized phenomenon. In practical terms and as illustrated in Figure 14, sound pressure levels in the straight case are 60 dBA. In the case of the curved trajectory, as already mentioned, up to 10 dBA higher sound pressure levels are observed, reaching 70 dBA. Regarding the \(L_{Aeq,T}\) indicator, the difference between the two cases analyzed lies at 10 dBA, similar to what was observed for the general frequency spectrum. Therefore, the indicator's value for the straight track is 74 dBA, while for the section of track on a curve, a value of 84 dBA is recorded.  
Since the differences between straight and curved alignment are so evident, the analysis of the spectrograms illustrated Figure 15 can be useful to allow a more detailed analysis of this case. As can be seen in the spectrograms corresponding to receivers M3 and M4 the energy corresponding to the passage of the vehicles is higher in the curved alignment compared to the straight alignment, exciting a wider range of frequencies. Moreover, the passage of the vehicles in the curved section presents very noticeable energy peaks, unlike the spectra referring to the straight alignment, which are more diffuse. In these peaks, one can perceive that the highest noise levels (about 80 dBA) correspond to the passage of the wheels on the measurement section. Finally, the spectra analysis allows corroborating the non-existence of squeal, since this type of noise is easily identified in the spectrograms due to its harmonic character.
## 3 Results and Discussion  
### Empirical Experimental Results  
The results obtained for the field tests ranging from 1 m to 10 m for the cell's shell's radiative patterns at 868 MHz and 2.4 GHz, when the cells were vertically and horizontally oriented toward the reflective ground plane, are shown in Figures 7 and 8. According to the graphs, the communication link behaviour indicates a better link at lower frequencies, supporting the theoretical simulation results. Due to its larger size and proximity to the top lid opening, the wire loop structure performs better than the open SMA terminal.  
The results show no difference between the vertical and horizontal shell orientation of the cells within the interest range of 1 m to 5 m: in both frequency cases, the signal decreased close to the 3 m test point while displaying an ascending tendency to around \(-90\) dBm for 868 MHz and \(-95\) dBm for 2.4 GHz. Due to the fixed test points and integer metric system, small variations in signal amplitude at receiving locations can be attributed to the signal's max and min relative to its spatial wavelength.  
Figure 7: Experimental measurements received signal strength indicator (RSSI), bit error rate (BER) and packet error rate (PER) for 868 MHz and 2.4 GHz communication over the 1 m up to 10 m separation between the two RF transceivers. The radiative cell’s shell is in **vertical** orientation towards the ground plane.  
The 2.4 GHz communication encounters non-null BER and/or PER on all test ranges. The scenario, however, is not different from what might be corrected by simple acknowledgement and retransmission protocols. More than half of the 100 sent packages reached their destination intact, with the exception of the 4 m position on SMA in a vertical orientation, where the BER is slightly higher.  
Although 868 MHz communication is a more suitable option for communication, it may be regulated through international standards at certain duty-cycle active times on certain channels [40; 41], contrary to 2.4 GHz, which benefits from no on-air restrictions. In addition, since 868 MHz covers a wider range than 2.4 GHz with lower attenuation, it may require more resources at vehicle level for compliance with the current standards in terms of EMC/EMI. Accordingly, both tested operating frequencies are appropriate for battery cell level communication, although each one presents different advantages and challenges [45].
# A Novel Link-to-System Mapping Technique Based on Machine Learning for 5G/IoT Wireless Networks
## 5 Numerical Results and Analysis  
Table 2 shows the system parameters used. To make AWGN curve, we used a DNN regression method. Actually, we already showed that a DNN regression is a more suitable method than a fitting function under 4G LTE-A systems in our previous work [12]. The fitting curve became over-fitted as BLER approached zero, as shown in Figure 4. In contrast, a DNN regression followed AWGN curve as closely as possible through a multi-dimensional mapping. Moreover, Table 4 shows MSE results for fitting function (\"FIT\") and DNN regression (\"DNN\") for all CQIs under 4G LTE-A systems. Actually, the deep neural network gave little improvement compared to fitting algorithm under AWGN channel condition since AWGN was an almost static channel environment. Accordingly, in this study, 5G NR AWGN curve was generated with a DNN regression method without comparison of FIT and DNN.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**CQI Index** & **CQI 1** & **CQI 2** & **CQI 3** & **CQI 4** & **CQI 5** & **CQI 6** & **CQI 7** & **CQI 8** \\ \hline FIT & 0.033 & 0.027 & 0.033 & 0.038 & 0.017 & 0.032 & 0.017 & 0.012 \\ DNN & 0.018 & 0.014 & 0.016 & 0.013 & 0.014 & 0.011 & 0.015 & 0.016 \\ CQI Index & CQI 9 & CQI 10 & CQI 11 & CQI 12 & CQI 13 & CQI 14 & CQI 15 & - \\ FIT & 0.019 & 0.021 & 0.011 & 0.025 & 0.013 & 0.015 & 0.030 & - \\ DNN & 0.011 & 0.008 & 0.013 & 0.013 & 0.010 & 0.013 & 0.010 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: The comparison of MSE under 4G AWGN channel [12].  
Figure 4: SNR vs. BLER for CQI 1 under 4G AWGN channel [12].
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 2 Study Area and Data
### Experimental Data  
#### 2.2.1 Luojia1-01  
Luojia1-01, successfully launched at JSLC (Jaquan Satellite Launch Centre) on 2 June 2018, is a scientific experimental microsatellite jointly developed by Wuhan University and CGSTL (Chang Guang Satellite Technology Co., Ltd., Changchun, China). It is not only the first professional night-time light remote sensing satellite in the world but also the first low-orbit satellite with Earth observation and satellite navigation enhancement functions. It is capable of forming images with 130 m resolution and 250 km wide fields. Moreover, the Luojia1-01 satellite is ideally able to take global night-light images and provide data within 15 days, which provides a basis for measuring the global gross domestic product (GDP), the carbon emission index, and the urban housing vacancy index [48]. All of these data can be downloaded free from the Hubei Data and Application Network of the High-Resolution Earth Observation System ([[http://59.175.109.173:8888/app/login.html](http://59.175.109.173:8888/app/login.html)]([http://59.175.109.173:8888/app/login.html](http://59.175.109.173:8888/app/login.html))).  
For this paper, we selected Luojia1-01 NTL imagery from 15 July 2018, with better image quality, i.e., cloudless data. Then, we converted the digital number value (DN) to the spectral radiance by radiation correction [46]. The following radiance conversion formula was as follows:  
\[L=DN^{\frac{3}{2}}\cdot 10^{-10} \tag{1}\]  
where \(L\) is the radiance value after absolute radiation correction, for which the unit is \(W/\left(\text{m}^{2}\cdot\text{\upmu m}\cdot\text{sr}\right)\). The \(DN\) value is the gray value of the image.
# Characteristics of Ordinary Portland Cement Using the New Colloidal Nano-Silica Mixing Method
## 3 Results and Discussion  
### Flow and Setting Times  
The measured flow values of the paste according to the substitution ratio of CNS are shown in Figure 1. The 0% CNS sample without CNS substitution overflows the flow table due to the high w/b ratio. However, from 10% CNS paste, flow rate decreases steeply as the substitution ratio of CNS increases. In particular, the 50% CNS sample showed a low flow value of 127.5 mm. This substitution of CNS promotes the hydration reaction of OPC and reduces the flow. This is similar to the flow characteristics of previous studies using PNS.  
In a previous study using most nano-silicas, a superplasticizer was used. This is due to the heterogeneous dispersion due to the high specific surface area of the nano-silica particles and the sudden drop in fluidity. Particularly, in the case of powdered nano-silica, since the fluidity is seriously deteriorated, a lot of experiments using the superplasticizer are performed. This study used CNS with relatively homogeneous dispersibility than PNS. And a high w/b ratio of 0.5 was chosen to exclude the effect of the superplasticizer. However, as shown in Figure 1, the abrupt decrease in fluidity is due to a decrease in the w/b ratio with an increasing CNS replacement ratio.  
The results of measuring the setting time according to the Gillmore needle method are shown in Figure 2. As the substitution rate of CNS increased, initial and final setting time decreased. [PERSON] et al. [19] also showed the setting times of colloidal nano-silica substituted for OPC by 0.5%, 1%, 1.5%, 2%, 2.5%, and 3%. As the CNS replacement rate increases, the setting time decreases. In particular, the reduction of the final setting time is more steep than before. Decreasing the setting time can be thought of as being caused by two reasons in this study. One is the pozzolanic reaction and nuclear effects of nano-silica particles. This is a known effect from previous studies [8; 19; 21; 26; 39; 40]. The other is the effect of w/b reduction due to the mixing water weight substitution of CNS.  
Figure 1: Flow values by CNS replacement rate.
## 5 Conclusions  
This paper proposes a high-precision SLAM framework for multi-scene applications. In this framework, dual lidars are fused to make up for the shortcomings of lidars' narrow FOV and hence improve the completeness of mapping. Meanwhile, dual lidars and IMU are tightly coupled to improve the localization accuracy. Extensive experiments were carried out and the results showed that compared with the commonly used LeGO-LOAM and LIO-SAM methods, our proposed method can produce more precise localization and more accurate mapping results with more details.  
Conceptualization, K.X.; methodology, K.X. and W.Y.; software, K.X., W.Y., W.L. and F.Q.; validation, W.Y. and Z.M.; formal analysis, K.X. and W.L.; investigation, W.L. and K.X.; resources, K.X. and W.Y.; data curation, K.X. and W.Y.; writing--original draft, K.X.; writing--review and editing, K.X. and W.Y.; visualization, K.X.; supervision, K.X. and Z.M.; project administration, K.X. and W.Y.; funding acquisition, K.X. and W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation (grant nos. 61602529 and 61672539). This work was also supported by Hunan Key Laboratory of Intelligent Logistics Technology (2019 TP1015) and Scientific Research Project of Hunan Education Department (No. 17C1650).  
Not applicable.  
Figure 16: Comparison of mapping details in the city street scene showing that **(a)** the mapping of LeGO-LOAM lacks structural details of the building, **(b)** the mapping of LIO-SAM lacks the height of the building structure, and **(c)** the mapping of HSDLIO provides finer structural details of the building.