esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Video Transmission Latency Measurement Method for Intelligent Cloud Computing
## 4 Evaluation
### Method 3: Lossless Remote Video Online Measurement  
It is necessary to prepare one CC3030 at the transmitter and one at the receiver before measurement. Since it is impossible to ensure that both the transmitter and receiver of remote video transmission can receive GPS signals, the IEEE1588 protocol is used to realize time synchronization between the transmitter and receiver. In the measurement process, two transmission channels are carried out synchronously. One method is the transmission of the original video to obtain the reconstructed video. The other method is to process the original video and reconstruct the video. At the sending end, the original video is input to CC3030 and the hash code of the original video is obtained. At the receiver, the reconstructed video is input into CC3030 and the hash code of the reconstructed video is obtained. The original video hash code with time stamp is transmitted to the receiver CC3030 through the network, and the original video frame hash code matching the reconstructed video frame hash code is found. The video transmission latency is obtained by using the timestamp carried in their hash codes.  
The codec system used in this measurement is the H.264 codec. Table 1 shows the timestamp of the first 10 s of the reconstructed video and the matched original video frame and calculates the delay value. Due to the limitation of the distance between the sender and the receiver, the latency measurement results are designed to be real-time output in the form of web pages, where the horizontal coordinate is time, the unit of second represents the monitoring once a second in actual operation, and the vertical coordinate is the latency value, the unit of which is millisecond, as shown in Figure 11.
## 3 Results and Discussion
### Roughness Analysis  
The roughness analysis is shown in Figure 11. As is known to us all, the roughness influences the pipeline wettability [49; 50; 51]. As is shown in Figure 11a, the roughness for iron casting, steel, PVC and PE without vibration were 76.2 nm, 58.7 nm, 50.6 nm, and 41.2 nm, respectively. However, 300 Hz vibration decreased the pipelines' roughness by 10-20%, and the corresponding roughness were 65.4 nm, 52.3 nm, 44.6 nm, and 38.6 nm, respectively. For sewage, the roughness of different pipelines' surfaces was increased by 10-20%. Without vibration, the roughness for iron casting, steel, PVC and PE were 112.4 nm, 96.3 nm, 84.3 nm, and 72.2 nm, respectively. In addition, 300 Hz vibration decreased the roughness of pipelines' surfaces, and the reason was that the vibration made the pipelines' surfaces smoother and the vibration was able to hold energy.  
Figure 11: The roughness of iron casting, steel, PVC and PE in **(a)** clean water; **(b)** sewage water.  
Figure 10: The gas/solid/water dynamic contact angles of sewage water drops onto the **(a)** iron casting; **(b)** steel; **(c)** PVC; **(d)** PE, with or without 300 Hz vibration.
## 3 Results and Discussion
Figure 8: Antenna inside skin-mimicking-gel; (**a**) perception view and (**b**) the experimental setup to measure the radiation pattern and S\({}_{11}\) inside the skin. Figure 9: Contrast between the simulation and measurement results of S\({}_{11}\) inside skin-gel. The E and H planes of the simulated and measured values of the radiation pattern inside the skin-gel are shown in Figure 10. The operation of the antenna within the skin was defined by the 2D radiation pattern. At 915 MHz, the antenna inside the skin showed a broadsided directional pattern in both planes. The simulated realized gain within skin at 915 MHz was \(-\)22 dBi. However, the measured results revealed a realized gain value of \(-\)28 dB. Figure 11 depicts the simulated and observed radiation efficiency and gain graph. The antenna had a simulated radiation efficiency of 2.2%, whereas the measured radiation efficiency within the skin was 2.1%.
# What Lies Beneath One's Feet? Terrain Classification Using Inertial Data of Human Walk
## 3 Results
### Binary Classification
#### 3.1.1 Indoor-Outdoor Classification  
In the case of indoor-outdoor classification, the best classification accuracy of 97.48% (\(\pm\)0.29) was achieved with SVM for the lower back sensor followed by the chest sensor 97.11% (\(\pm\)0.32) when trained with all features (194 features per stride). A comparable classification rate on all features was observed with RF where the best accuracy was achieved with the lower back sensor 96.52% (\(\pm\)0.34) followed by the chest sensor 95.72% (\(\pm\)0.41). The precision, recall, and f1-score remained above 95% in all cases. Figure 4 shows the confusion matrices of indoor-outdoor classification computed with SVM and RF using the different sets of features computed from the lower back sensor data. Detailed classification results for different sensor placements against different features sets are presented in Table 6. In general, for all three sensor placements, models trained with _all features_ produced the highest classification accuracies.  
Figure 4: Confusion matrices of _indoor–outdoor terrain classification_ computed with lower back sensor. Each column represents a feature set: (**a**) all features (194 features), (**b**) time (110 features), (**c**) frequency (84 features), (**d**) accelerations (97 features) and (**e**) angular velocities (97 features). Each row represents a classifier (from **top**) to (**bottom**): RF and SVM. Classes: \(C_{T}^{l}=indoor\) (carpet, concrete, tiles) and \(C_{T}^{0}=outdoor\) (grass, Asphalt, soil).
## 3 Analysis of Factors Affecting Flexural Performance of Reinforced Beams
### Effect of the Amount of FRP Grid Reinforcement on the Flexural Performance of Reinforced Beams  
To study the effect of the amount of FRP grid reinforcement on the flexural performance of the reinforced beams, BG-L-PCM is used as the reference beam. The rest of the parameters remain unchanged; the grid thickness and the number of grid layers are adjusted; the grid thicknesses are taken as 3, 4, and 5 mm; and the number of grid layers are taken as 1 and 2 layers.  
In this subsection, a total of six FRP grid-reinforced beams with different amounts of reinforcement are subjected to finite element analysis, and the load-midspan deflection curves obtained are shown in Figure 8. The characteristic loads, deflection values, and damage modes are shown in Table 4. As can be seen from Figure 7, the simulated beams show a clear three-stage variation. In the first two stages, the curves of the six beams are in good agreement, and in the third stage it is found that the stiffness of beam B5T50-50 is greater than that of beam B3O50-50; the stiffness of the beam decreases with the decrease in the amount of FRP grid reinforcement. As can be seen from Table 4, the ultimate load of the reinforced beams increases nonlinearly. With the increase of the reinforcement amount by 51.5-56.3%, while the deflection of the beams decreases from 21.14 mm to 9.79 mm, the strain of the FRP grid decreases from 0.0163 mm to 0.0066 mm. A total of three damage modes occur in the simulated beams: beam B3O50-50 undergoes the damage of the composite layer peeling off after the FRP grid fracture; beam B4O50-50 and beam B5O50-50 both undergo damage after the composite layer peeling damage, when the concrete in the compression zone was crushed; and beam B3T50-50, beam B4T50-50, and beam B5T50-50 all undergo damage only after the composite layer peeling damage. In conclusion, as the amount of FRP grid reinforcement increases, its flexural stiffness and ultimate load are improved to different degrees, and the midspan deflection of the beams is also reduced.
# Blind Detection of Broadband Signal Based on Weighted Bi-Directional Feature Pyramid Network
## 2 Methods
### System Model  
According to the first half of the frame shown in Figure 2 (the first three steps), we perform signal modeling. The communication process of a general wireless communication system can be represented as  
\[r_{0}(t)=s(t)h(t)+n(t) \tag{1}\]  
where \(r_{0}(t)\) is the signal received at the receiver; \(s(t)\) is the modulated signal transmitted at the transmitter; \(h(t)\) is the channel response, and \(n(t)\) is the additive Gaussian white noise at the receiver and in the channel with a mean of 0 and a variance of \(\delta^{2}\). We assume that the receiver detects transmitter signals consisting of n modulation types and treats these signals as time-domain mixed signals. At this point, the communication process is expressed as  
\[r(t)=\sum_{i=1}^{\mathrm{k}}s_{i}(t)h_{i}(t)+n(t)\quad(k=1,2,\ldots,N_{s}) \tag{2}\]  
where \(r(t)\) denotes the mixed signal detected and received by the receiver in the time domain detection process, \(s_{i}h_{i}(t)\) denotes the \(i\)th target signal, the number of modulation types of radiation source signals that can be received by the receiver during operation is denoted by \(k\), and the number of all possible signal modulation types in the target source is denoted by \(N_{s}\). We assume that each target signal contains only one modulation parameter.
# Application of a Hybrid Model in Landslide Susceptibility Evaluation of the Western Tibet Plateau
## 3 Results
### Landslide Susceptibility Mapping by IVM-LR and WoE-LR Models  
The relationships between the corresponding factors and the disasters are listed in Table 6 by logistic regression (LR). Through the coefficients of the LR, the weight of factors from IVM and WoE were weighted superposition. Then, the results of IVM-LR and WoE-LR  
Figure 6: Weight of factors for the WoE model. (**a**) W\({}^{+}\). (**b**) W\({}^{-}\).  
Figure 7: Landslide susceptibility mapping by the WoE model. (**a**) W\({}^{+}\). (**b**) W\({}^{-}\).  
were divided into five levels using the natural breakpoint method: very low, low, moderate, high, very high (Figure 8).  
Based on the IVM-LR model (Figure 8a), the LSI values of all units were between \(-8.93\) and \(-3.83\). Statistical analysis revealed that the proportion of landslide areas in each hazard level from very low to very high was 0.42%, 1.91%, 7.38%, 29.84%, and 60.47%, respectively (Table 5).  
Based on the W\({}^{+}\)-LR model (Figure 8b), the LSI values of all units were between \(-4.64\) and \(-2.10\). Statistical analysis revealed that the proportion of landslide areas in each hazard level from very low to very high was 0.12%, 2.31%, 9.38%, 25.95%, and 62.25%, respectively (Table 5).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Factors** & **Slope** & **Aspect** & **Elevation** & **Lithology** & **Distance to Faults** & **Distance to Roads** & **Distance to Rivers** \\ \hline LR & 0.480 & 0.716 & 0.150 & 0.614 & 0.702 & 0.367 & 0.633 \\ MLP & 0.053 & 0.135 & 0.144 & 0.164 & 0.217 & 0.131 & 0.157 \\ \hline \end{tabular}
\end{table}
Table 6: Coefficient of evaluation factors by LR and MLP.  
Figure 8: Landslide susceptibility mapping by the hybrid models. (**a**) IVM-LR. (**b**) W\({}^{+}\)-LR. (**c**) W\({}^{-}\)-LR.  
Based on the W\({}^{-}\)-LR model (Figure 8c), the LSI values of all units were between \(-5.75\) and \(-3.55\). Statistical analysis revealed that the proportion of landslide areas in each hazard level from very low to very high was 0.10%, 4.54%, 14.50%, 33.30%, and 47.56%, respectively (Table 5).
## 3 Methodology
### Derivation and Upscaling of Landsat 8-Based Indices
#### 3.1.3 Landsat 8 LST and TVDI Estimation  
Although Landsat 8 TIRS sensor measures thermal infrared radiance with two bands (band 10 and band 11) located at 10 and 12 \(\upmu\)m, significant calibration errors caused by stray light have been reported for band 11 [49]. As suggested by United States Geological Survey (USGS), a single channel algorithm by [PERSON] _et al._[50] was adopted to derive land surface temperature from band 10 in this study. Water vapor content required by the algorithm was derived from the measurements at weather stations or the MODIS water vapor product (MOD5L2 Collection 5.1) if weather station measurements were not available within the study area [36].  
TVDI, which represents water stress of vegetation, was estimated on the basis of the triangular shape of LST _vs._ NDVI scatter plots [51].  
\[TVDI\ =\ \frac{Ts-\ Ts_{min}}{a+\ b\cdot NDVI-\ Ts_{min}} \tag{6}\]  
where _Ts_ is LST for each pixel in the image, _Ts\({}_{min}\)_ is the minimum LST, _i.e._, wet edge in the LST-NDVI space. Coefficients a and b are determined by a linear fit between maximum LST for a given NDVI (_Ts\({}_{max}\)_) and NDVI at the dry edge [51].
## 7 Conclusions  
Practices of vehicular communication network's deployment, research, and scientific projects are developed in two directions: direct vehicle-vehicle (V2V) communication and vehicle-to-infrastructure (V2I) communication.  
In the study, the authors provided the multi-layer methodology for the assessment of heterogeneous services provision for sustainable cargo transportation.  
The network topology varies very rapidly, situations change dynamically, and service control systems have support possibilities to contain communications from one to several hundred of the network nodes.  
For the provision of heterogeneous services, it is necessary for new routing protocols and channel access methods for the large volume of fast-changing topology networks. The investigations in VANET communication development are essential. Some possibilities to solve the problem are provided. They are associated with communication between the sender and the receiver moving to opposite directions in the road. This approach is based on the construction of service provision infrastructure by developing a knowledge base for rapidly changing communication networks with the standardised networks supporting possibilities by evaluating the data transferring capacities for cargo transportation processes.  
The authors emphasise the information flows that are created for the transmission of messages and electronic documents used in freight transport. All these flows are essential for adequate transport safety, paperless management, driver comfort, and optimal traffic development, and are focused on reducing pollution. All the required data, which is essential during the process, is linked with network elements and content presented in electronic format.  
Further research directions could focus on the review of heterogeneous services for cargo transportation by rail, and also include multi-modal and autonomous transportation aspects. Additionally, the authors plan to extend the research to the infrastructure of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication.  
**Author Contributions:** [PERSON] contributed with the methodology for assessment of heterogeneous services Provision for sustainable cargo transportation, the requirements for infrastructure of service provision and the classification of heterogeneous services for transport means based on wireless networks. [PERSON] contributed with information flows in the context of traffic jumps, accidents, tracking and e-documents and the integration of heterogeneous service infrastructure for management of cargo transportation processes by roads and process monitoring. [PERSON] constructed UML diagram. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by Research Council of Lithuania (LMTLT) grant number 01.2.2-LMT-K-718-03-0030.  
**Acknowledgments:** This project has received funding from the European Regional Development Fund (project No 01.2.2-LMT-K-718-03-0030) under the grant agreement with the Research Council of Lithuania (LMTLT).  
**Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
In coastal areas, coconuts are a common crop. Everyone from farmers to lawmakers and businesses would benefit from an accurate forecast of coconut production. Internet of Things (IoT) sensors are strategically positioned to continuously monitor the environment and gather production statistics to obtain accurate agricultural output predictions. To effectively estimate coconut prediction, this study presents an enhanced deep learning classifier called Bi-directional Long Short-Term Memory (BILSTM) with the integrated Levy Flight and Seagull Optimization Algorithm (LFSOA). LASSO feature selection is applied to eliminate the superfluous characteristics in the yield estimation. To further enhance the coconut yield estimate, the optimal set of hyperparameters for BILSTM is tuned by the LFSOA, which helps to avoid the overfitting issue. For the results, the BILSTM is compared against different classifiers such as Recurrent Neural Network (RNN), Random Forest Classifier (RFC), and LSTM. Similarly, LFSOA-based hyperparameter tuning is contrasted with different optimization algorithms. The outputs show that LFSOA-based hyperparameter tuning in BILSTM achieved accuracy, precision, recall, and f1-score of 98.963%, 99.026%, 99.155%, and 95.758%, respectively, which are higher when compared to existing methods. Similarly, the BILSTM-LFSOA accomplished better results in statistical measures, including the Root Mean Square Error (RMSE) of 0.105, Mean Squared Error (MSE) of 0.011, Mean Absolute Error (MAE) of 0.094, and coefficient of determination (R\({}^{2}\)) of 0.954, respectively. From the overall analysis, the proposed BILSTM-LFSOA improves coconut yield prediction by achieving better results in all the performance measures when compared with existing models. The results of this study are important to many stakeholders, including but not limited to policymakers, farmers, banks, and insurance companies. As coconuts are an important crop in developing countries, accurate coconut yield forecasting will lead to greater financial and food security in these regions.  
bi-directional long short-term memory; coconut yield estimation; internet of things; least absolute shrinkage and selection operator; Levy flight; seagull optimization algorithm +
Footnote †: journal: Applied Sciences
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### Tropospheric Correction
#### 2.3.4 Protection Level Calculation  
Before the protection level calculation, user needs to first calculate the noise variance \(\sigma^{2}_{i}\) of the pseudo-range observation value between himself and satellite \(i\), which can be expressed as follows:  
\[\sigma^{2}_{i}=\sigma^{2}_{i,flt}+\sigma^{2}_{i,\text{UIRE}}+\sigma^{2}_{i,air }+\sigma^{2}_{i,tropo} \tag{15}\]  
with  
\[\sigma^{2}_{i,air}=\left(0.0741+0.18e^{(E_{i}/27.7^{\circ})}\right)^{2} \tag{16}\]  
where \(\sigma^{2}_{i,air}\) is the multipath and receiver thermal noise, and the calculation of \(\sigma^{2}_{i,flt}\), \(\sigma^{2}_{i,\text{UIRE}}\) and \(\sigma^{2}_{i,tropo}\) have already been described in above.  
Thus, the weight matrix \(W\) can be constructed as follows:  
\[W_{ii}=\sigma^{-2}_{i} \tag{17}\]  
where \(W_{ii}\) denotes the \(i\)-th diagonal element of \(W\).  
On this basis, the weighted covariance matrix can be obtained by using matrices \(G\) and \(W\) as follows:  
\[\left(G^{T}WG\right)^{-1}=\left[\begin{array}{cccc}d^{2}_{E}&d_{EN}&d_{EU}&d _{ET}\\ d_{EN}&d^{2}_{N}&d_{NUI}&d_{NT}\\ d_{EU}&d_{NUI}&d^{2}_{U}&d_{UT}\\ d_{ET}&d_{NT}&d_{UT}&d^{2}_{T}\end{array}\right] \tag{18}\]  
where indices \(E\), \(N\), and \(U\) refer to the east, north and up directions of the user receiver, respectively, and \(T\) refer to the receiver clock.  
Therefore, the horizontal protection level (HPL) and the vertical protection level (VPL) can be calculated as follows:  
\[\text{HPL}=\left\{\begin{array}{ll}&K_{\text{H,NPA}}\cdot d_{major}&\text{En }-\text{route to LNAV}\\ &K_{\text{H,PA}}\cdot d_{major}&\text{LNAV}/\text{VNAV},\text{LP},\text{LPV} \end{array}\right. \tag{19}\]  
\[\text{VPL}=K_{V}d_{U} \tag{20}\]  
With  
\[d_{major}=\sqrt{\frac{d^{2}_{E}+d^{2}_{N}}{2}+\sqrt{\left(\frac{d^{2}_{E}-d^{ 2}_{N}}{2}\right)^{2}+d^{2}_{EN}}} \tag{21}\]  
where \(K_{H,NPA}=6.18\) m, \(K_{H,PA}=6.0\) m, and \(K_{V}=5.33\) m.
###### Abstract  
The coupling of drones and IoT is a major topics in academia and industry since it significantly contributes towards making human life safer and smarter. Using drones is seen as a robust approach for mobile remote sensing operations, such as search-and-rescue missions, due to their speed and efficiency, which could seriously affect victims' chances of survival. This paper aims to modify the Hata-Davidson empirical propagation model based on RF drone measurement to conduct searches for missing persons in complex environments with rugged areas after manmade or natural disasters. A drone was coupled with a thermal FLIR lepton camera, a microcontroller, GPS, and weather station sensors. The proposed modified model utilized the least squares tuning algorithm to fit the data measured from the drone communication system. This enhanced the RF connectivity between the drone and the local authority, as well as leading to increased coverage footprint and, thus, the performance of wider search-and-rescue operations in a timely fashion using strip search patterns. The development of the proposed model considered both software simulation and hardware implementations. Since empirical propagation models are the most adjustable models, this study concludes with a comparison between the modified Hata-Davidson algorithm against other well-known modified empirical models for validation using root mean square error (RMSE). The experimental results show that the modified Hata-Davidson model outperforms the other empirical models, which in turn helps to identify missing persons and their locations using thermal imaging and a GPS sensor.  
ToT; propagation model; tuning models; remote sensing; drones; complex environment; thermal imaging; rescue operations +
Footnote †: journal: Sensors  
doi:10.3390/s22051786