esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Problem Definition
### Field of View (FoV) of Camera in 3D Space  
FoV is defined as the visible area of a camera, which can be calculated from the horizontal and vertical view angles (\(\phi\)), the working distance (\(R\)), and the pose (\(\psi\)), as shown in Figure 1. The horizontal and vertical camera view angles (\(\phi_{h}\) and \(\phi_{v}\), respectively) can be specified from the camera specifications. On the other hand, the horizontal and vertical poses (\(\psi_{h}\) and \(\psi_{v}\), respectively) describe the azimuth and elevation of a camera and they are decision variables. The positions of surveillance targets and cameras are represented by using the Cartesian coordinate system, while the poses and view angles of cameras are represented by using a spherical coordinate system.  
Figure 1: Field of view (FoV) of camera.  
As shown in Figure 1, the distance vector (\(\vec{u_{SO}}\)) between a camera position \(S\) and a given target point \(O\) in the monitoring space is:  
\[\vec{u_{SO}}=\vec{O}-\vec{S} \tag{1}\]  
Based on Equation (1), the following three Equations (2)-(4) must be satisfied for the point to be covered by the FoV of the camera:  
\[\vec{\psi}\cdot\vec{u_{SO}}\geq\vec{u_{SO}}\text{cos}\left(\frac{\phi_{\gamma }}{2}\right) \tag{2}\]  
\[\vec{\psi}\cdot\vec{u_{SO}}\geq\vec{u_{SO}}\text{cos}\left(\frac{\phi_{h}}{2}\right) \tag{3}\]  
\[\vec{u_{SO}}\leq R \tag{4}\]  
To cover a larger area than that covered by the typical pin-hole cameras and to reduce the number of cameras, we consider PTZ cameras for surveillance applications. A vertical rotation and a horizontal rotation correspond to the tilt and the pan, respectively. To specify the FoV of PTZ cameras while considering pan and tilt, we employ the concept of a reachable region from a previous study [22]. The reachable region of a camera is defined as the intersection of the two regions that the camera can cover in the worst-case scenario. Further, we incorporate zooming into the working distance (\(R\)) of a camera, as shown in Figure 2. The detailed specifications of three different PTZ cameras are shown in Table 2.
## 12 Conclusions  
Based on the analysis and simulation of data from earlier studies to forecast the compressive strength of SCC for 400 different mix proportions, the following conclusions were made:  
1. Based on data collected from the literature, the fly ash enhanced the compressive strength of normal concrete as a partial replacement for cement. Depending on the statistical analysis, the median percentage of superplasticizers for the production of SCC was 1.33%. Furthermore, the percentage of fly ash inside all mixes ranged from 0 to 100%, with 1 to 365 days of curing and sand content ranging from 845 to 1066 kg/m\({}^{3}\).
2. This study developed the NLR, MLR, and ANN models to predict the compressive strength of SCC mixes. The ANN model outperformed other models in the training and testing datasets, with higher R\({}^{2}\) values, and lower RMSE, MAE, and SI values.
3. The SI values for all models and phases (training and testing) were less than 0.2, indicating good performance. Furthermore, compared to the NLR model, the ANN model has smaller SI values in all phases, for example, 60% lower in training and 35% lower in the testing dataset.  
Figure 12: Sensitivity analysis to investigate the effect of input variables on the CS of CSS using the ANN model.  
Using several evaluation criteria, including the root mean square error (RMSE), the coefficient of determination (R\({}^{2}\)), the SI, and the mean absolute error (MAE). The sequence of ANN models was the best model provided in this research based on data acquired from the literature and produced a higher R\({}^{2}\) and lower MAE and RMSE.
* The sensitivity analysis test was performed in order to check the most effective dependent variables on independent variables' output performance. The results indicated that the most effective parameters causing the output result were curing time and fly ash content.
* The box plot for the proposed models indicated that the ANN model had better centered mean square error and standard deviation performance.
* The NLR model as a reliable mathematical model can predict the compressive strength of self-compacted concrete with a high coefficient of determination value.
* The overall findings and analysis showed that it was possible to effectively modify SCC's strength and other properties by producing replacement SCC with cement containing 35% fly ash. However, percentages more than 35% might be detrimental to SCC's performance.
* The sensitivity analysis showed that the curing time is the key input parameter that influences the improvement rate and forecasts the compressive strength of SCC.  
This research received no external funding.  
Not applicable.  
The data are available upon request.  
College of Engineering, the University of Sulaimani supported this study.  
The authors declare no conflict of interest.
## 7 Experimental Results
### Performance Metrics  
We use the following performance metrics:  
* Deadline success ratio (DSR): This metric is the ratio of successfully received data packets in the time deadline to all generated data packets from the source node. It is one of the most important metrics in soft real-time systems.
* Energy consumption (EC): This metric is the total energy consumption for the real-time service. Energy consumption is an important metric in wireless sensor networks. The power consumption values of transmitting and receiving are 21 mW and 15 mW, respectively, [40].
## 3 Data Acquisition, Processing, and Analysis of Magnetotelluric Sounding  
### Data Acquisition  
The magnetotelluric sounding profile started from the Guanting Reservoir in the east and reached the northwest of Xuanhua County in the west, with a total length of 80 km. There were 16 magnetotelluric sounding points, with an average distance of 5 km. The measuring instrument was an MTU-5A (Phoenix Company, Toronto, ON, Canada). We measured three mutually orthogonal magnetic field components (Hx, Hy, and Hz) and two mutually orthogonal electric field components (Ex and Ey). The subscripts x, y, and z indicate the north-south direction, the east-west direction, and the vertical direction, respectively. The acquisition frequency band of the instrument was 320-0.0005 Hz. To reduce the influence of random noise on data quality, the observation time was more than 18 h.
# An Ultra-Wideband Vivaldi Antenna System for Long-Distance Electromagnetic Detection
## 3 Verification and Discussion  
### Detection System Positioning Test  
A miniaturized and easily fabricated ultra-wideband (UWB) antenna system for long-distance electromagnetic detection is proposed. The transmitting antenna is the ultra-wideband wide beam antenna discussed in Section 2.1, and the photograph is shown in Figure 10a. The receiving antenna is the ultra-wideband narrow beam eight-element array designed in Section 2.2 and the photograph is shown in Figure 10b, which was placed on a turntable support. The distance between the two antennas was fixed. During the test, the transmitting and receiving antennas were connected to two ports of the E5071C vector network analyzer (VNA) to form the Ultra-wideband Vivaldi antenna system for long-distance electromagnetic detection.  
We measured the distance, w, between the two antennas. The VNA transmits electromagnetic wave signals through the transmitting antenna through port one. The receiving antenna has a smaller beam width. We placed it on the rotating table and connected it to port two of the VNA, and the schematic diagram of the experiment is shown in Figure 11. We observed the S21 parameter of the VNA and adjusted the angle of the rotating table to find out the azimuth angle \(\theta\) of the target. We maintained the azimuth angle and then changed the VNA to time-domain. We then measured the time difference T from the moment when the transmitting antenna sends a signal to the moment when the receiving antenna receives the signal, and the sum distance. Then, the target position can be obtained.  
Figure 9: (**a**) Return loss of the simulated 8-element Vivaldi array antenna. (**b**) The gain of the simulated 8-element Vivaldi array antenna. (**c**) Group delay profile of the proposed transmitting antenna and the receiving array antenna.
# Study on the Residual Strength of Nonlinear Fatigue-Damaged Pipeline Structures
## 2 Residual Strength Model of Materials with Fatigue Damage Accumulation
### Peak Stress Response  
The CT110 sample used for low-cycle fatigue (LCF) tests was cylindrical. The diameter of the sample was 6 mm, and the gauge was 12 mm. Strain-controlled fatigue tests were conducted at room temperature by using a closed-loop servo hydraulic testing machine. And the tests employed tension-compression fatigue loading. In the testing, the strain amplitude was 1.0%, the mean strain was 0%, and a triangular strain waveform with the strain amplitudes was used at a constant total strain rate of 4 \(\times\) 10\({}^{-3}\)/s [37]. The data on the peak stress of the three tests with the number of cycles were selected and averaged, and the level of uncertainty on the testing data does not exceed 5%.  
It can be seen from Figure 1 that when the strain amplitude is 1.0% under symmetrical cyclic loading, the low-cycle fatigue behavior of CT110 steel has obvious cyclic softening characteristics, and its peak stress drop rate shows significant differences. The peak stress response mainly presents three stages. In the first stage, the peak stress decreases rapidly, which is about the first 20% of the entire fatigue life cycle. In the second stage, the peak stress decreases steadily, and this stage is about 20% to 80% of the whole fatigue life cycle. In the third stage, the peak stress drops rapidly to coiled tubing failure, and this stage is the last 20% of the fatigue life cycle. But for pressurized pipelines, when the peak stress of the material drops to 80% of the fatigue life cycle, the material is considered to fail. Therefore, the third stage needs to be discarded in this research.  
According to the peak stress response curve of CT110 steel with a strain amplitude of 1.0%, the peak stress curves of the first stage and the second stage are fitted, respectively. Combined with the hysteretic loop model, the stress corresponding to the plastic strain of 0.2% is defined as the yield strength, and the residual yield strength models of CT110 steel at different fatigue stages can be obtained. The residual strength model is as follows [38]:  
\[\left\{\begin{array}{ll}\sigma_{a1}=aN^{b}&(D<0.2)\\ \sigma_{a2}=cN+d&(0.2<D<0.8)\\ \varepsilon_{a}=\frac{\sigma_{a1}}{E}+\left(\frac{\sigma_{a1}}{E^{b}}\right) ^{\frac{1}{\sigma}}\end{array}\right. \tag{2}\]  
where \(\sigma_{a1}\) and \(\sigma_{a2}\) denote the residual yield strength of the first and second stages of damage, respectively; \(a\), \(b\), \(c\), and \(d\) denote the fitting coefficients of the fitting functionsfor different damage stages, respectively; \(\epsilon_{a}\) denotes the total strain amplitude; \(\sigma_{ai}\) denotes the residual yield strength, \(i=1\) or \(2\); \(E\) denotes the elastic modulus; \(K^{\prime}\) denotes the cyclic strength coefficient; and \(n^{\prime}\) denotes the cyclic strain hardening exponent.
# Methods for Noise Event Detection and Assessment of the Sonic Environment by the Harmonic Index
###### Abstract  
Noise annoyance depends not only on sound energy, but also on other features, such as those in its spectrum (e.g., low frequency and/or tonal components), and, over time, amplitude fluctuations, such as those observed in road, rail, or aircraft noise passages. The larger these fluctuations, the more annoying a sound is generally perceived. Many algorithms have been implemented to quantify these fluctuations and identify noise events, either by looking at transients in the sound level time history, such as exceedances above a fixed or time adaptive threshold, or focusing on the hearing perception process of such events. In this paper, four criteria to detect sound were applied to the acoustic monitoring data collected in two urban areas, namely Andorra la Vella, Principality of Andorra, and Milan, Italy. At each site, the 1 s A-weighted short L\({}_{\text{Aeq,1s}}\) time history, 10 min long, was available for each hour from 8:00 a.m. to 7:00 p.m. The resulting 92-time histories cover a reasonable range of urban environmental noise time patterns. The considered criteria to detect noise events are based on: (i) noise levels exceeding by +3 dB the continuous equivalent level L\({}_{\text{Aeq,T}}\) referred to the measurement time (T), criteria used in the definition of the Intermittency Ratio (IR) to detect noise events; (ii) noise levels exceeding by +3 dB the running continuous equivalent noise level; (iii) noise levels exceeding by +10 dB the 50 th noise level percentile; (iv) progressive positive increments of noise levels greater than 10 dB from the event start time. Algorithms (iii) and (iv) appear suitable for notice-event detection; that is, those that (for their features) are clearly perceived and potentially annoy exposed people. The noise events detected by the above four algorithms were also evaluated by the available anomalous noise event detection (ANED) procedure to classify them as produced by road traffic noise or something else. Moreover, the assessment of the sonic environment by the Harmonic index was correlated with the single event level (SEL) of each event detected by the four algorithms. The threshold value of 8 for the Harmonic index, separating the \"noisy\" from the \"very noisy\" environments, corresponds to lower SEL levels for notice-events as identified by (iii) and (iv) algorithms (about 88-89 dB(A)) against those identified by (i) and (ii) criteria (92 dB(A)).  
road traffic noise; noise event detection; sound source recognition; annoyance; anomalous events +
Footnote †: journal: Applied Sciences
# A Novel Batch Streaming Pipeline for Radar Emitter Classification
## 4 Experiment
### Dataset  
For the experiment in this paper, 500 episodes of radar signals were generated, using a simulated signal generator. Among these episodes, 400 episodes were utilized as train data, while the remaining 100 episodes were used as test data. The radar data were episode-wise alike but had different values of AOA, Frequency, PW, and PA within the margin of errors, and they were organized in different TOA order. Thus, by validating the model, which was designed based on train data, with test data, we could check whether the generalization performance was guaranteed. Within each episode, 43 different radar emitters were active, emitting a total of 1500 pulses. These pulses were captured and stored as PDWs, serving as the input for the subsequent deinterleaving and recognition stages. The PDW dataset consisted of a wide range of radar emitter classes, each characterized by distinct PRI patterns and frequency patterns. The frequency patterns encompassed agile, fixed, hopping, negative, positive, sine, and triangular, while the PRI patterns included options such as dwell and switch, stagger, negative, positive, stable, triangular, and sine. Figure 4 and Figure 5 illustrate examples of these frequency patterns and PRI patterns, respectively. We performed one-hot encoding on the class information, yielding a total of 43 classes used as experimental data.
# Routing Protocols for Mobile Internet of Things (IoT): A Survey on Challenges and Solutions
## 4 Solutions to the Issues Faced by RPL during Mobility
### Position-Based Solutions
#### 4.4.2 Mobility Prediction Based RPL (MP-RPL)  
RPL poorly adapts to mobility scenarios as the routes are not updated as fast as they are updated in the case of static sensor nodes. Authors [67] have proposed a mobility prediction model known as sensor node speed classifier (SNSC) which classifies sensor nodes in different speed or velocity classes using the Maximum Likelihood Estimation (MLE) classification technique. Average Encounter Rate (AER) is computed by each node in SNSC in a time period T with a given velocity value. To predict the one-hop neighborhood, Hellos beacons short frames are broadcasted periodically for correctly estimating AER. Validation of the SNSC model is implemented in the Cooja simulator. 50 nodes are simulated and have a velocity value between 1 to 10 m/s. The result shows that the error prediction rate of SNSC is 4% sustaining the proposed speed classification technique accuracy.  
Authors [67] have proposed a Mobility prediction-based RPL (MP-RPL) where authors have exploited the SNSC model for accurate prediction of speed classes of sensor nodes. Velocity value is periodically estimated using SNSC and are injected in the DIOs messages. In each Trickle period, these DIOs messages are broadcasted which helps in making nodes aware of their neighbor's velocities. MP-RPL transmits the network traffic via the least-mobile nodes by making stable routes in the DODAG. Neighbors having increasing order of speed values are sorted by sensor nodes upon receiving DIOs. Neighbors that have minimum speed values are eligible preferred parents hence routes to the sink via the least mobile (LM) nodes are ensured. Routes can break quickly in the case of LM with high-speed values creating a high packet loss rate. To solve this problem authors have proposed intermediate nodes (IN) between preferred parents of LM and high-speed descendants lengthening routes to the sink. Working principles, issues addressed, strengths and weaknesses of solutions under the category of Position-based solutions are discussed in Table 4.
## 7 Discussion
Each configuration is designed to optimize the resonator's performance by reducing parasitic feedthrough, enhancing port-to-port signal isolation, and improving the suppression of spurious modes. This makes the hybrid MEMS resonator exceptionally suitable for advanced applications in telecommunications, aerospace, and beyond, where precision, efficiency, and reliability are of paramount importance. The ongoing development of these hybrid devices continues to push the boundaries of MEMS technology, promising significant advancements in the field of microfabrication and RF electronic systems.
## 6 Opportunities and Future Research Directions
### Energy Efficiency  
In all wireless networks, energy-related constraints are considered the important factors that determine networks' performance and characteristics because the nodes are battery-powered. Such constraints result in lower reliability of the communication medium and cause the loss of data during transmission from the end devices to the gateways and then the servers. Even though LPWAN technologies are low in power in comparison with other wireless networks, energy saving is among the key considerations to improve a LoRaWAN network's performance. Most of the existing studies focused on Class A LoRaWAN devices, which are the most energy-efficient ones compared with the other classes. Therefore, more research needs to be conducted to evaluate the performance and energy efficiency of Class B and Class C devices. Additionally, scheduling algorithms and adaptive data rates that consider energy constraints are in high demand. Therefore, to maintain the nature of the low energy consumption of LoRa, it needs to be further studied to solve the energy issues caused by other factors. LoRaWAN no-battery communications are also feasible according to the results in [117] if the right configuration is chosen (i.e., the capacitor size and turn-on voltage threshold) for different application behaviors (e.g., transmission interval and UL and DL packet sizes (PSs)) as well as environmental conditions, (e.g., the energy harvesting rate). Only small-DL PSs should be considered for these devices because the DL in the second reception window significantly impacts performance. The authors in [118] presented a joint distributed queueing system for LoRa that balances inter-channel traffic. Compared with the DQ, the suggested technique decreases the control overhead by up to 70%. Regardless of the number of arrivals, the protocol achieves not only near-optimal throughput and access delay but also power efficiency.
## 3 Results and Discussion
### Investigation on Classification Performance  
Table 2 summarizes the state-of-art COVID-19 prediction algorithms to test the data (Radiological (chest X-rays and C.T. images), RT-PCR, and Clinical data) along with the highest prediction results of the selected previous studies. Only the best-obtained results of different ML/DL techniques on C.T., X-ray images, RT-PCR, and clinical blood test data were mentioned. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Reference** & **Test Data** & \begin{tabular}{c} **COVID-19** \\ **Positive** \\ \end{tabular} & \begin{tabular}{c} **Pneumonia/Other** \\ **Infection** \\ \end{tabular} & \begin{tabular}{c} **Healthy** \\ **Total** \\ \end{tabular} & \
\begin{tabular}{c} **Total** \\ \end{tabular} &
\begin{tabular}{c} **Sources/Links** \\ \end{tabular} \\ \hline
[71] & \
\begin{tabular}{c} Clinical Blood \\ Test \\ \end{tabular} & 160 & 5333 & * NA & 5493 &
\begin{tabular}{c} University Medical Centre \\ Ljubljana (U.M.C.L.), \\ Slovenia \\ \end{tabular} \\ \hline
[72] & \
\begin{tabular}{c} Clinical Blood \\ Test \\ \end{tabular} & 82 & * NA & 32 & 114 &
\begin{tabular}{c} Taizhou Hospital Zhejiang, \\ China \\ \end{tabular} \\ \hline \hline \end{tabular}  
* Not Applied (N.A.) represents those kinds of images that were not utilized; ** Not Specified (N.S.) shows the images that were utilized but did not explain the number clearly.In Table 1, some significant available data sources were also presented and utilized by other researchers to explore more valuable solutions to deal with this pandemic. The datasets using X-ray images were higher in number than those with C.T. images. The percentage contribution of different diagnostic methods used for COVID-19 detection is presented in Figure 5. It can be noticed that X-ray image-based detection was the most utilized diagnostic method, contributing 47% of the total. C.T. image-based models were the second-largest diagnostic methods accounting for 30%, whereas the approaches with the least utilization for diagnosis of the virus were X-ray and C.T. image-based combined models (10%), clinical blood test (8%), and RT-PCR data-based models (5%).  
\end{table} 
Table 1: _Cont._  
Figure 5: Percentage utilization of various diagnostic techniques for the detection of COVID-19.