esa-sceva/satcom-chunk-collection huggingface dataset:
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 3 Methodology
### \(\sigma^{\circ}\) SAR Backscattering at Plot Scale  
The proposed method is based on detecting the change in the \(\sigma^{0}_{P}\) backscattering coefficient at plot scale. When the surface soil moisture (SSM) increases between two consecutive SAR acquisitions, \(\sigma^{0}_{P}\)-value between these two dates increases. Since irrigation is an artificial application of water, it causes obviously an increase in the SSM values. Thus, the SAR \(\sigma^{0}_{P}\)-value could increase between two SAR dates if an irrigation event occurred between these two SAR acquisitions [21; 22]. It is good to mention that only the VV polarization was considered in this study since VV is more sensitive to the soil water content than VH which is more sensitive to the vegetation cover [32].  
Not only could the increase in the \(\sigma^{0}_{P}\)-values be an indicator of an irrigation event, but also the stability or a slight decrease of \(\sigma^{0}_{P}\)-values between two dates could be linked to an irrigation event if the \(\sigma^{0}_{P}\)-values attain high values. In fact, with no actions, such as irrigation, rainfall, soil work, or vegetation development, the SAR signal between two near dates (at maximum 6 days) tends to decrease, especially during spring and summer season, due to several water cycle parameters such as the infiltration, evaporation and evapotranspiration that cause a decrease in the SSM values. Thus, stabilization or slight decrease of \(\sigma^{0}_{P}\) between time \(t_{1}\) and time \(t_{2}\) could be evidence of an additional water supply if and only if the level of the SAR signal at \(t_{1}\) is already high due to a previous irrigation or rainfall event occurring at time \(t_{1}\).  
Since the method is based on detecting the change is \(\sigma^{0}_{P}\) values, we propose to calculate, at each SAR acquisition, the difference between the \(\sigma^{0}_{P}\) value at this acquisition and \(\sigma^{0}_{P}\) at the previous SAR acquisition. We calculate therefore \(\Delta VV_{P}\) at plot scale as:  
\[\Delta VV_{P}=VV_{Pi}-VV_{Pi-1} \tag{1}\]  
where \(VV_{Pi}\) is the \(\sigma^{0}\) in VV polarization at the present SAR date and \(VV_{Pi-1}\) is the \(\sigma^{0}\) in VV polarization at the first previous date.  
Finally, for the \(\sigma^{0}_{P}\) value, three main thresholds were determined for probable irrigation events. First, if \(\Delta VV_{P}\) is less than or equals \(-0.5\) dB (which correspond to a decrease in \(\sigma^{0}_{P}\)-value more than 0.5 dB), then we assume that no chance of irrigation exists. If the \(\Delta VV_{P}\) is greater than or equal to 1 dB, then a possible irrigation event could have existed. In addition, if the \(\Delta VV_{P}\) is between \(-0.5\) dB and 1 dB (stabilization or slight decrease) then an irrigation event could have also existed. However, it is necessary to add other criteria to confirm the possible irrigation event between date \(t_{i-1}\) and \(t_{i}\). These additional criteria will be discussed in the coming sections.
# Hamming Distance Optimized Underwater Acoustic OTFS-IM Systems
## 2 OTFS-IM for Underwater Acoustic Communication System
### Underwater Acoustic Channel  
The fading time-varying underwater acoustic channel model can be approximated by a time-delay Doppler double extension function. In the time-delay Doppler domain, the double-extended channel function has the characteristics of two-dimensional sparsity. In this paper, we adopt the channel based on the statistical model proposed in [22], and the transfer function of the time-varying multipath underwater acoustic channel can be expressed as:  
\[H(f,t)=\overline{H}_{0}\underset{p}{\sum}h_{p}\widetilde{\tau}_{p}(f,t)e^{-j 2\pi ft_{p}} \tag{9}\]  
where \(\widetilde{\gamma}_{p}(f,t)=\gamma_{p}(f,t)e^{j2\pi a_{p}ft}\) represents the overall small-scale path coefficient and \(p\), \(\tau_{p}\), \(h_{p}\), and \(a_{p}\) represent the number of paths, delay of each path, path gain, and Doppler factor, respectively.
###### Abstract  
Due to the limited availability of fault samples and the expensive nature of marking fault samples in Electric Multiple Unit (EMU) gearbox monitoring data, a study was conducted to simulate the degradation process of key components in the CRH5 gearbox using rigid-flexible coupling dynamics. Vibration acceleration data from the simulation were utilized to create a six-dimensional hybrid feature domain representing the degradation process. By leveraging the capabilities of the Hidden Markov Model (HMM) for handling hidden transitive probabilities in temporal data and Gated Recurrent Unit (GRU) for addressing long-distance and high-dependence temporal data, a GRU-HMM fault prediction model was developed. This model was validated using monitoring data and the six-dimensional hybrid feature domain from the CRH5 gearbox and compared against actual maintenance records. The findings indicated that the GRU-HMM fault prediction model can effectively recognize the degradation patterns of multiple components, offering higher accuracy in fault prediction compared to traditional models. These research outcomes are expected to optimize EMU maintenance schedules based on usage conditions, enhance EMU utilization rates, and reduce operational and maintenance costs, thereby providing valuable theoretical support.  
EMU; gear box; GRU-HMM; dynamics simulation; fault prediction +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Modelling Results  
#### 3.5.1 Pollutant Concentration Levels in the Study Area  
BOLCHEM was first used to evaluate the air quality in the study area for the measurement campaign period (June-October 2012). Figure 4 shows the spatial distribution of NO\({}_{\text{X}}\), O\({}_{3}\), PM\({}_{2.5}\) and PM\({}_{10}\) ground concentrations averaged over the whole period. The ability of BOLCHEM was verified against data from the measurement campaign (available only for PM\({}_{2.5}\)) and data of monitoring stations managed by ARPA-Puglia. Figure 5 shows the time series of modelled and measured daily averaged concentration over the whole period. To quantify the model performance, the Pearson correlation coefficient (R), the mean bias (MB) and the root mean square error (RMSE) were also calculated. In the calculation of the statistical indicators, mean daily value of modelled and observed data were used. The mean concentration averaged over the whole period, in addition to the statistical indices, are reported in Table 5.  
BOLCHEM overestimation of NO\({}_{\text{X}}\) can be due to the fact that output refers to the model cell representing the port, where also ship emissions are located, as well as to the uncertainty of the employed emissions inventory (see Section 2.4.1). As for PM\({}_{2.5}\) and PM\({}_{10}\), they showed an overestimation probably due to the overestimation of sea salt by the model, while Saharan dust events occurred at the end of September and October were not reproduced by the model. However, since the focus is on the impact of ship emissions, we expect such overestimations slightly affected the results.  
Results show that BOLCHEM acceptably agreed with measured data. The model was then used in combination with ADMS-Urban to estimate the contribution of ship emissions on pollutant concentration levels following the zero-out method.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\mu\)g m\({}^{-3}\) & PM\({}_{2.5}\) & SIA & SOA & Combustion & Crustal & Anthropogenic & **Sea Salt** & **Under.** \\ \hline Urban-Industrial & \(17\pm 6\) & \(6.2\pm 1.6\) & \(3.1\pm 1.2\) & \(3.3\pm 0.2\) & \(3\pm 2\) & \(0.040\pm 0.019\) & \(0.6\pm 0.2\) & \(0.3\pm 0.4\) \\ Port & \(14\pm 5\) & \(5.5\pm 1.6\) & \(2.4\pm 0.9\) & \(2.4\pm 0.2\) & \(3\pm 2\) & \(0.031\pm 0.025\) & \(0.5\pm 0.2\) & \(0.2\pm 0.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Mean mass concentration (\(\mu\)g m\({}^{-3}\)) and standard deviation of PM\({}_{2.5}\) and of the main mass classes: secondary inorganic aerosol (SIA), secondary organic aerosol (SOA), combustion, crustal matter, anthropogenic metals, sea salt, and the undefined mass (UM).  
Figure 4: Average surface concentrations (\(\rm{\mu g~{}m^{-3}}\)) of (**a**) \(\rm{NO}_{\rm{X}}\), (**b**) \(\rm{O}_{\rm{3}}\), (**c**) \(\rm{PM}_{\rm{2.5}}\), and (**d**) \(\rm{PM}_{\rm{10}}\) obtained from BOLCHEM for the period June–October 2012 in the area surrounding the port.
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline
**Diagnostic Approaches** & **Aim for Usage** \\ \hline \multirow{7}{*}{CT preoperatively} & Estimate bone fractures and displacement; \\  & Indicate a potential conflict between the zygomatic bone and the coronoid process of the mandible; \\  & Evaluate the scope of fractures; \\  & Estimate any blood accumulation in the maxillary sinus, frontal sinus, and eye socket; \\  & Indicate if the skull base fracture is present with/or without fractures of the anterior and posterior \\  & frontal sinus walls; \\  & Zygomatic bone/arch fractures in 1, 2, 3 or more pieces; \\  & Evaluate the zygomatico-maximillary buttress, inferior, and superior orbital rims; \\  & Estimate if the globe position is symmetrical in both eye sockets and if the volume of the orbital \\  & socket has not decreased/increased; \\  & Possible blow-out or blow-in of the orbital floor/roof presence; \\  & Scope of a temporal bone fracture: displacement more or less than 5 mm; \\  & The anatomy of the external auditory canal and any disruption in the temporal bone pyramid; \\  & Temporo-mandibular joint (TMJ) injury: condylar head, condylar process, and rupture of the glenoid fossa; \\  & Coronoid process: fracture, displacement, defragmentation, or malposition after trauma \\  & Co-existing mandibular fractures; \\  & Other case-related (...) \\ \hline \multirow{7}{*}{CT postoperatively} & Evaluate proper bone alignment; \\  & Establish if superior, inferior, lateral, and medial orbital rims/walls are symmetrical; \\  & Proper eye socket floor/roof repositioning; \\  & Adequate stable osteosynthesis on the zygomatico-maximillary suture, fronto-zygomatic suture, and \\  & zygomatic arch position after reduction; \\  & Exclude any potential conflict between the coronoid process of the mandible and the zygomatic bone; \\  & If frontal sinus walls are fractured, estimate wall continuity and fronto-nasal duct adequate drainage; \\  & Establish proper maxillary sinus wall position and non-disrupted flow towards the osteomeat \\  & complex/OMC/; \\  & Other case-related (...). \\ \hline \multirow{7}{*}{CT late follow-up} & Evaluate good healing and proper bone alignment; \\  & Exclude any pathological fluid accumulation in the maxillary or frontal sinuses; \\  & Estimate if using stable titanium osteosynthesis plates/screws/mesh does not have any indications \\  & of inflammation or others. \\ \hline \multirow{7}{*}{MRI preoperatively} & TMJ disc position and its capsule; \\  & No fluid accumulation in the TMJ area; \\ \cline{1-1}  & Possible scarring, contusions, and contractions in the temporalis muscle; \\ \cline{1-1}  & Proper condylar head position in the glenoid fossa and adequate lateral pterygoid muscle function; \\ \cline{1-1}  & Other secondary findings (..). \\ \hline \end{tabular}
\end{table} 
Table 1: The scope of diagnostic procedures suggested by the authors. Abbreviations: CT—computed tomography; MRI—magnetic resonance, Panx—panoramic radiograph.
## 5 Parameter Estimation for Trajectory Prediction
### Parameter Estimation Equation

Table 2: Correlation equation between optimal distance parameter and intersection geometry related parameter. Figure 10: Parameter estimation for the Triclothoidal curve. There was a slightly strong linear correlation between the intersection parameters and the distance parameter \(d_{pre}\). Figure 12: Prediction results with estimated distance parameter \(\hat{d}_{pre}\) from experiment at intersection ID 2-2. Figure 13: Prediction results with estimated distance parameter \(\hat{d}_{pre}\) from experiment at intersection ID 2-3. The trajectory predictions were calculated at 20 Hz. The absolute prediction errors in Figures 11-14 refer to the deviation of the predicted vehicle rear axle trajectory in the curvilinear normal direction concerning the actual measured driving path. These errors were measured at every 0.5 m interval of each predicted trajectory. To evaluate trajectory prediction errors only around the intersections, in Figure 14, the evaluated section was limited to the displacement \(y\geq-25\) [m]. As can be seen in Figures 11-13, the trajectory predictions with the estimated parameters could predict the right turning trajectory overall, and the method could predict the vehicle trajectory before the vehicle drove into the intersection. The graphs that show absolute prediction errors indicate that the errors were suppressed under 1.6 m in all cases. After the turning started, the prediction errors became suppressed to around 0.5 m. In the result of the intersection ID 2-3, some absolute errors became larger at the end of the predictions; however, these errors were from the displacement after the vehicle completed the turning, therefore these errors were not included in the evaluation. Moreover, in most predictions, prediction errors at prediction positions that were shorter than 15 m ahead of the vehicle position were very low, at around 0.5 m. Thus, for driver assistance systems that require short-range trajectory prediction, such as emergency braking or steering assistance, the results showed that the proposed method could effectively predict the future positions of the ego vehicle. As can be seen in Figure 14, 75% of absolute errors in all cases were less than 1.2 m and, especially in the intersection ID 2-3, 75% of prediction errors were less than 0.4 m. In addition, 50% of absolute errors in all cases were less than 0.5 m. These results indicate that the proposed method had high prediction precision and accuracy. However, there was a variance in prediction errors by the intersection shapes. To improve the accuracy of the prediction, more intersection turning data may be needed to tune the parameter estimation in Equation (25).
###### Abstract  
It is challenging to measure the environmental impact of concrete with the absence of a consensus on a standardized methodology for life cycle assessment (LCA). Consequently, the values communicated in the literature for \"green\" concrete alternatives vary widely between 84 and 612 kg eq CO\({}_{2}\)/m\({}^{3}\). This does not provide enough evidence regarding the acclaimed environmental benefits compared to ordinary Portland cement concrete knowing that the average for the latter was concluded in this study to be around 370 kg eq CO\({}_{2}\)/m\({}^{3}\). Thus, the purpose of this study was to survey the literature on concrete LCAs in an attempt to identify the potential sources of discrepancies and propose a potential solution. This was done through examining 146 papers systematically and attributing the sources of error to the four stages of an LCA: scope definition, inventory data, impact assessment and results interpretations. The main findings showed that there are 13 main sources of discrepancies in a concrete LCA that contribute to the incompatibility between the results. These sources varied between (i) user-based choices such as depending on a cradle-to-gate scope, selecting a basic volume-based functional unit and ignoring the impact allocation and (ii) intrinsic uncertainty in some of the elements, such as the means of transportation, the expected service life and fluctuations in market prices. The former affects the reliability of a study, and hence, a concrete LCA methodology should not allow for any of the uncertainties. On the other hand, the latter affects the degree of uncertainty of the final outcome, and hence, we recommended conducting scenario analyses and communicating the aggregated uncertainty through the selected indicators.  
Keywords:green concrete; life cycle assessment; environmental impact assessment; inventory data; allocation; functional unit; service life +
Footnote †: journal: Applied Sciences
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
## 2 System Model
### Grouping Rule  
As shown in Figure 1, different delays of the asynchronous multi-users would lead to various symbol detection cases. It is hard to perform signal sampling and detection algorithms without grouping. Therefore, a grouping rule will help improve the system performance through categorizing similar delays into one group.  
Assuming that all users are in the same environment but only with different distances to the base station in the system model, we can divide the users with similar distances into one group.  
For example, there are two groups in the system model. The users with the distances similar to the \(d_{g_{1}}\) could be grouped as \(g_{1}\) and the users with the distances similar to the \(d_{g_{2}}\) could be grouped as \(g_{2}\). The users in both groups are defined as \(u_{g_{1}}^{(j)}\) and \(u_{g_{2}}^{(j)}(1\leq j\leq J)\). The transmitting power of these users is normalized to \(P_{g_{1}}\) and \(P_{g_{2}}\) as the identity. The modulation order is \(M\). The delay of two different user groups to the base station is \(\tau_{g_{1}}\) and \(\tau_{g_{2}}\) respectively and the arrows mean the transmitting direction, as Figure 2 illustrates.  
Figure 1: Different delays in the detection of asynchronous multi-users.  
Figure 2: Multi-user grouped model.
## 5 Conclusions  
The fundamental target of the current research involves the use of integrated geological and near-surface geophysical techniques to optimize the land-use plan for construction purposes and avoid the high-risk areas in the extension of Helwan city, Egypt. The applied geophysical techniques were utilized for monitoring the variations in subsurface distribution of geoelectrical and seismic velocity heterogeneities, in addition to the GPR signatures. The findings concluded that the subsurface sequence of the investigated site is represented mainly by two main layers: the first layer that is equivalent to highly weathered and argillaceous limestone, and the second layer that is corresponding to bedrock of limestone.  
The optimal land-use map was generated for the concerned study area by merging the integrated results of applied geological and geophysical techniques to determine the most suitable areas for construction purposes and avoid the high potential of risk areas. The investigated site was branched to two main units by avoiding the highly weathered, low seismic velocities and low true resistivity values, which also can be described as very steep lowland in southwestern part of the study area.  
Finally, the current study encourages to utilize and apply the specifically designed integrated geological and geophysical techniques of seismic refraction, geoelectrical resistivity sounding, and GPR for obtaining a reasonable subsurface image, which can perform the optimal geotechnical investigative program for the study area and its surroundings to maintain the sustainability of the future urban communities.  
It is so important and vital to understand the subsurface geological conditions of the new planned areas for constructional projects to prepare a sustainable land-use plan for  
Figure 15: Suggested land-use map of the investigated site: A unit, that represent the optimum area for construction purposes, and B unit, that equivalent to open, parking, garden areas.  
the investigated site. Therefore, the application, integration, and merging of a quantitative geological and geophysical survey program prior to the design and planning of the new urban communities, as applied through the current study, will provide a clear subsurface image and minimize the related costs of the geotechnical boring studies. Finally, the presented approach through the current study can be applied to more general situations.  
The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for supporting this work by Grant Code: 22 UQU4330935 DSR02.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction
## 2 Method and Model Establishment
### Sound Field Model of UAV Sound Signal  
The acoustic signals of UAVs are typically generated by friction between their motors, propellers, and air. As the motor drives the propeller to rotate at a relatively fixed frequency, the directly produced signal is the fundamental frequency signal. Nonlinear vibrations of the propeller combined with aerodynamic effects generate harmonic signals at integer multiples of the fundamental frequency. Thus, UAV acoustic signals are a superposition of the fundamental frequency signal and harmonic signals.  
In a complex practical environment, the sound signal of the UAV propagates in space simultaneously with various other sound source signals. Assume that the source sound signal is \(s_{i}(t)\), where \(i\) represents the serial number of different sound sources, and environmental noise can also be regarded as the source sound signal. \(s_{i}(t)\) is a continuous analog signal. The source sound signal reaches different sound sensors after intensity attenuation through different paths and different distances in space. Let \(x_{i}(t)\) be the acoustic analog signal received at the microphone. Then it can be expressed as Equation (1).  
\[x_{i}(t)=\sum_{j}a_{j}(i)s_{j}(t) \tag{1}\]  
where \(a_{j}(i)\) denotes the equivalent attenuation weight of the \(j\)-th source signal arriving at the \(i\)-th microphone. This model can be simplified using the matrix Equation (2).  
\[X(t)=AS(t) \tag{2}\]  
where matrix \(A\) consists of the attenuation weights, \(S=[s_{1},s_{2},\dots,s_{n}]^{T}\), and \(X=[x_{1},x_{2},\dots,x_{m}]^{T}\). In practice, microphones introduce minor Gaussian white noise due to their electrical characteristics and output discrete digital signals:  
\[X(k)=X(t)_{t=k}+X_{noise} \tag{3}\]  
According to the Nyquist sampling theorem, to prevent aliasing-induced distortion, the sampling frequency \(f_{s}\) must satisfy  
\[f_{s}\geq 2f_{max} \tag{4}\]  
Here, \(f_{max}\) represents the maximum frequency of UAV sounds. Smaller propellers generate higher fundamental frequencies and harmonic components, with typical small UAVs having maximum harmonic frequencies below 16 kHz. Consequently, the minimum required sampling frequency is 32 kHz. In our experiments, a 48 kHz sampling rate was adopted. Exceeding the Nyquist frequency provides denser sampling points, reducing reconstruction errors by better approximating the original analog signal.
# Optimization of the AODV-Based Packet Forwarding Mechanism for BLE Mesh Networks
## 4 Optimized-AODV Protocol Topologies and Testbed Design
### How the Optimizations Were Achieved
#### 5.1.3 BLE Mesh Network and AODV Layer Optimization  
Furthermore, in order to reduce the likelihood of unwanted retransmissions, we reduced the channel utilization and probability of collision due to retransmissions, as well as the packet loss by lowering the packet's time-to-live in both the application and network layers after making the necessary changes in the mesh protocol's network layer's bt_mesh_net_send and bt_mesh_net_recv functions. In addition, we improved the AODV protocol's rreq_send, rreq_recv, and rrep_send functions to reduce packet retransmission.