esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Mass Yields of PM\({}_{10}\)  
The mass-based yields of PM\({}_{0.5}\) and PM\({}_{0.5-10}\) formed during the air and oxy-fuel combustion of Zhundong coal are depicted in Figure 5. Firstly, it can be seen that the yields from OXY32 go up to 200 mg\({}_{\text{PM}}\)\({}_{10}\)/g\({}_{\text{ash}}\); such a high PM\({}_{10}\) yield from Zhundong coal compared to other power coals was also found in the study on the air condition by [PERSON] et al. (2012). This implies a terrible burden for dust-cleaning apparatuses such as electrostatic precipitators compared to other power coals. Secondly, the promoted O\({}_{2}\) fraction during oxy-fuel combustion increases the PM\({}_{10}\) yields, especially of PM\({}_{0.5-10}\), as explained in Section 3.1. Lastly, the yield of PM\({}_{0.5}\) from AIR is similar to that from OXY32 and higher than that from OXY21 and OXY27, while the yield of PM\({}_{0.5-10}\) from AIR is lower than that from OXY32 but similar to the OXY27 case. This can be explained by the effect of the combustion atmosphere on the partitioning of major ash-forming inorganic elements.  
Figure 4: Inorganic elemental composition of PM10 formed during the air and oxy-fuel combustion of Zhundong coal. (**a**) OXY21; (**b**) OXY27; (**c**) OXY32; (**d**) AIR. Stages 1–13 have a D50 size of 0.039, 0.072, 0.122, 0.205, 0.32, 0.488, 0.765, 1.231, 1.951, 3.08, 5.144, 8.099 and 10.747 \(\upmu\)m, respectively.  
Influence of Characterized Oxygen Input Fractions on the Emission of PM\({}_{0.5}\) and PM\({}_{0.5-10}\)  
Figure 6 presents the elemental PSDs of PM\({}_{10}\) while Figure 7 depicts the absolute mass of inorganic elements in PM\({}_{0.5}\) and PM\({}_{0.5-10}\), further indicating the mass contribution of various elements to the inorganic PM\({}_{0.5}\) and PM\({}_{0.5-10}\). As shown in Figure 6, Si and Al are only enriched in PM\({}_{0.5-10}\); a weak peak can be noted in PM\({}_{0.5}\) and an obvious peak in PM\({}_{0.5-10}\) can be found in PSDs of Mg; as with Ca, Na and S, two obvious peaks occur in both PM\({}_{0.5}\) and PM\({}_{0.5-10}\).  
Figure 5: Mass yields of PM\({}_{10}\) formed during the air and oxy-fuel combustion of Zhundong coal. (**a**) PM\({}_{0.5}\); (**b**) PM\({}_{0.5-10}\).  
Figure 6: Elemental PSDs of PM\({}_{10}\) generated from the air and oxy-fuel combustion of ZD coal. (**a**) Ca, (**b**) Mg, (**c**) Na, (**d**) S, (**e**) Si and (**f**) Al.
# Applications of Artificial Intelligence in Fire Safety of Agricultural Structures
###### Abstract
The information presented in Figure 1 is connected to the main body of the review given AI systems facilitate basic operations, information management, emergency evacuation, intelligent fire protection, and user management. The specific applications of the AI system functions in the prevention of fire in agricultural structures are reviewed in Section 2. The discussion is centered on Type B fuel fires caused by liquid fuels (oils, solvents, gasoline, and flammable liquids) [22]. Such fires are much more difficult to extinguish compared to Type A and Type C fires attributed to the burning of crop residues, dry grass and wood, and electrical faults, respectively [23]. Other rare types of fires are attributed  
Figure 1: AI system functional structure for fire safety [21]. to natural phenomena such as lightning [24]. The severity of fuel fires is evident from the scale of damage in tunnel fires, fires in fuel storage plants in China, the US, and Norway, among other countries [25; 26]. The Ministry of Agriculture in Canada estimates that barn fires caused damage worth USD 24 million in farms in 2014 [27]. Similar claims were made by the Animal Welfare Institute [28]. The cost of fires in the US was estimated to be about USD 11 billion [29]. The actual economic losses could be higher, taking into account the time for recovery and long-term environmental and social costs. The practical benefits and constraints associated with temperature, ionization, photoelectric, and CO sensor fusion in wireless sensor networks are discussed to affirm or reject the use of the naive Bayes classifier [30], Haar cascade classifier [11], PCA and SVM classifiers [31], WSNs [32], and probabilistic neural network classifiers [33], embedded into genetic algorithms (GAs), adaptive neurofuzzy inference systems (ANFISs), artificial neural networks (ANNs) [34], probabilistic neural networks (PNNs), recurrent LSTM neural networks (R-LSTM-NNs), and deep belief networks (DBNs).
## 4 Results and Discussion
### Fit of Durations Simulation
#### 4.2.2 Mazar Dudas Hydroelectric Project  
The evaluation was performed in months 5, 10, and 15 after starting the simulated execution of the project, and in months 10, 8, 6, and 1 before the simulated completion of the project. Figure 3 presents the time evolution of how the EVM and CTCR methods fit in their forecasts of the final completion time of the Mazar Dudas project, according to the ten thousand scenarios of the simulation model. It is apparent that for the Mazar Dudas project, from the first months of execution, the CTCR methodology presents a greater probability of fit than the EVM method. This indicates that CTCR predicts the period to completion of the project more efficiently than EVM. It is apparent that in month 10 before completion of the project, CTCR has a probability of 42.8% of correctly predicting the project's period of completion, while in the same period, EVM achieves a probability of only 30.7%. Similarly, in month 6 prior to completion, CTCR shows a probability of 43.7% compared with 38.2% for EVM.  
Figure 2: Evolution of the probability of fit of the EVM and CTCR methods to the forecast of the final period of the Cardenillo hydroelectric project.
## 4 Distributed Computation Offloading and Resource Allocation
### Computation Offloading is Modeled as a DRL Problem
#### 4.1.1 State Space  
The system state constitutes the factors such as the available offloading mode user \(k\), the maximum transmission rate, and the available distributed computing resources at an edge. Therefore, the system state is represented as \(s_{t}=\{s_{k}^{m},f_{1}^{k},f_{2}^{k},\ldots,f_{L}^{k},R_{k}\}\), where \(s_{k}^{m}\) represents the cloud and edge computing mode for the user \(k\). The maximum achievable transmission rate, \(R_{k}\), is dominated by the E-AP and cloud modes' power resources. Distributed computing resources \(\sum_{l\in L}f_{l,k}\) can be aggregated to meet the computation demand for the user \(k\)[36].
## 4 Expected Area-Based Real-Time Routing (EAR2)  
### Expected Area of a Mobile Sink  
For data dissemination to a sink, a source that detects an event needs to know the location of the sink through sink location services. Because we consider a mobile sink that randomly changes its own location in a sensor field, the obtained location cannot be exactly matched with the current location of the sink [10]. To deliver data to the sink within the desired time deadline \(T_{setdeadline}\), we expect that the mobile sink will move around during a space of time in a circular area, i.e., the expected area (\(EA\)). \(EA\) is able to be calculated by the obtained location and the velocity of the sink as in previous works [30; 31; 32]. The center point of \(EA\) is the initial obtained location of the sink. Let \(t_{0}\) be the time when the sink locates the initial location, \(t_{1}\) be the time when the source obtains the location of the sink, \(t_{2}\) be the time when the source generates its data to deliver to the mobile sink, and \(t_{3}\) be the time when the data from the source is received by the sink. The \(T_{setdeadline}\) is \(t_{3}-t_{2}\). The maximum moving distance \(r\) of the mobile sink, i.e., the radius of \(EA\) with the sink speed \(V_{sink}\) is calculated as follows according to the method in [31]:  
\[r=(t_{3}-t_{0})\times V_{sink}. \tag{2}\]  
As shown in Figure 1, because \(r\) is the maximum distance that the sink can move in a straight line during \(t_{3}-t_{0}\), the sink can be located at any position in \(EA\) at \(t_{3}\) due to its unrestricted movement, as mentioned in the paper [32].
## 5 Conclusions  
In this paper, an application for diabetes education is proposed. The proposed application was designed to provide a software tool to help support therapy in patients with diabetes, seeking to improve the quality of life. The simulation is fixed, but an interactive simulation functionality can be added to provide a richer user experience. The software is based on four mathematical models originally designed as a desktop computer program. An exercise option in the interface helps patients to schedule their routines and to estimate in advance its impact on blood glucose. This app could also be developed for Apples' iOS products. Besides, this app could potentially be implemented as a smartwatch app. The most important conclusions are:  
1. The App includes glucose regulation associated with metabolism and the application of an insulin injection.
2. The App is capable of taking into account the effects of food intake and physical activity.
3. The developed application can simulate the behavior of glucose levels for long periods.
4. The App is capable of considering three types of physical activity: light, moderate and heavy.
5. The developed application is focused on type-1 diabetes, but this can be extended to consider type-2 diabetes.
6. As a future improvement, the application should be modified to split the simulation processing into smaller operations running on multiple threads. This could be beneficial in devices with more than one core.  
Conceptualization, M.H.-O., M.A.N.-M.; Formal Analysis, C.A.C.-A., A.R.-L.; Investigation, M.A.N.-M., G.E.O.-C., J.M.C.-C.; Methodology, M.H.-O., C.A.C.-A., G.E.O.-C., R.S.-H., O.M.-R.; Project Administration, M.H.-O., R.S.-H.; Software, M.H.-O., G.E.O.-C., J.M.C.-C., O.M.-R.; Supervision, M.H.-O., M.A.N.-M., A.R.-L., O.M.-R.; Validation, A.R.-L., R.S.-H., J.M.C.-C.; Visualization, A.R.-L., J.M.C.-C., O.M.-R.; Writing Original Draft, M.H.-O., M.A.N.-M., C.A.C.-A., G.E.O.-C.; Writing Review and Editing, M.A.N.-M., C.A.C.-A., R.S.-H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Device**} & \multicolumn{4}{c}{**Simulation Time Duration**} \\ \cline{2-5}  & **4 Days** & **1 Week** & **2 Weeks** & **3 Weeks** \\ \hline Polaroid Tab & 12.30 & 13.53 & 16.24 & 21.11 \\ Galaxy S2 & 9.53 & 10.97 & 13.16 & 18.16 \\ Galaxy S4 & 6.53 & 7.64 & 9.17 & 12.84 \\ LG G3 Stylus & 5.53 & 6.59 & 7.90 & 11.30 \\ Galaxy Tab 10.1 & 5.53 & 6.70 & 8.04 & 11.73 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time to complete computation of glucose concentration plots (in seconds) using several simulation time durations.  
**Acknowledgments:** The present project was partially funded by the National Council of Science and Technology of Mexico (CONACYT) through a scholarship (no. 337087) granted to [PERSON]. The authors are grateful for the support provided by TNM-ITV, CONACYT and PRODEP.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Practical Discussions  
Using unmanned aerial vehicles (UAVs) and the Stackelberg game theory model can play a critical role in improving security and protecting privacy in disaster situations requiring rapid and accurate diagnosis and treatment of injured individuals. Whether it be a natural disaster such as a hurricane, tornado, or flood, or a human-made disaster such as a terrorist attack or mass casualty incident, the use of UAVs and the Stackelberg game theory model can be useful. Combining the Stackelberg game theory model with UAVs can help ensure efficient and secure communication between healthcare providers and emergency  
Figure 10: End-to-end delay (ms) vs. no of nodes.  
responders. In the aftermath of a disaster, one of the main challenges facing healthcare providers and emergency responders is collecting and transmitting accurate medical data from the affected individuals. These data are critical for making informed decisions about how to treat patients and allocate resources. However, traditional communication networks may be unable to handle the volume of data transmission required in these situations and may also be vulnerable to cyberattacks and other security threats. This is where UAVs and the Stackelberg game theory model can make a significant difference. The UAVs can assist the patients and rescue centers with Internet of Medical Things (IoMT) devices, such as vital signs monitors, patient health records, and other medical sensors. These devices can collect and transmit data in real time to the UAVs, which can then relay the data to hospital centers and emergency response teams. This allows for more rapid and accurate diagnosis and treatment of injured individuals and can help to save lives in critical situations. The Stackelberg game theory model can also protect patient privacy and prevent unauthorized access to sensitive medical information. The model uses a lightweight encryption scheme that ensures secure data transmission between the UAVs and the IoMT devices, preventing third-party access to patient data.  
Additionally, the hierarchical approach of the model allows UAVs to act as leaders in the game, making strategic decisions to optimize network performance and ensure secure data transmission. In contrast, IoMT devices act as followers, responding to the decisions made by the UAVs. Recently, an earthquake came to Turkey, which destroyed the main architecture of wireless networks it was critical to find the victims and offers first aid services and in this case, UAVs can play a crucial role in informing the rescue staff or hospitals about the patient's locations. Moreover, UAVs can also transport medical first aid staff to remote areas. In these scenarios, the patient's life is more important; therefore, it is necessary to have secure communication.
# Practical Use of Starlink Downlink Tones for Positioning
## 4 Multi-Epoch Positioning
### Positioning Algorithm
The line of \(H\) corresponding to innovation \(\Delta\hat{\rho}^{\widetilde{k}}_{j}\) is \(H\bigg{(}\Delta\hat{\rho}^{\widetilde{k}}_{j}\big{)}=\Big{[}h^{k}_{r,j^{ \prime}},h^{k}_{c\delta t_{r},j^{\prime}}\left(1+\hat{t}^{1}_{c\delta t_{r},j }\Delta t_{j,1}\right),h^{k}_{\lambda\delta f}\Big{]}\), with \(h^{k}_{\lambda\delta f}\) being a \(1\times K\) vector whose \(k\)th-element is set to \(-1\) and the remaining elements are set to \(0\). Every \(m\) epochs, the EKF filter gain is computed as \(K=PH^{T}\big{(}HPH^{T}+R\big{)}^{-1}\) and the state and its covariance are respectively updated according to \(x=x+K\Delta z\) and \(P=P-KHP\).
# GPSR: Gradient-Prior-Based Network for Image Super-Resolution
###### Abstract
We propose two Residual Spatial Attention Adaptive Aggregation Modules (RS3 AMs) to fully utilize the hierarchical features from the original LR images and embed them in the SR branch and the gradient branch, respectively. Specifically, on the one hand, considering that most SR methods [10; 13; 15] suffer from the hierarchical features being learned in a local way, which hinders the SR performance, we propose a novel Contextual Residual Fusion Structure (CRFS). Our CRFS removes over-dense connections and frequent concatenation operations from the dense block of RDN [11], and adaptively learns global hierarchical features in a holistic way, which promotes its feature representation ability. On the other hand, to enhance the feature representation of CRFS, we propose an efficient Large-Receptive-field-based Spatial Attention Module (LRSAM) to adaptively recalibrate the local spatial information of the feature map. To the best of our knowledge, large receptive fields facilitate the network to fully exploit the interdependence of local spatial information and significantly strengthen the sensitivity of the SR model toward critical spatial content. Finally, we combine LRSAM with CRFS to acquire two RS3 AMs for effective feature extraction in the SR branch and the gradient branch, respectively. It is worth mentioning that GPSR achieves better SR performance with smaller parameters (8.18M) compared to most state-of-the-art SR methods, such as SAN [15] (15.9M), HAN [12] (16.07M), and NLSN [13] (46.52M). Overall, our contributions are summarized as follows. * We propose a Gradient-Prior-based Super-Resolution network (GPSR) for images. The experimental results show that the proposed GPSR achieves superior performance against state-of-the-art methods in terms of subjective visual results and objective evaluation metrics. * We propose a detail-preserving Gradient-Guided Strategy (GGS) to prompt the model to focus more attention on the most critical high-frequency components, and to suppress undesired geometric distortions as much as possible. * We propose two Residual Spatial Attention Adaptive Aggregation Modules (RS3 AMs) and further incorporate them in the SR branch and the gradient branch, respectively, to fully explore and utilize the intermediate features to enhance feature representation capability.
# The Development of the Pipe Jacking Guidance Technology
## 3 Laser Target Method  
### System Configuration  
The laser target method is a relative approach to the manual visual inspection method. It involves the implementation of a corresponding transformation by the laser target, thereby replacing the manual visual inspection. As illustrated in Figure 2, the laser target structure is composed of multiple components. The entire equipment for the laser target is placed in a hermetically sealed metal box, the interior of which consists of thin lenses, a CCD camera and a dual-axis inclinometer, among other things. Since the distance of the light-sensitive surface from the thin lens is the focal length f of the thin lens, the laser beam will form a spot with a certain uniform radius on the light-sensitive surface after passing through the thin lens. The CCD camera records the position of the laser spot noted as P(x\({}_{t}\),y\({}_{t}\)). The azimuth angle is calculated using spatial relationships, and the dual-axis inclinometer captures the pitch and roll angles.  
Given that the thickness of the thin lens is considerably less than the focal length f, the offset due to the refraction of the laser beam is neglected in the localized enlargement in Figure 2, i.e., \(\Delta\)m = 0. Since the optical axis of the laser target is parallel to the central axis of the pipe jacking machine, the angle \(\delta_{\rm L}\) between the laser beam and the optical axis is also the angle between the laser beam and the central axis of the pipe jacking machine. Based on the spatial geometry, \(\delta_{\rm L}\) is calculated as shown in Equation (1).  
\[\delta_{L}=\arctan\biggl{(}\frac{1}{f}\sqrt{x_{f}{}^{2}+y_{f}{}^{2}}\biggr{)} \tag{1}\]  
The system composition of the laser target method is illustrated in Figure 3. The laser target is set up at the pipe jacking machine, and it is ensured that the optical axis of the laser target (shown in Figure 2) remains parallel to the central axis of the pipe jacking machine. The target prism is close to the laser target, and the laser total station is set up in the starting shaft, and the rear-view prism with a known coordinate is set up on the wall of the starting shaft. The ground control terminal is responsible for resolving the collected data to obtain the required guidance parameters.  
Figure 2: Structure composition of the laser target.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 3 System Architecture
### OneWeb's System  
According to FCC filings, the global communications company OneWeb has planned to launch the WorldVu constellation of satellites in two phases [30; 31; 32], with its first launch in 2018. The initial phase is composed of 716 satellites, and the second phase is focused on covering Earth regions with a higher population, adding 5656 satellites to the constellation [18]. The services will start in 2023, due to a pause caused by economic problems [14].  
Antennas between 2.4 and 3.5 m will be used in gateway earth stations. Each satellite will have assigned at least two gateways, and a supplementary antenna for handovers. They expect to have a minimum of five gateway earth stations in the USA, building the initial network using existing stations. These stations are also responsible for the transmission and reception of satellite payload control channels and gateway link power control. Communication with satellites is only allowed after reaching the elevation angle of 15 degrees [31]. The gateway of OneWeb will use the K-band in both uplink (27.5-30 GHz) and downlink (17.8-19.3 GHz) [2].
# Reduction of Satellite Signature Effect in High-Accuracy Satellite Laser Ranging to Etalon
## 3 Pre-Processing Algorithm with a Center of Mass Corrections Filter
Variations in the RMS of center of mass can be explained by the acceptance level of range residuals for normal points. By r setting to 3.34 mm, the RMS of center of mass corrections will be minimized, which means the range error introduced by the variation of the satellite signature effect will be reduced to a minimum. We propose a center of mass corrections filter that is applied to the normal point algorithm in order to correct for satellite signature effect on SLR data. In the suggested method, the residual histogram is located first by using a least-squares approach. Afterwards,  
Figure 6: Relationship between RMS of CoM and clipping location of the range residual distribution for normal points. residuals from the selected range are clipped with a \(-\)2.5 \(\times\) RMSrange in order to remove the noise points caused by the satellite signature effect. The selected residuals are then clipped by the second clipping step using 2.5 \(\times\) RMS criteria to form the SLR production. A flowchart of the SLR post-processing algorithm with a center of mass corrections filter is shown in Figure 7.
## 3 Results and Discussion
### Construction of the CDI
#### 3.1.3 Response of Agricultural Drought-Affected Area to Different Drought Indices  
We examined the temporal variation in the drought-affected area of agriculture and various drought indices of farmland areas in the study area from 1986 to 2015 to compare their applicability to agricultural drought in NEC (Figure 6). The results revealed that the drought-affected area was highly significantly (\(p<0.001\)) and correlated with each drought index, with the CDI (r = \(-0.732\)) best reflecting the impact of drought on agriculture and much better than the scPDSI (r = \(-0.639\)). Among the soil, atmospheric, and vegetation drought indices, SMCI (r = \(-0.712\)) had the highest correlation, indicating that a soil moisture deficit is most critical in agricultural drought. The VHI (r = \(-0.584\)) has the worst correlation with the drought-affected area of agriculture, and this may be because the reduction in VHI is not only due to drought, but also other meteorological disasters or pests. In addition, factors such as coarse resolution (crops mixed with forests in one pixel) and cloud pollution can also affect the accuracy of the VHI in monitoring agricultural drought. Considering the response relationship of each drought index with SIF and the agricultural drought area, we discovered that the CDI has the advantages of wide spatial range, long time range, and high accuracy in drought identification, which can be used for further study of agricultural drought hazards in NEC.