esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### The SAVANNA Modeling System  
This study applies the SAVANNA model (Figure 2) ([17,18,20,21, 22], a grid-based, ecological modeling system. SAVANNA is a spatially explicit and process-oriented model that was developed to address localized management questions for different ecosystem types that include grasslands, shrublands and forests within the savanna biome. The early development and application of the SAVANNA model was in Turkana district of Kenya and improvements to the model were made in subsequent applications [23,24, 25]. The model was designed to address the spatio-temporal variability of ecosystems by using remotely sensed data, GIS databases as well as spatial simulations in order to compute rates of plant production, forage intake by animals and ecosystem functions [26]. It simulates processes in an ecosystem using a weekly time step, simulating vegetation quantity and distribution in response to climate inputs as well as vegetation types consumed by herbivores [19]. The model also simulates the spatial redistribution of herbivores in response to changes in vegetation quantity as well as herbivore demographic responses to changes to biotic and abiotic factors. One advantage of the more meso-scale-focused SAVANNA model to more globalized, savanna ecosystem models such as G-Range [27], aDGVM [28] and LPJ-GUESS [29] is that localized conditions, impacts and potential mitigation alternatives can be simulated and explored in spatial and temporal detail that are meaningful to local managers and stakeholders.  
Figure 1: Location of Nkayi district in Matteeland north province of Zimbabwe (Data source: International Crop Research Institute for the Semi-Arid Tropics (ICRISAT)).
## 3 Case Study
### Results  
In the training procedure, 0-12 cables are simulated to be dropped, corresponding to a 0-85.7% missing rate. Figure 7(a) shows the loss curves of the DNN-structured and the LSTM-structured autoencoder model trained with a 50% missing rate (i.e., 7 randomly missing cable data are dropped out). Figure 7(b) shows the training errors of DNN and LSTM networks under different data loss rates. It can be observed from Figure 7 that in the missing data cases, the training error of DNN and LSTM networks increases exponentially with the increase of the missing rate (i.e., the number of missing cables k).  
The result shows that the LSTM-structured AE model performs a lot better than the DNN-structured AE model. Compared with the DNN-structured AE model, the LSTM-structured AE model can form a memory of historical data within the network, thereby extracting spatiotemporal correlation features of cable forces in the cable group. Therefore, under the missing date occasion, the performance of the LSTM-structured AE model and spatiotemporal correlation of cable tension far exceeds that of the DNN-structured AE model and spatial correlation alone.  
Figure 8 shows the prediction results of the pre-trained LSTM network for cable tension on 1 November 2011. While cable tension of SJX08, SJS13, and SJX13 are really missing, cable tension of SJS08, SJS09, SJS11, SJX11, SJX12, SJS13, and SJX13 are dropped out. Eight cable forces were really missing in the input data. With the pre-trained model, the prediction is illustrated in Figure 6. Indicating that the LSTM-structured AE model can reconstruct the missing data and diagnose cable health based on the pretrained agent model of the cable group's initial performance. The real cable tension value of cable SJS11 in Figure 8c is lower than the benchmark prediction value, indicating a decrease in the actual carrying capacity of the cable and thus indicating the cable damage, which is consistent with the cable state evaluation results released in [39].  
Figure 9 shows the diagnosis results of the cable group based on the 3-\(\sigma\) criterion. The results indicate that only cable SJS11 is damaged based on the standard values of the prediction error calculated using the pre-trained LSTM-structured AE model, while the other cables are in healthy condition, which is consistent with the cable state evaluation results in [39].  
Figure 7: Comparison of DNN-structured and LSTM-structured AE models.  
Figure 8: Prediction results of DNN and LSTM in a 50% data loss rate case (1 November 2011).
# Analysis and Optimization of Truck Windshield Defroster
## 4 Results and Discussion  
### Performance Evaluation of the Existing Defrosting Duct System  
Table 2 shows the airflow distribution, outlet volume flow rate, outlet air speed, and pressure loss at the outlet of the defrost duct based on the specification of the truck. The total air volumetric flow was 250 CMH (cube meters per hour). It shows that around 48% of the air flow was from the center outlet and another 52% of the air flow was almost equally distributed to the other four outlets, with a slightly lower distribution in the side outlets. It was also found that the difference of the pressure losses of the air flow at the five outlets was very small. This indicates that the design of the defrost duct is based on the equal pressure loss method in this truck.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Outlet Number**} & **Air Distribution** & **Outlet Volume** & **Outlet Air Speed** & **Pressure Loss—Static** \\  & **(100\%)** & **Flow Rate (CMH)** & **(m/s)** & **Pressure (Pa)** \\ \hline LH & 12.2 & 30.5 & 6.8 & 204.73 \\ CL & 13.6 & 34 & 1.7 & 217.67 \\ CC & 47.9 & 119.75 & 3.5 & 217.68 \\ CR & 14.1 & 35.25 & 1.7 & 217.70 \\ RH & 12.2 & 30.5 & 7.8 & 201.56 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Defrosting duct calculation results.  
Figure 5: Qualitative comparison of the defrost effect between the numerical results and experimental data: (**a**) 15 min defrosting effect; (**b**) 20 min defrosting effect.  
Figure 6 shows the defrosting performance duct for 25 min, which is the test time required in the national standard (GB11555-2009). It can be seen that the defrosting performance was not ideal since the frost did not melt at the two triangle zones as highlighted in the figure (black circle). These two positions could affect the driver's vision. These two zones were found mainly due to the low air volume flow rate and the low speed at the air outlets center left (CL) and center right (CR).
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
## 3 Method of This Paper
### Loss Function  
The network model proposed in this paper uses two loss functions, denoted as Loss_mse and Loss_ssim, where Loss_mse is the minimum mean square error of the predicted outcome and the corresponding Groundtruth, also known as the MSE loss function, can be expressed by the formula:  
\[L(y_{i},\mathbf{f}(x_{i}))=\frac{1}{N}\sum_{i=1}^{i=N}(y_{i}-f(x_{i}))^{2} \tag{6}\]  
where \(x_{i}\) denotes the \(i\)-th group of fogged images;\(y_{i}\) denotes the \(i\)-th group of non-fogged images; \(f(x_{i})\) denotes the result of the network model after defogging the fogged images; and \(i\) = 1,2,..., \(N\) denotes the number of samples for training. Loss_ssim is the SSIM loss, and SSIM is also called the structural similarity, which is a measure of the similarity of two images, comparing the defogged image with the real standard fog-free image, the larger the value of SSIM, the smaller the distortion of the defogged image. Moreover, SSIM takes values in the range of 0 to 1. Therefore, 1-SSIM was taken as the SSIM loss. The total loss function is the sum of Loss_mse and Loss_ssim, which can be expressed by the formula:  
\[\text{Loss\_sum}=\text{Loss\_mse}+\text{Loss\_ssim}=\text{Loss\_mse}+(1-\text {ssim}) \tag{7}\]  
The whole training process was optimized by the ADAM solver, the initial learning rate was set to 0.0001, where \(\beta_{1}\) = 0.9, \(\beta_{2}\) = 0.999, and the learning rate was adjusted using cosine annealing to set eight iterations to complete one cosine cycle, the number of iterations was set to 10, and the number of batch images was set to 4.
# A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming
## 2 Related Works  
There are many related studies on UAV resource allocation. Usually, they are different from three aspects: the goal, the object, and the method. Different allocation goals will bring totally different results. Unlike some methods for improving the physical layer parameters, the resource allocation for video transmission should naturally be set from the application layer. The object determines the main body of the allocation. At this point, the UAVs flying in cluster are not only different from single UAV, but also different from other mobile networks. Allocation methods are the concrete algorithms and steps adopted in the process. Because of the particularity of UAV cluster, a decentralized method may guarantee more robustness of the system.
## 6 Data Transfer and the Creation of a 3D Model for VR and AR
### Finalisation of Results
Ambient Occlusion (AO) mapping is a technique used to simulate soft shadows in areas like creases, holes, and spots where surfaces are close together, resulting in a darker appearance as ambient light is blocked. By estimating the amount of ambient light that reaches each point on a surface, AO enhances depth and realism, adding shadows to occluded areas [51]. After researching and testing the technology (high and low poly mesh, mapping) in the new game engine and Agisoft Metashape 2.0, a complete VR museum of the entire project was redesigned and recreated, which includes photos from the development of the reconstruction, scale models (also available in AR), and 3D models of all the stages that can be viewed at actual size with high quality texture.
## 3 Analysis of the Dual-Peak Resonance Restructured by Thermal Strains
### Analysis of Type C SMC-FBG  
In this case, the parameters of coating satisfy \(\alpha_{1}<\alpha_{2}=\alpha_{3}\) and \(E_{2}=E_{3}\), and the coating on the same one Bragg grating is divided into two sections with the same material but the different thickness. We use \(E_{2}\) for coating's Young's modulus and \(\alpha_{2}\) for coating's thermal expansion coefficient. Figure 9 shows the diagram of the thermal strains. \(\varepsilon_{1}L_{1}\) is caused by the thinner coating on the half-length of FBG and the thicker coating on the other half-length of FBG. Let \(A_{N2(N=d,s)}\) represent \(A_{d2}\) and \(A_{s2}\), which means the cross-section of the thicker coating and the thinner coating. And let \(\sigma^{\prime\prime}{}_{siz}\) and \(\sigma^{\prime\prime}{}_{siz}\), which means the thermal stress of the thicker coating and the thinner coating.  
Figure 8: Diagram of the thermal strains of the Type B SMC-FBG.  
\(\sigma^{\prime\prime}_{\mathit{diz}}\) and \(\sigma^{\prime\prime}_{\mathit{siz}}\) can be induced by Equations (15a) and (15b). Then the temperature sensitivity \(K^{\prime\prime}_{\mathit{dT}}\) and \(K^{\prime\prime}_{\mathit{sT}}\) can be expressed using Equations (16) and (17).  
\[\sigma^{\prime\prime}_{N1z}A_{1}=\sigma^{\prime\prime}_{N2z}A_{N2} \tag{15a}\] \[\alpha_{1}\Delta T\frac{L_{1}}{2}+\frac{\sigma^{\prime\prime}_{N1z}}{E_{1}} \frac{L_{1}}{2}=\alpha_{2}\Delta T\frac{L_{1}}{2}-\frac{\sigma^{\prime}_{N1z} }{E_{2}}\frac{L_{1}}{2}\] (15b) \[K^{\prime\prime}_{\mathit{dT}}=\lambda_{B}\bigg{[}(\alpha+\xi)+(1-p_{e})* \frac{(\alpha_{2}-\alpha_{1})A_{d2}E_{2}}{E_{2}A_{d2}+E_{1}A_{1}}\bigg{]}\] (16) \[K^{\prime\prime}_{\mathit{sT}}=\lambda_{B}\bigg{[}(\alpha+\xi)+(1-p_{e})* \frac{(\alpha_{2}-\alpha_{1})A_{s2}E_{2}}{E_{2}A_{s2}+E_{1}A_{1}}\bigg{]} \tag{17}\]  
In three cases, two different strains make the original single reflectance spectrum split into two peaks. Comparing \(K_{\mathit{sT}}\), \(K^{\prime}_{\mathit{sT}}\) and \(K^{\prime\prime}_{\mathit{sT}}\), we can find that they are of the same expression with different parameters. We use \(\Delta K_{\mathit{T}}=K_{\mathit{dT}}-K_{\mathit{sT}}\) to describe the temperature sensing difference between the two peaks for Type A SMC-FBG, \(\Delta K^{\prime}_{\mathit{T}}=K^{\prime}_{\mathit{dT}}-K^{\prime}_{\mathit{ sT}}\) for Type B SMC-FBG, and \(\Delta K^{\prime\prime}_{\mathit{T}}=K^{\prime\prime}_{\mathit{dT}}-K^{\prime \prime}_{\mathit{sT}}\) for Type C SMC-FBG. Considering \(h_{1}\) and \(h_{2}\), \(\Delta K_{\mathit{T}}\) and \(\Delta K^{\prime}_{\mathit{T}}\) can be expressed as Equations (18) and (19), whereas, the values of \(\alpha_{2}\), \(\alpha_{3}\), \(E_{2}\) and \(E_{3}\) are different. Then \(\Delta K^{\prime\prime}_{\mathit{T}}\) can be expressed as Equation (20).  
\[\Delta K_
# AVO-Friendly Velocity Analysis Based on the High-Resolution PCA-Weighted Semblance
## 2 Method  
### Review of Conventional AB Semblance  
The NMO-corrected seismic event can be denoted as a vector \(\mathbf{a}\in\mathbb{R}^{N}\). Assuming that there is no variation of amplitudes along with seismic events, the reference can be regarded as a constant vector \(\mathbf{c}\in\mathbb{R}^{N}\) whose elements are all equal to 1. Then the coherence coefficient of vectors \(\mathbf{a}\) and \(\mathbf{c}\) can be defined as  
\[\gamma^{2}(\mathbf{a},\mathbf{c})=\frac{\mathbf{a}\cdot\mathbf{c}}{|\mathbf{a }||\mathbf{c}|}=\frac{\left(\sum\limits_{i=1}^{N}a_{i}\right)^{2}}{N\sum\limits _{i=1}^{N}a_{i}^{2}} \tag{1}\]  
where \(\mathbf{a}\) is the amplitude of the NMO-corrected seismic event. Considering that a series of scanning velocities is used for NMO correction, the semblance spectrum can be expressed as:  
\[S(it,v_{k})=\frac{\sum\limits_{i=i-w/2}^{it+w/2}\left(\sum\limits_{j=1}^{N}a_ {\text{k}}(i,j)\right)^{2}}{N\sum\limits_{i=i-w/2}^{it+w/2}\left(\sum\limits_{ j=1}^{N}a_{k}^{2}(i,j)\right)} \tag{2}\]  
where \(a_{\text{k}}(i,j)\) is the amplitude of NMO-corrected CMP gather at the time index \(i\) and trace numbers \(j\) and \(N\) are the number of traces. \(S(it,v_{k})\) represents the coherence coefficient at the time index \(it\) with a scanning velocity \(v_{k}\) and \(w\) is the length of the window along with the time axis.  
The reference vector \(\mathbf{c}\) is not a constant sequence when the AVO anomalies exist. It was supposed that the reference vector has a trend \(b(i,j)=A(i)+B(i)\theta(j)\)[27], where \(A(i)\) and \(B(i)\) represent the AVO intercept and gradient, respectively. \(\theta(j)\) is a known function can be replaced by the offset \(x(j)\) and \(b(i,j)\) is the seismic amplitude of the Shuey approximation at offset \(x(j)\). The coefficients \(A(i)\) and \(B(i)\) can be estimated from the least-squares fitting:  
\[b(i,j)=\arg\min_{A(i),B(i)}(\sum\limits_{j=1}^{N}a(i,j)-A(i)-B(i)x(j)). \tag{3}\]  
Here Appendix A gives the exact expressions for solving \(A(i)\) and \(B(i)\). Then, substituting Equation (3) into Equation (2), we obtain the well-known AB semblance with an analytic form.  
\[S_{AB}(it,v_{k})=\frac{\sum\limits_{i=i-w/2}^{it+w/2}\left(\sum\limits_{j=1}^ {N}a_{\text{k}}(i,j)b_{k}(i,j)\right)^{2}}{N\sum\limits_{i=i-w/2}^{i+w/2}\sum \limits_{j=1}^{N}a_{\text{k}}^{2}(i,j)\sum\limits_{j=1}^{N}b_{k}^{2}(i,j)} \tag{4}\]  
The AB semblance uses a trend-based reference \(b(i,j)\) to replace the constant reference \(\mathbf{c}\). This trend function represents the fully corrected seismic events with the classic AVO anomalies. Compared with the conventional semblance, the AB-semblance can recover the energy clusters in the AVO region. However, the trend function significantly reduces the resolution of the AB semblance spectrum, and even the resolution of the AB semblance spectrum is lower than that of the conventional spectrum.
## 6 Uses of UAVs in Agriculture Advancement
### Planting  
Today, millions of acres are not cultivated worldwide due to the lack of appropriate manpower or human inaccessibility. The safety issue of the rough terrain is the major problem for not utilizing these areas for agricultural or forestry purposes. To this end, drone-based sowing methods are being established that can reduce sowing expenses by up to 85% [149]. Not only is it cost-effective, but it even expedites the planting because many newly formed drones can plant about 100,000 trees within one day [150]. These systems shoot pods comprise seed, which are essential nutrients required for plant growth. This technique is very effective for rough terrain; significantly, the success rate is more than 75% [151]. Because of the flexibility and success, the recommended drones can be excellent plantation systems worldwide [152; 153].
## 3 Test Result
### Influence of the Slurry Concentration on the Friction Characteristics of the Interface
#### 3.1.2 Concentration Greater Than 20%  
Table 2 shows that the peak shear stress and friction coefficient increased with the increase in the concentration, which is greater than 20%. This is occurred because the slurry with a concentration greater than 20% was semi-solid, its \"support-barrier\" soil effect was remarkable, and the adhesion degree with the pipe wall was greatly improved. When the sand slid relative to the pipe wall, shear failure occurred in the slurry interior (as shown in Figure 6d). Therefore, when the pipe soil slid relatively, it had to overcome the spatial structure strength of the slurry, which is the gel strength. With the further development of shear, the relative sliding of the pipe soil was subjected to the cohesion of particles in the slurry, which is internal friction. As the gel strength (or static shear stress) of the slurry increased with the increase in the solid particle content, and the internal friction increased with the increase in the solid particle content, the greater the slurry concentration, the greater the peak shear stress and friction coefficient.  
After comparing the test data of the slurry with a concentration of 20% and 25%, it was found that \(\mu^{\omega=25\%}<\mu^{\omega=20\%}<\tau_{\max}{}^{\omega=25\%}<\tau_{\max}{} ^{\omega=20\%}\). This is because shear failure of the former occurred between the pipe wall and the slurry while the shear failure of the latter  
Figure 6: Friction characteristics of the pipe–soil interface with different slurry concentrations. (**a**) Shear failure surface of slurry with a concentration equal to 12%. (**b**) Shear failure surface of slurry with a concentration equal to 14%. (**c**) Shear failure surface of slurry with a concentration equal to 20%. (**d**) Shear failure surface of slurry with a concentration equal to 25%.  
occurred in the slurry interior, and the slurry with a concentration equal to 25% slurry had a lower solid particle content than the other higher-concentration slurry, so the lubricity was better.  
In summary, the concentration has a critical value, which is 20%. When the concentration does not exceed the critical concentration, the slurry state is liquid; otherwise, it is semi-solid. Liquid slurry is mainly used for lubrication, and semi-solid slurry is mainly used to support soil. Different concentrations of slurry have different effects on the peak shear stress and friction coefficient of the pipe-soil interface. For liquid slurry, the peak shear stress of the interface depends on the structural shear strength of the interface, and the friction coefficient depends on the degree of friction between the slurry and the pipe wall. For semi-solid slurry, the peak shear stress depends on the spatial structure strength of the slurry, which is the static shear stress, and the friction coefficient depends on the internal friction of the slurry.
# Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
## 3 Results
### Cross-Validation within a Location  
The results of cross-validation are shown in Figures 7 and 8. Diagonal components, for which the training and test locations were the same, represent the results of 10-fold cross-validation within a location. The MAEs and R-squared values were improved by both linear regression-based and ANN-based calibration. ANN calibration performed better at all locations, confirming the effectiveness of our machine learning-based approach.  
Figure 6: Indoor air temperatures measured by low-cost and high-accuracy (reference) sensors. The indoor experiment was conducted for 15 days, and the air temperatures were very close in the whole period.  
Figure 7: MAE between air temperatures from high-accuracy sensor and low-cost sensor (original), linear regression-based calibration (linear) and ANN-based calibration (ANN).  
Example results are shown in Figure 9. The air temperatures measured by the low-cost sensor were clearly higher than those measured by the high-accuracy sensor when solar radiation was strong. These abnormal values were corrected by applying ANN-based calibration, bringing the calibrated air temperatures close to those from the high-accuracy sensors.  
Figure 8: R-squared values between air temperatures from high-accuracy sensor and low-cost sensor (original), linear regression-based calibration (linear) and ANN-based calibration (ANN).  
Figure 9: Examples of successful calibration. (**a**) 25 January 2015; (**b**) 28 September 2015.  
In applications such as crop modeling, daily average temperatures and the diurnal range of temperature are more important than instantaneous data. Figure 10 shows the daily average temperature and diurnal range of temperature before and after calibration. Our method greatly improved both, yielding a further advantage over calibration based on linear regression.  
Figure 10: Average and diurnal temperatures before and after calibration. “Low-cost sensor”, “linear” and “ANN” indicate original values, linear regression-based calibration values and ANN-based calibration values of the observations by the low-cost sensors respectively. (**a**) Daily average temperature; (**b**) diurnal range of temperature.  
Histograms of relative errors between reference and calibrated air temperatures are shown in Figure 11, where diagonal elements represent the result of cross-validation within a location. The histograms peak at a relative error of approximately \(=0\). Some calibration failures are picked up in Figure 12, where solar radiation plotted at 10-min intervals is also shown. The absolute errors became large when the solar radiation, which is the most important factor in outdoor air temperature measurement, changed abruptly.  
Figure 11: Histogram of relative error between reference and calibrated air temperatures.  
Figure 12: _Cont._
## 2 Materials and Methods
### Use Cases and Validation  
The MedicalForm system has designed a set of use cases to validate its proper management of semantics for:  
1. Representing international datasets, structures and forms: This use case will be validated through the definition of semantic structures and forms specified as part of the European Heart Failure Summary defined by SemanticHealthNet project [24].
2. Representing datasets, structures and forms for large healthcare providers: This use case will be validated through the definition of semantic structures and forms according to the Andalusian Stroke Integrated Care process EHR [25].
3. Export semantic structures as compliance instances with multiple relevant health informatics specifications. This use case will be validated by exporting CIMs as an implementable interoperability specification such as ISO 13606 and CDISC.
4. Export semantic structures with W3C semantic web technology stack. This use case will be validated exporting CIMs according to the Web Ontology Language (OWL).
5. Define implementable forms for research platforms, EHR systems and mHealth solutions: This use case will be validated by implementing a questionnaires in platforms (ITCBio, openClinica and Redcap), Diraya EHR system and CronCare mobile app for patient follow-up from hospital [21; 22].
6. Define a mechanism for reusing collected data in data analytics tools: This use case will be validated with the database configuration in TranSmart and i2b2 analytics tool. As a result, it will be required to be translated to define a semantic structure in the analytics tools in the form of a tree of concepts consistent with the MedicalForm definition in order to ensure the appropriate management of semantics in the performed analysis.