esa-sceva/satcom-chunk-collection huggingface dataset:
# Agricultural Residues of Lignocellulosic Materials in Cement Composites
## 2 Materials and Methods
### Mechanical Properties of Composites  
The mechanical properties of the composites were determined according to the Deutsches Institut fur Normung (DIN 52362) [26] and American Society for Testing and Material (ASTM D1037) [24] in an AROTEC (Sao Paulo, Brazil) universal machine with a load cell of 20 MN and testing rate of 5 mm/min. The internal bonding (IB) (Equation (1)), compression strength (CS) (Equation (2)),modulus of rupture (MOR) (Equation (3)), and modulus of elasticity (MOE) (Equation (4)) in static bending were evaluated.  
\[IB=\frac{P_{s}}{b\times l} \tag{1}\]  
\[CS=\frac{P_{b}}{b\times l} \tag{2}\]  
\[MOR=\frac{3\times P_{b}\times L}{2\times b\times h^{2}} \tag{3}\]  
\[MOE~{}=~{}\frac{P_{bp}\times L^{3}}{4\times b\times h^{3}\times\gamma_{p}} \tag{4}\]  
where \(P_{s}\)--the rupture load (N); \(b\)--the width of the specimen (mm); \(l\)--the length of the specimen (mm); \(P_{b}\)--the maximum load (N); \(P_{bp}\)--the load at the proportional limit (N), \(\gamma_{p}\)--the deflection corresponding to \(P_{bp}\) (mm); \(h\)--the thickness of the specimen (mm); \(L\)--the span (mm).  
For the internal bonding (IB) and compression strength (CS) tests, four samples per panel were used for each test. Six samples per panel were tested for the modulus of rupture (MOR) and modulus of elasticity (MOE) tests. Three samples per panel were tested after 28 days of curing, and three samples per panel were tested after accelerated ageing.
## 2 Real-Time Measurement System
### Software and Test Procedure  
The digital image processing software developed in this paper integrates the functions of image processing, calibration, and displacement calculation of cooperative targets. It can realize real-time measurement, the output of ion optics deformation, and grid gap variation. The measurement software was developed based on MATLAB 2017b and consists of a calibration module, an online measurement module, an offline measurement module, and a data display/save module. The software interface is shown in Figure 3.  
The measured data processing flow is shown in Figure 4. Firstly, the telemicroscope was adjusted to place the probes and cooperative targets in the middle of the image, and several frames of images were collected in the calibration module. In the calibration module, the images were partitioned, the edge detection threshold was selected, and edge detection was then executed. The program used the least squares method to fit the center of the cooperative targets as displacement origin and calculated the image magnification factor. Next, the measurement area parameters (region of interest, ROI) and the measurement frequency were inputted to the online detection module; the ion thruster started to operate; and the system continuously measured the ion optics deformation data, and outputted the deformation curve of the screen grid, the accelerator grid, and the decelerator grid in real time.  
Figure 2: The grid deformation real-time measurement system.  
When the ion thruster was shutdown, the deformation of the ion optics during the cooling process was continuously measured until the deformation amount returns to zero. When the measurement is finished, all the collected original image data was saved as a video file. The offline analysis module was used to load the video file, so that the calibration parameters can be adjusted again and so that the related algorithms can be modified to further analyze the thermal deformation of ion optics.
## 3 The Proposed Method  
### Data Decomposition and Parameter Setting  
Before decomposing the original Raman spectral sequence using VMD, the penalty factor \(\alpha\) and the number of modal decompositions \(K\) need to be pre-determined. They decide the bandwidth size of each modal component. When \(\alpha\) increases, the bandwidth of each modal component decreases, and vice versa. Similarly, the value of \(K\) directly affects the decomposition results. When \(K\) is small, the original signal cannot be fully decomposed, resulting in large decomposition errors and low prediction accuracy. When \(K\) is too large, the original signal is over decomposed. The accumulation of errors from excessive decompositions leads to decreased prediction accuracy and increased computational burden.  
The number of modal decompositions \(K\) is adaptively optimized based on the mean squared error loss function [23], which is specifically defined as:  
\[E_{de}=\frac{\sum\limits_{i=1}^{T}{(f(t)-\overset{\wedge}{f}(t))^{2}}}{T} \tag{10}\]  
In Equation (10): \(f(t)\) represents the original signal, \(\overset{\wedge}{f}(t)\) denotes the sum of the modal signals from each decomposition, \(T\) is the total time. Specifically, \(\overset{\wedge}{f}(t)\) is mathematically defined as:  
\[\overset{\wedge}{f}(t)=\sum\limits_{k=1}^{K}{u_{k}(t)} \tag{11}\]  
In Equation (11): \(u_{k}(t)\) represents the \(kth\) modal component. After defining the loss function, the iterative optimization of the VMD decomposition is performed. The smaller the \(E_{de}\) is, the more thorough the VMD decomposition.  
After VMD decomposition, the residual sequence is further decomposed using EWT to reduce the complexity of the load sequence. Having too few or too many modal decompositions in the EWT will affect the decomposition results. Considering the convergence characteristics of the center frequencies in VMD, the boundaries and center frequencies of the modal components can be adaptively detected. Therefore, before decomposing the residual sequence using ETW, VMD is first utilized to decompose the residual sequence and the number of ETW modal decompositions is adaptively determined based on the convergence of the center frequencies.
## 4 Simulation and Experimental Verification
### Quadratic Optimization Best Value Verification
#### 4.2.1 Stress-Strain Verification  
By analyzing the stresses and displacements under the three conditions, the maximum stress and displacement values are listed in Table 15. The corresponding stress and displacement diagrams for the three conditions are shown in Figure 25. Figure 25a-c show the equivalent stress distributions for conditions 1 to 3, while Figure 25d-f show the displacement distributions for conditions 1 to 3.  
According to the finite element analysis results, the maximum equivalent stress among the three conditions is 460.5 MPa, occurring in the third condition at the lower transverse plate. The material used was SD600, which has a yield strength of 600 MPa, and the stress values met the requirements. According to the relevant standard, the maximum strain  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working Condition** &
\begin{tabular}{c} **Static** \\ **Compression** \\ \end{tabular} & **Uphill Lifting** & **Steering Rollover** \\ \hline Maximum stress (MPa) & 252 & 321 & 461 \\ Maximum displacement (mm) & 0.0643 & 0.351 & 0.219 \\ Minimum fatigue life (cycles) & 1.0 \(\times\) 107 & 1.0 \(\times\) 107 & 5.142 \(\times\) 106 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Equivalent stress, displacement, and fatigue properties under three conditions.  
Figure 25: Diagrams of stress and displacement distribution under three conditions.  
in the component should be less than 0.2%. The maximum displacement in condition 1 is significantly smaller than in the other two conditions, occurring in the saddle body, which is also the widest part, and its strain ratio is much lower than in the other two conditions. The maximum displacement in condition 2 is 0.3506 mm at the lower transverse plate, corresponding to a strain of 0.15%, which is below the 0.2% limit. The maximum displacement in condition 3 is 0.2191 mm, located in the force-transforming element, corresponding to a strain of 0.08%, which is below the 0.2% limit, meeting all requirements.
# CBM: An IoT Enabled LiDAR Sensor for In-Field Crop Height and Biomass Measurements
###### Abstract
Additionally, the system architecture of IoT systems mostly consists of open-source packages and software which support a wide range of interfacing solutions and options to adapt and extend implementations in different experimental scenarios [28]. Raspberry Pi (Raspberry Pi Foundation, Cambridge, UK), a low-cost system-on-chip computer, has been used in broader IoT applications. The system has been used for collecting high-quality imaging data in different scientific and engineering applications [29; 30; 31]. In plant phenotyping, such systems were used in applications such as the tracking of leaf movement using time-lapse images [30], investigation of crop disease response in controlled environment conditions [32], evaluation of crop health [33], and in-field crop monitoring [34]. To this end, this research developed a low-cost sensor that was named CropBioMass (CBM) with simplified data acquisition and processing and the seamless extraction of plant traits measurements crop biomass, and height. The CBM was packaged with a solid-state LiDAR sensor for range measurements, an onboard computer with WiFi connectivity to the internet for easy data transport to a cloud platform, and a global navigation satellite system (GNSS) logger to measure the spatial geolocation of the range measurements. This study reports the design implementation details, including the system architecture, 3D design, and the software architecture processing workflow of CBM. Field testing was conducted on a wheat field trial containing multiple genotypes and results showed that the crop fresh- and dry-biomass and the plant height that was estimated by CBM had a high correlation with the manually measured data.
## 5 Discussion
### Possible Improvements and Comparison with Other Studies  
Monitoring of the budding rate of cotton was also conducted by [PERSON] et al. [11], and their study achieved an estimation accuracy ranging from 81.0% to 99.5%, with an average of 88.6% in terms of counting germinated cotton plants. In our study, the presented method achieved an accuracy of 91.13%, which is slightly higher than that of the previous study. The possible reasons for the improvement are: (1) the study filed was covered by plastic, which decreased the influences of weeds and reduced background interference; (2) the studies used different cotton plant identification methods. In their study, a maximum likelihood method was used to identify the cotton plant, but no accuracy of the maximum likelihood method was reported in their study. Hence, in this study, we also used the maximum likelihood method to obtain the classification result, and the accuracy validation shows the maximum likelihood method achieved accuracy is 87.85%, and the Kappa coefficient of 80.67%. While the SVM method used in our study performed better classification accuracy, this would improve the final accuracy; (3) this study also used a method for decreasing the influence of overlapping cotton.  
While the study presented a reasonable monitoring accuracy by using an RGB true color camera, it would benefit more if the study employed a near-infrared (NIR) camera [30; 31]. The NIR camera can provide more information on the cotton plants than the RGB camera, and it would greatly highlight the presence of the cotton plants. Hence, a NIR camera can be considered to make a comparison between the RGB camera further, especially, to identify if the NIR camera can provide more information of cotton plants which under the plastic film than the RGB camera. Besides, the method presented in the study to decrease the influence of the overlapping of cotton relies on the correct data collection date. This means that when the sizes of the cotton plants are large, or the overlaps are heavy, this method may not work well.
# A Review of Early Injection Strategy in Premixed Combustion Engines
## 6 Summary and Conclusions
Generally, decreasing the injection angle was not good for the control of soot emission, but NOx emissions could be suppressed by the rich fuel-air mixture and low combustion temperature. As the same time, the narrow injection angle is also beneficial to reduce the BSFC, due to avoiding incomplete combustion caused by wetting-wall phenomena. * The impingement target is an important factor influencing engine emissions, and the impingement target was commonly determined by the injection timing, injection angle, and piston structure. When the spray impinged at the bowl lip bottom edge, the secondary atomization process was enhanced, resulting in a drop of HC and CO emissions. * For HCCI and PCCI engines fueled with alternative fuels, the blending ratio of the alternative fuel directly affected the performance and emission characteristics. Oxygenated fuel was beneficial for the reduction of NOx and soot emissions, but was not good for decreasing HC and CO emissions. For gasoline-diesel blended fuel, the increased gasoline ratio resulted in reduced NOx and soot but increased HC and CO emissions. * For HCCI and PCCI engines, an appropriate early injection strategy can effectively improve engine performance and improve engine emissions characteristics. However, due to the actual working process of the engine, its performance and emissions are affected by many factors. Therefore, it is also necessary to study the effects of multiple factors (Injection pressure; Injection Timing; Injection Angle and fuel) on engine performance and emissions. The advantages and disadvantages of the early injection parameters have been summarized, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Early Injection Parameters**} & \multirow{2}{*}{**Injection Pressure \(\uparrow\)**} & \multicolumn{4}{c}{**Injection Timing**} \\ \cline{3-5}  & & **Single \(\leftarrow\)** & \multicolumn{2}{c}{**Two-Stage**} & \multicolumn{1}{c}{**Injection Angle \(\downarrow\)**} \\ \hline  & & & **First Injection Timing \(\leftarrow\)** & **Second Injection Timing \(\leftarrow\)** & **First Injection Quality \(\uparrow\)** \\ \hline  & Better air-fed mixing and fewer fast-rich regions. Higher heat release rate and temperature. & Longer premising time. Better air-fed mixing and more homogeneous early injection timing. & & Before air-fed mixing and more homogeneous early injection timing. Lower NOA emissions. & & Decreased wall-wetting field. \\ Advantage & Shorther combustion duration. & Longer homogeneous early injection timing. & & Lower combustion duration. & mixture for fast combustion stage. & & better air-fed mixing and more homogeneous true
## 3 Results and Discussion
### DEM Evaluation  
The accuracy of the Sentinel-1 DEM was evaluated against the airborne Lidar DEM from the Korea Hydrographic and Oceanographic Agency. In Figure 5, the mean RMSE and MAE were averaged into 0.1 bins of incremental GVF. The results show RMSE values, ranging from 0.1 to 2.6 m, and MAE values from 0.1 to 1.7 m. A drastic decrease in the RMSEs and MAEs is observed in GVF values higher than 0.2. Therefore, areas with GVF values higher than 0.2 were defined as tidal flats in this study and entered into our logical function to estimate intertidal topography.  
We also compared our Sentinel-1 DEM to the Landsat DEM from waterline extraction, as well as the airborne Lidar DEM (Figure 6). The Sentinel-1 DEM outperformed the Landsat DEM in terms of the evaluation indices of RMSE and MAE. The Sentinel-1 DEM better captured high topographic reliefs (>5 m) compared to the Landsat DEM. Our pixel-based method allowed more accurate measurement of small morphologic changes than the waterline-based method. This can also be explained by the difference of intrinsic spatial resolutions for the 10 m Sentinel-1 images and the 30 m Landsat images.  
Figure 5: The RMSE and MAE of the Sentinel-1 DEM against the reference Lidar DEM for each 0.1 bins of incremental GVF value.  
Figure 6: The comparison of different DEM products from (**a**) the airborne Lidar, (**b**) the pixel-based Sentinel-1, and (**c**) the waterline-based Landsat ETM+ data. The solid white line indicates the topographic profiles in Figure 7.  
Figure 7 illustrates the topographic profiles of the airborne Lidar, Sentinel-1, and Landsat DEMs demonstrated by the solid white line in Figure 6a. Overall, the topography profiles show a decreasing trend from north to south and the height difference amongst the three DEMs on the y axis from 2 to 5 m. The Sentinel-1 DEM better captured small scale topographic reliefs in heights ranging from 3.5 to 4.5 m on the northern latitudinal profile (>36.57 in degree) whereas the Landsat DEM seemed to be smoothly interpolated with low reliefs. Also, our Sentinel-1 DEM well-represented the steep slopes on the southern latitudinal profile which was close to the boundary between tidal flats and ocean. This can be supported by the fact that the waterline-based method tends to produce low vertical precision on steep slopes due to the edge detection error of the waterline.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
###### Abstract  
Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however not considered the fact that multiple vehicular contents can be transient, i.e., they expire after a certain time period since they were generated, the so-called _FreshnessPeriod_ in NDN. In this paper, we study the effects of caching transient contents in Vehicular NDN and present a simple yet effective freshness-driven caching decision strategy that vehicles can implement autonomously. Performance evaluation in ndrSIM shows that the _FreshnessPeriod_ is a crucial parameter that deeply influences the cache hit ratio and, consequently, the data dissemination performance.  
Caching; Named Data Networking; Information Centric Networking; Vehicular Ad Hoc Networks +
Footnote †: journal: Sensors
# A Neural Network-Inspired Matrix Formulation of Chemical Kinetics for Acceleration on GPUs
## 2 Methodology
### Reaction Decomposition and Classification
In the most general case, for each set of reversible and irreversible reactions, the decomposition into reaction types ensures  
\[N_{R,irv} =N_{R,irv}^{\mathrm{S}}+N_{R,irv}^{\mathrm{F}}+N_{R,irv}^{\mathrm{ P}}, \tag{12}\] \[N_{R,rev} =N_{R,rev}^{\mathrm{S}}+N_{R,rev}^{\mathrm{F}}+N_{R,rev}^{\mathrm{ P}}, \tag{13}\]  
where the superscripts \(S\), \(F\), and \(P\) indicate standard, falloff, and pressure-log reactions respectively (e.g., \(N_{R,irv}^{\mathrm{S}}\) is the number of irreversible, standard-type reactions in the mechanism). Additionally, the total number of reaction types present in the mechanism discounting reaction reversibility is given by  
\[N_{R}^{\mathrm{S}} =N_{R,irv}^{\mathrm{S}}+N_{R,rev}^{\mathrm{S}}, \tag{14}\] \[N_{R}^{\mathrm{F}} =N_{R,irv}^{\mathrm{F}}+N_{R,rev}^{\mathrm{F}},\] (15) \[N_{R}^{\mathrm{P}} =N_{R,irv}^{\mathrm{P}}+N_{R,rev}^{\mathrm{P}}, \tag{16}\]  
such that \(N_{R}=N_{R}^{\mathrm{S}}+N_{R}^{\mathrm{F}}+N_{R}^{\mathrm{P}}=N_{R,irv}+N_{R,rev}\). In practice, for memory coalescence reasons, the matrices \(\mathbf{A}_{irv}\) and \(\mathbf{A}_{rev}\) are again partitioned in a similar manner as described above, creating a two-level hierarchy. The top-most level, to reiterate, is \(\mathbf{A}=[\mathbf{A}_{irv};\mathbf{A}_{rev}]\). The second level then provides \(\mathbf{A}_{irv}=[\mathbf{A}_{irv}^{\mathrm{S}};\mathbf{A}_{irv}^{\mathrm{F} };\mathbf{A}_{irv}^{\mathrm{P}}]\) and \(\mathbf{A}_{rev}=[\mathbf{A}_{rev}^{\mathrm{S}};\mathbf{A}_{rev}^{\mathrm{F} };\mathbf{A}_{rev}^{\mathrm{P}}]\). A schematic of this decomposition is provided Figure 2b. For a given mechanism, the above decomposition (or classification) into reaction types applies to all reaction matrices, i.e., matrices that are of size \(N_{\mathrm{C}}\times N_{R}\). Note that this is a by design an overly general classification. Most mechanisms do not contain every aforementioned reaction type. For example, one mechanism may only contain reversible reactions (\(N_{R,irv}=0\)), and another may contain both reversible and irreversible reactions, but no falloff reactions (\(N_{R}^{\mathrm{F}}=0\)). Each chemical mechanism can therefore be characterized not only by the total number of reactions, but also by the distribution of the reaction types. The mechanisms considered in this work are detailed in Table 1, and their corresponding reaction type distributions are visualized in Figure 3. Section 3 shows how knowledge of these distributions gives additional insight into the speedup and GPU performance behavior, since each reaction type brings forward a different amount of numerical complexity.
## 6 Novel mHealth Predictive Outbreak for COVID-19 (mPOC) Algorithm and IoMT Protocol
### Initial Stage  
Once the mPOC application is successfully installed and launched on the smartphone, the algorithm initiates and collects user-related information, such as the user's location, including the suburb, city, and country. This location data can be obtained through various means, such as GPS, Wi-Fi, or mobile networks. Additionally, the application prompts the user to provide essential demographic details, including age, gender, ethnicity, height, weight, and any pre-existing health conditions that may influence the risk of COVID-19 complications; examples of such conditions encompass cancer, immunocompromised disorders, diabetes, pulmonary, renal, and cardiovascular diseases.  
One notable complication that can arise from COVID-19 infections is the hyperactive immune response known as Cytokine Storm (CS) [38], wherein the immune system inundates the infection site (e.g., lungs) with inflammatory white blood cells, potentially leading to multiple organ failure. By conducting a blood test to assess the cytokine/chemokine profiles, it becomes possible to identify risk factors associated with Cytokine Storm, which can be considered a pre-existing condition. The DRI value, which indicates the health risk, is updated accordingly to reflect this information.  
Figure 2: The mHealth Pandemic Outbreak COVID-19 (mPOC) System [5].  
Concurrent with the mPOC algorithm's execution on the smartphone, the Bio-Watch device attempts to establish a connection with the smartphone, calibrate its sensors, commence capturing the user's physiological indicators, and adapt to the typical readings. This stage occurs once and can be subsequently updated, as depicted in Table 2, to ensure accurate and reliable data captured for the mPOC system.
## 4 Experimental Results
### Model Verification
#### 4.1.2 Comparison with the Passive-Only Model  
In order to test the usefulness of the include active observations into the TC intensity estimation model, the [PERSON] et al. (2017) method was chosen to compare with the proposed model. The selected parameters, the coefficients and RMSEs of the 10 candidates of regression equations are shown in Equation (3) and Table 4.  
\[V_{\text{max}}=a\times P_{i}+b\times P_{j}+c\times P_{k}+d \tag{3}\]  
As shown in Table 4, the average RMSE of the [PERSON] model was 6.57 m/s. This RMSE is larger than the estimation of the proposed method, comparing with both the training dataset or the test dataset (see Figure 7a,d). It is also indicated that synergy by using active and passive microwave observations can obtain better estimation results than by only using passive observation as model predictors.