esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Analysis of Simulation Model and Experimental Results
### Simulation Analysis of the Impact of OCR on Compressor Performance
#### 5.2.1 Experimental Results of OCR with Changing Conditions
As the pressure ratio increases, internal leakage also increases, resulting in lower volumetric efficiency, as described by Equation (3). At low compressor speeds, less oil is drawn from the compressor oil sump, leading to poor sealing and a rapid decline in volumetric efficiency. As the compressor speed increases, sealing improves and eventually reaches an optimal level, causing volumetric efficiency to stabilize. Figure 9 demonstrates that the discharge temperature is related to evaporation and condensation temperatures. According to [PERSON] [18], a lower evaporation temperature leads to a higher discharge temperature under the same condensation temperature. For example, in transitions from 10/50 \({}^{\circ}\)C to 0/50 \({}^{\circ}\)C and 5/55 \({}^{\circ}\)C to 0/55 \({}^{\circ}\)C, a 10 \({}^{\circ}\)C drop in the evaporation temperature results in approximately a 10 \({}^{\circ}\)C increase in the discharge temperature. As the evaporation temperature decreases, the pressure ratio increases. Therefore, the refrigerant mass flow rate decreases under the same condensation temperature. Since there is an irreversible loss in the compression process, the refrigerant steam absorbs this part of the heat, resulting in an increase in the discharge temperature. Additionally, a higher condensation temperature leads to a higher discharge temperature. This is shown by transitions from 0/50 \({}^{\circ}\)C to 0/45 \({}^{\circ}\)C and 10/50 \({}^{\circ}\)C to 10/45 \({}^{\circ}\)C, where a 5 \({}^{\circ}\)C drop in the condensation temperature results in approximately a 15 \({}^{\circ}\)C decrease in the discharge temperature. Because the condensation temperature decreases, the pressure ratio decreases. Therefore, the refrigerant mass flow rate increases under the same evaporation temperature. Then, the discharge temperature decreases. The figure also shows that with increasing speed, the discharge temperature decreases, primarily because as the compressor speed in  
Figure 9: Discharge temperature vs. compressor speed with different temperature conditions. creases, the oil content in the refrigerant increases, and more liquid refrigerant is dissolved within the oil, thereby lowering the discharge temperature. However, at significantly high compressor speeds, growing mechanical losses reduce the cooling effect of increased oil content, causing the discharge temperature to stabilize or rise slightly.
# Role of Forster Resonance Energy Transfer in Graphene-Based Nanomaterials for Sensing
## 3 Application of Graphene-Based Nanomaterials in Sensing
### Biomolecule, Bacteria, and Virus Sensor
During this process, the antigen and antibody were involved in binding because of the antibody-antigen interaction to make the complexation of Fe-N-GQD/Ab/Ag. Therefore, the gap between the antigen and antibody with GO increased and the fluorescence was recovered. This immunosensor was applied in human serum for the detection of the _salmonella Typhi_ Vi antigen (LOD = 1 pg/mL). Figure 16: Schematic representation of the rGQDs-aptamer (**G14**) preparation and DX sensing. Reprinted from [71] with permission from Elsevier. Figure 17: Plausible FRET mechanism for the Fe-N-GQDs/GO-composite (donor/acceptor) and its fluorescence responses. Reprinted from [72] with permission from Elsevier. [PERSON] and co-workers [73] constructed a sandwich-structured upconversion nanoparticle (UCNP) **G16** based on the combination of an HA aptamer, GO, and polyacrylic acid for the detection of H5N1 IAV hemagglutinin (HA). In **G16**, the energy transfer process occurred because of the \(\pi\)-\(\pi\) interaction between the aptamer and GO, which reduced the gap between the UCNP and GO (Figure 18). In the presence of HA, **G16** enabled the changes in conformations, and moved away from GO with a detection of 60.9 pg mL\({}^{-1}\). Based on the above results, the **G16** aptamer was also used for the specific detection of H5N1 HA in blood and HA protein in human serum.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Real Data Experiments
#### 4.2.5 Incremental Verification Experiments  
The difficulty of evaluating extrinsic calibration lies in the difficulty to obtain the ground truth of rotation and translation between the camera and LiDAR. For this reason, we employ incremental verification experiments to evaluate the proposed method. As shown in Figure 2b, the camera is fixed on precise rotation stage and LiDAR is fixed on precise motorized linear stage. First, we calibrate extrinsic parameters of system using five pairs of frames through our method. Then, we move the precise motorized linear stage 80 mm away from camera, and we calibrate extrinsic parameters of system again. The translation vector between the first time and second time is shown in Table 3. Similarly, we rotate the precise rotation stage \(11^{\circ}\) clockwise, and figure out the rotation matrix before and after the rotating stage as well. The result is shown in Table 3.  
The increment of translation is 79.383 mm, and the error is within 0.8%. The increment of rotation is 11.041\({}^{\circ}\), and the error is within 0.4%. This indicates that the error of extrinsic parameters is within the tolerance range and the results of our method are basically accurate.
# Correlation between Coda Wave and Stresses in Uni-Axial Compression Concrete
## 2 Correlation Model of Sound Velocity and Stress in Concrete
### Sound Velocity in Concrete without Stress Action  
Since the 1960s, research has focused on the relation between the elastic modulus of solids and ultrasonic wave velocity. For the analysis and application of a coda wave in concrete crack detection, acoustic slowness is an important concept employed to describe the mechanism of coda wave development, and it is very useful for visualizing the direction of the velocity [21]. Acoustic slowness was adopted to express the speed of the sound wave propagation in concrete. It is the reciprocal of the sound velocity \(V\), i.e., \(S=1/V\).  
Using the time average formula [22], we can obtain:  
\[\frac{1}{V_{p0}}=\frac{1-\phi}{V_{pm}}+\frac{\phi}{V_{a}}\text{ or }S_{p0}=(1-\phi)S_{pm}+\phi S_{a} \tag{1}\]  
where \(V_{pm}\), \(V_{p0}\), and \(V_{a}\) represent the velocity of the waves propagating in the elastic matrix, concrete, and air, respectively; \(S_{pm}\), \(S_{p0}\), and \(S_{a}\) denote the acoustic slowness in the elastic matrix, concrete, and air, respectively; \(\phi\) is the porosity of the concrete.  
We simplified the cracks in concrete as circular holes [22] and only considered a one-dimensional situation where the diameter of hole \(d\) can be equivalent to \(\phi\), as shown in Figure 1. When the sound wave meets the hole, it travels along the interface between the hole and the cement mortar matrix. Then, we can derive the followed relations:  
\[\frac{1}{V_{\text{p0}}}=\frac{1-\phi}{V_{\text{pm}}}+\frac{\pi\phi/2}{V_{\text {pm}}} \tag{2}\]
# Power Grid Load Forecasting Using a CNN-LSTM Network Based on a Multi-Modal Attention Mechanism
## 3 Methodology
### CNN Module
The output vector of the CNN module can be represented as follows:  
\[f_{out}=S(f_{in}\times W_{1}+\sigma_{1}), \tag{1}\]  
\[f_{p}=M(f_{out})+\sigma_{2}, \tag{2}\]  
where \(f_{out}\) is the output of the convolutional layer, \(f_{p}\) is the output of the pooling layer, \(W\) and \(\sigma\) are the weight matrix and bias vector, respectively, \(S\) is the activation function, \(\times\) represents the convolution operation, and \(M(\cdot)\) represents the max function.
# Preparation of Freeze-Dried Porous Chitosan Microspheres for the Removal of Hexavalent Chromium
## 3 Results
### The Morphology of Porous Chitosan Micospheres
#### 3.2.4 Effect of the Freezing Time  
The formation and distribution of ice crystals should be influenced by freezing time. Therefore, the effect of freezing time on the morphological structures of porous chitosan microspheres, obtained using various freezing times (24 h, 48 h and 72 h) with the chitosan solution concentration kept constant at 3% (\(w/v\)) and using a 500-\(\upmu\)m syringe needle, were investigated. It can be seen from Figure 5 that the morphological structures of the surface and the cross section of the chitosan microspheres prepared over different freezing times were very different. With the increase in freezing time, the surface of the chitosan microspheres gradually became rough and uneven; when the freezing time increased to 72 h, a \"honeycomb-like\" hexagonal pit structure with visible edges was formed. At the same time, porous chitosan microspheres had a larger pore when the freezing time was longer, as observed from the cross section structure. These might be explained by the fact that, at the beginning of ice crystal formation, crystalline ice readily released itself from any dissolved solute, in this case chitosan, which were concentrated in the empty areas where the ice initially resided [28]. The ice grew larger and \"pore pressure\" is produced with the increasing of freezing time [30], which caused chitosan to flow to the surface to form the edge of the pit. This conjecture could be confirmed by thinner pore walls appearing in the interior of the porous chitosan microsphere prepared by freezing for 72 h.  
Since the porous chitosan microspheres prepared with 3% (\(w/v\)) chitosan solution at a freezing temperature of \(-20\)\({}^{\circ}\)C (using a 500-\(\upmu\)m syringe) had a good spherical structure, the above conditions were taken as constants for further tests. Because of the electrostatic interaction and chelation between active functional groups of chitosan and heavy metal ions [31; 32], the abundant pores and ultra-high specific surface area were more conducive to the adsorption of heavy metal ions. The above results showed that a change in freezing time caused great differences in the porosity and surface area of the porous chitosan microspheres. The porosity of porous chitosan microspheres prepared at \(-20\)\({}^{\circ}\)C for 24 h, 48 h, and 72 h were 23.14 \(\pm\) 0.89%, 46.72 \(\pm\) 1.20% and 64.15 \(\pm\) 1.07%, respectively. Therefore, in further adsorption experiments, the effect of freezing time on the adsorption activity of the chitosan microspheres for Cr(VI) was investigated.
# LTE: Lightweight Transformer Encoder for Orbit Prediction
## 2 Materials and Methods
### Prediction Model
For the loss function used in training the prediction model, we employ MSE, as shown in Equation (5). Here, \(Y_{i}\) denotes the actual data value, \(\Upsilon^{\prime}_{i}\) represents the predicted data value, and \(n\) is the length of the entire dataset. Figure 4: A representation of 200 data points of the Semi-major axis with PE applied. The blue line indicates the input data with PE, while the yellow line represents the input data without PE. \[\text{MSE}=\frac{1}{n}\sum_{i=1}^{n}(Y_{i}-Y_{i}^{\prime})^{2} \tag{5}\]
## 5 Conclusions  
In this study, we demonstrated the capability to differentiate monocultures accurately and to correctly detect _E. coli_, _L. monocytogenes_, _S. enterica_, and _S. aureus_ after 24 h of growth with an accuracy of 0.93 (\(\pm\)0.20) using a low-cost portable Raman handheld device equipped with Orbital Raster Scan technology combined with PCA and SVM as a classifier. To our knowledge, this is the first report using a Raman handheld device with ORS technology for the detection of suspended cells on media broth using minimal sample preparation, i.e., 1 mL of 24 h growth cell suspension, making it a very convenient technique for a large volume of samples achieving high accuracy in a shorter time compared to conventional culture methods, and with lower cost compared to other Raman techniques such as SERS that require a substantial initial capital investment. Therefore, this detection method could be viable in low-resource settings, such as temporary laboratories set up in remote areas or temporary emergency units by minimally trained personnel.  
Conceptualization, A.C.S. and M.H.; methodology, A.C.S., M.H., and C.R.G.; software, M.H.; validation, C.R.G.-G., A.C.S., and M.H.; formal analysis, M.H.; investigation, C.R.G.-G.; resources, M.H., and A.C.S.; data curation, C.R.G.-G. and M.H.; writing--original draft preparation, C.R.G.-G.; writing--review and editing, A.C.S. and M.H.; visualisation, A.C.S. and C.R.G.-G.; supervision, A.C.S.; project administration, M.H. and A.C.S.; funding acquisition, M.H. and A.C.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by an internal competitive grant awarded to [PERSON] and [PERSON] by the University of the West of England, Bristol.  
Not applicable.  
We thank the UWE Microbiology technical team for kindly providing the strains used in this work.  
The authors declare no conflict of interest.
## 3 Results
### Surface Runoff Reduction Capacity of perennial Sweet Pea  
The experiments on the reduction of runoff and sediment yield in the perennial sweet pea planting area were conducted in the low hilly granite region of Yi County. Table 4 shows that following heavy rainfall, the surface runoff volume in the four-year-old perennial sweet pea plot was significantly lower than in the control plot featuring bare ground (CK) and the plot with well-established natural vegetation. During severe rainfall, the four-year-old perennial sweet pea plot demonstrated a substantial 90.4% reduction in runoff compared to the bare ground (CK) and a notable 42% reduction compared to the mature natural vegetation plot. These findings underscore the robust rainwater interception and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Age** & **Phenological Period** & \
\begin{tabular}{c} **Maximum Water Absorption** \\ **of Branches and Leaves** \\ \end{tabular} &
\begin{tabular}{c} **Canopy Intercepted** \\ **Precipitation (mm)** \\ \end{tabular} \\ \hline
4 & Branching period & 31.42\% & 0.712 \\
4 & Initial flowering period & 26.40\% & 0.95 \\
5 & Branching period & 25.03\% & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of natural conditions in the experimental zones for perennial sweet pea introduction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample Quadrat** & **Weight of Litter before** & **Weight of Litter after** & **Maximum Water Absorption** & **Litter Water** \\
**(1 \(\times\) 1 m)** & **Water Absorption (kg)** & **Water Absorption (kg)** & **Rate of Litter (\%)** & **Capacity (mm)** \\ \hline
1 & 0.452 & 1.696 & 2.75 & 1.244 \\
2 & 0.344 & 1.28 & 2.72 & 0.936 \\
3 & 0.32 & 1.188 & 2.71 & 0.868 \\ Average & 0.372 & 1.388 & 2.73 & 1.016 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Litter water holding capacity of four-year-old sweet pea.  
runoff reduction capacities of the four-year-old perennial sweet pea trial area, surpassing the runoff mitigation performance of the mature natural vegetation. Furthermore, during heavy rainfall events, the surface sediment production from the four-year-old perennial sweet pea plot was effectively nil in contrast to the bare ground (CK) and the plot with well-established natural vegetation, which yielded discernible sediment volumes. The four-year-old perennial sweet pea's remarkable sediment interception ability contributes to mitigating soil erosion and minimizing its occurrence. Nevertheless, it is noteworthy that during heavy rainfall, the surface runoff volumes from the four-year-old perennial sweet pea plot and the well-established natural vegetation plot were comparable yet distinctly lower than those from the bare ground (CK).
# Performance Evaluation of IM/DD FSO Communication System Under Dust Storm Conditions
## 2 Dusty Channel Model
### Dusty Channel Model
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Distribution** & \(\mathbf{R^{2}}\) & **RMSE** & **Estimated Parameters** \\ \hline Beta & 0.85 & 1.30 & \(\alpha=7.34,\beta=0.71\) \\ Power & 0.76 & 1.63 & \(a=9.83\) \\ Weibull & 0.44 & 2.48 & \(a=0.95,\;b=14.03\) \\ Lognormal & 0.18 & 3.01 & \(\mu=-0.10,\;\sigma=0.15\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Goodness-of-fit metrics and estimated parameters for fitted distributions. Figure 2: Comparison between the empirical histogram of the measured signal intensity data and the fitted probability density functions (PDFs) of different theoretical distributions. tractable distribution that balances statistical fidelity with mathematical simplicity, allowing closed-form performance analysis. It is worth mentioning that the statistical distribution of the received irradiance can be influenced by the physical characteristics of the dust particles, such as size distribution, chemical composition, and geometric shape as these factors govern the scattering and absorption behavior of the optical signal. In this study, the dust sample was collected from a real dust storm event and used without modification, resulting in an average particle size of approximately 17.3 \(\upmu\)m. The particle properties were kept constant throughout the measurements; however, due to hardware limitations, detailed characterization of the dust composition and morphology could not be performed. Variations in these properties such as smaller particle sizes, different mineral contents, or irregular geometries could lead to different irradiance distributions.
# Comparison of Fatigue Performances Based on Shape Change of Rail Fastening Spring
## 3 Comparison of Fatigue Performance Based on Fastening Spring Shapes
### Height of the Spring Arm  
The effect of the spring arm height on the fatigue performance of the fastening spring was investigated. Figure 8 shows the perspective view of the change in the spring arm height. Four different cases with spring arm height increments of \(-20\%\), \(-10\%\), \(+10\%\), and \(+20\%\) were compared with the original fastening spring (\(+0\%\)). Figure 9 plots the global response (fastening force vs. displacement) for various spring arm heights. Displacement values corresponding to the rail fastening force of 9 kN were applied to the analysis. Figure 10a plots the maximum principal stresses during the fastening when varying the spring arm height, while (b) plots the range of maximum principal stress due to the vertical deflection caused by train use. The results showed that the spring arm height variable also only slightly affected the stress conditions; thus, the fatigue performance was only slightly affected by this variable. As discussed in Section 3.1, this result may also be attributed to similar internal forces on the critical sections of the fastening spring with the unchanged  
Figure 6: Fastening force vs. displacement diagram varying the spring end height difference.  
Figure 7: Stress variation with the height of spring end: (**a**) maximum principal stress; (**b**) maximum principal stress range.  
moment arm. The analysis results showed that the shear force and the bending moment at the critical section of stress concentration increased by about 8% and 7%, respectively, when the spring arm height increased from \(-20\%\) to \(+20\%\).
## 3 Analysis of the Selected Case Studies
### Computer Simulation Analysis
#### 3.4.1 Incident Solar Radiation and Total Radiation on Blocks  
The results of the total radiation test on the area, in terms of the importance of the compact urban fabric in reducing the surfaces exposed to the hot solar rays, are shown in Figures 7 and 8, with the daily average of solar insolation as follows: in Yazd, 4.5-5.5 kWh/m\({}^{2}\) with 3200 sun hour/year; in Baghdad, 4.4-5 kWh/m\({}^{2}\) with 3244 sun hour/year; in Damascus, 5.5-5.8 kWh/m\({}^{2}\) with 3634 sun hour/year; and in Sana'a, 5-6 kWh/m\({}^{2}\) with 3000 sun hour/year.
# A Review of the Evolution Characteristics and Argillization of Clay Interbeds in Rockslides
## 3 Parameter Changes during the Argillization Process
### Clay Minerals in the Process of Argillization  
Because of the inhomogeneous stress resulting from the hydrophilic swelling of clay minerals, scholars believed that clay minerals played a controlling role in the process of argillization. It is more accurate to use mineral composition or chemical composition directly to describe the state of clay interbed.  
Clay interbeds mainly consist of an illite, montmorillonite, kaolinite or illite/ montmorillonite mixed layer. It should be noted that clay minerals of different types and origins exhibit variations in layer charge magnitude and distribution, ion exchange capacity, and expansibility. Clay minerals can also be transformed into each other. The study of clay minerals should be synchronized with the characteristics of structural change at each stage. Then, a more comprehensive understanding of each of them could be obtained (Figure 7) [43].  
The type and content of clay minerals should be determined first. The standard methods used to measure clay content are the hydrometer method and the pipette method, and they are complex and difficult to replicate. [PERSON] [44] provided alternatives by using the clay estimation model based on vapor adsorption. By comparing the clay content estimated via the modified model, it is evident that the estimation of the measured clay content has significantly improved. And thus, it had great practicality.  
Figure 6: Contact mode of clay particles during disintegration (modified from [PERSON] [40]).
# Prediction of Oleic Acid Content of Rapeseed Using Hyperspectral Technique
## 4 Conclusions  
Hyperspectral technology possesses the advantages of being fast, non-destructive, and highly efficient and, therefore, it can play an important role in crop nutrition diagnosis. In this paper, we attempt to use the hyperspectral characteristics of seeds to construct the inversion model of rapeseed's oleic acid content. The proposed inversion model provides a helpful experience for estimating the oleic acid non-destructively. In practice, based on rapeseed hyperspectral data, four types of spectral features are considered, namely multifractal parameters, trilateral parameters, spectral indices, and sensitive bands. In order to select optimal characteristic parameters, we first choose two features in each type of spectral features according to correlation analysis. Then, by using the selected features as the model input of univariate (one feature) and multivariate combination (at least two features), the BP neural network model is established for an oleic acid prediction.  
The results show that multivariate parameters can greatly improve the model's accuracy and stability. An interesting finding shows that the combined features including multifractal parameters will bring about better model performance. The best model performance comes from the combined parameters {MF-\(h\)(0), SB-DR\({}_{574}\), SPI-NDSI(\(R_{575}\), \(R_{576}\))}. Model test shows that our model has good robustness.  
Conceptualization, F.L.; methodology, F.L. and G.L.; software, F.L.; validation, F.L.; formal Analysis, F.L. and F.W.; investigation, F.L. and X.L.; resources, G.L.; data curation, F.L.; writing--Original Draft Preparation, F.L.; writing--review and editing, F.W. and J.Y.; visualization, F.L. and X.L.; supervision, G.L.; project administration, G.L.; funding acquisition, G.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Chinese National Natural Science Foundation (Grant No. 61973111) and the Natural Science Foundation of Hunan Province (CN) (Grant No. 2020 JJ377).  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The authors declare no conflict of interest.  
Figure 6: Three evaluation indicators of \(RMSE\), \(R\), and \(RE\) with different sample sizes of training set.