esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
Also, \(Q\) describes either the essential thermal energy for a specific process or thermal losses. As reference state for the above process, we considered \(T_{0}=298\) K and \(P_{0}=1\) atm. The optimal value for the air excess at the inlet was found to be 100% (i.e., stoichiometric requirement), while the extent of reforming was set to 80%. In the fuel cell, we considered the reactions  
\[H_{2}+O_{2}^{-}\to H_{2}O+2e^{-}\ \ (+\Delta H) \tag{2}\]  
\[CO+O_{2}^{-}\to CO_{2}+2e^{-}\ (-\Delta H) \tag{3}\]  
that can be summarized as  
\[H_{2}+CO+2O_{2}^{-}\to H_{2}O+CO_{2}+4e^{-}\ (+\Delta H) \tag{4}\]  
which produces electricity and heat. This reaction is supposed to take place in an extent of 80%. The performance analysis of such an innovative charge station in terms of energy efficiency sounds insufficient if not performed along with financial feasibility over time. Note that the initial costs of a SOFC-based project are quite high, while the maintenance and the operational costs are lower than those of internal combustion engine (ICE) technology. The overall comparison between the several simulated projects will be based on the optimisation of the Net Present Cost (_NPC_), expressed as  
\[NPC=\left|CAPC+\sum ADICFL\right| \tag{5}\]  
where _CAPC_ is the capital cost of each project in the initial (zero) year, and _ADICFL_ refers to the annual discounted cash flows per year summed over the 5 years of the project's lifetime. In order to calculate _ADICFL_, it is necessary to estimate the discounted factor (_FD_), which is used to calculate the present value of a future cash flow varying throughout the whole project's lifetime, as the following expression indicates:  
\[FD=\frac{1}{\left(1+\left(\frac{j}{100}\right)\right)^{N}} \tag{6}\]where \(N\) is the year of calculation, and \(i\) is the real discount rate using it in the above expression as a percentage. For the scope of the present research study, the _ADICFL_ (see Equation (5)) incorporates the discounted cost of emissions per year calculated as follows:  
\[DTCEMI=-FD\frac{0.606\text{-}TPCON}{1000}CEMI \tag{7}\]  
where 0.606 is the mass of CO2 emissions in kilogram per produced kWh of electric energy, as announced by the Greek government, the _TPCON_ is the total power consumption in kWh, and _CEMI_ represents the cost of a produced CO2 ton, as specified by EU (30 \(\copy\)/ton) [31].
# On the Accuracy of Turbulence Model Simulations of the Exhaust Manifold
## 3 Exhaust Manifold Analysis
### Results and Discussion
#### 3.6.2 Velocity Comparison  
Figure 7 depicts the velocity profiles at the exhaust manifold outlet in the ZX plane. The k-kl-\(\omega\) model indicates the presence of the highest velocity regions, reaching up to 80.63 m/s, signifying strong transitional flow effects. In contrast, the RNG k-epsilon, k-omega STD, transition SST, and Reynolds stress models exhibit maximum velocities in the 63-65 m/s range. These discrepan  
Figure 4: Temperature distribution at the outlet of the exhaust manifold (90 s).  
Figure 5: Temperature distribution comparison.  
The diagram shown in Figure 8 illustrates how velocity varies across the manifold, with positions ranging from 0.2 m to \(-\)0.105 m in the XZ plane. Initially, the velocity at the three inlets is set to 20 m/s as per the boundary condition. As it moves through the exhaust manifold, the velocity gradually increases. The k-kl-\(\omega\) model stands out with higher velocities, peaking at around 65 m/s and maintaining higher velocities along the exhaust path \(y\)-axis; the RNG k-epsilon, k-omega STD, transition SST, and Reynolds stress models exhibit similar peak velocities, at around 63-65 m/s.  
These models predict slightly lower peak velocities compared to the k-kl-\(\omega\) model but follow a nearly identical trend, increasing steadily up to 0.00 m and then maintaining or slightly decreasing.  
Although all models follow a similar curve in the velocity distribution graph, despite varying peak values, it highlights their general agreement in predicting flow behavior within the exhaust manifold. This consistency across different turbulence models indicates reliable modeling of the exhaust flow dynamics, which is crucial for optimizing the manifold's design for efficient exhaust gas expulsion and for minimizing backpressure. The k-kl-\(\omega\) model's higher-velocity predictions provide additional insights into transitional flow effects, offering a more detailed analysis for fine-tuning manifold performance.  
As can be seen, Figure 9 depicts the contour of the streamlined velocity contours across the exhaust manifold, captured for various turbulence models, revealing that all models predict a similar trend in flow patterns with smooth streamlines. The k-kl-\(\omega\) model predicts the highest velocities (up to 72.27 m/s) and exhibits more complex patterns, effectively capturing transitional flow effects. In contrast, the other models (RNG k-epsilon, k-omega STD, transition SST, and Reynolds stress) show slightly lower maximum velocities (around 65-66 m/s) but maintain well-structured and consistent flow patterns. The k-kl-\(\omega\) turbulence model uses the length scale variable, which enhances its capability to handle the flows with the manifold while considering the curvature of the inlets. This feature allows the model to represent the domain's exhaust gases and turbulent structures accurately.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 4 Imaging Fusion Algorithms
### Sparse Representation and Compressive Sensing Based Methods  
Recently, the sparse representation of signals has become a popular topic of research. The method assumes the input image (usually expressed as a column vector) can be represented by a linear combination of several elements (a series of column vectors), which is referred to as atoms. The atoms compose a dictionary. In this technique, the most important issue is the dictionary choice. Two methods are usually employed: (1) an analytical dictionary built by selecting a specified transformation matrix via Fourier, wavelet, Curvelet and Gabor transform, and (2) dictionary learning built based on data training. The second method effectively achieves higher accuracy in extracting complex image features, a better space representation of various features of images and good adaptability. The common procedures in this category include K-means generalized SVD (K-SVD) [126; 127; 128; 129; 130], PCA [131; 132], online dictionary learning, optimal directions and adaptive sparse representation [133]. However, such multidimensional signal/image processing usually involves a large amount of data. According to the compressive sensing theory [134], the image can be compressed with a few random projections if the image is sparse in a certain transform domain and can be sparsely represented [135; 136; 137; 138].  
Experimental results show that the image fusion using compressive sensing could preserve the rich texture information of the input images while reducing the amount of data required and the complexity of the algorithm processing [135; 136]. During training, the dictionary for the sparse representation, the test sample (input images), can be represented as a column vector. Similarly, we create a dictionary matrix of a column vector which represents the training samples (atoms). Compressive sensing, using random projection, can be applied to reduce the dimensions of both the test vector and the dictionary matrix. Then, representation coefficients are obtained using sparse coding techniques, such as orthogonal matching pursuit, simultaneous orthogonal matching pursuit, a joint sparse representation model, approximate sparse representation with multi-selection strategy and convolutional sparse representation. At last, the sparse representation coefficients from the input images are fused by a certain fusion rule. Finally, the fused image is reconstructed by fused sparse representation coefficients. For instance, PET/CT/MRI images are fused using the K-SVD-based learning dictionary and Orthogonal Matching Pursuit (OMP) algorithm [139]. Similarly, Sparse representation-based methods have been applied for fusions of CT/MRI and MR images [138; 140; 141].
## 4 Conclusions  
A prototype test was performed to investigate the strengthening effect of external prestressed steel strands on a PCCP. Based on the test results, the following conclusions can be drawn:  
(1) The strains in prestressing wires showed slight variation with the change of internal water pressure during the entire process. Breakage of the wires leads to significant prestress loss within a certain distance from the broken point. The prestress resumes partially beyond a certain length due to the bond quality of the mortar coating with the prestressing wires.  
(2) The operation of breaking wires mostly affected the strains of the inner concrete at the crown. It significantly influenced the outer concrete core at spring-line which is near the broken point. The onset of visible cracks in the outer concrete core first appeared at spring-line near the broken point. The debonding between mortar coating and outer concrete core was found at the same position according to the observations.  
(3) After the tensioning operation of the strands, the strands contribute to compensate for the prestress loss induced by the wires breakage. The crack propagation in the core is also constrained by the strands simultaneously. The test pipe was able to sustain the design internal water pressure after strengthening and the water tightness property was in a good condition. In addition, the maximum width of the cracks in the outer concrete core at the spring-line was reduced from the 1.2 mm to 0.1 mm because of the contribution of the strands. The effect of PCCP strengthened with external prestressed strands is evident.  
(4) The strains of the steel strands were all below the tensile strain level. The method of PCCP strengthened with the external prestressed strands is able to meet the strengthening requirement of the test and is an effective method for strengthening PCCP. The study presented in this paper lays the groundwork for future research into the reinforcement of PCCP with external prestressed steel strands.  
Figure 20: The strain curves of prestressed strands.  
**Author Contributions:** Conceptualization, T.D., S.X. and L.Z.; Methodology, T.D. and L.Z. and B.C.; Software, T.D. and L.Z.; Writing-Original Draft Preparation, L.Z.; Writing-Review and Editing, B.C., X.L. and M.L.; Funding Acquisition, J.Y. and Q.Z.  
**Funding:** This research was supported by the Beijing south-to-North water Transfer Line management Office (GXGLC-JSZX -2017-CG01), the National Natural Science Foundation of China (Grant No. 5097911), Beijing Municipal Science and Technology Commission (Z141100006014058) and China Institute of Water Resources and Hydropower Research (SM0145B632017).  
**Acknowledgments:** The authors would like to thank the anonymous reviewers for their constructive suggestions to improve the quality of the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Carcass Yield and Meat Quality of Broiler Chicks Supplemented with Yeasts Bioproducts
## 3 Results
### Texture Profile Analysis  
The broiler breast and thigh meat texture profiles analysis are displayed in the Tables 6 and 7. In this study, the broiler breast and thigh meat texture profiles were not affected by the SC (\(p>0.05\)) nor the RM dietary supplementation (\(p>0.05\)). Similar results for hardness, adhesiveness, chewiness, cohesiveness, springiness, gumminess, and resilience were found on all experimental groups (1-8). \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline
**Group** & **SC** & **RM** & **Hard** & **Adhe** & **Cohe** & **Spri** & **Gumm** & **Chew** & **Resi** \\ \hline
**1** & **0** & **No** & 2806.00 & 0.266 & 0.260 & 2.238 & 729.69 & 13.27 & 0.204 \\
**2** & **0.6** & **No** & 2789.20 & 0.264 & 0.238 & 2.280 & 665.52 & 12.10 & 0.186 \\
**3** & **1** & **No** & 2721.00 & 0.266 & 0.266 & 2.392 & 724.54 & 13.17 & 0.203 \\
**4** & **1.3** & **No** & 2720.60 & 0.278 & 0.262 & 2.216 & 711.07 & 12.93 & 0.199 \\
**5** & **0** & **Yes** & 2779.40 & 0.264 & 0.240 & 2.390 & 667.35 & 12.34 & 0.187 \\
**6** & **0.6** & **Yes** & 2758.80 & 0.266 & 0.256 & 2.194 & 709.41 & 12.90 & 0.198 \\
**7** & **1** & **Yes** & 2709.40 & 0.270 & 0.252 & 2.268 & 683.92 & 12.44 & 0.191 \\
**8** & **1.3** & **Yes** & 2709.60 & 0.280 & 0.236 & 2.316 & 635.26 & 11.55 & 0.178 \\
**SEM** & & & 82.146 & 0.010 & 0.012 & 0.074 & 39.473 & 0.718 & 0.011 \\ \hline \multicolumn{2}{l}{\(p\) value} & \multicolumn{3}{c}{0.999} & \multicolumn{1}{l}{0.992} & \multicolumn{1}{l}{0.279} & \multicolumn{1}{l}{0.186} & \multicolumn{1}{l}{0.439} & \multicolumn{1}{l}{0.439} \\ \hline \multicolumn{2}{l}{} & \multicolumn{3}{c}{**Main effect—SC levels**} \\ \hline
**0** & & 2792.70 & 0.265 & 0.250 & 2.314 & 698.52 & 12.70 & 0.195 \\
**0.6** & & 2774.00 & 0.270 & 0.247 & 2.237 & 687.47 & 12.50 & 0.192 \\
**1** & & 2715.20 & 0.268 & 0.259 & 2.330 & 704.23 & 12.80 & 0.197 \\
**1.3** & & 2714.90 & 0.279 & 0.249 & 2.266 & 673.16 & 12.24 & 0.188 \\ SEM & & 58.086 & 0.007 & 0.008 & 0.052 & 27.911 & 0.507 & 0.008 \\ \hline \multicolumn{2}{l}{\(p\) value} & \multicolumn{3}{c}{0.700} & \multicolumn{1}{l}{0.467} & \multicolumn{1}{l}{0.759} & \multicolumn{1}{l}{0.571} & \multicolumn{1}{l}{0.867} & \multicolumn{1}{l}{0.867} & \multicolumn{1}{l}{0.867} \\ \hline \multicolumn{2}{l}{} & \multicolumn{3}{c}{**Main effect—RM addition**} \\ \hline
**No** & & 2759.10 & 0.269 & 0.257 & 2.28 & 707.71 & 12.87 & 0.20 \\
**Yes** & & 2739.30 & 0.270 & 0.246 & 2.29 & 673.98 & 12.25 & 0.19 \\
**SEM** & & 41.073 & 0.005 & 0.006 & 0.037 & 19.98 & 0.359 & 0.006 \\ \hline \multicolumn{2}{l}{\(p\) value} & \multicolumn{3}{c}{0.735} & \multicolumn{1}{l}{0.835} & \multicolumn{1}{l}{0.225} & \multicolumn{1}{l}{0.841} & \multicolumn{1}{l}{0.236} & \multicolumn{1}{l}{0.236} & \multicolumn{1}{l}{0.236} \\ \hline \end{tabular}
\end{table} 
Table 7: Yeasts supplemented broilers’ thigh meat texture profile.
# Blind Deconvolution with Scale Ambiguity
## 4 Discussion and Experimental Results
### Experimental Results
#### 4.2.2 Deblurring on Real Blurred Images  
We note that the existing two methods [10; 14] have very similar formulation to (4). So we first show an example in Figure 12 compared with these two methods. The deblurring results shown in Figure 12b,c still contain severe blur effects and ringing artifacts. In comparison, our result shown in Figure 12d is better.  
Figure 13 shows another real blurred example compared with the state-of-the-art deblurring methods [1; 13; 14; 15; 10; 15; 17; 29]. The close-ups of the deblurred results by [10; 15], which are shown in Figure 13c,h, contain significant blur, especially in the text region. The deblurred results by the methods [13; 29] contain significant ringing artifacts, as shown in Figure 13d,e. The deblurred result by [PERSON] et al. [17], which is shown in Figure 13k, contains some unpleasing effects. Although the method [1; 3; 5; 14; 23] handle this image well, their deblurred results contain some noises around the text in the background as shown in Figure 13b,f,g,i,j, respectively. In contrast, our method generates clear result with less noise. The text and background of the deblurred result by our method are much clearer than other methods.  
Another one real blurred image is shown in Figure 14a. The results by methods [10; 23; 27], shown in Figure 14b,g,i, contain some blur effects. In Figure 14c,d,e,h, the results by methods [13; 14; 17; 29] contain significant ringing artifacts. The result by [3] is visually better than the above results as shown in Figure 14f. Compared with it, our method generates the deblurred image with clearer details as shown in Figure 14j.  
Figure 15 shows one example from the dataset [44] compared with [1; 3; 10; 11; 13; 14; 15; 23]. The deblurring methods [1; 13; 23] produce ringing artifacts as shown in Figure 15b,e,h. The deblurring results by methods [3; 11; 15] are over smoothed as shown in Figure 15c,g,i. The deblurred result by [PERSON] et al. [10] in Figure 15d still contains some noise. The deblurred result by [PERSON] and [PERSON] [14], which is shown in Figure 15f, is also unpleasing. In contrast, our method generates a much clearer result, as shown in Figure 15j.  
In addition, we provide visual comparisons with the state-of-the-art deep learning based methods in Figure 16. The deep learning based methods [35; 37] are less effective in handling this blurred image as shown in Figure 16b,c. The result by the method [27] in Figure 16d is visually acceptable. In comparison, our result shown in Figure 16e is clearer than their result as shown in Figure 16d.
# A Secure Opportunistic Network with Efficient Routing for Enhanced Efficiency and Sustainability
## 7 Performance Evaluation
### Simulation Results  
#### 7.2.1 Layer 1 Simulation Results  
In this experiment, we conducted a comparison between the detection time achieved by our proposed system and that of FT-OLSR. Figure 9 illustrates the time taken to isolate attackers from communication with other nodes, with respect to the percentage of attackers within the OppNet. As indicated in [37], the fuzzy logic system operates with two inputs and three fuzzy sets for each input, using 10 discretization levels for the universe of discourse. Based on these parameters, the fuzzy logic system involves approximately 9127 operations. Assuming the implementation of our FT-OLSR in hardware with a CPU clocked at 700 MHz and an average instruction execution time of 10 clocks, a single inference requires approximately 0.13 ms.  
Figure 9: Detection time.
## 5 Simulation Experiments
### Discussion  
In the experimental result analysis part, the improved UWB/IMU tight combination model was numerically validated. The experimental results showed that the improved UWB/IMU tight combination model remarkably enhanced the positioning accuracy with a maximum deviation of 0.232 m (reduced by 83.93% compared to a pure UWB model and 43.14% compared to the conventional UWB/IMU model) and standard deviation of 0.09981 m (reduced by 88.35% compared to a pure UWB model and 22.21% compared to the conventional UWB-IMU model).  
In the application of the real-world scene, positioning accuracy will be affected by obstacles or moving pedestrians in most real-world scenes. It is necessary to consider the matching problem of the IMU cumulative error elimination step and the UWB elimination error step in future research, and it is also important to modify the parameters in the extended Kalman filter to increase the generalization ability of the dynamic double-loop cumulative error estimation model.
## 3 Approach
### Seedling Floating and Qualified State Recognition and Improvement  
#### 3.2.1 YOLOv8n Network Model  
In the realm of one-stage object detection models, YOLOv8 is known for its efficiency and flexibility [22], excelling in both speed and accuracy. YOLOv8 is the optimal model in the YOLO series for detection speed and precision. Specifically, the YOLOv8n [23] network model is compact, fast in inference speed, and provides a powerful choice for lightweight detection within the YOLO series. It is well suited for scenarios with real-time requirements. Therefore, this paper selects the YOLOv8n model as the foundational model for real-time detection of seedling planting conditions. The improved YOLOv8n network structure is divided into four parts, as shown in Figure 8: input, backbone, neck, and prediction.  
In the YOLOv8n model, the original seedling images are resized to 640 \(\times\) 640, maintaining the aspect ratio, with the remaining areas filled using background padding. These resized images are then input into the backbone section. Within the main network, feature extraction is performed through three effective feature layers. Subsequently, the AFPN (Asymptotic Feature Pyramid Network) in the neck section is employed, utilizing upsampling operations to segment and concatenate features, achieving feature fusion at different stages [24]. In the prediction section, a decoupled head structure is utilized for separate classification and regression tasks. This not only reduces the number of channels but also enhances detection speed and accuracy. The output includes seedling detection categories, bounding box coordinates, and predicted confidence scores.
## 3 Validation of MANTiS-32 Sensing Performance
### Integrated Measurement Performance Evaluation of the MANTiS-6050 Sensor and MANTiS-32 System
#### 3.4.1 Test Structure for System Validation  
To validate the measurement capability of the system on relatively flexible structural elements, a simple cantilever beam specimen was selected, as depicted in Figure 12. The beam was fabricated from standard structural steel, with dimensions of 770 mm in length, 60 mm in width, and 5 mm in thickness. The mechanical properties of the material are presented in Table 5. The geometric configuration of the test structure and the spatial arrangement of the sensors for the MANTiS-32 measurement experiment are shown in Figure 12.
# Interference of Spread-Spectrum EMI and Digital Data Links under Narrowband Resonant Coupling
## 5 Impact of Spread Spectrum Modulations in a 4-QAM Channel under Resonant EMI Coupling  
The effects of SS EMI on a communication channel under resonant EMI couping, which have been discussed in the previous section in a completely general, coding-independent way in terms of equivalent channel capacity loss, are now investigated with reference to a synchronous buck DC-DC power converter interfering with a 4-QAM digital link featuring Turbo coding, which makes it possible to achieve a data rate approaching the channel capacity and is therefore expected to be more sensitive to EMI-induced capacity loss [37].
# A Multi-Gateway Behaviour Study for Traffic-Oriented LoRaWAN Deployment
## 2 Fundamentals of LoRaWAN Technology  
The best-known low-power wide area network (LPWAN) technologies working on the unlicensed band are Sigfox [2] and LoRa [3]. However, the latter, which is the subject of our study, is the most popular thanks to some of its characteristics that are essential for some IoT applications such as smart cities and industry 4.0. Among these characteristics are the downlink channel, which is necessary for the functionalities of the control-command mode, the capacity of this technology to support traffic, and the consideration of mobility. LoRa remains the most widespread technology on the market and the most documented in terms of research thanks to its free media access control (MAC) LoRaWAN protocol promoted by the LoRa alliance [3]. LoRaWAN presents a better quality ratio compared to the investment costs for the smart city, smart grids, smart farming, and remote monitoring systems [4]. Its architecture is star-of-star, as depicted in Figure 1, where each element works at one or more OSI layers (physical (L1), datalink (L2), network (L3), transport (L4), session (L5), presentation (L6), application (L7)). Specifically, the end device works at layers L1 and L2. The GW works at layers L1, L2, L3, and L4. The servers work at all layers.