esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 IPTMS Analysis Method  
The IPTMS designs in the fields that are mentioned in the previous sections are mostly based on energy analysis relating to the first law of thermodynamics. The energy-based designs aim to minimise aircraft energy usage. However, in 1987, [PERSON] et al. [91] stated that energy analysis cannot detect most of the problems of thermodynamic imperfection. In 1999, [PERSON] [92] defined a concept, named \"exergy\", to measure the quality of various types of energy. The exergy analysis is based on the second law of thermodynamics. A process can satisfy the first law, but compliance does not mean the process is feasible because entropy cannot decrease according to the second law. An exergy analysis is an optimal approach based on the objective functions of entropy generation. Design problems can be identified because aircraft inefficiency can be quantified using exergy analysis. Additionally, researchers can compare the performance of different subsystems with exergy analysis.  
[PERSON] et al. [93] applied an exergy analysis to an IPTMS where the optimisation objectives were exergetic efficiency and total take-off weight. The entropy generated by the IPTMS subsystems is estimated and the total entropy generation of the overall system is found. In this research, the exergy approach can help with the design, but its results are similar to the results of the energy approach and its advantages in IPTMS design need to be validated further. [PERSON] et al. [94] investigated the thermodynamic irreversibility for IPTMSs with exergy approach. They found that exergy optimisation can help to obtain absolute and consistent measurements for aircraft performance trade-off.  
In addition to thermodynamic characteristic optimisation, IPTMS optimisation can also achieve light weight at the system level. [PERSON] et al. [95] proposed a system-level analysis method to minimise the total fuel weight penalty for a simplified IPTMS with energy recovery. They adopted the equivalent mass method for analysis and compared the performance of three cooling strategies of the IPTMS. It was found that the optimal cooling strategies are different under different operating conditions.  
The development of IPTMS analysis methods can contribute to the trade-off study of an IPTMS. The IPTMS analysis methods can be beneficial for engine cycle designs and lightweight system-level designs. Additionally, the IPTMS analysis methods require validation to ensure their stability.
# Hybrid Backbone-Based Deep Learning Model for Early Detection of Forest Fire Smoke
## 4 Experimental Results
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Models** & **Backbone** & **Precision** & **Recall** & **F1** & **mAP** \\ \hline Yolov5-n & Yolov5 & 0.957 & 0.946 & 0.951 & 0.973 \\ Yolov5-n & EfficientNet\_b1 & 0.920 & 0.914 & 0.916 & 0.953 \\ Yolov5-n & MobileNetV3s & 0.926 & 0.931 & 0.928 & 0.959 \\ Yolov5-n & ResNet34 & 0.929 & 0.942 & 0.935 & 0.958 \\ Yolov5-n & ResNet50 & 0.980 & 0.964 & 0.971 & 0.986 \\ \hline Yolov5-s & Yolov5 & 0.950 & 0.940 & 0.944 & 0.956 \\ Yolov5-s & EfficientNet\_b1 & 0.907 & 0.894 & 0.900 & 0.946 \\ Yolov5-s & MobileNetV3s & 0.965 & 0.931 & 0.947 & 0.961 \\ Yolov5-s & ResNet34 & 0.877 & 0.836 & 0.856 & 0.908 \\ Yolov5-s & ResNet50 & 0.963 & 0.960 & 0.961 & 0.976 \\ \hline Yolov5-m & Yolov5 & 0.962 & 0.958 & 0.959 & 0.988 \\ Yolov5-m & EfficientNet\_b1 & 0.814 & 0.791 & 0.802 & 0.905 \\ Yolov5-m & MobileNetV3s & 0.982 & 0.905 & 0.941 & 0.969 \\ Yolov5-m & ResNet34 & 0.911 & 0.894 & 0.902 & 0.932 \\ Yolov5-m & ResNet50 & 0.941 & 0.973 & 0.956 & 0.971 \\ \hline Yolov5-l & Yolov5 & 0.967 & 0.980 & 0.973 & 0.986 \\ Yolov5-l & EfficientNet\_b1 & 0.786 & 0.834 & 0.809 & 0.878 \\ Yolov5-l & MobileNetV3s & 0.956 & 0.932 & 0.947 & 0.974 \\ Yolov5-l & ResNet34 & 0.915 & 0.820 & 0.864 & 0.941 \\ Yolov5-l & ResNet50 & 0.977 & 0.950 & 0.963 & 0.972 \\ \hline Yolov5-x & Yolov5 & 0.971 & 0.932 & 0.951 & 0.982 \\ Yolov5-x & EfficientNet\_b1 & 0.944 & 0.918 & 0.930 & 0.957 \\ Yolov5-x & MobileNetV3s & 0.964 & 0.942 & 0.952 & 0.963 \\ Yolov5-x & ResNet34 & 0.923 & 0.931 & 0.926 & 0.963 \\ \hline Yolov8-n & Yolov8 & 0.908 & 0.918 & 0.912 & 0.950 \\ Yolov8-s & Yolov8 & 0.940 & 0.940 & 0.940 & 0.959 \\ Yolov8-m & Yolov8 & 0.914 & 0.953 & 0.933 & 0.965 \\ Yolov8-l & Yolov8 & 0.882 & 0.870 & 0.875 & 0.917 \\ Yolov8-x & Yolov8 & 0.888 & 0.831 & 0.858 & 0.928 \\ \hline Proposed & & & & & \\ Model & & 0.988 & 0.967 & 0.977 & 0.973 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Model training results. structure had been replaced with ResNet50. This model was followed by the Yolov5-s model, whose backbone structure was replaced with ResNet50 with a mAP value of 96.5%. Because the test models are implemented using image data that the algorithms have not seen before, they best reflect the performance of the proposed models in a real application environment. When all hybrid models and original YOLO models were examined, the hybrid recommended model provided the best performance. A heatmap is given in Figure 5 showing the performance metrics (precision, recall, F1, mAP) for various models listed on the x-axis. Color intensity represents performance, where dark red corresponds shows higher values and blue corresponds shows lower values. The performance of the proposed model is generally better or equal to other models in all metrics (precision, recall, F1, mAP). It is seen that the proposed model has the highest value, especially in the mAP (97%) metric.
# Towards Flexible Integration of 5G and IIoT Technologies in Industry 4.0: A Practical Use Case
## 5 Use Case
### Scenario  
As previously introduced, the use case presented here consists in the monitoring of a broaching process. This broaching process is performed by a broaching bench installed in the CFAA.  
Broaching is the process of removing material from a surface (usually metal) in order to shape it, which enables the manufacturing of complex profiles. Broaching has a wide range of application in the aeronautic and automotive industries. For example, it is the standard process for producing fir-tree slots in turbine discs due to the complex and delicate cuts that need to be applied to the pieces. For the broaching process, a broaching tool is employed.  
A broaching tool is equipped with a series of cutting teeth placed throughout a row. There is a progressive rise in height per tooth, which indicates the amount of material that each tooth can remove on every pass. Broaching is a very expensive process for various reasons. The complex geometry of a broach results in a high tooling cost, and the large number of teeth that needs to be constantly grounded into the work piece leads to high tool wear, negatively impacting their lifetime.  
To overcome these shortcomings, ML techniques will be introduced in the broaching process in order to monitor it and improve performance. The ML method will imply the analysis of real-time contextualized data obtained from the broaching machine and its environment, combined with historical records, in order to obtain a model that can accurately represent the process and predict the results. This ML approach is intended to achieve early detection of tool wear and disturbances,leading to an increased lifetime of the tool and helping to achieve the quality requirements for the aeronautical industry in the resulting pieces.
## 4 Discussion  
### Limitations of the Landsat-based coastline Detection Method  
Coastline detections are difficult to validate due to their highly dynamic nature and the spatial variability of their dynamics [8]. Therefore, a cloud-free scene of a very-high resolution sensor had to be selected for the same day and time at which a Landsat scene was available, to avoid the effect of local short-term coastline changes. The determined accuracy of 8.4 m lies in the sub-pixel range of Landsat sensors. The validation area, however, only covers an area in central Vietnam that consists of urban areas, rocky coasts, sandy beaches, estuaries, and vegetated coast and shows a low tidal range [77]. Thus, while the validation area contains some heterogeneity in coastal types, it does not provide an indication of accuracy in mangrove areas, marshlands, or tidal flats of southern and northern Vietnam. Furthermore, the validation is limited to the coastline extraction method only. In this respect, it can be stated that although the validation method yielded a high sub-pixel accuracy, the value should be treated with caution. Neither the error sources of the annual composites nor those of more complex coastal types are reflected. Accordingly, further validation of the dataset would be recommended, particularly covering the delta regions in Vietnam and the temporal component. For this purpose, using additional VHR images as well as land-survey GPS and sea level data would be conceivable [29]. Potential causes for errors are discussed in the following paragraphs.  
The availability of valid Landsat pixel observations is temporally and spatially variable (compare Figure 2), which may be related to sensor operation periods, acquisition paths, storage and distribution issues of the Landsat archive, and sensor failures (such as the Landsat 7 Scan Line Corrector (SLC) failure in 2003) [78]. Weather conditions play an additional critical role in the available number of valid pixel observations. Since each image was cloud-masked prior to compositing, occasional misclassification of bright targets, such as building tops, snow, ice, sand dunes, salt lakes, and beaches as clouds is possible [60]. Figure 12 shows the average number of valid pixel observations per year after cloud-masking, revealing an overall mean of 10 observations between 1986 and 2021. From these statistics, a temporal quality characteristic of the individual annual coastlines may be obtained: the coastline for 2012 is thus significantly less reliable than the coastline from 2015.  
MNDWI composites with few valid observations per pixel were found to occasionally produce incomplete or false coastlines at cloud residuals or remaining tidal flats. Furthermore, it has been shown that the representability of the mean-high water level in annual MNDWI composites increases with the number of available acquisitions (compare Figure 11).
# Prediction of Unconfined Compressive Strength in Cement-Treated Soils: A Machine Learning Approach
## 2 Materials and Methods
### Machine Learning Methodology
#### 2.3.1 Data Preprocessing and Feature Engineering  
All available experimental data were used for model development, with no values excluded as outliers. A visual inspection using boxplots and descriptive statistics was performed [58; 59], but no data points were identified as experimental errors or aberrant values. This approach was chosen to avoid bias and to ensure that the influence of all investigated parameters--including compaction velocity, even if small--would be represented in the model.  
Standard data preprocessing steps were applied as needed for each algorithm, including feature scaling for distance-based and gradient-based methods. The main input features used for modeling were cement content, curing time, and compaction velocity. Other derived or composite features (such as cement/water ratio or porosity/cement ratio) were considered during preliminary testing, but models using the original input variables achieved similar or better predictive performance. As a result, all three primary variables were retained in the final modeling workflow.  
Given the wide range of UCS values (157-5055 kPa) and the potential for heteroscedasticity in cement-stabilized soil data, logarithmic transformation of the target variable was investigated as a preprocessing option. Preliminary evaluation using nested cross-validation revealed that log transformation provided inconsistent benefits across algorithms: while Gradient Boosting showed marginal improvement, both Random Forest and XGBoost demonstrated slightly better performance without transformation. Since the primary objective was developing a robust uncertainty quantification system rather than maximizing point prediction accuracy for any single algorithm, the original UCS scale was retained to maintain interpretability and avoid the additional complexity of inverse transformation in the final prediction system.
## 2 Device Structure Modeling  
Figure 1 shows a cross-sectional view of the 1.2-kV 4H-SiC trench-gate MOSFET with a BPW structure. The 4H-SiC trench-gate MOSFET was constructed using Sentaurus TCAD simulation tools. The doping profiles of the P base, n\({}^{+}\) source, p\({}^{+}\) source, and BPW were formed by the Monte Carlo method. For a voltage rating of 1.2-kV, the drift doping concentration (N\({}_{\text{drift}}\)) and the drift thickness (t\({}_{\text{drift}}\)) were 6 \(\times\) 10\({}^{15}\) cm\({}^{-3}\) and 13 \(\upmu\)m, respectively. These parameters determine the breakdown voltage using a punch-through diode model and consider the two-dimensional effect and the reduction in the effective thickness between the trench and n-drift length [21]. The doping concentration and depth of the n\({}^{+}\) source, p\({}^{+}\) source, and P base were 1 \(\times\) 10\({}^{20}\) cm\({}^{-3}\), 1 \(\times\) 10\({}^{20}\) cm\({}^{-3}\), and 3 \(\times\) 10\({}^{17}\) cm\({}^{-3}\), respectively. The length and depth of n\({}^{+}\) source, p\({}^{+}\) source, and P base were 1.25/0.3 \(\upmu\)m, 0.5/0.4 \(\upmu\)m, and 1.75/0.7 \(\upmu\)m, respectively. The width, depth, and slope of the trench were 1 \(\upmu\)m, 2 \(\upmu\)m, and 88\({}^{\circ}\), respectively; the trench depth effectively prevented the degradation of the current flow from the JFET resistance. At the trench bottom, a p-type region for BPW was constructed using multi-implantation energies and a spacer thickness of 0.1 \(\upmu\)m. The implantation energies and spacer thickness were considered to prevent lateral penetration of the trench sidewall. N\({}_{\text{BPW}}\) values from 1 \(\times\) 10\({}^{17}\) cm\({}^{-3}\) to 3 \(\times\) 10\({}^{18}\) cm\({}^{-3}\) were used to analyze electrical characteristics, and the contact metal was placed at the bottom of the trench to set BPW as ground [15; 22].  
Figure 1: Cross-sectional view of the 1.2-kV 4H-SiC trench-gate MOSFET.
# Retrofit Fatigue Cracked Diaphrag Cutouts Using Improved Geometry in Orthotropic Steel Decks
## 5 Results and Discussion
### Stress Spectra due to a Longitudinally Moved Vehicle  
The stress spectra of zone A and zone B for the standard vehicle tests are presented in Figure 17. The stresses in these figures are the average value and the error bars reflect the deviations from the average value.  
(1) Under longitudinal wheel loading, the value of the maximum stress obtained from the tests is generally less than the FEA value, and the difference is less than 12.5%. Considering that it is difficult to experimentally capture the maximum stress at the locations of the strain gauges, the error is within an allowable range.  
(2) For the longitudinal loading, the measured stresses in zone A are all compressive stresses. The maximum compressive stress measured before retrofit is 146.2 MPa with a measurement error of 10.8 MPa. In comparison, the maximum compressive stress measured after retrofit is 58.6 MPa with  
Figure 16: Stress distributions along the edge of diaphragm cutouts: (**a**) longitudinal movement L3, (**b**) longitudinal movement L10.  
a measurement error of 13.6 MPa. The maximum reduction in stress is 87.6 MPa, at the location L3. The maximum stress reduction ratio is 58.4%, at the location L5.  
(3) The measured stresses in zone B under various longitudinal movement conditions are tensile stresses. The maximum tensile stress measured before retrofit is 95.8 MPa with a measurement error of 8.6 MPa, while the maximum measured stress after retrofit is 42.4 MPa with a measurement error of 6.2 MPa. The maximum reduction in stress is 53.4 MPa, at the location L3. The maximum stress reduction ratio was 62.5%, at the location L11.  
(4) The principal compressive stress is large, when the deck is subjected to the load cases L2, L3 and L4. For these cases, the central axis is located at the front of the diaphragm and the rear axis is located at the back of the diaphragm simultaneously. The wheel load does not directly act on the upper part of the diaphragm. Because the wheel load acting on the bridge deck is mainly transmitted to the diaphragm through the U-ribs.
# Bindings and RESTlets: A Novel Set of CoAP-Based Application Enablers to Build IoT Applications
## 6 Implementation and Evaluation
### Performance Evaluation
#### 6.4.1 Performance Evaluation of Bindings  
A. Memory Footprint  
As described above the original Erbium code has been modified in order to support the binding concept. This modification induces a slight increase in memory space, mainly in the code (text) segment. Table 1 shows the increased memory footprint of the binding solution compared to gateway or cloud-based solutions. As every observer's information needs to be stored in memory, the memory required in the BSS section increases proportionally to the number of observers. However, the difference between the two approaches is only 38 bytes per observer for the two cases.  
Despite the slight increase in memory footprint, the code can still fit in constrained devices. Given the advantage of the binding solution, the increase in memory footprint is acceptable and the binding solution is viable to be applied in constrained devices. However, this does not come without a limitation. An increased number of bindings leads to an increase in memory space requirement. The BSS section of both solutions in the table shows that when the number of observers increases, the size of the BSS increases as well because at boot time the program always reserves the maximum amount of memory needed to store all potential observers. As memory is a very scarce resource of constrained devices, this will limit the number of observers allowed to register simultaneously and thus the number of bindings that can be supported. Here the gateway/cloud solution has an advantage since it may achieve scalability by aggregating multiple observe requests at the gateway avoiding one to one relationships between multiple actuators and a sensor.
## 3 Results and Discussion
### Characterization of Nanoparticles of Garlic Extract by Microplate Spectrophotometer  
The UV-Vis spectrophotometric characterization was carried out using a microplate spectrophotometer, and the absorbance was scanned in the wavelength range from 200 nm to 750 nm. The spectrophotometer results showed that the maximum absorbance of nanoparticles of garlic extract was in the visible wavelength range of around 240-300 nm, which is typically the range of allicin [48], which is an organosulfur compound found in garlic and plays the vital role in the antibacterial property of garlic extract.  
During this study, it was observed that the sonicated samples showed high absorbance compared to the unsonicated sample (control); however, no significant differences in the peak location or absorbance was observed among the four sonicated samples, prepared at various times and at different sonication power (Figure 4).  
A slight difference was observed in sonication time which indicated that as sonication time increased, the absorbance of the four sonicated samples slightly increased. The marginal differences might be due to high extraction efficiency when the sonication time increased; however, the power (Watt) did not show an impact on the peak location or absorbance.  
Figure 3: Graphical presentation of the total viable count of 40 mg/mL of garlic extract treated in different conditions on tested bacteria. Values are means of determinations of three replicates, and bars represent standard deviations (S.Ds.) of the means. S.Ds. followed by different letters, are significantly different at \(p\leq 0.05\) level by [PERSON]’s multiple range test.
# LaneFormer: Real-Time Lane Execution and Detection via Transformer
## 3 Results
### Experimental Parameters  
For the Tusimple data set, we set the input resolution to 360 \(\times\) 640, the learning rate to \(5e-4\), the batch size as 20, the number of prediction curves N set as 6, and the number of training iterations as 400 k. We scale the raw data, rotation, image channel increase or decrease, and cropping for data augmentation. In addition, for the CULane data setting, the learning rate is set to \(e-5\), and the other parameters remain unchanged. Except for the ablation experiment, the hyperparameter settings of all experiments are the same. All results are tested on the dual 2080 Ti graphics card platform.  
In order to illustrate the performance of our model, we compare it with the excellent models for lane line detection: VPGNet [16], Lanenet [20], Ultra-Fast Lanenet [25], SAD [14], Fast-Draw [30], SCNN [22], LSTM [31]. We test on the Tusimple data, comparing the dimensions of the frame rate, MACS, Para, PP, ACC, FP, FN, and place the comparison results in Table 1.  
Table 1 shows the performance comparison between our method and the current excellent lane line detection method. The evaluation index is based on the Tusimple data  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Method** & **FPS** & **MACS** & **Para** & **PP** & **Acc** & **FP** & **FN** \\ \hline VPGNet [16] & 45 & - & - & \(\surd\) & 98.25 & 0.0048 & 0.0250 \\ \hline Lanenet [20] & 52.6 & - & 20.68 & \(\surd\) & 96.45 & 0.0617 & 0.0244 \\ \hline UltraFast [25] & 75 & - & 0.95 & \(\surd\) & 95.58 & 0.0602 & 0.0205 \\ \hline SAD [14] & 70 & - & 0.95 & \(\surd\) & 96.60 & 0.0601 & 0.0213 \\ \hline FastDraw [30] & 90 & - & - & - & 96.88 & 0.0742 & 0.497 \\ \hline SCNN [22] & 7 & - & 20.45 & - & 97.36 & 0.0642 & 0.0133 \\ \hline LSTM [31] & 420 & 0.574 & 0.77 & - & 96.18 & 0.291 & 0.338 \\ \hline Ours & 310 & 0.425 & 0.60 & - & 97.31 & 0.0290 & 0.0332 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of accuracy (%) on TuSimple testing Set. The number of multiply-accumulate (MAC) operations is given in G. The number of parameters (Para) is given in M (million). The PP means the requirement of post-processing.  
set. Compared with the LSTM network of the same transformer structure, our method has a slightly lower speed but a large improvement in accuracy. At the same time, the parameter page is lower than LSTM. Compared with CNN-based lane line detection frameworks (VPGNet, Lanenet, SAD, FastDraw, SCNN), our speed is between 5-50 frames faster than theirs, and our accuracy is equal to or even slightly higher than the above methods.
## 2 Methodology, System Description, and Problem Formulation  
### Methodology  
Figure 1 illustrates the methodology of this work. By utilizing proactive caching and mobile edge computing technologies, we firstly design the 360\({}^{\circ}\) VR video service system in Section 2.2, where we detail the workflow and latency model, video coding and FoV prediction model, and fairness-oriented transmission and computing resource allocation strategy. Based on the system design, in Section 2.3, we formulate the multiuser access control problem as a probability-constrained integer programming problem. In Section 3.1.1, we analyze the E2E delay using the diffusion process analysis method, whereby we derive the delay violation probability for the users who are granted access in the system, as presented in Section 3.1.2. Finally, by exploring the relationship between communication and computing resource consumption and the users' delay violation probability, we design an efficient solver for the multiuser access control problem in Section 3.2.