esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation and Discussion
### Day-Ahead DRO Model Simulation Analysis
#### 5.2.3. CHB Hydrogen Blending Ratio Distribution Pattern  
The distribution results of the hydrogen blending ratio \(r\) after optimization for Case 4 across eight scenarios are shown in Figure 6. From the figure, it can be seen that the optimized hydrogen blending ratio, \(r\), is concentrated at 0.2 during the 0-7 h and 11-18 h periods and at zero during the 8-10 h and 19-23 h periods. The ratio exhibits a pattern that alternates between two constant values at different times. Furthermore, the hydrogen blending ratio exhibits pronounced step changes across temporal intervals, which further verifies the reasonableness of the THB rule.  
The binarized THB strategy significantly simplifies the demands of real-time control. This approach only requires switching the hydrogen blending ratio at predefined time intervals, which is more feasible in practical engineering applications and substantially reduces the design and control complexity of on-site valves and actuators. Such switching operations can be accomplished using the logic of existing industrial control systems, thereby obviating the need for developing high-frequency, high-precision continuous proportional regulation algorithms and hardware. This makes the optimization strategy proposed in this paper more readily implementable in the actual management of integrated energy systems.
# Birds Detection in Natural Scenes Based on Improved Faster RCNN
## 4 Experiment and Result Analysis
### Result and Analysis  
#### 4.4.1 Ablation Studies  
To test the effectiveness and contribution of the model using different strategies, experiments were carried out on the bird dataset. The same learning rate 0.001 and iteration times 20,000 were set during the experiment. Experimental results are shown in Table 2, where \(\times\) means unused and \(\surd\) means used.  
According to Table 2, the average detection accuracy of the original Faster RCNN model is 73.68%. Different improvement strategies have different effects on model enhancement, among which multi-scale feature map fusion has a more obvious effect on model  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Num** & **Anchor** & **KM** & **MSFIF** & **SNMS** & **MT** & **NET** & **mAP** & **FPS** & **P** & **R** & **F** \\ \hline
1 & 9 & \(\times\) & \(\times\) & \(\times\) & \(\times\) & VGG19 & 84.6 & 12.52 & 73.15\% & 74.22\% & 73.68\% \\
2 & 15 & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & VGG19 & 86.07 & 11.86 & 74.99\% & 75.37\% & 75.18\% \\
3 & 15 & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & VGG19 & 85.23 & 11.99 & 75.83\% & 76.51\% & 76.17\% \\
4 & 15 & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & VGG19 & 85.08 & 12.43 & 77.61\% & 76.13\% & 76.86\% \\
5 & 15 & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & VGG19 & 85.81 & 12.18 & 76.39\% & 77.43\% & 77.41\% \\
6 & 15 & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & VGG19 & 86.52 & 12.63 & 77.22\% & 75.48\% & 76.34\% \\
7 & 15 & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & VGG19 & 87.59 & 13.91 & 76.84\% & 78.91\% & 77.86\% \\
8 & 15 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & VGG19 & 88.67 & 13.78 & 77.67\% & 78.35\% & 78.00\% \\
9 & 15 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & INet-V3 & 88.72 & 13.17 & 78.94\% & 79.76\% & 79.35\% \\
10 & 15 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & Res-50 & 89.04 & 13.63 & 80.03\% & 79.68\% & 79.85\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect comparison of model promotion by different strategies.  
enhancement. The average detection accuracy of the final model using all the improved strategies reached 79.85%, which was 6.17% higher than that of the original model.
## 2 Related Model and Methods
### Theory of the Multipath Error  
Multipath error is mainly induced by the reflection and diffraction of a signal; these non-direct signals can be received by a GPS receiver, which further distorts the received signal and causes errors in carrier phase and pseudorange observations. Since the physical surrounding environment of the receiver dictates the multipath effect, the multipath is significantly different for land, water, glass, concrete, etc. The general multipath effect environment is demonstrated in Figure 1.  
From Figure 1, it can be seen that all multipath signals (including the reflected signal and scattered signal) pass a longer distance compared to the direct signal. Therefore, the direct signal will be received first by the receiver antenna, compared with other multipath signals. The relative delay of multipath signals can be determined based on electromagnetic theory. In terms of the relative amplitude of the multipath signal, it is mainly dependent on the shape, size, and reflection coefficient of the object's surface. For example, the reflection coefficient of dry soil and grassy field are 0.27 and 0.33, respectively. However, the reflection coefficient of fresh water and sea water are 0.80 and 0.81, which are about three times that of dry soil. Thus, the amplitude of multipath error induced by the water is larger than the dry soil and grassy field. Moreover, the reflection coefficient is also relevant to the incidence angle of the signal. Thus, the amplitude of attenuation decreases with the decreasing satellite elevation angle. For more information, refer to [PERSON] [11]. In conclusion, the multipath error is more serious during a flood compared to in a normal environment without flooding.
## 3 Results and Discussion  
### Microstructure  
Figure 2 shows the optical microscopic images of CS_QAT and CS_IQAT. It can be observed that both specimens are composed of ferrite and carbide. The microstructure of CS_IQAT (Figure 2b) is significantly finer and more homogeneous than that of CS_QAT (Figure 2a).  
Figure 3 shows the SEM images of CS_QAT and CS_IQAT. It can be observed that the tempered microstructure of both consists of ferrite and Fe\({}_{3}\) C particles. Compared with CS_QAT, the Fe\({}_{3}\) C particles of CS_IQAT are finer and more uniform. The ultra-fast cooling rate during the intensive quenching process increases the amount of subcooling, nucleation drive, and nucleation sites, resulting in finer and more uniform martensite. The subsequent high-temperature tempering process induces martensite decomposition and promotes a uniform redistribution of carbon, resulting in the formation of finer Soxhlet.  
Figure 4 shows the XRD images of CS_QAT and CS_IQAT. Diffraction peaks of \(\alpha\)-phase (ferrite) and Fe\({}_{3}\) C were detected, which is consistent with the results of optical microscopy and SEM.  
Figure 1: MFT-5000 (from Rete Silicon Valley, San Jose, CA, USA) working principle diagram.  
Figure 2: Optical microscopic images of (**a**) CS_QAT and (**b**) CS_IQAT.
# Fault Diagnosis Across Aircraft Systems Using Image Recognition and Transfer Learning
## 5 Explanation and Evaluation of Results
### Grad-CAM Visualisation for 2D-CNN TL Result
However, since the ImageNet dataset contains over 14 million images in 1000 classes, the vast number of features that the pre-trained 2D-CNN can identify enabled the knowledge transfer to identify the ECS fault patterns in the target domain despite the content of the images in the source and target domains being unrelated. In comparison, the other choice of source domain, the fuel system data images, is a more similar set of images. However, since the fuel system dataset is much smaller than ImageNet, containing only 3989 images in 32 classes, there was a more limited pattern recognition capacity transferred to the ECS problem, hence the slightly worse performance of the 2D-CNN-based TL solution using the fuel system as the source. Nonetheless, the  
Figure 14: Plot of Grad-CAM heatmaps superimposed on the input image of an (**a**) ACM fault case; (**b**) RAI blockage case; (**c**) TCV fault case; (**d**) healthy case; (**e**) PHX fault case; and (**f**) SHX fault case. transfer from the fuel system to ECS and APU improved the accuracy, demonstrating the successful cross-system transfer ability of this method. Although the cross-system TL solution underperforms the TL solution based on ImageNet for the three datasets used in this work, it is still a remarkable achievement that is worth further discussing in the future for two reasons. Firstly, it is more sensible from an engineering perspective to leverage diagnostic knowledge from one system to another. Using both engineering systems in the source and target domain, any physical similarity between them would lead to similar fault patterns and, hence, better diagnostic knowledge transfer based on their physics. The three systems used in this work were an extreme demonstration of completely different systems set up to fully test the feasibility of the cross-system TL methods. However, for systems resembling higher physical similarity, there may be more similar patterns between their faults that would promise higher cross-system TL accuracy. Secondly, despite the fuel system dataset being orders of magnitude smaller than ImageNet, the cross-system TL solution achieved a comparable level of improvement as the TL solution was based on ImageNet, which indicated a higher efficiency of diagnostic knowledge transfer. If another aircraft system was found to produce richer data and more fault patterns, there was the possibility that the cross-system transfer from that system could perform better than ImageNet.
# An Improved Cuckoo Search Algorithm and Its Application in Robot Path Planning
## 2 Relevant Theoretical Basis  
### Cs  
The CS emulates the brood parasitism of cuckoo birds, where each cuckoo lays a single egg and randomly deposits it in a chosen host nest. Cuckoos pass on eggs with the highest parasitization levels to subsequent generations within all infested nests. The number of available host nests remains constant, and the likelihood of a host discovering a parasitic egg is denoted by \(Pa\in(0,1)\).  
CS initiates a random population and employs the Levy flight mechanism to refine population positions. The initial population position, \(X_{0}\), is randomly generated as illustrated in Equation (1):  
\[X_{0}=low+(high-low)\times rand(d), \tag{1}\]  
where \(rand(d)\) represents a \(d\)-dimensional random number in \([0,1]\), and \(low\) and \(high\), respectively, denote the upper and lower bounds of the search space.  
CS employs a strategy of Levy flight for the population search, and the population position is updated as Equation (2):  
\[X_{i}^{t+1}=X_{i}^{t}+\partial\otimes Levy(\lambda), \tag{2}\]  
where \(X_{i}^{t+1}\) and \(X_{i}^{t}\) represent the \(i\)-th population positions at the time \((t+1)\) and \(t\), respectively. \(\partial\) is a step-size factor, typically taken as a positive value.  
The direction of Levy flight follows a uniform distribution and the flight step size follows the Levy distribution shown in Equation (3):  
\[\text{Levy}\sim u=t^{-p},1\leq p\leq 3, \tag{3}\]  
Therefore, the population positions formula can be given as Equation (4):  
\[X_{i}^{t+1}=X_{i}^{t}+\alpha\frac{u}{\left|v\right|^{1/\beta}}(X_{i}^{t}-X_{ best}^{t}),\beta\in[1,2], \tag{4}\]  
where \(X_{best}^{t}\) is the best individual in the population at time \(t\), \(\beta\in[1,2]\) is the flight parameter, typically set to \(1.5\), \(u\) and \(v\) are random numbers following a normal distribution; that is, \(u\sim\text{N}\big{(}0,\sigma_{u}^{2}\big{)}\) and \(v\sim\text{N}\big{(}0,\sigma_{v}^{2}\big{)}\), where \(\sigma_{u}\) and \(\sigma_{v}\) satisfy Equation (5):  
\[\sigma_{u}=\{\frac{\Gamma(1+\beta)\times sin(\pi\beta/2)}{\Gamma[(1+\beta)/2] \times 2^{\beta-1/2}\times\beta}\}^{1/\beta},\sigma_{v}=1, \tag{5}\]  
where \(\Gamma\) represents the gamma function.  
After updating the population positions using the Levy flight mechanism, CS proceeds with a random walking by generating a random number \(r\in(0,1)\). Assuming the elimination probability is \(Pa\), when \(r>Pa\), a new population position is generated as Equation (6):  
\[X_{i}^{t+1}=X_{i}^{t}+rand(0,1)\otimes H(\partial)\otimes\big{(}X_{m}^{t}-X_{ k}^{t}\big{)}, \tag{6}\]  
where \(X_{m}^{t}\) and \(X_{k}^{t}\) represent any two population positions other than the current population \(X_{i}^{t}\), \(H(\partial)\) denotes the Heaviside function, and rand \((0,1)\) represents a random number from \(0\) to \(1\).
# A Traceable Spectral Radiation Model of Radiation Thermometry
### Real Body Radiation Thermometry (RBRT)  
Some authors previously attempted to incorporate the emissivity characterisation within correction of thermal calibration by expressing common parameters [23]; however, such solutions are not applicable upon a change of environmental conditions and require re-calibration.  
Currently, best practice in radiation thermometry of real bodies (RBRT) is to adjust the input parameters of the GBRT model while matching the measurement and reference temperature of the surface, obtained with a contact thermometer.  
Repeated evaluations of required instrumental parameters are required even in radiation thermometry of grey bodies because of the thermal dependence of spectral influence parameters. While thermal dependence of influence parameters is documented for many materials and often expected in practice, spectral interactions of spectral influence parameters in the system of measurement are always neglected in the GBRT model; therefore, GBRT is not suitable for RBRT, where spectral parameters are non-homogenously distributed.  
In order to correctly perform RBRT, instrumental parameters of GBRT should take into account the spectral distribution of influence parameters and the spectral radiances that are emitted and absorbed at each stage of the physical process of RBRT.  
The objective of this study was to define a mathematical model for the process of radiation thermometry (including BBRT, GBRT and RBRT) and to define inverse models that serve to compensate for the inevitable influences within the physical model, namely emission, reflection and atmospheric transmission.
# Prediction of Arrival Time of Vessels Considering Future Weather Conditions
## 2 Previous Studies
### Overview
#### 2.1.1 (A) Route Calculation  
The Dijkstra method is used to determine the route that minimizes the travel distance from the current position to the destination port under the constraints of the topographical information and congestion status of vessels at each candidate transit site. The Dijkstra method is one of the algorithms available for determining the shortest route from a departure site to a destination site. The transit sites are represented at the vertex of the graph, the connection between the sites is represented at the edge, and the route is calculated in a way that minimizes the cost of traversing an edge. [PERSON] et al. set the cost of each edge in accordance with the distance traveled between sites, the congestion status at each candidate transit site, and the topographical information of each site regarding its location on land or sea.
# Multi-Source Satellite Imagery and Machine Learning
## 6 Ablation Analysis
### Combinations of Parameter Sets and Classifiers  
The goal is to identify which data combinations most influence classification performance by comparing dataset combinations of geographic parameters (DGPs), OLI spectrometric parameters (DOLIPs), and PCA parameters (DPCAPs). Algorithm performance is measured using criteria such as accuracy, MCC (Matthews Correlation Coefficient), and execution time.  
The DGP-DOLIP combinations achieve similar results in terms of accuracy, with XGBoost and Random Forest reaching 51.53% and Gradient Boosting slightly lower (50.51%). Performance in MCC follows a similar pattern, with XGBoost and Random Forest delivering close results at 40.35% and 40.28%, respectively.  
DGP-DPCAP combinations perform better, especially with Random Forest, which achieves the best accuracy (55.29%) and high MCC (45.12%), outperforming other algorithms in this combination.  
The DPCAP-DOLIP combinations show relatively lower performance, with Random Forest still performing best (45.05%) but with a significantly lower MCC (31.81%) than in the other combinations.  
Regarding execution time, Random Forest remains the fastest across all combinations, followed by XGBoost and Gradient Boosting.  
Table 11 provides essential insight into the impact of different dataset combinations on classification performance. It suggests that DGP-DPCAP and DGP-DOLIP combinations  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Algorithm** & **Time (ms)** & **Accuracy (\%)** & **MCC (\%)** \\ \hline DPCAP & Gradient Boosting & 8.68 & 39.93 & 25.42 \\ DPCAP & XGBoost & 4.19 & 37.20 & 22.23 \\ DPCAP & Random Forest & 2.60 & 41.98 & 28.23 \\ DGP & Gradient Boosting & 10.27 & 51.53 & 40.31 \\ DGP & XGBoost & 8.07 & 47.78 & 35.72 \\ DGP & Random Forest & 2.58 & 49.14 & 37.39 \\ DOLIP & Gradient Boosting & 8.68 & 43 & 29.16 \\ DOLIP & XGBoost & 4.19 & 43.34 & 30.09 \\ DOLIP & Random Forest & 2.60 & 52.59 & 30.32 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Algorithm performance evaluation on different datasets (DGP, DOLIP, DPCAP).  
improve classification results, with Random Forest showing consistent, high-level performance across all data combinations.
###### Abstract  
Purpose: The main purpose of the research conducted was the analysis of kinematic and biomechanical parameters measured during manual wheelchair ramp-climbing with the use of the anti-rollback system and the comparison of the values tested with the manual wheelchair climbing the same ramp but without any modifications. The paper presents a quantitative assessment relating to the qualitative research of the anti-rollback system performed by another research team. Method and materials: The article presents the measurement results of the wheelchair motion kinematics and the activity of four upper limb muscles for eight subjects climbing a 4.58\({}^{\circ}\) ramp. Each subject propelled the wheelchair both with and without the anti-rollback system. The kinematic parameters were measured by means of two incremental encoders with the resolution of 500 impulses per single revolution of the measurement wheel. Whereas, the muscle activity was measured by means of surface electromyography with the use of Noraxon Mini DTS apparatus equipped with four measurement channels. Results: The surface electromyography measurement indicated an increase in the muscle activity for all four muscles, during the use of the anti-rollback system. The increase was: 18.56% for deltoid muscle anterior, 12.37% for deltoid muscle posteriori, 13.0% for triceps brachii, and 15.44% for extensor carpi radialis longus. As far as the kinematics analysis is concerned, a decrease in the measured kinematic parameters was observed in most participants. The medium velocity of the propelling cycle decreased by 26%. The ratio of the generated power and the loss power in a single propelling cycle \(\lambda\) had decreased by 18%. The least decrease was recorded for the measurement of mechanical energy \(E\) and the propelling cycle duration time. For the total mechanical energy, the decrease level was 3%, and for the propelling cycle duration it was 1%. The research carried out did not demonstrate any impact of the anti-rollback system use on the push phase share in the entire propelling cycle.  
wheelchair propulsion; motion resistance force; manual wheelchair +
Footnote †: journal: Applied Soft Soft
## 4 Experiment Results
### the Role of Gen Branch  
As shown in Table 4, compared with the fine-tuned VGGNet, our method has an average performance improvement of 4.2%, which suggests the effectiveness of sentimental interaction characteristics in image emotion classification task.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FI**} & \multirow{2}{*}{**Flickr**} & \multicolumn{3}{c}{**Twitter I**} & \multirow{2}{*}{**Twitter II**} & \multirow{2}{*}{**EmotionROI**} \\ \cline{3-3} \cline{5-7}  & & & **Twitter I-5** & & **Twitter I-4** & & \\ \hline Fine-tuned VGGNet & 83.05 & 70.12 & 84.35 & 82.26 & 76.75 & 76.99 & 77.02 \\ Ours-single & **88.12** & **72.31** & **89.24** & **85.19** & **81.25** & **80.59** & **83.62** \\ \hline \hline \end{tabular}
\end{table}
Table 4: The model performance comparison across image datasets. Results with bold indicate the best results compared with other algorithms.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FI**} & \multirow{2}{*}{**Flickr**} & \multicolumn{3}{c}{**Twitter I**} & \multirow{2}{*}{**Twitter II**} & \multirow{2}{*}{**EmotionROI**} \\ \cline{3-3} \cline{5-7}  & & & **Twitter I-5** & & **Twitter I-4** & & **Twitter I-3** & \\ \hline GCH & - & - & 67.91 & 97.20 & 65.41 & 77.68 & 66.53 \\ LCH & - & - & 70.18 & 68.54 & 65.93 & 75.98 & 64.29 \\ SentiBank & - & - & 71.32 & 68.28 & 66.63 & 65.93 & 66.18 \\ DeepSentiBank & 61.54 & 57.83 & 76.35 & 70.15 & 71.25 & 70.23 & 70.11 \\ VGGNet [27] & 70.64 & 61.28 & 83.44 & 78.67 & 75.49 & 71.79 & 72.25 \\ PCNN & 75.34 & 70.48 & 82.54 & 76.50 & 76.36 & 77.68 & 73.58 \\ Yang [9] & 86.35 & 71.13 & 88.65 & 85.10 & 81.06 & 80.48 & 81.26 \\ \hline Ours-single & **88.12** & **72.31** & **89.24** & **85.19** & **81.25** & **80.59** & **83.62** \\ \hline Wu [8] & **88.84** & 72.39 & 89.50 & **86.97** & 81.65 & 80.97 & 83.04 \\ Ours-ensemble & 88.71 & **73.11** & **89.65** & 84.48 & **81.72** & **82.68** & **84.29** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sentiment classification accuracy on FI, Flickr, Twitter I, Twitter II, EmotionROI. Results with bold indicate the best results compared with other algorithms.
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 1 Introduction
Integrated OPAs generate narrow beams and have a wide steering angle with tuning speeds in the MHz to GHz range [18; 44]. Particularly, Si photonics offers a low-cost OPA technology thanks to its compatibility with mature complementary metal-oxide-semiconductor (CMOS) process infrastructure. Moreover, Si photonics promises large-scale implementation with CMOS electronics, efficient optical phase shifters, and laser integration [17; 19; 45]. The non-mechanical beam steering based on an integrated OPA offers better reliability and reconfigurability. Compared with similar technologies such as MEMS, they are more tolerant to mechanical stresses from vibrations that cause a misalignment in assembled devices [46]. Many pioneering works of integrated OPAs were demonstrated with applications, ranging from free-space communications [47; 48; 49; 50; 51], LiDAR [11; 17], imaging [52; 53; 54], to biological sensing [55; 56]. Integrated OPAs are a promising candidate to become the next marketable solid-state dynamic beam steering solution. Figure 1: Integrated OPAs. (**a**) The wire-bonded device on the chip carrier plugged into the breadboard. Reprinted with permission from [40] © The Optical Society. (**b**) Blue light OPA. Reprinted with permission from [41] © The Optical Society. (**c**) A set of three 32-channel and one 240-channel OPAs on a coin. Reprinted with permission from the author of [42] published on Arxiv, 2018. (**d**) A photo of an OPA on a penny. Reprinted with permission from [43] © The Optical Society. In this review, integrated OPAs for beam forming and steering are discussed. The paper is organized as follows. Section 2 gives an overview of the principles of integrated OPAs. The building blocks of integrated OPAs will be given in Section 3. The discussion of configuration is given in Section 4. We then focus our attention on the material platforms of integrated OPAs (Section 5). Various wavelength ranges used in integrated OPAs are discussed in Section 6. The performance of integrated OPAs is given in Section 7. Applications and potential alternatives are discussed in Section 8. We offer a brief outlook for future research potential in Section 9. The conclusions are then summarized in Section 10.