esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Multi-Feature Fusion Distance Measure Model of MFF-SLIC
#### 3.2.1 Color Feature Calculation of Digital Image  
The transformation from RGB color space to Lab color space needs to use intermediate variables XYZ. Therefore, the color feature calculation of digital images could be divided into two sections.  
1. RGB to XYZ [42]  
Suppose the three original color variables \(r\), \(g\) and \(b\), and the range of values is [0, 255]. A function is defined as Equation (11),  
\[\gamma(x)=\left\{\begin{array}{cc}\left(\frac{x+0.055}{1.055}\right)^{2.4},&x> 0.04045\\ \frac{1}{12.92},&\text{other}\end{array}\right., \tag{11}\]  
where \(\gamma(x)\) is used to improve contrast by non-linear hue adjustment. The three original variables \(r\), \(g\) and \(b\) are transformed by Equation (11),  
\[\left[\begin{array}{c}R\\ G\\ B\end{array}\right]=\left[\begin{array}{c}\gamma(r/255)\\ \gamma(g/255)\\ \gamma(b/255)\end{array}\right], \tag{12}\]  
Let \([R,G,B]^{\text{T}}\) is transformed to \([X,Y,Z]^{\text{T}}\) by matrix \(\mathbf{M}\), which is shown as Equation (13),  
\[\left[\begin{array}{c}X\\ Y\\ Z\end{array}\right]=\mathbf{M}*\left[\begin{array}{c}R\\ G\\ B\end{array}\right], \tag{13}\]  
Figure 5: The sketch map for the difference of global \(k\)-means and local \(k\)-means.  
Figure 6: The flowchart of distance measure model construction.  
where,  
\[\mathbf{M}=\left[\begin{array}{ccc}0.4124&0.3576&0.1805\\ 0.2126&0.7152&0.0722\\ 0.0193&0.1192&0.9505\end{array}\right], \tag{14}\]  
2. XYZ to Lab [42; 43]  
Suppose a function \(f(y)\) as Equation (15),  
\[f(y)=\left\{\begin{array}{ccc}y^{1/3},&y>\left(\frac{6}{29}\right)^{3}\\ \frac{1}{3}\left(\frac{29}{6}\right)^{2}\!y+\frac{4}{29},&\text{other}\end{array} \right., \tag{15}\]  
where \(f(y)\) is also a correction function, then the calculation formula of color feature in CIELAB color space is described as Equation (16),  
\[\left[\begin{array}{c}L\\ a\\ b\end{array}\right]=\left[\begin{array}{c}116f(Y/Y_{0})-16\\ 500[f(X/X_{0})-f(Y/Y_{0})]\\ 200[f(Y/Y_{0})-f(Z/Z_{0})]\end{array}\right], \tag{16}\]  
where \(X_{0},Y_{0}\) and \(Z_{0}\) is respectively used for the normalization of \(X,Y\) and \(Z\). Generally, the value of \(\left[X_{0},Y_{0},Z_{0}\right]^{\mathrm{T}}\) is set to \(\left[95.047,100,108.883\right]^{\mathrm{T}}\).
## 5 Evaluation
### Classification and Prediction  
#### 5.2.1 ROC Curve and AUC Value  
We use the ROC curve and AUC value to further prove the rationality of our feature selection results, and find the prediction model that best fits our data structure in the comparison of various model effects.  
The ROC (Receiver Operating Characteristic) curve is a curve drawn on a two-dimensional plane. The abscissa is the false positive rate (FPR) and the ordinate is the true positive rate (TPR). The ROC curve shows the trade-off between TPR and FPR. As a baseline, a random classifier is expected to give points lying along the diagonal (FPR = TPR). Classifiers that give curves closer to the top-left corner indicate a better performance.  
Figure 5 shows the ROC curve results of five classification models based on 11 characteristics selected by [PERSON] and OMP as well as all the features of the original data, respectively. We can intuitively obtain two conclusions. Firstly, the performance of the ROC curve of the data through the feature selection is significantly improved than the one of the original data, which proves the rationality and validity of our feature selection results. Secondly, the classification performance of SVM is better than other models, which means that we could choose SVM to be the predictive model for the following research on the possibilities of paper publication by the first-year students.  
Except for the intuitive observation of using SVM as the predictive model from the performance of the ROC curve, we also use the AUC value as another criterion for judging the quality of the models. AUC (Area Under Curve) is a standard used to measure the quality of a classification model. The AUC value is the area covered by the ROC curve. The larger the AUC, the better the model's classification effect. AUC = 1 is a perfect classifier. When using this predictive model, a perfect prediction can be made no matter what threshold is set. However, in most cases of prediction, there is no perfect classifier. When the AUC value is between 0.5 and 1, the prediction result is better than the random guess, and the classifier may have the value when the threshold is properly set.  
Figure 6 shows the AUC values of our models as the criterion for classification accuracy. It can be seen that the original data have the smallest AUC value, which further verifies the effectiveness of our method for feature selection. In addition, from the AUC values of the five classification methods, SVM performs significantly better than others when classifying the features selected by Lasso and OMP. This indicates the same conclusion that it is more appropriate to choose the SVM model when predicting.  
Figure 5: ROC curve for five classification methods.
## 2 Smoothing Method Analysis
### Quadratic Exponential Smoothing  
Quadratic exponential smoothing introduces a trend component to simple exponential smoothing and is suitable for dealing with time series with linear trends [20]. The basic idea is to perform exponential smoothing on the data twice, the first time for extracting the smoothed base-level values and the second time for extracting the trend components. The model equation for quadratic exponential smoothing is as follows:  
\[S_{t}=\alpha\cdot Y_{t}+(1-\alpha)\cdot(S_{t-1}+T_{t-1}) \tag{4}\]  
\[T_{t}=\beta\cdot(S_{t}-S_{t-1})+(1-\beta)\cdot T_{t-1} \tag{5}\]  
\[\hat{Y}_{t+k}=S_{t}+k\cdot T_{t} \tag{6}\]  
where Equation (4) represents the smoothed level update, Equation (5) represents the trend update, Equation (6) represents the prediction equation, \(S_{t}\) represents the smoothed value at time \(t\), \(T_{t}\) is the trended value at time \(t\), \(\alpha\) and \(\beta\) are the coefficients controlling the strength of the smoothing and the trend, respectively, \(Y_{t}\) is the measured bridge deflection value at time \(t\), and \(\hat{Y}_{t+k}\) is the output after time \(t\) has predicted k time.  
Figure 1: Gaussian smoothing.  
In bridge health monitoring, deflection data reflect the deformation of a bridge under load. These data usually contain a short-term noise component and a long-term trend component. Therefore, quadratic exponential smoothing is good at extracting deflection data trends while reducing short-term fluctuation effects.
# Unmanned Aerial Vehicle Routing Problems: A Literature Review
## 2 UAV Routing with an Emphasis on the Problem Type, Transportation Mode, and Degree of Automation
### Degree of Automation  
This paper also considers the degree of automation of transportation as automation and autonomy play a large role in practical applications of the UAVRP [55; 56]. Furthermore, they play an increasing role in various modes of transport. Driverless trains are already in operation; the degree of automation in cars is continuously rising, and even the air transport sector is discussing the use of pilotless aircraft [57]. It is observed that certain modes of transportation are fully automated and certain modes are semi-automated [56; 58]. Automated guided transport (AGT) systems and AGVs fall under the fully automated modes of transportation and have been subjected to intense study in literature [25; 27; 59; 60; 61; 62]. In maritime transport, automated vessel routing has been introduced and unmanned vessels will be the future of maritime transportation [63].  
As UAVs flight and navigation tasks are increasingly automated to gain economies-of-scale and speed of operations and support the large-scale operations, UAV routing and execution are evolving from teams of operators managing a single UAV to a single operator managing multiple UAVs as illustrated in Figure 1. The increasing degree of autonomy and automation has created a continuous push for developing methods for managing complex UAV operations. Such systems will naturally require the development of advanced prediction, routing, and scheduling methods and implementation of various systems to support decision-makers in handling the complexity of operations [11; 12; 13; 14]. It is also worth noting that most contributions focus on the VRP characteristics, specific or multimodal transportation modes, and to some degree, on the VRP for automated land-based transportation (typically indoor robotic solutions such as AGVs, mobile robots). While the classical VRP is well-studied, the methods and approaches found within this domain are still very much applicable for the advancement of new technology in the area of UAV operations [13].
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Indium-Doped Zinc Oxide Films  
The In-doped ZnO thin films were synthesized on glass and p-type silicon substrates [176] at 100 deposition cycles. The biggest grain size could be observed when the indium concentration was 2.5%, while small and more uniform morphology took place at a higher doping concentration (7.5%) based on the FESEM images. The structural analysis, such as crystallite size (69.3 to 67.5 nm), reduced, but the microstrain (1.67 to 1.73 \(\times\) 10\({}^{-3}\)) and dislocation density (2.08 to 2.19 \(\times\) 10\({}^{13}\), lines/m\({}^{2}\)) increased, and were reported for the films prepared at various In-doping concentrations (from 2.5% to 7.5%). Transparency increased initially and then dropped at a higher indium (In) concentration because of the structural disorders (inclusion of more indium) in the obtained samples.
# Enhanced Localisation and Handwritten Digit Recognition Using ConvCARU
## 4 Experimental Results and Discussion  
This section provides a detailed analysis of the experimental results, offering an in-depth examination of the model's performance and practical applications. Key metrics and their visual representations are explored first to provide a comprehensive evaluation of the effectiveness and limitations of the proposed approach. The results are then compared with relevant benchmarks to highlight this method's advantages and areas for improvement. In this experiment, the MNIST dataset [61] is used as a fundamental benchmark for recognising handwritten digits. This dataset comprises 70,000 grayscale images, each measuring \(28\times 28\) pixels and featuring a wide range of handwriting styles. This structured composition provides a valuable testing platform for classification models, enabling researchers to optimise performance without worrying about data inconsistencies. Additionally, this study employs ConvCARU to improve localisation accuracy and classification robustness. The consistency of the MNIST dataset enables reliable evaluation and assessment of the method's ability to adapt to different handwriting styles while maintaining high recognition accuracy.
## 3 Experiment and Results  
### Datasets  
Data used in experiments comes from Fujian Marine Forecasts Station, we use the data from January 2009 to August 2011 as shown in Table 1. In this dataset, the station dimensions have up to 14 different types of sensing data, including SDO (Saturated Dissolved Oxygen), DO (Dissolved Oxygen), Temperature, Salt, Chlorophyll-a (Chl-a), Tides, Turbidity, PH, Air Temperature, Press, WIND_SPEED, WIND_DIRECTION, FLOW_SPEED, and FLOW_DIRECTION. We choose the index of Chlorophyll-a(Chl-a) index as an indicator of the algal biomass, which means we use the Chl-a value as the target series. In order to select the correlation factors related to red tide, we use the Pearson Correlation Coefficient (PCC) to measure the correlation between the related factors and red tide. We choose the higher value of PCC as the related factors, including Air Temperature, SDO, DO, Temperature, Press, Wind Speed, and Wind Direction. Table 2 shows the PCC value between related factors and Chl-a values. Data sensing interval is 30 minutes. The trend of related factors is showed in Figure 2. In the experiments, we partition the data into training and test data by a 9:1 ratio, we use the 6337 data points as the training data, and the 834 data points as the test data. Owing to the discontinuity of the data recorded at the station, also in order to enhance the generalization ability of the model, we decided to shuffle the train data to train our model.
# Analytic Time Reentry Cooperative Guidance for Multi-Hypersonic Glide Vehicles
## 4 Multi-HGV Time Coordination Strategy  
Multi-aircraft cooperative guidance requires multiple flights to reach the target point near the specified flight time under the coordination of the system guidance strategy. Using the trajectory optimization method [28], the flight time range of each aircraft is obtained. Only when there is an intersection within the time adjustment range of each aircraft, can the cooperative reentry flight time exist and the cooperative reentry flight be realized. The desirable cooperative flight time is as follows:  
\[\left\{\begin{array}{ll}T_{\text{min}}\leq T_{\text{c}}\leq T_{\text{max}} \\ T_{\text{min}}=\max(T_{\text{min},1},T_{\text{min},2},\cdots,T_{\text{min},n}) \\ T_{\text{max}}=\min(T_{\text{max},1},T_{\text{max},2},\cdots,T_{\text{max},n}) \end{array}\right. \tag{32}\]  
where \(T_{\text{min},n}\) is the minimum flight time of the \(n\)th aircraft, \(T_{\text{max},n}\) is the maximum flight time of the \(n\)th aircraft, \(T_{\text{min}}\) is the minimum value of the adjustable range of the flight time, \(T_{\text{max}}\) is the maximum adjustable range of the flight time, and \(T_{\text{c}}\) is the final cooperative flight time.  
It can be seen from Section 2 that the algorithm in this paper has three design parameters that need to be optimized. After starting the reentry mission, an improved sparrow optimization algorithm proposed by the author in a previous work [28] is used to generate the profile parameters of the AOA and bank angle, and the AOA command is obtained by Equation (9). When each aircraft enters the initial reentry phase, it maneuvers at a fixed bank angle until it satisfies Equation (12) and enters the glide phase. When flying in the gliding phase, the terminal speed that can simultaneously satisfy the cooperative time constraint and the range constraint is obtained by Equation (28), and then the bank angle command is calculated by Equation (29). Finally, the bank angle symbol is determined by the heading angle deviation corridor. The analytical time-cooperative guidance strategy of multi-HGVs proposed in this paper is shown as Figure 1:
## 4 Experiment Results
### Experiment Setup  
#### 4.3.1 Evaluation Methodology  
The evaluation methodology of this study focused on deriving and comparing results for three cases: the basic structure, DHS-CNN, and changes in the loss function for the InceptionV4 and EfficientNet models. The following evaluations were conducted:  
* Basic structure evaluation: First, the performance of the basic structures of the InceptionV4, EfficientNet, and ViT models was evaluated. This step was important for establishing the baseline performance of the models.
* Application of DHS-CNN: Next, the performance of both models with DHS-CNN applied was evaluated. This approach was crucial for understanding the impact of DHS-CNN on model performance.
* Changes in the loss function: Finally, the effect of modifying the loss function on the performance of both models was evaluated. This analysis helped determine how applying different weights to specific abnormal types influenced the results.  
This evaluation methodology provided a comprehensive understanding of how structural changes and adjustments to the loss function affected overall model performance.
# Design of a Fixed IF Down-Conversion Double-Balanced Mixer for UHF Band Applications
## 3 Proposed Down-Conversion Mixer Design
### Single-Ended to Balanced RF Stage Driver Design and Optimization  
Implementing a Gilbert-cell mixer presents a challenge in using differential amplifiers within the cell itself. The optimal differential driver of the RF port is the most critical trade-off. Here, a better solution is employed in place of costly and potentially asymmetrical transformers, such as those found in diode mixers. Such a transformer is costly and difficult to implement at the RF stage. The proposed solution is to use first a pre-driver stage to convert a single-ended line to a balanced line [3]. As shown in Figure 2, we use the phase shift of 180\({}^{\circ}\) of the first RF stage transistor and drive the second transistor. Ideally, the first RF common emitter stage will generate a 180\({}^{\circ}\) phase shift between the base and the collector. At the second stage, the base-collector phase shift will be converted back to 0\({}^{\circ}\). In reality, the phase shift between two transistors is never ideal. Figure 3 shows the simulated stand-alone RF stage to analyze the single-ended RF signal behavior to balanced signal conversion.  
Figure 4 shows the time domain collector current waveforms of RF stage transistors. Since the device operating point is around 1.7 V, 10 mA of bias current, the collector current is swinging from 10.8 mA to 9.2 mA.  
The gain of the second RF stage is \(g_{m}\) times higher than the first RF stage. Balanced differential signal pairs carry signals of equal amplitude but with a 180\({}^{\circ}\) phase shift. If not properly balanced, the differential signaling becomes a source of electromagnetic interference or cross-talk [13], appearing as common-mode noise or voltage at the IF port.  
Therefore, in order to achieve a close match to the first stage, the gain at the second RF stage must be reduced. This can be done by an emitter degenerating the second stage. Both RF stages are emitter degenerated with chosen impedance, improving input stage linearity and providing noise match. Figure 5a shows the gain and phase of two RF stages; the gain of the second stage is 3 dB higher than the first stage, and the phase difference between the two stages is roughly 154\({}^{\circ}\) at 1 GHz (Figure 5b).  
Figure 3: RF stage amplifier.  
A 180-degree phase shift achieves optimal symmetry cancellation, resulting in much improved isolation. However, in practice, it is unusual. As a result, RF-to-IF isolation is inferior to the LO-to-IF and LO-to-RF stages. The RF amplitude difference between the two RF stages is negligible, as balanced symmetry is largely generated from the LO stages, although it may have a slightly deteriorated excellent noise figure.
# Construction Project Cost Prediction Method Based on Improved BiLSTM
## 6 Experimental Results and Analysis
### Data Preprocessing
#### 6.1.1 Relevance Analysis  
The correlation values of the candidate indicators are listed in Table 1, and the candidate indicators with correlation values greater than 0.9 are designated as feature indicators. The foundation type (\(x_{1}\)), structure type (\(x_{2}\)), fortification intensity (\(x_{3}\)), management level (\(x_{4}\)), aboveground floor area (\(x_{5}\)), number of aboveground floors (\(x_{6}\)), number of underground floors (\(x_{7}\)), aboveground floor height (\(x_{8}\)), basement floor height (\(x_{9}\)), concrete prices (\(x_{10}\)), reinforcing steel prices (\(x_{11}\)), and duration (\(x_{12}\)) indicators were selected as the model inputs, and the unit cost (\(y\)) was defined as the model output.
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 4 Experiments and Results
### Parameter Setting  
The server configuration used in this experiment was Intel (R) Core (TM) i7-10875H (2.3 GHz), with 32 GB memory, NVIDIA Geforce RTX 2060 GPU, and a Windows 11 64-bit operating system. The version of the PyTorch framework the experiment used was 1.7.1. The version of Word2 vec used was Tencent-ailab-embedding-zh-d100-v0.2.0-s from Tencent AILab. The experiment also used the transformers model, whose version number was 2.5.1, and the coding layer used the Roberta model and the BiGRU model.  
The main parameter settings of the Roberta are illustrated in Table 2.  
The learning rate was optimized based on cosine annealing. According to Formula (11), the initial maximum learning rate was set to 0.01 and the cycle period of the learning rate was set to 5 epochs. The optimization of the learning rate in our method is illustrated in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline hidden\_size & 768 \\ max\_position\_embeddings & 512 \\ num\_attention\_heads & 12 \\ num\_hidden\_layers & 12 \\ pooler\_fc\_size & 768 \\ pooler\_num\_attention\_heads & 12 \\ pooler\_num\_fc\_layers & 3 \\ pooler\_size\_per\_head & 128 \\ vocab\_size & 21,128 \\ \hline \end{tabular}
\end{table}
Table 2: The parameter settings of the Roberta model.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline input\_size & 768 \\ hidden\_size & 64 \\ dropout & 0.4 \\ \hline \end{tabular}
\end{table}
Table 3: The BiGRU model parameter settings.
## 4 Sorting Out Feasible Solutions
### Displacement Analysis  
If all link lengths \(r_{i}\), \(i=0\),..., \(5\), and the offset \(h\) are given, for a given value of the input crank angle, the Watt II and Stephenson III six-bar slider-crank mechanisms can each be assembled into four different configurations called assembly modes or branches, and some of the prescribed positions of a synthesized mechanism may lie on different assembly modes. If all the prescribed positions do not lie on a single stroke of the slider on one branch, the solution is unacceptable. Therefore, it is necessary to select the solution mechanisms that satisfy this condition by analyzing the displacement of each mechanism.  
For the analysis of the six-bar slider-crank mechanism, the four-bar linkages commonly used in the two mechanisms need to be analyzed first. The angular displacements \(\alpha\) of link \(r_{3}\) and \(\phi\) of link \(r_{2}\) shown in Figures 1 and 2 can be determined as [39]  
\[\alpha=2\tan^{-1}\Biggl{(}\frac{-B\pm\sqrt{B^{2}-C^{2}+A^{2}}}{C-A}\Biggr{)},\ -\pi\leq\alpha\leq\pi, \tag{45}\]where  
\[\begin{array}{ll}A=2r_{0}r_{3}\cos\eta-2r_{1}r_{3}\cos\theta,\\ B=2r_{0}r_{3}\sin\eta-2r_{1}r_{3}\sin\theta,\\ C=r_{0}^{2}+r_{1}^{3}+r_{3}^{2}-r_{2}^{2}-2r_{0}r_{1}(\cos\eta\cos\theta+\sin \eta\sin\theta),\end{array} \tag{46}\]  
and  
\[\phi=\tan^{-1}\biggl{(}\frac{r_{0}\sin\eta+r_{3}\sin\alpha-r_{1}\sin\theta}{r_ {0}\cos\eta+r_{3}\cos\alpha-r_{1}\cos\theta}\biggr{)}, \tag{47}\]  
respectively.  
Notice that when only the link lengths of the four-bar mechanism and the angle of the ground link \(\eta\) shown in Figures 1 and 2 are given, there are two sets of \(\alpha\) and \(\phi\) for a given crank angle \(\theta\) due to the positive (\(\alpha^{+}\)) or negative (\(\alpha^{-}\)) sign in Equation (45), which correspond to the two assembly modes. However, since the solution of the synthesis equations in this study yields the \(x\)- and \(y\)-components of each link in its initial position, the sign to be used in Equation (45) and the assembly mode of the four-bar can be readily identified.