esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Waste Express Bag as a Novel Bitumen Modifier
## Discussion
### Morphology  
The morphology of WEB-modified bitumen was evaluated by fluorescence microscope (FM) observations which providing information on the polymer in the modified bitumen. Both the bitumen and polymer will be lighter when they are exposed to fluorescence where the polymer will be brighter than the bitumen. Therefore, the distribution of the polymer can be observed. In the early study, it had been found out that after the modification, the SBS additives would become little particle and mixed with virgin bitumen homogeneously [29]. Interestingly, the FM experiment results of WEB-modified bitumen have similar conditions. Figure 6 shows the FM images of several typical samples in this research. It can be seen in Figure 6 that the virgin bitumen's image was very pure, indicating that the four fractions of virgin bitumen (saturates, aromatics, resins, and asphaltenes) were distributed homogeneously. The images of the 6% and 8% WEB-modified bitumen are shown in Figure 6b,c. In Figure 6b, many little polymer particles were homogeneously distributed in the bitumen. This demonstrates that the high-speed shearing process can make the WEB additives become much smaller and produce homogeneous WEB-modified bitumen. In Figure 6c, a new structure can be observed in the 8% WEB-modified bitumen. The WEB additives were combined together and became a bigger structure, which was like a skeleton structure. This \"tiny aggregate\" may significantly improve the rutting resistance of WEB-modified bitumen.  
Figure 5: Results of the Fourier transform infrared spectroscopy (FTIR) tests: (**a**) Express plastic bags; (**b**) Matrix bitumen; (**c**) 6%WEB-modified bitumen; (**d**) 8%WEB-modified bitumen.
## 2 Problem Definition
### Cost Analysis
#### 2.2.2 Vehicle Management and Use Cost  
It is more practical to adopt a calculation method for vehicle management and use costs that change with the travel time [21]. There are three types of expenses for each MCV dispatched by the distribution center: (1) vehicle dispatch cost, (2) vehicle usage cost and (3) labor cost. Among them, a change in travel time will lead to changes in the vehicle usage cost and labor cost. The travel time of the vehicle on the current route is the sum of the travel time of the vehicle on all the road segments along the current route. The vehicle management and use cost \(C_{1}\) of the MCV can be calculated according to Equation (3), where \(\varphi\) represents the unit vehicle dispatch cost (CNY/vehicle), \(\mu\) represents the unit time vehicle usage cost (CNY/hour) and \(\psi\) represents the unit time labor cost (CNY/hour).  
\[C_{1}=\varphi\cdot\sum_{j\in N^{\prime}}\sum_{k\in V}x_{0j}^{k}+(\mu+\psi) \cdot\sum_{i\in N}\sum_{j\in N}\left(x_{ij}^{k}\cdot t_{ij}^{k}\right) \tag{3}\]
# Urbanization and Grain Production Pattern of China: Dynamic Effect and Mediating Mechanism
## 4 Results
### Robustness Test  
The above tests of dynamic effect and mediating mechanism basically confirm the research hypotheses proposed in this paper, and to further improve the accuracy of the estimation results, robustness tests are next conducted by replacing the dependent variable to re-estimating the model. The location quotient (LQ) is usually used to measure the spatial distribution of an industry in a specific region, and it is an indicator of the degree of concentration and specialization of the industry [51]. The agricultural LQ reflects the level of scale and dominance of grain production in a region, so we replace the GPP with the agricultural location quotient to re-estimate the model. The agricultural LQ is the ratio of the share of total agricultural output in the total economic output of a region to the share of total agricultural output in the total economic output of the country [52], which is calculated as follows:  
\[LQ_{i}=\frac{A_{i}/E_{i}}{A/E}=\frac{A_{i}/A}{E_{i}/E}\]  
where \(LQ_{i}\) is the agricultural location quotient of province \(i\), \(A_{i}\) is the total agricultural output value of this province, \(E_{i}\) is the total economic output value (GDP) of the province, \(A\) is the national total agricultural output value, and \(E\) is the national total economic output value (GDP). The larger the \(LQ_{i}\) is, the higher the concentration degree of agricultural production in this province.  
The re-estimations were all done under W3 (Tables 7 and 8). The result of dynamic spatial econometric estimation shows that the time lag effect and spatial spillover effect remain positive and significant, and the coefficient of urbanization is significantly negative, which is consistent with the results of the previous paper and verifies the existence of a significant negative impact of urbanization on the GPP. The results of the mediating effect test show that MCI, PS, and ATECH all play a positive mediating effect in the influence process of urbanization, with the difference that the mediating effect of ATECH (8.68%) is greater than that of MCI (3.91%) and PS (2.61%). Overall, the conclusions of this study have high robustness.
# A Multi-Layer Secure Sharing Framework for Aviation Big Data Based on Blockchain
## 4 Data Security Sharing Modeling
### Constraints for Secure Data Sharing
#### 4.2.1 Storage Space Constraints  
Each participant has limited actual storage capacity, especially lightweight participants like aircraft with extremely constrained storage. The total stored data must be restricted:  
\[\forall A_{i}\in Participants,\ \sum_{D_{j}\in\mathit{Stored}(A_{i})}\mathit{Size} \big{(}D_{j}\big{)}\leq C_{i}\]  
where \(C_{i}\) is the maximum storage capacity of participant \(A_{i}\), and \(\mathit{Size}\big{(}D_{j}\big{)}\) is the size of data \(D\).
## 1 Introduction
In addition, by directly determining the observation adequacy and re-observation simultaneously during observation, the expense in terms of time and cost due to the reduced precision in the GPS post-processing can be minimized. In this paper, we describe GPSQC, a Windows-based quality control program that gives the variety of information to judge the suitability of GPS observations within the observation time in the field and enhances the precision of data processing. The GPSQC program was designed to directly calculate the geometric distribution, multi-path effect, ionospheric delay and cycle slip effect from observation and navigation files at a single point. This program checks the observation data from a single station and is available to run on MS Windows operating systems with GUIs (Window 9x, 2000, XP and higher). The quality of data from any GPS receiver can be checked if the observation data are in the RINEX format. If satellite position information is to be calculated and used by GPSQC, then a RINEX navigation file must also be used. RINEX translators developed at the University of Bern [7] are publicly available. In addition, batch and script files translate raw data to a RINEX format, process the data with GPSQC, then display the output plot files using a graphical display from a single command line. Batched runs can generate hard copies of the quality control summary and graphics files.
## 5 Discussion
### Hydrodynamic Changes  
#### 5.1.1 Alteration in Annual Flow Process  
Here, years featuring flood discharge within distinct time series (1998, 2007, and 2012) are chosen to construct the annual discharge curve. Figure 6 depicts the annual discharge curves for hydrometric stations situated along the MYR. The results indicate that the standard deviation of the average annual discharge in the MYR is 12,704 m\({}^{3}\)/s. Following the impingement of the TGD, the annual discharge curve became flatter (the standard deviation of the annual discharge decreased by approximately 37%), and the number of days with overbank flows progressively diminished (from 83 days in 1998 to 40 days in 2012, a reduction of 52%). It is noteworthy that the differences in discharge standard deviation reduction among various hydrometric stations are relatively small. For instance, the most modest reduction is observed at Station 6 (\(-\)36%), while the most substantial reduction takes place at Station 4 (\(-\)39%). Consequently, after the TGD operation, the less fluctuating flow process in the MYR may diminish the impact on the bar to some degree, thereby offering opportunities for the development and growth of the bar and its vegetation.  
Figure 6: Daily discharge pluses for different hydrometric stations along the Middle Yangtze River from 1998 to 2012.
## 3 Simulations
### Wind Farm Configurations and Cases
#### 3.2.2 Idealized Farm to Farm Interaction Case  
Two idealized wind farms of 4 \(\times\) 4 wind turbines with an internal spacing of 14 \(R\) are considered, see Figure 4. The first wind farm's front row turbines are placed at z = 100 \(R\) in the streamwise direction and are centered around \(x\) = 150 \(R\) in the spanwise direction. The total domain has the extensions 300 \(R\)\(\times\) 392 \(R\)\(\times\) 25 \(R\) in the x, z respectively y directions. The inner equidistant region covers the \(x\)-coordinates 150 \(\pm\) 72.5 \(R\), the \(z\)-coordinates 10-392 \(R\) and has a height of 8 \(R\). The extension of the equidistant region covers in the \(z\)-direction the second wind farm that starts at 292 \(R\), which corresponds to 150 \(R\) (6 km) behind the first wind farm.  
The turbulence is added at z = 13 \(R\) and has side extensions which cover with some margin the equidistant region, i.e., 153.6 \(R\) (covering 77.5-222.5 \(R\) in Figure 4). Vertically between 0.4 \(R\) and 10 \(R\) in height is added.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Case:** & **ws** & **veer** & **ws cor** & **veer cor** \\ \hline Mean wind shear only in streamwise direction & X & & X & \\ \hline Mean wind shear including a wind veer & & X & & X \\ \hline Coriolis correction is used & & X & X \\ \hline \hline \end{tabular}
\end{table}
Table 1: Four cases are performed with different combinations of setup regarding mean wind shear and if a Coriolis correction is included.  
Figure 3: The placement of the turbines (\(\bullet\)) in the the row in the LES domain covering 100 \(R\)\(\times\) 392 \(R\) with an equidistant region (marked with the rectangle) of 34 \(R\)\(\times\) 382 \(R\). The turbulence is added at z = 13 \(R\), at the red line.
# Performance Tradeoff Analysis of Hybrid Signaling SWIPT Systems with Nonlinear Power Amplifiers
## 2 System Model
### Memoryless Nonlinear PA Models
#### 2.1.2 Saleh Model  
The Saleh model was first obtained by statistical analysis of the input and output data of travelling-wave tube amplifiers (TWTA) [54]. It has a simple structure, but can describe the nonlinear characteristics of TWTA accurately. The model also has high fitting performance for the measured data of other memoryless PAs. Therefore, it is widely used as a memoryless PA model in modern communication systems [55].  
Our paper adopts a normalized Saleh PA model, whose AM-AM and AM-PM characteristics are given by  
\[g(r)=\frac{a_{1}\frac{r}{r_{\max}}}{1+b_{1}\bigg{(}\frac{r}{r_{\max}}\bigg{)}^ {2}}, \tag{6}\]  
\[f(r)=\frac{a_{2}\bigg{(}\frac{r}{r_{\max}}\bigg{)}^{2}}{1+b_{2}\bigg{(}\frac{r }{r_{\max}}\bigg{)}^{2}} \tag{7}\]  
respectively. Without loss of generality, the values of model parameters are taken from numerical values reported in [52]. Specifically, we have \(a_{1}=r_{o,\max}\), \(b_{1}=1/4\), \(a_{2}=\pi/12\), and \(b_{2}=1/4\).
# Cross-Regional Pavement Temperature Prediction Using Transfer Learning and Random Forest
## 1 Introduction
The integration of transfer learning with machine learning models is expected to enhance both predictive accuracy and cross-regional adaptability, offering an intelligent and scalable solution. In view of this, a pavement temperature prediction method combining random forest with transfer learning is proposed in the present study. The aim is to improve the model's adaptability and prediction accuracy across different regions. Firstly, meteorological and measured pavement temperature data from several typical climatic regions are collected and organized. Correlation analysis between meteorological factors and pavement temperature is then conducted. Secondly, random forest models are separately constructed for each region to establish a baseline prediction framework. Thirdly, a transfer learning strategy is introduced to effectively enhance the model's adaptability in the target region by transferring feature information from the source region. Finally, the proposed models are validated and compared using measured data to evaluate their prediction accuracy and generalization ability under various regional conditions. This study provides a practical AI-based solution to address the limited regional applicability of existing temperature prediction models, and it offers technical support for intelligent pavement thermal environment control, climate-resilient infrastructure design, adaptive maintenance planning, real-time pavement condition forecasting, and the development of smart transportation systems.
# Real-Time, Content-Based Communication Load Reduction in the Internet of Multimedia Things
## 3 Methodology
### System Composition
#### 3.2.1 Data Acquisition  
Acquisition of video data and conversion of color spaces, prior to actual processing, is carried out in this module. For simplicity, raw YUV videos (Y is the luminance value and UV are the two color component) are opened prior to conversion to the RGB format, which eases video processing. The trial video sequences, used in experiments, are obtained from open source repositories, selected on the basis of their different motion and color characteristics. Each of the video sequences used has differing content type, extent of motion, and frame sizes. These sequences consisted of five open-source videos named throughout this paper as Walk, Ground, Exercise, Duval Street and Football. Ground and Football were obtained from the VIRAT video dataset (release version 2.0) [59] and Xiph.org [60]. At a particular instance, a new frame is read from the stored video source. This new frame and a previously acquired frame are converted to RGB color space.
# Design of W-Band GaN-on-Silicon Power Amplifier Using Low Impedance Lines
## 3 Measurement Results  
Figure 6 is a graphic representation of the fabricated W-band power amplifier in a 60 nm GaN-on-Si HEMT foundry. Overall chip size is 2.4 mm \(\times\) 2.0 mm including DC and RF pads. The chip was mounted on the gold-plated copper block by eutectic bonding for a good heat sink. The small- and large-signal performances were measured by on-wafer probing at the drain bias voltage \(V_{\mathrm{DD}}\) of 10 V. The measured S-parameters are given in Figure 7a. The maximum gain (\(S_{21}\)) of 16.1 dB is achieved at 92.2 GHz with a 3-dB bandwidth of 6.3 GHz from 87.8 to 94.1 GHz, where \(S_{11}\) and \(S_{22}\) are better than \(-8.9\) and \(-9.5\) dB, respectively.  
Figure 5: Design of final three-stage power amplifier using Lange couplers. (**a**) Circuit schematic. (**b**) Simulated S-parameters. (**c**) Simulated output power, gain, and PAE as a function of input power at 90 GHz.  
Figure 6: Fabricated W-band power amplifier IC using 60 nm GaN-on-Si HEMT foundry (chip size: 2.4 mm \(\times\) 2.0 mm).  
The power measurement was performed in the continuous-wave condition. Figure 7b shows the measured output power (P\({}_{\text{out}}\)), gain, and PAE with frequency. The input power is fixed at 16.0 dBm in this measurement. The measured P\({}_{\text{out}}\) is higher than 26.5 dBm with gain > 10.5 dB and PAE > 8.5% from 88 to 93 GHz. Figure 7c shows the measured power performance with the input power at 90 GHz. The peak power is as high as 28.9 dBm at an input power of 19.0 dBm, corresponding to the power density of 1.94 W/mm. The peak PAE was measured to be 13.3%. Figure 8 shows the performance comparison between the reported W-band GaN-on-SiC and GaN-on-Si power amplifier ICs. To the best of the authors' knowledge, the power amplifier IC in this work presents the highest output power and power density performance among the reported GaN-on-Si power amplifier ICs operating at a W-band. It is also worthwhile to note that its output power and PAE performances are comparable to those of GaN-on-SiC power amplifier ICs, even though they are worse, as demonstrated in Figure 8.  
Figure 8: Comparison of the reported W-band GaN power amplifier ICs (circles: GaN-on-SiC, squares: GaN-on-Si). (**a**) Output power versus frequency. (**b**) PAE versus frequency.  
Figure 7: Measurement results. (**a**) S-parameters. (**b**) Output power, gain, and PAE versus frequency with input power of 16.0 dBm. (**c**) P\({}_{\text{out}}\), gain, and PAE versus input power at a frequency of 90 GHz.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 4 Challenges That Influence the Design of In-Body Antennas  
The development of in-body antennas faces numerous design challenges. Miniaturizing the effective electrical size of an antenna leads to a reduction in its electromagnetic performance [78]. Furthermore, it is necessary to consider some important factors to ensure patient safety during the design phase of in-body antennas. First, the in-body antenna is required to be biocompatible, and the SAR must be controlled within the standard limit. This section describes the factors that influence the design specifications of in-body antennas.
# Experimenting with Extreme Learning Machine for Biomedical Image Classification
## 2 Background
### Relm  
RELMs are an extension of the traditional extreme learning machine (ELM) algorithm that incorporates regularization techniques to improve generalization and prevent overfitting. The regularization term is added to the objective function during the training process, encouraging the model to find a balance between fitting the training data and reducing complexity.  
Below, an overview of how RELMs work is given, building upon the steps described for ELMs:1. Input Layer: The input layer represents the features or attributes of the input data, similar to ELMs.
2. Hidden Layer: The hidden layer consists of randomly initialized neurons. Each neuron has its own set of weights connecting it to the input layer.
3. Activation Function: An activation function is applied to the weighted sum of inputs for each neuron in the hidden layer, introducing nonlinearity.
4. Output Layer: The output layer produces the final predictions or classifications, using an appropriate activation function.
5. Random Weight Initialization: The weights connecting the input layer to the hidden layer are randomly generated, as in ELMs.
6. Calculate Hidden Layer Outputs: The random weights and the input data are multiplied, and the activation function is applied to obtain the output of the hidden layer.
7. Regularization Term: RELMs introduce a regularization term to the objective function used during the training process. This term penalizes large weights and adds a constraint on the model's complexity. It helps prevent overfitting, where the model becomes too specialized to the training data and performs poorly on unseen data.
8. Solve Regularized Output Weights: The output weights are determined using a regularized least squares method. This process involves minimizing the sum of the squared errors between the actual outputs and the desired outputs while considering the regularization term.
9. Prediction: Once the output weights are obtained, the RELM can make predictions on new data by propagating the input through the network and computing the output using the learned weights.  
The addition of the regularization term in RELMs encourages the model to find a balance between fitting the training data and reducing complexity. By penalizing large weights, RELMs promote simpler models that are less likely to overfit and generalize better to unseen data.  
Regularized extreme learning machines are particularly useful when dealing with limited training data or when there is a high risk of overfitting due to complex models. The regularization term allows for better control over the model's complexity, leading to improved generalization and more robust performance.  
It is important to note that different types of regularization techniques can be used in RELMs, such as L1 or L2 regularization (also known as Ridge or Lasso regression). The specific choice of regularization method depends on the characteristics of the problem at hand and the desired properties of the learned model.
# Implementation and Evaluation of Activity-Based Congestion Management Using P4 (P4-ABC)
## 6 Performance Evaluation
### Evaluation Methodology  
We describe the experimental design of our evaluation, the experimental environment, and summarize applied parameters.
# Novel Methods of AI-Based Gait Analysis in Post-Stroke Patients
## 4 Discussion
### Limitations of Current Studies  
Gait analysis is usually a complementary test, supporting a diagnosis made on the basis of other tests, especially in the diagnosis of various diseases and the evaluation of rehabilitation progress. Gait parameters are influenced by many different factors (height, age, gender, lower limb structure, etc.). Furthermore, the above-mentioned basic gait parameters are not sufficient, especially in medical diagnosis, so the classical gait regularity test includes the following:  
* static examination;
* measurement of joint mobility;
* gait analysis.  
When it comes to diagnosis and the evaluation of rehabilitation results, there is no single universal tool, technique, or method. Old solutions are constantly being improved and new ones developed for specific (mostly narrow) applications. The field of technical solutions for diagnostic support is developing particularly rapidly. This poses a challenge for medical staff, as they must be familiar with all of these solutions in order to ensure that the optimal diagnostic method is selected for each specific patient, disease, or therapy phase. There are several reasons for this:  
* there are no universal methods;* simple and quick methods are not very accurate;
* accurate, reliable, and reproducible methods are time-consuming, costly, and require complex procedures;
* some methods require organizational changes for their successful implementation.  
Given this context, there is a need to pre-define the requirements of clinicians so that the designers of diagnostic systems (other clinicians, scientists, engineers) can propose more appropriate solutions. The development of smartphone apps and wearable devices means that some runners and triatheltes are already using simple gait analysis solutions.  
More advanced studies may include:  
* the measurement of kinematic parameters (the analysis of human movements without analyzing the forces causing these movements), i.e., ranges of movement in the joints measured during gait as a trajectory of selected points on the body,
* the measurement of kinetic parameters, i.e.,the measurement of forces and moments of forces causing human movement, orthe measurement of muscle activity during gait using electromyography (EMG).