esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Examples  
### Heat Conduction  
A thick-wall cylinder with inner radius \(a=20\) mm and outer radius \(b=150\) mm serves as a benchmark for validating the thermo-mechanical coupling (see Figure 3). In the displacement field, the cylinder's inner and outer walls are free boundaries. In the temperature field, the cylinder's initial temperature is 25 \({}^{\circ}\)C, with the inner wall at 400 \({}^{\circ}\)C and the outer wall at 25 \({}^{\circ}\)C. A two-dimensional thick-wall cylinder is a solution to a plane strain problem.  
According to previous studies [16; 45], the analytical solution of the temperature field at any distance from the center of the thick-walled cylinder is given:  
\[T_{r}=\frac{\ln(b/r)}{\ln(b/a)}T_{a}+\frac{\ln(a/r)}{\ln(a/b)}T_{b} \tag{32}\]  
Figure 3: Schematic diagram of the thick-walled cylinder.  
Figure 2: Segregated scheme for the TM-PFM.  
The radial and tangential stresses in the thick-wall cylinder are:  
\[\left\{\begin{array}{l}\sigma_{r}=-\frac{\mathrm{E}_{T}(T_{\mathrm{z}}-T_{ \mathrm{b}})}{2(1-v)}\Big{[}\frac{\ln(b/r)}{\ln(b/a)}-\frac{b^{2}/r^{2}-1}{b^{2}/ a^{2}-1}\Big{]}\\ \\ \sigma_{\varphi}=-\frac{\mathrm{E}_{T}(T_{\mathrm{z}}-T_{\mathrm{b}})}{2(1-v)} \Big{[}\frac{\ln(b/r)-1}{\ln(b/a)}+\frac{b^{2}/r^{2}+1}{b^{2}/a^{2}-1}\Big{]} \end{array}\right. \tag{33}\]  
where \(r\) represents the radial distance from any point within the cylinder to its central axis. \(T_{r}\) donates the temperature at distance \(r\). \(\sigma_{r}\) and \(\sigma_{\varphi}\) are the radial and tangential stresses at distance \(r\), respectively. Compressive stress is considered positive while tensile stress is taken as negative. The corresponding parameters in the simulation are as follows: \(\rho=2300\) kg/m\({}^{3}\), \(E=20\) GPa, \(v=0.3\), \(C_{p}=880\) J/(kg\(\cdot\)K), \(k=3\) W/(m\(\cdot\)K), and \(\alpha_{T}=1.0\times 10^{-6}\) /K.  
Figure 3(a) displays the temperature distribution within the thick-walled cylinder, comparing analytical and numerical temperature solutions along the radial direction. The temperature decreases monotonically from the inner to the outer surface, and the numerical results exhibit excellent agreement with the analytical solution. Figure 3(b) further compares numerical and analytical solutions for radial and tangential stresses. Along the radial coordinate, the radial stress first increases and subsequently decreases while remaining compressive throughout the entire thickness; concurrently, the tangential stress transitions from initial compression to tension with increasing radius. The close correspondence between numerical and analytical solutions of stress and temperature strongly confirms the accuracy of the coupled thermo-mechanical model.
# An Enhanced Tree Routing Based on Reinforcement Learning in Wireless Sensor Networks +
## 5 Performance Evaluation
### Discussion  
As the traditional tree routing selects a parent node based on the number of hops to the destination, it cannot cope with various problems, such as link breakage and congestion. To address this problem, the linear weighted sum-based scheme jointly considers multiple cognitive metrics, such as the hop count, link quality, buffer capacity, and residual energy in the decision-making process. However, in the process of deriving the weighted sum, the weights between the multiple metrics are determined to be the same; thus, it cannot cope with changes in network conditions.  
To solve this problem, the MCDM-based scheme proposed in our previous study aims to respond to changes in network environment by logically determining the weights between multiple metrics. However, as the decision maker pre-determines the weights of each metric based on prior knowledge, there is a clear limitation in that the weights cannot be changed at runtime. To overcome this limitation, the proposed scheme recognizes the network environment using multiple cognitive metrics and finds the optimal policy to jointly optimize the performance metrics through Q-learning. In addition, we use WMA to prevent sudden changes in cognitive metrics. As a result, the proposed scheme provides a flexible trade-off between conflicting performance metrics by adaptively changing the parent node according to changes in network conditions.
## 4 Factors Influencing Chitosan Properties
### Degree of Deacetylation (DDA)  
The deacetylation degree is calculated by the amount of deacetylated glucosamine units present in the total glucosamine units. It is one of the most basic structural parameters to investigate chitin/chitosan. The degree of deacetylation (DDA) has a great influence on the solubility, viscosity, crystallinity, ion exchange capacity and flocculation of chitosan. Generally, chitin with more than 55% \(N\)-acetyl can be dissolved in 1% acetic acid or hydrochloric acid, which is called chitosan, the DDA varies from 60% to 100% due to different preparation conditions and requirements, but only the chitosan with more than 70% DDA can be used as industrial products, the completely deacetylated chitosan is extremely difficult to prepare. The deacetylation degree classification standard of chitosan is shown in Figure 3.  
\(N\)-deacetylation degree is the basis for determining the chitosan solubility. The charge density on the carbon chain increases with the increase of DDA, and the -NH2 group in the chitosan molecule  
Figure 3: The deacetylation degree classification standard of chitosan.  
will be protonated to form the NH\({}_{3}\)+ ion in the acid environment, so it will dissolve in the acid condition. The higher the deacetylation degree is, the more free-amino groups are on the molecular chain and the better the solubility is in the acid. The chain elasticity of chitosan can be changed by changing DDA, with the increase of deacetylation, a more flexible chitosan chain is produced, consequently forming a random spiral structure in the chain and more intramolecular hydrogen bonds. The mechanical properties of the microspheres are generally weaker than those of the deacetylated microspheres. The weaker the interaction between molecules, the less tangled the chitosan chain. The preparation of chitosan with high DDA is very important in the process of developing chitosan products, because the DDA can determine the chitosan solubility, and it is also the precondition for its chemical modification and functional modification. Chitosan with high DDA and low molecular weight and low viscosity usually need to be further hydrolyzed and degraded. By the X-ray diffraction test, with the increase of the deacetylation degree, the X-ray diffraction peak becomes sharper and the crystallinity is higher. Furthermore, acetylation regulates cell proliferation and adhesion, however not altering the cytocompatibility of chitosan. It was reported that decreased DDA is beneficial to cell growth and adhesion [32; 33; 34; 35].
# Safety Assessment of the Cover-and-Cut Method Under Blasting Vibration Induced by Tunnel Excavation
## 6 Discussion
### Comparison with Existing Literature  
The findings complement previous research on blast-induced vibrations by confirming that vibration impact decreases with distance from the blast source. The vibration frequencies observed (40-80 Hz) are consistent with studies on concrete structures, which typically vibrate within a similar range. Notably, the absence of overlap with vehicle vibration frequencies (0.5-25 Hz) eliminates the risk of resonance, supporting the feasibility of combining metro operations with ongoing construction.  
Unlike other studies focusing on vertical settlement or lateral displacement, this research emphasizes dynamic responses across multiple structural components. By integrating field data with numerical predictions, the study offers a more comprehensive assessment of both vibration speed and structural resilience, adding depth to the existing body of knowledge on underground construction safety.
## 2 Materials and Methods
### Inertial Measurement Unit (IMU)  
The IMU (MicroStrain, Inertia-Link-3 DM-GX2, Williston, ND, USA) measures orientation, velocity, and gravitational forces in a three-dimensional space, using a combination of a gyroscope and an accelerometer [28; 29]. The IMU was carefully fixed to the skin over the sacrum with double adhesive tape. Data were collected via a wireless Bluetooth connection, and real-time data from the sensor were stored on a computer with a sampling frequency of 100 Hz. Data analysis of the step test parameters was performed by proprietary, non-disclosed algorithms provided by the manufacturer based on the algorithms by [PERSON] et al. [30]. Inertial sensors have shown potential in functional tests [31; 32], and stepping has been shown to be a powerful performance-based test with good discriminate capacity to differentiate between healthy subjects and patients with osteoarthritis [31]. The IMU-based method has been shown to be reliable and reproducible in assessing gait and performance-based tests in healthy subjects [33; 34], and has also shown potential for performance-based tests such as ascending-descending movements in step tests and sit-to-stand transfers [22; 31; 35; 36]. Asymmetry scores (ASs) between the implant-side leg (ASi) and the non-implant-side leg (ASn) were calculated for each parameter using Equation (2):  
\[\text{ASs}=100\%\times\left(\left(\frac{\text{ASi}-\text{ASn}}{\text{ASi}+ \text{ASn}}\right)/2\right) \tag{2}\]
## 5 Discussion
### Discussion of the Bearing Capacity Factors \(N_{q}\) and \(N_{c}\)
Second, the model assumes homogeneous and isotropic soil properties; extending it to layered or anisotropic soils would necessitate assigning spatially variable strength parameters and refining the energy dissipation formulation accordingly. Finally, the method may not be suitable in scenarios involving highly compressible materials, strain-softening behavior, or footing-soil interfaces with significant roughness. In such cases, incorporating compressibility corrections, softening laws, or interface roughness factors would be necessary to maintain predictive accuracy.
# Rate of Penetration Prediction Method for Ultra-Deep Wells Based on LSTM-FNN
## 4 Model Construction
### Parameter Optimization  
In this paper, well depth is the basis of the time series data division. From the perspective of the model, different time series data lengths contains different information, which leads to a difference in model training accuracy. From the analysis of the actual drilling process, the depth range reflects how much information of engineering geological parameters before the time step is considered when the model predicts the drilling rate of the next time step, so the step length of the time series data is a model hyperparameter that needs to be prioritized. The hyperparameters to be optimized in the process of model establishment include: the timing data input step, that is, the well depth range of each time series data; the number of LSTM layers; the number of LSTM hidden layer neurons; the number of FNN layers; the number of FNN neurons; the FNN activation function; and the model learning rate. The specific hyperparameter search range is shown in the Table 2.  
80% of the drilling data of well 1 were randomly selected as the training set and 20% as the test set to optimize the hyperparameter of the model. The mean relative error (MRE)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**LSTM**} & \multicolumn{4}{c}{**FNN**} & \multicolumn{2}{c}{**Model Parameters**} \\ \hline
**Step Length** & **Layers** & **Neural** & **Layer** & **Neural** & **Active Function** & **Learning Rate** \\ \hline
5 & 1 & 10 & 1 & 32 & ReLU & \(1\times 10^{-3}\) \\
10 & 2 & 20 & 2 & 64 & ReLU & \(1\times 10^{-3}\) \\ \hline
15 & 3 & 30 & 3 & 128 & & \\
20 & 4 & 40 & 4 & 256 & Tanh & \(1\times 10^{-4}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: LSTM–FNN model parameter combination.  
Figure 6: LSTM–FNN network framework.  
and regression determination coefficient (\(R^{2}\)) were selected as the standards to test the prediction accuracy of the model, As shown in Equations (13) and (14).  
\[\text{MRE}=\frac{1}{n}\sum_{i=1}^{n}\left[\frac{\left|y_{i}^{p}-y_{i}\right|}{y_ {i}}\right]\times 100 \tag{13}\]  
\[R^{2}=1-\frac{\sum_{i=1}^{n}\left(y_{i}-y^{p}\right)^{2}}{\sum_{i=1}^{n}\left( y_{i}-\overline{y}\right)^{2}} \tag{14}\]  
where, n is the number of samples, \(y_{i}^{p}\) is the predicted value, \(y_{i}\) is the true value and \(\overline{y}\) is the average of the actual values.  
According to the calculation results, when the parameters in Table 3 are selected for the model, the model achieves the optimal prediction accuracy. At this time, the prediction accuracy of the model on the test set, where the MRE is 12%, \(R^{2}\), is 0.90.
## 1 Introduction  
Knowledge of complex permittivity of materials at terahertz (THz) frequencies is required for a variety of applications, including material identification [1, 2], observation of phase state or morphology [3, 4], detection of impurities or degradation [5, 6], determination of electronic properties [7, 8], non-destructive testing [9, 10, 11, 12, 13, 14], and optical design for high-speed communications [15].  
Currently, there are two main instrumental platforms capable of providing accurate broadband measurements of refractive indices and absorption coefficients of materials at THz frequencies. These are time-domain spectroscopy (TDS) and photonic-based frequency-domain spectroscopy (FDS). To date, the vast majority of THz measurements have been performed using THz TDS. Hundreds of papers have been published describing the technique and analysing its various aspects, including numerous excellent reviews [16, 17, 18]. TDS is inherently broadband in that it uses single-cycle THz pulses containing the complete source spectrum. The data are acquired in the time domain, i.e., it follows the time evolution of the THz pulse. Spectral data are derived from the time-domain data by applying Fourier transform, which yields both amplitude and phase information. In contrast, FDS is continuous-wave and frequency-tunable; i.e., the signal is narrow-band, and spectral data are acquired by tuning the signal frequency over the band of interest [19]. The THz signal is generated by photomixing, as the difference-frequency of two infra-red lasers whose wavelengths are offset by the requisite amount.  
The main advantage of FDS is high frequency resolution, better than 50 MHz as compared to \(>\)1 GHz for TDS. Related to that is the ability to select the range of frequencies measured, or to measure at a single constant frequency. However, current FDS systems are temperature-tuned, and therefore their frequency scanning rate is relatively slow [20, 21]: a full-range high-resolution scan can take up to 1.25-1.5 h.  
In this paper, we report operation of a novel pre-production optoelectronic FDS system capable of fast scanning similar to TDS. The system, named \"T-Sweeper\", is designed for industrial applications requiring rapid, robust measurements. We report extensive tests of this system, including frequency and amplitude calibration, and examples of material measurements. We also compare its performance with that of TDS and FDS. To our knowledge, this is the first such detailed comparison of the performance of the three different types of THz spectrometer.
## 4 Discussion
Although sagittal [11; 15; 16; 17; 18] slices have generally been used for meniscal segmentation, there have also been studies in coronal slices [12] or both slices [35]. More research has been completed on the segmental section because it contains more information about the meniscus and better defines the clinical pattern [16; 25]. [PERSON] et al. [12] proposed a U-Net-based method for meniscal segmentation and calculated DSC for MM and LM to be 85.18% and 84.33%, respectively [12]. In another U-Net-based study for the automatic segmentation of knee cartilage and meniscal structures, DSC and overall performance for MM and LM were calculated as 0.87%, 0.89%, and 0.88%, respectively [15]. In another U-Net-based study, DSC was reported in the range of 094-0.93 [16]. In the proposal of [PERSON] et al. [11] for a fully automatic 3D DCNN for the detection and segmentation of meniscal clefts in knee MR images, DSC and Sensitivity were 0.924 and 0.95, respectively. The overall performance evaluations of the proposed method (Method 3) were 0.8976 (\(\pm\)0.0071) and 0.9004 (\(\pm\)0.0064) for DSC in the test and external set, respectively, indicating that our study contributes to the literature. While the related studies are based on a single model for meniscus segmentation, this study uses ensemble methods and operates according to the masks obtained from five different models. In this way, the performance of YOLO models is improved. In this study, beyond proposing a new convolutional neural network model, a new model is presented. YOLO models offer real-time performance and provide a significant advantage in overcoming the difficulties of manual segmentation. Furthermore, the accuracy of YOLO is improved by using a dynamic weighted multiple voting method with grid search, which is unique in the literature. The internal dataset was divided into train, valid, and test sets using stratified random sampling to maintain class balance, make model performances consistent, and make quantitative and qualitative observations more consistent. One of the limitations of this study is that the total number of healthy and torn meniscus images in the dataset is different. Model 1 showed a superior performance due to its high number of parameters and complexity. However, this also reveals that the model needs more computational power. Other models are not as powerful as Model 1. The existence of another model close to the performance of Model 1 will improve the performance of ensemble methods and contribute to the performance of the proposed method (Method 3). The ability of the proposed model to automate the segmentation of the meniscus could significantly reduce the time required for manual labeling by radiologists and orthopedic surgeons. This efficiency gain could improve workflows in the clinical setting. In addition, the dynamically weighted ensemble approach increases the reliability of the model by reducing inter-observer variability, making it a promising tool for both diagnostic and surgical planning applications.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 3 Integrated System and Algorithm Design
### Throughput Optimization Model  
We developed a model to evaluate throughput optimization achieved by CCLF. Notably, we adopted average bandwidth as a key performance indicator for assessing network throughput. Average bandwidth is defined as mean data transfer rate over a specified period, accurately reflecting network actual transmission efficiency.  
First, we calculate the throughput capacity and average bandwidth denoted as \(\overline{b}_{\text{exist}}\) under existing congestion control mechanisms. In Section 3.3.1, we measure the network throughput and average bandwidth denoted as \(b_{\text{CCLF}}\) when using the CCLF approach. Then, in Section 3.3.2, by comparing the values of \(b_{\text{CCLF}}\) and \(\overline{b}_{\text{exist}}\), we validate that the network throughput under the CCLF method significantly exceeds that of existing methods.  
In this study, we investigate data transmission within the contact windows of satellite communications. During this period, the direct path from the producer to the consumer can be regarded as a stable connection. We define \(N\) as the number of forwarding hops from the producer to the consumer. In LEO satellite networks characterized by dynamic available bandwidth, the available bandwidth of the \(i\)-th link at time \(t\) is denoted by \(b_{i}(t)\).  
For existing congestion control schemes, when the data traffic exceeds the available bandwidth, packets accumulate in the queue of the output interface, potentially causing queue overflow and packet loss. Consequently, the throughput capacity of the network will not exceed the bandwidth of its least capable link. This relationship can be expressed as  
\[b(t)=\min_{i}\{b_{i}(t)\} \tag{1}\]  
where \(b(t)\) represents the throughput capacity of the network at time \(t\). The average bandwidth of the entire network within \((0,t)\) time can be derived as follows:  
\[\overline{b}_{\text{exist}}(t)=\frac{1}{t}\int_{0}^{t}b(\tau)\mathrm{d}\tau= \frac{1}{t}\int_{0}^{t}\min_{i}\{b_{i}(\tau)\}\mathrm{d}\tau \tag{2}\]  
It indicates that under existing schemes, the throughput capacity of the network is constrained by the bottleneck bandwidth. Our research objectives are to improve bandwidth  
Figure 4: Comparison of end-to-end average throughput in time-varying networks.  
utilization and enhance network throughput. Specifically, in our subsequent analysis, we employ average bandwidth as the metric for evaluating network throughput.
# Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
## 1 Introduction
* An Extended Kalman Filter (EKF) has been built for fusing the UWB ranging estimates with the on-board data of the vehicle. Moreover, an algorithm for post-processing the UWB ranging estimates has also been developed. The combination of both algorithms computes an accurate and reliable position over time, enhancing the individual performance of the different data sources. * Validation of the proposed solution by different tests performed in both a simulated environment and a real environment. On the one hand, the simulations have been carried out for proving the correct operation of the proposed solution in its initial design. On the other hand, the real tests have been carried out on the Astazero proving ground [13] in Sweden, using a relevant setup for the PRoPART use case. For these tests, a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) has been used as testing platform. * Accurate UWB sensor based on an external antenna connected through a cable to achieve a flexible deployment of our solution. To our best of knowledge, none of the research works found in the literature proposes such a flexible solution. The remainder of this paper is organized as follows. In Section 2, some related work are introduced. In Section 3, the methodology is described. The details of the implementation of our approach are presented in Section 4. Section 5 provides the experimental results and comparison of the accuracy and range of the UWB solution for different trajectories. Finally, the conclusions are summarized in Section 6.