esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Pore Size Distribution of Coal before and after Sc-CO\({}_{2}\) Extraction  
The pore size distributions of raw and Sc-CO\({}_{2}\) extracted coal are shown in Figure 3. According to previous study [7; 8], macro- and mesopores decrease and micropores increase with an increase in coalification. In this study, the volume percentages of the macro- and mesopores were very high, especially for high rank coals. This is likely because the gaps between the sample particles were tested. Hence, a modified Hodot classification [29] was used in this study, and the pores that were over 10,000 nm in diameter were eliminated to reduce the interference of the gaps on the pore distribution between sample particles. The classification is shown as follows: macropore (>1000 nm), mesopore (100-1000 nm), transitional pore (10-100 nm), and micropore (\(\leq\)10 nm).  
Previous study [30] showed that when the pressure becomes large enough, the mercury no longer intrudes into the pores and the coal is compressed to some extent, which make the incremental pore volume change suddenly. In this work, the incremental pore volumes of samples before and after the Sc-CO\({}_{2}\) extraction showed such a sudden change when the pore diameter reduced to a particular range. This indicates that the samples were compressed around the points.  
Figure 3 shows significant changes in the pore size distribution of samples by Sc-CO\({}_{2}\) extraction. The volumes of incremental big pores increased after Sc-CO\({}_{2}\) extraction, perhaps mainly caused by the pore size enlargement, while the increase of incremental small pores volumes was mainly caused by the increasing number of pores. This indicates that Sc-CO\({}_{2}\) extraction not only increases the number of micropores, but also enlarges the pore diameter size. As mentioned above, the change in micropore size  
Figure 2: The flow chart of Sc-CO\({}_{2}\) extraction in this work.  
distribution was not only due to the characteristics of micropores but also caused by the compressibility of all of the samples.  
Figure 3: Pore distribution of the coals before and after Sc-CO\({}_{2}\) extraction: (**a**) Qiyi coal; (**b**) Liulin coal; (**c**) Dengjiazhuang coal; (**d**) Shuangliu coal; (**e**) Maozequ coal.
## 4 Classification of MAC Protocols Based on Channel
### Distributed Single-Channel MAC Protocols
#### 4.1.5 AveMAC  
Adaptive vehicular MAC is proposed to adaptively vary the channel condition and to handle unbalanced traffic in the opposite direction in a two-way intersection road scenario. It also eliminates merging collision and access collision and improves channel utilization. AveMAC is an enhanced VeMAC [53], which is a TDMA-based protocol supporting one control channel and several service channels.  
The frames in the control channel are partitioned into disjoint sets of time slots analogous to moving vehicles in the left or right directions [71]. The partitioning of the channel is not fixed as in VeMAC; rather, it varies adaptively to the traffic condition. Here, vehicles cannot use the time slots occupied by vehicles one-hop or two-hop away but can use the slots occupied by at least three-hop neighbors. Before adapting to timeslot partitioning, vehicles need to listen to the channel for N successive time slots to obtain information about a set of one-hop and two-hop neighbors. Then, the set of timeslots available for allocation can be determined so that the vehicle can randomly select a timeslot to be allocated for further transmission. It then checks whether the reservation of that slot is successful by listening to the channel for the next N-1 slots. Suppose that the reservation is found to be unsuccessful. In that case, it is estimated that some other vehicle in a two-hop neighbor attempts to reserve that slot, so the vehicle tries to reserve a new slot, thus avoiding access collision. The vehicle continues to access the same time slot until a merging collision occurs detected based on frame information (FI) messages.  
The protocol improves channel utilization. However, it does not consider the hidden terminal problem. QoS matrices such as reliability, packet delivery ratio, and network throughput are also not analyzed.
# Small-Sample Bearings Fault Diagnosis Based on ResNet18 with Pre-Trained and Fine-Tuned Method
## 3 Proposed Framework  
In many practical scenarios, the bearing is in a normal working state most of the time, so the data available for FD are very limited. The limited availability of data poses challenges to training neural network-based diagnostic models. The insufficient fault data makes it challenging for the model to learn effectively, potentially impacting both its generalization ability and the accuracy of fault identification. To enhance the accuracy and adaptability of FD with small samples, this study introduces a VSDP-TLResNet18 framework for FD, employing a pre-trained and fine-tuned method, outlined in Figure 6. The methodology consists of the following steps:  
Initially, the original vibration signal is decomposed and processed using the VMD method. In VMD, the count of modal components \(k\) influences the outcome of the decomposition, and incorrect parameter settings can result in either incomplete or excessive decomposition. The \(k\) value is set based on the center frequency. Each IMF component's kurtosis is calculated, and the top-six IMFs are selected according to  
Figure 5: Principle of pre-training and fine-tuning.  
Figure 6: Diagnostic framework of the proposed method.  
To fully leverage deep learning in image processing, the six IMF components selected are successively generated into a two-dimensional image by SDP transform. Reasonable selection of parameters \(g\) and \(l\) can improve the image quality and strengthen the discrimination between fault signals, making the distinction between various fault vibration signals more obvious. In order to maximize the difference and visualize different fault images, the optimal parameters \(g\) and \(l\) of SDP are determined by Pearson correlation analysis. After generating SDP fault images, the SDP images representing various fault types are split into training and testing sets, proportionally.  
The VSDP-TLResNet18 model employs the SDP image as its input. Through the pre-trained and fine-tuned method, the knowledge of the ResNet18 model pre-trained on large datasets is transferred to the task of FD; this is done to enhance the model's performance on small-sample bearing datasets. The training set is utilized for training the VSDP-TLResNet18 model. Simultaneously, the last epoch of model weight will be saved for testing, and the diagnostic results will be output.
## 3 Materials and Methods
### Anomaly Classification
#### 3.5.1 Spatial Stream  
The spatial stream consists of a pre-trained CNN model base with its classification and dense layers are removed. The network performs prediction on an individual frame basis, and every third video frame is provided to the CNN model to match the predicted computational performance of the temporal stream. The spatial stream uses a ResNet50 model that effectively acts as a feature extractor from the RGB images obtained from the video stream.
## 1 Introduction  
Quantum error correcting codes are an essential part of proposals for quantum computation. Topological error correcting codes are currently one of the most well studied examples [1]. No code is useful without a decoder, which takes the classical information produced by measurement of the code and uses it to determine how best to counteract the effects of noise. Multiple algorithms have been proposed to decode topological codes all with various advantages and disadvantages.  
Here we present an algorithm whose main advantage is its simplicity, allowing straightforward generalization to a multitude of topological codes as well as more exotic decoding problems, such as non-Abelian decoding [2]. It can be considered to be an example of an HDRG decoder, which have recently begun to attract attention [3; 4; 5], and is related to the 'expanding diamonds' algorithm studied in [6].  
To compare the method to existing ones we apply it to a specific choice of code and error model. The most widely studied topological codes are the surface codes [7]. It is therefore these that we use for our benchmarking. We specifically use the planar code, in order to demonstrate the manner in which our algorithm deals with boundaries. Many decoding methods have been applied to this code [8; 9; 10], making it the best choice for comparing our algorithm.  
The primary error model we consider is the standard one applied to the planar code. This has errors acting independently on each spin, and bit and phase flips occurring independently of each other. However, since realistic physical systems will likely experience errors that are correlated between spins [11; 12; 13], we also consider an error model with nearest neighbour correlations between errors.  
We also make use of a recent study of HDRG decoders [5], of which our proposed method is an example. This work suggests an improved distance metric that can be used in such decoders in order to enhance performance for codes such as the planar code. We study the performance of our decoder both with and without this modification.
# Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video Streaming
## 4 Approach
### Extending LQ for Improving QoE in Mobile Streaming
#### 4.2.2 Counter-Based Switching Logic  
In addition to the adjustment factor, we introduce a bit rate switching logic using a counter mechanism to more aggressively prevent short-term bit rate fluctuations. Since the LQ output is a continuous value, immediate switching based on the quantized value from the LQ output can incur oscillations at boundaries. The counter-based switching logic avoids such oscillations by introducing some hysteresis to the LQ ABR. To this end, LQE selects a bit rate \(R_{k}\) higher than \(R_{k-1}\) only if the estimated bit rates from the all controller outputs in previous \(m\) consecutive chunks (\(\hat{R}_{i\in[k-m,k]}\)) indicate to increase bit rate, that is, \(\forall i\in[k-m,k]\), \(\hat{R}_{i}>R_{i}\Rightarrow R_{k}=\hat{R}_{k}\). LQE uses the same counter-based switching for the case of switching-down bit rate.
## 4 Experimental Validation
### Case 1: Public Bearing Dataset of Paderborn University (PU)
#### 4.1.2 Experimental Setup of Case 1  
To accurately evaluate the performance of the proposed ISCV-ViT model, three groups of experiments are conducted on the public dataset PU: ISCV-ViT, ViT, and ISCV-ResNet50. Since the current signal is sensitive to the load, two working conditions, N15_M01_F10 and N15_M07_F10, are selected. At the same time, five fault categories are selected from the accelerated life test for each working condition in addition to the health category. The details are shown in Table 4. A total of 25,600 data points are taken as a sample, and 200 samples are extracted from each category. Finally, 1200 samples are divided into a training set and a validation set at a ratio of 4:1. To eliminate accidental errors, each experiment was run for 200 epochs and repeated three times.
# Assessment of Characteristics of Acid Mine Drainage Treated with Fly Ash
###### Abstract  
Acid mine drainage (AMD) occurs naturally in abandoned coal mines, and it contains hazardous toxic elements in varying concentrations. In the present research, AMD samples collected from an abandoned mine were treated with fly ash samples from four thermal power plants in SinaviaIcalfield in the proximate area, at optimized concentrations. The AMD samples were analyzed for physicochemical parameters and metal content before and after fly ash treatment. Morphological, geochemical and mineralogical characterization of the fly ash was performed using SEM, XRF and XRD. This laboratory-scale investigation indicated that fly ash had appreciable neutralization potential, increasing AMD pH and decreasing elemental and sulfate concentrations. Therefore, fly ash may be effectively used for AMD neutralization, and its suitability for the management of coalfield AMD pits should be assessed further.  
abandoned mine; fly ash; acid mine drainage; SEM
# Study on Solar Radiation and the Extreme Thermal Effect on Concrete Box Girder Bridges
## 2 Calculation Method of Solar Radiation and Field Temperature Observing
### Determination of the Heat Transfer Parameters  
According to the heat transfer theory, the thermal boundary of the box girder belongs to the second boundary condition, in which the heat flux is known, and the third boundary condition, in which the heat exchange coefficient and the external ambient temperature are known. In engineering studies, it is usually unified as the third boundary condition for convenience of analysis. In many bridges, it is assumed that the temperature field is invariant along the bridge's longitude direction. Therefore, the temperature field distribution of the cross-section can be simplified as a 2D problem, and the thermal conductivity sub-equation is expressed as:  
\[\lambda\bigg{(}\frac{\partial^{2}T}{\partial x^{2}}+\frac{\partial^{2}T}{ \partial y^{2}}\bigg{)}=\rho\cdot c\cdot\frac{\partial T}{\partial\tau} \tag{11}\]where \(\lambda\) is the thermal conductivity, \(T\) is the temperature, \(\rho\) is the density, \(c\) is the specific heat, and \(\tau\) is time.  
The heat exchange between the box girder and the outside environment consists of solar radiation, heat exchange with the atmosphere, and atmospheric longwave radiation. The heat exchange between the box girder and the inter-space mainly includes the heat exchange with the interior air and the longwave radiation. The heat exchange diagram of the box girder is shown in Figure 1.  
According to the unified comprehensive heat transfer method, the outside thermal boundary condition is expressed as  
\[q_{c}=(h_{c}+h_{r})(t_{c}-t) \tag{12}\]  
where \(q_{c}\) is the feat flux from the air to the box girder, \(h_{c}\) is the convective heat coefficient, which is calculated as Formula (13), \(h_{r}\) is the heat radiation coefficient, which is calculated as Formula (14) [1; 27], \(t_{c}\) is the comprehensive air temperature, which is calculated as Formula (15), and \(t\) is the girder surface temperature.  
\[h_{c}=3.9\;v+5.53,\;(v\leq 5\;\text{m}/\text{s}) \tag{13}\]  
\[h_{r}=\epsilon_{r}\text{-}C_{0}\cdot\left[K^{2}+{K_{a}}^{2}\right](K+K_{a}) \tag{14}\]  
\[t_{c}=\frac{\epsilon\cdot I}{(h_{c}+h_{r})}+t_{a} \tag{15}\]  
where \(v\) is the wind speed, \(\epsilon_{r}\) is the radiation coefficient of girder surface, \(C_{0}\) is the Stefan-Boltzmann constant, \(K\) is the thermodynamic temperature of the girder surface, \(K_{a}\) is the thermodynamic temperature of the air, \(\epsilon\) is the solar radiation absorption coefficient, and \(t_{a}\) is the measured air temperature.  
The inside thermal boundary condition is expressed as  
\[q_{ci}=(h_{c}+h_{r})(t_{ai}-t) \tag{16}\]  
where \(t_{ai}\) is the inside air temperature. It changes slightly in a day, around 1-3 \({}^{\circ}\)C, and it could be valued as average daily temperature plus 1.5 \({}^{\circ}\)C if there are no measured data [28].
# Information Relaying Methods in VANET: Algorithms, Standards and Tests
## 4 Enhanced K-Means-Type Algorithm  
Recall that the conventional K-means-type algorithm was designed for describing the process of partitioning an N-dimensional observation into \(k\) sets on the basis of a sample [5]. When clustering points with the squared Euclidean distance metric, the procedure described in [5] gives the following objective function that updates the mean center of a cluster to find the closest center for each point:  
\[\phi=\sum_{v\in V}\min_{c\in C}\lVert v-c\rVert^{2}, \tag{3}\]  
where \(v_{1},v_{2},\cdots,v_{m}\in V\) are the \(m\) vehicle nodes defined in the system settings of Section 3 and \(C\) is the centroid set, with each element \(c\) representing the center of a corresponding cluster.
###### Abstract  
The purpose of this study was to examine the impact of a variety of supplemental feeds on the composition and quality of milk in a pasture-based dairy system. Four pasture-supplemented feeding systems were compared: Group 1 supplementation with 16% crude protein parlour concentrate (CONC); Group 2 supplementation with palm kernel expeller plus parlour concentrate (PKE); Group 3 supplemented with soya hulls plus parlour concentrate (SOYA); Group 4 was supplemented with molassed beet pulp plus parlour concentrate (BEET). Supplemental feeding system was demonstrated to have a significant effect on the size of native casein micelles and the gelation properties of milks. While CONC feeding produced significantly higher casein micelle size, gel strength (Young's Modulus) was significantly negatively correlated with casein micelle size. Supplemental feeding system had a significant effect on a number of fatty acids (FA) and indices derived therefrom, including total saturated and unsaturated fatty acids, _de novo_ produced FA, omega 3, and omega 6 FA. The volatile profile of milks was also affected by supplemental feed choice, whereby multivariate analysis demonstrated that the CONC diet was distinctly different to that of the PALM, SOYA, and BEET milks. Multivariate analysis demonstrated that it is possible to distinguish milks from different pasture-supplemented feeding systems by their FA profile.  
Keywords:dairy; fatty acids; pasture; feed supplements; milk composition; cow diet +
Footnote †: journal: _foods_
# DeCGAN: Speech Enhancement Algorithm for Air Traffic Control
## 5 Conclusions  
In this paper, we present a DeCGAN speech enhancement algorithm specifically designed for ATC. The algorithm effectively removes noise and irrelevant phonemes from audio, thereby restoring higher speech quality. In the proposed algorithm, a unique generator structure is introduced to restore both magnitude and phase information. The metric discriminator in adversarial training addresses the non-differentiability issue of evaluation metrics, ensuring that the generated speech better aligns with both subjective and objective perception standards. Our DeCGAN model has broad prospects for applications in civil aviation radio air-ground communication as it can increase the quality of speech, make the words more understandable, and reduce the chance of misjudgments, thereby ensuring efficiency and safety of communication.  
In the future, to address the high computational costs caused by the overall complexity of the algorithm, we will explore model compression and optimization techniques to enhance real-time processing capabilities. Additionally, future work will focus on evaluating and adapting the model to diverse domains and noise environments to further extend its applicability beyond ATC communications. After that, we plan to evaluate the performance of our model under babble noise interference in future work, which may inspire future research on speech enhancement techniques across various noise environments. Lastly, we will also consider conducting a sensitivity analysis on the key parameters involved in the loss function, to better understand their impact on model performance. Such investigations will not only support the reproducibility and interpretability of our framework but will also provide a stronger foundation for designing more effective and perceptually aligned speech enhancement systems in future studies.  
Conceptualization, H.L. and Y.H.; methodology, Y.H.; software, J.K.; validation, H.L., Y.H. and H.C.; formal analysis, Y.H.; investigation, Y.H.; resources, H.L.; data curation, Y.H.; writing--original draft preparation, Y.H. and H.C.; writing--review and editing, H.L. and J.K.; visualization, Y.H.; supervision, H.C. and J.K. All authors have read and agreed to the published version of the manuscript.  
This research has been supported by the Key Laboratory of Flight Techniques and Flight Safety, Civil Aviation Administration of China (No. F2024 KF01A).  
This study utilized the following open-source dataset: the VoiceBank + Demand dataset: \"[[https://datashare.ed.ac.uk/handle/10283/1942](https://datashare.ed.ac.uk/handle/10283/1942)]([https://datashare.ed.ac.uk/handle/10283/1942](https://datashare.ed.ac.uk/handle/10283/1942)) (accessed on 1 March 2025)\". The private dataset used in this study is not publicly available, due to privacy restrictions. For more information, please contact the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
studied the secrecy performance of the FSO system and analyzed the influence of atmospheric turbulence, pointing error, and other related parameters, as well as the position of eavesdroppers in different eavesdropping scenarios. In [18; 19], the RIS and source nodes were combined, respectively, to form one and multiple RIS-equipped sources. The above studies showed that the RIS could improve performance such as the OP of a system, but the application of RIS to the mixed RF/FSO system to resist eavesdropping and improve the security performance of the physical layer has not been reported in the literature. Drawing from the aforementioned discussion, we propose a mixed RF/FSO system where the jamming signals via RIS reflection cause interference to the eavesdropper. It was found that the RIS could significantly improve the system security performance. The uniform cumulative distribution function (CDF) for the end-to-end signal-to-noise ratio (SNR) of the communication system was derived for the eavesdropper subjected to the interference signal reflected by the RIS. We further derived the expressions for the SOP and ASC, which were verified for accuracy using Monte Carlo methods. The changes in the important system parameters, such as time switching factor, energy conversion efficiency, and average interference noise ratio were analyzed by simulations. In addition, the impact on the system security performance before and after the use of RIS was compared. The influence of important parameters of the RIS on the secrecy performance of the system was further investigated. These parameters included the number of reflecting elements, the link distances before and after reflection, and the fading severity parameter.
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 3 Proposed Dynamic Cross-Domain Authentication Scheme
### User Update Algorithm  
We used a simple and effective update algorithm to implement the dynamic addition and removal of users. The main idea is to make each leaf node represent a user, where the current value of the leaf node is the binary string of the public key held by the user. When the user state changes, it only needs to modify all the values in the path from the leaf to the root, without changing the entire Merkel tree. As shown in Figure 2, we provide an example of a tree with \(2^{3}=8\) leaf nodes. When the status of the user \(u_{101}\) changes, we only need to change the values of the yellow nodes. It can be seen that the time complexity of the proposed algorithm is O(log\(N\)).