esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Development of the Fluoropolymer Industry from 1930s \(\sim\) 1990s
### 1950s  
In 1953, a new fluoropolymer, polychlorotrifluoroethylene (PCTFE) was commercialized by M. W. Kellog Company under the trade mark Kel-F. PCTFE is produced by the free radical polymerization of chlorotrifluoroethylene (CTFE) with a linear polymer chain structure (Figure 2).  
High molecular weight PCTFE can be prepared by polymerization in bulk, in solution, in suspension and in emulsion forms [12]. Compared to PTFE, only one fluorine atom was replaced by the chlorine in PCTFE. The introduction of chlorine atom in the polymer structure interrupted the crystallization ability of the polymer main chain, and resulted in lower crystallinity, lower melting temperature, better intermolecular interaction, and better mechanical properties of PCTFE compared to  
Figure 2: The polymerization of CTFE.  
PTFE. PCTFE showed higher hardness, higher tensile strength, higher resistance to creep, and less water vapor and gas permeability. One important advantage of PCTFE is its melt processability.  
PCTFE is easily melt processed and the products are more transparent. PCTFE can be used continuously from \(-100\)\({}^{\circ}\)C to \(200\)\({}^{\circ}\)C. Especially, its better cold-flow characteristics made it more competitive to PTFE materials. It showed similar flame retardancy and better radiation resistance compared to PTFE. The chemical resistance and electrical properties of PCTFE are not as good as those of PTFE, but still better than most polymer materials.  
The price of PCTFE is higher than that of PTFE due to the expensive monomer as well as the small market size. Therefore, its main application is limited to use as a moisture barrier film in packaging and special engineering devices, where PTFE cannot meet the high performance requirements. Some examples are the aeronautical and space applications for cryogenic seals and gaskets. Low molecular weight PCTFE are used as oils, waxes and greases, inert sealants, lubricants for oxygen-handling equipment or corrosive media, plasticizers for thermoplastics and gyroscopic floatation fluids.  
PCTFE was originally produced by M. W. Kellog and 3M under the trade name of Kel-F [13]. After 3M discontinued the production of PCTFE (1995) [14], Daikin purchased the manufacture rights to PCTFE and produced it under the trade name Neoflon. PCTFE resin is also produced by Honeywell as Aclar and by Arkema as Voltalef.
# Physical and Mechanical Properties and Damage Mechanism of Sandstone at High Temperatures
## 3 Test Result
### Uniaxial Compression Test
#### 3.2.4 Variation Law of Peak Strain  
Figure 8a shows the variation rule of peak strain \(\varepsilon\) with temperature, which shows a general trend of decreasing and then increasing as the temperature increases, and the sandstone changes from brittle to ductile in the range of 500-600 \({}^{\circ}\)C. According to the results of Zhu [29], 800 \({}^{\circ}\)C is the initial temperature for the transition from brittle to ductile in the studied sandstones, and the different mineralogical composition of the studied sandstones also causes this difference. In this paper, it is found that the peak strain is 1.47 \(\times\) 10\({}^{-2}\) at room temperature 25 \({}^{\circ}\)C and 1.36 \(\times\) 10\({}^{-2}\) at 200 \({}^{\circ}\)C, which is a decrease of 7.48% compared with the same period, which is due to the closure of microfractures and the increase in the brittleness of sandstones due to the increase in temperature. Between 200 \({}^{\circ}\)C and 1100 \({}^{\circ}\)C, the peak strain increases continuously, and the peak strain at 1100 \({}^{\circ}\)C is 2.69 \(\times\) 10\({}^{-2}\), a rise of 97.79%. When the temperature continues to increase to 1200 \({}^{\circ}\)C, the peak strain decreases again to 2.36 \(\times\) 10\({}^{-2}\), which is due to the fact that the sandstone has been highly deteriorated at this temperature, and the ability to resist damage is greatly reduced.  
Figure 8b shows the heating rate's effect on the sandstone's peak strain \(\varepsilon\). The peak strain was 2.69 \(\times\) 10\({}^{-2}\) at the heating rate of 5 \({}^{\circ}\)C/min. The peak strains were 2.36 \(\times\) 10\({}^{-2}\), 2.35 \(\times\) 10\({}^{-2}\), and 2.32 \(\times\) 10\({}^{-2}\) at the heating rates of 10 \({}^{\circ}\)C/min, 20 \({}^{\circ}\)C/min, and 30 \({}^{\circ}\)C/min, respectively, with a fluctuation of not more than 2%, but compared to 5 \({}^{\circ}\)C/min, the peak strains were decreased by 12.26%, 12.63%, and 13.75%, respectively. This indicates that the rate of warming has little effect on the peak strain, and the temperature is the main influencing factor.
## 2 Related Work
### Techniques for Converting Time Series Data into 2D Representations
#### 2.3.2 Methods Based on Mapping Data into Images  
Inspired by advancements in computer vision, researchers have applied image processing techniques and deep learning models to map raw vibration data into two-dimensional images. Instead of directly rearranging data into matrices, this approach transforms time series data into the image domain, enabling the application of image-based techniques to vibration data.  
A common method involves three steps: normalization, grayscale mapping, and image construction. First, the time series data are normalized to a specific range, such as [0, 1] or [\(-\)1, 1], ensuring consistency and preparing them for further processing. Normalization adjusts each data point \(x\), based on the formula:  
\[x^{\prime}=\frac{x-min(x)}{max(x)-min(x)} \tag{5}\]  
where \(x\) represents an original data point, while \(min(\cdot)\) and \(max(\cdot)\) are the minimum and maximum values in the dataset, respectively.  
The normalized data are mapped to grayscale pixel values (0-255), preserving contrast and details. Each value is scaled and converted to an integer, representing a corresponding pixel intensity. Finally, these pixel values are arranged to form a grayscale image, with dimensions based on the time series' length and specific requirements.  
This transformation enables the use of deep learning models for fault diagnosis and pattern recognition, leveraging the strengths of image processing techniques. It also allows for enhancements like interpolation and smoothing to improve data representation. However, the method has limitations, including potential information loss during conversion and increased computational demands. Additionally, interpreting results can be challenging, as pixel values may lack direct physical meaning, requiring further analysis to ensure accurate conclusions.  
However, this conversion process may result in information loss, particularly when transforming continuous time series data into discrete image pixels, as some key details may be omitted, potentially impacting the accuracy and efficiency of the model. Furthermore, processing image data generally requires significant computational resources and storage space, which can increase processing time and costs, especially when dealing with large-scale datasets. Despite the intuitive nature of the images facilitating the analysis, each pixel does not always correspond directly to specific physical meanings of the original time series data, which can diminish model interpretability and necessitate more in-depth analysis and expertise to accurately interpret the results.
# DNNs Based Computation Offloading for LEO Satellite Edge Computing
###### Abstract  
Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user's offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning-based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near-optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user's bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory.  
LEO satellites; mobile edge computing; deep learning; task offloading; bandwidth allocation +
Footnote †: journal: 9  
on the LEO satellites, and users offload tasks to the LEO satellites to obtain smaller delay and energy consumption [6; 7]. With the rapid development of artificial intelligence, many learning algorithms are used in MEC networks for generating offloading decisions and resource allocation [8; 9]. Therefore, we consider applying the deep learning to the LEO satellites edge computing system.  
The main contributions of this paper are as follows:  
* We design a LEO satellite edge computing system where the LEO satellite equipped with MEC server serves ground users, and the effect of free space loss on channel gain is also considered in the simulation.
* We investigate a multi-user multi-task LEO satellite edge computing network. For the user's offloading decisions problem, we use multiple parallel deep neural networks (DNNs) to generate corresponding offloading decisions. For the user's bandwidth allocation problem, we derive the optimal bandwidth allocation scheme of the system.
* We conduct a large number of simulation experiments. Simulation results show the reliability and performance superiority of the proposed algorithm. Compared with the other comparative algorithms, the proposed algorithm in this paper can obtain the optimal system utility function values. Moreover, our proposed algorithm can obtain a good convergence effect and a low average time cost.
## 5 Performance Evaluation
### Simulation Results
#### 5.3.1 Impact of Traffic Load
Comparatively, ALTR shows better performance than TARA as it avoids excessive packet transmission because of employing the shortest path routing. The QoS-aware protocols outperform both TARA and ALTR considering reliability as one of the routing metrics for reliability sensitive packets. Both TMQoS and TLQoS treat the \(Cr\) and \(Rc\) packet separately in taking their routing decision. Because of employing both path reliability and data reliability, \(Cr\) packet achieves higher reliability than \(Rc\) for both the protocols. Overall, the reliability performance of TLQoS is better than TMQoS for both the packet types, as during high traffic load, TLQoS adapts the dynamic changes in network status much more efficiently than TMQoS because of its localized approach in routing decision. We evaluate the average temperature rise for different traffic loads as shown in Figure 4d. ALTR shows the best performance in this regard as it route all types of packet through the \"cooler\" node and avoids unnecessary packet transmission along the network applying the shortest path algorithm after a certain hop-count. Comparatively, TLQoS shows a bit poor performance than TMQoS at low traffic load. The reason is, although it avoids hotspot in routing all packet types, however, the packets might move around more nodes (nodes in the same depth or even in higher depth) in the network that increases the average temperature. However, at high traffic load, TLQoS achieves lower temperature rise than TMQoS due to the less number of retransmissions, and choosing the lower temperature node with more accuracy for Rg data packets employing localized approach. Although, the temperature is chosen as the only routing metric in TARA, still it achieves the highest average temperature rise at high traffic load because of the involvement of more nodes in the network unnecessarily for withdrawal strategy. As the traffic load increases, the energy consumption also increases in all the protocols as illustrated in Figure 4e. Similar to the temperature rise, ALTR also shows the best performance in energy consumption. The reason is that, although ALTR has lower reliability than QoS-aware protocols but it involves less number of nodes in packet transmission that affects the lower average energy consumption of the nodes. The higher reliability and shorter hop-count path selection also causes to achieve lower energy consumption of QoS aware protocols. Although at low traffic load, TMQoS shows a bit better energy consumption than TLQoS, however, TLQoS outperforms TMQoS at high traffic load due to its superior performance in reliability at that traffic condition. TARA, having poor reliability performance and spreading the packets along more nodes in the network due to withdrawal strategy, exhibits the worst energy consumption performance among all the protocols.
# Optimal Lane Allocation Strategy in Toll Stations for Mixed Human-Driven and Autonomous Vehicles
## 3 Methodology
### Optimization Model
#### 3.1.1 Highway Toll Station Lane Allocation  
In accordance with Section 2, the lane allocation problem at toll stations under heterogeneous traffic flows is presented from the perspective of traffic planners and managers. They adhere to the principle of system optimization by determining and adjusting the number of ETC lanes in the queueing system to minimize total travel time. We use the subscripts \"up\" and \"low\" to differentiate between the upper and lower models in the bilevel programming framework. Let \(Z_{up}\) represent the objective function; thus, the objective function for the toll station lane allocation problem can be expressed as follows:  
\[\min_{n_{di}}Z_{up}=\sum_{d\in\mathbf{D}}\sum_{i\in\mathbf{I}}\lambda_{di}Ws_{di} \tag{8}\]  
\begin{table}
\begin{tabular}{c l} \hline \multicolumn{2}{c}{**Indices and Sets**} \\ \hline \(\mathbf{D}\) & Set of lane directions \\ \(\mathbf{I}\) & Set of toll collection methods \\ \(\mathbf{I}\) & Set of vehicle categories \\ \(d\) & Direction: 1 for entrance, 2 for exit \\ \(i\) & Lane type: 1 for ETC lane, 2 for MTC lane \\ \(j\) & Vehicle type: 1 for CAVs, 2 for MTC-HVs, and 3 for ETC-HVs \\ \(c\) & Indicates that the decision variable is relaxed \\ \(*\) & Indicates that variables are treated as parameters \\ \hline \multicolumn{2}{c}{**Parameters**} \\ \hline \(\mu_{di}\) & The service rate of a single lane of type \(i\) in direction \(d\) \\ \(n_{sum}\) & The total number of lanes at the highway toll station \\ \(p_{di}\) & The proportion of vehicle type \(j\) in direction \(d\) \\ \(\theta_{di}\) & The proportion of ETC-HVs choosing the lanes of type \(i\) in direction \(d\) \\ \(\lambda_{d}\) & The arrival rate of vehicles in direction \(d\) \\ \(Ws_{di}\) & The average passage time of vehicles in lane type \(i\) in direction \(d\) \\ \(k\) & Lagrange multiplier \\ \hline \multicolumn{2}{l}{**Decision variables**} \\ \hline \(n_{di}\) & The number of lanes of type \(i\) in direction \(d\) \\ \(\lambda_{di}\) & The vehicle arrival rate for lane type \(i\) in direction \(d\) \\ \hline \end{tabular}
\end{table}
Table 2: List of Notations.  
subject to  
\[\sum_{d\in\mathbf{D}}\sum_{i\in\mathbf{I}}n_{di}=n_{sum}, \tag{9}\]  
\[n_{di}>\frac{\lambda_{di}}{\mu_{di}},\quad\forall d\in\mathbf{D},i\in\mathbf{I},\lambda_ {di}\
eq 0, \tag{10}\]  
\[n_{di}\in\mathbb{N}, \tag{11}\]  
where Equation (9) corresponds to the total number of lanes constraint; Equation (10) is derived from Equation (2) and ensures the stable operation of the toll station, confirming that \(Ws_{di}\) is a non-negative value; Equation (11) ensures each lane number is an integer.
# Pricing Strategies of Logistics Distribution Services for Perishable Commodities
## 4 Comparison Analysis of Four Pricing Models
### Comparison of Applied Range  
The factors affecting the distribution pricing of perishable goods under the fixed pricing model are consumer service satisfaction and the decay index of commodities. The characteristic of this pricing model is constant distribution price during the distribution time. A fixed pricing model will lead to longer delivery times and consumers will give a lower degree of service satisfaction, which will lead to a reduction in the number of consumers, and then affects the overall profit of logistics service providers. A fixed pricing model with time constraints has largely reduced the decline of consumer service satisfaction caused by long delivery times. From Section 3.1, it can be seen that the commodity decay index increases and the logistics distribution pricing decreases under the fixed pricing model. Therefore, it is very important for the logistics service provider to choose the appropriate commodity decay index according to the distribution time in this mode.  
The factors that influence the price of the dynamic pricing model are consumer service satisfaction, storage cost of perishable goods, and their decay index. A dynamic pricing model distributes perishable goods with dynamic price, which avoids the reduction of distribution quantity caused by a decrease in customer service satisfaction. Therefore, this pricing model is more suitable under these circumstances. Under the dynamic pricing model, distribution price decreases with the increase of distribution time, decay index and storage cost of perishable goods. The larger the increase of decay index and storage cost of perishable goods, the more the distribution pricing decreases.
# Fuzzy-Based Control System for Solar-Powered Bulk Service Queueing Model with Vacation
## 3 Materials and Methods
### Fuzzification Analysis
#### 3.2.2 Threshold Design Justification  
The transition thresholds for the three service modes--(A) High Bulk Service, (B) Medium Single Service, and (C) Low Vacation--were initially established using expert domain knowledge and refined through empirical simulation to maintain queue stability and minimize energy consumption under typical traffic and solar conditions. These thresholds define the fuzzy rule base, which dynamically adjusts service behavior in response to real-time variations in arrival rate, queue length, and solar energy availability. However, we acknowledge that the fixed nature of these thresholds may limit responsiveness during rapidly changing solar conditions (Figure 2). To address this, a promising enhancement involves integrating dynamic vacation thresholds based on real-time or forecasted solar irradiance. By anticipating fluctuations in solar power availability, the system could preemptively adjust vacation periods, enabling proactive energy management and further improving service continuity. This extension will be considered in future work, potentially through the incorporation of solar forecasting models within the fuzzy inference framework.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rule No.** & **Queue Length** & **Server Status** & **Arrival Rate** & **Service Adjustment** \\ \hline
1 & Short & Idle & Low & Decrease \\ \hline
2 & Short & Busy & Moderate & Maintain \\ \hline
3 & Medium & Busy & High & Increase \\ \hline
4 & Long & Busy & High & Increase \\ \hline
5 & Long & Broken & Moderate & Maintain \\ \hline
6 & Medium & Idle & Low & Decrease \\ \hline
7 & Long & Idle & High & Maintain \\ \hline
8 & Short & Broken & Low & Decrease \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy rule base.  
Figure 2: Membership function graphs for input and output variables in the Mamdani Fuzzy Inference System: (**a**) arrival rate, (**b**) queue length, (**c**) server utilization, and (**d**) service mode.  
The figure displays four membership function (MF) graphs used in the Mamdani Fuzzy Inference System for the proposed FBCS:  
1. Arrival Rate: Defined over the range [4; 10], with linguistic terms _Low_, _Medium_, and _High_ modeled using triangular MFs. This helps determine input traffic intensity.
2. Queue Length: Spanning [0; 20], it includes _Short_, _Medium_, and _Long_ queue states, used to assess congestion levels.
3. Server Utilization: Ranges from [0, 1], with _Low_, _Medium_, and _High_ MFs reflecting the current workload of the server.
4. Service Mode: The output variable (Low = A, Medium = B, High = C) guiding the operational state--vacation, single, or bulk service--based on fuzzified input.  
These MFs, designed via expert input and refined through fuzzy c-means clustering, form the foundation for rule evaluation and adaptive service control.
# Quantum Perspective on Digital Money: Towards a Quantum-Powered Financial System
## 3 Verification Methods for Quantum Money in Quantum Wallets
### Blind Verification Protocols  
Blind verification protocols enable quantum wallets to interact with a trusted quantum server or bank to authenticate the amount of quantum money without directly measuring or collapsing the quantum states stored within [21,22]. In this approach, the quantum wallet can perform a sequence of quantum operations as specified by the server. The results of these operations are then returned to the server, which interprets them to confirm the amount without requiring direct access to the quantum states themselves. By keeping the verification process blind, these protocols maintain the coherence of the states within the wallet and offer a secure verification method.
## 3 System Framework and Algorithm Procedure
### Overview  
In DMAC, we partition the channel utilization into two time slots: CP and TP. During the CP, all of the nodes transmit a DTR frame to the AP. The nodes that send successfully the AP inserts the DTR into a transmission queue, meanwhile the AP does not transmit the ACK frame to the nodes. During the TP, these nodes will be sequentially scheduled to transmit data by a beacon or ACK from the AP.