esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Future 7G/8G networks are expected to integrate both terrestrial Internet and space-based networks. Space networks, including inter-planetary Internet such as cislunar and deep-space networks, will become an integral part of future 7G/8G networks. Vehicle-to-everything (V2X) communication networks will also be a significant component of 7G/8G networks. Therefore, space networks will eventually integrate with V2X communication networks, with both space vehicles (or spacecrafts) and terrestrial vehicles involved. DTN is the only candidate networking technology for future heterogeneous space communication networks. In this work, we study possible concatenations of different DTN convergence layer protocol adapters (CLAs) over a cislunar relay communication architecture. We present a performance characterization of the concatenations of different CLAs and the associated data transport protocols in an experimental manner. The performance of different concatenations is compared over a typical primary and secondary cislunar relay architecture. The intent is to find out which network relay path and DTN protocol configuration has the best performance over the end-to-end cislunar path.  
Keywords:space vehicles; space vehicular networks; vehicle-to-everything (V2X) networks; space networks; sensor networks +
Footnote †: journal: Sensors
## 3 Preliminaries
### Network Model  
Figure 1 illustrates the structure of the WSNs.  
The following factors are taken into account when creating the network model:  
* In WSNs, every sensor node has the same initial energy.
* Distances between nodes are calculated using Euclidean distance.
* Within the sensing area, sensor nodes are haphazardly placed and maintain a fixed location once deployed.
* The BS can be positioned either within or outside the screening region, and it remains stationary.
* The sensor node as a cluster member transmits the collected environmental data to the CH.
* The aggregated data are transferred to the BS via CH.
## 3 Results and Discussion
### Optical Microscopic Analysis of Hardened Foam Concrete  
Figure 7a-d shows photographs of pore structures of hardened foam concrete specimens with a density of approximately 550 to 800 kg/m\({}^{3}\). Numerous well-distributed air pockets/voids are observed in the foam concrete samples. Void sizes are found to be highly dependent on the percentage of foam content used. When the percentage of foam content is high, and the cement paste volume is kept low, easily moveable air bubbles tend to be close to each other during the mixing and before setting. As a result, bigger-sized air pockets are formed in the foam concrete with a high percentage of foam content, as shown in Figure 7b. Also, the water-to-cement and water-to-binder ratios can play an important role in forming air voids. If a higher percentage of water content is used in a mix, it reduces the viscosity of the cement paste. Consequently, bubbles in the fresh foam concrete mix coalesce easily and form larger-sized air pockets [41].  
Larger air pockets improve thermal insulation, which is beneficial if the product is intended to be used for building materials. However, larger air pockets decrease the strength of the material significantly. In contrast, bigger air pockets increase the permeability of foam concrete, which can be useful if foam concrete is intended to be used for soil replacement.  
PP fibres are expected to restrict the movement of air bubbles. As a result, the formation of larger air pockets in fresh foam concrete were prevented. Foam concrete with smaller-sized air pockets contributes to achieving higher compressive strength. Also, PP fibres improve the tensile strength and ductility of the foam concrete by creating bridges between pores.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multicolumn{2}{c}{**Blended (Foam Content 70\%)**} & \multicolumn{2}{c}{**GP Mixes (Foam Content 70\%)**} \\ \cline{2-5}  & **Conventional** & **Permeable** & **Conventional** & **Permeable** \\ \hline Initial (hour) & - & 10.00 & 6.50 & 7.33 \\ \hline Final (hour) & - & 22.00 & 11.33 & 14.00 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Setting time of the mixes prepared with conventional foaming liquid and permeable foaming concentrate.  
Figure 6: Comparing compressive strength between two foaming agents.  
Fly ash (FA) and silica fume (SF) affect the formation of pore structures in foam concrete. Replacing Portland cement with FA decreases the apparent viscosity of the fresh cement paste [42]. Consequently, larger-sized air voids could be formed in the foam concrete with a compromise in compressive strength. In contrast, replacing Portland cement with SF enhances the plastic viscosity of the paste substantially because the specific surface areas of SF particles are approximately 50 times larger than that of cement particles [43]. Therefore, SF could contribute to preventing the free movement of air bubbles in the fresh cement paste, resulting in a more stable void network, as described in Sections 3.1 and 3.2.
## 4 Experimental Setup and Results
### Development Environment  
The details of the development environment to conduct this study are given below in Table 1.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Component** & **Technology/Tool** & **Purpose** \\ \hline Programming Languages & Python 3.8, C++14 & Python for data processing and AI model development, C++ for system control and performance optimization. \\ \hline AI Model Framework & TensorFlow 2.15.0 & Deep learning framework used for implementing the LSTM and DQN models. \\ \hline AI Model 1 (Time Series) & LSTM (Long Short-Term Memory) & Used for predicting future temperature changes using historical sensor data. \\ \hline AI Model 2 (Control) & DQN (Deep Q-Network) & Reinforcement learning algorithm for optimizing HVAC control parameters. \\ \hline Backend Framework & Flask & Backend service used for handling HTTP requests and serving the application. \\ \hline Application Server & Gunicorn & Web server for running the Flask application in a production environment. \\ \hline Real-Time Data Visualization & Grafana & Tool for visualizing system performance and real-time data, providing an intuitive graphical interface. \\ \hline Frontend Development & HTML5, CSS3, JavaScript (ES6) & Front-end development for user interfaces, enabling interaction with system data and control mechanisms. \\ \hline Database & InfluxDB & Time-series database for storing and managing real-time sensor data. \\ \hline \end{tabular}
\end{table}
Table 1: Development Environment.  
The data processing and system control algorithms were developed using Python 3.8 and C++. The AI model was implemented using TensorFlow 2.15.0, a widely used deep learning framework. Multithreading and asynchronous communication techniques were C++employed to enhance system responsiveness and real-time data processing capabilities. Flask served as the backend service, Gunicorn was used as the application server, and Grafana provided real-time data visualization and system configuration through an intuitive graphical interface. Front-end development was performed using HTML, CSS, and JavaScript.
## 3 The Present and the Future of the Fluoropolymer Industry
\begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline
**Industries** & **Functions** & **Forms** \\ \hline Automotive & Mechanical property, thermal property, chemical property, and friction property. & O-rings, gaskets, valve stem seals, shaft seals, linings for fuel hoses, power steering, and transmission. \\ \hline Chemical industry & Chemical resistance, mechanical property, thermal property, and weather stability. & Coatings for heat exchangers, pumps, diaphragms, impellers, tanks, reaction vessels, autoclaves, containers, flue duct expansion joints, heavy-wall solid pipe and fittings. \\ \hline Electrical/electronic & Dielectric constant, flame resistance, and thermal stability. & Electrical insulation, flexible printed circuits, ultra-pure components for semiconductor manufacture. \\ \hline Architectural and domestic & Weatherability, flame retardancy, friction property, thermal stability. & Water-repellent fabric, architectural fabric, non-stick coatings for cookware, and fiberglass composite for constructions. \\ \hline Engineering & Mechanical property, thermal stability, chemical stability, weatherability, and surface energy. & Seats and plugs, bearings, non-stick surfaces, coatings for pipes, fittings, valve and pump parts, and gears. \\ \hline Medical & Surface energy, biological stability, mechanical property, chemical resistance. & Cardiovascular grafts, ligament replacement, and heart patches. \\ \hline \end{tabular}
\end{table} 
Table 1: Typical applications of fluoropolymers for different industry segments. \begin{table}
\begin{tabular}{|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|p{113.8 pt}|} \hline Fluoropolymer & Starting Year & Melting temperature (°C) & Tensile (MPa) & Break (\%) & Dielectric Strength (\%V/mm) & Appl. Temp (°C) & Main Applications \\ \hline PTFE & 1947 & 317–337 & 550 & 300-550 & 19.7 & 260 & Chemical processing, wire and cable \\ \hline PCTFE & 1953 & 210-215 & 60-100 & 100-250 & 19.7 & 200 & Barrier film, packaging and sealing \\ \hline FEP & 1960 & 260-282 & 345 & \(\sim\)300 & 19.7 & 200 & Cable insulation \\ \hline PVF & 1961 & 190-200 & 2000 & 90-250 & 12–14 & 110 & Lamination, film and coating \\ \hline PVDF & 1961 & 155–192 & 1,040-2,070 & 50-250 & 63–67 & 150 & Coating, wire, cable, electronic \\ \hline ECTFE & 1970 & 235–245 & 240 & 250-300 & 80 & 150 & Flame resistant insulation \\ \hline PFA & 1972 & 302–310 & 276 & \(\sim\)300 & 19.7 & 260 & Chemical resistant components \\ \hline ETFE & 1973 & 254–279 & 827 & 150-300 & 14.6 & 150 & Wire and cable insulation \\ \hline THV & 1996 & 145–155 & 82–207 & 500-600 & 48–62 & 93 & Barrier film and insulation \\ \hline \end{tabular}
\end{table} 
Table 2: The summary of fluoropolymers with their general properties. In summary, fluoropolymers have come a long way in the past 70 years. No doubt many new fluoropolymer materials will be developed and new applications will be found for old fluoropolymers. The importance of the fluoropolymer industry will not be destabilized before the emergence of new substitute materials.
# Energy Efficient Underlaid D2D Communication for 5G Applications
## 3 Proposed Modified Derivative Algorithm
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline K (no of bands) & 8 \\ Wi (bandwidth of ith band) & 25 MHz \\ \(\alpha\) (Path loss exponent) & 4.1 \\ e\({}_{\text{c,i}}\) (cellular probability for ith band) & 0.1 \\ e\({}_{\text{d,i}}\) (D2D probability for ith band) & 0.1 \\ Tc\({}_{\text{c,i}}\) (cellular threshold for ith band) & 0 dB \\ Td\({}_{\text{i}}\) (2D threshold for ith band) & 0 dB \\ \(\left[\text{R}_{\text{c,00,1}},\text{ R}_{\text{c,00,2}},\dots,\text{R}_{\text{c,00,5}}\right]\) & [50,60,70,80,90,100,100,90] m \\ \(\left[\text{R}_{\text{d,00,1}},\text{ R}_{\text{d,00,2}},\dots,\text{ R}_{\text{d,00,5}}\right]\) & [10,20,30,20,10,10,10,10] m \\ \(\left[\lambda_{\text{c,1}},\text{ }\lambda_{\text{c,2}},\dots,\lambda_{\text{c,5}}\right]\) & [10,1,10,10,10,10,10,10]\(\times\)\(\lambda_{\text{c,ref}}\) \\ \(\left[\lambda_{\text{d,1}},\text{ }\lambda_{\text{d,2}},\dots,\lambda_{\text{d,5}}\right]\) & [10,1,10,10,10,10,10]\(\times\)\(\lambda_{\text{d,ref}}\) \\ D\({}_{\text{P}}\) (total transmission power of D2D users) & 65 mW \\ D\({}_{\text{p,i,up}}\) & 25 mW \\ \(\epsilon\) (tolerance) & \(1.5\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation parameters by using the modified derivative algorithm with different parameters. Figure 7: The energy efficiency of D2D users against the reference density of cellular users by using the modified derivative algorithm with different parameters. D2D users increased, the EE rose and then it slowly decreased if the cellular user's transmission power is 325 mW, 375 mW, and 425 mW are fixed. This is a result of the increasing distortion brought on by cellular transmission. We also observed that EE decreases with the increase in cellular power because the increase in cellular power creates interference. Figure 9 shows the graph between D2D power and EE. We observed that initially, EE rises with D2D power until the maximum point after which EE decreases with D2D power. In Figure 9, the feasible area lies to the left of the dashed line. Then \(\rm f_{i}(D_{p,i})\) grows steadily in the feasible area, hence \(\rm f_{i}(D_{p,i})\) reaches its highest value at \(\rm D_{p,i}=D_{p,i,sup}\) which results from \(\rm D_{p,i,max}=D_{p,i,sup}\), and the feasible area lies to the right of the dashed line, then \(\rm f_{i}(D_{p,i})\) uniformly drops in the feasible area.
## 1 Introduction
Several MIMO-OFDM PAPR reduction techniques that use cross antenna processing or something similar were also introduced in [36; 37; 38; 39]. Cross antenna processing can reduce the PAPR of the OFDM signal using an extended space domain, but this can only be applied to a special type of signal structure. In addition, a large amount of side information is needed to apply it to LS-MIMO-OFDM systems [37].
## 4 Classification of Auto-Configuration Protocols
### Stateful Protocols
#### 4.1.6 [PERSON] _et al._' s Protocol  
The auto-configuration protocol introduced in [17] proposes a scheme where the nodes are classified into coordinator and common nodes. The first coordinator assigned to initiate the IP address assignment is the so-called _C-root_. The coordinators manage the IP address pool and they are responsible for assigning an IP address to a node which has just joined the network. The nodes that wish to join the network will interchange _HELLO_ messages to find the coordinator node nearest and to obtain a new IP address from that coordinator node. To maintain the IP address pool efficiently, the coordinator nodes are distributed in a tree topology called _C-tree_ by exchanging _HELLOs_. This protocol does not consider network partition and merging.
# Low Temperature RF-Plasma Initiated Rapid and Highly Ordered Fracture on Ag Nanowires
## 3 Results and Discussion
As shown in Figure 5a, the \(\lambda_{sp}\) at the junction area marked by yellow dotted circle are much greater than that away from this area. With the increase of the aggregation of AgNPs, insufficient coarsening on AgNWs can be observed and the periodicity of AgPCs is thoroughly broken. When AgNPs are combined into high density networks, plasma treatment can lead to uniform wire-wire welding, and will not cause fracture or coarsening on AgNWs during a short time treatment. This is probably because of the scattering and propagating of the plasma kinetic energy [24]. It is well known that the plasma kinetic energy offers diffusion forces to Ag atoms on AgNPs surfaces in all directions within an ultra-short time, but then decrease rapidly with the distance from the AgNPs surfaces due to the scattering and propagating of kinetic energy (Figure 5b), which means the average kinetic energy exerted on AgNWs could be significantly reduced, thereby greatly reducing the diffusion capacity of Ag atoms on AgNWs surfaces. Meanwhile, Rayleigh instability on nanowires is broken because of the changes in nanowire surface energy. When AgNPs are overlapped, the surface energy of AgNWs at the junction is changed  
Figure 3: (**a**), Schematic illustration of the AgNWs during the plasma treatment. (**b**), TEM results of the evolution of AgNWs after 30 s and the possible evolution process of AgNWs to AgPCs. RF plasma power is 60 W.  
due to the changes in their mass and curvatures on nanowires. AgNPs network with a great number of junctions in a large-scale leads to a random surface energy distribution on AgNPs, which causes the invalidation of the Rayleigh instability. In this case, Ostwald Ripening could be the main reason for the uniform wire-wire welding in AgNPs networks [26], which prevents further fracture and coarsening on AgNPs during short time plasma treatment. Apparently, this rapid and uniform and low temperature needed wire-wire welding technology meets the urgent needs of Ag nanowire-based transparent conductive electrodes with high performances.
# Fine-Grained Fault Sensitivity Analysis of Vision Transformers Under Soft Errors
## 2 ViT Model and Fault Resilience Evaluation Platform
### Fault Resilience Evaluation Platform
#### 2.2.2. Quantization  
Quantization is a widely adopted model compression technique that reduces the computational and memory footprint of DNNs by mapping high-precision floating-point values to lower-precision fixed-point representations. This process enables efficient inference on resource-constrained hardware, such as edge devices and embedded systems, without significantly compromising model accuracy [36; 37; 38]. Among the various quantization schemes, post-training quantization and quantization-aware training are commonly used to transform model weights and activations from 32-bit floating-point (FP32) to 8-bit integer (Int8) formats. As shown in Figure 2, the FP32 format, as defined by the IEEE 754 standard [39], consists of 1 sign bit, 8 exponent bits, and 23 mantissa (fraction) bits, allowing for a wide dynamic range and high numerical precision. In contrast, the Int8 format represents data using a fixed 8-bit signed integer, typically ranging from \(-128\) to \(127\).  
To enable accurate mapping between FP32 and Int8 representations, quantization relies on _scale_ factors and _zero_point_ to calibrate the distribution of floating-point values within the limited range of integers. This transformation from FP32 to Int8 can be written as follows:  
\[x_{q}=\text{Clip}(\text{Round}(\frac{x_{f}}{scale}+zero\_point)) \tag{8}\]  
where \(x_{q}\) is the original floating-point value, and \(x_{f}\) represents the quantized integer result. _zero_point_ is an integer offset that aligns the zero value of the floating-point domain with that of the quantized domain. \(\text{Round}(\cdot)\) applies standard rounding to the nearest integer. \(\text{Clip}(\cdot)\) ensures the quantized output is bounded within the target range, typically \([-128,127]\) for 8-bit signed integers. _scale_ is a positive scaling factor that can be calculated as follows:  
\[scale=\frac{x_{max}-x_{min}}{Q_{max}-Q_{min}} \tag{9}\]  
Figure 2: Single-precision floating-point data type and 8-bit quantization.  
Here, \(x_{max}\) and \(x_{min}\) represent the maximum and minimum values in the distribution of weights or activations. \(Q_{max}\) and \(Q_{min}\) denote the upper and lower bounds of the quantized integer range, respectively. Dequantization can be easily written as follows:  
\[x_{f}=scale\cdot(x_{q}-zero\_point) \tag{10}\]
## 4 Results and Analysis  
### Training Results of the Proposed Model  
During the training process of the proposed model, the loss curve and accuracy curve reveal the key dynamics of training, as shown in Figure 7. First, at the beginning of training (i.e., the first few epochs), the loss curve decreases rapidly, indicating that the model is effectively learning from the training data and gradually improving the fit. As training progresses, the training and validation losses gradually level off, showing that the model has fitted the training data better. In terms of Precision, Recall, and mAP, the correlation curves show a similar upward trend and eventually stabilize at a higher level, further validating the effectiveness of the model. This series of performances indicates that the proposed tomato ripeness detection model has not been overfitted during the training process, the indexes have maintained good stability, and the model has demonstrated ideal convergence. The smoothness of the loss curve reflects that the model has effectively fitted the training data, while the continuous improvement of the validation metrics indicates that the model's performance on the validation set has also been significantly improved.  
Figure 6: The structure of the confusion matrix.  
Therefore, it can be inferred that the model exhibits good generalization ability and stability in both the training and validation phases.
# Evaluation of Remote Sensing-Based Evapotranspiration
## 4 Results and Discussion
### Multi-Variable Calibration (Scheme II)
#### 4.3.1 Performance Analysis at Different Temporal Resolutions
At the seasonal scale, KGE and PBIAS values for runoff simulations based on Scheme II are compared in Figure 9. In the figure, it is obvious that all three variants of Scheme II present good performance in the four seasons, with high KGE (>0.5) and low PBIAS (<20%). As for KGE, the three ET datasets perform comparably well in spring, summer, and winter, next to autumn, in the calibration and validation periods. Similar conclusions can be derived for PBIAS. Among the three ET datasets, PML performs better than GLEAM and FLUXCOM. The runoff simulation based on Scheme II (PML) has the highest KGE for the four seasons (KGE_Spr = 0.88, KGE_Sum = 0.89, KGE_Aut = 0.77, KGE_Win = 0.81) in the calibration period, followed by Scheme II (GLEAM) (KGE_Spr = 0.75, KGE_Sum = 0.71, KGE_Aut = 0.59, KGE_Win = 0.77) and Scheme II (FLUXCOM) (KGE_Spr = 0.73, KGE_Sum = 0.69, KGE_Aut = 0.58, KGE_Win = 0.78). Similar conclusions can be drawn for the validation period. Furthermore, the runoff simulation under Scheme II (PML) has the lowest value of PBIAS in the calibration period, which is 5.9% for the average value of the four seasons, followed by Scheme II (GLEAM) (14.3%) and Scheme II (FLUXCOM) (14.5%). Meanwhile, the average PBIAS values of the four seasons in the validation period are 7.1%, 5.2%, and 4.6%, respectively. Figure 7: Comparison of KGE and PBIAS values for runoff simulations in different months based on Scheme II. Above all, applying these three ET datasets in model calibration is proven to be capable to improve hydrological model simulations, with enhanced ET simulation and good runoff simulation at different temporal resolutions. The result is consistent with the conclusions obtained in previous studies, which indicate that incorporating ET data into model calibration can maintain runoff simulation and improve ET simulation [7,8]. However, our study reveals more information related to the effect of remote sensing-based ET datasets in improving model performance and the differences among the three ET datasets. The findings in this study can provide a reference for related studies and users under the advancement of remote sensing-based ET datasets. Among the three datasets, PML performs the best in improving model performance, followed by GLEAM and FLUXCOM. The reason for the best performance of PML is because the algorithm used in PML is similar to the ET calculation method adopted in the DHSVM model. The ET  
Figure 8: Comparison of observed and simulated monthly runoff in the calibration and validation periods based on Scheme I and Scheme II. calculation in the DHSVM model is based on the Penman-Monteith approach [37]. The method used to estimate ET for the PML dataset is the Penman-Monteith-Leuning energy balance model, which is based on the Penman-Monteith equation, gridded meteorology, and a simple biophysical model for surface conductance (i.e., used with remotely sensed leaf area index to calculate surface conductance) [48; 49].