esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In terms of their prospective use, as well as their use under demanding conditions, the application of high-performance materials in components manufactured using the FFF technique will be unavoidable, and this is associated with the need to know their tribological properties. The present study aims at determining the tribological properties of PEI (polyetherimide) production parts manufactured using the FFF technique. The summary of research, in Table 1, shows that the polymers and binders have good tribological properties, and their application in technical practice is beginning to be justified [32; 33; 34; 35]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Research Objective** & **Materials** & **Methodology** & **Findings** \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] \\ et al., 2020 \\ \end{tabular} } & Effect of varying & \begin{tabular}{c} ABS and VeroGray\({}^{\text{TM}}\) \\ contact loads on \\ friction and wear \\ \end{tabular} & \begin{tabular}{c} Loads of 1. 5 and 10 N \\ (Stratasys\({}^{\text{\tiny\textregistered}}\), Eden \\ Prairie, USA) polymers \\ \end{tabular} & \begin{tabular}{c} Loads of 1. 5 and 10 N were \\ sliding contact with a \\ 52,100-steel counter \\ face at room \\ temperature \\ \end{tabular} & \begin{tabular}{c} Loads of 1. 5 and 10 N were \\ applied under dry \\ applied under dry sliding contact \\ with a 52,100-steel counter face at room \\ room temperature. \\ \end{tabular} \\ \hline \multirow{4}{*}{\begin{tabular}{c} [PERSON] \\ et al., 2019 \\ \end{tabular} } & Mechanical and & Epoxy resin blended & \begin{tabular}{c} Epoxy resin blended \\ with graphite/talc \\ micro fillers for 15 min \\ with graphite/talc \\ micro fillers \\ \end{tabular} & \begin{tabular}{c} Epoxy resin blended \\ at a constant speed of \\ 1200 rpm and an \\ applied load of 100 N \\ as per the ASTM D4172 \\ under dry conditions \\ \end{tabular} & \
\begin{tabular}{c} Epoxy resin blended \\ with graphite/talc \\ micro fillers for 15 min \\ at a constant speed of \\ 1200 rpm and an \\ applied load of 100 N \\ as per the ASTM D4172 \\ under dry conditions \\ \end{tabular} &
\begin{tabular}{c} Graphite (10 wt\%)/talc (10 \\ wt\%)/epoxy demonstrated a \\ significant reduction by \textasci{}63\% \\ (from 0.103 to 0.276) in COF and a \\ moderate increment by \textasci{}34\% in \\ wear resistance as compared with \\ pure epoxy bearing balls under \\ dry conditions. Hardness \\ improved by \textasci{}5\% in the graphite \\ (10 wt\%)/talc (10 wt\%)/epoxy \\ composite bearing ball. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: A summary of studies on the tribological properties of polymers, testing methods, and findings.
# Efficient Transcoding and Encryption for Live 360 CCTV System
## 2 Related Work  
### Live CCTV System  
The availability of CCTV camera poses challenges for massive video processing for transmissions and analysis. CCTV camera data are received from a variety of sources, including traffic intersections, retail shops, convenience stores, and traffic intersections. This condition has promoted the development of computer vision, AI and machine learning. The great point comes from the direct video system that  
Figure 1: Conceptual architecture of live CCTV System with real-time transcoding.  
is extracting the value from the video to impact on the scientific, social and business fields. To meet the requirements of video analytics, CCTV systems need to be developed in real-time transmission, low cost of operation and the ability of accurate data analysis from CCTV live videos. To achieve the increasing demands of CCTV video data, an efficient solution is essential to handle the large bandwidth, real-time, quality of service and quality of experience requirements. Using one transcoder is one of most effective choices to propose reasonable solutions.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 2 Preliminaries
### A* Algorithm for Path Planning  
The A* algorithm (A-star algorithm) is a classical heuristic search algorithm that is widely applied in path planning and graph traversal problems. It has demonstrated outstanding performance in various domains, including robotic navigation, game AI, and mapping services. The core strength of the A* algorithm lies in its ability to guarantee the optimality of the solution while significantly enhancing search efficiency. Its fundamental principle involves initiating the search from a designated starting point and progressively exploring toward the goal, guided by an evaluation function that prioritizes paths with the highest potential to reach the destination--thereby avoiding exhaustive or uninformed searches.  
At the core of the A* algorithm lies the evaluation function f(n), defined as f(n) = g(n) + h(n), where n denotes the current node under exploration. The term g(n) represents the actual cost incurred from the start node to node n, calculated as the cumulative cost along the known path. In contrast, h(n) is the estimated cost from node n to the goal node, commonly referred to as the heuristic function. The choice of heuristic function plays a critical role in determining both the efficiency of the algorithm and the quality of the resulting solution. Importantly, the heuristic does not need to provide an exact prediction of the remaining cost--it suffices that the estimate does not exceed the true cost (a property known as admissibility). When this condition is met, the A* algorithm is guaranteed to produce an optimal path.The basic structure of this algorithm is illustrated in Figure 2.  
During execution, the algorithm maintains two sets: the open list and the closed list. The open list contains nodes that are pending exploration, while the closed list records nodes that have already been processed. At each iteration, the algorithm selects the node with the smallest f(n) value from the open list as the current node for expansion. It then evaluates all the neighboring nodes of the current node. For each neighbor, the algorithm computes updated values for g, h, and f, and based on these values, either adds the neighbor to the open list or updates its information if a more efficient path is found. The current node is subsequently added to the closed list to prevent redundant processing.
## 5 Experimental Result and Analysis
### Ablation Study  
This ablation study evaluated the impact of each component within the proposed multi-scale transformer framework on detection performance. By selectively removing key elements--including the subframe-level transformer, pairwise subframe-level transformer, frame-level transformer, and regression task--the study quantifies their individual contributions to classification accuracy. This analysis underscores the significance of the hierarchical Transformer structure and multi-task learning in enhancing steganographic detection. The results are presented in Table 3.  
The results highlight the contribution of each component to detection performance. Removing the subframe-level transformer reduces accuracy from 92.45% to 92.01%, indicating its role in capturing local features. Excluding the pairwise subframe-level transformer lowers accuracy to 91.86%, demonstrating its importance in modeling inter-subframe dependencies. Eliminating the frame-level transformer results in a more significant drop to 91.72%, emphasizing its role in capturing the global context. Removing the regression task decreases accuracy to 91.90%, confirming the benefit of incorporating continuous embedding rate information. The full model achieves the highest accuracy of 92.45%, demonstrating the collective effectiveness of all components.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Index** & **Network Description** & **Accuracy** \\ \hline \#1 & without the subframe-level transformer & 0.9201 \\ \#2 & without the pairwise subframe-level transformer & 0.9186 \\ \#3 & without the frame-level transformer & 0.9172 \\ \#4 & without the regression task & 0.9190 \\ \#5 & the whole proposed method & 0.9245 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection accuracy with different model variants.  
Figure 7: The ROC results of multiple steganography methods. (**a**) The ROC result on [PERSON]’s steganography method; (**b**) the ROC result on [PERSON]’s (\(\eta=1\)) steganography method; (**c**) the ROC result on [PERSON]’s (\(\eta=2\)) steganography method; (**d**) the ROC result on [PERSON]’s (\(\eta=4\)) steganography method.
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 2 Proposed WRTU-16T SRAM Architecture  
### Proposed Cell Design  
The schematic and layout of the WRTU-16T cell are presented in Figures 1 and 2. The proposed WRTU-16T cell is composed of 16 transistors and incorporates two storage nodes (Q, QB) and two internal nodes (S0, S1). Both the SEU recovery capability and write performance are enhanced by the 16-transistor architecture through three design features: dual feedback paths between the storage and internal nodes, the physical isolation of sensitive nodes, and an access structure that employs both PMOS and NMOS transistors. The four access transistors are implemented using one pair of PMOS and one pair of NMOS transistors [7]. Only two pairs of nodes are considered critical in terms of SEU susceptibility, thereby enhancing recovery capability [8]. Transistor sizing is configured as follows: P1, P2, P3, and P4 are each assigned a width-to-length (W/L) ratio of 3; P7 and P8 are each assigned a ratio of 9; and N7 and N8 are each assigned a ratio of 3. All other transistors are designed with the minimum feature sizes allowed by the process design rules. Thesesizing decisions are implemented to strengthen immunity against SNUs, improving write performance and power efficiency. Read operations are performed through two access transistors, N7 and N8, while write operations are executed using all four access transistors, N7, N8, P7, and P8. When a logic '1' is stored, the node states are established as Q = S1 = 1 and QB = S0 = 0.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 2 Spatial Deformation and Soil Pressure Distribution of Retaining Structure
### Spatial Deformation of Retaining Structure
#### 2.1.4 Anchor Cable Deformation  
When assuming that the anchor cable is connected to the waist beam and remains stationary without displacement, the horizontal deformation of the waist beam can be determined by calculating the horizontal component of the anchor cable deformation. The deformation of the anchor cable, represented as \(\Delta\), can be mathematically expressed using Equation (4):  
\[\Delta=\frac{u(x,z)\sin(\frac{x}{l}\pi)}{\cos\theta} \tag{4}\]  
In this formula, \(\theta\) is the angle between the anchor cable and the horizontal plane.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### Operation Flow  
The operation and process flow of BDSBAS is shown in Figure 3. The three monitoring receivers of the MS are used to collect the raw observations of pseudo-ranges and carriers of GNSS satellites at each signal frequency in real time, and the meteorological data are collected by meteorological equipment and then transmitted to the DPC through the ground communication network. The DPC performs redundancy inspection, preprocessing, differential corrections calculation for orbit corrections, clock corrections, grid ionospheric vertical delay (GIVD), integrity information calculation for user differential range error (UDRE), dual frequency range error (DFRE), grid ionospheric vertical error (GIVE), degradation parameters, and consistency checking processing on the raw observations to generate augmentation information before transmitting it to the OCC. The OCC arranges the augmentation information in a standard format, forms the augmentation messages, and transmits it to the US. The US uploads the augmentation messages to the BDSBAS GEOs and broadcast the messages in the service area. Finally, the user receives the GNSS observations and the BDSBAS augmentation messages in real time, and simultaneously performs augmentation positioning and protection level calculation. On this basis, the protection level and the alarm level are compared to realize the integrity alarm function.  
Figure 2: The 27 MSs located in mainland China.
# Respiration Symptoms Monitoring in Body Area Networks
## 5 Overview of the Proposed Method
#### Noise Filtering Process  
The noise contained in the data collected using the wearable sensor as well as in the wireless channel information should be removed. The wavelet filter presented in [20] is used to remove the noise and smoothens the rising and falling edges appeared in the received signals critical for tracking the normal and abnormal breathing. The advantage of using wavelet filter over other filters such as Chebyshev or Butterworth is due to its better performance considering the high-frequency noise.
# Study on Water Inrush Characteristics of Hard Rock Tunnel Crossing Heterogeneous Faults
## 5 Results
### Evolution Law of Velocity
#### 5.2.2 The Law of Change of Water Inrush Velocity with Time  
The longitudinal analysis presented in Figure 6 clearly delineates the temporal variations of water inrush velocity at uniform advance lengths. Meanwhile, Figure 8 reveals how the water inrush velocity evolved over time within the crown across various advance lengths. Notably, in the damage zone, there was a marked reduction in water inrush velocity across different depths as time progressed. For instance, at x = 70 m, the velocity decreased from a peak of 1.5 m/s to 0.13 m/s, translating to a reduction rate of 91.3%. In contrast, within the fault core (x = 90 m), the water inrush velocity remained remarkably stable, hovering around 0.5 m/s with negligible variation.  
Significantly, the distance between the excavation surface and the fault core markedly influences the duration required for the water inflow velocity to stabilize. Specifically, at locations x = 70 m, x = 80 m, and x = 85 m, the time required for the water inrush velocity to decrease from its peak to a steady state is 120,000 s, 250,000 s, and 350,000 s, respectively. This pattern indicates that the nearer the tunnel face is to the fault core, the longer the period for the water inrush velocity to diminish. This analysis can be attributed to the different water storage and supply characteristics between the damage zone and the fault core. The fault core, as the main fracture, provides a water source supply to the damage zone. The closer the excavation face is to the fault core, the stronger the water supply capacity, resulting in a smaller pore pressure gradient and slower velocity reduction.  
Figure 7: The change of the maximum water inrush velocity \(v_{max}\) with the advance length: (**a**) numerical calculation curve of the \(v_{max}\) at the tunnel face as a function of the advance length; (**b**) on-site real water inrush photos in the damage zone; (**c**) on-site real water inrush photos in the fault core.
# Efficient Implementation of Matrix-Based Image Processing Algorithms for IoT Applications
###### Abstract  
This paper analyzes implementation approaches of matrix-based image processing algorithms. As an example, an image processing algorithm that provides both image compression and image denoising using random sample consensus and discrete cosine transform is analyzed. Two implementations are illustrated: one using the Blackfin processor with 32-bit fixed-point representation and the second using the convolutional neural network (CNN) accelerator in the MAX78000 microcontroller. Implementation with Blackfin can be considered a classic approach, in C language, possible on all existing microcontrollers. This implementation is improved by using two cores. The proposed implementation with the CNN accelerator is a new approach that effectively uses the dedicated accelerator for convolutional neural networks, with better results than a classical implementation. The execution time of matrix-based image processing algorithms can be reduced by using CNN accelerators already integrated in some modern microcontrollers to implement artificial intelligence algorithms. The proposed method uses CNN in a different way, not for artificial intelligence algorithms, but for matrix calculations using CNN resources effectively while maintaining the accuracy of the calculations. A comparison of these two implementations and the validation using MATLAB with 64 bits floating point representation are conducted. The obtained performance is good both in terms of quality of reconstructed image and execution time, and the performance differences between the infinite precision implementation and the finite precision implementation are small. The CNN accelerator implementation, based on matrix multiplication implemented using CNN, has a better performance suitable for Internet of Things applications.  
discrete cosine transform; random sample consensus; visual DSP++; blackfin processor; CNN accelerator; internet of things +
Footnote †: journal: Applied sciences  
1 Telecommunications Department, Faculty of Electronics, Telecommunications and Information Technology, POULTEHNICA Bucharest National University for Science and Technology, 060042 Bucharest, Romania; [EMAIL_ADDRESS]
# WiFi FTM, UWB and Cellular-Based Radio Fusion for Indoor Positioning
## 2 Overview of Location Technologies
### WiFi Fine Time Measurement  
IEEE 802.11 mc includes a fine time measurement (FTM) for range estimation in timing protocols using RTT [20; 21]. This release will transform the indoor positioning industry in the next years because WiFi infrastructure is widely deployed. The protocol estimates precisely the distance to any WiFi access point (AP) which supports the protocol without needing to be connected to them [22]. The information is calculated on the device for privacy preserving, since sensitive location information is not shared among network peers. In [23], the accuracy for positioning of WiFi FTM is computed with a precision of a meter-level accuracy in real scenarios with dense deployments of WiFi APs.
# IOb-Cache: A High-Performance Configurable Open-Source Cache
## 2 IOb-Cache
### Replacement Policy  
The line replacement policy in a k-way set-associative cache is implemented by this module. Different available replacement policies can be selected using the \"REP_POLICY\" synthesis parameter. The module has three main components: the Policy Info Memory (PIM), the Policy Info Updater (PIU) datapath, and the Way Select Decoder (WSD).  
The PIM stores information of the implemented policy. Note that replacement policies are dynamic and use data from the past, so memory is needed. The PIM has as many positions as the number of cache sets, addressed by the _index_ part of the main memory address. The width of the PIM depends on the chosen policy. The PIM is implemented using a register-file so that during a system reset or cache invalidation, it can be set to default initial values.  
When a cache hit is detected, the information stored in the PIM is updated based on the information previously stored for the respective set and the newly selected way. This function is performed by the PIU. When a cache miss is detected, the information for the respective cache set is read from the PIM and analyzed by the WSD to choose the way where the cache line will be replaced.  
The currently implemented policies are the least-recently-used (LRU) and the pseudo-least-recently-used (tree and MRU-based).
###### Abstract  
In vehicular ad hoc networks (VANETs), safety applications require a reliable and delay-guaranteed broadcasting service to disseminate safety messages. However, channel fading and the high mobility of vehicles make it very challenging for a broadcasting scheme to meet the strict service demands of safety messages. On the other hand, cooperative retransmission is effective in mitigating wireless channel impairments by utilizing the broadcast nature of wireless channels. Therefore, this paper proposes a repetition-based cooperative broadcasting (RBCB) scheme for safety messages in VANETs. The proposed scheme enables the selected helper vehicles to perform the cooperative rebroadcasting along with the source vehicle during the source vehicle's slot in order to increase the transmission reliability of safety messages and reduce rebroadcasting times. The performance of RBCB scheme is mathematically analyzed in terms of packet delivery probability and average packet delay under the Rayleigh fading channel. Moreover, extensive simulations are conducted to evaluate the performance of RBCB scheme. Both mathematical analysis and simulation results demonstrate that RBCB scheme significantly improves the packet delivery probability and decreases the average packet delay.  
c 2020, 10, 212; doi:10.3390/app10010212
## 3 Results
### Enhanced Lipid Production through Cultivation of C. curvatus ATCC 20509 in Fed-Batch Conditions  
C. curvatus ATCC 20509 was cultivated in treated second CW (S\({}_{0}\approx\) 60 g/L) without adding salts or other ingredients into the medium. Thereafter, fed-batch cultivation performed by pulsed feeding with condensed solution of CW derived lactose was carried out. Intermittent lactose feeding was carried out when the remaining, into the medium, lactose was found withing the range of 10-20 g/L. As in all previous cases, trials were conducted in 250-mL Erlenmeyer flasks. The kinetics of the experiment is presented in Figure 3a, and 321 h after inoculation, significant TDW production (X = 38.1 g/L) containing 57% \(w/w\) of lipid in TDW (therefore L concentration was =21.7 g/L) was recorded. A total lactose consumption of 153.9 g/L was observed, whereas the consumed quantities of IP, FAN, protein and TKN of the treated CW were 0.21 g/L, 43.8 mg/L, 0.70 g/L, and 0.41 g/L, respectively. The fed-batch trial can be divided into two well-separated phases, namely the phase in which lactose was mainly consumed in order to produce lipid-free cellular material (in this phase the conversion yield of lipid produced per unit of lactose consumed was c. 0.07 g/g), and the main lipid-producing phase, in which lactose was mainly channeled towards the synthesis of SCO (in this phase, the conversion yield of lipid produced per unit of lactose consumed was c. 0.21 g/g) (Figure 3b). Evidently the second phase of the culture (Y\({}_{\text{L/S}}\) = 0.21 g/g) coincided with the late growth phases, where assimilable nitrogen had already been depleted and lactose was mainly converted to microbial lipid. Microscopic observations throughout the fed-batch culture, demonstrated the evolution of the yeast cells, which as the culture time progressively passed, the size of lipid droplets significantly increased (Figure 4).  
Figure 2: The viscous culture broth of _Papiliotrema laurentii_ NRRL Y-2536 cultivation (**a**), after the addition of acetone (**b**), forming a blur (**c**), which precipitates (**d**), resulting in a thick, elastic, gummy precipitate.  
Figure 3: **(a)** Kinetics of _C. curvatus_ ATCC 20509 fed-batch cultivation in the complex substrate, centrifugated, and filtrated second cheese whey, with initial lactose concentration S\({}_{0}\)\(\approx\) 60 g/L, presenting the pulsed feeding, dry biomass, and lipid production. **(b)** Fed-batch trial divided into two well-separated phases; the phase in which lactose is mainly consumed in order to produce lipid-free biomass (in this phase the conversion yield of lipid produced per unit of lactose consumed was c. 0.07 g/g), and the main lipid-producing phase, in which lactose was mainly channeled towards the synthesis of SCO (in this phase, the conversion yield of lipid produced per unit of lactose consumed was c. 0.21 g/g).