esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on Parameters Affecting MANET Performance
## 2 Routing in MANETs
### AODV Routing Protocol
During this blocking time, node A discards any  
Figure 3: The AODV routing protocol flowchart. RREQ from neighbor node B until the blacklist timeout is reached. After the blacklist timeout expires, node B is removed from the blacklist [14]. \[\text{BlackListTimeout}=\text{RoutRequestRetry}\times\text{NetworkTransversalTime}\] (2)
* Route request rate limits: The route request rate limit is the maximum number of RREQ packets for the source node to originate per second. The route request rate limit's default value is ten packets per second. * Active route timeout: The neighbor node is recorded in the routing table and considered an active node when the active route timeout is not exceeded. When a neighbor node is active, the recorded route to this neighbor should be used [15]. The active route timeout default value is 3000 ms.
* Hello interval: All MANET nodes should reveal their existence in the network within a hello interval time [16]. If a node does not contribute to the routing activities for a hello interval time, it should broadcast a hello message with TTL = 1. Hello interval default value is set to be 1000 ms.
* Allowed hello loss: If a node does not receive any contribution to routing activities from its direct neighbor node for more than (HelloInterval \(\times\) AllowedHelloLoss), the node should assume a link failure to this neighbor [17]. The allowed hello loss default value is two link failures. * DPC: After the delete period constant time is expired, the expired route will be deleted from the routing table [18]. The default value for DPC is 5 s.  
Table 2 summarizes all AODV configuration parameters and their default values.
# Vision-Based Traffic Sign Detection and Recognition Systems: Current Trends and Challenges
###### Abstract  
The automatic traffic sign detection and recognition (TSDR) system is very important research in the development of advanced driver assistance systems (ADAS). Investigations on vision-based TSDR have received substantial interest in the research community, which is mainly motivated by three factors, which are detection, tracking and classification. During the last decade, a substantial number of techniques have been reported for TSDR. This paper provides a comprehensive survey on traffic sign detection, tracking and classification. The details of algorithms, methods and their specifications on detection, tracking and classification are investigated and summarized in the tables along with the corresponding key references. A comparative study on each section has been provided to evaluate the TSDR data, performance metrics and their availability. Current issues and challenges of the existing technologies are illustrated with brief suggestions and a discussion on the progress of driver assistance system research in the future. This review will hopefully lead to increasing efforts towards the development of future vision-based TSDR system.  
Traffic sign detection and tracking (TSDR); advanced driver assistance system (ADAS); computer vision +
Footnote †: journal: Sensors **2019**, 19, 2093; doi:10.3390/s19092093
## 2 Proposed Simulation and Experimental Methods
### Dataset Construction and Deep Learning Framework  
In order to improve the generalization ability and robustness of the model in defect detection tasks, this study constructed a dataset that integrates finite-element simulation images and infrared thermal imaging images and uses the YOLO11 deep neural network as the core detection framework. Data construction includes simulation image annotation, format conversion, image augmentation, and other preprocessing steps to ensure that defect features are fully expressed.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Component** & **Material** & \begin{tabular}{c} **Density** \\ **(kg/m3)** \\ \end{tabular} & \
\begin{tabular}{c} **Thermal Conductivity** \\ **(W/(m-K))** \\ \end{tabular} &
\begin{tabular}{c} **Heat Capacity** \\ **(J/(kg\cdot\)K)** \\ \end{tabular} \\ \hline Substrate & FR-4 & 1850 & 0.3 & 1200 \\ Pad & Cu & 8960 & k\({}_{\text{pad}}\)1 [FOOTNOTE:1]Footnote 1: k\({}_{\text{pad}}\) = 469.9464 + 0.1761 \(\times\) T,2 \(\text{k}_{\text{s}\text{o}\text{o}\text{o}\text{o}\text{  
The simulated images represent the steady-state temperature distributions on the bottom surface of the package substrate under various defect conditions. These images were extracted from finite-element analysis results and serve as the basis for constructing the simulation dataset.  
To enhance the diversity and generalization capability of the dataset, a series of data augmentation techniques were applied to both simulated and experimental images. These include geometric transformations such as rotation and flipping as well as thermal noise injection. Specifically, a probabilistic augmentation strategy was employed, involving random rotation (30% probability, with a maximum angle of \(\pm\)45\({}^{\circ}\)), random flipping (50% probability), and random cropping (40% probability, preserving at least 60% of the original frame). In addition, Gaussian noise injection (20% probability) and random occlusion (30% probability, with 2-20% of the area covered by randomly colored blocks) were introduced to simulate real-world image variability. A typical example of the augmented images is shown in Figure 4.  
The original simulation samples are 68 images of missing solder balls, missing solder ball and pad, and bridge and mixed defects, which were expanded to 768 images after augmentation and divided into training, validation, and test sets (8:1:1). The model labels include four categories: missing solder balls, missing solder ball and pad, and bridge, corresponding to category indexes 0 to 2, respectively.  
The YOLO11 network structure is divided into three parts: the backbone network, the neck network, and the detection head [26]. The backbone adopts the improved structure of CSPDarknet-Deep and introduces the C2 PSA attention mechanism and SPPF module to improve the feature extraction capability. The neck adopts a bidirectional weighted feature fusion module to enhance the multi-scale information integration. Finally, the detection head supports three-scale output and has a good effect on small target defect positioning.
## 3 Methodology
### Cybersecurity Optimizer (CYO)
#### 3.1.1 Mathematical Model  
The Cybersecurity Optimizer as shown in Algorithm 1, is a sophisticated algorithm that emulates adaptive and resilient behaviors found in cybersecurity systems by integrating evolutionary strategies to tackle complex optimization challenges. The algorithm begins with an Initialization Phase, where a random seed is set, and an initial population is generated within the defined bounds, laying the foundation for the evolutionary process. Following this, it transitions into the Adaptive Learning Phase, which utilizes dynamic memory structures for scaling factors and crossover rates. This allows the algorithm to adapt its mutation and crossover strategies based on previous successes, thereby improving its efficiency in exploring and exploiting the solution space.  
In the Defense Mechanism Phase, the optimizer performs mutation and crossover operations to produce new candidate solutions, which are then evaluated for their fitness. The fittest solutions are retained to guide future generations, mirroring the continuous improvement processes inherent in cybersecurity defenses. The final stage, known as the Evaluation and Update Phase, involves selecting superior solutions based on their performance and updating the solution archive, ensuring the algorithm's robustness and adaptability across iterations.  
This carefully structured sequence of phases allows the Cybersecurity Optimizer to maintain an essential balance between diversification and intensification, which is critical for navigating complex and dynamic optimization landscapes.  
```
1:Input:\(pop\_size\), \(Max\_iter\), \(lb\), \(ub\), \(dim\), \(fobj\)
2:Output:\(bestFitness\), \(bestSolution\), \(convergenceCurve\)
3:Initialize random seed according to Equation (3)
4:Initialize population \(P(0)\) according to Equation (4)
5:Initialize \(Memory_{SF}\) and \(Memory_{CR}\) according to Equations (5) and (6)
6:\(bestFitness\leftarrow\infty\)
7:\(convergenceCurve\leftarrow\) array of size \(Max\_iter\) filled with zeros
8:for\(iter=1\)to\(Max\_iter\)do
9:for\(i=1\)to\(pop\_size\)do
10: Select \(mem\_idx\) randomly
11: Calculate \(CR_{i}\) and \(SF_{i}\) using Equation (7)
12:\(pbest\leftarrow\) select one of the top performers in \(P\)
13: Select \(r1\), \(r2\) randomly from population and archive
14: Generate mutant vector \(V_{i}^{(g)}\) using Equation (8)
15: Perform crossover to generate \(U_{i}^{(g)}\) using Equation (9)
16:if\(f(U_{i}^{(g)})<f(P[i])\)then
17:\(P[i]\leftarrow U_{i}^{(g)}\)
18: Update fitness if \(f(U_{i}^{(g)})<bestFitness\)
19:\(bestFitness\gets f(U_{i}^{(g)})\)
20:\(bestSolution\leftarrow U_{i}^{(g)}\)
21:endif
22:endfor
23:\(convergenceCurve[iter]\gets bestFitness\)
24:endfor
25:return\(bestFitness\), \(bestSolution\), \(convergenceCurve\)
```  
**Algorithm 1** Cybersecurity Optimizer Algorithm
## 5 Performance Evaluation
### Case Study: Wi-Fi Power Savings by LDWuR  
#### 5.1.1 Power Consumption  
We observe that the packet rate affects the power consumption at a given beacon interval. Figure 7a shows a relationship between power consumption and packet rate with short data packets (notated as sp) of 34 bytes and long data packets (notated as lp) of 2334 bytes, respectively. The beacon interval is 100 ms. The conventional MAC protocol (notated as 802.11) incurs a higher power consumption than our MAC protocol (notated as WuR), as the conventional MAC protocol has idle-listening on high-power Wi-Fi radio. Our MAC protocol takes advantage of our LDWuR to save listening power. The long data packet induces higher power consumption than the short data packet, as the high-power Wi-Fi radio transmits a longer data packet from the access point to the station. Figure 7b shows a relationship between improvement and packet rate with short data packets and long data packets, respectively. In terms of a short data packet, the improvement percentage decreases slightly when the packet rate increases, as our MAC protocol has more wake-up signal activity before beacons and data packets. In terms of the long data packet, the improvement percentage decreases rapidly when the packet rate increases, as the high-power Wi-Fi radio deals with a long data packet that causes our MAC protocol's power consumption to be closer to the conventional MAC protocol.  
We explore the beacon interval impact on power consumption at a given packet rate. The packet rate is 10 packet/s. Figure 8a shows that beacon interval affects power consumption. A small beacon interval has a slightly higher power consumption then a large beacon interval, as a small beacon interval has more beacon activity in a certain time period. Therefore, a small beacon interval has lower improvement in Figure 8b. As the short data packet takes advantage of fewer activities on high-power Wi-Fi radio, it provides higher improvement than the longer data packet.
## 2 Materials and Methods
### qPCR Tests  
For qPCR analysis using the SaITyppm&PriSal-T qPCR Duplex kit (GPS(tm), Orihuela, Spain), reaction mixtures were prepared according to the manufacturer's instructions using 5 \(\upmu\)L of extracted DNA per reaction. Experiments were performed on a QuantStudio5 (Applied Biosystems, Waltham, MA, USA) or a CFX96 (Bio-Rad, Hercules, CA, USA) thermocycler. The thermal protocol consisted of an initial activation step at 95 \({}^{\circ}\)C for 2 min, followed by 40 amplification cycles organised as follows: denaturation at 95 \({}^{\circ}\)C for 5 s, annealing/extension at 60 \({}^{\circ}\)C for 20 s, and data acquisition. Positive control, negative control (nuclease-free water), and internal control were included in all assays. The internal control included in the kit is directed against an external target, the concentration of which is minimised to detect the presence of possible qPCR inhibitors in the case of negative samples without affecting the sensitivity of the main targets. Fluorogenic signals were detected using the FAM channel for the PriSal-T, the Cy5 channel for SaITyppm, and the HEX channel for the internal control. Calibration of the qPCR was performed using a standard curve prepared from 10-fold serial dilutions from 10\({}^{6}\) to 10 copies of a standard template.
# FLAME-VQA: A Fuzzy Logic-Based Model for High Frame Rate Video Quality Assessment
## 1 Introduction
### Paper Structure  
In the subsequent sections of this paper, we provide an in-depth overview of recent video quality assessment methods that also employ subjective ratings to deliver the output (Section 2). Next, we offer a concise explanation of the dataset [9] that we utilized for our modeling (Section 3). We then discuss the process of applying fuzzy logic in quality assessment, highlighting its effectiveness in handling uncertainty and vagueness in the data (Section 4). Furthermore, we present and verify the results obtained from our fuzzy logic-based video quality assessment model--FLAME-VQA (Section 5). Lastly, in Section 6, we conclude by discussing the implications of our findings, shedding light on the limitations of the model, and outlining potential directions for future research.
## 4 Results and Discussion  
### Calibration  
Figure 3 shows the correlation between the calibrated light source and the PRI sensor. The plots demonstrate the linearity of the sensor response to incident radiance across a wide dynamic range and show correspondence close to the 1:1 line and a high coefficient of determination.  
The angular response of the PRiAnalyze was assessed using the goniometer measurements and radiance values normalized by the radiance at the normal-incidence angle exhibited a shape between linear and cosine (Figure 4). At an incidence angle of 70\({}^{\circ}\) from the normal, the 532 nm waveband reached zero, while readings from the 568 nm waveband reached a minimum. No larger range of angles was investigated, as the goniometer's range was limited to 70\({}^{\circ}\). Deviations from a cosine shape can be explained by shadowing from the rim of the sensor head and the rubber washers enclosing the Teflon  
Figure 3: Correlations between the PRiAnalyze waveband data (\(x\)-axis) and the calibrated light source (\(y\)-axis).  
diffusers that restrict the field of view to a range smaller than 2\(\pi\cdot\)sr. As a result, the sensor field of view (FOV) was approximately 140\({}^{\circ}\). Effects of angular incident radiation on bandwidth and center wavelength of the interference filters were not investigated in this study, and potentially can explain additional discrepancies between the angular responses of the two channels.
## 5 Advantages, Limitations, and Future Research Directions of the Model
### Model Limitations and Future Research Directions
#### 5.2.3 Influence of Text Prompt Setting on Image Quality  
In our experiments, we found that image quality depends mainly on the quality of the input text prompts when the image generation module is used to convert the text into an image. How to set a better text prompt? We use two examples to show our thoughts on this issue and provide some ideas for subsequent researchers.  
As shown in Figure 4, a more concise and clear expression can guide the image generator to generate higher-quality pictures and obtain higher classification accuracy. At the same time, through Figure 4a, we find that complex expressions are more likely to produce images of people's broken limbs. Therefore, compared with complex text prompts, we prefer to use concise and clear text prompts to guide image generation. In addition, we still need to pay attention to the ambiguity of some words in the text prompt, and the image generation module will be misled by the ambiguous text prompt to generate incorrect images that do not conform to the semantics, as shown in Figure 5:  
From our human perspective, we can understand that \"Do not let worry get you down. Remember that [PERSON] started as a basket case\" is an example of encouraging people to cheer up. The phrase \"basket case\" in the sentence is more likely to mean \"people in a helpless situation\" than \"basket\". However, the stable_diffusion model cannot understand the hidden meaning of the sentence like humans do, so it understands \"basket case\" as \"basket\" and generates an error image like Figure 5a. This situation is common in the generated images, which reduces the performance of our model. If we change the expression method  
Figure 4: (**a**) The picture is generated from a text prompt without any processing. (**b**) The picture is generated using the reduced text prompt.  
of the above ambiguous sentences, we will find that it guides the image generation module to generate a more semantic image, as shown in Figure 5b.  
We input Figures 4 and 5 into the CLIP model for classification. The higher the quality of the generated image, the more consistent it will be with the semantics of the text, and the classification accuracy will be improved accordingly. Unfortunately, we still need to find a universal solution to the problems contained in the two examples in this chapter. Figures 4 and 5 are just examples we listed in which the text prompt is modified manually. How to let the computer solve this problem by itself? We are still looking for a better way. So all the experiments in this paper are affected by these two problems. It can be seen from the above examples that different settings of text prompts in the image generation model do affect the generation of images and further affect the performance of the model we propose. Therefore, how to process text prompts so that they can guide the image generation module to generate more semantic images is one of the problems we will solve later.
## 2 Electrical Resistivity/Impedance Measurement: Is It Really that Simple?
### AC Measurement of Electrical Resistivity
#### 2.2.1 2-Electrode Configuration
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reference** & **Specinems** & **Electrodes** & **Material** & **Additions** & **Frequency** \\ \hline [PERSON] et al. [72] & Cubes: 50 mm & Stainless steel pins (2.4 mm diameter, 10 mm & Mortar & - & 20 Hz–1 MHz \\  & (side) & electrode spacing), embedded for 30 mm & Mortar & - & 20 Hz–1 MHz \\ [PERSON] et al. [71] & Pisms: & Stainless steel electrodes (20 \(\times\) 30 \(\times\) 0.5 mm) & Paste & Steel/carbon fibers & 5 mHz–10 MHz \\  & \(25\times 25\times 100\) mm & Cubes: 150 mm & Brass electrodes (6 mm diameter), at a & Concrete & SCMs & 107, 108, and 120 Hz \\  & (side) & center-to-center distance of 50 mm & Concrete & - & 120 Hz \\  & \(100\times 100\times\) & Stainless steel screws, at depths of 10 mm & Concrete & - & 120 Hz \\  & \(300\) mm & and 50 mm & Concrete & - & 120 Hz \\ [PERSON] et al. [75] & Pisms: & Copper gauge strips (\(18\times 18\) mm), & Paste & - & 0.5 Hz–10 MHz \\  & \(25\times 180\times 4(8)\) mm & with 63 mm spacing & Paste & - & 0.5 Hz–10 MHz \\ [PERSON] et al. [22] & Pisms: & Titanium mesh & Mortar & Steel fibers & 100 mHz–1 MHz \\  & \(40\times 40\times 160\) mm & Pisms & Stainless steel nets & Mortar & - & 10 Hz–1 MHz \\ [PERSON] et al. [21] & \(40\times 40\times 160\) mm & Iron nets & Concrete & - & - \\ [PERSON] et al. [76] & - & Pisms & - & & \\  & \(40\times 40\times 160\) mm & Brass plates (\(30\times 100\) mm) & Concrete & - & 40 Hz–1 MHz \\  & (side) & & & & \\  & (side) & & & & \\  & (disler) & (diameter) \(\times 200\) mm & Stainless steel plates & Concrete & - & 1 kHz, according to ASTM C1760 \\  & (height) & & & & [37] \\ [PERSON] et al. [77] & Cubes: 50 mm & Stainless steel plates (\(50\times 50\) mm) & Paste & - & 1–100 kHz \\  & (side) & & & & \\  & (side) & & & & \\  & & & & & \\  & & & & \\  & & & & & \\ \end{tabular}
\end{table} 
Table 3: AC measurements with 2-point probe method.
## 2 Background Work
### Analysis of the State of the Art
Such a paper also emphasizes the importance of opportunistic networks in situations where traditional networks are unavailable or overloaded, such as indisaster zones or densely populated urban regions. Despite substantial progress, the paper indicates that universal deployment has not been achieved and recommends continued research into new use cases and integration with emerging technologies such as 5G and IoT. Related to the previous article, in [30] the authors utilize wireless network identifiers (beacons) for information dissemination in opportunistic networks. This solution makes use of existing wireless network infrastructure, such as Wi-Fi or BLE, to transmit messages without requiring active connections. It is especially useful in situations requiring quick information circulation, such as emergency notifications or public announcements. With respect to Bluetooth, research has been carried out on how to apply and optimize BLE for opportunistic networking, with an emphasis on data dissemination and energy efficiency. For instance, a mesh network protocol using BLE technology is presented in [31] and is aimed at smart homes, IoT, and industrial automation. It makes use of the publish/subscribe paradigm, which enables nodes to accept data from publishers and to subscribe to certain types of messages. Other authors have made use of BLE for opportunistic networking, as in [32], which describes techniques to improve data dissemination and to decrease energy consumption by making effective use of BLE advertising and scanning modes. Similarly to opportunistic networks, the proposed protocol ensures efficient data exchange even in situations with sporadic connections. After reviewing the current state of the art, it can be concluded that the previously mentioned studies have been mostly focused on simulations and on the theoretical part, providing barely any experimental evaluation in real-world scenarios. Moreover, few fully address security and privacy concerns, and there is little emphasis on integrating emerging technologies like Bluetooth Mesh with opportunistic networks. In contrast, this article provides both a theoretical description and a practical validation in real scenarios, including specific details on security, privacy, and novel strategies to optimize power consumption in BLE devices. Furthermore, this work also focuses on making opportunistic networks more robust and adaptable to changing conditions, such as poor coverage situations, which fills an important gap in the existing literature.
## 3 Improved LEACH Protocol
### LEACH Protocol Based on Replication Computing  
#### 3.4.1 Principle of Replication Computing  
Replication computing is a technique that uses unnecessary and redundant computations in parallel computing to reduce communication or execution time [23]. Figure 3 illustrates an example of leveraging replication computing to reduce communication in parallel computing. In this example, the task involves summing two input variables \(x_{0}\), \(x_{1}\) and storing the result in processors A and B. In Figure 2(a), processor A first acquires the inputs \(x_{0}\) and \(x_{1}\) via communication, computes their sum, and then transmits the result to B. Conversely, Figure 2(b) depicts a scenario where both inputs \(x_{0}\) and \(x_{1}\) are broadcasted to A and B. Then, each processor independently computes the sum. This example shows that, in contexts where broadcasting incurs no extra communication cost (e.g., bus structures or one-hop networks), the latter approach reduces one instance of inter-processor communication at the cost of duplicating computational tasks. This advantage makes replication computing attractive to underwater acoustic communication that is characterized by a large propagation delay.