esa-sceva/satcom-chunk-collection huggingface dataset:
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 5 Experimental Results and Analysis  
In this paper, we constructed a simulation experiment to run the proposed routing algorithms on a controller. It uses fuzzy logic to infer the optimal path weights between CHs and then implements FPO-MST and FCO-MST.
## 3 Proposed System Model
### Reinforcement Learning Technique for Proposed DAWPC-MAC Protocol  
Reinforcement Learning (RL) is the foundation of the DAWPC-MAC protocol detailed above. Within this protocol, local receiver nodes perform intelligent communication management in underwater networks to enhance localization. In reinforcement learning, a local agent (the courier node, representing the receiver node here) is trained to make optimal decisions to maximize a reward signal over time. The agent operates within the network environment, taking actions in discrete time slots based on the current policy and receiving rewards as feedback. This feedback in reinforcement learning guides the agent's learning process through trial and error, thereby enhancing strategies to optimize network performance.  
The courier node, acting as the receiver node, monitors the current network status, including the number of nodes seeking access and their priority levels. It then determines an appropriate course of action, such as assigning specific time slots to nodes, based on a learned policy. This policy is maintained and refined using predictions generated from the \(Q\)-value function. This function links state-action pairs to their anticipated total rewards while considering discounts. Unlike conventional Q-learning methods that utilize a local table for reference, DQN employs a Deep Neural Network DNN to address the continuous and complex state-action landscape typical of underwater networks. The Q-values are continuously updated through iterations based on the Bellman equation shown in Equation (1).  
\[Q(s_{t},a_{t})\leftarrow(1-\alpha)Q(s_{t},a_{t})+\alpha\Big{(}r_{t}+\gamma\max_ {a}Q(s_{t+1},a)\Big{)}, \tag{1}\]  
where \(\alpha\) is the learning rate, \(\gamma\) is the discount factor, \(r_{t}\) is the immediate reward, and \(s_{t},a_{t}\) represent the current state and action, respectively. The reward function is carefully designed to incentivize energy-efficient operation, reduced collisions, and timely delivery of critical data. Through repeated interactions, the DQN optimizes the protocol's performance by balancing energy consumption, channel utilization, and packet delivery priorities. In summary, by incorporating RL through DQN, the DAWPC-MAC protocol dynamically adapts to changing network conditions, learns to resolve collisions effectively, and enhances the overall efficiency and scalability of the underwater communication system.
## 2 System Model  
We consider a wireless communication network which consists of \(N\) TPs and \(K\) users to be served by the network. A set of users and a set of TPs are denoted by \(\mathcal{K}=\{1,2,\ldots,K\}\) and \(\mathcal{N}=\{1,2,\ldots,N\}\), respectively. Here, the set of TPs is re-ordered in order of frequency, i.e., \(\mathcal{N}=\mathcal{N}_{1}\cup\mathcal{N}_{2}\cup\cdots\mathcal{N}_{t}\), where \(\mathcal{N}_{t}\in FR_{t}\). In other words, \(\mathcal{N}=\{1,2,\cdots,n_{1},n_{1}+1,\cdots,n_{1}+n_{2},n_{1}+n_{2}+1, \cdots,n_{1}+n_{2}+n_{3},\cdots\sum_{\forall t}n_{t}\}\), where \(\mathcal{N}_{t}=\{1,2,\ldots,n_{t}\}\). In addition, a set of users to be served by TP \(n\), where \(n\in\mathcal{N}\), is denoted by \(\mathcal{K}_{n}\), i.e., \(\mathcal{K}=\mathcal{K}_{1}\cup\cdots\cup\mathcal{K}_{N}\). We also consider that each user is served by at least one TP. We focus on the transmissions from the TP and multiple users, i.e., downlink transmissions, where at least one serving TP for each user is selected in advance at each time. Each user reports the channel quality of TPs (i.e., \(\mathcal{K}_{n}\)) to its associated TP. Accordingly, we assume that the channel quality from TP \(n\) to user \(k\) with the achievable data rate, denoted by \(r_{k}^{n}\), is available at each time slot of performing the TPS. Since a user \(k\) is served by at least one TP, the achievable data rate from the selected TPs in a given time is given by  
\[R_{k}=\sum_{n\in\mathcal{N}}r_{k}^{n}x_{k}^{n}, \tag{1}\]  
in which \(x_{k}^{n}\) denotes the service indicator, where \(x_{k}^{n}=1\) when the TP \(n\) is selected to serve the user \(k\), and \(x_{k}^{n}=0\), otherwise. In other words, the number of TPs serving user \(k\), denoted by \(t_{k}\), is obtained by \(t_{k}=\sum_{n\in\mathcal{N}}x_{k}\). Note that if single connectivity is allowed, \(t_{k}=1\). Here, for user \(k\), the achievable data rate [in bps] served by TP \(n\), i.e., \(r_{k}^{n}\), can be obtained according to [PERSON]'s capacity formula [16] by  
\[r_{k}^{n}(p_{n})=B\log_{2}(1+\gamma_{k}^{n}(p_{n})), \tag{2}\]  
in which \(B\) denotes the system bandwidth and \(\gamma_{k}^{n}(p_{n})\) denotes the received SINR of user \(k\) from TP \(n\) with the given power \(p_{n}\) stated by \(\gamma_{k}^{n}(p_{n})=\frac{\sum\limits_{n\in\mathcal{N}_{n}}^{k}p_{n}}{\sum \limits_{n\in\mathcal{N}_{n}}^{k}p_{n}+\epsilon_{n}^{k}}\), where \(g_{n}^{k}\) denotes the channel gain from TP \(n\) to user \(k\) and \(\sigma_{n}^{k}\) denotes the thermal noise power on the user \(k\). We also assume that the length of the data packet for user \(k\)where \(t_{k}^{n}\) denotes the service time for a user \(k\) served by TP \(n\), i.e., \(t_{k}^{n}=d_{k}^{n}/r_{k}^{n}\). Note that we assume all packets are the same length for all users, for simplicity.
# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 3 Materials and Methods
### Image Classification  
Prior to picture classification, all of the photos used for the model training were downsized (1280 \(\times\) 720) in Irfan View (version 4.58) to obtain the best possible accuracy value. Cropping helps to remove the unnecessary details and helps the deep learning model to learn patterns that are relevant to the task at hand. Another reason for cropping images is that specific neural network models expect images of specific dimensions. YOLOv4 is example of such a network that expects images to be in 224 \(\times\) 224 dimensions. The pictures were chosen to be 224 \(\times\) 224 pixels in size, with a base learning rate of 0.001 and a decay learning ratio strategy. However, 0.95 was chosen as the value of momentum. The lessons were improved upon using a transfer learning strategy. Transfer learning involves retraining a model for new classes using the existing weights (a set of weights) from a model that has already been fully trained on a specific dataset, such as ImageNet [24]. Transfer learning is effective in many sensing applications, as confirmed by the study of [PERSON] et al. [25]. Further steps during the DCNN network training showed that the lowest value was saved for the validation set later. The outcomes of all the saved models were then computed in the fashion of statistical metrics such as accuracy, precision, and recall with the test dataset. The training of all the detection models, including YOLOv3-tiny, YOLOv4-tiny, and variations of YOLOv5, was performed on the powerful discrete NVIDIA RTX 2070 GPU.
# Experimental Study of the Volumetric Error Effect on the Resulting Working Accuracy--Roundness
## 2 Experimental Setup
### Measurement Setting
#### 2.4.2 Volumetric Accuracy  
The volumetric accuracy of the machine was verified for the calibration cycle (LTc1) and the verification cycle (LTc2) according to the procedure described in [5]. Intermediate rigid body type 3 (IRB3) was chosen as a kinematic model. During the measurement, 1112 points in measured space are measured, including reversal measurement. The feed rate of the machine axes was chosen to be 6000 mm/min in order to minimise measurement time. From the calibration measurement, a compensation file (volumetric compensation systems (VCS), Siemens) was also obtained and activated in the machine tool.  
Table 4 shows the technical specifications of the measuring device LaserTRACER.  
Ambient conditions were monitored during calibration and the verification measurements. These are listed in Tables 5 and 6. In the calibration measurements, temperature change on the X-axis did not exceed 0.3 \({}^{\circ}\)C, 0.2 \({}^{\circ}\)C on the Y-axis and 0.6 \({}^{\circ}\)C on the Z-axis. These conditions can be considered stable for calibration measurements LTc1 (see Section 3.1). The principle of measuring and determining LaserTRACER positions (P1, P2, P3 and P4) is described in publications [4; 5].  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Specification** & **Units** \\ \hline Resolution & 0.001 & \(\upmu\)m \\ Measuring uncertainty (k = 2) & 0.2 + 0.3 \(\times\) L & \(\upmu\)m \\ Measuring range & 0.2–18 & m \\ \hline \end{tabular}
\end{table}
Table 4: LaserTRACER specifications.  
\begin{table}
\begin{tabular}{c c c} \hline
**Item** & **Specification** & **Units** \\ \hline Sensor accuracy (at 20 \({}^{\circ}\)C) & \(\pm\)0.5 & \(\upmu\)m \\ Sensor resolution & 0.1 & \(\upmu\)m \\ System accuracy & \(\pm\)1.25 & \(\upmu\)m \\ Measuring uncertainty (k = 2) & 0.7 + 0.003 \(\times\) L & \(\upmu\)m \\ Maximum sampling rate & 1000 & Hz \\ \hline \end{tabular}
\end{table}
Table 3: Ballbar QC20-w specifications [33].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Position of Measurement** & **Time of Measurement** & **X-Axis Temperature [\({}^{\circ}\)C]** & **Y-Axis Temperature [\({}^{\circ}\)C]** \\ \hline P1 & 16 & 19.8 & \(19.5\) & 20.6 \\ P2 & 13 & 19.7 & \(19.5\) & 20.4 \\ P3 & 9 & 19.7 & \(19.5\) & 20.3 \\ P4 & 12 & 19.6 & \(19.4\) & 20.1 \\ \hline \end{tabular}
\end{table}
Table 5: Conditions of calibration measurement.  
In the verification measurements, temperature change on the X-axis did not exceed 0.3 \({}^{\circ}\)C, 0.6 \({}^{\circ}\)C on the Y-axis and 1.0 \({}^{\circ}\)C on the Z-axis. These conditions can be determined as an evaluation of the verification measurement LTc2 of the volumetric accuracy of the machine tool (see Section 3.1).
## 4 Underwater Image-Enhancement Experiments and Result Analysis
### Qualitative Evaluation  
This study focuses on image clarity and texture rendition, which are both critical for underwater structural inspection. To test the proposed WWSF algorithm, eight inspection images--showing concrete spalling, cracking, corrosion, and voids--were enhanced, andthe results were compared against six established methods (CLAHE, UDCP, RGHS, UCM, MLLE, and WWPF).  
In terms of color fidelity, the CLAHE, WWPF, RGHS, and WWSF methods demonstrate relatively accurate color restoration. In contrast, the UDCP method, based on image restoration principles, causes severe color distortion, while the UCM method introduces an excessive reddish tone. These distortions can be attributed to the high turbidity and strong optical variability in lake water, which hinder accurate modeling of underwater light attenuation. Furthermore, in shallow waters where red light absorption is not yet significant, forcibly applying red-channel compensation may lead to unnatural reddish hues.  
Regarding contrast performance, CLAHE and WWSF show superior contrast enhancement. Conversely, RGHS performs poorly in this aspect due to its global histogram stretching strategy, which lacks the ability to enhance local contrast, a critical factor in emphasizing fine structures. The WWSF method, which incorporates CLAHE for contrast enhancement, effectively improves the local contrast of structural details, making it well-suited for underwater hydraulic imagery.  
As for detail preservation, the WWPF, MLLE, and WWSF methods achieve better visual recovery of fine features, such as cracks and surface textures, while other approaches fail to adequately reveal these structural cues. These three methods belong to the fusion-based enhancement category, which excels in detail preservation. Specifically, both WWSF and WWPF utilize wavelet transform-based fusion to highlight edge and texture features, while the MLLE method enhances details and color by adopting a minimum color-loss principle and maximum attenuation-guided fusion strategy.  
In terms of structural integrity, our WWSF method demonstrates excellent performance. It substantially reduces shadowing across the image, and avoids overexposed edges or false contours. In contrast, WWPF tends to produce overexposed boundaries and more noticeable artifacts. This difference stems from WWSF's incorporation of gamma correction, which effectively adjusts global brightness and suppresses structural distortions, thereby preserving the visual coherence of the enhanced image.
###### Abstract  
The quality of ultrasound images of welds is highly penalized by the dendritic structure of the material that forms during cooling. The image of a flaw is all the more degraded as a reliable description of such a medium is most of the time not possible, due to the poor knowledge on the weld at time of inspection. In a previous work, we demonstrated the efficiency of an optimization procedure to correct a degraded Total Focusing Method (TFM) image of a point-like reflector inside a homogeneous weld, with uniaxial grain orientation. In the present contribution, this procedure is extended to more realistic welds with a varying grain orientation, and it is evaluated with a 64-element array emitting at 5 MHz on stainless steel weld samples featuring side-drilled holes of 1 mm diameter. A first proof of concept with simulated echoes, and then two experiments show that defects that were hardly visible on TFM images before the optimization are now well reconstructed and with positioning errors inferior to 1 mm.  
Keywords:non-destructive testing; ultrasonic array; total focusing method; adaptive imaging; austenitic welds; optimization algorithm +
Footnote †: journal: Medical  
describes the weld, by means of an optimization algorithm, until no further increase in the maximum amplitude of the defect echo. In the current contribution, we extend the optimization procedure to deal with unknown and inhomogeneous austenitic welds as encountered in nuclear power plants.  
This paper is organized as follows: in Section 2, we recall the different steps of the optimization procedure with details on the [PERSON]'s weld model, used herein, and the particle swarm algorithm to which has been added a warm restart strategy; in Section 3, we then evaluate the optimization procedure with a numerical proof of concept; and in Section 4, the procedure is experimentally evaluated with austenitic weld samples containing artificial defects located either in the middle of the weld or on its chamfer.
# Research on Part Tolerances for RV-40E Reducer Based on Virtual Prototype Technology
## 1 Introduction
[33] optimized the tolerance allocation using a state-space model to significantly reduce transmission errors and proposed a dynamic accuracy weighting strategy to balance cost and accuracy. [PERSON] et al. [34] used a hybrid simulation PCE approach to evaluate various sensitivity indices and determine the optimal tolerance scheme for the transmission mechanism. Despite these valuable contributions, current research on the RV-40E reducer demonstrates two significant limitations regarding practical applications. Firstly, these studies fail to conduct a systematic assessment of how the tolerance design of individual components in the RV-40E reducer affects its transmission error. Secondly, they do not provide sufficient justification for the accuracy requirements or tolerance design of RV reducer components, nor do they establish a systematic approach for evaluating the appropriateness of tolerance design. The above series of tolerance studies, although not related to the RV-40E reducer, can guide the selection of accuracy levels and the design of tolerance values for various components and parts of the RV-40E reducer. To improve the transmission accuracy and reduce the manufacturing costs, this article will deeply analyze the structural composition and tolerance value design of the RV-40E reducer. Specifically, we propose a method for selecting the accuracy level of externally purchased components, such as bearings. Based on the principle of multi-body dynamics, a virtual prototype of the RV-40E reducer will be established. The design of experiment (DOE) method is then adopted to analyze the reasonability of the tolerance values. Finally, the optimal scheme for the part tolerance values will be proposed to achieve the high-accuracy and low-cost manufacturing of the RV-40E reducer.
## 1 Introduction  
Landslides, as one of the most frequent geological disasters, have caused casualties, property damage and a series of geological environment problems [1; 2]. According to the statistics report of the China Institute of Geo-environmental Monitoring [3], a total of 2966 geological disasters occurred in 2018, including 1631 landslides, resulting in 112 deaths and a direct financial loss of CNY 1.47 billion.  
Hence, to minimize the damage caused by the landslides, it is necessary to carry out landslide susceptibility research in a region [1, 4, 5]. Generally, landslide susceptibility can be roughly defined as the probability of landslide occurrence in a certain area under the coupling action of a series of geological environmental factors and human activities [6, 7, 8, 9]. Currently, many approaches have been applied in landslide susceptibility research and their results have been applied to landslide prevention [10, 11, 12]. According to previous studies [13, 14, 15, 16], the approaches applied in landslide susceptibility assessment can be roughly divided into three types: statistical models, machine learning models and hybrid integration models. The commonly used statistical models are statistical index (SI) [17, 18], index of entropy (IoE) [19, 20], frequency ratio (FR) [21, 22, 23], weights of evidence (WoE) [24, 25], certainty factor (CF) [26, 27], evidential belief function (EBF) [28, 29], analytical hierarchy process (AHP) [30, 31, 32], and logistical regression (LR) [33].  
As the prediction ability of the above models has needed to be improved, machine learning methods have been introduced, such as support vector machine (SVM) [34, 35, 36], random forest (RF) [37, 38], adaptive neuro-fuzzy inference systems (ANFIS) [39, 40], artificial neural network (ANN) [41, 42, 43], decision tree (DT) [44, 45], and classification and regression tree (CART) [46, 47]. Many researchers have gradually found that the prediction ability of a single model is limited [48]. Therefore, it is necessary to develop hybrid integration approaches, such as support vector machine-evidential belief function (SVM_EBF) [49], decision tree-weights of evidence (DT_WoE) [50], adaptive neuro-fuzzy inference systems-statistical index (ANFIS_SI) [51], frequency ratio-logistic regression (FR-LR) [23, 52], rotation forest-naive Bayes tree (RF_NBT) [53], and random subspace-classification and regression tree (RS_CART) [54].  
In order to obtain an appropriate model that is suitable for the study area, this paper introduces an advanced artificial intelligence algorithm (SysFor) with a frequency ratio and index of entropy to carry out a landslide susceptibility assessment. A logistic regression model is used as the benchmark model to compare the performance of the hybrid models.
## 3 Design and Development
### The FPGA Implementation Detail
Additionally, the pipelined structure helps reduce the impact of endpoint effects, i.e., errors that may be introduced due to improper handling of data block boundaries during signal processing. By processing each computational step in staged processing, the pipeline can smoothly transition between data blocks, ensuring the overall stability and accuracy of the computational results. Compared to the ESP32 implementation at 240 MHz with a 20.60-millisecond delay, this method achieves a minimum delay of approximately 0.002 milliseconds at a 200 MHz frequency, sufficient to update the judgment result once each sample is generated and meeting the conditions for extracting each level of IMFs. For the simulation samples, the proposed method yielded a SNR of 13.93 dB for IMF7, with a variance-normalized MSE of 0.0358, a variance-normalized RMSE of 0.1891, and a correlation efficiency of 98.29%.
## 2 Results and Discussion
### Cell Proliferation  
Biocompatibility of cell-laden FGSr scaffolds were considered using MTT (Figure 8) and live/dead (Figure 9) assays. Quantification results were shown as percentage of viable cells normalized to the control FG. It is a common knowledge that FG is non-toxic to cells as it is a natural component derived from fish. The cytotoxicity assay demonstrated that FGSr was non-toxic to cells and there were significant differences between cell viability at all time points of 1, 3 and 7 days. After 7 days of culture, it can be noted that there was almost a 1.7-fold increment in cellular viability between FGSr and FG. Therefore, this result concluded that not only was FGSr non-toxic, it was also able to increase cellular proliferation and viability. As seen, there was considerable live cells (green stained) noted in all images at all time points and there was no dead cell (red stained) noted. This result proved that both FG and FGSr was non-toxic to cells. At the same time, it can be noted that there were noticeably more green stains on FGSr at all time points as compared to FG. In recent years, several studies demonstrated the CS-contained scaffolds were able to improve cellular behaviors and activities via raising Si ion influx through L-type Ca channels thus leading to enhance phosphorylation of MAPK pathways in primary cells [46]. Thus, we deduce that the FGSr hydrogel scaffold is favourable for WJMSC growth even when the cells are kept homogenously afloat in the 3D microenvironment [30].  
Figure 8: Proliferation of WJMSC on FG and FGSr scaffolds for 1, 3, and 7 days. Data presented as mean \(\pm\) SEM, \(n=6\) for each group. “*” indicates a significant difference (\(p<0.05\)) when compared to FG.  
Figure 7: (**A**) Ca, (**B**) Si, and (**C**) Sr ion concentrations in SBF after immersion for different durations. Data presented as mean \(\pm\) SEM, \(n=6\) for each group.