esa-sceva/satcom-chunk-collection huggingface dataset:
# 3D Global Path Planning Optimization for Cellular-Connected UAVs under Link Reliability Constraint
## 4 Results and Discussions
### Results Validation  
To validate the ability of the proposed strategy to solve the 3D UAV path planning problems, this subsection compares the simulation results of Scenario I with the results of the conducted aerial drive test on the UKM campus. Figures 17 and 18 indicate the RSRP and RSRQ values for different routes and heights. The change of the color spectrum from cold to warm indicates the improvement of the RSRP and the weakening of the RSRQ. The result comparison reveals a better aerial coverage on Route II in terms of RSRP and RSRQ. Additionally, by comparing the results of Route II with the BSs' locations in Figure 3, it can be revealed that the UAV is served with better RSRP and RSRQ values when it is inside the proposed reliable zone.  
Figure 16: Comparison of the algorithms’ execution time in different scenarios.  
Figure 15: Statistical results of Scenario III: (**a**) optimal path, (**b**) reliable path, and (**c**) ratio of the reliable path.  
Figure 17: RSRP measurements for the aerial drive test on the UKM campus at different heights (65, 85, 105, and 125 m) and different routes: (**a**) Route I, (**b**) Route II, and (**c**) Route III.  
Figure 18: RSRQ measurements for the aerial drive test on the UKM campus at different heights (65, 85, 105, and 125 m) and different routes: (**a**) Route I, (**b**) Route II, and (**c**) Route III.  
Figure 19 summarizes the RSRP and RSRQ measurement results for different routes and heights in the form of box plots. As can be seen, by increasing the flight height, the mean values of both RSRP and RSRQ degrade. On the other hand, Route III provides the worst result, since throughout the flight mission, the distance between the UAV and BSs is greater compared with Routes I and II. Based on the minimum, maximum, and mean values of presented results as well as their distribution, Route II with flight heights of 65 and 85 m provides the best RSRP and RSRQ results.  
Finally, Figure 20 projects the results of the optimized flight path (computed by the PSO-GA algorithm) on the results of the three measured routes (at a height of 85 m). The results reveal that the computed optimal path is close to Route II, and the proposed strategy can effectively solve the UAV path planning problem in real-world scenarios.  
Generally, the results show that the PSO-GA algorithm can provide the best solution for the 3D UAV path planning problem among all three algorithms. PSO-GA has the best performance in terms of generating feasible solutions, reliable path length, reliable path ratio, convergence speed, and execution time. In addition, PSO-GA outperforms the other two algorithms in terms of searching ability and stability. The comparison of simulation and measurement results also validates the effectiveness and feasibility of the proposed solution in a suburban environment.  
Figure 19: Statistical results of the conducted aerial drive test on the UKM campus at different routes and flight heights: (**a**) RSRP and (**b**) RSRQ.
# An Improved Approach to Detection of Rice Leaf Disease with GAN-Based Data Augmentation Pipeline
## 5 Experimental Results and Comparative Analysis
### Synthetic Data Generation  
Our experiment with the proposed GAN-based augmentation pipeline used an SG2-ADA model, which shows that we can generate high-quality refined rice leaf disease images with a few available training datasets which can be used directly for data augmentation to improve Faster-RCNN and SSD models for rice disease detection. See Figure 11 for real images and Figure 12 for synthetic images.  
The visual quality of the synthetic images generated by the proposed pipeline is good and would be difficult to distinguish between real and fake images by a non-expert.  
The model generated 50,001 images of 4 classes of rice leaf disease, which include BL, TG, BS, and RB though 46% of the synthetic images were blurry and distorted, which was detected and filtered out using the variance of the Laplacian method. Afterimage preprocessing, we produced 26,694 synthetic images. Table 2, shows the evaluation results of the network using the FID, KID, and P&R metrics. The network achieved an FID score of 26.67, KID score of 0.08, Precision of 0.49, and Recall of 0.14 after training for 144 h. It was also observed that the FID value decreased during the course of training.  
Figure 11: Real images.  
Figure 12: Synthetic images.
###### Abstract
Other studies have focused on the detection and localization of structural damage using decision tree ensembles (DTEs); in particular, the authors of [22] developed a methodology, based on a set of decision trees, which belongs to the class of vibration-based approaches as a method for the assessment of the health of a structure, obtained by analyzing the dynamic properties of the structural system, in particular, mode shapes and natural frequencies. The proposed damage detection method was validated for three different test cases, comprising both numerical simulations and experimentally recorded data. In [23], a new generalized auto-encoder (NGAE) is proposed and supplemented with a statistical pattern-recognition approach using power cepstral coefficients of acceleration responses. This method was validated using numerical simulations and experimental data and shows better performance than the traditional auto-encoder (TAE) and principal component analysis (PCA). In this paper, we investigate the ability of a framework based on hybrid-based models to identify damage under environmental conditions, herein applied for damage identification due to the generalized corrosion of a steel structure [24]. The proposed framework, implemented in the MATLAB environment [25], is able to identify the damage through supervised learning algorithms, such as the SVM, which can classify damage states with a good accuracy, even under stochastic conditions. The accuracy achieved in the damage prediction is a function of the goodness of selected features and artificial intelligence (AI) hyperparameters. For the AI training, stationary signals are generated through response history dynamic analyses on a finite element (FE) model. The second part of the paper explores the possibility of estimating the remaining useful life (RUL) of a structural system using a Bayesian updating model. The potential of condition monitoring to support high-level decision making, such as equipment replacement, maintenance planning, and spare parts management, is indeed a topic of growing interest. In order to make the most effective use of condition information, it is useful to identify a degradation signal, i.e., a quantity computed from sensor data that is suitable for estimating the condition of the structure and thus providing information on how this condition is likely to evolve in the future. The approach studied was successfully applied to a simple structural system to test the concept.
## 3 System Architecture
### Deep Learning for Decoding Data  
When the communication distance is extended, SNR values tend to diminish, posing challenges for the receiver in establishing a clear threshold between ON and OFF values. Addressing this concern, a matched filter method was previously proposed in [29] to optimize SNR values and extend the communication distance. However, in a mobility environment, the SNR is very harsh to decode data; therefore, we propose a neural network model that is trained by various datasets to increase our decoding performance.  
After the LED is detected, the signals of all LED areas are converted from image form (parallel) to serial form for data decoding. The signal obtained after conversion is shown in Figure 2. The blur effect in OCC systems is generally caused by the relative movement of the transmitter and receiver. Consequently, the deployed system experiences inter-symbol interference, which significantly reduces system performance. In a mobileenvironment, identifying the preamble is crucial for decoding the signal, as only afterward can the remaining data segments be easily decoded. To simplify the decoding of the signal on the camera side of the system when deploying the system in a mobile environment, we implemented a deep-learning decoder to identify the preamble of each data packet. Using the RMSE metric, we calculated and evaluated the accuracy of the deep-learning decoder model to assess its performance. In 200 epochs, the system demonstrated high accuracy, which was reflected by the low value of error (<0.1) during the forecasting process.  
After the images are recorded by the camera, utilizing the YOLO algorithm, we can determine the image area with the LED array and then adopt image-processing and coding techniques to decode data from the LED region. In particular, we extract OCC signals from the image format (parallel data format) to serial format (serial data stream) in the identified LED region using the down-sampling method, and we extract the 2D-MIMO signal using the center intensity point of the LEDs. The dataset (9000 data samples) for the deep-learning decoder was gathered using a variety of commercial camera types at varying distances (between 2 and 22 m) and under different circumstances, including mobility and rotation. We used a simple two-layered deep-learning neural network model to prevent the model from overfitting. After preamble detection, the threshold of the 2D-MIMO signal can be accurately identified when compared to traditional technology in a mobile environment. Six or more hidden layers would compromise the test dataset's accuracy in the event of overfitting.
# Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?
## 3 Results  
### Impacts of Assimilation on Land Surface Variables Prediction  
The output time step of MLDAS is 30 min, so H, LE, and SM predictions were daytime-averaged (09:00-18:00, local time) for further estimates within all assimilation-prediction scenarios. As with each assimilation scenario, a short-term prediction (forecasting lead time of 30 days) for LAI, SM, H, and LE was conducted separately in MLDAS and OL. \(NIC_{RMSD}\) was used to show the advances in land surface variables prediction by MLDAS compared with Noah-MP OL.
## 1 Introduction
### Highlights of this Article  
The main highlights of our article are as follows:  
* The IoT devices in our system are divided into multiple clusters with each cluster having a CH. A UAV flies over these CHs in such a trajectory that optimizes the number of tasks executed, the number of devices served, along with consumption of energy. Here we have avoided the round-robin mode of task execution because tasks are generally scheduled according to priority.
* Each CH maintains two tables, one corresponding to IoT devices under itself and the other corresponding to other CHs. Information about IoT devices is required to compute the priority of the current task submitted by one particular IoT device whereas cluster information is required to compute the credibility of the CH to be visited next. This trajectory planning of UAV is completely dynamic and with every traversal of CH, training data is refined with the most recent experiences(that is, made updated with the most recent records), so that the next CH to visit can be computed efficiently. Dynamic planning of energy-conserving UAV trajectory is an important contribution of this article.
* Energy is preserved not only during trajectory planning of UAVs but also during task propagation from a non-CH IoT device to its CH. When submitting the next task, this information is supplied to an IoT device by the CH so that its chance of being dropped becomes lower. This preserves the energy that would have been required otherwise for resubmitting the task. This is accomplished using the ARMA model [23] and decision tree [24].
* Use of GA enables incorporating a large number of clusters of IoT devices providing a scalability feature of the system.
* Simulation results demonstrate that our proposed scheme EETO-GA outperforms other state-of-the-art UAV trajectory optimization algorithms in terms of performance Metrics like UAV energy saved (UAVE), energy saving in IoT devices (IoTDEC), the average delay in execution of the task (ADET), and the percentage of tasks that could be completed (PTSK). Proposed EETO-GA improved average UAVE: 43%, IoTDEC: 56%, PTSK: 7.5%, and ADET: 38% over the state of the art.
## 2 Related Work
In most routing protocols, the query packets are broadcasted to repair a broken path from source node to sink, which causes significant communication overhead in terms of both energy and delay. In order to repair the broken path and maintain the available route from the source nodes to the mobile sink through a multi-hop network with lower communication overhead, IOLPSOR (immune orthogonal learning particle swarm optimization algorithm based routing recovery method) [11] is proposed to provide more efficient routing recovery capability to MWSNs. Furthermore, a more efficient ECPSOAR (efficient routing recovery protocol with endocrine cooperative particle swarm optimization algorithm) [10] is proposed to repair the broken path caused by both the movement of the sink and failure of sensor nodes. However, neither IOLPSOR nor ECPSOAR considers the following two cases. One case is that, the lifetime of the routing tree depends on the relay node that has the least lifetime. The other case is that, the improved PSOAs used by these two protocols do not well fit the discrete routing scenario.
## 5 DynDLOff: An Offline Algorithm
### Phase I: Assign Data-Local Tasks  
This phase generates a balanced assignment that only contains data-local tasks and evenly distributes data-local tasks across the cores. The balanced assignment, denoted as \(\mathcal{B}\), satisfies the following constraints. * Constraint 1: all tasks are assigned to their preferred cores, such that \[\forall t\in T,\mathcal{B}(t)\in DL^{\mathit{core}}(t).\] In the following, we refer to the assignment strategies that satisfy Constraint 1 as data-local assignments. * Constraint 2: among the cores that task \(t\) prefers, \(\mathcal{B}(t)\)'s load is the smallest, such that \[\forall t\in T,L_{\mathcal{B}}(\mathcal{B}(t))-1\leq\min_{p\in DL^{\mathit{ core}}(t)\setminus\mathcal{B}(t)}L_{\mathcal{B}}(p).\]  
**Property 1**: _A balanced assignment \(\mathcal{B}\) has the following properties 1. For cores \(p_{i}\) and \(p_{j}\), if \(L_{\mathcal{B}}(p_{i})-1>L_{\mathcal{B}}(p_{j})\), the data-local tasks on \(p_{i}\) do not prefer \(p_{j}\). 2. \(\mathcal{B}\) is optimal among all the data-local assignments in terms of the makespan._  
Property 1 is correct because of Constraint 2. Property 2's correctness is proved as follows. Assume data-local assignment \(\mathcal{B}^{\prime}\) is optimal and does not satisfy Constraint 2. Without loss of generality, let task \(t\) be the task that dissatisfies Constraint 2. Let \(p^{k}\) be a core that holds \(L_{\mathcal{B}^{\prime}}(\mathcal{B}^{\prime}(t))-1>L_{\mathcal{B}^{\prime}}( p^{k})\). By moving \(t\) to core \(p^{k}\), we obtain another data-local assignment \(\mathcal{B}^{\prime\prime}\) that holds \(\mathit{makespan}(\mathcal{B}^{\prime\prime})\leq\mathit{makespan}(\mathcal{ B}^{\prime})\). We continue the motions until Constraint 2 is satisfied, and then generate \(\mathcal{B}\). Because \(\mathcal{B}^{\prime}\) is optimal and \(\mathit{makespan}(\mathcal{B})\leq\cdots\leq\mathit{makespan}(\mathcal{B}^{ \prime\prime})\leq\mathit{makespan}(\mathcal{B}^{\prime})\), it is easy to discern that \(\mathcal{B}\) is also optimal. To find a balanced assignment, we base Phase I on two steps. The first step (lines 1-2 of Algorithm 2) uses a greedy approach to assign all the tasks to their preferred cores. For each task \(t\), the algorithm chooses the least-loaded server \(s\) from \(DL^{\mathit{src}}(t)\), then assigns \(t\) to the least-loaded core of \(\mathit{core}(s)\). The data-local assignment \(\mathcal{B}_{0}\) generated by this step is referred to as an _initial assignment_. Figure 2(a) shows the initial assignment for Table 2's scheduling instance. The second step (lines 3-17 of Algorithm 2) continuously moves tasks from high-load to low-load cores until the task assignment satisfies Constraint 2. Specifically, this step achieves the goal by finding cost-reducing paths [48] on a bipartite graph.
# Designing a User-Centered Inspection Device's Handle for the Aircraft Manufacturing Industry
## 4 Discussion
### Strengths and Limitations  
This study has several strengths and limitations. The main strength lies in providing a methodological approach to achieve a user-centered design of a handle for a novel device to be used in inspection tasks of the aircraft manufacturing industry. It should be highlighted that the study was preceded by a study focused on the assessment of the initial conditions [20]. This previous study was relevant to gather information about the real end-users and industrial needs and constraints. Based on these, three handles were designed following a user-centered design, incorporating several ergonomic requirements (as explained in Section 2.1). However, to select the most suitable handle, each one was evaluated from a thorough scientific study, combining subjective judgments with several quantitative metrics. We believe that this approach allows a detailed assessment of the handles and could be adopted by other researchers/designers in similar studies to support handles' design.  
However, the results of the current study are limited for some reasons. Firstly, to avoid unbiased results, and due to the difficulty of recruiting a significant sample of aircraft inspectors (since it is an uncommon professional occupation), the participants recruited for the experimental tests did not have prior experience performing inspection tasks. Furthermore, the inspection tasks were performed in a simulated environment instead of in a real-industry scenario. These factors always introduce uncertainty about what the results would be under real-industry conditions. Therefore, the assessment under real conditions in an aircraft manufacturing industry is recommended, and further research is necessary to understand how the handle will be adequate in different inspection tasks and over longer periods of use. Moreover, as defended by [24] and [23], improvements made as part of the digitalization of the working processes, as we proposed, must be developed respecting an ergonomic participatory approach with active workers' involvement. Thus, the ergonomic assessment, including the end-users (in this case, the inspectors), will assume an important phase for the final design and deployment of the novel device, respecting a user-centered design in this digitalization of the inspection process.
# Machine Learning Ship Classifiers for Signals from Passive Sonars
## 3 Classifier Models
### Multi-Layer Perceptron  
A Multi-Layer Perceptron (MLP) is composed of an input layer, one or more hidden layers, and an output layer [1]. Each layer consists of neurons that perform transformations on the data via weighted sums and activation functions.  
Given an input vector \(\mathbf{x}=[x_{1},x_{2},\ldots,x_{n}]\), the output of the \(j\)-th hidden neuron is computed using  
\[z_{j}=\sum_{i=1}^{n}w_{ji}x_{i}+b_{j},\quad h_{j}=f(z_{j}),\]  
where \(w_{ji}\) are the weights, \(b_{j}\) the bias, and \(f\) an activation function (e.g., sigmoid or ReLu [27]).  
The output layer receives the transformed values \(h_{j}\) and computes  
\[y_{k}=g\left(\sum_{j=1}^{m}v_{kj}h_{j}+c_{k}\right),\]  
where \(v_{kj}\) and \(c_{k}\) are the weights and biases of the output layer, and \(g\) is the output activation function (e.g., softmax for classification or linear for regression).
# Rapid and Accurate PPA Prediction for the Template-Based Processor Design Methods
## 2 Related Work
### PPA Prediction for HLS  
The relation between the knobs and the PPA for HLS design is a black box; therefore, machine learning is widely used for PPA prediction in HLS design to accelerate the design process. [PERSON] et al. proposed a machine-learning-based simulated annealer method for high-level synthesis design space exploration in 2014 [15]. To foresee the correlation between power consumption and HLS-based applications at an early designstage, [PERSON] et al. introduced HL-Pow [16], a power modeling framework for FPGA HLS based on state-of-the-art machine learning techniques. HL-Pow incorporates an automated feature construction flow to efficiently identify and extract features that exert a major influence on power consumption, simply based upon HLS results, and a modeling flow that can build an accurate and generic power model applicable to a variety of designs with HLS. Moreover, [PERSON] et al. presented models for fast and accurate PPA prediction that can reduce the manual optimization iterations with EDA tools in 2021 [17]. They investigated techniques to automate PPA optimization using evolutionary algorithms. For PPA prediction, a baseline model is trained on a known design using Latin hypercube sample runs of an EDA tool, and transfer learning is then used to train the model for an unseen design. With transfer learning, the same accuracy was achieved on a different (unseen) design in only 15 runs, indicating the viability of transfer learning to generalize PPA models. However, PPA prediction for HLS is usually used for prototype design and algorithm verification, while HDLs are preferred for chip tape-out in the industry [1]. On the one hand, HLS approaches promise to raise the level of abstraction and compile regular C/C++ functions into logic elements. There is still no clear way to develop an optimized design without prior hardware engineering experience. Moreover, the parameters for HLS are 3 kinds of knobs, which cannot control circuit details such as the ways cache numbers. On the other hand, PPA prediction methods based on machine learning require large samples for training. Therefore, we propose a rapid and accurate PPA prediction method for HDL designs from template-based chip design methods. On the one hand, there is a describable relation between the parameters of an HDL design and its PPA. The circuit details can be directly controlled by modifying the HDL codes. However, the proposed PPA prediction method requires a few samples to estimate the PPA. Table 1 shows a comparison among the proposed method and the best machine learning model, XGBoost [18], for HLS PPA prediction, which is mentioned in [17].
## 4 Clustering and Fault Surface Extraction
If done manually, this would take several weeks or more. However, by applying the clustering method, the fault extraction could be completed within 4-5 h. Therefore, we demonstrate that the proposed method can obtain an effective fault model in a significantly shorter time than the conventional methods. Figure 9: Fault point groups extracted individually by DBSCAN clustering from original point group data shown in Figure 8a. Figure 10: Results of fault modelling using (**a**) original fault cube, (**b**) conventional (automatic) approach, and (**c**) clustering (the proposed method). Automatic fault extraction methods show low connectivity between fault models; however, the proposed method shows high connectivity with the possibility of individual fault extraction through clustering.