esa-sceva/satcom-chunk-collection huggingface dataset:
# Olive Oil with Ozone-Modified Properties and Its Application
## 3 Materials and Methods
### Cytotoxicity Testing
#### 3.7.4 MTT Assay  
Cytotoxicity was evaluated quantitatively by a reduction of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolzolium bromide) assay. First, 1 \(\times\) 10\({}^{4}\) cells in the logarithmic growth phase were seeded in each well of a 96-well plate in complete culture medium and incubated overnight (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)). Next, the medium was aspirated, and tested oils were added to achieve final concentrations from 39 to 1250 \(\upmu\)g mL\({}^{-1}\). The negative control contained cells in DMEM. Cells were exposed to extracts for 24 h (37 \({}^{\circ}\)C, 5% CO\({}_{2}\)). After incubation, the samples were aspirated, MTT (0.5 mg mL\({}^{-1}\) in PBS) was added, and they were incubated for further 3 h. Next, it was removed and formazan precipitates were solubilised by DMSO. Absorbance was measured at 550 nm (with a reference filter of 620 nm) using a microplate reader (TriStar\({}^{2}\) LB 942, Berthold Technologies GmbH & Co. KG, Bad Wildbad, Germany).  
The absorbance of the control sample (untreated cells) represented 100% cell viability. Cell viability (%) was calculated as: (sample OD/control OD) \(\times\) 100%; and cytotoxicity (%) was calculated as: 100--cell viability. Results were presented as mean of four individual evaluations \(\pm\) standard deviation (\(\pm\) SD). Cytotoxicity responses were evaluated according to ISO 10993-5:2009(E) [9]. The mean error of MTT method is up to 10%.
# Insights into the Issue of Deploying a Private LoRaWAN
## 3 Experimental Environment
### Private Lonawan  
The private network includes one single gateway with LORIOT cloud server. Specifically, we use the Lorank 8+ gateway with the following properties:  
* Transmit power up to +27 dBm (500 mW, the power was always based on the selected channel and allowed value from the regulation recommendation [31]).
* Received signal sensitivity up to \(-138\) dBm.
* Five dBi antenna.
* Communication range up to 25 km.  
* Up to eight simultaneous receiving channels.
* Up to 60 thousand nodes.
* Whole gateway covered in an IP67 case.  
Figure 3 shows the different parts of the university campus. Each letter indicates one part of the building. The gateway was positioned to cover both the building and part of the city in the E-part of the building. Further, the campus building is one of the highest points in the city. The Lorank 8+ gateway was placed on the roof of the E-part of the building of our campus with the coordinates of the site 49.2269133\({}^{\circ}\) N, 16.5752194\({}^{\circ}\) E. The E-part of the building provides a power panel for outdoor gateways and metal pillars for deployment. Using the 3D preview of the selected area, it can be observed that from the roof of the E-part of the building, which is marked in the picture (black dot), it is possible to observe most of the buildings in the city of Brno in the line-of-sight (see Figure 4). From the selection of multiple positions, this position seemed the most strategic given the above-mentioned parameters.  
The gateway is connected via a basic commercial switch through the 100 MB/s Ethernet to the campus network. The optical connection was chosen to protect network equipment on campus from lightning damage. For this reason, however, Ethernet-to-optic media converter must be used at the gateway side and optic-to-Ethernet media converter on the campus side of the network. The LORIOT cloud itself is not operated locally on campus, but is run directly on LORIOT's servers, for which gateway access is through the Internet. The complete network topology is displayed in Figure 5.  
Figure 4: Location of our private one-gateway LoRaWAN experimental network with view of the city of Brno (website: [[http://webmaps.kambrno.cz/](http://webmaps.kambrno.cz/)]([http://webmaps.kambrno.cz/](http://webmaps.kambrno.cz/)) (accessed on 15 February 2022)).  
Figure 3: Location of our private one-gateway LoRaWAN experimental network with view of the individual buildings of the Electrical faculty (A–H are the names of the buildings).
## 3 Results and Discussion
### Studies Involving Post-Stroke Individuals  
The created application was subjected to preliminary testing. This study took place under the supervision of a qualified occupational therapist, who qualified the patients for this study, supervised their progress, and qualitatively assessed the patients' progress. This study involved five post-stroke patients between the ages of 59 and 72 residing in a long-term care facility (Table 1). Therapy using the created app was applied for 5 days a week for 10 min each for 4 weeks.
_J. Sens. Actuator Netw._**2013**, \(2\), 675-699; doi:10.3390/jsan2040675  
_Journal of Sensor and Actuator Networks_  
**ISSN 2224-2708**  
www.mdpi.com/journal/jsan/  
_Article_  
**iMASKO: A Genetic Algorithm Based Optimization Framework for Wireless Sensor Networks**  
**[PERSON] * and [PERSON]**  
Heterogeneous Systems Design Group, Lyon Institute of Nanotechnology UMR CNRS 5270, University of Lyon, Ecole Centrale de Lyon, Ecully 69134, France; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +33-472-186-059.  
_Received: 29 August 2013; in revised form: 15 September 2013 / Accepted: 17 September 2013 / Published: 9 October 2013_  
**Abstract:** In this paper we present the design and implementation of a generic GA-based optimization framework iMASKO (**i**NL@**MATLAB Genetic **A**lgorithm-based **S**ensor **N**etwork**K** **O**ptimizer) to optimize the performance metrics of wireless sensor networks. Due to the global search property of genetic algorithms, the framework is able to automatically and quickly fine tune hundreds of possible solutions for the given task to find the best suitable tradeoff. We test and evaluate the framework by using it to explore a SystemC-based simulation process to tune the configuration of the unslotted CSMA/CA algorithm of IEEE 802.15.4, aiming to discover the most available tradeoff solutions for the required performance metrics. In particular, in the test cases different sensor node platforms are under investigation. A weighted sum based cost function is used to measure the optimization effectiveness and capability of the framework. In the meantime, another experiment is performed to test the framework's optimization characteristic in multi-scenario and multi-objectives conditions.  
**Keywords:** WSNs; optimization; MATLAB; genetic algorithm; performance metrics; simulation; evaluation; weighted sum; multi-objective; multi-scenario
# IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring
## 4 Experiment and Results
### Seatbelt Monitoring Results  
We tested the seatbelt monitoring algorithm by changing the status of the five seatbelts in the vehicle while driving. In this experiment, smartphone 1 was also used. Each seatbelt was unfastened 50 times while driving at a speed of over 20 Km/h. The proposed system (using an edge and a fog layer) was compared with the system in [17] (using only an edge layer) to determine the influence of adding the fog layer. The results of the seatbelt monitoring at the traffic station were as in Table 6. The accuracy of detecting seatbelts for the same vehicle status was high, which means that the proposed system can be used for seatbelt monitoring.  
Figure 7: Speed estimation using smartphone 1, which shows a comparison of estimation between the baseline (edge computing alone) and the proposed system (edge + fog layers). The proposed system’s estimation is more accurate than that of the baseline.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Accuracy**} \\ \hline
**Door** & **without Fog Layer** & **with Fog Layer** \\ \hline Front left & 95\% & 98\% \\ Front right & 94\% & 97\% \\ Rear left & 93\% & 98\% \\ Rear right & 94\% & 94\% \\ Average & 94\% & 97\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: The accuracy of the passenger counting algorithm.
# Throughput Enhancement in Downlink MU-MIMO Using Multiple Dimensions
## 5 Implementation Issue  
This section presents some additional considerations for system application and limitation under practical constraints. In addition, this section gives some ideas for additional performance gain and presents some methods to reduce some negative effects in the proposed scheme.
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 1 Introduction
Additionally, some dehazing models trained on synthetic hazy datasets fail to produce satisfactory results on real-world hazy images, resulting in domain-shift issues. To address these challenges, zero-shot dehazing methods [13; 14; 15; 16] that use a single hazy image for model training have been proposed to improve model generalizability. For instance, [PERSON] et al. [15] proposed a re-degradation haze imaging model for remote sensing (RS) image dehazing without any pre-training or fine-tuning on specific datasets. In this study, we introduce a zero-shot dehazing approach for RSI that leverages the strengths of both the zero-shot learning strategy and an embedded prior-based dehazing module. The former enhances the network's generalizability without requiring a huge quantity of training data, while the latter bolsters the haze removal capability by correcting hazy image priors. Given a hazy image, we first process it using a prior-based module to obtain the initial transmission map, atmospheric light, and dehazed outcome. We opt for _DCP_ as the chosen prior-based dehazing module as it yields superior preliminary results for subsequent refinement than the other dehazing priors [17]. We proceed to propose two shallow UNet-based CNNs to refine the initial transmission map and dehazed result, addressing the failure cases of the prior-based module. To facilitate zero-shot training, we regenerate a hazy image employing atmospheric light, a refined transmission map, and refined dehazed result in accordance with the haze imaging model. Our tailored loss function encourages the regenerated hazy image to be close to the input hazy image, resulting in a cycle-consistent network. The main contributions of this paper are summarized as follows:1. This paper presents a novel zero-shot dehazing framework embedded with hazy image priors as pre-dehazing. The proposed method generates a cycle-consistent hazy image from the input, enabling zero-shot training with a single image, eliminating laborious data collection, and improving generalizability. Importantly, the prior-based pre-dehazing contributes to an accelerated convergence rate within the zero-shot learning paradigm of the network;
2. The \(DCP\) is embedded into the proposed framework to improve the dehazing capability and convergence efficacy, while two CNN-based RefineNets are implemented to refine the outputs of the \(DCP\) module. Consequently, the proposed method can produce pleasing results, even in scenarios where the \(DCP\) fails, embracing the advantages of both prior-based and learning-based methods;
3. Comprehensive experiments were conducted to evaluate the effectiveness of the proposed method. Both the quantitative and visual results demonstrate that our method is superior to other dehazing methods in processing both uniform and non-uniform RS hazy images. Moreover, the proposed method yields a substantial enhancement over the chosen prior-based method (\(DCP\)) in this study.
## 3 Methodology
### Convolutional Autoencoder Model  
The main purpose of using a deep learning model in this work is a dimensionality reduction through the feature extraction, and thus the extracted features represent the conditions of every moment of the ball bearing. Among the developed deep learning models, the CAE was selected in this study. CAE is a type of autoencoder (AE) neural network that is used to extract hidden features from unlabeled images. CAE is characterized by having identical input and output sizes and is trained to predict the input in the output (\(h_{w,b}(x)\approx x\)). One CAE algorithm consists of three layers: the input layer, the hidden layer(s), and the output layer. The idea is that one or several hidden layers have lower dimensions than the visible layers (input and output), and thus the input information is reconstructed and compressed in the hidden layer(s). The hidden layer that contains the fewest nodes is known as a bottleneck. The bottleneck layer represents the maximum point of compression of the input data, which contains all necessary data to reconstruct the input data again. Therefore, the CAE is constituted by two main parts: an encoder that maps the input into the code, and a decoder that maps the code to a reconstruction of the original input in the output layer.  
In the encoding section, some convolutional layers and pooling layers are stacked on the input image to extract hierarchical features. Then, all units in the last convolutional layer have been flattened to form a vector followed by a fully connected layer(s). The bottleneck layer usually has 2 neurons. Accordingly, the input 2D image (for this study, the input image was \(60\times 60\) RGB pixels) is transformed into a two-dimensional vector space (\(\mathbb{R}^{3\times 60\times 60}\rightarrow\mathbb{R}^{2}\)). To train the CAE model in an unsupervised manner, we used the mirror architecture of the encoder in the decoder section. Thus, fully connected layer(s) followed by some deconvolutional and upsampling layers are used to transform the embedded features back to the original image. The structure of the proposed CAE model is shown in Figure 4. After setting up the CAE, it is essential to optimize weights and biases by minimizing the reconstruction error, i.e., the loss function. Backpropagation algorithm [43] is used to compute the gradient of the loss function with respect to any weight and bias in the network. To make the output of the decoder as equivalent as possible to the input, the binary_crossentropy function is employed as the standard CAE loss function, and the Adam optimizer is used to optimize the loss function [43].
## 2 Materials and Methods
### Point-to-Pixel Comparative Method  
Several different methods can be used to compare precipitation products on different spatial grids. Generally, point data such as that supplied by rain-gauge networks are interpolated on a defined grid according to different methods which can be divided into two categories [55]:  
* Deterministic methods (e.g., the inverse distance weighting or the Thiessen polygons; [56; 57]);
* Geostatistical methods (e.g., the different Kriging methods; [58]).  
The choice of the selected interpolation mainly depends on factors such as the complexity of the topography or the density of the rain-gauge network [59].  
In this study, we consider the original spatial resolution of the RADOME network as a reference. Hence, we compare the observed punctual rain-gauge values to their respective nearest estimated pixel by using the widely used point-to-pixel approach [8; 59; 60; 61; 62]. Moreover, the comparisons between the rain-gauge data and the diverse rain products were performed on the accumulated rain values obtained at the end of both case studies presented in Section 2.3. For each point-pixel considered (\(i\)), an absolute bias was calculated according to: \(BIAS_{i}=E_{i}-O_{i}\) where \(O\) was the rain-gauge observations from the RADOME network while \(E\) was the estimations given by each precipitation product (i.e., radar or satellites) used in this study. Thus, a positive or negative bias corresponds, respectively, to an overestimation or an underestimation of the used precipitation product compared to rain-gauge observations.
## 2 Related Work
### Deep Learning Methods  
With the ongoing advancement of drone technology equipped with high-definition cameras, deep learning techniques have been widely applied in the field of target detection, including maintenance and monitoring of WTBs ([PERSON] et al., 2017; [PERSON] et al., 2018). Detection methods based on deep learning leverage large volumes of annotated data for training, enabling automatic learning and recognition of features within images, thereby achieving precise target detection. Damage detection methods based on deep learning offer advantages such as non-contact, high precision, and long-range detection. Compared to traditional detection approaches, this method can significantly reduce labor costs and risks while being less susceptible to environmental factors. [PERSON] et al. (2019) employed Haar-like features, an Adaboost cascade classifier, and convolutional neural networks (CNN) to construct a classification network for detecting the presence and location of damage. [PERSON] et al. (2019) utilized deep learning models, transfer learning, and ensemble learning classifiers to identify damage in images of WTBs. [PERSON] et al. (2019) detected damage to the blades using an enhanced SSD algorithm. [PERSON] et al. (2019) suggested using a lightweight YOLOv5s as the basis for a surface flaw detection model for wind turbine assembly, which not only exhibits high accuracy but also delivers strong instantaneous performance. [PERSON] et al. (2019) presented a modified algorithm named Attention and Feature Balance YOLO (AFB-YOLO) based on YOLOv5s. This updated model can identify subtle and low-resolution damage characteristics on WTBs, greatly improving the capacity to identify small-scale flaws. [PERSON] et al. (2019) have made improvements to the yolov-tiny algorithm, resulting in a modified model that demonstrates greater detection accuracy, especially in terms of small-sized objects.  
Although current methods can effectively detect damage scenarios of simple WTBs, they mainly focus on identifying single damage types. When it comes to detecting various types of damage in complex environments, their performance often leaves much to be desired, frequently resulting in missed and false detections. This phenomenon indicates that existing technologies still have significant limitations in improving the accuracy of detecting multiple types of damage. To address these shortcomings, firstly, the present research employed drones equipped with high-resolution cameras to conduct an extensive collection of blade damage images across various locations. Based on the acquired data, the damages were classified into two categories: cracks and peelings. Secondly, this study utilized the efficient YOLOv8 model, which is widely applied in the field of damage detection. Finally, to improve the model's performance even further, improvements were made in three key aspects of the algorithm: the introduction of DSConv to enhance feature extraction capabilities, refinement of the upsampling process, and customized optimization of the loss function.
## 4 Experimental Results
### Analysis of Results for the Proposed Pipe Leak Detection  
Figure 7 illustrates the classification results of the models with TL and without TL (non-TL) using confusion matrices, which display the correct and incorrect classifications of leak and normal states. The non-TL model was trained end-to-end in Stage 2 without leveraging Stage 1 pre-training, as shown in Figure 4. The confusion matrix on the left demonstrates the performance of the non-TL model, correctly classifying 12,940 noise instances and 13,450 leak instances, while misclassifying one noise instance and nine leak instances. In contrast, the matrix on the right shows the results of TL model, with 12,939 noise instances and 13,458 leak instances correctly classified, and only two noise instances and one leak instance incorrectly classified. These results confirm that the proposed TL model achieves superior classification accuracy for leak and normal states by leveraging pre-trained fused features compared to the non-TL model.  
The performance of the leak detection model was further evaluated based on precision, recall, and F1-score metrics. Precision, defined in Equation (13), quantifies the proportion of true leaks among all samples identified as leaks. Recall, defined in Equation (14), measures the proportion of actual leaks correctly detected by the model. The F1-score, shown in Equation (15), combines precision and recall into a single metric by calculating their harmonic mean.  
\[Precision=\frac{TP}{TP+FP}, \tag{13}\]  
\[Recall=\frac{TP}{TP+FN}, \tag{14}\]  
\[\text{F1}-\text{score}=\frac{2\times(Precision\times Recall)}{Precision+Recall}, \tag{15}\]  
where true positive (TP) refers to cases where the model correctly identifies a sample as a leak when it truly is one. A false positive (FP) occurs when the model incorrectly classifies a noise sample as a leak, while false negative (FN) signifies instances where the model mistakenly classifies a leak sample as noise.  
Table 5 summarizes the micro-leak detection accuracies of the proposed model compared to various existing ML and DL approaches. Each model was independently trained on 1D features derived from the time and frequency domains, with their outputs combined using an ensemble approach. The multi-layer perceptron (MLP) [36; 37] ensemble employed an MLP model to learn RMS and frequency features, achieving an ensemble accuracy of 80.18%. The SVM-ensemble, which employed an SVM model, achieved a higher ensemble accuracy of 85.36%. The LSTM-ensemble, combining results learned through an LSTM  
Figure 7: Comparison of confusion matrices for model performance with and without TL: (**a**) results of the proposed model without TL; (**b**) results of the proposed model with TL.  
network, achieved an accuracy of 74.47%. The 1D CNN-based model introduced in Stage 1 learned RMS and frequency features with accuracies of 98.42% and 98.97%, respectively, culminating in an ensemble accuracy of 99.32% (1D CNN-ensemble). In contrast, the TL-based model introduced in Stage 2 of the proposed framework exhibited superior performance, achieving an exceptionally high accuracy of 99.99%.