esa-sceva/satcom-chunk-collection huggingface dataset:
# GNSS Code Multipath Mitigation by Cascading Measurement Monitoring Techniques
## 4 Detection, Exclusion and De-Weighting Strategies
### Exclusion and De-Weighting of Affected Measurements
Although, this procedure does not absolutely eliminate multipath errors, when the number of measurements is limited and exclusion is not desirable, de-weighting of affected measurements can be useful. The computational burden depends on the increasing function and the maximum number of iterations required to reach the predefined geometry threshold.
## 4 Experiments and Analysis
### Datasets and DCNNs  
The proposed method is evaluated on widely used datasets, i.e., CIFAR10 and CIFAR100 [33]. Specifically, the CIFAR10 dataset consists of 60,000 images with dimensions of 32 \(\times\) 32 for the classification of 10 classes of natural objects. In addition, there are 50,000 images and 10,000 images in the training set and the testing set, respectively. The CIFAR100 dataset consists of the same number of images as CIFAR10 but has 100 class labels.  
State-of-the-art DCNNs including VGG-16, VGG-19, ResNet-18, and ResNet-34 are considered [34; 35]. VGG-16 is a 16-layer single-path convolutional neural network with 13 convolutional layers, while VGG-19 is a 19-layer single-path convolutional neural network with 16 convolutional layers. ResNet-18 is a residual network composed of a series of residual blocks with 17 convolutional layers, while ResNet-34 has 33 convolutional layers.
## 3 Types of Catalysis in Biodiesel Production
### Transesterification  
Transesterification reaction is also known as alcoholysis, involving the transformation of one ester into another therefore known as transesterification. In the case of a biodiesel synthesis process, a triglyceride (formed by a combination of fatty acids with glycerol) facilitated by a catalyst (strong acid/alkoxide) reacts with alcohol that results in production of biodiesel and glycerin. Base catalyzed transesterification is frequently used for biodiesel production. Equation (2) shows transesterification reaction.  
\[\text{Triglyceride}\,+\,\text{Alcohol}\,\xrightarrow{\text{ Catalyst}}\,\text{Fatty acids alkyl esters}+\text{Glycerol} \tag{2}\]  
Both acid catalyzed and base catalyzed transesterification reactions are used to produce esters (biodiesel). Transesterification involves the generation of a highly potent electrophile when a proton is donated to carbonyl group in presence of a strong acid catalyst. When a  
Figure 3: Mechanisms of catalysis of biodiesel synthesis process (**a**) Mechanism of acid esterification (**b**) Mechanism of acid catalyzed transesterification (**c**) Mechanism of alkaline transesterification reaction [23].  
proton is removed from alcohol, a nucleophile is produced, and the reaction rate is enhanced by strong base catalyst. Almost 96% Fatty acid methyl esters (FAMEs) and fatty acid ethyl esters (FAEEs) are produced in reaction mixture depending on the alcohol used [10]. In base catalyzed transesterification, an alkoxide ion is formed in the first step then a carbonyl group of triglyceride act as electrophile as a result a tetrahedral intermediate is formed followed by synthesis of an alkyl ester. At the final step, the regeneration of alkali occurs with H+ attached to anionic diglyceride [24]. However, same reaction pattern is followed by acid catalysts except formation of carboxylic acid due to nucleophilic attack. These catalysts are highly reactive and have potential to provide a 100% transesterification reaction.
# Review of Physical Layer Security in 5G Wireless Networks
## 5 Future Research
### Security Solutions for 5G Networks  
Opportunities and Collaborative: Efforts Further research is needed to develop more effective physical layer security solutions for 5G wireless networks. This could involve exploring new techniques and technologies, such as machine learning and artificial intelligence, to enhance the security of the physical layer. More research is needed to understand the vulnerabilities of the physical layer in 5G wireless networks and how they can be exploited by attackers. This could involve conducting more in-depth studies of the physical layer and its components to identify potential weaknesses. Future research should focus on developing more comprehensive risk assessment frameworks for physical layer security in 5G wireless networks. These frameworks should consider not only technical vulnerabilities but also organizational, social, and economic factors that may affect the security of the physical layer. There is a need for more collaboration between industry, academia, and government agencies to address the physical layer security challenges in 5G wireless networks [44]. This could involve establishing partnerships to share knowledge and resources, develop new solutions, and address common challenges.
###### Abstract  
Image dehazing is a crucial task in computer vision, aimed at restoring the clarity of images impacted by atmospheric conditions like fog, haze, or smog, which degrade image quality by reducing contrast, color fidelity, and detail. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown significant improvements by directly learning features from hazy images to produce clear outputs. However, color distortion remains an issue, as many methods focus on contrast and clarity without adequately addressing color restoration. To overcome this, we propose a Color-Correction Network (CCD-Net) based on dual-branch fusion of different color spaces for image dehazing, that simultaneously handles image dehazing and color correction. The dehazing branch utilizes an encoder-decoder structure aimed at restoring haze-affected images. Unlike conventional methods that primarily focus on haze removal, our approach explicitly incorporates a dedicated color-correction branch in the Lab color space, ensuring both clarity enhancement and accurate color restoration. Additionally, we integrate attention mechanisms to enhance feature extraction and introduce a novel fusion loss function that combines loss in both RGB and Lab spaces, achieving a balance between structural preservation and color fidelity. The experimental results demonstrate that CCD-Net outperforms existing methods in both dehazing performance and color accuracy, with CIEDE reduced by 40.81% on RESIDE-indoor and 45.57% on RESIDE-6K compared to the second-best-performing model, showcasing its superior color-restoration capability.  
Keywords:image dehazing; color correction; image restoration; deep learning; attention mechanism +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Biocompatibility  
In this study, TCPs were used as the control group. The results showed that MG63 cells experienced no cytotoxicity after 24 and 72 h of culture (Figure 6). The MTT assay showed that the TNT-treated and NaOH-treated test pieces in the CaP solution group had cell survival rates significantly higher than that of pure titanium after 72 h of culture (\(p<0.05\)). However, rates of the TNT CaP and NaOH CaP groups did not significantly differ from that of the TCPs (\(p<0.05\)) (Figure 6A). In addition, the biocompatibility result of LAB CoHA solution is Figure 6B. Rates of the TNT CoHA, NaOH CoHA, and HCl CoHA groups were all significantly higher than that of the Ti CoHA group (\(p<0.05\)). In particular, the cell survival rate of the TNT CoHA group after 72 h of culture was significantly higher than that of the TCPs (\(p<0.05\)). This may have been caused by the release of cobalt ions. A previous study found that cobalt ions can mimic a hypoxic environment and activate HIF-1\(\alpha\) to promote vascular epithelial growth factor (VEGF) production [42]. The differentiation of MG63 cells improved under the influence of cobalt ions released by TNT CoHA [43]. In addition, [PERSON] et al. reported that synthesized CoHA was implanted in the mandible of a rat model of osteoporosis, and results showed that the mineral deposition rate of cobalt ion-filled sites was higher than that of pure HA, and cobalt promoted the rate of bone formation [44]. This is similar to results of our study, indicating that cobalt ions contributed to the growth and differentiation of MG63 cells.
## 2 Literature Review
### Pricing Scheme  
The pricing scheme used for mobile data service has evolved in the past two decades. Because flat-rate pricing is easy to implement and quite effective in stimulating data demand [10], it was popular when the total market demand for data service was low. As data demand grew, mobile service providers moved to metered pricing, where a customer is charged a fixed fee first and then a per-unit fee. In contrast to flat-rate pricing, metered pricing allows mobile service providers to facilitate price discrimination and thereby increase their revenue [11; 12]. In practice, there are two versions of metered pricing: two part pricing and three part pricing. The difference is that three part pricing bundles some allotted data into the fixed fee, but two part pricing does not (see Figure 1a,b). [PERSON] [13] studied two part pricing and found the optimal fixed fee and per-unit fee for the revenue-maximizing strategy. In addition, they showed that the fixed fee and per-unit fee have different relative effects on the demand and retention of users.  
A similar investigation analyzed three part pricing. [PERSON] [14] assumed that each consumer has a predetermined demand. He showed that for consumers who are not overconfident, the firm's optimal strategy is a high fixed fee and thus takes all of the consumers' surplus. Furthermore, the firm's profit increases if the consumers are overconfident. Later, [PERSON] [15] characterized the optimal three part pricing plan under more general conditions. Based on a global bound for the service provider's profit, they employed a methodology that was different from the standard first-order conditions approach and showed that this bound is attained using the optimal plan. In addition, some researchers compared two part pricing and three part pricing and tried to find which one was better for service providers. [PERSON] [12] concluded that a relatively small menu of three part pricing can be more profitable than a menu of two part pricing of any size. In contrast, Reference [16] showed that the optimal three part pricing outcomes are identical to the optimal two part pricing outcomes when the market demand follows an increasing price elasticity or when the consumer distribution approximately follows an increasing hazard rate.  
Since we entered the era of 4G, connection-speed-restriction pricing has rapidly become a popular pricing scheme used by the industry. Figure 1 shows the differences among two part pricing, three part pricing, and connection-speed-restriction pricing. In an overview of smart data pricing, [PERSON] et al. [17] addressed connection-speed-restriction pricing as a new trend of data pricing. However, to the best of our knowledge, few studies have been devoted to connection-speed-restriction pricing. Our study aims to fill this gap.
## 1 Introduction
[32] conducted a study of automatic DR text extraction using the bug reports of Chrome Browser as the research object. [PERSON] et al. [33] extracted user rationale about how to select software application decisions from 32,414 online reviews of 52 software applications on Amazon, using multiple classifier algorithms. To address the problem that the existing DR models largely rely on human efforts in capturing DRs along design activities, [PERSON] et al. [34] proposed an ISAAL (Issue, Solution, Artifact Layer) model for rationale information extraction from the design archival documents. On the basis of the ISAAL model, [PERSON] et al. [35] proposed the ISAAL model, classifying the DR information into four classes, issues, solutions, arguments, and artifacts, which can be utilized to construct a DKN. However, the ISAAL model does not consider how DR is automatically extracted from documents, and the ISAAL model does not support knowledge networks. Researchers have conducted numerous studies on the integration, sharing, and rapid retrieval of design knowledge. However, even so, how to build a DKN for knowledge in one document, and how to build an Open Design Knowledge Network (ODKN) for knowledge in multiple documents, which can be automatically interpreted by computers and can facilitate quick understanding by designers, thus providing designers with creative and inspirational stimuli, is still a problem. To this end, this paper proposes a three-dimensional knowledge network metamodel, including the literature, the artifact, and the rationale. The metamodel can be used for the knowledge extraction of technical documents, and the construction of Knowledge Graphs (KG), thereby facilitating the discovery, understanding, transfer, and reuse of knowledge. The main contributions of this paper can be summarized as follows:  
* The DKSN, a knowledge network metamodel based on DR theory, is proposed to provide designers with innovative ideas, inspirations, and creative stimuli. * The DKSN takes into consideration the requirements of text mining, making it a convenient, rapid, and efficient approach for the capture, sharing, and dissemination of design knowledge in documents. * It can be used to integrate DR from patents, scholarly articles, and other publicly available documents to build an ODKN. The rest of this paper is organized as follows: Section 2 reviews the relevant work on DR representations, design knowledge networks, and semantic network and knowledge fusion. Section 3 presents a three-dimensional knowledge network metamodel, and a knowledge fusion method. Section 4 sets up an illustrative example. Finally, the conclusions are discussed in Section 5, together with potential future work.
# Agricultural Waste-Based Biochar for Agronomic Applications
## 3 Biochar as a Soil Amendment
### Impacts of Biochar on Heavy Metals and Chemicals Pollution Remediation 3.6.1. Heavy Metals
#### 3.6.2 Pesticide
Another aspect that has to be considered with respect to biochar addition to remediate pesticide in soils is the release of pesticides previously adsorbed onto biochar, which can impact its bioavailability in soils and in surface- and groundwater as well [179]. Thanks to biochar properties, not all adsorbed pesticides can be readily desorbed [179; 180; 181]. [PERSON] et al. [182] showed in a one-step desorption experiment that some amounts of fomesafen adsorbed on soils amended with rice hull biochar at an application rate of 2% were desorbed; this could be due, according to the authors, to the relatively low surface area of rice hull biochar promoting the detachment of weakly adsorbed molecules of fomesafen molecules from biochar micropores [182]. [PERSON] et al. [183] observed a significant reduction in the bioavailability of atrazine in soil amended with cassava wastes-derived biochar, due to its irreversible chemical binding or sequestration in biochar pores. [PERSON] et al. [184] studied the effect of pyrolysis temperature on the desorption rate of herbicide terbuthylazine in a soil amended with sawdust biochar; the authors showed that the desorption rate is lower and slower in the presence of biochar produced at 700 \({}^{\circ}\)C as compared to that produced at 350 \({}^{\circ}\)C. The effect of biochar addition on pesticide sorption-desorption behavior in soils may also be affected by biochar ageing. [PERSON] et al. [185] showed that biochar ageing decreases biochar's specific surface area, which negatively affects the biochar's sorption capacity. Similarly, [PERSON] et al. [186] reported that ageing of rice straw biochar in soil significantly decreased its sorption capacity to pentachlorophenol. [PERSON] et al. [187] found that despite the decrease in the sorption capacity of rice hull biochar after 6 months of its application, its ability to sequesterfomesafen is still higher as compared to non-amended soil. In the study performed by [PERSON] et al. [146], the authors showed that the immobilization of triazine pesticides such as simazine varied little as a function of biochar ageing, suggesting the persistent of biochar and its long-term efficiency in soil.
## 3 Experimental Environment
### Monitoring IoT Device with TPU Accelerator  
We built the monitoring device based on the RaspberryPi 4 model B mini-computer with Broadcom BCM2711 quad-core 64-bit ARM-8 Cortex-A72 1,5 GHz processor and 4 GB of RAM (Raspberry Pi Foundation, Cambridge, UK) (Figure 1). The device is powered by an external source of electricity in the form of a power bank. Monitoring is performed with the use of an ArduCam OV5647 5 MPx camera with an LS-2718 CS lens sourced from Botland, Brain, Poland. The camera supports the HD 1080 px mode, providing 30 frames per second. The camera is connected to a dedicated connector that the RaspberryPi is equipped with, ensuring fast data transfer. An additional GSM modern is used for transmitting data to the data center. The modern is connected to the central unit via a USB 3.0 connector, and it is equipped with its own SIM card. In such a way, it is independent of the availability of the broadband network in the vicinity of the beehive.  
While having a network connection, it is possible to connect to the Cloud data center through the AWS IoT Cloud gateway service. For each frame of the recorded video stream, the IoT device performs bee identification (segmentation of bees) and _Varroa destructor_ detection (the classification process). If the bee is classified as infected, the IoT device sends an MQTT message to the Cloud data center for further processing and notifying a beekeeper. The software organization is presented in Figure 2.  
Detection of bees and of varroosis is carried out through the use of the Google Coral USB Accelerator. Thanks to the built-in Edge TPU co-processor, the Coral accelerator enables quick inference with Machine Learning (ML) models. The Edge TPU co-processor allows performing up to 4 trillion operations per second using only 2 watts of power [48]. This enables the implementation of modern mobile vision models with a speed of almost 400 frames per second in an energy-efficient manner. Moreover, the accelerator is compat  
Figure 1: Overview of the IoT device used for monitoring a beehive.  
ible with the Tensor Flow Lite library. A general model of the monitoring IoT device is shown in Figure 1.  
The IoT device monitors the beehive and records a video data stream. Selected video frames of the video stream (images) are then analyzed in order to identify bees. Identified bees are marked with a surrounding rectangle (a frame indicating the region of interests, ROI) and extracted from the image. Then, extracted pictures of bees (ROIs) are analyzed again to detect _V. destructor_ on bees (classification process). In such a way, each frame from the video stream delivers many smaller pictures of bees classified as infected or not. Both phases of the analysis are TPU-accelerated. In case of infection, the pictures of the infected bees are transferred to the data center for further analysis, storing, upgrading ML models, and sending a notification to the relevant beekeeper.
## 3 Compensation in Three-Phase Three-Wire Systems with Balanced Voltages
### Reactive Power Compensator  
The use of reactive power compensators (SVC) has been extensively studied. The two methods, frequently used to calculate the reactance values, are sinusoidal and balanced current (SBC) and minimum line loss (MLL). We use the SBC method in this study. In the SBC method, the values of reactance in each phase are identical and determined from the positive-sequence voltages and currents according to (14). \(X_{P}\) with a positive value represents a coil, while a negative value represents a capacitor. Normally, this compensator is capacitive in nature, as the charges are usually inductive. The only positive sequence reactive power to compensate for the balanced voltages is that of the load; therefore, \(Q_{P+}\) is equal to \(-Q_{L+}\).  
\[X_{P}=3\frac{{V_{+}}^{2}}{Q_{P+}}=-3\frac{{V_{+}}^{2}}{Q_{L+}} \tag{14}\]  
Here  
Figure 2: Compensation of a system with an unbalanced load. NSCC: negative-sequence current compensator; SVC: static VAR compensator.  
\(X_{P}\) is the reactance value of SVC compensator.
* \(V_{+}\) is the positive-sequence line-to neutral voltage.
* \(Q_{P+}\) is the total positive-sequence reactive power to compensate.
* \(Q_{L+}\) is the total positive-sequence reactive power of the load.
# Security Issues in IoT-Based Wireless Sensor Networks: Classifications and Solutions
## 3 Security Protection Schemes
### Secure Data Aggregation Schemes  
Data aggregation protocols aim to eliminate redundant data transmission over the networks. They are essential for energy-constrained WSNs. Traditional data aggregation techniques include simple query types such as SUM, COUNT, AVERAGE, and MIN/MAX [83]. Secure data aggregation requires authentication, confidentiality, and integrity. This also requires the cooperation of sensor nodes. Figure 7 shows the classification of secure data aggregation tools in WSNs. According to the operation of the protocol, it can be classified into two types: plain text-based and cryptographic-based.
## 5 Results and Analysis
### First-Stage Recognition
#### 5.2.1 The Evaluation Indexes  
We used precision, recall, mAP (mean average precision), AP (average precision), accuracy, and FPS (frames per second) as measurement indicators. These metrics are defined as follows:  
\[precision=\frac{TP}{TP+FP} \tag{8}\]  
\[recall=\frac{TP}{TP+FN} \tag{9}\]  
\[accuracy=\frac{TP+TN}{TP+FN+FP+TN}, \tag{10}\]  
where TP, FP, FN, and TN represent the number of true positives, false positives, false negatives, and true negatives, respectively. When the category of defect prediction is correct, and the intersection over union (IoU) is greater than a threshold (0.6 in our experiments), we consider the detection to be correct. The AP is the area under the precision-recall (P-R) curve. The mAP is the mean value of the AP of all categories. FPS represents the number of images that the target detection method can process per second, which can evaluate  
Figure 12: Some of the images from the NEU-DET and Enriched-NEU-DET datasets.  
precision of Improved-YOLOv5 on the VOC2007 dataset is 2.3% higher than that of original YOLOv5, and the mAP value is also 1.5% higher. On the Enriched-NEU-DET dataset, the recall value is 4.9% higher than that of YOLOv5, and the mAP value is 7.1% higher, which shows that the improvement made by the YOLOv5 model for small target detection has a very good effect. These results show that the improved YOLOv5 detection method achieves the best result in terms of several evaluation indicators, and fully meets the requirements for further application in industrial production situations.  
In the Table 4, a is \"crazing,\" b is \"inclusion,\" c is \"patches,\" d is \"pitted_surface,\" e is \"rolled-in_scale,\" and f is \"scratches.\"
## 3 Results and Analysis
### swHPFM Validation and Algorithm Results  
Both the 2340-core-scale and the 28080-core-scale programs of the swHPFM converged at \(-10\), after about 9429 iteration steps, as shown in Figure 11. While the residual convergence curve fluctuated slightly, the residual declined. What is more, these trends are the same as those of the Tianhe-2A supercomputer, whose processors are Intel Xeon E5-2692v2. The residual convergence of hydromechanics corresponds to the accuracy of the swHPFM parallel framework.  
In addition, the simulated generated mash and press data were processed by postprocessing the software, Tecplot, to obtain the results of the flow field simulation, which are presented in Figures 12 and 13. All of these results prove that the swHPFM framework is accurate.  
Figure 11: Residuals of the algorithm of a whole blade row of the axial compressor. The trends are the same as those of the Tianhe-2A supercomputer. The residual convergence of hydromechanics corresponds to the accuracy of the swHPFM parallel framework.