esa-sceva/satcom-chunk-collection huggingface dataset:
eye diseases. More specifically, the evaluation of fundus images has been done manually and requires a highly skilled ophthalmologist. Through the morphological and topological changes of the retinal vessels, the latter can detect the existence of pathological situations. Moreover, manual segmentation can be challenging due to the variety of morphological structures eye vessels can have [7]. Automatic segmentation of retinal vessels in fundus images is crucial since manual segmentation can be time and cost-demanding. All things considered, computer-aided detection systems for automatic vessel segmentation are in high demand. The work of [PERSON] et al. was one of the first efforts in the literature to present a methodology for retinal image analysis, which is focused mainly on vessel segmentation [8]. Retinal imaging is now the primary way to care for patients with retinal and other systemic diseases [9]. Segmenting the vessels from eye fundus photos constitutes a tedious and demanding procedure in terms of time and carefulness that should be gotten and can require up to three days for all the observations to be gathered accurately. Blood vessel segmentation is a procedure performed manually by a specialist doctor and may be prone to errors. In addition, the daily costs associated with the expert decisions (e.g. ophthalmologist) on eye care and the augmenting number of retinal photos to be examined and analysed constitute the main reasons why an automatic vessel segmentation system should be adopted. This article proposes a convolutional autoencoder model, a special stream of convolutional neural networks used to segment retina images. The remainder of the article is as follows. In Section 2, we present a complete review of the literature and examine recent related works in the area of eye blood vessel segmentation. After that, Section 3 presents our model, describes all the input data preprocessing steps, and illustrates the proposed architecture of the convolutional autoencoder we designed and developed. The experimental results of our study are presented briefly also in this section. Then, Section 4 explains the experimental study and the assessment of the proposed architecture on different public datasets. Furthermore, it provides a deep and complete comparison of our model with other recent works in the literature. Finally, Section 5 provides our work's main conclusions and draws the main directions for future work.
## 8 Performance Comparison
### Computation Cost  
Table 2 offers a comparative analysis of computational costs, contrasting our proposed scheme with those referenced. The evaluations were conducted using a personal computer equipped with an Intel(R) Core(TM) i5-1035G1 CPU @ 1.00 GHz, 1.19 GHz, 16.0 GB RAM, and a Windows 10 64-bit operating system. We denote the computation times for cryptographic one-way hash functions, elliptic curve point operations, and symmetric encryption/decryption functions as \(T_{h}\), \(T_{m}\), and \(T_{se}\), respectively, with respective durations of 0.056 ms, 2.806 ms, and 0.062 ms.  
Notably, the initial authentication in our scheme is a one-time occurrence, followed by successive handover authentications. Thus, our analysis primarily focuses on the handover authentication overhead for comparison. In our scheme, the computation cost for vehicles is quantified as \(6T_{h}\), and for the RSU nodes, it is \(10T_{h}\). The total computation cost for our scheme, therefore, is approximately \(6T_{h}+10T_{h}\approx 0.896\) ms. In contrast, the total computation costs for the schemes in [14; 19] are approximately 20.508 ms and 12.12 ms, respectively. Moreover, the schemes described in [28; 29] entail computation times of approximately 17.284 ms and 9.034 ms, respectively. These findings, graphically represented in Figure 10, highlight the lower computational overhead of our proposed scheme compared to the aforementioned alternatives.
## 2 Related Work
### Anomaly Detection in Binary Classification  
While most studies on time-series anomaly detection focus on the one-class classification strategy using only normal data, there have been recent attempts to identify anomalous data through binary classification. [PERSON] et al. [6] proposed a hybrid approach to recognize fraudulent credit card transactions. This study generated latent representations of the given data using a long short-term memory (LSTM) [18] based autoencoder pretrained on normal data, and then classified these representations using a support vector machine (SVM) [19].  
[PERSON] et al. [7] proposed a method for detecting anomalous sounds. Instead of presenting a novel detection model, this study focused on collecting anomalous samples from a given dataset by careful selection of unrelated data to serve as proxy outliers. Then, a detection model based on ResNet [20] was trained as a binary classifier using normal samples and proxy outliers.  
[PERSON] et al. [21] proposed an RNN-based model for detecting intrusions in Internet of Things (IoT) networks. The proposed model, designed with LSTM, bidirectional LSTM, or gated recurrent unit (GRU) [22], was evaluated on various IoT datasets. This study trained the detection model as a binary classifier and employed techniques such as weighted loss and borderline SMOTE [23] to overcome the limitations of imbalanced datasets.  
[PERSON] et al. [24] proposed a binary time-series classification model for detecting gas emissions from the ocean. This study adopted a Bayesian convolutional neural network to detect gas leaks and introduced Monte Carlo dropout [25] to maximize generalization.  
[PERSON] et al. [26] proposed a model for detecting anomalous data in quasi-periodic time-series. This study first split the quasi-time-series into successive quasi-periods through two-level clustering and then detected anomalies using a hybrid attentional LSTM-CNN model.
# Narrative Review on Methods of Activating Irrigation Liquids for Root Canal Treatment
## 3 Types of Irrigants and Irrigation Protocols
Chlorhexidine has antibacterial properties while causing less tissue irritation than NaOCl [23]. Some experiments showed that a 2% solution of CHX exceeds the 5.25% solution of NaOCl in eliminating _E. faecalis_[24]. On the contrary, there are results showcasing sodium hypochlorite as superior in terms of bactericidal properties [25; 26]. There are also studies demonstrating that NaOCl and CHX have comparable antimicrobial efficacy but that their operation mechanisms are distinct [27]. Moreover, the literature indicates that CHX does not induce dentin erosion in the same way that NaOCl does when used as a subsequent irrigant after EDTA [28]. Although NaOCl, in the concentration of 1-6%, remains a golden standard in endodontics, it is noticed that none of the currently used irrigants are completely satisfactory in all respects.
## 1 Introduction
aeruginosa_ down-regulated the expression of _pha_ genes, and therefore synthesis and accumulation of PHA [30]. In _P. putida_ and _P. chlororaphis_ 1391, the _rpoS_ gene is expressed in stationary phase and positively regulates PHA synthesis after the onset of the stationary phase [26; 31; 32]. In _Ralstonia eutorpha_ H16 _spoT_2 (pppGpp synthase), mutants synthesized only minor amounts of PHB [33; 34]. However, in _P. putida_ KT2440, there was no effect of _relA/spoT_ mutations on the accumulation of PHAs [35]. In an earlier study examining the relationship between PHZ and PHA production, we discovered that the PHZ-minus mutant _P. chloroformis_ PA23-63 accumulated greater concentrations of mcl-PHAs compared to the wild type [23]. In the current study, we sought to determine whether changes in carbon partitioning and/or altered _pha_ gene expression account for the higher PHA production observed for PHZ and PRN mutants. Further, we investigated whether regulatory genes that control PHZ/PRN also affect PHA production and transcription of _pha_ genes.
# Object-Oriented Remote Sensing Image Change Detection Based on Color Co-Occurrence Matrix
## 3 Experimental Results and Discussion
### Optimal Feature Combination Filtering  
The features reflect the ground object information of the remote sensing images. Generally speaking, a large number of features can provide rich information, but too many features not only bring redundancy but also affect the accuracy of detection. In this study, all features are filtered through a cumulative backward search strategy, and the importance of the features is calculated through training for all the features. The ranking results of the feature importance in a single training will be slightly different. In this study, after repeating the training many times, the cumulative importance value of each feature is taken for the feature ranking. The experiments show that the cumulative ranking result is almost consistent with the statistical sorting result, which sorts features on the basis of the highest number of occurrences at the location.
## 5 Conclusions  
This paper introduced a novel vehicular VLC system aimed at improving road safety in tunnel environments. The system utilizes adaptive beam alignment to maintain reliable communication between vehicles, even when navigating curves. Additionally, this can be seamlessly integrated into existing vehicles by repurposing built-in LED tail-lights as communication sources, which required minimal modification. The empirical experiments conducted with the RB073 Arduino Car Learning Kit demonstrated the system's effectiveness in transmitting real-time driving dynamics data, such as target speed and acceleration, between vehicles. The results indicate the system's ability to sustain stable communication links in various tunnel scenarios, including sudden stops and traffic congestion, showcasing its potential to reduce the risk of chain collisions in GPS-deprived settings.  
Conceptualization, Y.W.; methodology, Y.P. and H.L.; validation, Y.W. and Y.P.; validation, H.L.; supervision, Y.S.; project administration, Y.S.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
Figure 12: Performances of the proposed vehicular VLC system with and without adaptive beam-alignment mechanisms.  
Figure 13: SINR, received signal power, and noise power of the proposed vehicular VLC system using commercial LED tail-light under a change in distance between transmitter and receiver.  
**Funding:** This work was supported in part by the project titled \"Development of Polar Region Communication Technology and Equipment for Internet of Extreme Things (IoET)\", funded by Ministry of Science and ICT (MSIT), Korea.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original data underlying this study are available from the co-authors listed above upon reasonable request.  
**Acknowledgments:** We extend our sincere thanks to our colleagues and students for their unwavering support throughout this research. We are especially grateful to all participants in experiments for generously sharing their time and insights.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# IRS-Enabled Ultra-Low-Power Wireless Sensor Networks: Scheduling and Transmission Schemes
## 3 Transmission Scheme
### CSI Acquisition and Feedback  
First, we denote the signal sent from the BS to ISN \(l\) as \(\mathbf{x}_{l}=\sqrt{P_{l}}\frac{\mathbf{a}_{l}}{\|\mathbf{a}_{l}\|}\). Thus, the user receives the following signal in time slot \(t\):  
\[Y(t)=\mathbf{x}_{l}^{H}\mathbf{h}_{l}(t)+\mathbf{x}_{l}^{H}\sum_{j=1,j\
eq l}^ {L}\mathbf{h}_{j}(t)+Z(t), \tag{11}\]  
where \(\mathbf{h}_{j}(t)=\mathbf{H}_{1,j}\mathbf{\Theta}_{j}(t)\mathbf{h}_{2,j}\) as defined in (6), where \(Z(t)\sim\mathcal{CN}(0,\sigma^{2})\) is the noise at the user and \(\sigma^{2}\) is the power of the noise.  
The other IRSs receive negligible power from the BS due to favorable propagation, hence the second term in (11) is small; specifically, the signals from other ISNs are small, and are thus neglected. As such, (17) simplifies to  
\[Y(t)=\mathbf{x}_{l}^{H}\mathbf{h}_{l}(t)+Z(t), \tag{12}\]The instantaneous effective SNR at the user through the cascaded \(l\)th IRS link is provided by (cf. (12))  
\[\gamma_{l}=\frac{p_{l}|\mathbf{f}_{l}^{H}\mathbf{H}_{1,l}\mathbf{\Phi}_{l}\mathbf{ h}_{2,l}|^{2}}{\sigma^{2}}=|\mathbf{f}_{l}^{H}\mathbf{H}_{1,l}\mathbf{\Phi}_{l} \mathbf{h}_{2,l}|^{2}\gamma, \tag{13}\]  
where \(\gamma=\frac{P_{l}}{\sigma^{2}}\). This initial CSI acquisition sweep generates \(\gamma_{l}\)s at the user. The user can use this information to schedule an ISN for transmission based on a scheduling scheme (detailed in Section 4), then send the index of the scheduled ISN to the BS. After that, the BS activates the ISN with a beacon beam to awaken it, which is explained next.
# Sentinel: Automated Detection of Android Vishing Apps Using Optical Character Recognition
## 2 Related Work
### Current Vishing Defenses  
In practice, attackers circumvent safeguards by sharing vishing applications through websites or emails, rather than through application marketplaces like the Google Play Store [7]. Due to the difficulty in preventing such distribution, companies offer applications that provide blacklist-based warnings or utilize Android APIs to present phone numbers. The Google Play Store hosts vishing defense applications, including Whowho [11], Truecaller [12], SafeVoice [13], Hiya [14], Whoscall [15], Phishingeyes [16], CallApp [17], Show Caller ID & Spam Blocker [18], and SmartAntiPhishing [19]. Blacklist-based defenses add the attacker's number to the blacklist after the victim has already suffered an incident, while defense applications utilizing Android APIs simply verify Call Redirection based on the numbers. These found in Table 2. However, these defense techniques are not effective against new vishing applications or Display Overlay Attack.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Blacklist-based approches** \\ \hline Advantages & 100\% detection when phone number exists in list \\ \hline Disadvantages & Damage caused by post incident detection \\ \hline \multicolumn{3}{c}{**Voice recognition-based approaches**} \\ \hline Advantages & Detecting voice phishing through specific text \\ \hline Disadvantages & LLM dataset limitations and response generation delays \\ \hline \multicolumn{3}{c}{**Provide number-based approches**} \\ \hline Advantages & Enable Call Redirection Attack defense \\ \hline Disadvantages & Warning is passive and there is no direct defense \\ \hline \multicolumn{3}{c}{**API trace-based approaches**} \\ \hline Adavantages & Various API call information checked \\ \hline Disadvantages & Impossible to detect when API changes \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vishing defense methods advantages and disadvantages.
# Automated Surface Defect Inspection Based on Autoencoders and Fully Convolutional Neural Networks
###### Abstract  
This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation of templates from test targets for defect detection. Because the templates are produced from the test targets, the position alignment issues for the matching operations between templates and test targets can be alleviated. The FCN is employed for the segmentation of a template into a number of coherent regions. Because the AE has the limitation that its capacities for the regeneration of each coherent region in the template may be different, the segmentation of the template by FCN is beneficial for allowing the inspection of each region to be independently carried out. In this way, more accurate detection results can be achieved. Experimental results reveal that the proposed algorithm has the advantages of simplicity for training data collection, high accuracy for defect detection, and high flexibility for online inspection. The proposed algorithm is therefore an effective alternative for the automated inspection in smart factories with a growing demand for the reliability for high quality production.  
surface inspection; defect detection; artificial intelligence; autoencoder; convolutional neural networks +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Biochemical Composition  
#### 3.6.1 Proximate Composition  
The proteins, lipids, carbohydrates, and ashes contents were determined to further characterize the biochemical composition of the biomass resultant from both heterotrophic and photoautotrophic media optimization (Table 8).  
Proteins are the main product of several microalgal-based biorefineries [30]. Microalgae proteins are high quality since these micro-organisms can synthesize all the essential amino acids and also have functional biological activities with increased human health benefits [4; 31; 32]. Furthermore, algal sources of protein are largely used as feed additive in aquaculture [8]. The cultures grown with the optimized media registered the highest protein contents, 61.49-73.45% dry weight (DW) (\(p<0.05\)). Therefore, _C. amblystomatis_ might strengthen the microalgal protein market, which is expected to cover 50% of the total alternative protein market by 2054 [33].  
The lowest carbohydrate content was registered in the photoautotrophic cultures (\(p<0.05\)). The cells grown with a heterotrophic-optimized medium contained a much lower content of carbohydrates (14.11% of DW) than those obtained from cultures grown with heterotrophic basal medium (47.68% of DW). In microalgae, the high content of carbohydrates might be stored under unfavorable conditions at the expense of proteins [32; 34], which can also explain the low content of protein in the cultures grown with the heterotrophic basal medium.  
The cultures grown in heterotrophy registered the lowest lipid content (7.41-8.88% of DW) (\(p<0.05\)). The highest content was achieved in the cultures grown with de basal photoautotrophic medium: 19.74% of DW, which is in concordance with the results reported for this microalga [35]: 18.33% of DW, using the same cultivation medium in a 2.5 m\({}^{3}\) closed tubular photobioreactor.  
The ash contents were also similar to those reported for this microalga [35] (9.88-15.85% of DW), being the lowest value registered in the cultures grown with the optimized photoautotrophic medium (6.07% of DW) (\(p<0.05\)).
# Resource Scheduling in Cloud Computing Based on a Hybridized Whale Optimization Algorithm
## 5 Practical Simulations, Analysis, and Discussion
### Tests with Standard Benchmark Functions
#### 5.1.3 Comparative Analysis and Discussion
As a general conclusion, by conducting empirical experiments and by performing theoretical analysis, it can be concluded that the WOA-AEFS considerably improves basic WOA version by addressing its deficiencies of inadequately established balance between the exploitation and exploration and by avoiding the premature convergence behavior. However, as stated above, there always must be some kind of a compromise. The basic WOA utilizes fewer number of controls parameters. In order to control the ABC exploration and the FA's search process, the WOA-AEFS employs three more control parameters, two dynamic (\(\mathit{eir}\) and \(\alpha\)) and one static (\(\mathit{limit}\)). In addition to the comparative analysis with the basic WOA, we also wanted to see how the WOA-AEFS relates to other state-of-the-art metaheurisitcs, which results in the same benchmark instances could be found in the modern computer science literature. With this objective, we performed another comparative analysis between the WOA-AEFS and the particle swarm optimization (PSO) [93], gravitational search algorithm (GSA) [94], differential evolution (DE) [95] and fast evolutionary programming (FEP) [96]. Results for all approaches are taken from [22; 97]. As in the first comparative analysis, we used the same performance metrics as in [22]--the best values averaged over 100 independent runs (mean indicator) and the standard deviation (std). All algorithms are tested under the same experimental conditions (\(N=30\) and \(maxIter=500\)). Comparative analysis between the WOA-AEFS and the above mentioned approaches is given in Table 5. We note that we also added basic WOA in comparative analysis because we wanted to evaluate its performance against other state-of-the-art algorithms. Similarly as in the previous comparative analysis, we formatted the best results for each category of tests with bold style. We have also included an additional row, where we counted for each column the number of times a particular algorithm obtained the best results. Based on the comparative analysis with other state-of-the-art algorithms that is shown in Table 5, it can be stated that the WOA-AEFS on average performed better than all other approaches included in analysis. For example, for the standard deviation indicator, the WOA-AEFS obtained the best results even for 10 benchmarks, while in the case of the mean indicator, the WOA-AEFS performed the best for six benchmarks. The second best algorithm is DE, while the GSA is ranked as the third best algorithm included in the analysis.
###### Abstract  
The plant density of soybean is a critical factor affecting plant canopy structure and yield. Predicting the spatial variability of plant density would be valuable for improving agronomic practices. The objective of this study was to develop a model for plant density measurement using several data sets with different spatial resolutions, including unmanned aerial vehicle (UAV) imagery, PlanetScope satellite imagery, and climate data. The model establishment process includes (1) performing the high-throughput measurement of actual plant density from UAV imagery with the You Only Look Once version 3 (YOLOv3) object detection algorithm, which was further treated as a response variable of the estimation models in the next step, and (2) developing regression models to estimate plant density in the extended areas using various combinations of predictors derived from PlanetScope imagery and climate data. Our results showed that the YOLOv3 model can accurately measure actual soybean plant density from UAV imagery data with a root mean square error (RMSE) value of 0.96 plants m\({}^{-2}\). Furthermore, the two regression models, partial least squares and random forest (RF), successfully expanded the plant density prediction areas with RMSE values ranging from 1.78 to 3.67 plant m\({}^{-2}\). Model improvement was conducted using the variable importance feature in RF, which improved prediction accuracy with an RMSE value of 1.72 plant m\({}^{-2}\). These results demonstrated that the established model had an acceptable prediction accuracy for estimating plant density. Although the model could not often evaluate the within-field spatial variability of soybean plant density, the predicted values were sufficient for informing the field-specific status.  
PlanetScope; random forest; partial least squares regression; spatial variation; spectral reflectance; YOLOv3 Article  
28 June 2021
## 5 Results and Discussion
The same three ad hoc routing protocols (AODV, DSR and OLSR) were utilized to examine how these protocols improved the throughput, delay and data dropped of the WSN, as shown in Figure 5:0  
Figure 5:1 _Cont._  
As seen in the Figure above, these routing protocols improved the delay, throughput and data dropped QoS parameters of the Wireless Sensor Networks. AODV achieved the least delay and the highest throughput and AODV also improved the traffic dropped of the network.