esa-sceva/satcom-chunk-collection huggingface dataset:
# Zeroes: Robust Derivative-Based Demodulation Method for Optical Camera Communication
## 3 Method Description
### First-Order Differential Calculation  
Once the extracted signal is correctly filtered by the moving average and shaped by normalization, the first-order derivative \(x_{n^{\prime}}\) is computed while setting all the points with an absolute derivative value lower than the threshold \(T_{h}\) to zero (Figure 7). In fact, removing the low-amplitude variations of the derivative provides an improvement in the identification of the logical state changes, lowering the probability of a false zeros detection. Since the derivative is computed on a normalized signal \(x_{n}\), the value of \(T_{h}\), defining the minimal amplitude of a lobe, remains the same from one image to another and is fixed to 1, assuming that the low noisy fluctuations derivative of the normalized signal would not exceed 1. However, the derivative thresholding results in the emergence of isolated points in the signal, characterized by a derivative sign different from its neighbor points. When a sudden change in the derivative sign occurs on a single point, the corrupted values of \(x_{n^{\prime}}(i)\) become equal to the average of its neighboring values \(x_{n^{\prime}}(i-1)\) and \(x_{n^{\prime}}(i+1)\), as follows:  
Figure 5: Representation of the extracted signal.  
Figure 6: Representation of the extracted signal after moving average filtering and normalization.  
\[{x_{n}}^{\prime}(i)=\begin{cases}\frac{{x_{n}}^{\prime}(i-1)+{x_{n}}^{\prime}(i+1)}{ 2},&\text{if }sgn(x_{n}^{\prime}(i))\
eq sgn(x_{n}^{\prime}(i-1))\\ &\text{and }sgn(x_{n}^{\prime}(i))\
eq sgn(x_{n}^{\prime}(i+1))\\ &\text{}\forall i\in[1,i_{max}-1]\\ {x_{n}}^{\prime}(i),&\text{otherwise}\end{cases} \tag{4}\]  
where \(sgn()\) is the sign function. Then, the thresholding operation described above is once again repeated in order to eliminate the resulting values with an absolute value below \(T_{h}\).  
As illustrated in Figure 7, the derivative filtering mechanism described above combined with the moving average ensures a nearly free-noise derivative by setting all the values between the red dotted lines to zero and smoothing the outlier values.
# Early Fault Detection and Operator-Based MIMO Fault-Tolerant Temperature Control of Microreactor
## 9 Conclusions  
In this study, the fault-tolerant control system was designed for the MIMO system that controls the temperature of two regions of the microreactor system. This extends the fault-tolerant control system, which was effective for only one region in previous studies. In addition, the fault detection system combining One-Class SVM and ChangeFinder was applied to detect faults; for training the One-Class SVM, the evaluation function for the classification model was created, and the hyperparameters were optimized by the real-coded genetic algorithm. Finally, the effectiveness of the system was verified through simulations and experiments. The fault detection system combining One-Class SVM and ChangeFinder detected faults even when the amplitude of the fault elements was small. The results show that the system is more effective than One-Class SVM alone in detecting failures in microreactor systems.  
[PERSON] used a combination of One-Class SVM and ChangeFinder for training to create the fault classification model for temperature sensors. Parameter optimization of the fault classification model was performed using a real-coded genetic algorithm. In addition, we proposed a method to extend the fault-tolerant temperature control system of the microreactor to a MIMO fault-tolerant temperature control system, and proposed a control system combined with fault classification. Simulation and actual experiments were also conducted and this paper was written. M.D. suggested technical support and gave overall guidance on the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 3 Methodology
### Correlated Multi-Objective Energy-Efficient Routing Scheme for SDWSN-IoT
#### 3.2.5 Algorithm Time Complexity Analysis  
When dealing with RL-based routing protocols, making the time-complexity of that algorithm is crucial for reasons such as: making the performance analysis and determining if the algorithm is suitable for the intended application; resource allocation; and assessing how well the algorithm scales with the increasing problem size, real-time, and dynamic environment requirements. Study [40] suggests that the time complexities of RL algorithms require updating the Q-table of each state \(\forall s_{t}^{i}\in S_{i}\) for each state in the state space \(S\). This means, in a worst-case scenario, the time complexity of the RL-based algorithm is when the learning agent visits all the states in the state and action spaces. This suggests that the time complexity of RL algorithms depends on the size of the state space. The state space size is \(O(N^{2})\), where \(N\) could be the number of nodes in the network, the amount of traffic between a source node and a destination node (traffic matrices), or static vectors. The size of the action space is limited to the \(k\) potential paths associated with each state. The complexity for RL is, then, \(O(kN^{2})\), where \(k\) is a constant.
# Discrete Semantics-Guided Asymmetric Hashing for Large-Scale Multimedia Retrieval
Specifically, DSAH handles the nonlinear relations in different modalities with a kernelization technique, then an asymmetric learning scheme is proposed to effectively perform the hash function learning and hash code learning processes; meanwhile, our proposed DSAH considers the following aspects. First, we leverage both label information and similarity matrix to enhance the semantic information of the learned hash codes. Then, we solve the problems of matrix sparsity and outlier processing. In addition, a discrete optimization algorithm is proposed to solve the discrete problems. Our major contributions can be summarized as follows:  
1. A novel supervised cross-modal hashing method, i.e., DSAH, is proposed to learn the discriminative compact hash codes for large-scale retrieval tasks. DSAH takes the label information and similarity matrix into consideration, which can improve the discriminative capability of the learned hash codes, and solves the problems of matrix sparseness and outlier processing. 2. An asymmetric learning scheme with real-valued embeddings is proposed to effectively learn the hash function and the hash codes. 3. Comprehensive experiments are conducted on two famous datasets. The experimental results demonstrate that DSAH outperforms some state-of-the-art baselines. The remainder of this paper is organized as follows. Section 2 briefly reviews the related works. Section 3 introduces the details of the DSAH model and presents the alternative optimization algorithm. In Section 4, we give the results of experiments performed. Finally, we present the conclusions in Section 5.
# A PUF- and Biometric-Based Lightweight Hardware Solution to Increase Security at Sensor Nodes
## 2 Application Scenarios
### Sensor Nodes Used for Regulation Compliance  
Many regulations that prevent occupational and environmental risks establish that the measurements of certain variables should meet accepted levels (for example, levels of noise, radioactivity, carbon-dioxide emissions, etc.). Environmental, occupational, health and safety inspectors perform on-site analysis of those variables to prevent harm to the environment, citizens, and workers. They wear sensor nodes conveniently calibrated and tested to take the needed measurements.  
Since the sensors proposed herein send ciphered measurements to a base station, falsification of the data in order to bypass the regulations is more difficult. In this scenario, ciphering may not be enough. Fake sensors measuring false data as well as impostor inspectors assuming the identity of a legitimate one should be avoided because otherwise, dangerous environments can be certified as healthy. Repudiation attacks are reduced because it can also be proven that wrong or malicious messages were sent by the sensor of an inspector who carried out wrong evaluations or who became malicious.
# Development of a Self-Powered Magnetorheological Damper System for Cable Vibration Control
## 2 Description of the Self-Powered MR Damper System for Cable Vibration Control
### Linear-to-Rotational Conversion  
To ensure that the reciprocating linear motion of the stay cable can be converted into the bidirectional rotational motion of the sprocket and the rotary DC generator, a pre-tensioned spring is connected to the chain to guarantee that the chain is able to move back and forth with the cable (Figure 1). The stiffness coefficient of the spring should be suitable so that the chain can always keep straight without providing excessive extra tension force to the stay cable. In addition, the initial elongation of the spring should be larger than the predicted amplitude of the cable at the location of the generator. Thus, the linear motion of the cable is successfully translated into the rotational motion of the rotary DC generator. The relationship between the angular velocity of the generator \(\dot{\theta}\) and the linear velocity of the stay cable \(\dot{u}\) is given as  
\[\dot{\theta}=\eta\dot{u} \tag{1}\]  
where \(\eta\) is the transmission efficiency of the linear-to-rotational conversion, expressed as  
\[\eta=\frac{1}{r}\dot{u} \tag{2}\]  
where \(r\) is the effective transmission radius of the sprocket.  
Figure 1: Schematic diagram of the self-powered MR damper system for cable vibration control.
# Charting Past, Present, and Future Research in the Semantic Web and Interoperability
## 1 Introduction
This is because data utilization requires sources to be linked or interoperable to be accessible by end-users. This standardization and interoperability can be addressed by developing the knowledge graphs used by organizations such as Google, Uber, Amazon, Facebook, and Netflix. A novel approach is to create a Decentralized Knowledge Graph (DKG), which has been made possible by combining graph technology and blockchain technology. Blockchain provides a consensus layer that verifies the authenticity of the data entering the DKG. It is gated by an identifier of source (i.e., data provenance) and conforms to W3C standards. Smaller digital technology firms such as Slovenia-based OriginTrail have developed an open-source ecosystem comprising tools and protocols that connect blockchain to blockchain and blockchain to legacy. While OriginTrail has numerous case studies [41; 42] they have migrated towards implementing a DKG with full compliance to W3C and GS1 standards in supply chains. A significant development is the transition from stove-piped data held in organizational siloes to interoperable Web3-based ecosystems where multiple datasets can be discovered, queried, and integrated. Thus, this paper developed from the need to understand how the World Wide Web is currently using data and to define how the semantic web can use it. The research presented here will help define how the future development of a DKG can evolve using semantic web technology, and we posit that DKGs will be transformative for industrial and social systems. Finally, newcomers to this intriguing and relevant discipline will benefit from the study's self-contained nature. More specifically, in this study, we aim to answer the following research questions:  
1. How has semantic web and interoperability research evolved since its inception? 2. What countries are at the intersection and forefront of semantic web and interoperability research? 3. Which scholars are the most contributive to the field? 4. What kinds of collaborative relationships exist between countries and institutions in this field? 5. What is the present status of research, and what are the future directions in the semantic web and interoperability field? The paper continues as follows. Section 2 presents the methodology used in the review. In Section 3, the descriptive results are analyzed, followed by the results from the network analysis in Section 4. In Section 5, we perform a content analysis based on co-citation clustering, and next, we discuss the review's findings, its limitations, and potential suggestions for future research. Finally, we briefly conclude the work.
## 1 Introduction
Although many scholars have carried out research on the permeability coefficient, most of the research objects are a single type of soil, and the tailings are a kind of sand-silt-clay mixed soil structure in the accumulation process of actual engineering, so it is necessary to study the prediction model of the tailings permeability coefficient with different particle sizes. In general, the previous studies mainly focused on the discussion of the macroscopic mechanical properties of tailings, but the structural characteristics and permeability characteristics of tailings from a microscopic perspective were rarely studied. The microscopic pore structure and particle morphology of tailings can well characterize their physical properties, which are of great significance for the microscopic analysis of tailings materials ([PERSON], 2018). In recent years, with the advancement and innovation of image processing technology (DIP), X-ray computed tomography (X-ray CT) has been widely used in the field of material microscopy; it can be used to perform 3D imaging and nondestructively analyze the microscopic features of objects ([PERSON], 2018). This technology has been widely used in the study of rock and soil, but the study of the microstructure and permeability characteristics of tailings has rarely been reported. In this paper, the Zeiss Xradia 410 Versa (Oberkochen, Germany) high-performance micron CT scanner was used to scan tailings samples with different particle sizes, and the microscopic information of the tailings was obtained to characterize the morphological characteristics of the grains. Based on the pore throat model of the tailings, a microscopic seepage simulation was carried out in combination with the indoor variable head test, and a feasible permeability coefficient model based on different particle size tailings was proposed, which provided a new method for the study of the permeability characteristics of tailings, and at the same time, provided a new perspective for the study of permeability characteristics of other types of soil.
Sensors_**2012**, _12_, 15308-15337; doi:10.3390/s121115308  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Decentralized Cooperative TOA/AOA Target Tracking for Hierarchical Wireless Sensor Networks**  
**[PERSON] and [PERSON] ***  
Department of Electrical Engineering, Graduate Institute of Communication Engineering,  
National Chung Hsing University, Taichung 402, Taiwan; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-4-2285-1549 (ext. 811); Fax: +886-4-2285-1410.  
_Received: 6 July 2012; in revised form: 23 October 2012 / Accepted: 1 November 2012 / Published: 8 November 2012_  
**Abstract:** This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.  
**Keywords:** wireless sensor networks; target tracking; Bayesian filtering; neural networking; covariance intersection
## 8 Chitosan-Based Nanoparticles for Posterior Segment of Eye
### Bacterial and Fungal endophthalmitis  
The human eye is susceptible to various bacterial infections due to its constant exposure to the environment. Bacterial endophthalmitis is an ocular inflammation caused by bacterial species of _Staphylococcus aureus_, methicillin-resistant _S. aureus_ (MRSA) and Streptococcus species that causes irreversible damage to the photoreceptor cells in the posterior segment of the eye, leading to blindness.  
Daptomycin is a conventional lipopeptide antibiotic used against gram-positive bacteria, which is approved for the treatment of the bacterial endophthalmitis. Direct administration of daptomycin leads to an insufficient concentration of the drug at the site of action due to lacrimal fluid secretion and protective barriers. The development of chitosan [79] or chitosan-coated alginate nanoparticles [80], which can act as drug carriers for daptomycin, provides an alternative for improvising the efficiency and residence time. The favorable properties of chitosan and alginate contributing in the opening of tight junctions present between the epithelial cells allow enhanced intraocular drug transport.  
Amphotericin B is widely explored for fungal endophthalmitis. Amphotericin B administration to the eye has been studied using various approaches including corneal shields, intravenous and intracameral injections. However all these approaches have certain limitations, like chances of expulsion, diminished ocular bioavailability, chances of developing cataract. Chitosan, when conjugated with lecithin, a phosphatidylcholine, helps to improvise the mucoadhesive properties and increases the delivery of Amphotericin B to eye [81]. Table 2 summarizes chitosan-based nanoparticles and the potential applications in ophthalmology.
## 4 Applications in Optimization
### Differential Evolution (DE)
#### 4.2.3 Evolutionary Chaos Control Optimization
In this way, optimal solutions were obtained securing very fast and precise stabilization for a convenient cost function surface in the \(CF_{SIMPLE}\) case and a very chaotic and nonlinear cost function surface in the \(CF_{UNIVERSAL}\) case.
## Materials and Methods
### In Vitro Isothermal Adsorption
MS analysis was performed with an electrospray ionization (ESI) interface and operated in a positive ion mode. The spray voltage was maintained at \(5\) kV. The source temperature was kept at \(500\)\({}^{\circ}\)C. The mass spectrometer was monitored at \(m/z\)\(305.1\) to \(169.2\) for precursor and product ions of diazinon [38]. For paraquat, LC/MS/MS with a hydrophilic interaction liquid chromatography (HILIC) column (\(2.1\times 100\) mm, \(3\)\(\mu\)m) at \(30\)\({}^{\circ}\)C was used to determine its concentration. Separation using a mobile phase containing \(10\) mM ammonium acetate with \(0.1\%\) formic acid (eluent A) and acetonitrile (eluent B) was carried out at a flow rate of \(0.2\) mL/min and injection volume of \(10\)\(\mu\)L. The following gradient program was used for elution: \(60\%\) eluent B (initial), \(60\)-\(20\%\) eluent B (from \(0\)-\(7\) min) and \(20\%\) eluent B (\(7\)-\(8\) min). MS analysis was operated in positive mode with capillary voltage at \(4.5\) kV. The source temperature was kept at \(350\)\({}^{\circ}\)C. Positively charged molecular ions of paraquat were monitored for precursor and products at \(m/z\)\(186\) to \(171\) and \(155\)[39]. For both methods, the mass spectrometer was operated under multiple reaction monitoring (MRM) mode. The unit mass resolution was used for ion mass analyzers. The enhanced product ion (EPI) scan rate was \(1000\) amu/s, and the scan range was \(106\) to \(396\) amu. Nitrogen gas was used as the collision and curtain gas, and argon gas was used as the nebulizer and heater gas. Empower(r) analyst software was used to control the LC/MS/MS system and acquire the data. The limits of detection (LOD) for each toxin were \(500\) ppb for PCP and \(2,4,6\)-TCP, \(5\) ppb for lindane and linuron, \(12.5\) ppb for diazinon, \(0.1\) ppb for trifluralin and \(10\) ppb for paraquat, with excellent reproducibility and sensitivity of the detection methods. Standard toxin solutions were spiked before and after \(2\) h of agitating, and the relative standard deviations (RSD) were \(<\)\(5\%\), showing a high recovery percentage and limited nonspecific binding. The detection methods were validated using standard calibration curves. Standard solutions of each toxin were individually prepared in mobile phase at concentration gradients between \(25\) ppm and \(0.1\) ppm to plot the standard curves. The standard curves of all toxins were linear (r\({}^{2}>0.99\)) between signal intensity and toxin concentration.
## 3 Results and Discussion
### Residual Drift Ratio  
The residual drift ratio \(D_{r}\) at each drift ratio level can be calculated by Equation (5):  
\[D_{r}=\frac{|D_{r}^{+}|+|D_{r}^{-}|}{2} \tag{5}\]  
where \(D_{r}^{+}\) and \(D_{r}^{-}\) are the positive and negative drift ratios when the lateral load in the first cycle of each drift ratio level is 0. In Figure 14, the relationship between the residual drift ratios and drift levels for all the specimens is presented. In general, when the drift ratio was less than 1%, all the specimens' residual drift ratio was relatively small and similar. This could be because all the specimens were in the elastic stage at that moment. Therefore, the specimens could return to their original position when the load was removed. At a larger  
Figure 13: Equivalent viscous damping ratios of the specimens: (**a**) Hysteretic loop; (**b**) All column specimens; (**c**) BMRC1 and NEWRC1; (**d**) NEWRC1, 2 and 3; (**e**) NEWRC1 and 4; (**f**) NEWRC1 and 5; (**g**) NEWRCF.  
drift ratio of 4%, all the NEWRC columns had a residual drift ratio lower than 1%, which indicates that the self-centering capacity of the NEWRC columns is better than BMRC1.  
In Figure 14, the residual drift ratio of BMRC1 and NEWRC1 is compared. It is evident that after a 1.5% drift ratio, the residual drift ratio of BMRC1 is significantly higher than NEWRC1. The residual drift ratios of NEWRC1 at 4% and 8% drift ratio is 56.2% and 26.5% smaller than that of the specimen BMRC1. Hence, it can be concluded that the material strength has an apparent impact on the residual drift ratio. On the other hand, the transverse reinforcement spacing had no apparent influence on the residual drift ratio, as the residual drift ratio curve for NEWRC1, NEWRC2, and NEWRC3 were similar, as shown in Figure 14.  
Further, in Figure 14, the residual drift ratio between NEWRC1 is compared with NEWRC4 and NEWRC5. From the comparison made, it can be observed that NEWRC4 has a slightly lower residual drift ratio than NEWRC1. Hence, it can be concluded that an increase in the axial load ratio reduces the residual drift ratio by a small margin. In the case of NEWRC5, until the 5% drift ratio, the residual drift capacity of NEWRC5 and NEWRC1 is similar. However, after that, NEWRC1 has a slightly higher residual drift ratio than NEWRC5.  
In Figure 14, also the residual drift ratio of NEWRC5 is shown. At a larger drift ratio of 3.5%, the residual drift ratio of NEWRC7 is smaller than the repairable limit of 1%, as specified by [36]. Hence, it can be concluded that NEWRC exhibits excellent residual drift capacity even at a higher drift ratio.  
Figure 14: Residual drift ratios at different drift ratios: (**a**) All column specimens; (**b**) BMRC1 and NEWRC1; (**c**) NEWRC1, 2, and 3; (**d**) NEWRC1 and 4; (**e**) NEWRC1 and 5; (**f**) NEWRCF.