esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Catalytic Properties of Au-ZrO\({}_{2}\)
The Au-ZrO\({}_{2}\) nanocatalyst may facilitate the transfer of an electron from BH\({}_{4}\)\({}^{-}\) ions to the nitro group of 4-nitropenol and reduce it to 4-aminophenol with improved reaction rate. Upon addition of Au-ZrO\({}_{2}\), the intensity of the absorption band of 4-nitropenol at ~400 nm gradually decreased with the emergence of a new band at ~300 nm indicating the formation of 4-aminophenol. The reaction was completed within a short period of time between 5 to 30 min depending upon the amount of catalyst used. With increasing the amount of Au-ZrO\({}_{2}\) nanocatalyst from 0.25 mL to 1 mL the reaction time decreased from 30 min to 5 min, respectively, as shown in Figure 5. Notably, in the absence of catalyst, the peak at the 400 nm decreased slightly even after a long reaction time (several hours). This clearly depicts the inefficiency of NaBH\({}_{4}\) towards the reduction of 4-nitropenol and the importance of Au-ZrO\({}_{2}\) during this process. Furthermore, the stability of the Au-ZrO\({}_{2}\) nanocatalyst was tested by repeated use of the same catalyst for the reduction of 4-nitropenol several times. The reusability of the catalyst has significant importance from both academic and commercial aspects. In this case, to evaluate the reusability of Au-ZrO\({}_{2}\), the reduction of 4-nitropenol was performed five times using the same catalyst. After every reaction, the catalyst is isolated by centrifuge and reused for another reaction. All the reusability tests are carried out using 0.25 mL concentration of catalyst; however, the substrates and catalysts are taken in large amounts to recover a sufficient amount of catalyst required for further reactions. The results of the recycling of Au-ZrO\({}_{2}\) are shown in Figure 6. The results revealed that the catalytic activity of Au-ZrO\({}_{2}\) was only slightly affected even after five cycles. During the reusability tests, the conversion of 4-nitropenol was slightly decreased in each reaction which could be due to the weight loss of catalyst while recovery (during filtration). For instance, during the first reaction using fresh catalyst, 4-nitropenol was completely converted to 4-aminophenol in 30 min, however in subsequent reactions conversion was slightly reduced after every reaction during the same reaction time, as reflected by the decrease in the intensity of the absorption peak of 4-aminophenol at ~300 nm (cf. Figure 6). Figure 5: UV spectra of 4-nitrophenol reduction by the addition of different volumes of Au-ZrO\({}_{2}\) nanocatalyst: (**a**) 0.25 mL, (**b**) 0.5 mL, (**c**) 0.75 mL, (**d**) 1.00 mL, and (**e**) digital photos of the color transformation during the conversion ofnitrophenol to aminophenol the amount of nanocatalyst was taken from freshly prepared dispersion of Au-ZrO\({}_{2}\) with a concentration of 0.25 mg/mL. Figure 6: Reusability of Au-ZrO\({}_{2}\) nanocatalyst for the reduction of 4-nitrophenol. All the reactions are performed for 30 min using the same catalyst which was recovered by centrifugation.
## 5 Results and Evaluation
### Comparative Evaluation  
We compared our protocol with two other protocols designed for mobile sensor networks. AODVjr protocol is from the group of MANET protocols, which have been modified for sensor networks. Another protocol is the Epidemic routing, which is aimed for mobile sensor networks.  
We implemented AODVjr, a simplified version of AODV protocol, which is aimed for sensor networks. It was originally proposed in [7] and we added support for multiple sinks according to [8], which is another modification of AODV protocol for anycast routing. AODV protocol belongs into the group of reactive protocols, thus when a node does not know the route to the destination, it broadcasts route request message and waits for reply with the route towards the destination.  
Figure 3: TEAR protocol performance _vs._ minimum conductivity threshold (\(\xi_{E_{MIN}}\) varies from 0 to 0.5).  
Epidemic routing is protocol for mobile sensor networks. We implemented this protocol according to a protocol design description in [9]. Epidemic protocol is based on the exchange of message vectors between newly connected neighbors and then, based on the requirements, the sensor nodes exchange required messages, which they do not have in their buffers. The performance of this protocol depends on the buffer size for messages (it stores also messages of other sensor nodes) and on the repetition of the anti-entropy period, which defines how often two connected neighbors exchange their updated message vectors. Based on the results in [9], we set the buffer size of sensor node to 100 messages and re-initiation of the anti-entropy period to 10 seconds.  
We implemented our proposed TEAR protocol based on the previously described design and we set the period for beacon (message carrying temperature value) to 1 second. Every sensor stores table of the actually connected neighbors with their temperatures and a time when the most recent beacon came.
## 4 Simulations Results and Discussion with Performance Evaluation
### Parameters to Optimize  
To obtain the best possible results, a mathematical optimization model is adopted. In this subsection, linear constraints are used to formulate the problems. The main objective of the proposed work is to minimize the EC and maximize the throughput. An efficient mechanism is proposed to monitor the constraints of objective functions.
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
## 5 Development of 1-D CNN Models  
### 1-D CNN Model Using Time-History Response  
#### 5.1.1 Database of Time-History Responses  
The concatenated time-history responses were processed with controlled noise contamination to improve the CNN model's adaptability to real-world conditions. Noise was introduced at 1-10% standard deviation levels across ten iterations. Figure 12 illustrates examples of contaminated responses compared to their original responses.  
The time-history responses were used to generate datasets for CNN model development. The responses were acquired in 15 ensembles for each damage level, of which 80% were used for training and validation and 20% reserved for evaluation. A total of 120 responses obtained from eight levels (D0-D7) were divided into 96 responses for training/validation and 24 responses for evaluation. The noise-contamination process generated 4800 responses from the 96 original responses, resulting in a dataset of 4896 responses for training and validation. The evaluation dataset was constructed using the same method to maintain the consistency of the databank structure. The evaluation dataset had 1224 responses, consisting of 24 original responses and 1200 responses generated through noise contamination.  
Figure 13 shows the 4896 responses from the foundation damage levels (D0-D7) used for training and validation. Each response consisted of 5000 data points with magnitudes ranging from -0.6 to 0.6. The time-history responses from eight damage levels were labeled accordingly before input into the 1-D CNN model.  
Figure 11: SVD responses of five accelerometer sensors.  
Figure 12: Illustration of noise contamination to time-history responses: (**a**) 5% noise; (**b**) 10% noise.
## 1 Introduction
Most immunoassay for detecting SARS-CoV-2 active infections utilize the nucleocapsid (N) or the spike protein (antigen) as targets. Many commercially available immunoassay-based COVID-19 self-tests in the US use the N protein (antigen) as a target of detection (Flowflex, BinaxNOW, QuickVue, InteliSwab, BD Veritor, iHealth) [22]. N-based antigen assays have been reported to have higher sensitivity than spike-based assays in nasopharyngeal specimens [23]. According to the literature, 1000 copies of the N protein are expressed by each SARS-CoV-2 virion, compared with an estimated 100 spike protein trimers, which equates to 300 monomers [24]. Here, we target the N protein as our diagnostic marker for SARS-CoV-2 infection in nasal samples. In this work, we report the use of a magneto-controlled immuno-capture of the SARS-CoV-2 N protein in nasal samples and subsequent EC detection using gold nanoparticle (AuNP) labels (Figure 1). The feasibility of using AuNP labels for EC detection on an SPCE has been well established. The authors direct the interested reader to several noteworthy resources wherein the characterization of the EC mechanism has been described theoretically and experimentally [25; 26; 27; 28]. Magnetic beads (MBs) conjugated with anti-nucleocapsid protein were used to preconcentrate the N protein from nasal samples while simultaneously labeling with AuNPs, forming a sandwich. We identified the ideal size for AuNPs to be used in the assay by considering both the strength of the signal they produce and the ease of attaching antibodies to them. We also determined the ideal deposition time that yields a maximum EC signal and lower noise. Utilizing these parameters, we can report the detection of the N protein in PBS buffer and COVID-19-positive nasal samples. To the best of our knowledge, this is the first protocol to demonstrate the simultaneous magnetic particle preconcentration and AuNP labeling of human-nasal-sample-extracted N proteins for EC detection. Once generated, the MB-N protein-AuNP sandwich is transferred to an SPCE for EC detection utilizing Differential Pulse Voltammetry (DPV).
# An Innovative Enhanced JAYA Algorithm for the Optimization of Continuous and Discrete Problems
## 3 Research Methodology (Proposed Algorithm)
### Modified JAYA Algorithm
#### 3.2.3 Applying Solutions' Position  
The modification has been implemented here. Referring back to the standard version of JAYA in Section 3.1, two members, the best and worst members, have the highest involvement in updating the positions of all individuals. This, in turn, may have a negative effect by causing the algorithm to become stuck in local minima due to reduced population diversity. Therefore, some modifications are introduced here to tackle this problem. This is elaborated as follows:  
\[\begin{array}{l}\text{for}\;n\;=\;1:\;\text{PS}\;\text{do},\\ \text{for}\;m\;=\;1:\;\text{DV}\;\text{do},\\ \text{if}\;r_{j}<AW\\ X_{n,m}^{t+1}=X_{n,m}^{t}+rand[0,\;1](X_{m,best}^{t}-|X_{n,m}^{t}|)-rand[0,\;1] (X_{m,worst}^{t}-|X_{n,m}^{t}|)\\ \text{else}\\ MU=2\;\times\;\text{Iter}\;\times\;(a/MI)\\ X_{n,m}^{t+1}=X_{m,best}^{t}-(2\;\times\;\text{MU}\;\times\;\text{rand}[0,\;1] -\text{MU})\;\times\;\text{abs}(2\;\times\;X_{m,best}^{t}-X_{n,m}^{t})\\ \text{end}\\ \text{end}\\ \text{end}\\ \text{end} \tag{7}\]  
Here, \(AW\) is a user-defined parameter ranging between [0.1, 0.9], Iter is the iteration counter, and \(MI\) signifies the maximum number of iterations. \(MU\) demonstrates a self-adaptive mutation.  
As can be seen, compared to the original JAYA algorithm, which uses a single learning strategy, the position in the proposed method is updated in two steps, which leads to better population diversity. Here, \(r_{j}\) denotes a random number and \(a\) is a constant number that takes a value between 1 and 5.
# Mechanical Behavior of Ti6 Al4V Scaffolds Filled with CaSiO\({}_{3}\) for Implant Applications
## 3 Biomaterial Production and Characterization  
Figure 2 shows the five different TPMS scaffolds with a diameter of 20 mm and a 15 mm height. The five different TPMS scaffold types are: (a) Rectangular with a vertical flat area, (b) primitive, (c) lattice, (which is a very common type of structure produced using SLM [32]), (d) gyroid and (e) honeycomb (which is another common structure that is produced by SLM). Similarly Figures 3-7 exhibits the stress distribution and deformation in the five different TPMS scaffolds.  
Figure 4: Primitive scaffold simulation: (**A**) Stress and (**B**) displacement (final height after compression \(15-6.91=8.09\) mm; the red dots are separated particles in contact with compressive punches).  
Figure 3: Rectangular scaffold simulation: (**A**) Stress and (**B**) displacement (final height after compression: \(15-4.26=10.74\) mm; red dots are separated particles in contact with compressive punches).  
Figure 2: Scaffolds (their weight indicated in parentheses) with: (**A**) Rectangular (3.97 g), (**B**) Primitive (5.35 g), (**C**) Lattice (3.78 g), (**D**) Gyroid (4.04 g) and (**E**) Honeycomb (5.52 g) structures with 20 mm diameter and 15 mm height manufactured by selective laser melting.  
The 99.9% purity wollastonite (CaSiO\({}_{3}\)) with a particle size of 1-5 \(\upmu\)m (supplied by NYAD, grade 1250) is filled and sintered (frittage) inside an argon-atomized Ti6 Al4V cellular lattice structure via an SPS machine (made by FCT Systeme), as shown in Figure 8. The process was performed with an optimal pressure of 30 MPa at a temperature of 1100 \({}^{\circ}\)C, with a heating rate of 100 \({}^{\circ}\)C/min and a holding time of 5 min [33].  
Figure 5: Lattice scaffold simulation: (**A**) Stress and (**B**) displacement (final height after compression \(15-9.11=5.89\) mm; red dots are separated particles in contact with compressive punches).  
Figure 6: Gyroid scaffold simulation: (**A**) Stress and (**B**) displacement (final height after compression \(15-8.1=6.9\) mm; these red dots are separated particles in contact with compressive punches).  
Figure 7: Honeycomb scaffold simulation: (**A**) Stress and (**B**) displacement (final height after compression \(15-4.24=10.76\) mm; red dots are separated particles in contact with compressive punches).  
Wollastonite composition consists theoretically of 48.28% CaO and 51.72% SiO\({}_{2}\), while usually, the natural mineral may contain small amounts of iron, aluminum, magnesium, potassium and sodium (Figures 8A and 9A). An SLM-manufactured Ti6 Al4V lattice with 1 mm cell size filled by CaSiO\({}_{3}\) powder and sintered in SPS (Figures 8B and 9B) are shown. The results show a good boundary between ceramic and lattice after SPS and polishing (Figures 8C and 9C).
# An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
## 4 Numerical Results and Validation
### Model Parameters  
The specific parameters are shown in Table 1.  
Most of the energy excited by the guide rail is located at low frequencies. The white noise signal passing through the low-pass filter is selected to simulate actual rail excitation. To approach the actual excitation signal of the guide rail more closely, select the appropriate amplitude of white noise. The white noise signal is output through the model in Simulink. Set the noise power to \(1\times 10^{7}\), the sample time to \(1\times 10^{3}\), and the seed to 23,341. The transfer function of the low-pass filter [26] is shown in Equation (18):  
\[w_{d}=\frac{2\pi\times 10}{s+2\pi\times 10} \tag{18}\]  
In practical applications, both the magnitude of the control force that activates the actuator and the travel of the mechanism should be considered while suppressing elevator vibration. Therefore, control costs should be limited so that the designed controller can  
Figure 4: Flow chart of multi-objective genetic algorithm.  
meet actual needs. The optimal design model obtained after limiting the control costs is as follows:  
\[\begin{array}{c}min:J_{1}\;and\;J_{2}\\ subjectto:p_{1}=max(Re(eig(LFT(G,C))))<0\\ p_{2}=J_{2}<J_{2 max}\\ q\in(q_{0},q_{1})\\ Designvariable:q\end{array} \tag{19}\]  
The corresponding regularization coefficients are: w\({}_{1}\) = 1000, w\({}_{2}\) = 10, w\({}_{3}\) = 0.02, \(w_{4}\) = 0.001.  
When using the MOGA algorithm to optimize the parameters, it is necessary to determine the search interval of the controller parameters first. Due to the elevator system being a complex MIMO system, the root locus method or the Routh Hurwitz method used to determine the search interval of the SISO system is no longer applicable. The initial interval of MIMO system parameters is generally determined using random area testing or decision statistics.  
The FOPID controller designed in this paper is similar in structure to a PID controller. All parameters of the controller are nonnegative, and the lower bound of the initial search interval does not need to be searched. However, the designed controller has many parameters; it is hard or even unable to find a solution when all parameters are tested in a random area together. Referring to the parameter tuning method of conventional PID controllers, the parameter range of the designed controller is determined step by step, and the parameter range is gradually optimized in the order of proportional parameters, integral parameters, and differential parameters, as shown in Figure 5.
# Semantic Similarity Analysis for Examination Questions Classification Using WordNet
###### Abstract  
Question classification based on Bloom's Taxonomy (BT) has been widely accepted and used as a guideline in designing examination questions in many institutions of higher learning. The misclassification of questions may happen when the classification task is conducted manually due to a discrepancy in the understanding of BT by academics. Hence, several automated examination question classification systems have been proposed by researchers to perform question classification accurately. Most of this research has focused on specific subject areas only or single-sentence type questions. There has been a lack of research on question classification for multi-sentence type and multi-subject questions. This paper proposes a question classification system (QCS) to perform the examination of question classification using a semantic and synthetic approach. The questions were taken from various subjects of an engineering diploma course, and the questions were either single- or multiple-sentence types. The QCS was developed using a natural language toolkit (NLTK), Stanford POS tagger (SPOS), Stanford parser's universal dependencies (UD), and WordNet similarity approaches. The QCS used the NLTK to process the questions into sentences and then word tokens, such as SPOS, to tag the word tokens and UD to identify the important word tokens, which were the verbs of the examination questions. The identified verbs were then compared with the BT's verbs list in terms of word sense using the WordNet similarity approach before finally classifying the questions according to BT. The developed QCS achieved an overall 83% accuracy in the classification of a set of 200 examination questions, according to BT.  
question classification; bloom taxonomy; similarity; POS tagger; universal dependencies
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 3 System Model  
In this section, we consider a new kind of distributed resources allocation structure based on the slicing technology of the space-ground integrated communication system.
## 4 Discussion
Secondly, a direct validation was made with experimental measures [26]. The experimental results demonstrated the validity of the hypotheses of our simplified model. Thus, in all cases, the variance explained by the knee angle was very high (lower quartile 98.5%), which confirms the hypothesis that there is a single fDOF. Therefore, all generalized coordinates could be predicted accurately from one main variable (knee flexion in this movement). This result has already been verified in other joints when cyclical repetitions are performed [42]. Regarding the hypothesis of the quasistatic regime, there is a practically perfect correspondence between the estimates associated with the FDM and SGM models. Thus, the ICC between both models is higher than 0.999, with errors lower than 0.1% of the joint moment range. This shows that, in slow movements such as those usually applied in rehabilitation, the inertial forces are negligible. The coherence between the estimated forces and the EMG signals in the active muscles (vastus lateralis and vastus medialis) is significant. The correlation coefficients were greater than 0.931 in all cases. Furthermore, the correspondence between the forces estimated by the model and those obtained by the Force-EMG calibration curves was also very good, with ICC values higher than 0.966 and an SEM of the order of 5% of the range. Note that this verification was performed with different exercises than those used to obtain the calibration curves. Although this validation is indirect, since the forces in the tendons have not been measured, it shows that the model is consistent with the levels of muscle activation. Finally, the correspondence between the estimates of the joint forces of the model and those measured experimentally in the SGM has also been verified. The ICC obtained for the compression force is 0.945, with an error of 6% over the range of the registered force. For tangential forces, the result was somewhat worse, but in any case, very good, with an ICC of 0.827 and an error of 10% of the range. Obviously, the results obtained in the direct validation correspond to a single subject, and we should expand the sample to include other subjects and exercises. However, the results of this work confirm that assuming the appropriate hypotheses and adjusting the model to the essential muscles, it is possible to obtain a very simple model capable of offering estimates that are consistent with muscle activity and in vivo measurements of joint forces. Moreover, the stationary geometric approach used is compatible with performing offline calculations, which will help provide efficient models that are capable of operating in real time. These are essential requirements for its use in the control of rehabilitation robots.
# Topology-Aware Anchor Node Selection Optimization for Enhanced DV-Hop Localization in IoT
###### Abstract  
Node localization is a critical challenge in Internet of Things (IoT) applications. The DV-Hop algorithm, which relies on hop counts for localization, assumes that network nodes are uniformly distributed. It estimates actual distances between nodes based on the number of hops. However, in practical IoT networks, node distribution is often non-uniform, leading to complex and irregular topologies that significantly reduce the localization accuracy of the original DV-Hop algorithm. To improve localization performance in non-uniform topologies, we propose an enhanced DV-Hop algorithm using Grey Wolf Optimization (GWO). First, the impact of non-uniform node distribution on hop count and average hop distance is analyzed. A binary Grey Wolf Optimization algorithm (BGWO) is then applied to develop an optimal anchor node selection strategy. This strategy eliminates anchor nodes with high estimation errors and selects a subset of high-quality anchors to improve the localization of unknown nodes. Second, in the multilateration stage, the traditional least square method is replaced by a continuous GWO algorithm to solve the distance equations with higher precision. Simulated experimental results show that the proposed GWO-enhanced DV-Hop algorithm significantly improves localization accuracy in non-uniform topologies.  
Internet of Things; node localization; DV-Hop; anchor node selection Article