esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on Operator Allocation in Consideration of Fatigue in Cell Manufacturing System +
## 2 Methods
### Learning Rate with Fatigue  
The decrease in physical and cognitive resources that accompanies an increase in the number of tasks results in fatigue, which has been shown to negatively affect the progress of learning [16]. Accumulated fatigue can lead to a decline in operators' judgment, decision making, and reaction time [37; 38]. Furthermore, it has been shown that low levels of fatigue improve operator performance, while high levels of fatigue negatively affect operator performance [39; 40]. Examples of tasks with high levels of fatigue include heavy-load tasks and tasks with long cycles [41].  
[PERSON] et al. [17] proposed Equation (4), which indicates a learning index that takes fatigue into account. This equation incorporates the effect of fatigue accumulation in the learning index by setting a fatigue index for each task. This is a crucial consideration, as fatigue accumulated through the repetition of tasks can deteriorate operator performance. Understanding and accounting for this factor is essential in our efforts to optimize operator performance.  
\[\alpha(n)=\alpha+f_{p}+1-\exp\Bigl{(}f_{p}\left(n^{f_{p}}-1\right)\Bigr{)} \tag{4}\]  
Figure 1: Learning curve (learning rates of 70%, 80%, and 90%).  
where \(\alpha(n)\) is learning index that varies with the number of iterations \(n\). \(\alpha\) is shown in Equation (3). Also, \(f_{p}\) (\(0\leq f_{p}\leq f_{u}\)) is the fatigue index, with \(f_{u}\ \in\ [0,1]\). A small value of the fatigue index \(f_{p}\) indicates a slow rate of fatigue accumulation, while a large value of the fatigue index \(f_{p}\) indicates a fast rate of fatigue accumulation. The fatigue index \(f_{p}\) is a task-specific value. By incorporating \(\alpha(n)\) from Equation (4) into Equation (2), the assembly time of the \(n\)th unit can be estimated, taking fatigue into account. Figure 2 shows the learning-fatigue curves when the learning rate is 80% and the fatigue index \(f_{p}\) is 0.15, 0.20, and 0.25.  
From Figure 2, the accumulation of fatigue at the end of the workday results in a U-shaped curve. This is consistent with the study of [PERSON] et al. (2019), who found that the performance of most operators subjected to learning and fatigue effects improved to a certain threshold and worsened beyond it.
# Mechanical Performance of Built-Up Columns
## 4 Numerical Analysis
### Effect of Web Height of Connector  
According to the experiments, the connectors having web height of 40 mm cannot effectively restrain the deformation of tubes, so the web height of the connector becomes a factor to be considered in the numerical analysis. The influence of web height of the connector on the mechanical performance of built-up column was analyzed by means of a dimensionless parameter named as web height/spacing ratio, that is, the ratio of the web height of connector to the spacing between tubes. The models for comparison were established based on S1 and the web height of the connectors was 50 mm, 60 mm, 80 mm and 100 mm respectively, and the corresponding web height/spacing ratios were 0.25, 0.3, 0.4 and 0.5 respectively. The number of screws in each junction were kept at two.  
The change of bearing capacity with the web height/spacing ratio is shown in Figure 12. From the figure, we can see that the bearing capacity of the built-up column increased obviously with the increase of web height/spacing ratio. When the web height/spacing ratio is 0.5, the local plastic deformation of tubes was observed during the post-buckling period (see Figure 13). This indicates that with the increase of the web height/spacing ratio, the ability of the connector to restrain the deformation of tubes is improved, and the composed action between tubes is also strengthened. However, when the parameter increases from 0.4 mm to 0.5 mm, the bearing capacity of the built-up column increases only 2.6 %. The increasing range decreases significantly after the parameter is greater than 0.4. This shows that the increase in the web height/spacing ratio does not result in a sustained significant increase in the bearing capacity of the built-up column. Therefore, for the built-up column with length of 3000 mm, the ratio of the web height of connector to the spacing between the tubes is suggested to be 0.4.  
Figure 11: Numerical simulation results and details (**a**) Load-axial shortening curves of S1; (**b**) Curve of spring force and displacement.  
Figure 12: Effect of web height of connector and adding connector at column ends.  
Figure 10: Mises stress of specimens (unit: N/m\({}^{2}\)).
## 3 ADRC Design and Parameterization
### TD Design  
As an important component of ADRC, the TD mainly enables the differential extraction of rotational speed signals for motor systems, tracks the given speed, and avoids the amplification of noise that occurs with classical differentiation. The speed overshoot issue brought on by the usage of PI controllers in the current loop is improved by the TD's arrangement of the transition process to take into consideration the \"tolerance\" of the motor control object. The transition mechanism set up by the PMSM speed control system for a particular \(\overline{\omega}_{m}\) is represented by Equation (28). The following methods can be utilized with the PMSM speed control system:  
\[\left\{\begin{array}{l}fh=fhan\Big{[}\omega_{ref}(t)-\overline{\omega}_{m}, \dot{\omega}_{ref}(t),r_{0},h\Big{]}\\ \omega_{ref}(t+h)=\omega_{ref}(t)+h\cdot\dot{\omega}_{ref}(t)\\ \dot{\omega}_{ref}(t+h)=\dot{\omega}_{ref}(t)+h\cdot flt\end{array}\right. \tag{28}\]  
where \(\overline{\omega}_{m},\omega_{ref},\dot{\omega}_{ref}\) stands for the specified angular velocity, the transition process between that value's differential value and the real angular velocity, \(r_{0}\) represents the speed parameter, and \(h\) represents the filtering parameter. It typically takes the ADRC calculation period of two to obtain \(h=10\times 10^{-4}\). Equation (8) is the form of the nonlinear TD using the most rapid synthesizing function \(fhan()\). Its discrete expression is given below:  
\[f=fhan(x_{1},x_{2},r,h)=\left\{\begin{array}{l}d=rh^{2},a_{0}=hx_{2},y=x_{1} +a_{0}\\ a_{1}=\sqrt{d(d+8|y|)}\\ a_{2}=a_{0}+sign(y)(a_{1}-d)/2\\ s_{y}=(sign(y+d)-sign(y-d))/2\\ a=(a_{0}+y-a_{2})s_{y}+a_{2}\\ s_{a}=(sign(a+d)-sign(a-d))/2\\ f=-r(\frac{a}{d}-sign(a))s_{a}-rsign(a)\end{array}\right. \tag{29}\]  
where \(x_{1},x_{2}\) represents the inputs, \(f\) is the output, and \(r\) is the limit value of the output \(f\). After system debugging, when \(r_{0}\) is set to 100,000, the system has a faster response speed and improved tracking performance.  
Since the TD itself also has a filtering function to reject wild values, this paper introduces a TD in the real feedback loop of \(i_{q}\) to perform smoothing of the \(i_{q}\) signal and improve the control accuracy of the current.
## 5 Evaluation
### Total Makespan Results  
Table 7 compares the maximum _makespan_ results for each job type when the testbed UPC system runs the jobs by following the assignments found by the algorithms. Furthermore, it shows the _lower bound (LB)_ on the maximum _makespan_ found at _First Step_ of the proposed algorithm for the reference of them.  
The results indicate that for any job type, the maximum _makespan_ result by the proposal is better than the results by the two compared algorithms and is close to the lower bound. Thus, the effectiveness of the proposal is confirmed. It is noted that the results by _FCFS_ are far larger than the ones by the others because FCFS does not consider simultaneous multiple job executions for a worker.
## 4 Performance Analysis  
In this section, the experiments are introduced from four aspects. First, the data augmentation process is introduced. Second, three data sources and components of these datasets are explained. Third, deep learning models are described in detail, and these models are applied to NLP. Finally, the superiority of the MDA is verified by comparing the performance of the deep learning models in different tasks.
# Metallic Nanoparticles Obtained via \"Green\" Synthesis as a Platform for Biosensor Construction
## Materials and Methods
### Biosensor Evaluation
#### 2.5.2 Immobilization of the Enzymes onto Electrodes  
In order to construct the enzyme-based amperometric biosensors, GEs were previously modified by dropping 5 \(\upmu\)L gPdNPs (or not modified for control electrodes). After formation of solid films, aliquots of a freshly prepared enzyme solution (see Section 2.2) were dropped. A GE with an enzyme only, but without gPdNPs, was used as a control. For construction of the lacase-based ABS, 5 \(\upmu\)L lacase solution was dropped onto the surfaces of bulk GEs and gPdNPs-modified GEs. For construction of the AO-based ABS, 5 \(\upmu\)L HRP solution was dropped onto the surface of both GEs (control and modified). After formation of solid films, 7 \(\upmu\)L of AO solution were dropped on the electrodes. After drying for 10 min at room temperature, the layers of enzymes on the electrodes were covered with supporting polymers.  
Several fixing methods were tested, including a dialysis membrane, solutions of Nafion, chitosan and polyethyleneimine. The main principle for choosing the most effective covering film was the maximal amperometric output of the bioelectrode on the correspondent substrate and the stability of the bio-recognition layer on the surface of the GE with the enzyme. Experimentally, the best composition was selected: 10 \(\upmu\)L of the mixture, which contains 1% chitosan and 0.5%polyethyleneimine solutions (1:1, \(v/v\)), were dropped on the surfaces of laccase- and AO-based electrodes. The prepared bio-functionalized electrodes were rinsed with corresponding buffers (see Section 2.2) and kept in these buffers with 0.1 mM EDTA at +4 \({}^{\circ}\)C until used. The electrodes were washed with corresponding buffer solutions before and after each measurement in order to stabilize the basic signal.
## 2 Problem Definition
### Problem Modeling
#### 2.2.2 Satellite Information  
The performances of available satellites and payloads, as well as orbital information, are also very important considerations in making imaging schedules. In this study, the satellite is a small satellite under 500 kg equipped with an active phased array SAR sensor. The satellite's orbital motion is simulated using the J2 Perturbation propagator in the Systems Tool Kit (STK) program, forming a Walker Delta constellation with a total of 40 satellites. This use of J2 perturbation reflects the study's emphasis on optimal imaging planning rather than precise orbit prediction, with the model providing a sufficient balance between simplicity and the accuracy required for our analysis. More specific parameters will be mentioned in Section 4.1, Test Scenario. There are two assumptions related to the satellite in this study: 1. It is assumed that communication between the ground station and the satellite for transmission and reception is out of research scope, and that the satellite has already received the imaging scheduling command; 2. Contingencies such as functional failures of available satellites are not considered, and it is assumed that all satellites are operating normally.
# Approximate Triangulations of Grassmann Manifolds
## 4 Conclusions  
In this paper, we demonstrated the utility of using Vietoris-Rips and witness complexes to obtain approximate triangulations of the Grassmann manifolds \(G_{k}(\mathbb{R}^{n})\). We were able to construct such spaces with relatively few vertices, but some questions remain for further study.  
1. How small of a sample can we use to generate an approximate triangulation? For example, a result in [3] asserts that any triangulation of \(G_{2}(\mathbb{R}^{4})\) must have at least 14 vertices. We built an approximate triangulation using a witness complex on 100 landmarks. Surely, our algorithm will not work with only 14 points, but we plan to investigate how few we can get away with. A theorem of Niyogi-Smale-Weinberger [16] provides lower bounds on the number of points required to compute homology correctly with high probability, but these are certainly too high and can be improved in practice.
2. Can we push the computations further? The next Grassmannian to study is \(G_{2}(\mathbb{R}^{5})\). This is a nonorientable 6-manifold, and, using our procedure, we would embed it in \(\mathbb{R}^{25}\). The machine used to compute the persistent homology of the witness complexes on \(G_{2}(\mathbb{R}^{4})\) in MATLAB ran out of memory on 100 landmarks in \(G_{2}(\mathbb{R}^{5})\). We therefore need either a bigger machine running MATLAB, or software that can handle witness complexes. The GUDHI package [17] is one option, but we have not attempted it yet.
3. The author expects to gain access to a new GPU based supercomputer at his institution in the next year. This may allow for similar computations on higher-dimensional \(G_{k}(\mathbb{R}^{n})\).  
This research received no external funding.  
This problem was suggested to me by [PERSON]; I thank him for the inspiration and helpful conversations. [PERSON] provided useful tips for Javaplex. I am also grateful to [PERSON] for the use of his rather powerful computer. [PERSON] had a good suggestion about the proof of Proposition 2. Finally, thanks are due to a pair of anonymous referees for many useful comments.  
Figure 13: Barcodes for a witness complex on 100 points in a 5000-point sample on \(G_{2}(\mathbb{R}^{4})\) (**a**) \(\mathbb{Z}/2\) coefficients and (**b**) \(\mathbb{Z}/3\) coefficients.
## 3 Metabolic Responses to Different Antibiotic Classes
### Glycopeptides and Carbon Metabolism  
Glycopeptides, such as vancomycin and teicoplanin, inhibit peptidoglycan polymerization by binding to D-Ala-D-Ala residues [53; 54].  
However, their effectiveness is diminished under conditions of slow growth or dormancy [12].  
Progressive exposure to vancomycin induces a series of complex physiological and metabolic adaptations in _Staphylococcus aureus_, associated with the evolution toward an intermediate-resistant phenotype.  
Metabolomic analyses have revealed extensive remodeling of intracellular metabolism consistent with an energy-saving strategy.  
This reorientation includes changes in the profiles of over 200 metabolites, with reduced virulence, diminished adhesion capacity, and potential reorganization of anabolic and catabolic fluxes, although no specific activation of gluconeogenesis or direct inhibition of glycolysis or the Krebs cycle has been documented.  
The observed alterations appear aimed at enhancing survival under antibiotic and immune stress, rather than supporting active growth [55].  
In certain _Enterococcus faecalis_ strains subjected to selective pressure from glycopeptides, an increase in the synthesis of cell wall precursors via alternative biosynthetic pathways has been observed, supported by a reorientation of carbon metabolism [56].  
In particular, the activation of anaplerotic fluxes along the phosphoenolpyruvate-pyruvate-oxaloacetate axis suggests a functional adaptation aimed at ensuring the availability of intermediates for muramyl biosynthesis under conditions of vancomycin-mediated inhibition of conventional peptidoglycan synthesis [56].  
Such metabolic remodeling may represent an ancillary mechanism supporting non-van gene-mediated resistance.
# Securing IoT Data Using Steganography: A Practical Implementation Approach
###### Abstract
Existing data protection solutions such as steganography and cryptography are inapt for direct adaptation due to their computational complexity, application specificity, and inflexibility. Furthermore, the IoT environment is depicted as having: limited device capability, high data rate traffic, massive scalability and a large spectrum of heterogeneous devices [26]. IoT cybersecurity challenges, considering the aforementioned limitations, can be addressed using lightweight audio steganography algorithms. Image and video steganography requires a high data rate with additional energy for data transmission, increased processing time, energy consumption, and storage requirement. Audio signals (e.g., speech and music), in this case, are better candidates to facilitate the accommodation of IoT devices' constrained resources. In addition, the number of voice-enabled IoT devices is expanding across industries and has become a standard in connected devices such as: mobiles, tablets, sensors, wearable devices, and smart speakers. The shift from touch to voice is a need that is highlighted by the current pandemic to improve safety. In health care, there are hospitals in the US that allow parents to gain access to high-quality clinical information and specific treatment protocols on Alexa-enabled devices. Manufacturing plants, smart agriculture, construction sites and production lines also require hands-free mobility that IoT voice recognition systems can provide. These voice recordings are transmitted over untrusted public networks and then stored and processed on untrusted third-party cloud-based infrastructures. It is important to protect the information fed to or received by the voice-enabled IoT device such as authentication data, personal and private information. While many research proposals have been presented in the steganography literature, audio steganography schemes addressing cybersecurity issues in IoT devices are still limited in number, with modest contributions. In this paper, we propose an audio steganography solution designed for IoT implementation through a scalable, noise-resilient, and lightweight algorithm that can accommodate mass deployment in an IoT environment. The remaining part of this paper is organized as follows. The related work is discussed in Section 2 and then our methodology for secure IoT communication is presented in Section 3. The experimental setup and performance evaluation results are given in Section 4 and we conclude the paper and state some future directions in Section 6.
## 4 Numerical Examples
### Numerical Example 3
#### 4.3.2 Case B: Inclined Initial Hydraulic Head  
In Case B, the head of the seaside is a function of distance \(x\). The initial head is given as  
\[h(x,t=0)=\frac{H_{L}}{L_{f}}x. \tag{27}\]  
The boundary conditions at both left and right sides are given as  
\[h(x=0,t)=A\sin(\frac{2\pi}{t_{0}}t), \tag{28}\]  
\[h(x=L_{f},t)=H_{L}. \tag{29}\]  
In this example, \(H_{L}=3\) m, and \(H_{L}/L_{f}=10^{-3}\). Other parameters including the dimension of the space domain, the transmissivity, the storage coefficient, the leakage coefficient, the elapsed time, the amplitude of the tidal change, and the tidal period are the same as Case A. The schematic illustration for Case A is shown in Figure 8b.  
This example is solved using both FDM and our method. For the FDM analysis, the temporal and spatial discretizations for this problem are separately considered. The implicit scheme and the central difference approximation are considered for the temporal and spatial discretization, respectively. For the SCA analysis, we consider a source point and 163 boundary points. We consider the basis function order to be 25. Figure 10 depicts the results computed utilizing the FDM and our method. It is found that the tide-induced groundwater response computed using the proposed method agreed well with those of the FDM.  
Figure 9: Result comparison of example 3 (case A).
## 5 Reduction Factor Analysis Considering Particle Shape Effect on Mixture Porosity  
From the above illustration, a porosity prediction model for sphere element has been established. However, as mentioned in the Introduction, paving materials in reality generally have complex but similar particle shape combinations, which indicate that shape effect is non-negligible but consistent. Therefore, a reduction factor to consider particle shape effect was introduced in this chapter.  
A gradation was selected randomly for comparing the mixture porosity between sphere elements model and real aggregate shape model. The volume of each particle size group was: \(Volume_{13.2-16.0~{}mm}:Volume_{9.5-13.2~{}mm}:Volume_{4.75-9.5~{}mm}:Volume_{2.36-4.75~{}mm}=1:5:4:1\).  
A total number of 15 DEM models with different real aggregated shapes were built based on previous researches [33; 34; 35; 36; 37]. It should be noted that there is only one particle shape in each specimen to amplify its effect, as shown in Figure 12.  
Figure 11: Porosity comparison among lab test, virtual test and BP neural network model for steel ball test.  
Figure 10: Steel balls in lab experiment.  
All the aggregate shapes used in this chapter were quite common. Although there are some special shapes in civil engineering, for instance, elongated particle, their volume percentages in mixture were generally low and strictly controlled, due to their poor engineering properties. The model parameters were identical with sphere element-based models. In this way, these mixtures' porosities were recorded and plotted on the following Figure 13.  
From Figure 13, the red line represents the mixture porosity of the sphere elements-based model, which was 0.381, while the green points refer to 15 mixture porosities in 15 real aggregate shapes respectively. It can be observed that there is an obvious difference between these two models. Nevertheless, this difference is quite stable and consistent among 15 green points. Detailed statistical analysis has been done as shown in the following Table 15.  
Figure 12: The compacted mixture with real aggregate shape and 15 aggregate shapes.  
Figure 13: Mixture porosity comparison between sphere elements model and real aggregate shape model.  
From Table 15, 95% confidence interval has been determined and plotted as shown in Figure 13. Therefore, a shape reduction factor range was recommended considering the shape effect through Equations (10) and (11).  
Upper limit of shape reduction factor \[=\frac{\text{Confidence upper limit}}{\text{{Mixture porosity of sphere model}}}=\frac{0.231}{0.381}=0.606\] (10) Lower limit of shape reduction factor \[=\frac{\text{Confidence lower limit}}{\text{{Mixture porosity of sphere model}}}=\frac{0.206}{0.381}=0.541\] (11)  
When the prediction results based on sphere element models are multiplied by shape reduction factor, a more accurate result could be obtained.
# Challenges, Threats, Security Issues and New Trends of Underwater Wireless Sensor Networks
## 2 Particularities and Constraints
### Transmission Error  
As discussed above, the underwater acoustic channel is significantly affected by many factors such as water temperature, low speed of acoustic wave, ambient noise, transmission loss, multipath effect, and Doppler effect [2]. All these factors may cause delay variance and bit error, which result in high bit error rate and packet loss probability in UWSNs. Moreover, the underwater acoustic channel has the character of an open channel, which is shared by all nodes within the communication range. In this case, an attacker can passively intercept and analysis acoustic signals, and even worse actively disrupt network services such as localization, time synchronization and routing. Hence, it is a great challenge to design an effective secure protocol to protect UWSNs from eavesdropping and other malicious attacks.