esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experiments and Results
### Experiment 1: EMG Measurements
#### 3.1.1 Method  
Seven males (age 21-23 yo) participated in the experiment. The subjects consisted of people who had no muscle aches, fatigue, or malaise and who had no trouble in walking. Prior to the experiments, each participant answered verbal questions on their health and gave informed consent to participate. Participants with nonideal health were rescheduled to a later date. The EMG measurements were performed under the following conditions:  
* Level walking without feedback;
* Stair-climbing without feedback (physical stair-climbing);
* Level walking with visual and force feedback (virtual stair-climbing).  
During level walking, the participants walked along a straight path of approximately 4 m. During physical stair-climbing, the participants climbed 10 steps, each with a height of 200 mm and a tread of 250 mm. In the virtual environment, the virtual stair height was set to 200 mm, and the air pressure supplied to the PGM was 200 kPa. All tasks were carried out three times at the natural walking speed of the participant. The motions of the vastus lateralis, biceps femoris, tibialis anterior, and gastrocnemius muscles of the dominant leg were measured. The EMG signals were measured by a wireless surface EMG measurement device (DELSYS Trigno, Natick, MA, USA). The sampling frequency was 2000 Hz. The EMG waveforms were evaluated after bandpass filtering at 20-450 Hz and a smoothing process. At the end of the task, the EMG of each participant was normalized by the maximal voluntary contraction (MVC) of the associated muscle, which was measured in isometric contraction for each participant.
# Measurement of Wheel Radius in an Automated Guided Vehicle
## 3 An Overview of Selected Measurement methods
### Methodology of Measurements in Curved Motion  
With a large deviation of the actual track from the assumed theoretical course, the vehicle performs arc motion (Figures 4 and 5). Determination of rolling radii from the dependence (8) for such a course is subject to a significant error. The distance traveled along a curve differs from the length of the section connecting the beginning and the end of the curve.  
In many cases, the recorded measurement data was characterized by high interference. A fragment of such a course is shown in Figure 5. Cyclic deviations and measurement disturbances are visible on the course. This type of data has been subjected to appropriate filtration processes described in [15].  
There are many ways to determine the path traveled in curvilinear motion. In the conducted preliminary tests, two methods were used, shown in Figure 6. The first method determines the distance \(s_{c}\) between the start and end position of a selected point of the vehicle. In the second method, characteristic markers on the reference surface can be used to measure distance \(s_{w}\), e.g., door openings. In both methods, the distance \(d\) from the reference surface is recorded and the counter reads the data from the encoders of individual wheels.  
Knowledge of the value of the radius of curvature \(R\) and half arc angle \(\phi\) makes it possible to determine the lengths of arcs traveled by the wheels and their rolling radii.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 2 Related Work
#### Healthcare Scheduling and Resource Optimization  
Efficient healthcare scheduling and resource management is critical for operational efficiency. Literature in this domain applies mathematical optimization to crucial areas like staff and patient scheduling, admission planning, and resource allocation. For staff scheduling, the research aims to optimize shift scheduling and nurse routing models using techniques such as mixed-integer programming and genetic algorithms [19; 20; 21]. Approaches consider nurse availability, hospital policies, and workload balancing. Optimization in patient admission and appointment systems reduces wait times through simulation, algorithms, and queuing models [22; 23; 24]. Methods incorporate patient priorities and time slot preferences [25; 26]. Additional studies apply optimization techniques such as stochastic programming, network modeling, and machine learning for resource allocation, bed management, and operating room scheduling [27; 28; 29]. The healthcare scheduling literature leverages operations research to optimize staff planning, patient scheduling, and resource allocation. Mathematical modeling provides quantitative approaches for maximizing utilization, service quality, and productivity.  
The existing literature centers around specific aspects of healthcare management and scheduling, while the proposed model uniquely targets healthcare scheduling. It emphasizes the intricate balance between resource allocation, patient satisfaction, and operational efficiency. Unlike previous work, the model thoroughly examines healthcare resource allocation, encompassing staff, patients, and resources over specified periods. This approach enables more precise and context-sensitive optimization aligned with healthcare organizations' demands. Despite the focused nature of prior models, our approach offers a comprehensive and integrated approach to optimize healthcare operations. It employs mathematical procedures with simulated data to handle a wide array of healthcare operations based on staff scheduling, patient assignment, and resource allocation using linear and integer programming for a holistic optimization approach.
## 5 The Proposed Routing Method
### Distributed and Lightweight Trust Mechanism (W-Trust)  
In this section, the weighted trust mechanism (W-Trust) is presented. W-Trust is a lightweight and distributed mechanism that evaluates the trust level of IoT nodes based on their interactions when sending and receiving data packets. If W-Trust detects that an IoT node behaves maliciously, it reduces the direct trust related to this node with regard to a penalty factor to isolate this node and prevent its malicious activities in the network. Furthermore, if W-Trust detects that an IoT node has satisfactory activities in the network, it will improve the direct trust level of this node with regard to a reward factor to increase the participation of this node in the routing process. Therefore, W-Trust must calculate three parameters, namely direct trust, indirect trust, and total trust. In the following, these parameters are explained.
## 3 Experiments  
Experiments have been conducted to test this hypothesis in this paper. The strategy of the experiment is to polish several lines with different overhang ratio \(\tau\) to compare their edge profiles. Figure 5 shows the experimental prototype. R80 SR bonnet tool was used in this experiment. Considering that the TIF with a diameter of 28 mm of R80 SR bonnet has high material removal efficiency and stability, and Gaussian-like shape, it is mostly adopted in the practical pre-polishing process. Hence, TIF with the diameter of 28 mm was also adopted in this experiment. The polishing experiment was done on a flat surface made of fused silica, with the size of 200 mm \(\times\) 200 mm. The flat surface had been finely polished before this experiment. Considering that the polishing pad would be rapidly damaged when the overhang ratio is larger than 0.5, the overhang ratio adopted in these experiments varied from 0 to 0.5. Other experimental conditions have been shown in Table 1. In total, 11 lines were polished in this experiment, and the length of each line was about 85 mm.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Conditions** & **Value** \\ \hline Precession angle(deg) & 23 \\ Inner pressure(MPa) & 0.25 \\ H-axis speed (rpm) & 1500 \\ Feeding speed (mm/min) & 100 \\ Radius of bonnet (mm) & 80 \\ Polishing slurry & \(\sim\)2 wt.\% CeO\({}_{2}\) \\ Overhang ratio \(\tau\) & 0–0.5, with the increment of 0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental conditions.  
Figure 4: Demonstration of several practical edge effect cases about the error profile of the edge.  
The measured results using the interferometer have been shown in Figure 6. Figure 6a shows the removed form shape of each polishing line (Around the half line close to the edge has been shown, and some data were missing due to the large gradient of the form error). It indicates that the width of the upstand edge becomes smaller with the increasing \(\tau\). To compare these profiles quantitatively, six sectional profiles among them were extracted as shown in Figure 6b. It hints that both the width and height of the upstand edge become smaller with the increasing \(\tau\). When \(\tau\) = 0, the width of the upstand edge was \(\sim\)23 mm and the height is \(\sim\)4.2\(\lambda\). When \(\tau\) = 0.5, its width and height dramatically reduced to \(\sim\)8 mm and \(\sim\)1.25\(\lambda\), respectively. Hence, the hypothesis made in part 2 is proved to be correct. With this in view, in the pre-polishing process of optical surfaces using the SR bonnet, the tool can overhang the workpiece and the adopted overhang ratio can be increased to a threshold without damaging the polishing pad.
## 4 Application Example
### Finite Element Model  
To verify the applicability of the analytical algorithm, a three-dimensional beam element model of self-anchored suspension bridge with three towers and four spans was established by ANSYS. The main tower and stiffening beam were simulated by Beam44, which has the ability to bear tension, compression, torsion and bending. In order to connect the stiffening beam with the sling, the Beam4 element was used to simulate the rigid beam, and the element is given enough rigidity to make it a rigid arm. The main cable and sling were simulated by Link10 unit, which has the functions of stress stiffening and large deformation. The finite element model did not consider the influence of pile caps, foundations and side piers. The model has a total of 1210 units and 1087 nodes. The finite element model was shown in Figure 6. The bottom of the tower was set as a fixed boundary, and the supports of the main tower and the stiffening beam were simulated by coupling the degrees of freedom of the corresponding nodes. The edge pier support was realized by restricting the node degree of freedom of the edge pier support position of the stiffening beam. For the consolidation of the main cable saddle and the main tower, it was realized by coupling the three-way displacement of the corresponding nodes X, Y and Z. The anchorage between the main cable and the stiffening beam is simulated by coupling the degrees of freedom of the corresponding nodes. The live load refers to the grade I lane load standard of highway [20]. After considering multi-lane and longitudinal and transverse reduction, the line load is 40.74 KN/m, and the concentrated load is 1396.8 KN.  
Figure 5: Layout of the Luozhou Bridge.  
Figure 6: Finite element model of the Luozhou Bridge.
# Beam Training for Millimeter-Wave Communication Based on Tabu Table Enhanced Rosenbrock Algorithm
## 3 Rosenbrock-Algorithm-Based Beam Training
### Simulated Annealing-Based Rosenbrock Search  
In order to solve the problem of falling into the local optimum, the SA algorithm is added to the Rosenbrock search, which applies the probabilistic acceptance criteria to the probe movement. Specifically, two temperature parameters \(T_{I}\) and \(T_{g}\) are adopted in this method, i.e., the local temperature and the global temperature, which correspond to the probe moving and the pattern moving, respectively. The temperature parameter \(T_{g}\) is updated after the new search directions have been constructed, while \(T_{I}\) is updated after each probe moving stage.  
Despite the fact that, the combination of the SA algorithm with the RA makes it possible to avoid falling into the local optimum because of the probabilistic property of the SA algorithm. It should be emphasized that certain problems still remain unsolved due to the intrinsic features of the beams search. For example, practical beam indexes are both integers, while the output beam indexes for the next probe moving should be rounded to the nearest integer. Simulation analysis found that rounding to the nearest integer makes the algorithm suffer the repeat search problems. The repeated search problem not only consumes a lot of search steps but also cause unavoidable search failure.
# Image Encryption Algorithms: A Survey of Design and Evaluation Metrics
## 2 Image Encryption Algorithms
### Chaotic Systems  
Some of the most widely used image encryption algorithms are based on chaos theory; this is due to several desirable properties, such as non-linearity, sensitivity to initial parameters, speed, and robustness [7]. In chaos-based algorithms, the pseudorandom chaotic sequences that are generated by the chaotic system are used to permute and diffuse a plaintext image [8].  
Some of the chaotic maps that are commonly utilized in image encryption methods are as follows: logistic map [9; 10], Baker map [11; 12], Arnold map [13; 14], tent map [15; 16], hyperchaotic maps [17; 18], etc. Researchers have proposed a plethora of image encryption algorithms based on chaotic systems; the following is a brief overview of some of the proposed algorithms for each chaotic system.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 4 Experimental Section  
### Experimental Dataset and Processing  
We utilized the BuildingNet [43] public dataset for the point cloud generation task. This dataset comprises 1849 complete building point cloud models, encompassing various architectural categories, including houses, churches, skyscrapers, city halls, libraries, and castles, among others. The dataset was randomly divided into training, testing, and validation sets in a ratio of 80%, 15%, and 5%, respectively. From each complete building point cloud, we uniformly sampled 20,480 points. We randomly selected one viewpoint from multiple viewpoints as the center and removed points within a certain radius to obtain incomplete point cloud data. We chose building point cloud data with a 5% missing rate for training and testing in the experiments. All input point cloud data were centered around the original points and their coordinates were normalized to [-1,1].
## Appendix C Vector Potential and Displacement Vector  
The physical meaning of the relation between the vector potential and displacement vector is not straightforward. The former is associated with the gauge field and the latter is with the material field. As far as the field equation is concerned, the following argument holds, which allows us to replace \(\mathbf{A}\) with \(\mathbf{\xi}\).  
The resistive forces exerted by an elasto-plastic material are the following three.  
\[F_{e}^{\ l} = -G\mathbf{j}_{e}=(\lambda+2G)\
abla(\
abla\cdot\mathbf{\xi}_{e}) \tag{11}\] \[F_{p}^{\ l} = -G\mathbf{j}_{p}=-\sigma_{0}\rho(\
abla\cdot\mathbf{v})\frac{\partial\mathbf{ \xi}_{p}}{\partial t}\] (12) \[F_{p}^{\ l} = -G(\
abla\times\mathbf{\omega}_{p}) \tag{13}\]  
Here, \(F_{e}^{\ l}\) is the elastic longitudinal force, \(F_{p}^{\ l}\) is the plastic (energy dissipative) longitudinal force, and \(F_{p}^{\ l}\) is the transverse elastic force in the plastic regime. If we consider the total displacement, the equation of motion becomes as follows.  
\[\rho\frac{\partial^{2}(\mathbf{\xi}_{e}+\mathbf{\xi}_{p})}{\partial t^{2}}=(\lambda+2G )\
abla(\
abla\cdot\mathbf{\xi}_{e})-\sigma_{0}\rho(\
abla\cdot\mathbf{v})\frac{ \partial\mathbf{\xi}_{p}}{\partial t}-G(\
abla\times\mathbf{\omega}_{p}) \tag{14}\]  
Use the following conditions.  
\[(\lambda+2G)\
abla(\
abla\cdot\mathbf{\xi}_{p})=0 \tag{15}\] \[-G(\
abla\times\mathbf{\omega}_{e})=0\] (16) \[-\sigma_{0}\rho(\
abla\cdot\mathbf{v})\frac{\partial\mathbf{\xi}_{e}}{ \partial t}=0 \tag{17}\]  
Adding Equations (15)-(17) into Equation (14) and defining toal displacement and rotation vectors as \(\mathbf{\xi}_{t}\equiv\mathbf{\xi}_{e}+\mathbf{\xi}_{p}\) and \(\mathbf{\omega}_{t}\equiv\mathbf{\omega}_{e}+\mathbf{\omega}_{p}\), rewrite equation of motion (14) as follows.  
\[\rho\frac{\partial^{2}\mathbf{\xi}_{t}}{\partial t^{2}}=(\lambda+2G)\
abla(\
abla \cdot\mathbf{\xi}_{t})-\sigma_{0}\rho(\
abla\cdot\mathbf{v})\frac{\partial\mathbf{\xi}_{ t}}{\partial t}-G(\
abla\times\mathbf{\omega}_{t}) \tag{18}\]  
By dropping suffix \(t\), and rearranging terms, we obtain the following wave equation.  
\[\rho\frac{\partial^{2}\mathbf{\xi}}{\partial t^{2}}+\sigma_{0}\rho(\
abla\cdot \mathbf{v})\frac{\partial\mathbf{\xi}}{\partial t}=(\lambda+2G)\
abla(\
abla\cdot \mathbf{\xi})-G(\
abla\times\mathbf{\omega}) \tag{19}\]  
Using the mathematical identity \(\
abla\times\mathbf{\omega}=\
abla(\
abla\cdot\mathbf{\xi})-\
abla^{2}\mathbf{\xi}\) and dividing by \(\rho\), we can rewrite Equation (19) as follows.  
\[\frac{\partial^{2}\mathbf{\xi}}{\partial t^{2}}+\sigma_{0}(\
abla\cdot\mathbf{v}) \frac{\partial\mathbf{\xi}}{\partial t}-\frac{G}{\rho}\
abla^{2}\mathbf{\xi}=\frac{ \lambda+G}{\rho}\
abla(\
abla\cdot\mathbf{\xi}) \tag{20}\]  
Equation (20) is identical to wave Equation (25). It should be noted that the material constant \(\lambda\) and \(G\) can be considerably different from those defined in elastic theories as this formulation includes plastic dynamics. It is necessary to evaluate these quantities experimentally.
# An Ultra-Wideband Microwave Photonic Channelized Receiver with Zero-IF Architecture
## 2 Principle
\right\} \tag{23}\]  
where \(\alpha\) refers to polarization azimuth, and \(\phi\) refers to the phase difference between the two polarization components. To achieve balanced detection, PC is adjusted and \(\alpha=45^{\circ}\). In the balanced photodiode (BPD), the two photocurrents subtract each other, and the following current is generated after BPD [11]:  
\[ \begin{array}{l}i(t)\propto\left|E_{1}(t)\right|^{2}-\left|E_{2}(t)\right|^ {2}\propto\\ \cos AJ_{2}(B)J_{2}(m_{LO2})J_{1}(m_{RF1})\cos[(-2\omega_{LO1}-2\omega_{LO2}+ \omega_{RF1})t-2\varphi_{1}(t)-\phi]\end{array}  \tag{24}\]  
The main purpose of usingbalanced detection is to supress even-order distortion and eliminate DC offset. Since the phase of the baseband signal is related to \(\phi\), which can be arbitrarily tuned through polarization control. In the first channel, the PC is adjusted to set \(\phi=0^{\circ}\), and the photocurrent after BPD can be written as:  
\[i_{l}(t)\propto\cos AJ_{2}(B)J_{2}(m_{LO2})J_{1}(m_{RF1})\cos[(-2\omega_{LO1} -2\omega_{LO2}+\omega_{RF1})t-2\varphi_{1}(t)] \tag{25}\]  
Thus, an in-phase down-converted IF signal is obtained. In the other channel, the PC is adjusted to set \(\varphi=90^{\circ}\), and the photocurrent after BPD can be written as:  
\[i_{Q}(t)\propto\cos AJ_{2}(B)J_{2}(m_{LO2})J_{1}(m_{RF1})\sin[(-2\omega_{LO1} -2\omega_{LO2}+\omega_{RF1})t-2\varphi_{1}(t)] \tag{26}\]  
The two PCs are adjusted so that the phase difference between one optical carrier and the RF signal is \(0^{\circ}\), so that the channel information outputs after passing through the PBS and the BPD. The phase difference between the other optical carrier and the RF signal is \(90^{\circ}\), so the Q channel information outputs after passing through the PBS and the BPD. The five channels in parallel are identical after the narrowband OBPF. Therefore, the five sub-IF signals output from each channel is sent to the ADC for electrical domain signal processing. The parameters used in the simulation are shown in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Component** & **Parameters** \\ \hline LD & power of 10 dBm; linewidth 1 kHz; relative intensity noise (RIN) of \(-\)160 dB \\ \(LO_{1}\) & amplitude of 10 dBm; frequency 0.6 G \\ DPMZM1 & insertion loss of 6 dB; \(V_{\pi}=3.5\) V; \(V_{1a}=3.15\) V; \(V_{1b}=-6.23\) V; \(V_{1c}=0\) V \\ \(LO_{2}\) & amplitude of 8 dBm; frequency 10 G \\ DPMZM2 & insertion loss of 6 dB; \(V_{\pi}=3.5\) V; \(V_{2a}=0\) V; \(V_{2b}=0.83\) V; \(V_{2c}=0\) V \\ OBPF & center frequency of 20 G; bandwidth of 30 G \\ MZM & insertion loss of 6 dB; \(V_{\pi}=3.5\) V; \(V_{dc}=1.75\) V; \\ narrowband OBPFs & center frequencies of 18.8 GHz, 19.4 GHz, 20 GHz, 20.6 GHz, and 21.2 GHz, respectively; bandwidth of 600 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The main parameters of the building blocks used in the Virtual Photonic incorporation (VPI) simulation.
# A Framework for Enhancing Big Data Integration in Biological Domain Using Distributed Processing
## 2 Background
### Ontology  
Ontology is a computational structure used to represent entities and relationships in a given domain in a structured format. Ontologies usually consist of classes, attributes, relations, function terms, restrictions, rules, axioms, and events. The essential elements of the gene ontology are classes, metadata, relations, and axioms: [10]  
Classes are used to represent a type of thing in a given domain. Each class has a unique identifier within the ontology namespace. If a class is no longer needed, it is not deleted, but it is marked by \"obsolete\" to save it for historical reasons. Obsolete classes may have some metadata pointing to an alternative class identifier.  
Metadata is textual information associated with a class; it may include alternative identifiers, obsolete flags, definitions, synonyms, cross-references to external databases or web data source, textual comments, and other information.  
Relations are used to link classes in hierarchal relationships, from more general classes at the higher levels to the more specific ones at the lower levels. Relations should be directional, such as the hierarchical relationships to build a directed acyclic graph (because any class can have multiple parents). The most common relations are \"is a,\" \"part of,\" \"has parts,\" \"regulates,\" etc.  
Axioms are used to define the constraint on the classes' definitions; this is called description logics. In Web Ontology Language (OWL), they are called logical axioms, and include quantifiers (universal and existential), cardinalities (minimum and maximum), logical connectives (intersection and union), negation, disjointedness, and equivalence.  
Ontologies can be stored in different formats; the most common format is the Open Biomedical Ontology (OBO) format, designed specifically for biomedical ontologies. In recent years, a new format called Web Ontology Language (OWL) was designed to be applicable with the semantic web standards. There are some tools to convert OBO to OWL and vice versa [11]. Portege [12] is the most common ontology editor for editing ontology classes, relationships, logical axioms, and metadata. Moreover, it provides ontology visualization and reasonings, such as HermiT [13] and Pellet [14].