esa-sceva/satcom-chunk-collection huggingface dataset:
# The Self-Calibration Method for the Vertex Distance of the Elliptical Paraboloid Array
## 2 Materials and Methods
### Optical Slope Sensor  
The optical slope sensor used for the self-calibration method consists of a laser, a reflector, a spatial filter, a beam splitter, two lenses, a CCD (charge coupled device) camera, and more, as shown in Figure 3. The laser emitted from a laser source (power 5 mW, wavelength \(\lambda\) = 650 nm, unpolarized light) is reflected by a reflector (offset 135\({}^{\circ}\)angle in a horizontal direction) and passes through a spatial filter (diameter 300 \(\upmu\)m). After going through the spatial filter and focusing Lens 1 (focal length 30 mm), the laser beam is divided into two beams by the beam splitter (reflection factor of 50%). The reflected  
Figure 1: The model of the elliptical paraboloid and detection results of the 3D surface profile: (**a**) the model of the elliptical paraboloid; (**b**) the 3D surface profile for the model.  
Figure 2: The linear elliptical paraboloid array.  
beam, as the measuring beam, reaches the surface of the elliptical paraboloid array and is reflected again. Finally, the measuring beam is received by a CCD camera (pixels number \(2592\times 1944\), pixel size \(2.2\times 2.2\) um) located on the focal plane of object Lens 2 (focal length 100 mm) and is transmitted to the computer for analysis and processing. The actual spot image is displayed on the computer screen in Figure 3, whose central position will be determined by the centroid algorithm. Through the processing of the spot image, the angle and displacement can be calculated precisely. In short, the optical slope sensor is simple, portable, and accurate.
# CoMP-Aware BBU Placements for 5G Radio Access Networks over Optical Aggregation Networks
## 3 Problem Formulation  
The problem of BBU pool placement is described as follows [23]:  
**Given**: Network topology, number of wavelengths per connection, maximum allowable front-haul latency, and computational effort required by a pool serving given the number of cell sites;  
**Output**: Placement of BBUs so that BBU pools are connected to several RRHs;  
**Objective**: Minimizing the following:  
i. The number of BBU pools;  
ii. The number of fiber connections used.
## 4 Numerical Simulation
### Verification of the Numerical Model Using NEWRCF  
To further verify the reliability of the numerical model, the numerical model was applied to the specimen NEWRCF. Figure 19 shows the experimental hysteresis loop and backbone curve comparisons with that of the numerical model; it can be seen that the latter's curves gave a close prediction of the experimental envelope curves. The ratio of the maximum force between the experimental results and the numerical model was 0.94. The drift at longitudinal rebar buckling from the experimental results was 5.98%, whereas, from the numerical model, it was 5.80%. The numerical model accurately predicted the initial stiffness, peak load, unloading stiffness, and pinching effect. The magnitude of the strength degradation was also predicted well, as it remained within the acceptable boundaries. Overall, the numerical model can accurately simulate the nonlinear response of high-strength columns and frame structures.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**References** & **Column** & \(\begin{matrix}(f_{e}^{\prime})\\ \textbf{(MPa)}\end{matrix}\) & \(\begin{matrix}f_{\textbf{w}}\\ \textbf{(MPa)}\end{matrix}\) & \(\begin{matrix}f_{\textbf{w}}\\ \textbf{(MPa)}\end{matrix}\) & \(\begin{matrix}b\\ \textbf{(mm)}\end{matrix}\) & \(\begin{matrix}h\\ \textbf{(mm)}\end{matrix}\) & \(\begin{matrix}a/d\end{matrix}\) & \(\begin{matrix}p\\ \mathcal{T}/A_{\textbf{w}}\end{matrix}\) & \(\begin{matrix}\varrho l\\ \textbf{(\%)}\end{matrix}\) & \(\begin{matrix}\varrho l\\ \textbf{(\%)}\end{matrix}\) & \(\begin{matrix}\varrho t\\ \textbf{(\%)}\end{matrix}\) & \(\begin{matrix}\textbf{Test}\\ \textbf{Type}\end{matrix}\) \\ \hline \multirow{5}{*}{This research} & NEWRC1 & 80.9 & 736 & 813 & 500 & 500 & 7.2 & 0.066 & 2.44 & 1.03 & C \\  & NEWRC2 & 80.9 & 736 & 813 & 500 & 500 & 7.2 & 0.066 & 2.44 & 0.52 & C \\  & NEWRC3 & 80.9 & 736 & 813 & 500 & 500 & 7.2 & 0.066 & 2.44 & 0.34 & C \\  & NEWRC4 & 80.9 & 736 & 813 & 500 & 500 & 7.2 & 0.132 & 2.44 & 1.03 & C \\  & NEWRC5 & 80.9 & 736 & 341 & 500 & 500 & 7.2 & 0.066 & 2.44 & 1.03 & C \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Paultre and** & No. 10013015 & 94.8 & 729 & 637 & 305 & 305 & 6.5 & 0.136 & 2.15 & 0.76 & C \\  & & C2 & 74.6 & 738 & 1448 & 152.4 & 152 & 3.9 & 0.1 & 2.45 & 0.62 & C \\  & & C3 & 81.8 & 738 & 1448 & 152.4 & 152 & 3.9 & 0.2 & 2.45 & 0.62 & C \\ \hline \hline \end{tabular}
\begin{tabular}{c c c c c c c c c c c} \hline \hline Xiao and & HC4-8L19-T10-0.1P & 76 & 510 & 510 & 254 & 254 & 2 & 0.1 & 2.46 & 1.5 & DC \\  & & HC4-8L19-T10-0.2P & 76 & 510 & 510 & 254 & 254 & 2 & 0.2 & 3.5 & 1.5 & DC \\  & & HC4-8L16-T10-0.1P & 86 & 510 & 510 & 254 & 254 & 2 & 0.096 & 2.46 & 1.5 & DC \\  & & HC4-8L16-T6-0.1P & 86 & 510 & 510 & 254 & 254 & 2 & 0.096 & 2.46 & 0.75 & DC \\ \hline \hline This research & NEWRCF & 80 & 723 & 833 & 500 & 500 & 8 & 0.1 & 2.3 & 1.29 & DC \\ \hline \hline \end{tabular}
\end{table}
Table 4: Column database.  
Figure 18: Comparisons of hysteretic curves and backbone curves from numerical simulation and experimental results.
## 2 Materials and Methods
### Application of Noise Reduction Algorithms
#### 2.1.1 Optimization of \(\sigma\)-Value for the Block Matching and 3D Filtering Algorithm
The BM3D algorithm goes through a process of transforming from the spatial domain to the frequency for processing. Therefore, an inverse 3D transform is utilized to convert the noise reduced frequencies back to the spatial domain in order to derive final denoised image. After the grouping process, each 3D block is subjected to a denoising procedure using collaborative filtering, which produces an estimation for each constituent patch in the block. The reference patch determining the basis for block grouping can be redundantly processed, since the patches are randomly selected from the whole region of the image. In order to compensate for this, the weighted average removes repeated estimates and obtains a final estimate. \[\hat{y}_{step1}=\frac{\sum_{X_{R}\in X_{x_{m}}}\sum_{x_{m}\in P_{2D}^{ht}}w_{x _{R}}^{ht}E_{P_{2D}^{ht}}^{ht}(x)}{\sum_{X_{R}\in X_{x_{m}}}\sum_{x_{m}\in P_{ 2D}^{ht}}w_{x_{R}}^{ht}\chi_{x_{m}}(x)}, \tag{5}\]  
where \(\hat{y}_{step1}\) represents the estimate acquired through the first step of the BM3D algorithm, \(w_{x_{R}}^{ht}\) is the weight, and \(\chi_{x_{m}}\) denotes the square support function located in \(x_{m}\in X\). In Equations (3)-(5), the superscript \(ht\) of the modifiers means that the hard-thresholding technique is used in the first step as the noise reduction method. Replacing ht with wie results in a equation that means the second stage of removing noise using Wiener filtering, since the process of grouping and collaborative filtering in each stage is identical. The first estimate obtained after completing the first step is used as a pilot signal for Wiener filtering to improve the performance. Figure 1 is the flowchart of the noise reduction process of the BM3D algorithm.
## 4 Validations
### Example 1: RC Beam with Pre-Existing Crack  
Figure 4 shows the geometry of the RC with a pre-existing crack. The objective of this newly developed benchmark is to demonstrate the feasibility of the SRAM for structures with identified pre-existing crack. In this scenario, the crack length \(L_{f}\) is variable and SRAM results are compared to theoretical results using the S&T model shown on the same Figure 4.  
Table 1 provides the material properties used in the example. In the simulations, reinforcement rebars are modeled as truss elements embedded within 3D solid elements. Five rebars, each with a cross-sectional area of 300 mm\({}^{2}\) were used (20 M rebar). The mesh density is shown in Figure 4. Due to symmetry, only half the beam model was used in simulations. To model the pre-existing crack, a frictionless contact interface is used. This interface transfers only normal compressive contact forces and is present at the beginning of the analysis before application of load P.  
The schematic representation of the S&T model employed in theoretical calculations is depicted overlaid on the FE mesh in Figure 4. As the crack length \(L_{f}\) increases, the effective width of the inclined strut diminishes, impacting the strength of the theoretical model. Figure 5 presents a comparison between numerical (SRAM) and theoretical (S&T) results. The strength of cracked or uncracked beam corresponds to the maximum transferrable force P (Figure 4) before failure. Each point in the curve represents a different numerical simulation/theoretical computation. A good correlation is observed between both results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Material Property** & & **Value** \\ \hline Compressive strength of concrete & \(f_{c}^{\prime}\) & 30 MPa \\ \hline Tensile strength of concrete & \(f_{t}^{\prime}\) & 2.9 MPa \\ \hline Young modulus of concrete & \(E_{c}\) & 26,600 MPa \\ \hline Poisson coefficient of concrete & \(v\) & 0.18 \\ \hline Reinforcement ratio & \(\rho\) & 0.011 \\ \hline Reinforcement yield stress & \(f_{y}\) & 400 MPa \\ \hline Reinforcement Young modulus & \(E_{s}\) & 200,000 MPa \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties used in example 1.  
Figure 4: Example 1: RC beam with pre-existing crack.  
The strut path (principal compressive stresses) at the end of the reduction phase is also shown in Figure 5 for two different configurations: uncracked beam and pre-existing crack at 50% of the beam height. The disruptive effect of the crack is clearly evident. In all numerical simulations, the final failure mechanism was related to the compressive failure of the strut.  
This example demonstrates the possibility to include weak planes in existing RC structures. Friction and cohesion effects can also be integrated into contact interfaces to more realistically represent the behavior of crack planes.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 3 Datasets
### UR-Fall Dataset  
UR-Fall is a multimodal dataset of RGB, depth, and sensor data modalities [50]. The RGB data includes 70 sequences of 30 fall events and 40 activities of daily living (ADLs). Fall events are captured using two Microsoft Kinect cameras along with accelerometric data, while ADL events are recorded with only one camera (camera 0) and an accelerometer. Sensor data are collected using PS Move (60 Hz) and x-IMU (256 Hz) devices. The dataset is organized such that each row contains a sequence of depth and RGB images from both cameras (camera 0 is parallel to the floor, and camera 1 is ceiling-mounted), synchronization data, and raw accelerometer data.
# An Integrated Software for Computing Mechanical Properties of Crystalline Material by Means of XRD
## 3 Results and Discussion  
### Analysis of Stress  
The dialog box in Figure 6 allows us to select the material and its elastic constants used to determine stress. This also allows us to revise a material or add a new material and then save it to the program library. Figure 7 shows the display for choosing methods of peak position determination, and the correction factors used for stress computation. The corrected X-ray diffraction line, peak positions, and stress and their 95% confidential limits representing the reproducibility of the calculated value are shown.  
The sample was made from aluminum alloy 1060 with dimensions of 250 \(\times\) 50 \(\times\) 5 mm. The sample surface was ground to remove the surface layer of about 1 mm and then polished with emery paper to obtain the roughness of 0.64 \(\upmu\)m. Table 1 shows the experimental conditions for stress measurement using XRD.  
Figure 6: Elastic constants \(E\) and \(v\) of common materials for stress computation.  
Figure 7: Peak positions and stress determination of aluminum alloy 1060.  
Table 2 shows the residual stresses of aluminum alloy 1060 calculated from XPro 2.0. The peak positions are computed using four methods of peak position determination Parabola, Gaussian, Half-width, and Centroid with their 95% confidence limits. They are also compared to the result computed from the commercial Origin. The stress values determined from the Parabola method and Gaussian curve method strongly agree with the Origin. They have a precision coefficient R\({}^{2}\) of fitting regression of 0.91 \(\pm\) 0.06 and 0.89 \(\pm\) 0.08, respectively. In contrast, the Half-width and the Centroid methods gave much larger 95% confidential limits with lower R\({}^{2}\) of 0.72 \(\pm\) 0.15 and 0.68 \(\pm\) 0.18, respectively. Therefore, the Parabola and Gaussian methods are the most suitable in XRD stress investigation.
## 2 Testbed Setup and Measurement Methodology
### Experimental Setup for Timing Measurements
#### 2.1.3 Load Types  
In the context of a multiprogrammed computing systems, several kinds of tasks can be distinguished [19; 20]. According to this classification, we distinguish the following load scenarios and therefore create corresponding load types for the experiments in the testbed:  
* _Idle system_: In this load type, the operating system works in normal mode, without any additional working applications. An idle system represents the best-case scenario.
* _CPU-bound load_: This type creates a very high CPU load using up to 100% of available CPU resources. For this, a utility creates 100 threads with square root calculations. This type of load is often created by using video/audio conversion/compression algorithms or by applications for graphics processing.
* _IO/network-bound load_: This load type simulates typical VMs load, which can exist on hosting in a large data center. For this, we create an NFS server on the VM and start sending and receiving data calls as standard read/write calls. This workload results in a large number of context switches and disk interrupts. As an example, this scenario can be used for solving such problems as distributed processing of big data.
## 5 Simulations and Results
### Model uncertainties compensation:  
This simulation evaluates the UAV's ability to compensate for model uncertainties resulting from variations in actuator dynamics. This scenario captures a critical aspect of uncertainty compensation--adjustments in control system dynamics due to actuator nonlinearities or miscalibrations. The test is conducted by increasing the servomotor gain \(K_{s}\) by 20% at t = 2.0 s, simulating the actuator's sensitivity to hardware inconsistencies or miscalibrations. The sudden increment in \(K_{s}\) modifies the UAV's state-space model by changing key system parameters, and altering the elevator's control response characteristics, which potentially affects the UAV's pitch stability and tracking accuracy. The time-domain responses of the UAV's pitch angle, under the influence of each controller, are presented in Figure 13.
# Intersection-Based Link-Adaptive Beaconless Forwarding in Urban Vehicular Ad-Hoc Networks
## 2 Background
### Remote Monitoring Applications in VANETs
#### 2.1.1 Emergency Telemedicine  
Emergency telemedicine makes use of emerging wireless communication and information technologies to provide health-care expertise such as medical diagnosis, treatment and patient care, in remote sites [12]. Early medical diagnosis and treatment could be vital in patient's recovery or even saving patient's life. In addition, transmission of patient's medical data from ambulance to the hospital allows the doctors to make necessary arrangements for the incoming patient. The medical data sent includes still images of patient such as X-ray report, medical video of the patient and various signals from physiological sensors such as electrocardiogram (ECG), electroencephalogram (EEG), heart rate (HR) and blood pressure.  
Telemedicine projects in [13; 14; 15] aimed at developing an emergency telemedicine device to transmit important medical data such as still images and various signals from physiological sensors from a remote site to an emergency health-care center to enable prehospital assistance from doctors. Apart from this, a telestroke ambulance prototype was developed in [16] to enable realtime audio-video communication between the ambulance and the hospital site using cellular technology. The use of video technology enables doctors to perform more accurate diagnosis by visualizing different features of the patients such as colouring, rapid breathing, muscle stiffness, and state-of-shock evidence [17]. The work in [18] highlights a detailed explanation on various emergency telemedicine applications.
## 2 Materials and Methods
### Characterization Methods
#### 2.4.2 Fourier Transform Infrared Spectroscopy (FTIR) Investigation  
Fourier transform infrared spectroscopy (FTIR) analysis were carried out on a Perkin Elmer Spectrum 100 spectrometer (PerkinElmer, Zaventem, Belgium) in ATR (attenuated total reflection) mode. Spectra were obtained after 16 accumulations with a resolution of 4 cm\({}^{-1}\).
# State of Industry 5.0--Analysis and Identification of Current Research Trends
###### Abstract  
The term Industry 4.0, coined to be the fourth industrial revolution, refers to a higher level of automation for operational productivity and efficiency by connecting virtual and physical worlds in an industry. With Industry 4.0 being unable to address and meet increased drive of personalization, the term Industry 5.0 was coined for addressing personalized manufacturing and empowering humans in manufacturing processes. The onset of the term Industry 5.0 is observed to have various views of how it is defined and what constitutes the reconciliation between humans and machines. This serves as the motivation of this paper in identifying and analyzing the various themes and research trends of what Industry 5.0 is using text mining tools and techniques. Toward this, the abstracts of 196 published papers based on the keyword \"Industry 5.0\" search in IEEE, science direct and MDPI data bases were extracted. Data cleaning and preprocessing were performed for further analysis to apply text mining techniques of key terms extraction and frequency analysis. Further topic mining i.e., unsupervised machine learning method was used for exploring the data. It is observed that the terms artificial intelligence (AI), big data, supply chain, digital transformation, machine learning, internet of things (IoT), are among the most often used and among several enablers that have been identified by researchers to drive Industry 5.0. Five major themes of Industry 5.0 addressing, supply chain evaluation and optimization, enterprise innovation and digitization, smart and sustainable manufacturing, transformation driven by IoT, AI, and Big Data, and Human-machine connectivity were classified among the published literature, highlighting the research themes that can be further explored. It is observed that the theme of Industry 5.0 as a gateway towards human machine connectivity and co-existence is gaining more interest among the research community in the recent years.  
Industry 5.0; artificial intelligence; smart manufacturing; big data; internet of things; human-machine coexistence +
Footnote †: journal:  
0000-0001-5000-0001-5000][PERSON]
## 1 Introduction
This work uses some of the concepts proposed in the literature and proposes novel ideas and modifications to develop a low cost system suitable for previously mentioned studies. The following are some of the design requirements considered and realized:  
* Actuators for air conditioning, light spectrum adjustment, and on-demand water and gas exchange with airtight containment
* Measurement system for air quality, gas concentrations, air pressure, air and substrate temperatures, humidity, and substrate moisture
* Modular design using off-the-shelf components and 3D printable parts
* Software framework for experiment regime design and execution, user interaction, and data collection and analysis  
In the following sections, firstly, hardware component selection and electronic interface design are explained followed by software component design and integration of an example control regime. The results of sensor-actuator tests for performance evaluation of the designed system are then presented. Finally, the results of experiments executed for the study of growth of biological organisms are discussed.
## 2 Experimental Set-Up  
The homemade excimer laser manufacturing system is illustrated in Figure 1. The laser source used in the present work was a commercial KrF excimer LPXpro305 (Coherent Co., Ltd., Santa Clara, CA, USA) that works at 248 nm. The laser pulse width and frequency were 30 ns and 1 Hz, respectively. The excimer laser beam was well aligned using an He-Ne laser indicator light (4D-B, Tuoda Co., Ltd., Beijing, China). A homemade mask with a 2 mm \(\times\) 2 mm stainless-steel square pinhole was inserted into the beam axis to control the laser focal spot shape and size, through which the excimer laser beam was directed. After placement of the mask, the laser beam was deflected by 90\({}^{\circ}\) using a UV reflecting mirror, and subsequently tightly focused on the glass sample by a 10\(\times\) UV objective. To control the microchannel dimensions and ablation pattern, the glass sample position was adjusted using a three-dimensional (3D) stage. The stage resolution was better than 2 \(\upmu\)m in the 50 mm translation range, and the flight velocity could be varied from 0.01 to 6 mm/s along each axis. The laser output parameter settings and the stage translation were controlled by computer. After the ablation experiment, the etched depth and surface roughness were measured by a surface profiler (P7, KLA Tencor. Co., Ltd., Milpitas, CA, USA).  
A 1 mm thick UV transparent quartz glass was used in this experiment, and the content of SiO\({}_{2}\) was no less than 99.96%. We measured the transmittance spectrum of the fused quartz glass in the UV range prior to ablation using a spectrophotometer. As can be seen in Figure 2, such glass has a good transmittance window in the UV range, and the transmittance is greater than 92% at 248 nm. The 8% transmission loss is due to reflection at the quartz layer surface, such that the 248 nm laser beam is hardly absorbed by the substrates [21]. A 304-type stainless-steel plate (20 mm \(\times\) 20 mm \(\times\) 10 mm) was chosen as the target for plasma vaporization generation. The metal target was kept in contact with the rear surface of the fused quartz glass so that the glass could be etched directly by the high-temperature plasma. For preparation, both the glass sample and the stainless-steel target were ultrasonically cleaned in ethanol for 10 min to remove the oxide on the surface before the experiment.
# Multi-Line Prefetch Covert Channel with Huge Pages
## 3 Background
### Cache Coherence Covert Channels  
Covert channels exploit these coherence protocols by manipulating the state of cache lines to create detectable timing variations that can encode and transmit information secretly:  
1. State-Based Timing Differences: Access times vary significantly based on the state of the cache line. For instance, a line in the'modified' state in one core's cache being read by another core will result in a longer latency, as the line must be fetched from the owning core's cache and updated in the LLC and the requesting core's cache.  
2. Prefetching and Coherence State Manipulation: Prefetch instructions (e.g., PREFETCHW) are used to deliberately alter the state of a cache line. This instruction can prefetch data into a cache and set it to'modified', preparing it for faster subsequent write operations but also changing the coherence state detectably, which can be exploited in a covert channel to signal a '1' or '0' based on whether the prefetch operation took more time (indicating a state change) or less time (indicating no state change).