esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimating the Uncertainty of Measurements for Various Methods and 3D Printed Parts
## 1 Introduction
The dimensions of a part produced by an additive method often deviate from the nominal values of 3D model features due to factors such as printer resolution, printing parameters, printing technology, and measurement method. Using the example of a standard test artifact from the National Institute of Standards and Technology (NIST) containing a set of different features that can be used to characterize the performance of 3D printers, comparative tests were performed using different additive technologies [22]. The standard [23] for additive manufacturing recommends different shapes of master samples, for evaluating, among other things, the spatial uncertainty of parts that can be made using different 3D printing technologies. Achieving high dimensional accuracy in 3D printing technology, selecting a suitable measurement method for geometric dimensions, and estimating the uncertainty of measurement results is a considerable problem, as demonstrated in this work. In scientific research, a frequently used concept is measurement uncertainty, but in the presented work, the so-called production uncertainty was examined, which has been shown to be significantly influenced by the number of samples tested, and the final result varies depending on the measurement method used. The main purpose of the measurement is the diameter of the sample and the assessment of the impact of measurement accuracy on its values. Moreover, an additional goal of our research was to show how an accidental encounter of a seam by the operator of both the caliper and the coordinate measuring machine can be misinterpreted and falsify the result of the diameter, not the distance. We treat the seam as a defect that the operator taking the measurement is unaware of, because the seam may be scattered across the diameter and invisible at first view.
## 1 Introduction
Following such idea, the characteristics of multimedia coding techniques have been exploited in transport, network, MAC and physical layers, influencing procedures for congestion control, error recovery, multipath selection and energy preservation. There are many works in the literature that propose cross-layer architectures, addressing different issues of visual sensor networks. However, we classify as multimedia-based only the works that exploit the inner characteristics of the employed coding technique to achieve higher efficiency. For example, [9] is a cross-layer solution aimed at congestion mitigation based on the analysis of the memory status of sensor nodes and the current transmission trends, acting to forecast network congestion and actively adjust the transmission rate. In fact, this work presents a cross-layer architecture that according to our classification is not multimedia-based. We believe that high-efficiency VSN applications are particularly interesting in multimedia-based cross-layer optimization solutions. Several papers can be found in the literature surveying general aspects of wireless sensor networks [1-3] and wireless multimedia sensor networks [5-7,10]. In a different way, we survey in this paper data-based cross-layer optimization solutions where the inner characteristics of the available multimedia coding techniques are exploited to achieve higher efficiency. In short, we want help to answer the following questions: (1) what are the proper multimedia coding techniques for applications, architectures, frameworks and protocols in the VSN research field? How can some multimedia coding techniques be exploited to achieve higher efficiency in terms of energy consumption, end-to-end delay and quality of received data? and (2) what are the design issues that future multimedia-based cross-layer optimization solutions should follow? In this survey, we are not concerned with the details of the algorithms and techniques for multimedia coding, which are already covered by the academic community [5,6,11]. This survey is organized as follows. Section 2 presents some fundaments of image and video coding techniques. In Section 3, the expected benefits of cross-layer design are discussed. Section 4 surveys several papers concerning multimedia-based cross-layer optimization in visual sensor networks. Future research directions are discussed in Section 5, followed by Conclusions and References.
## 2 Materials and Methods
### Model Reduction and Optimization  
In this section, we present a comprehensive approach to model reduction and optimization that integrates numerical methods, Genetic Algorithms, and LSTM-based predictions. This hybrid approach enables more accurate and efficient model reduction while maintaining real-time adaptation capabilities.
## 3 Results
### Effect of TB on Volatile Flavor Profile of Yogurt
The addition of TB increased the relative percentages of undecane, dodecane, tridecane, tetradecane, pentadecane, 1,2-epoxy octadecane, and heptadecane in yogurt. In addition, traditional yogurt contained more cyclododecane than TB yogurt. Hydrocarbons are usually precursors for other volatile compounds and have no direct effect on the formation of the characteristic aroma in the products [32]. Three types of alcohols, including _trans_-2-methylcyclopentanol, 2-cyclohexene-1-ol, and nonadecanol, were identified during the process of the oxidation of lipids, and the reduction of various aldehydes and ketones, where they are usually associated with floral, fruity and alcoholic odors in dairy products [35]. The TB yogurt showed a lower concentration of _trans_-2-methylcyclopentanol, a higher concentration of nonadecanol, and an equal concentration of 2-cyclohexene-1-ol compared to the control. Despite the fact that most alcohols are used as intermediates in the synthesis of esters, they can lessen the sour taste that acid makes [36]. In the volatile fraction of TB yogurt, four ester compounds were detected, including methyl palmitate, methyl linoleic, methyl oleate, and methyl stearate, with varying degrees of concentration reduction in comparison with plain yogurt. Acids are key volatile compounds in yogurt and serve as precursors for the synthesis of alcohols and esters [37]. 5-Hexenoic acid was detected exclusively in ordinary yogurt, but pentadecanoic acid was not present. The palmitic acid content (1.86%) in TB yogurt was greater than that in the control (1.21%). Ketones, which contribute to the characteristic aroma of many foods, are mostly from oxidation, thermal degradation, and amino acid degradation products of unsaturated fatty acids [38]. The relative percentage of 2-nonanone in TB yogurt (1.27%) was higher than that in normal yogurt (0.44%). According to [PERSON] et al. [36], the presence of 2-nonanone was also capable of weakening pungent flavor. As for heterocycles, 2-pentylfuran (caramel notes and nut odor) was identified in both yogurt groups, and its concentration rose slightly when TB was added. Yogurts with TB addition or not had the same relative proportion of amyl-benzene in this experiment. Figure 5: Comparison of volatile compounds in common yogurt and TB yogurt. This suggests that adding TB to yogurt had a direct effect on the production of volatile aromatic compounds. Such an effect was most likely to be linked to a significantly higher level of total acidogenic bacteria counts in TB yogurt compared to common yogurt, due to bacteria's critical roles in altering the composition and concentration of volatile components in the milk base [39].
## 2 Materials and Methods
### Velostat-Film-Based Pressure Sensor with Individual Application of Lower Limb Pressure Ranges  
The Velostat film is a material that changes the electrical resistance and current by compressing the conductive layer when external pressure is applied. Therefore, the measurement of the changing resistance can confirm the magnitude of the applied pressure [30; 31; 32]. Figure 1b is a graph showing the pattern of resistance change when pressure is applied to a Velostat film of three different sizes. The larger the size of the Velostat film, the smaller the initial resistance value, which reflects the different characteristics of the resistance change at the time of pressure application and the saturation point reaching the minimum resistance. Therefore, if sensors of various sizes are fabricated using the Velostat film, different pressure change characteristics can be considered depending on the sensor attachment point. Figure 1c shows the method of fabricating a pressure sensor with a Velostat film. The sensor is fabricated by placing an electrode foil on both sides of the Velostat film and coating the outer surface with an adhesive film. This shape is similar to a sandwich structure. Figure 1d represents a voltage distribution circuit and a Wheatstone bridge circuit for the determination of the magnitude of the applied pressure.  
Since the resistance value of the Velostat film changes according to the applied pressure scale, the scale of the applied pressure can be obtained by constructing a circuit using the fabricated sensor as a factor. As the sensor is fabricated using a voltage distribution circuit, the amount of applied pressure can be obtained through the output voltage value according to Equation (1). The voltage distribution circuit may be advantageously used in a relatively simple configuration. The Velostat sensor consists of an upper part close to the applied voltage, and the internal reference resistance consists of a lower part close to the ground in order to verify the upward output in the pressure application. The internal reference resistance was configured to present the output of 500 mV in a state in which pressure was not applied. The possibility of applying the Velostat-based sensor inside the socket was confirmed through the previous research conducted by our research team [33].  
\[V_{out}=V_{cc}\times\frac{R_{reference}}{R_{velostat}+R_{reference}} \tag{1}\]
# Capturing Protein Domain Structure and Function Using Self-Supervision on Domain Architectures
## 4 Results
### Qualitative Evaluation  
\(RQ_{qualitative}\): Did Vectors of Each Domain Superfamily Form a Cluster in the \(V_{emb}\)?  
To explore the \(V_{emb}\) in terms of the last research question, \(RQ_{qualitative}\), we randomly selected five InterPro domain superfamilies to perform the visualization experiment. The selected domain superfamilies were _PMP-22/EMP/MP20/Claudin superfamily_ with parent InterPro id IPR004031, _small GTPase superfamily_ with parent InterPro id IPR006689, _Kinase-pyrophosphylase_ with parent InterPro id IPR005177, _Exonuclease_, _RNase T/DNA polymerase III_ with parent InterPro id IPR013520, and _SH2 domain_ with parent InterPro id IPR000980.  
We loaded the parent-child tree \(T_{hier}\), provided by InterPro, and for each domain superfamily starting from the parent domain, we included recursively all domains that had a subfamily relationship with this parent domain. For example, the _Kinase-pyrophosphylase_ domain superfamily had domain parent IPR005177, which in turn had two immediate domain subfamilies IPR026530 and IPR026565. The IPR026565 domain contained a subfamily domain with ID IPR017409, where consequently, the set of domains for _Kinase-pyrophosphylase_ domain superfamily was (IPR005177, IPR026530, IPR026565, and IPR017409). We retrieved the vectors for each domain in each superfamily in the \(V_{emb}^{best\ intrinsic}\). Finally, we applied principal component analysis (PCA) to produce a two-dimensional space.  
Visualization of the reduced space is depicted in Figure 4. Domain embeddings of each superfamily are organized in well-separated clusters. The cluster of the _Exonuclease_, _RNase T/DNA polymerase III_ superfamily had the highest dispersion of all presented superfamilies. By this finding, we could answer the research question with the following: Embedding vectors of the same superfamily are well-clustered in the trained \(V_{emb}\).
# A Survey on Video Big Data Analytics: Architecture, Technologies, and Open Research Challenges
## 2 Literature Overview in VBDA
### Problem Taxonomy and Applications of Video Processing in VBDA
#### 2.4.4 Crowd Analysis and Management  
Crowd analytics addresses the need for real-time understanding of group movement, density estimation, and behavioral anomalies in densely populated areas. These techniques are critical for ensuring event safety, optimizing public space design, and managing pedestrian movement in high-density environments [77]. Approaches have evolved from traditional optical flow and density map estimation to GNN-based and Transformer-based models for dynamic prediction. Benchmark datasets such as Mall [78], UCF-QNRF [79], and ShanghaiTech support comparative evaluation.
## 3 Error Correction Framework
### Stage 1: Static Error Correction
The flow chart of the static error correction for the system is shown on the right side of Figure 3. It shows that the robot has to stop for each waypoint to perform the error correction cycle. The system is capable of connecting to the tracker through the network and can obtain static and continuous measurements. Continuous measurement means that the Leica Tracker is able to provide the measurements in dynamic fashion or continuous mode. This is called tracking mode, in which the tracker tracks the device (by continuously locking into the SMR, TMAC or TSCAN) and enables the tracker to provide the measurement in fractions of milliseconds. At this stage, the Leica Tracker reflector is used to obtain 3D measurements and so the correction is using a 3D plane. Figure 4 shows the interface of the integrated system for the user. The process is described as follows:  
1. The user connects with the Leica Tracker using IP and port. The IP address and port number are required because the tracker is accessible through the network. \"Tracker Connection\" information turns green. 2. User initializes the tracker (if not pre-initialized after start). Figure 1: Basic system architecture. 3. The user gives bird bath using the tracker reflector. 4. The user presses the button \"Ready to accept Robot Connection\". 5. The user starts the robot program. 6. The \"Robot Connection\" information turns green. 7. The system then begins its process for correcting the errors.
# Design and Simulation of Electromagnetic Linear Actuators for Jet Dispensers
## 2 Analysis Method for Electromagnetic Actuators and Design Process
### Finite Element Analysis  
In the present study, the magnetic field produced by the electromagnetic actuators is simulated using ANSYS Maxwell software based on the Maxwell equations, which relate the electric and magnetic fields to one another and to the corresponding electric charges and currents. In particular, the simulations use the magnetostatic solution type in Maxwell software, which resolves the static magnetic field existing in a structure that is generated by a direct current (DC) or permanent magnet, as well as the external magnetic field, for both linear and nonlinear materials. Notably, such an approach is applicable to both simple and complex geometries. However, detailed geometries are usually ignored to reduce the possibly disturbing dynamics problem [41; 42].  
Thus, in the present study, the three-dimensional (3-D) structures of the SA, MCA, and MMA are assumed to be symmetric about the \(z\)-axis, and are therefore replaced by two-dimensional (2-D) models to simplify the modeling process and reduce the calculation time. ANSYS Maxwell simulations are then performed using the following magnetostatic field equations [42]:  
\[\
abla\times H=J \tag{1}\]  
\[\
abla\cdot B=0 \tag{2}\]  
\[H=\frac{B}{\mu_{r}\mu_{0}} \tag{3}\]  
\[B=\
abla\times A \tag{4}\]  
\[J=\
abla\times\left(\frac{1}{\mu_{r}\mu_{0}}\
abla\times A\right) \tag{5}\]  
where \(H\) is the magnetic field strength, \(J\) is the \(z\)-component of the current density field in the electric conductor, \(B\) is the magnetic flux density, \(A\) is the \(z\)-component of the magnetic vector potential, \(\mu_{r}\) is the relative permeability of the material. Note that solving Equations (1)-(5) requires prior knowledge of both the geometric dimensions of the model and the material properties. Furthermore, the relative permeability of nonlinear materials is not constant, and hence the \(B\) vs. \(H\) curve must also be defined in advance.
## 2 Materials and Method
### Determination of Antibacterial Activity
#### 2.3.2 The Disk Diffusion Method  
The sprout extracts were prepared as presented in Section 2.2.1. In order to verify the antibacterial potential of sprouts on the five microbial strains, the Kirby-Bauer diffusometric method was used, taking as a standard (positive control) different antibiotics depending on bacterial strain characteristics. The microbial suspension at an optical density (OD) of 0.5 McFarland was read at a wavelength of 565 nm, using standard saline for each tested strain and then seeding it on Mueller-Hinton agar (Sigma-Aldrich, Dortmund, Germany). After drying the inoculum on the surface of the culture medium, the microtablet containing the antibiotic of choice (Liofilchem, Roseto degli Abruzzi, Italy) was placed centrally for the positive control, and the microtablets using sterile Whatman filter paper no. 1 soaked with different uL of the extracts to be tested were placed lateral to it. It should be noted that the diameter of the microtablets is 6 mm. The positive control was different depending on microorganism as follows: _E. coli, Poteus mirabilis_, and _Klebsiella pneumoniae_--positive control for levofloxacin; _E. faecalis_--positive control for vancomycin; and _S. aureus_--had a positive control for linezolid.  
After placing the microtablets on the surface of the culture medium, the plates are incubated for 18-24 h at 35-37 \({}^{\circ}\)C, after which the size of the inhibition zones is checked by measuring the halo around the microtablets and expressing the results in millimeters [23].  
The size of the zone of inhibition indicates the antibacterial efficacy of the analyzed extract and was determined using the formula:  
\[\textit{Diameter of inhibition zone ($\varnothing\,mm$)}=[(DV-DC)+(DH-DC)]/2 \tag{2}\]  
where:  
\[\textit{DV--vertical diameter (mm);}\]  
\[\textit{DC--disc diameter (mm);}\]  
\[\textit{DH--horizontal diameter (mm)} \tag{3}\]  
The percentage of strain inhibition for each extract was calculated by relating the zone of inhibition generated by the sample to the zone of inhibition of the antibiotic; positive control was calculated using the following formula:  
\[\textit{Inhibition (\%)}=[\textit{DS/DPC}]\times 100 \tag{3}\]where:  
\(D\)S--Diameter of inhibition zone for sample (\(\varnothing\) mm);  
\(D\)PC--Diameter of inhibition zone for positive control (\(\varnothing\) mm).
# A Synthetic Aperture Radar-Based Robust Satellite Technique (RST) for Timely Mapping of Floods
## 6 Results
### Ouse River--England 2015  
The CEMS system was activated for this event on 28 December 2015 [60]. We considered the flood delineation map generated by analyzing a RADARSAT image acquired on 29 December 2015 as reference data for this case study. The flood map was generated by CEMS using a semi-automatic approach with an estimated geometric accuracy of 10 m [60]. This reference map was then compared with the SAR-RST-FLOOD map computed on the same date (Figure 4).  
The user accuracy value was 0.64, the produced accuracy was 0.98, and the Kappa index reached 0.77 (Table 3). From the image (Figure 4), it is evident that the proposed approach was able to detect more flooded pixels than those achieved by applying the threshold method to the RADARSAT image. Such a discrepancy, whose reliability cannot be assessed in the absence of ground data, is mostly related to the difference between the two used methods. In any case, the high Paf value indicates the good reliability of the RST approach.
## 3 MIMO System Model and the Implementation Steps of Channel Estimation
### Implementation Procedure  
In the MIMO system, channel assessment is essential to estimate the channel parameters from the receiver side antennas using pilot symbols. It is designed to simultaneously reduce the overlapping of frequency and allow multiple channel estimation at the same time. Channel estimation provides useful information for future processing of signals. The developed MIMO channel estimation model undergoes a certain implementation procedure, which is explained below.  
* The parameters used in the implemented MIMO channel assessment model are represented as time representation, subcarrier count, and regulation order.
* In the MIMO system, parallel data are connected to the antennas at the receiver side. These data are selected based on arbitrary functions \(Tz(G)\) in MATLAB.
* The input data and carrier signal are fed to the amplitude modulator for modulating the signal.
* The coefficient of the carrier signal is represented as the pilot signal. These signals are occupied based on diverse baseband algorithms and equilibrium access.
* If no data are sent through the transmission channel during communication, then loss of the signal occurs, which leads to a change in the time waveform. Inverse FT is applied in the time waveform to improve the efficiency of the system.  
Figure 1: Structural representation of the MIMO system model.  
* The Rayleigh channel model with impulse signal \(Lz(T)=\left[Lz(0),\ldots,Lz(t-1)^{e}\right]\) is represented as a system model. Here, \(t\) denotes a random variable.
* The vacant space is removed at the acceptor side using a demodulation process to improve the efficiency of the estimated channel.  
In MIMO, numerous antennas are used in the center and destination side of the antenna. The transmitter antenna sends the signals and is received by the receiver antenna. Then, these signals are combined at the receiver side in order to accomplish the error and reduction of capacity gain. The received data symbol at the receiver has transmitted symbols, the channel matrix, and noise vectors. The accurate value of the forwarded signal can be estimated through the detection methods. This can be attained via the cancellation of the unwanted signals and then determined using the desired subcarrier for the entire independent transmitter. In order to reduce the noises and distortion from the received signal, channel estimation is needed. The system is pre-trained to estimate the channel efficiency using the Minimum Mean-Square Error (MMSE) method, which needs the matrix of channel correlation and the received noise power. The channel estimation error is analyzed using these trained matrices. From these channel estimation methods, the appropriate variations in the channel are obtained.  
Proposed Hybrid Serial Cascaded Network for the Estimation of Channel State Information in MIMO with Hybrid Optimization Algorithm