esa-sceva/satcom-chunk-collection huggingface dataset:
# Green Strategies for Molecularly Imprinted Polymer Development
## Green Strategies in MIP Development
### Ultrasound-Assisted MIP Synthesis
The same group [89] also described the caffeine-imprinted polymers using MAA as monomer, EGDMA as crosslinker, and BPO as initiator, by ultrasound-assisted precipitation polymerization, in which polymer microspheres in high yields and with narrow size distributions were obtained, with number average particle size diameter (\(d_{\mathrm{n}}\)) between 0.28 to 0.56 \(\upmu\)m. Imprinted polymers prepared at 40 \({}^{\circ}\)C had the best caffeine binding performance, adsorbing 21.63 \(\upmu\)mol\(\cdot\)g\({}^{-1}\) and with IF of 6.54. MIP-coated Mn-doped ZnS quantum dots for specific fluorescent recognition of cocaine (COC) and metabolites for tracing drugs, were described by [PERSON] et al. [90]. Ultrasound technology allowed the production of MIP layer on QDs by precipitation polymerization, in which EGDMA was used as a functional monomer, DVB as crosslinker, and AIBN as initiator, to proceed the synthesesat 37 kHz for 4 h. Structures produced showed to be able to detect COC and the metabolites, benzoylecgonine (BZE), and ecgonine methyl ester (EME). Another group [91] reported MIP-layered magnetic supports towards to the same template-COC and metabolites, as BZE, cocaethylene (CE), and EME which were produced by ultrasonically-assisted polymerization. MIP syntheses were performed using EDGMA as functional monomer, DVB as crosslinker and AIBN as initiator under sonication conditions (37 kHz, 325 W, 30 \({}^{\circ}\)C) for 4 h. Then, MIP-magnetic particles could be obtained in an efficient way as agglomerates of spherical particles with approximately 50 nm of particle size diameter. Further, particles were packed on a solid phase extraction (SPE) column, showing selective behavior for COC and metabolites in urine samples. These examples describe well the high potential of ultrasound-assisted technology in MIP synthesis. This technology has been also used as an additional step to traditional processes to assist the template desorption from the final MIPs at the end of the polymerization [92].
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Slice-Based QoS Manager Solution
#### 4.6.6 DiffServ QoS  
While per-flow QoS can have a good behaviour as communication flows increase, the flow entries' number, which is set for each OvS switch, also grows. Hence, the per-flow QoS is considered not to be scalable. Contrarily, the DiffServ slice-based QoS implementation divides flows into the several QoS classes at the domain entrance (_br-int_) and applies DSCP marking, matching them with the associated queues in each slice's OvS switch. Therefore, packets will be forwarded according to the first six bits of the Type of Service (ToS) field in its IP header.
# Evaluation of RGB-D Multi-Camera Pose Estimation for 3D Reconstruction
## 1 Introduction
The most notable distortion model is the Brown-Conrady model [11]. The term calibration normally refers to methods of estimating the intrinsic parameters, distortion coefficients and extrinsic parameters. Quaternions are another way to express rotations in the three-dimensional vector space. This method has a compact representation and has some mathematical advantages [12; 13]. It is commonly used by the robotics industry because it is more mathematically stable and avoids the gimbal lock phenomenon, where two axes align and prevent the rotation in one dimension. The robotic arm used in this work, the ABB IRB 4600, uses quaternions for the orientation of its TCP. Equation (4) [14] shows the conversion method from quaternions to Euler angles used in the homogeneous transformation matrix which was used in this work. \[ \begin{bmatrix}\phi\\ \theta\\ \psi\end{bmatrix} = \begin{bmatrix}\text{atan2}(2(q_{0}q_{1}+q_{2}q_{3}),1-2(q_{ 1}^{2}+q_{2}^{2}))\\ \text{asin}(2(q_{0}q_{2}-q_{3}q_{1}))\\ \text{atan2}(2(q_{0}q_{3}+q_{1}q_{2}),1-2(q_{2}^{2}+q_{3}^{2}))\end{bmatrix}  \tag{4}\]
# Methodology for Verification of Geometrically Complex Components Through Reverse Engineering
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Control** & **Measurement** & **Tolerances** & **Deviation** & **Test** \\  & **(Flatness)** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline Surf 1 & Surface Distance & \(-\)0.109 & \(\pm\)1.000 & \(-\)0.109 & Pass \\ Surf 5 & Surface Distance & \(-\)0.092 & \(\pm\)1.000 & \(-\)0.092 & Pass \\ Surf 9 & Surface Distance & \(0.373\) & \(\pm\)1.000 & \(0.373\) & Pass \\ Surf 13 & Surface Distance & \(-\)0.202 & \(\pm\)1.000 & \(-\)0.202 & Pass \\ Surf 17 & Surface Distance & \(-\)0.282 & \(\pm\)1.000 & \(-\)0.282 & Pass \\ Surf 22 & Surface Distance & \(0.187\) & \(\pm\)1.000 & \(0.187\) & Pass \\ Surf 23 & Surface Distance & \(0.048\) & \(\pm\)1.000 & \(0.048\) & Pass \\ Surf 24 & Surface Distance & \(-\)0.024 & \(\pm\)1.000 & \(-\)0.024 & Pass \\ Surf 25 & Surface Distance & \(-\)0.486 & \(\pm\)1.000 & \(-\)0.486 & Pass \\ Surf 27 & Surface Distance & \(-\)0.487 & \(\pm\)1.000 & \(-\)0.487 & Pass \\ Surf 30 & Surface Distance & \(-\)0.203 & \(\pm\)1.000 & \(-\)0.203 & Pass \\ Surf 34 & Surface Distance & \(-\)0.232 & \(\pm\)1.000 & \(-\)0.232 & Pass \\ Surf 36 & Surface Distance & 0.315 & \(\pm\)1.000 & 0.315 & Pass \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Surface distance measurement results. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Name**} & **Control** & **Measurement** & **Tolerances** & **Deviation** & **Test** \\  & **(Flatness)** & **[mm]** & **[mm]** & **[mm]** & **[mm]** \\ \hline Plane 1 & \(\boldsymbol{\sigma}\) & 1.186 & 4.500 & 1.186 & Pass \\ Plane 2 & \(\boldsymbol{\sigma}\) & 0.010 & 1.000 & 0.010 & Pass \\ Plane 3 & \(\boldsymbol{\sigma}\) & 0.030 & 1.000 & 0.030 & Pass \\ Plane 4 & \(\boldsymbol{\sigma}\) & 0.002 & 1.000 & 0.002 & Pass \\ Plane 5 & \(\boldsymbol{\sigma}\) & 0.002 & 1.000 & 0.002 & Pass \\ Plane 7 & \(\boldsymbol{\sigma}\) & 0.035 & 3.000 & 0.035 & Pass \\ Plane 8 & \(\boldsymbol{\sigma}\) & 0.390 & 4.500 & 0.390 & Pass \\ Plane 9 & \(\boldsymbol{\sigma}\) & 0.075 & 3.000 & 0.075 & Pass \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Flatness measurement results. deformation could help prevent thermal stresses during welding, increasing the fixture's precision and lifespan. However, this approach would involve higher financial costs. Advanced simulation tools could predict the behavior of materials during welding, allowing for the optimization of component positioning and clamping even before physical production begins. This would reduce the need for physical testing and increase efficiency. Implementing online monitoring systems during welding would enable real-time parameter adjustments, ensuring that the welding process remains within optimal conditions, thereby minimizing potential errors.
# Improved Low-Complexity, Pilot-Based Channel Estimation for Large Intelligent Surface Systems
## 1 Introduction  
### Motivation  
The development of next-generation wireless communication systems has positioned Large Intelligent Surfaces (LISs) as a revolutionary technology to meet the increasing demands for higher data rates, better connectivity, and extended network coverage [1]. LIS systems, designed to improve signal transmission and minimize interference, employ a vast network of low-cost active antennas, making them particularly suitable for the promoting requirements of 5G-and-beyond networks [2]. However, achieving optimal performance in LISs heavily depends on precise Channel State Information (CSI), which is a critical component for advanced signal processing and reliable data transfer [3].  
One of the crucial technical challenges in LIS systems is channel estimation, a process that involves accurately determining channel characteristics from the received signals. This domain widely adopts pilot-based techniques due to their simplicity and effectiveness [4]. These methods utilize predefined pilot symbols sent through the channel, enabling the receiver to estimate its characteristics. Several methods are employed for channel estimation, including the following:  
* _Pilot-based techniques_: these include methods such as Least Squares (LS) and Minimum Mean Square Error (MMSE).  
* _Machine learning-based methods_: these leverage data-driven approaches for enhanced accuracy.
* _Compressed sensing_: this approach exploits the channel's sparsity to estimate its parameters efficiently.  
Among various techniques, the LS method stands out for its ease of implementation and minimal computational complexity [5]. Despite its advantages, the LS method is highly sensitive to noise and interference, which can lead to significant inaccuracies in large-scale systems with numerous antennas [6].
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 2 Methodology
### Experimental Setup
#### 2.1.2 Cable Selection  
In this experimental setup, for cable selection, we considered low cost, easy availability in the market, and durability as factors. Selecting the right cable for cable-assisted devices is essential for durability and for reducing maintenance [30; 39]. It is assumed that if cable  
Figure 1: (**I,II**) show the experimental setup mimicking the human upper limb in two different joint positions, i.e., 90\({}^{\circ}\) and 120\({}^{\circ}\), as represented in (**V,VI**). (**III,IV**) highlights the components of the experimental setup which are the motor, cable, and load cell with a display.  
performance is good in cyclic loading, then it should perform well in static loading for a similar load. Hence, a different experimental setup, as shown in Figure 2a, is used to test cable resistance and wear under cyclic loading. A motor-driven pulley system is used to move weights up and down linearly at 0.025 m/s for a distance of 0.15 m. \(V(t)\), shown in Equation (1), is the ramp signal of voltage used to drive the motor, where the function switches with the signal from IR (Infra-Red) proximity sensor. The parameters \(V_{0}\) and \(V_{max}\) represent the minimum and maximum voltage of the signal.  
\[V(t)=\begin{cases}V_{0}+(V_{\max}-V_{0})\frac{t}{T_{1}},&\text{for }0\leq t \leq T_{1},\text{ stroke down between IR sensors}\\ V_{\max},&\text{for }T_{1}<t<T-T_{1}\\ V_{\max}-(V_{\max}-V_{0})\frac{t}{T},&\text{for }T-T_{1}\leq t\leq T,\text{ stroke up between IR sensors}\end{cases} \tag{1}\]  
According to various studies, rehabilitation exercises can require up to 30,000 cycles over six months [40]. This led us to test three cables--monofilament nylon, nylon-braided, and steel-braided--for 100,000 cycles. We observe that nylon-braided cable performs well and surpasses the required cycles, as evident from Figure 2b. The preliminary results can be found in the article [30].  
A Proberos 4-strand nylon-braided cable with a diameter of 0.5 mm and a load-carrying capacity of 35 kg was chosen for the current experimental study. This cable was selected for its durability and suitability in exosuit applications. The cable serves as the actuator element in the setup, simulating the tension and forces encountered in actual elbow exosuits. The selection of the cable also considers low cost, easy availability in the market, and durability.
## 3 Illustrative Numerical Results
### Thermal Variations (AT)  
The change in the refractive index of a material as a function of the variation of temperature is known as the thermo-optic effect. An unintentional waveguide temperature variation turns into a resonance-frequency drifting: e.g., \(\Delta T=1\)\({}^{\circ}\)C, corresponds to 10 GHz frequency shift (in silicon) [13]. We have investigated the impact of such thermal perturbation on the overall backplane transfer function when a value within the range \(\pm 0.5\), \(\pm 1\), and \(\pm 1.5\)\({}^{\circ}\)C of thermal variation is applied to any RR with uniform probability distribution. Table 3 shows that a thermal drifting of \(\pm 1.5\)\({}^{\circ}\)C has a detrimental impact. In fact, the transfer functions are highly distorted and the variability on _IL_  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\lambda\) & _\textless{IL}\textgreater{}_ & \(IL_{m}\) & \(IL_{M}\) & \(B_{m}\) & \(B_{M}\) & \(X_{A}\) & \(X_{NA}\) & \(X_{FL}\) \\ Chan@GHz & [dB] & [dB] & [dB] & [GHz] & [GHz] & [dB] & [dB] & [dB] \\ \hline
16@100 & \(-12.5\) & \(-12\) & \(-14\) & \(10.2\) & \(20.9\) & \(-21.2\) & \(-27.2\) & \(-32\) \\
32@50 & \(-15.8\) & \(-15\) & \(-17.6\) & \(10.4\) & \(20.1\) & \(-14\) & \(-20.8\) & \(-29\) \\
64@25 & \(-19.8\) & \(-18\) & \(-22.5\) & \(10.5\) & \(17.6\) & \(-7.2\) & \(-15.2\) & \(-18\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Increasing channels at fixed spectrum.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \(\alpha\) & _\textless{IL}\textgreater{}_ & \(IL_{m}\) & \(IL_{M}\) & \(B_{m}\) & \(B_{M}\) & \(X_{A}\) & \(X_{NA}\) & \(X_{FL}\) \\
**[dB/turn]** & **[dB]** & **[dB]** & **[dB]** & **[GHz]** & **[GHz]** & **[dB]** & **[dB]** & **[dB]** \\ \hline
0 & \(-15.8\) & \(-15\) & \(-17.6\) & \(10.4\) & \(20.1\) & \(-14\) & \(-20.8\) & \(-29\) \\
0.2 & \(-20.2\) & \(-19.5\) & \(-21.5\) & \(12.2\) & \(31.4\) & \(-9.7\) & \(-17.3\) & \(-24\) \\
0.4 & \(-23\) & \(-22.6\) & \(-24.5\) & \(13.5\) & \(41.2\) & \(-(measured as gap between \(IL_{m}\) and \(IL_{M}\)) increases from almost 2 to 6 dB. Our architecture is clearly sensitive to thermal variations higher than 1.5 \({}^{\circ}\)C.
# Road Crack Detection by Combining Dynamic Snake Convolution and Attention Mechanism
## 3 Dataset Description and Performance Metrics  
### Dataset Description and Processing  
In this study, road defect data were collected from several datasets and a custom dataset. These include the Crack 500 dataset [29], which comprises 500 images of road surface cracks captured using mobile phones, each annotated with ground truth crack  
Figure 4: Attention mechanism module.  
Figure 5: The part of downsampling feature maps.  
images. The Crack Forest dataset [30] contains 206 images of road defect scenes. Totally, our custom dataset consists of 4699 defect images. The datasets were partitioned in a 7:2:1 ratio for training, validation, and testing purposes. The datasets include images with various disturbances, such as shadows and zebra crossings, as illustrated in Figure 6.  
To enhance the network's translational invariance and robustness, random horizontal and vertical flips, as well as rotations, were applied to the input images during data loading. Due to variations in image sizes and channel numbers across different datasets, all images were resized to 480 \(\times\) 320 dimensions and converted to a single-channel format for uniformity during network training. Additionally, the entire dataset was standardized to expedite model convergence.  
In the original dataset, label pixel values are either 0 or 255. For ease of subsequent processing, pixels with a value of 255 were modified to 1. During the prediction phase, the network assigns a probability value between 0 and 1 to each pixel. If the predicted probability is greater than or equal to 0.5, it is considered a target pixel; otherwise, it is considered a background pixel.
## 2 Materials and Methods
### GC-MS Determination of Flavor Enhancers  
The flavor substances in the samples were separated using a 7890B series gas chromatograph, and the target substances were quantified using a 5977B series mass spectrometer. Chromatographic conditions of the 7890B were: the chromatographic column was an Agilent 19091S-431 UI-5 MS capillary column (15 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m), the sample injection volume was 1 \(\upmu\)L, and the injection port temperature was 250 \({}^{\circ}\)C. The temperature rise program was 35 \({}^{\circ}\)C, containing for 0 min, rising to 220 \({}^{\circ}\)C at 5 \({}^{\circ}\)C / min, and then, rising to 280 \({}^{\circ}\)C at 10 \({}^{\circ}\)C / min and containing for 2 min. The carrier gas was high purity helium (99.9%), the flow rate was 1 mL/min, the column pressure was 12.04 psi, and the injection port was in the undivided mode. The GC column was directly connected to an Agilent 5977 B series mass selective detector of ion source for the mass spectrometry analysis. The EI source was used as the ion source, the analyte was ionized in the ion source at 70 eV and 230 \({}^{\circ}\)C, and the scanning mass range was 40-400 amu [32].  
The matrix effect (ME) in samples was calculated using the following equation:  
\[\text{ME}\%=\frac{\text{Peak area of standards in matrix -- Peak area of standards in solvent}}{\text{Peak area of standards in solvent}}\times 100\% \tag{1}\]
## 4 Simulation Results and Performance Analysis
### Throughput Compared to Traditional Methods  
To analyze the change in throughput, we simulate the MHSTFC-UD model when the number of users increases under the ideal scenario of uniform distribution of users within the beam and compare its throughput with the traditional scheme. User distribution density can fully utilize all frequency resources. It is also assumed that each user only sends a short message once during the satellite's one-time pass. The simulation results are shown in Figure 5 and the maximum throughput is shown in Table 4.  
The MHSTFC-UD model distinguishes between different users in four dimensions, time, space, frequency and code, compared to the UFFR [17] and BH [26] methods, which distinguish between different users only in the time or space domain or a combination of the two. Therefore, its throughput can reach 76.6 Gbps when the number of users reaches 6.69 billion. Compared with UFFR and BH, the maximum throughput is increased by about 162 times and 319 times, respectively. However, in practice, the distribution of users is random, so this throughput is often not achieved.  
Therefore, in the next section, the system's access performance is analyzed when the users are randomly distributed according to the actual situation. Moreover, to more  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **MHSTFC-UD** & **MHSTFC** & **UFFR** & **BH** \\ \hline Maximum throughput & 76.6 Gbps & 76.6 Gbps & 0.47 Gbps & 0.24 Gbps \\ Multiplication factor & 319.17 & 319.17 & 1.96 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of maximum user throughput in the case of an even user distribution.  
Figure 5: System throughput as a function of the number of users with a uniform user distribution.  
intuitively illustrate the number of users accessing the system, the throughput is converted into the number of users accessing the system for comparative analysis.
Plant Feed Additives as Natural Alternatives to the Use of Synthetic Antioxidant Vitamins on Livestock Mammals' Performances, Health, and Oxidative Status: A Review of the Literature in the Last 20 Years  
number of hydroxyl (OH) groups located on the flavonoid skeleton [7]. If more OH groups are present in the flavonoid skeleton, the PFA can potentially exert a stronger antioxidant activity because it can donate more electrons. Another important mode of action of the antioxidant components of PFAs is their ability to donate hydrogens to metals, thus inhibiting their pro-oxidative activity [9]. Other mechanisms by which PFA may exert an antioxidant activity in animal tissues are by quenching O2, decreasing O2 concentrations, thereby preventing peroxide formation, and by activating antioxidant enzymes [10]. Recently other mechanisms have been investigated to explain the antioxidant activity of PFA, namely interactions with specific proteins of the intracellular signaling cascades, modulation of the expression and activity of key proteins, influence on epigenetic mechanisms, or their effect on the gut microbiota [11]. Essential oils, plant extracts, and plant by-products (e.g., from the grape or citrus industries) can be freely fed to livestock reared under both conventional and organic conditions [11; 12; 13; 14]. Furthermore, PFAs are considered natural, harmless, and residue-free; hence, they could meet both consumer expectations and the need of the feed industry to find valid alternatives to synthetic antioxidants.  
In livestock mammals, the impact of PFA on the oxidative status of the animal has been investigated less than in poultry [15]. Indeed, in the poultry sector, PFAs have been widely studied in comparison with synthetic vitamins in order to assess their antioxidant activity on both animals' performances [16] and products traits [17]. In swine, PFA has been mostly tested as alternatives to antimicrobials rather than as antioxidants, particularly after the ban of in-feed antibiotic use in 2006 [18; 19; 20], whereas in ruminants, PFA (usually essential oils) have been widely investigated as rumen modifiers [21; 22]. Nevertheless, some trials comparing natural to synthetic antioxidants have also been conducted, in swine, rabbits, and ruminants, in order to assess their capacity in ameliorating products yield and quality [23]. However, only a few studies have been conducted in livestock to evaluate the antioxidant role of PFAs in comparison to synthetic molecules in affecting their performances. Nevertheless, it should be highlighted that controversy exists in the literature about the true effectiveness of PFA when compared with VitE, probably because they have different efficacies and modes of action [8]. Thus, it is important to summarize the results of their use in monogastrics and ruminants nutrition. The present review focused on the antioxidant capacity of plant extract, EO, and plant by-products as potential alternatives to synthetic antioxidant vitamins in livestock mammals, including their effect on health, performance, and physiological parameters.
## 2 Numerical Model and Simulations
### Correlations of Heat Transfer Coefficient and Friction Factor  
In 2014, [PERSON] et al. [21] reported the correlation of heat transfer coefficient for the stacked woven wire matrix regenerator, which had a diameter of 100 \(\upmu\)m and a porosity of 63%. The validity and reliability of this correlation has been demonstrated for the application of porous media simulations, and the expression is given by:  
\[Nu=1.91+0.17 Re^{0.80} \tag{7}\]  
The friction factor correlation has been widely studied for porous media. [PERSON] et al. [22] investigated the pressure drop under steady flow in a screen-based regenerator and obtained the Darcy friction factor correlation, which is now widely used under steady flow in the regenerator:  
\[f=\frac{33.6}{Re_{l}}+0.337 \tag{8}\]  
where \(l\) denotes the pore gap.  
[PERSON] et al. [23] investigated the oscillatory flow in the regenerator at ambient temperature and set up a one-dimensional model. Based on the experimental results, they proposed correlations for the Fanning friction factor, which depended on the mesh numbers:  
\[\begin{array}{l}f_{\mathrm{oscm}}=36.55/Re_{\mathrm{m}}+0.16\quad\text{for mesh 200}\\ f_{\mathrm{oscm}}=44.10/Re_{\mathrm{m}}+0.33\quad\text{for mesh 250}\\ f_{\mathrm{oscm}}=48.83/Re_{\mathrm{m}}+0.16\quad\text{for mesh 400}\end{array} \tag{9}\]  
In this study, the steady friction factor correlation by [PERSON] et al and the oscillating flow friction factor correlation by [PERSON] et al. are applied in the simulations, respectively. The results indicate that under the boundary condition of sinusoidal pressure, the steady friction factor correlation by [PERSON] is no longer valid, showing significant differences between steady flow and oscillatory flow. So, the friction factor correlation introduced by [PERSON] et al. is selected.
# Decentralization of Virtual Linkage in Formation Control of Multi-Agents via Consensus Strategies
## 3 Decentralization of Virtual Linkage Approach  
This section illustrates the decentralization of virtual linkage approach. First, the decentralized architecture is introduced to illustrate the advantages as compared to the hierarchical architecture. Then the consensus formation control is presented to enable each agent to decide its movement independently with only exchanging state information with its local neighbors.