esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Centralized Clustering Algorithm for Wireless Sensor Networks
## 3 Proposed Method  
The proposed method is a centralized algorithm that combines the decision about the CHs and the skip setting. The selection of the CHs is made by the identification of the best scores obtained from the inferred results of a fuzzy system. The BS runs this system applied to each node. The inputs for each fuzzy system are parameters that the base station gathers or estimates from the information obtained from the network. The output score ranges from 0 to 1. Thus, the BS manages the network in a rounds-based schedule that updates the score of the nodes after a variable interval of rounds called \(skip\). The exact round scheduling is detailed in Section 3.2.  
As previously commented, the way that the BS sorts the nodes to elect the CHs is based on the output of a Type-2 Fuzzy inference system (T2 FIS) or score. Precisely, a high value implies a high probability of converting a node into a CH. Finally, the BS only selects \(k\) nodes with the highest value to be CH and informs all the nodes of their role: cluster head or contributing node. The T2 FIS is explained in the next section. The parameter \(k\) is set in the configuration process.  
Concerning the skip configuration, this parameter is set in the configuration phase of the WSN. The goal of this setting is obtaining a good performance of the network in terms of reliability (making all the measurements from all the sensors available as long as possible) and durability (keeping the functionality of most sensors).
## 3 Results
### Characterization Part
#### 3.1.4 Structural Characterization of PMVEMA-CEF DFMAC  
The comparative FTIR/ATR spectra of PMVEMA, CEF and PMVEMA-CEF are given in Figure 5.  
Theoretically, the carbonyl group of the beta-lactam ring in vibration mode should appear around \(\
u=1770\) cm\({}^{-1}\)[26]. As seen in the FTIR/ATR spectrum of CEF in Figure 5, carbonyl group absorption for cefalexin monohydrate appeared around \(\
u=1754\) cm\({}^{-1}\) and the peak for the side amide link on the beta-lactam ring appeared at \(\
u=1688\) cm\({}^{-1}\).  
According to the FTIR/ATR spectrum of PMVEMA-CEF in Figure 5, peaks at \(\
u=3218\) cm\({}^{-1}\) (O-H stretching of broad c. acid), alkane at \(\
u=2930\) cm\({}^{-1}\) (CH\({}_{3}\) (a)symmetric stretch  
Figure 4: FTIR/ATR spectra of PEMA, AMP and PEMA-AMP.  
Figure 5: FTIR/ATR spectra of PMVEMA, CEF and PMVEMA-CEF.  
ing), peaks at \(\
u\) = 1765 cm\({}^{-1}\) (C=O group of beta-lactam), a carboxylic acid peak at \(\
u\) = 1700 cm\({}^{-1}\) (C=O stretching), three substituted alkene peaks at \(\
u\) = 1651 cm\({}^{-1}\) (C=C str.), a 2\({}^{\circ}\) amide (of \(\beta\)-lactam) at \(\
u\) = 1651 cm\({}^{-1}\) (C=O stretching) and monosubstituted benzene at \(\
u\) = 1496 cm\({}^{-1}\) (C-C stretching) were observed.  
The characteristic symmetric and asymmetric C=O stretching vibration peaks of anhydride of PMVEMA at 1849 and 1770 cm\({}^{-1}\) disappeared. The carbonyl group peak of the beta-lactam ring of CEF shifted from 1754 cm\({}^{-1}\) to 1765 cm\({}^{-1}\). After the PMVEMA-CEF conjugation, new peaks at 1765 cm\({}^{-1}\) (-N-C=O) and 1651 cm\({}^{-1}\) (CO-N-H) due to amide formation can be seen in Figure 5.  
According to the \({}^{1}\)H NMR spectrum of PMVEMA-CEF in Figure S1d, \({}^{1}\)H NMR (400 MHz, dmso-d6) 8: 8.65 ppm (1H, N-H), 8.13 ppm (1H, N-H), 7.39-7.33 ppm (5H, C-H), 4.98 ppm (1H, C-H), 4.79 ppm (1H, C-H), 4.41 ppm (1H, C-H), 3.37 ppm (3H, CH\({}_{3}\)), 3.06 ppm (1H, C-H), 2.97 ppm (1H, C-H), 2.68 ppm (1H, C-H), 2.28 ppm (3H, CH\({}_{3}\)), 1.00 ppm (2H, CH\({}_{2}\)), 0.97 ppm (2H, CH\({}_{2}\)).
# A Multi-Customer Vehicle Scheduling Optimization Method for Coal Intelligent Loading System
## 5 Case Verification
### Customer Prioritization  
Aiming at the order information of seven customers, the evaluation grade membership degree method is used for evaluation, and the obtained customer order evaluation indicator information is shown in Table 8.  
Because each indicator unit is different, the data normalization process is carried out. Here, \(X_{3}\) and \(X_{4}\) are benefit indicators, which are converted by Formula (1); \(X_{1}\), \(X_{2}\), and \(X_{5}\) are cost indicators, converted by Formula (2). Formulas (3) and (4) were used to calculate the positive and negative ideal solutions for each index, as shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Loading Bunker Serial** & **Customer** & **Vehicle** & **Load Capacity** & **Loading Start** & **Loading End** \\
**Number** \(k\) & **Number** \(i\) & **Number** \(j\) & **Q\({}_{ij}\) (ton)** & **Time** \(t_{s}\) & **Time** \(t_{e}\) \\ \hline
2 & 1 & 1 & 12 & 8:49 & 9:01 \\
1 & 1 & 2 & 12 & 9:18 & 9:30 \\
3 & 1 & 3 & 12 & 9:18 & 9:30 \\
3 & 1 & 4 & 8 & 9:43 & 9:51 \\
1 & 1 & 5 & 12 & 8:53 & 9:05 \\
1 & 1 & 6 & 8 & 10:25 & 10:33 \\
1 & 1 & 7 & 12 & 11:25 & 11:37 \\
1 & 1 & 8 & 12 & 10:53 & 11:05 \\
2 & 1 & 9 & 8 & 9:49 & 9:57 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Notification of loading operation before optimization.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Customer Orders** & \(X_{1}\) & \(X_{2}\) & \(X_{3}\) & \(X_{4}\) & \(X_{5}\) \\ \hline
1 & 0.8 & 0.6 & 0.6 & 0.4 & 0.4 \\
2 & 0.8 & 0.8 & 0.4 & 0.4 & 1 \\
3 & 0.4 & 0.6 & 0.6 & 0.8 & 0.6 \\
4 & 0.2 & 0 & 1 & 1 & 0 \\
5 & 1 & 1 & 0.2 & 0 & 0.8 \\
6 & 0.2 & 0.4 & 1 & 0.8 & 0.2 \\
7 & 0.2 & 0.4 & 0.8 & 0.8 & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Customer order evaluation indicator information.  
Formulas (5)-(7) were used to calculate the group benefit value, individual regret value, benefit ratio sum, and order ranking, as shown in Table 10.  
According to the ordering result, it can be seen that \(r_{4}>r_{6}>r_{7}>r_{3}>r_{1}>r_{2}>r_{5}\), and the order priority of customer is Order 4 > Order 6 > Order 7 > Order 3 > Order 1 > Order 2 > Order 5. Therefore, it can be inferred that each customer's prioritization penalty coefficients are as follows: \(R_{1}=30\), \(R_{2}=20\), \(R_{3}=40\), \(R_{4}=70\), \(R_{5}=10\), \(R_{6}=60\), and \(R_{7}=50\).
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### Analysis of Triaxial Test Results
#### 3.3.1 Analysis of Factors Affecting Peak Deviatoric Stress  
Unconsolidated undrained (UU) triaxial tests were conducted under confining pressures of 200-400 kPa on specimens with various mix ratios cured in water for 40 days. The peak deviatoric stress results are presented in Figure 7. As shown in the figure, the peak deviatoric stress of the specimens increased with higher confining pressure. Additionally, under the same confining pressure and recycled aggregate content, the peak deviatoric stress rose with an increase in stabilizer content, reaching its maximum when the stabilizer content was 20%. For confining pressures of 200-400 kPa, the peak deviatoric stress of R20C20 was 72.74%, 77.38%, and 97.57% higher than that of R20C10, respectively. Conversely, under the same confining pressure and stabilizer content, the peak deviatoric stress decreased as the recycled aggregate content increased. For confining pressures of 200-400 kPa, the peak deviatoric stress of R20C20 was 68.68%, 61.59%, and 59.45% higher than that of R60C20, respectively.  
The peak deviatoric stress results of different specimens indicate variability in the combined effects of stabilizer and recycled aggregate at varying dosages. Within acceptable limits, increasing the stabilizer content positively influenced shear strength. This effect was attributed to the hydration reaction of the stabilizer, which generated a significant amount of cementitious material, effectively filling the inter-particle voids and enhancing inter-particle cohesion. However, increasing the recycled aggregate content negatively impacted shear strength. This was likely due to slippage and displacement between recycled aggregate particles, which reduced the specimen's overall shear strength.
## 1 Introduction
### Research Scope  
The proposed evaluation strategy used in this work intend to evaluate the direct georeferencing quality of the UAV-based laser scanning system by using the calculated point cloud without any additional information or optimization. Since the processing chain recommended by the manufacturer includes point cloud optimization techniques, such as strip adjustment or insertion of GCPs, it is difficult to conclude the accuracy of the direct georeferencing itself. Therefore, the strategy adopted for quality assessment is chosen to evaluate the absolute accuracy and precision of a single measurement without optimization, for example, cross-flight measurements or additional point cloud processing interventions, similar to [PERSON] et al. [37]. This analysis to derive absolute accuracy is performed by comparing the point cloud obtained with the UAV-based laser scanning system with a TLS reference point cloud using point-based and parameter-based evaluation strategies. The objects for comparison are mainly TLS targets adopted for use with the UAV-based laser scanning system. Furthermore, additional objects are used for the analysis of the height component and, therefore, the accuracy in vertical direction. In addition to absolute accuracy, the precision of the system is also evaluated by performing repeated flights with identical flight planning. This approach allows a proper quantification of the precision of the UAV-based laser scanning system, especially, when the GNSS conditions change, particularly the satellite geometry. Another important factor affecting direct georeferencing is the GNSS master station used. To investigate this aspect in detail, four different master stations are employed: one own master station, one Continuously Operating Reference Station (CORS) and two Virtual Reference Stations (VRS). In addition to the analysis of single strip measurements, a second data set is considered with a cross-flight pattern and extended post-processing as it is commonly done.
## 4 Experimental Results  
### Dataset and Optical System  
There were six videos containing TV in our dataset and the frame number of each video is shown in Table 1. The image size of each frame was 1920 \(\times\) 1200. All videos were shot under this condition: adjusting the z position of the microscope stage to make the formed elements clearest. The positions and shapes of the TV were constantly changing due to its motion characteristics and most of the time it appeared as shadow regions in the videos. For the convenience of comparison, we manually labeled the TV in all video frames, obtaining a total of 2520 annotated images for analysis (ground truth of TV, pixel value 0 for background regions, pixel value 1 for TV regions). For the two convolutional neural networks, we used video1 to video2 as the training set, video3 as the validation set, and video4 to video6 as the test set.  
Figure 4: Examples of the generated random rough detection results. For convenience of display, we have used red to mark the TV regions on the original image. (**a**) Ground truth of TV; (**b**) two examples of the generated rough detection result.  
Figure 5: The inference phase of the fine detection network.  
As shown in Figure 6, the optical system for capturing TV videos contains a biological microscope and a charge-coupled device (CCD) camera. We used a CX31 biological microscope (Olympus, Tokyo, Japan) equipped with a 40\(\times\) objective lens (CFI BE2 Plan Achromat, Nikon, Tokyo, Japan) which has a numerical aperture (NA) of 0.65. An EXCCD01400 KMA CCD camera (Motic, Xiamen, China) with a pixel size of 6.45 \(\upmu\)m \(\times\) 6.45 \(\upmu\)m is used for exposure and the exposure time was 40 ms. The field of view (FOV) was 0.41 mm \(\times\) 0.26 mm.
# Parametric Analysis of Compliant End Face Gas Film Seals Considering Slip Flow Effects
## 2 Analytical Model
### Mathematical Model
#### 2.2.2 Reynolds Equation Considering Slip Flow Effects  
According to [PERSON]'s analysis of the velocity slip effect at the wall boundary, it can be seen that there is a step in the slip velocity of the low-pressure gas flowing near the solid wall; the normal velocity gradient and the thermal creep effect are two important components of the boundary velocity slip [36; 37]. However, in the compliant end face gas film sealing problem, the influence of the thermal creep effect is very small; therefore, the velocity slip equation for the compliant end face can be obtained. [PERSON] and [PERSON] analyzed the ultra-thin gas lubrication problem by introducing Poiseuille flow coefficients and establishing the F-K slip flow model [38; 39] based on the theory of the above study. The dimensionless modified Reynolds equation describing the boundary velocity slip flow effect considering thin gas action is obtained as:  
\[\frac{1}{R^{2}}\frac{\partial}{\partial\theta}\bigg{(}PH^{3}Q\frac{\partial P }{\partial\theta}\bigg{)}+\frac{1}{R}\frac{\partial}{\partial R}\bigg{(}RPH^{ 3}Q\frac{\partial P}{\partial R}\bigg{)}=\Lambda\frac{\partial}{\partial\theta }(PH) \tag{2}\]  
where \(Q=\frac{Q_{\rm p}}{Q_{\rm con}}\) is the dimensionless Poiseuille flow coefficient, \(Q_{\rm p}\) is the Poiseuille flow coefficient, and \(Q_{\rm con}\) is the continuous Poiseuille flow coefficient, which can be calculated by Equation (3) and Equation (4), respectively.  
\[Q_{\rm p}=\left\{\begin{array}{l}\frac{D}{6}+1.0162+\frac{10.653}{D}-\frac{2 1354}{D^{2}}(D\geq 5)\\ 0.13852D+1.25087+\frac{0.15663}{D^{2}}-\frac{0.00969}{D^{2}}(0.15\leq D<5)\\ -2.22919D+2.10673+\frac{0.01653}{D^{2}}-\frac{0.000694}{D^{2}}(0.01\leq D<0.15) \end{array}\right. \tag{3}\]\[Q_{\rm con}=\frac{D}{6} \tag{4}\]  
where \(D=\frac{p_{\rm o}h_{0}}{\mu\sqrt{2R_{\rm g}T_{0}}}\) is the inverse Knudsen number; \(R_{\rm g}\) is the gas constant, J/(mol\(\cdot\)K); \(T_{0}\) is the absolute temperature, K.
## 4 Experiments
### Experiments on Public Datasets  
On SROIE dataset, we conducted experiments using \(P\), \(R\), and \(F1\) as the evaluation metrics, and compared our results with other end-to-end models, including donut [10], dessurt [11], and DocParser [12]. The experimental results are presented in Table 6.  
On CORD dataset, we conducted experiments using \(F1\) and \(BTED\) as evaluation metrics and compared our results with other models, including three backbone networks for VDU tasks: BERT [5], BROS [4], and LayoutLMv2 [7], as well as two end-to-end models donut [10] and DocParser [12]. The experimental results are shown in Table 7 (for OCR-dependent models, the results are based on PaddleOCR).  
The experimental results on the CORD dataset demonstrate that our proposed SwFB model outperforms other VDU models, achieving superior performance. Additionally, in the experiments on the SROIE dataset, our model performs comparably to other end-to-end models. These results further confirm that our model has excellent generalization ability and is suitable for information extraction tasks in various document images. They also strengthen the feasibility of applying our model in diverse real-world scenarios.
# Ultrasound for Meat Processing: Effects of Salt Reduction and Storage on Meat Quality Parameters
## 3 Results and Discussion
### Bacterial Enumeration in Brine  
Even though meat itself is an implicit source of bacterial contamination, other sources such as improperly cleaned equipment or brine recirculation during processing could also be cause of contamination. An investigation on the bacterial contamination levels of recirculating brine used in the production of pork, found that the number of _L. monocytogenes_ increased with time, reaching a maximum of log\({}_{10}\) 2.34 after 2.5 h [51]. The potential use of ultrasound for decontamination purposes is based on the effect of acoustic cavitation that generates strong oxidizing agents, such as \({}^{\bullet}\)OH radicals, H\({}_{2}\)O\({}_{2}\) and ozone, initiating and enhancing redox reactions with the consequential effect of bacterial inactivation [16; 52]. The decontamination efficiency of US for the brining environment was evaluated by enumerating the number of _E. coli_ and _L. innocua_ present in the liquid after processing the inoculated meat, Figure 6.  
In meat samples primed with US, a significant reduction of 4 log\({}_{10}\) CFU/mL (_p_ = 0.005) was observed for _E. coli_ in both the brines used. In agreement with these results, previous studies have shown that the Gram-negative bacteria in particular _E. coli_, could be easily inactivated by sonication treatment in the presence of salts, due to its thinner peptidoglycan layer [17; 18; 53]. On the other hand, the _L. innocua_ population in the brine did not show a significant reduction for both US-assisted and static brining with both salt solutions, Figure 6. Generally, _Listeria_ spp. tends to be more resistant to high salt concentrations and ultrasound treatments than Gram-negative bacteria are; as observed by [PERSON] et al. [54], this organism could survive in a cheese brining system with a salt concentration up to 10%. Therefore, its persistency even under ultrasound treatment is not surprising; in order to be inactivated, it may require additional stressors such as temperature (e.g., thermo-sonication) or addition of antimicrobials [55; 56; 57].
# A Real-Time Network Traffic Classifier for Online Applications Using Machine Learning
## 3 System Design
### Dataset
This step was necessary because all dimensions of feature vectors extracted from input traffic data should be in the same range. This made the convergence of our ML models faster during the training phase. Statistical normalization (Z-transformation) was implemented by subtracting the input mean value \(\mu\) from each attribute's value \(I(i)\), and then dividing the result by the standard deviation \(\sigma\) of the input feature vector. The distribution of the output traffic values would resemble a Gaussian curve centered at zero. We used the following formula to normalize each feature vector in our training set:  
\[O(i)=\frac{I(i)-\mu}{\sigma} \tag{1}\]  
where \(I\) and \(O\) are the input and output feature vectors, respectively; and \(i\) is the current feature vector's index to be normalized.
## 3 SSMA Broadcast Protocol
### Discussion on Key Protocol Parameter
#### 3.4.3 The Lower Bound of BP  
Since every level except for level one takes one _BSS_ and the nodes at the highest level do not have to rebroadcast the message, the lower bound of _BP_ for a tree of depth \(H\) is given as follows:  
\[BP\geq len(BS)+(H-2)\times len(BSS) \tag{14}\]We need to estimate \(H\) that corresponds to the maximum level. From the cumulative distribution function in Reference [21], the distribution of \(Pr(level=k)\) is shown in Figure 8 where \(R\) and \(a\) indicate the transmission range and the width and length of network dimension, respectively.  
Figure 8: Probability distribution of tree levels.  
Figure 6: Distribution of node degrees.  
Figure 7: The maximum number of competing nodes at any tree level.  
From Figure 8, when \(R=10\) m and \(\alpha=30\) m, \(H=k\approx 5\) (i.e., \(Pr(level=5)\approx 0.0254\)), and when \(R=28\) m and \(\alpha=100\) m, \(H=k\approx 6\) (i.e., \(Pr(level=6)\approx 0.012\)).
# All-Optical Modulation and Ultrafast Switching in MWIR with Sub-Wavelength Structured Silicon
## 1 Introduction
showed a 50-MHz all-optical modulator with germanium-on-silicon waveguides in the wavelength range between 2 and 3 \(\upmu\)m [20]. In our previous work [6], we used a time of flight detection scheme based on the all-optical modulation in the range between 2.1 and 3.5 \(\upmu\)m, demonstrating a potential to reach sub-picosecond temporal resolution. Up to date, there have been only a few experiments conducted on MWIR all-optical modulators, and the reported results only covered the short wavelength part of the mid-infrared spectrum. In this work, we aim to explore for the first time a reflective-type silicon-based all-optical modulator in the wavelength range between 4 and 6 \(\upmu\)m, covering almost the entire MWIR range. Sub-wavelength structured silicon optical membranes were used as the active material for the modulator, and its reflectance in the MWIR regime was modified by external excitation of the free charge carriers (electron-hole plasma) using an intense laser beam of 800 nm. We show the overall performance of the modulator in the frequency and time domains and quantify the pump fluence required to achieve the optimised modulation. The results suggest the enhanced modulation depth of the sub-wavelength structured silicon in comparison to the bulk counterpart under the same experimental condition. An optical model was developed to fit the experimental data and extract the associated carrier concentration, charge carriers' scattering rate and the complex dielectric function of the optically-excited membranes. We explored the temporal response of the modulator and designed the experimental conditions to achieve the ultrafast modulation of 0.55 ps. These unique conditions for the ultrafast switching in the time domain take advantage of the dielectric function variation following the carrier concentration evolution before reaching the equilibrium state and before the onset of the carrier density decay.
## 4 System Architecture
### Beamformer  
The core operations of digital beamforming are the complex weight multiplication and summation of the sampled signals. This implies the simultaneous complex weight multiplication of every sampled channel when trying to reduce signal delay between input and output of the system. This can be computed using Equation (1):  
\[\mathrm{b(t)}=\sum_{\mathrm{k}=0}^{\mathrm{k}=\mathrm{N}}\mathrm{W_{k}(t)} \cdot\mathrm{X_{k}(t)} \tag{1}\]  
where \(\mathrm{X_{k}(t)}\) represents the complex sampled signal of channel k at a given time t. This is multiplied by the complex weight destined to the same channel \(\mathrm{W_{k}(t)}\). The output of the beamformer b(t), is the product of the summation of these N multiplications.  
Figure 6 presents the beamforming IP core developed with Vitis High-Level Synthesis (HLS) [35] and added to the block design in Vivado. This block can generate multiple beams in a parallel manner. Multiple beams can be generated simultaneously just by summing every beam pointing to different satellites. This was implemented within the FPGA fabric present in the SoC. The complex samples are transmitted from the RF-ADCs to the IP core using the AXI4-Stream protocol adopted by Xilinx. The complex weights are transmitted by the processing system using the AXI4-Lite protocol. Both the in-phase and quadrature weight components are stored back-to-back in register spaces, where they can be updated independently. In this way, the system can adapt to many use cases. The multiplication and summation of the samples uses the dedicated DSP48E2 blocks for faster processing.  
Figure 5: Mixing the signal to baseband.