esa-sceva/satcom-chunk-collection huggingface dataset:
# Directly Measuring the Pulse Front Distortion of High-Peak-Power Femtosecond Lasers
## 3 Experimental Results  
As described in [24], the PFD in a 200 TW/27 fs laser system mainly results from the four-stage lens-based beam expanders. The gratings-induced pulse front tilt has been carefully checked and controlled by the method referred to in [28]. Thus, the PFD in this laser system can be illustrated as Equation (2).  
\[\Delta T(r)=-0.0348\cdot r^{2},\ \ \text{with}\ r\in(-42.5,\ 42.5), \tag{2}\]  
The PFD measurement was performed after the compressor. Limited by the aperture of the output window in the vacuum compressor, the beam diameter of output pulse was decreased from 85 to 80 mm. The diameter of the iris diaphragm is designed to change through 13, 26, 39, 52, 65 and 80 mm, respectively. In the case of a different beam diameter, the spatial overlap of the two pulse beams after \(BS\) is realized by linearly translating \(M4\). The recorded correlation signals of the above six sampled pulses are shown in Figure 3, and the blue and red traces represent the results measured at two edges of the overlapped pulse after \(BS\). The temporal intervals between the red auto- and cross-correlation peaks were 140, 237, 224, 275, 270 and 264 fs, corresponding to the time delays of \(T_{2}\) measured at OA. The temporal intervals between the blue ones were 135, 220, 202, 231, 201 and 152 fs, corresponding to the time delays of \(T_{1}\) measured at BO. As a result, the corresponding difference values (\(T_{2}-T_{1}\)) of above six sampled pulses were 5, 17, 22, 44, 69 and 112 fs, respectively.  
Based on the Equation (1), the maximal PFD of the above six sampled pulses are 2.5, 8.5, 11, 22, 34.5 and 56 fs, respectively. In other words, the PFD values of 85 mm-diameter compressed pulse are \(-2.5\), \(-8.5\), \(-11\), \(-22\), \(-34.5\) and \(-56\) fs respectively, at the radii are 6.5, 13, 19.5, 26, 32.5 and 40 mm. As Figure 4 shows, the experimentally measured results fit well with the theoretical curve. The slight difference may have resulted from the little divergence (\(\approx\)50 \(\upmu\)rad) of the pulse beam; the maximal  
Figure 3: Measured correlation signals of the six sampled pulses.  
group delay of the pulse front induced by this divergence is below 2 fs. Based on the experimental results and theoretical calculations above, we can deduce the maximal PFD of an 85 mm-diameter compressed pulse is about \(-62.8\) fs. In addition, the experimental results also demonstrate that, by this improved self-reference cross correlator, the PFD at any position of pulse can be directly measured; meanwhile, the global PFD can be obtained by the scanning measurement.
## 5 Surface Roughness Estimation  
### Experiment and Data Acquisition  
In order to verify the effectiveness of the proposed method, a series of spheroidal graphite cast iron 500-7 milling cutting experiments were carried out. The milling cutting experiments are conducted on three machining centers (VMC650E/850E produced by Shenyang Machine Tool Company (Shenyang, China), XC63100 produced by Kaichuang Equipment Technology Company (Luoyang, China), and NBH800 produced by Huller Hille (Mosbach, Germany)). The dimension of the workpiece is 200 \(\times\) 140 \(\times\) 100 mm. The experimental setup for the milling test is shown in Figure 11. Machining parameters for the milling tests are shown in Table 1. A mobile roughness measuring  
Figure 10: Flow chart of the proposed method.  
device (type: Marsurf PS1 (produced by Mahr (Goettingen, Germany)), Figure 11d) is engaged to measure the machined surface roughness.  
Images used to train the proposed algorithm in this paper are collected by a self-made image acquisition system. The schematic sketch of the structure of the image acquisition system is shown in Figure 12.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Property** & **VMC650E/850E** & **XK63100** & **NBH800** \\ \hline Milling mode & Down milling & Up milling & Up milling \\ Milling tool & Carbide disc milling tool & Carbide disc milling tool & Carbide disc milling tool \\ Feeding speed & 200 mm/min & 500 mm/min & 700 mm/min \\ Spindle speed & 960 rpm & 450 rpm & 2228 rpm \\ Cutting depth & 0.5 mm & 4 mm & 0.5 mm \\ \hline \end{tabular}
\end{table}
Table 1: Information of the milling cutting experiment.  
Figure 11: Milling experiment setup (**a**) VMC650E/850E; (**b**) XK63100; (**c**) NBH800; and (**d**) mobile roughness measuring instrument.  
As can be seen in Figure 12, this image acquisition system is constructed by an industrial camera, a coaxial light source (blue light), and other auxiliary mechanisms. Coaxial lights generated by an LED array are reflected by a semi-reflective mirror to ensure their reflected light directions are identical to the axis of the camera lens, such that the reflected coaxial lights are sent to the workpiece without unevenness. After machining, the image acquisition device was placed on workpiece surfaces and captured digital images of the surface. Details about the parameters for capturing the images are listed in Table 2.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 6 Test Results
### Lossless Compression
#### 6.2.2 MRZ and SKM Datagrams  
The performance of FAPEC in KMALL files is even better than that in KMWCD files. It is at least 28% better than _bzip2_ and 35% on average. In this case, _bzip2_ does not outperform _gzip_ and _Zstd_, and all three exhibit similar performance in terms of ratio.  
Similar to the previous section, we have also plotted the throughput in Figure 11. The simplicity of our algorithm allows FAPEC to be even slightly faster than _Zstd_. Compared to _gzip_ or _bzip2_, FAPEC is between 5 and 6 times faster than the former and between 9 and 10 times than the latter. Finally, the outperformance of FAPEC is also observed in the time and inverse compression ratio scatter plot in Figure 12. In this case, the time axis has been cut at 10 s.  
Figure 11: Lossless compression ratio (**left** panel) and lossless compression throughput (**right** panel) for the tested KMALL files.  
Figure 10: Comparison of time and ratio performance of lossless compressors for KMWCD files.
## 4 UFMC RadCom Signal Model
### BER Analytical Model  
In this subsection, we elaborate an analytical model of the BER. The analytical expression of the M-ary QAM modulation over additive white Gaussian noise (AWGN) is given by Equation (24).  
\[BER=\frac{2(M-1)}{M\log_{2}M}Q\Bigg{(}\sqrt{6\frac{\gamma\log_{2}(M)}{M^{2}-1 }}\Bigg{)} \tag{24}\]  
where \(M\) is the modulation order, \(\gamma\) denotes \(\frac{Eb}{N_{0}}\), and \(Q\) is defined as the \(Q\) function:  
\[Q(x)=\frac{1}{\sqrt{2\pi}}\int_{x}^{+\infty}e^{-\frac{x^{2}}{2}}\;\mathrm{d}t\]  
BER calculation is easily performed using the following approximation of the \(Q\) function:  
\[Q(x)\approx\frac{1}{12}e^{-\frac{x^{2}}{2}}+\frac{1}{6}e^{-\frac{x^{2}}{3}}\]  
Figure 4: Universal filtered multicarrier (UFMC) transmitter.  
Hence, for OFDM, the BER over each subcarrier can be deducted from the Equation (24). However, as subcarriers in the UFMC system are being filtered, noise variance on the \(q_{th}\) subcarrier of the \(s_{th}\) sub-band is then divided by the equivalent filter response and can be expressed as follows.  
\[\sigma_{q}^{2}=\frac{\sigma_{z}^{2}}{|f_{k}|^{2}}\]  
Thus, the BER expression of the \(q_{th}\) subcarrier of the \(s_{th}\) sub-band can be written as  
\[BER(s,q)=\frac{2(M-1)}{M\log_{2}M}Q\left(\sqrt{6|f_{k}|^{2}\frac{\gamma\log_{2 }(M)}{M^{2}-1}}\,\right) \tag{25}\]
### Glare Evaluation by UGR
[18] introduced software for automated UGR calculation using imaging luminance measuring devices (ILMDs). In [19], evaluations were made by comparing UGRs obtained with the original method. In this research, the luminaires with non-uniform luminous intensity distribution were divided into smaller subdivisions. The results showed that UGR was not robust for luminaires with non-uniform luminous intensity distribution glare evaluation. Therefore, it is necessary to perform further research to find a robust method for glare evaluation for both luminaires with uniform and non-uniform luminous intensity distribution. [PERSON] et al. [20] defined an improved UGR standard method which took into account pixels of a luminaire with luminance above a threshold as the luminating part. The other pixels were considered as background luminance. The method can better describe the glare felt by observers than the standard CIE-proposed UGR calculation method. In [21], UGR for conventional fluorescent luminaires was obtained by three methods using DIALux software, LMK software, and a Python-based software. UGR was calculated by the mentioned methods for the same spaces. The results' similarity showed the applicability of UGR for luminaires with uniform luminous intensity distribution such as the fluorescent luminaires. [PERSON] et al. [22] used the UGR method for small sources to address glare produced by some types of LED sources via combining measurement and software analysis. Their study did not address LED luminaires with multiple light sources (LED matrix) or when several light sources were present in the field of view simultaneously. [PERSON] et al. [23] demonstrated the importance of light source positioning for controlling UGR. Additionally, they showed the correlation between UGR and observers' visual discomfort perception by simultaneously using a qualitative method. [PERSON] and [PERSON] suggested a procedure for using high dynamic range imaging (HDRI) techniques for UGR assessment by determining the best exposure time. Their method uses ILMD. Consequently, their proposed procedure requires the use of technical devices [6]. The mentioned studies showed that, despite limitations of the UGR method in some conditions as explained previously, research to improve UGR to cope with different scenarios and conditions, especially modern luminaires such as LEDs, is plentiful. Proposedmethods for UGR calculation improvement did not address reducing the number of calculations for obtaining UGR. The reduction and simplification of the calculations and models have received attention in science fields as can be seen in [24; 25; 26; 27]. Due to the prevalence of UGR application, it is useful to devise methods for decreasing the number of UGR calculations. The proposed tabular method makes it possible to decrease UGR calculations by using the UGR table and shows the UGR table's applicability for points inside a room which the table was not originally designed for.
## 4 Implementation of Proposed Architecture
### Distributed MEC/Cloud Orchestrator  
#### 4.3.1 Logical Overview  
In a distributed manner, the orchestrator function and GUI function are separately deployed to each Private/Local Telecom Operator and the Internet, respectively. An overview of this architecture is illustrated in Figure 8. The GUI functionality of the MEC/Cloud Orchestrator is deployed at a higher level, e.g., on the Internet, allowing end users to access it.  
Figure 7: Sequence of MEC/Cloud Orchestrator for implementation: (**a**) subscription registration process and (**b**) deployment process.  
Meanwhile, a part of the orchestration function, that is, the deployment function, is assigned to each Private/Local Telecom Operator.  
The GUI function determines which Private/Local Telecom Operator's service range is provided based on the end user's registration information and requests it via the internal management interface. Based on the requested information, the Orchestrator function of the Private/Local Telecom Operator receives the information at the API interface published by the Cloud Owner via the northbound interface. This is compared with MEC information acquired via the southbound interface and then the deployment destination is registered. Finally, the application is deployed based on the scenario's judgment logic. There are a plurality of merits when an MEC/Cloud Orchestrator is used. The distributed manner has several advantages when an MEC/Cloud Orchestrator is employed and managed for each Private/Local Telecom Operator, as listed as below:  
1. It supports the concealment of confidential information such as network information, physical context information, and server information, for each Private/Local Telecom Operator.
2. It can make recommendations to end users using the Private/Local service according to predicted regional information by algorithmizing the application in MEC which they hold as input information to AI/ML.
3. When a Private/Local Telecom Operator covers multiple areas, the log/tracking data and updating of applications used by end users can be shared among regions.  
Because of the advantages described above, a distributed architecture can be a solution for managing and utilizing MEC for each Private/Local Telecom Operator.
# Enzymatic Degumming of Soybean Oil for Raw Material Preparation in BioFuel Production
## 3 Results
### Pilot Industrial Implementation  
The pilot industrial implementation was carried out in two stages:  
1. enzymatic degumming of crude soybean oil;
2. production of BioFuel from degumming soybean oil (using a cavitation unit BIOTRON-R 1000 (CT Systems Corporation, 2910-567 Setubal, Portugal)). Enzymatic degumming improves soybean oil quality for further processing. Using Purifine(r) 3G, 20,000 kg of oil was degummed at the \"Agro Center\" farm in Ukraine. The process involved adding 2 kg of enzyme (100 ppm per ton) to 500 L of water (2.5% of oil mass) with pH control (6.9-7.1). The reaction occurred in a heated, stirred reactor at 57 \({}^{\circ}\)C for 2 h. After enzymatic treatment, the oil was heated to 80 \({}^{\circ}\)C for 30 min to inactivate the enzyme, followed by settling to separate impurities. The purified oil was used for biofuel production. Quality control assessed phospholipid content and acid value, ensuring compliance with processing standards. The hydraulic schematic diagram is shown in Figure 1. The operating principle is based on alternate filling of tanks E1 and E2 with source components, precise dosing and preliminary mixing, with subsequent alternate draining of the mixtures into the cavitation reactor R. Feed pumps D1, D2, and D3 are intended to supply source products into the system. Regulating valves V1, V2 and V3 are intended to control tank-filling speed with corresponding products. Pumps E1 and E2 are intended for product mixing and subsequent supply to the cavitation reactors. Regulating valve V4 is intended to control the necessary flow level (efficiency) of the preliminary processing system. Dosing of products 1, 2 and 3 is conducted via weighing with strain sensors W1 and W2. The conditions of the soybean oil enzymatic degumming process and the production of BioFuel are given in Table 9. Characteristics of crude soybean oil, degummed soybean oil, and BioFuel are given in Table 10. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Process Parameter** & **Units of Measurement** & **Value** \\ \hline \multicolumn{3}{c}{1. The stage of enzymatic degumming} \\ \hline Enzyme & - & Purifine® 3G enzyme \\ Enzyme content & Ppm & 100 \\ Temperature & \({}^{\circ}\)C & 56–58 \\ pH value & - & 6.9–7.1 \\ Dosage of water & wt. \% & 2.5 \\ Time & Min & 120 \\ \hline \multicolumn{3}{c}{2. The stage of biodiesel production by cavitation} \\ \hline \hline \multicolumn{3}{c}{32\% solution of potassium} \\ Catalyst & - & methoxide (CH\({}_{3}\)OK) in \\ \multicolumn{3}{c}{methanol} \\ Content catalyst & wt. \% & 3.9 \\ Desumming Soybean Oil & wt. \% & 86.6 \\ CH\({}_{3}\)OH (Methanol) & wt. \% & 9.5 \\ Cavitation temperature & \({}^{\circ}\)C & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Process conditions. Figure 1: Hydraulic schematic diagram (BIOTRON-R 1000).
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Routing Void Avoidance Scheme and Routing Process
#### 3.6.2 Routing Process  
In the FLGR protocol, neighbor information can be maintained by periodically sending Hello messages. Taking the node \(n_{x}\) for example, the key processing steps are as follows:  
1. Once node \(n_{x}\) receives a data message, it updates its local network node location table according to the location information of the destination node and all previous forwarding nodes in the data message. If node \(n_{x}\) is the destination node, the data message will be processed in the application layer, and the routing ends. Otherwise, go to step 2.
2. Looking up the neighbor information of node \(n_{x}\), if the destination node is its two-hop neighbor node, the next forwarding node can be selected according to the greedy forwarding mode. Otherwise, the updated location information of the destination node and node \(n_{x}\) will be added to the data message. The CNR and CNs can be determined by the updated location information of the destination node. If the CNR angle of node \(n_{x}\) is more than \(\pi\) (\(\theta^{\text{aero}}\geq\pi\)), then go to step 7. Otherwise, go to step 3.
3. If the CN set of node \(n_{x}\) is empty (\(C_{x}=\varnothing\)), or \(C_{x}\
eq\varnothing\), but there is no node in the CNR of all CNs of node \(n_{x}\) (\(Z=\varnothing\)), go to step 6. Otherwise, go to step 4.
4. For CNs with \(Z\
eq\varnothing\), the comprehensive evaluation values can be obtained through the fuzzification, fuzzy reasoning, and defuzzification of parameters (\(D\), \(\eta\), \(\Omega\)). In addition, the CNs with \(Z=\varnothing\) are discarded. Then, go to step 5.
5. The next forwarding node can be selected by ranking the comprehensive evaluation values of CNs. If multiple nodes have the best comprehensive evaluation value, the next forwarding node can be selected from them randomly.
6. Operate the routing void avoidance scheme, and angle \(\theta^{\text{aero}}\) will increase linearly by \(\hat{\theta}^{\text{aero}}\) until nodes occur in the CNR. If the adjusted angle is more than \(\pi\), go to step 7; otherwise, go to step 3.
7. Discard the data message.
## 4 Verification and Validation  
One of the methods in scientific research involves first verifying a proposed mathematical model before its application and then validating its applicability [40]. Accordingly, we use the terms'verification' and 'validation' in this work to refer to the correctness of the proposed model and its applicability in real-world scenarios, respectively. We employ Monte Carlo simulation experiments to verify the proposed model in Section 4.1, show their implementation using hypothetical scenarios in Section 4.2, and utilize real project data to validate the applicability of the proposed model in Section 4.3. Thus, verifying the model through simulation experiments ensures that the theoretical results align with the outcomes obtained from the simulations. Conversely, validating its applicability involves assessing whether the proposed model accurately represents real-world phenomena and scenarios.
## 5 Experiment and Discussion
### D-Q Synchronous Min-Max Coordinate System  
The reason why the diagnosis of ITSC using the PVA method is difficult is that the maximum and minimum values based on the vector scalar sum of the d-axis and the q-axis are expressed as ratios. This is based on the pulsation of the current based on the interlinked magnetic flux analysis described above.  
In this paper, a change in the magnitude of the short-circuit current is generated for linear diagnosis of ITSC. To apply it to the experiment, the D-Q synchronous min-max coordinate system over time was proposed.  
Figure 11 shows the results of monitoring the steady-state (black line), ITSC (red line), 4-turn short circuit (green line) and 6-turn short circuit (blue line) under full load using the proposed D-Q synchronous coordinate system. As shown in Figure 11, the induction motor in steady state contains irregular AC component pulsating due to a loss. It can be found that the amplitude in the ITSC state is not significantly different from that in the steady state. However, it can be found that the overall value of the current increases, and from the 4-turn short circuit, the amplitude becomes larger.  
Figure 12 shows the results of monitoring the no-load condition using the D-Q synchronous coordinate system. As shown in Figure 12, a short circuit over 4-turn can be diagnosed as the characteristic point as the amplitude becomes larger. In the case of ITSC, however, the pulse width is similar to that in the normal state, making diagnosis difficult.  
Figure 13 shows the maximum and minimum values of AC components pulsating from no load to full load by extracting 90 samples per second in the min-max coordinate system.  
In the 1690 rpm region in Figure 13, the results described in Figure 6 can be observed. Through this, it can be found that linear separation is possible through the max value of id and the min-max coordinate system in the region up to heavy load or full load and that the ITSC can also be linearly separated. However, under no-load and light load conditions, more than 6-turn short circuits may be linearly separated, but it is difficult to separate ITSC and 4-turn short circuits. Therefore, they will need diagnostic techniques combined with AI technology.