esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The induced polarization method has become a popular method for evaluating formation permeability characteristics in recent years because of its sensitivity to water and water-bearing porous media. In particular, the induced polarization relaxation time can reflect the macroscopic characteristics of the porous media of rock and soil. Therefore, in order to study the relationship between relaxation time and permeability, eight quartz sand samples of different grain sizes were used to simulate water-bearing sand layers under different geological conditions, and the induced polarization experiment and the Darcy seepage experiment were carried out on the same sand sample. The experimental results show that relaxation time and permeability are closely correlated with the grain size of quartz sand samples. According to the experimental data, the power function equation is a better fit for describing the relationship between permeability and relaxation time. It is worth noting that the equations obtained are only empirical equations for quartz sand samples, and they may not be applicable to all geological conditions.  
induced polarization method; Darcy seepage experiment; permeability; relaxation time +
Footnote †: journal: selected topics
# MEXN: Multi-Stage Extraction Network for Patent Document Classification
## 4 Experimental Results
### Ablation Studies
#### 4.4.2 Ablation Study on D-Block  
**D-Block variations.** We experiment on variations of D-Block to demonstrate the performance of summarizing paragraphs information. To fairly evaluate the addictive and productive attentions, we use the fine-tuned Bert for P-Block as described in the previous section, and L-Block that cross-validates the labels of section and sub-section. In Table 3, we observe that the productive method shows better performance compared to the addictive attention. Productive attention exploits more directly computing attention weight between paragraphs than in additive attention, which sequentially propagates information via RNN units so that productive attention can take into account the contextual significance of each paragraph well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Section**} & \multicolumn{3}{c}{**Subsection**} \\ \cline{2-7}  & **EM** & **HL** & **Jacc** & **EM** & **HL** & **Jacc** \\ \hline CNN & 49.981 & 0.0788 & 68.424 & 26.395 & 0.00984 & 35.943 \\ RNN(bi-LSTM) & 56.793 & 0.0745 & 71.594 & 36.531 & 0.00843 & 53.235 \\ pre-trained BERT & 54.954 & 0.0697 & 73.696 & 39.861 & 0.00836 & 58.364 \\ fine-tuned BERT & **66.692** & **0.0471** & **82.710** & **51.565** & **0.00616** & **72.716** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ablation study on P-Block variations.  
**Summarizing methods.** We also study the classification token method by comparing it with feature pooling approaches in D-Block in Table 3. The productive attention uses a classification token, \([DCLS]\), to summarize a document vector, and other compared methods aggregate paragraph features by pooling, We observe that the classification token shows better classification performance than others. The results show that the token method is also a useful approach with the transformer encoder process for summarizing feature vectors like \([CLS]\) token on word embedding classification.
## 8 Conclusions  
The Sentinel-1A SAR system has set new standards for the achievable geolocation accuracy of comparable spaceborne C-band SAR sensors, with the successful completion of the in-orbit commissioning phase. At the time of this writing, we believe that users can have high confidence in the consistent geometric quality of the level 1 S1A products provided through the data hub. The most significant uncorrected perturbation in these products is currently the atmospheric path delay, which can introduce cross-track (slant range) sample shifts of typically \(\sim\)3.5 m (_i.e.,_ usually \(\leq\) 1 sample, depending on the product type). Based on the results of this study, it can be expected that in the near future, new geolocation correction parameters--including the path delay--will be annotated in S1A image products, simplifying \"out-of-the-box\" corrections possible if needed. This would further improve the absolute geolocation quality of all standard level 1 SAR image products.  
IW SLC results may be published in the future, but for the moment their more complex handling has delayed accurate estimation of their geolocation accuracy. However, the preliminary results (not shown here) were encouraging, with a similar behavior observed as their SM counterparts.  
We measured the ALE in the range and azimuth dimensions for SM mode SLC and GRDF products based on a time series of over 40 acquisitions. We performed our analysis using two separately surveyed test sites and S1A SM beams S2 through S6. Considering the very good correspondence between the ALE estimates for the various beams and surveyed reflectors, we can conclude that our atmospheric PD model is working correctly.  
Both range and azimuth already exhibit geolocation consistency well within the sub-sample level (as reflected by the ALE standard deviations) when external perturbations are accounted for. For SM SLC the values are 6 cm in range and 17 cm in azimuth (better than \(\sim\)1/30 th of a sample). SM GRDF consistency is 14 cm in range and 28 cm in azimuth (better than \(\sim\)1/15 th of a sample).  
The remaining azimuth bias (mean azimuth ALE) of \(\sim\)1.8 m for both types of SM products may possibly be influenced by a combination of (a) instrument timing mismatches (small difference between GPS and SAR instrument time); (b) orbital state vector estimation in ITRF; (c) orbital state vector interpolation accuracy; (d) satellite center of mass _vs_. SAR antenna phase center position; (e) uncertainties inherent in the azimuth time stamp annotations caused by quantization of the clock rate (e.g., for TerraSAR-X this effect contributes up to 6.5 cm azimuth error [21]); or (f) a bias in the relativistic Doppler correction for along-track motion. These potential influences may be subject to future studies in order to even further improve the already remarkable S1A geolocation accuracy.
## 1 Introduction
The acoustic intensity at focus induced by MIFU is below the cavitation threshold. Its intensity range is generally 100-1000 W/cm\({}^{2}\) and the peak negative pressure at the focus is 1-4 MPa [16]. Studies show that the errors caused by the complexity of cavitation and high nonlinearity can be ignored in MIFU [17; 18]. During MIFU exposure, the temperature of the tissue still rises rapidly, and the general heating rate is \(\leq\)5 \({}^{\circ}\)C/s. [PERSON] and [PERSON] indicated that pulses lasting 10 s at an acoustic intensity of 100-400 W/cm\({}^{2}\) can be considered a threshold for tissue necrosis [19]. In this context, we studied the power setting of focused ultrasound in the palliative treatment of advanced pancreatic cancer. The primary goals of this study were to achieve accurate prediction of temperature in the target region and especially pain reduction with an improvement of the clinical condition. In this study, an infrared temperature measurement experimental system was set up to measure the temperature rise at different power in ex vivo bovine livers. This method not only has high temporal resolution and spatial resolution but also shows the temperature distribution on the two-dimensional plane and avoids the error caused by the inaccurate orientation in the axial direction when the temperature is measured by the thermocouple. The intervention of infrared glass in the experimental system made viscous heating (VH) artifacts inevitable. Viscous heating arose from the difference in density between the infrared glass and surrounding tissue; this difference in density led to relative motion (arising from the radiation force of FUS) between these two mediums and caused the viscous heating in the glass-tissue interface and then affected the measurement accuracy [20]. Therefore, based on Pennes' equation, this study established a numerical simulation model of tissue temperature rise considering the influence of VH, and verified the consistency between the simulation temperature and the measured temperature at a power range of 4 W to 8 W (acoustic intensity range was 200 to 500 W/cm\({}^{2}\)). Considering that there is no glass in the actual treatment, a numerical simulation model was established by removing the infrared glass to predict the temperature rise of the bovine liver tissue at different power. Finally, the influence of blood perfusion on the temperature of the target area was analyzed to provide a reference for the intensity and time of the palliative treatment of tumors. It showed that the use of MIFU was conducive to effectively controlling the rise of tissue temperature until reaching the thermal damage threshold temperature (55 \({}^{\circ}\)C), which could more safely achieve the palliative treatment of tumors and provide a reference for the establishment of a standardized FUS dosage.
## 3 Resource Scheduling and Collision Resolution in Sidelink Mode 4  
The resource reservation in the sidelink Mode 4 works in a distributed manner among UEs, so it provides a distributed mechanism to mitigate message collisions [19]. Specifically, the 3 GPP standards define parameters and possible mechanisms to cope with the channel congestion [20; 21], which is called the sensing-based SPS. SPS is prescribed by the standards due to the periodic nature and the predictable sizes of the safety messages such as BSM transmitted by a UE. Under the SPS scheme, therefore, a group of resources evenly spaced by the RRI on the time axis are reserved in one signaling exchange. For instance, a UE can be allowed to transmit ten BSMs each spaced 100 ms before another attempt to reserve resources for the next group of BSMs is made. Typical RRI values are 20 ms, 50 ms, and 100 ms, where longer intervals are also allowed. The other type of resource reservation, namely dynamic scheduling, requires signaling for each packet transmission. It has much larger signaling overhead, thus can delay the transmission of the time-critical safety messages.  
To determine which single-subframe resource(s) a UE can use, it relies on _sensing_ the resource use by UEs in the recent past. By allocating the resource using the sensing result, the SPS scheme tries to minimize the message collisions as congestion worsens. However, the sensing-based SPS should not be considered a fully-fledged congestion control algorithm, which would use various available means such as power control, MCS control, retransmission control, or packet drop [20; 36]. Below, we briefly discuss the sensing-based SPS algorithm as defined in the current standards.
# Measurement and Analysis of Industrial Forklifts Vibration Levels for Unit Load Testing Purposes
## 2 Materials and Methods
### Data Analysis  
The analysis was done using power spectral density (PSD) plots to analyze the effects of speed, road quality, forklift type, unit load weight, and sensor location on forklift vibration levels in more detail. PSD plots were prepared from the measured vibration data using fast Fourier transformation (FFT) of Xware software and MATLAB R2014a (MathWorks Inc, Natick, MA, USA). The values of the power density (PD) levels are presented between 1-200 Hz. This frequency range represents the vibration events that do not have enough intensity to influence the integrity of the product-package systems in general industry use. For easier comparability, the spectrum of the International Safe Transport Association (ISTA) 3B test procedure's [23] vibration profile is also shown in the figures.  
Furthermore, this frequency range contains vibration responses that can be observed in small vehicle delivery from the vehicle body structure, suspension system, tire type, etc. Additionally, the overall Grms were calculated from the PSD spectra in each case and layer between 1-200 Hz. This way each vibration environment could be compared by the vibration intensity in the frequency range between 1-200 Hz that was generated by each of the three industrial forklifts under the different handling scenarios.  
Statistical analyses of the collected vibration events were performed with the MATLAB R2014a software to find the calculated Grms values, which helped to more thoroughly understand the nature of the vibrations. These statistical characteristics show the _kurtosis_ (K) of the probability distribution functions for these events using a 95% confidence interval (CI). The definition of kurtosis is as follows: when the standard normal distribution has a kurtosis of zero, then a positive kurtosis indicates a \"heavy-tailed\" distribution, compared to a normal Gaussian distribution. Presenting kurtosis values is very important because the random vibration testing controller in the laboratory, when engineers want to simulate vibration circumstances, mostly generates a control signal from normal distribution so that field measured kurtosis can be used as an input parameter to perform realistic variability for the random signal during vibration simulation in the laboratory.  
Figure 3: Sensor locations for vibration data collection.
## 7 Processing Architecture
### CHP2 Physical Layer  
The CHP2 physical layer is distributed across the processing system (PS) and programmable logic (PL) components of the MPSoC, as depicted in Figures 13 and 14. The skeleton is based on the Analog Device SDR platform [89]. Timing-critical components are implemented as HDL IP cores in the PL section. The Tx and Rx engines are customized and placed on the IQ data paths. The correlator reference bank, the TRS board controller, and the carrier-synthesizer phase-reset controller are placed on the AXI bus. In the PS section, CHP2 processes are run on the ARM Cortex-A53 cores. This includes waveform encoding and decoding, channel equalization, frequency offset estimation and correction, and automatic gain control.  
Figure 12: CHP2 processing chain block diagram.  
Figure 13: CHP2 system functions organized by OSI layer.
## 2 Materials and Methods
### System Design
#### 2.1.2 Hardware Composition  
The hardware of the automatic feedback system mainly includes the elliptical cylindrical mirror M2 and the four-blade exit slit, Slit 2, of the beamline, a current-voltage (I-V) convertor developed by the SSRF, a MOXA ioLogik E2240 controller, a picoammeter, a gold mesh in the spectroscopic end station, and a photodiode (PD) detector in the STXM end station.  
The exit slit, Slit 2, of the beamline acts as a secondary light source, which is a critical device used to control the X-ray beam energy resolution and coherence in the downstream optical path to the end stations. Composed of four independently moving blades, Slit 2 can adjust the size and shape of the slit, as shown in Figure 2. From the beam incident direction, the left and right blades are installed in front of the up and down blades. The monochromator grating, placed horizontally at the upstream of the beamline, generates a vertical-strip-shaped X-ray beam that illuminates Slit 2, as shown in the embedded diagram in Figure 2. The M2 mirror placed vertically causes the beam spot on Slit 2 to move horizontally. Since the size of Slit 2 is usually set to tens of micrometers during experiments, and the current signal of the blade is proportional to the area illuminated by the incident X-ray, the horizontal blade edge current signals are usually bigger than the vertical blade edge currents. To achieve high precision readout, an I-V convertor with 8 channels made by the SSRF control group is installed to convert current signals to voltage signals. A MOXA ioLogik E2240 controller is connected for signal acquisition and range switching. When the incident X-ray beam center deviates from the center of Slit 2, the left and right blade edge current signals are different, which can be used as a reference index for the automatic feedback system.  
The gold mesh installed in the spectroscopic end station and the PD installed in the STXM end station are used to directly measure the intensity of X-ray beam. The signals from the gold mesh and PD can be read out using a picoammeter.  
All digital signals and control access of the hardware are connected and controlled by the control software through a local area network (LAN), as shown in Figure 2. The control software has access to the converted Slit 2 blade edge voltage signals, gold mesh current signal, PD current signal, and motor signals of M2. In addition, the software controls the movement of M2 position based on a suitable logic criterion to maintain the optimal X-ray flux of the sample.  
Figure 2: Layout of hardware connection by the local area network (LAN). The monochromatized X-ray was horizontally focused to the exit slit (Slit 2) by an elliptical cylindrical mirror. The four blade signals of Slit 2 were read out by an I-V convertor. The photon flux after Slit 2 was detected by a gold mesh and PD detector, and the signals were read out by a picoammeter. The signals of M2, Slit 2, gold mesh and PD are all connected into LAN and can be read out for control.
## 2 Materials and Methods
### Expression and Isolation of the Recombinant Protein  
The gene encoding KpSSB was cloned into pET21b, which incorporates a 6 xHis tag at the protein's N-terminus. The resulting plasmid, referred to as pET21b-KpSSB, was introduced into _E. coli_ BL21 (DE3) cells. These transformed cells were cultivated to an OD\({}_{600}\) of 0.9 in LB medium supplemented with 250 ug/mL ampicillin, under vigorous shaking at 37 \({}^{\circ}\)C. Recombinant KpSSB was then induced by the addition of 1 mM isopropyl thiogalactopyranoside for 9 h at 25 \({}^{\circ}\)C. The induced cells were harvested and resuspendedin Buffer A (20 mM Tris-HCl, 5 mM imidazole, and 0.5 M NaCl, pH 7.9), followed by disruption through sonication. The recombinant protein was purified from the soluble supernatant using Ni\({}^{2+}\)-affinity chromatography (HisTrap HP; GE Healthcare Bio-Sciences, Piscataway, NJ, USA). Elution was performed with Buffer B (20 mM Tris-HCl, 250 mM imidazole, and 0.5 M NaCl, pH 7.9), and the purified protein was subsequently dialized against a dialysis buffer (20 mM HEPES and 100 mM NaCl, pH 7.0; Buffer C). Protein purity was assessed by electrophoresis on a 12% SDS-PAGE gel.
# A Review of Fuzzy and Pattern-Based Approaches for Class Imbalance Problems
## 2 General Background
### Pattern-Based Classifiers  
Pattern-based classifiers can explain their results, through patterns, in a language closer to that used by experts, and the results have been shown to be more accurate than other popular classifiers, such as decision trees, nearest neighbor, bagging, boosting, SVM, and naive Bayes [25; 26]. The stages of building a pattern-based classifier are:  
* Mining stage: At this stage, several patterns are extracted from the training dataset. Mining contrast patterns is a challenging problem due to the high computational cost generated by the exponential number of candidate patterns [12; 39; 40; 41].
* Filtering stage: At this stage, there are set-based filters and quality measures that need to distinguish between patterns that have a high discriminative ability for supervised classification [41]. The quality is usually established by measuring reliability, novelty, coverage, conciseness, peculiarity, diversity, utility, and actionability [42]. All the previous measurements take into account two parameters: if the pattern covers an object and if the object is representative of the class determined by the pattern.
* Classification stage: The last stage is the classification of query objects. At this stage, the classifier combines the patterns and creates a voting scheme. Finally, it is necessary to evaluate the performance of the classifier to determine its quality.
## 2 Data Materials and Preprocessing
### Sentinel-3 Image Dataset
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image Acquisition Day**} & \multicolumn{3}{c}{**\# of Image Patches**} \\ \cline{2-3}  & **Pretraining Stage** & **Transfer-Learning Stage** \\ \hline
11 Jan & 1008 & 35 \\ \hline
29 Mar & 4715 & 74 \\ \hline
6 Apr & 5681 & 98 \\ \hline
26 Apr & 1908 & 22 \\ \hline
30 Apr & 2582 & 48 \\ \hline
15 Jan & 3471 & \\ \hline
22 Jan & 2017 & \\ \hline
7 Feb & 3722 & \\ \hline
8 Feb & 3681 & \\ \hline
19 Feb & 2877 & \\ \hline
2 Mar & 1654 & \\ \hline
10 Mar & 3393 & \\ \hline
26 Mar & 2766 & \\ \hline
10 Apr & 3984 & \\ \hline
21 Apr & 3772 & \\ \hline
**Total** & 47,231 & 275 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Sentinel-3 \(R_{rs}\) image patches from Laguna Lake. Considering the stability of the model training, the water patches from Laguna Lake and Lake Victoria containing \(R_{rs}\) at 16 spectral bands were normalized to the range 0, 1 using the minimal and maximal \(R_{rs}\) values at each spectral wavelength. The data normalization process was also performed for the in situ and simulated Chla concentration data.
## 3 Probability of Chip Error in Coherent O-QPSK
### Half-Sine Pulse Shaping
#### 3.2.2 Case (b): \(\sqrt{I}\geq\sqrt{C}\)  
When \(i\), which refers to the amplitude of the interferer signal at the sampling instance, is larger than or equal to \(\sqrt{C/2}\), the receiver can decide for the wrong symbol with a probability of \(\frac{3}{4}\). Regarding \(P_{c}\), a transition towards \(m_{2}\) or \(m_{1}\) will result in one chip error per symbol, while a transition towards \(m_{0}\) will result in two chip errors per symbol. Therefore, the error rates when \(i>\sqrt{C/2}\) are:\[P_{s} =\frac{3}{4}\] \[P_{c} =\frac{1}{4}\cdot\frac{1}{2}+\frac{1}{4}\cdot\frac{1}{2}+\frac{1}{4 }=\frac{1}{2}\]  
In general, \(P_{s}\) and \(P_{c}\) can be written as a function of \(i\) as follows:  
\[P_{s}(i)=\begin{cases}0,&i<\sqrt{C}\\ \frac{3}{4},&\sqrt{C}\leq i\leq\sqrt{I}\end{cases}\]  
\[P_{c}(i)=\begin{cases}0,&i<\sqrt{C}\\ \frac{1}{2}&\sqrt{C}\leq i\leq\sqrt{I}\end{cases}\]  
Finally, we can derive the error probabilities when \(\sqrt{I}\) is greater than \(\sqrt{C}\) by evaluating the expected values of \(P_{s}(i)\) and \(P_{c}(i)\):  
\[P_{s} =E[P_{s}(i)]=\int_{\sqrt{C}}^{\sqrt{I}}\frac{3}{4}f_{i}(i)di=\frac {3}{4}\cdot F_{i}(\sqrt{C}<i\leq\sqrt{I})=\frac{3}{4}\frac{2}{\pi}(\frac{\pi} {2}-\sin^{-1}(\sqrt{\frac{C}{I}}))=\frac{3}{4}-\frac{6}{4\pi}\sin^{-1}(\sqrt{ \frac{C}{I}})\] \[P_{c} =E[P_{c}(i)]=\int_{\sqrt{C}}^{\sqrt{I}}\frac{1}{2}f(i)di=\frac{1} {2}\cdot F_{i}(\sqrt{C}<i\leq\sqrt{I})=\frac{1}{2}\frac{2}{\pi}(\frac{\pi}{2} -\sin^{-1}(\sqrt{\frac{C}{I}}))=\frac{1}{2}-\frac{1}{\pi}\sin^{-1}(\sqrt{\frac {C}{I}})\]  
where \(f_{i}(i)\) is given in Equation (15) and \(F_{i}(i)\) is given in Equation (16).  
The computational complexity of \(P_{s}\) and \(P_{c}\) are \(O(1)\) in big O notation, since \(C\) and \(I\) are constant values.
# Evaluation of Modern Internet Transport Protocols over GEO Satellite Links
## 3 Modern Internet Transport Protocols
### Congestion Control Algorithms  
There have been many congestion control algorithms developed for TCP over the years. Currently, the most widely used algorithm is TCP Cubic which is the default TCP in Linux, Mac OS and latest Windows operating systems [23]. Cubic is a loss-based algorithm that was proposed to overcome the low utilization problem of the standard New Reno TCP over high bandwidth-delay-product (BDP) networks. Another recent congestion control algorithm called the Bottleneck Bandwidth and Round-trip time (BBR) was developed and used by Google in 2016 [24]. Unlike loss-based algorithms, BBR aims to avoid congestion before it occurs by estimating the BDP and trying to manage the bottleneck's queue. The two algorithms are briefly explained in the following subsections.