esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion of the Current Study
### Effect of Cement Content on the Liquid and Plastic Limits of the Soil  
As depicted in Figure 9, the results show that the crude oil in the soil voids tries to occupy more space than water. Further increase of the contaminant content produces an appropriate opportunity to coat the soil particle surface, and thus a stronger tendency of the soil to be granulated without plasticity. The results reveal that the liquid limit values are 48, 46, 45, and 42 at crude oil concentrations of 0, 5, 10, and 15%, respectively. The contaminant-filled soil spaces reduce the double-layer thickness by decreasing the pozzolanic reaction between the water molecules and the charges located at the clay mineral surfaces. For the plastic limit, the trend of water content variation with crude oil presents a somewhat proportional relation. More oil content makes the soil structure more dispersed. Thus, more brittle behaviour is observed as the contaminant content increases, as shown in Figure 10.  
Figure 10: Effect of cement additive on the Atterberg limits of the contaminated soil.  
Figure 9: Illustration of Atterberg limits values and crude oil content.  
As shown in Figure 10, the cement material efficiently reduces the liquid limit more than the plastic limit. The cement particles minimise the contaminant's potential to cover the soil particles completely, leading to more pronounced spaces and less plasticity. The admixture produces almost the same impact in reducing the plasticity index (P.I) for all the contaminant percentages. For the case of 5% crude oil, the plasticity indices decreased from 22.7 to 10.8 when 7% of cement was added, where the P.I values were 15.7, 11.7, 7.6, and 4.7 for the soil contaminated with 15% crude oil content and treated by 0, 3, 5, and 7% of cement, respectively. The results proved that the best percentage of the additive was 7%, irrespective of the crude oil content.
# A Context-Aware Neural Embedding for Function-Level Vulnerability Detection
## 3 Experiments and Evaluation
### Limitations and Future Work
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Software** \\ **Project** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Our** \\ **Framework** \\ \end{tabular} } & \multicolumn{4}{c}{\
\begin{tabular}{c} **The Number and the Proportion of Buffer Errors** \\ **Vulnerabilities in the Total Found Vulnerabilities in Top-\(k\)** \\ \end{tabular} } & \multicolumn{4}{c}{
\begin{tabular}{c} **The Number and the Proportion of Buffer Errors Vulnerabilities** \\ **Accounted for All Vulnerabilities in the Test Set** \\ \end{tabular} } \\ \cline{3-7}  & & **Top 10** & **Top 20** & **Top 30** & **Top 40** & **Top 50** \\ \hline \multirow{2}{*}{FFmpeg} & with RF & 4 (44\%) & 10 (56\%) & 12 (50\%) & 15 (48\%) & 17 (44\%) \\ \cline{2-7}  & with SVM & 4 (44\%) & 9 (56\%) & 10 (53\%) & 12 (52\%) & 13 (45\%) \\ \hline \multirow{2}{*}{LbTIFF} & with RF & 6 (86\%) & 10 (67\%) & 16 (73\%) & 18 (72\%) & 19 (68\%) \\ \cline{2-7}  & with SVM & 6 (75\%) & 10 (71\%) & 14 (78\%) & 17 (21\%) & 19 (70\%) \\ \hline \multirow{2}{*}{OperSSL} & with RF & 2 (22\%) & 5 (26\%) & 6 (21\%) & 8 (24\%) & 9 (23\%) \\ \cline{2-7}  & with SVM & 2 (22\%) & 5 (26\%) & 6 (21\%) & 8 (24\%) & 9 (22\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The number and the proportion of buffer errors (CWE-119) vulnerabilities accounted for in the found vulnerabilities in Top-\(k\) on projects: FFmpeg, LibTIFF, and OpenSSL. set so that the robust and statistically stable neural models can be trained to contribute to improved detection performance.
# HybridTabNet: Towards Better Table Detection in Scanned Document Images
## 6 Experiments and Results
### ICDAR-17 Pod  
We finetuned HybridTabNet with a ResNeXt-101 backbone on the ICDAR-17-POD [1] dataset. Table 2 quantifies the results of HybridTabNet on the ICDAR-17-POD dataset. This achieved the highest precision of 0.882 and recall of 0.997 on the IoU thresholds of 0.5 to 0.8. On the IoU threshold 0.9, it achieved a recall of 0.983 and precision of 0.869. Overall, the recall value was high and close to 1, whereas the precision value was low. This result means that it rarely failed to detect the table region in the image but also incorrectly labeled regions as tables. Figure 5 depicts the qualitative results of HybridTabNet. Figure 4(b) shows a False Positive predicted by our model where it confused an image containing a graph as a table. Figure 4(c) shows a False Negative of our approach where it failed to detect a clear table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **IOU Threshold** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{6}{*}{ResNeXt-101} & 0.5 & 0.954 & 0.953 & 0.953 \\ \cline{2-5}  & 0.6 & 0.937 & 0.948 & 0.942 \\ \cline{2-5}  & 0.7 & 0.927 & 0.948 & 0.933 \\ \cline{2-5}  & 0.8 & 0.920 & 0.9331 & 0.927 \\ \cline{2-5}  & 0.9 & 0.895 & 0.905 & 0.901 \\ \cline{2-5}  & W.Avg & **0.923** & **0.934** & **0.928** \\ \hline \multirow{6}{*}{ResNet-50} & 0.5 & 0.928 & 0.920 & 0.924 \\ \cline{2-5}  & 0.6 & 0.905 & 0.922 & 0.913 \\ \cline{2-5}  & 0.7 & 0.894 & 0.910 & 0.902 \\ \cline{2-5}  & 0.8 & 0.879 & 0.895 & 0.887 \\ \cline{2-5}  & 0.9 & 0.831 & 0.846 & 0.838 \\ \cline{2-5}  & W.Avg & **0.881** & **0.894** & **0.887** \\ \hline \hline \end{tabular}
\end{table}
Table 1: HybridTabNet results on the ICDAR-19 dataset with deformable ResNeXt-101 and ResNet-50 backbones. W.Avg denotes the weighted-average of the respective measure on the IoU threshold.  
Figure 4: HybridTabNet results on ICDAR-2019 Track-A Modern dataset. (**a–c**) True positive, False positive, and False negative results, respectively. The blue outline shows the predicted table, and the red outline highlights the ground truth table.
## 3 CNN-LSTM-MHSA Model-Based Multiple Bolt Looseness Detection
In real applications, it may be challenging to detect the looseness condition of each bolt individually, so the eight bolts in the beam-column joint are segmented into  
Figure 5: Diagram of the tested beam–column joint. four distinct subgroups by four rows. The outputs of the model are the local bolt looseness severities for the \(i\)th row (\(r_{i}\)). \(r_{i}\) varies from 0 (intact) to 1 (total loosed). Parameters within the neural network are updated during the training process based on the loss calculated by Equation (6) and the back propagation operation. \[Loss=\frac{1}{N}{\sum_{j=1}^{N}}\left(\frac{1}{4}{\sum_{i=1}^{4}}\big{(}f_{ij} -r_{ij}\big{)}^{2}\right) \tag{6}\]  
where \(N\) is the number of training samples, \(r_{ij}\) is the estimated local bolt looseness severity of the \(i\)th row for the \(j\)th bolt looseness case, and \(f_{ij}\) is the corresponding real value. The traditional CNN works well for extracting spatial features from input data, while LSTM is adept at capturing long-term dependencies in time series. By combining CNN and LSTM, the model can fully process both the time and frequency information of the received guided wave signals. LSTM comprises three key components: the forget gate, the input gate, and the output gate, as shown in Figure 7a. These gates are responsible for selecting significant information from the previous time step, filtering new input information, and controlling the effective output information, respectively [39]. Since LSTM may lose important information when dealing with excessively long input sequences, CNN is utilized to reduce the dimensionality of the feature map beforehand. Figure 7b illustrates the structure of the SA mechanism, which employs probability distribution to assign higher weights to critical information, thereby enhancing model accuracy [40]. Figure 6: Implementation flow of the proposed method.
# High Linearity DC-38 GHz CMOS SPDT Switch
## 4 Conclusions  
In conclusion, a wideband and high-performance SPDT switch structure, which covers the sub-6 GHz and the 28 GHz 5G bands, is desirable. Most SPDT switches are not applicable to these two bands due to limited bandwidth. Although there are a few SPDT switch structures covering these two bands, the overall performance, especially the IP\({}_{\mathrm{1 dB}}\), still has room for improvement. In this work, we demonstrate a low insertion-loss, high isolation, and high-linearity DC-38 GHz CMOS SPDT switch for sub-6 GHz and 28 GHz 5G communications. Wideband input matching is achieved by adopting the \(\pi\)-matching (CLCmatching). Broadband output matching and isolation enhancement (between the outputs) are attained by using the traveling-wave matching (CLCL network). Positive/negative gate-bias is adopted for linearity enhancement because larger \(\text{P}_{\text{in}}\) (i.e., AC signal with larger negative \(\text{V}_{\text{in}}\)) is required to conduct the off-state series switch transistor. Negative-body bias is used for insertion-loss reduction because the off-state series switch transistor is closer to open-state. At DC-6 GHz GHz, the SPDT switch obtains excellent insertion loss of 0.4-1.4 dB, and isolation of 37.5-59.4 dB. At 28 GHz, the SPDT switch accomplishes insertion loss of 4.2 dB, isolation of 25.9 dB, and prominent \(\text{IP}_{\text{1 dB}}\) of 25.6 dBm, one of the best \(\text{IP}_{\text{1 dB}}\) results ever reported for mm-wave SPDT switches. The decent result of the wideband SPDT switch demonstrates its potential for sub-6 GHz and 28 GHz 5G communications.  
Conceptualization, J.-F.C. and Y.-S.L.; methodology, J.-F.C.; software, J.-F.C.; validation, J.-F.C. and Y.-S.L.; formal analysis, J.-F.C. and Y.-S.L.; investigation, J.-F.C.; resources, J.-F.C.; data curation, J.-F.C.; writing--original draft preparation, J.-F.C.; writing--review and editing, Y.-S.L.; visualization, J.-F.C.; supervision, J.-F.C. and Y.-S.L.; project administration, J.-F.C.; funding acquisition, J.-F.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology (MOST), Taiwan, grant number MOST109-2222-E-035-009.  
Not applicable.  
This work was supported by the Ministry of Science and Technology (MOST) of Taiwan under Contract MOST109-2222-E-035-009. The authors would like to thank Taiwan Semiconductor Research Institute (TSRI) for supporting the chip fabrication and measurement.  
The authors declare no conflict of interest.
# Ultra-Wideband Analog Radio-over-Fiber Communication System Employing Pulse-Position Modulation
## 3 Experimental Setup
### RAU Receiver  
The receiver was located 5 m away from the RAU transmitter. The transmitted mm-wave signal was received by an identical Ka-band antenna and filtered using the bandpass filter (BPF) (Marki, Morgan Hill, CA, USA), which has a center frequency of 27.38 GHz and a 3-dB passband of 8.3 GHz. After filtering, the signal at a central frequency of 28 GHz was amplified by the SAGE SBL low-noise amplifier (LNA) with a gain of 40 dB, allowing further processing in the mm-wave RAU receiver. The amplified signal was then sent to the envelope detector (Spacek Labs, Santa Barbara, CA, USA) with 13.5 GHz bandwidth, which performed frequency down-conversion of the 28 GHz intermediate frequency signal. The down-converted baseband signal was then sent to the digital storage oscilloscope (DSO) and the ESTT 7 Series [35] event timer via a custom-made low-pass filter (LPF) with a bandwidth of up to 1.8 GHz for analysis. This specific LPF [40] was purposefully utilized to broaden the received pulses for accurate detection with the ESTT, as the group delay of this filter remains nearly constant for frequencies up to 3 GHz, thus reducing pulse shape distortions. The magnitude response of the ARoF communication system, including the custom LPF, can be seen in Figure 6. Additionally, the Keysight DSOZ334A DSO, with a sample rate of 80 GSa/s and a bandwidth of 33 GHz, was initially used for signal visualization and optimization after wireless transmission and was later replaced with the ESTT for experimental measurements. The pulse waveforms for various pulse duration values \(\tau\), captured by the DSO, for the back-to-back experimental setup and the setup involving 20 km SMF, can be seen in Figure 7 and Figure 8, respectively.  
Figure 5: Optical spectrum before PIN for (**a**) back-to-back setup, (**b**) setup with 20 km SMF.  
As shown in Figures 7 and 8, the pulses after the LPF in the RAU receiver are broadened to approximately 500 ps, facilitating their detection by the ESTT. However, the expansion comes at the cost of the pulse waveform amplitude.  
In the presented receiver RAU, ESTT is working as a TDC, triggering at the incoming pulses and converting the absolute times of the events into digital numerical values in ps, called \"time tags\". ESTT uses a variable voltage threshold, which can be set by software in a range between \(-2\) V and \(3\) V with step \(1.2\) mV. In the case of PPM, where pulses are transmitted with high amplitude, this device successfully works as a TDC, as presented in Section 4 of this and our previous research [37].  
ESTT is connected to the PC via a USB 3.0 interface, providing fast real-time transfer of time tags to PC. TR-PPM demodulation from time tags and forward error correction (FEC) is done in the receiver's software part, written in C++20 and running on the PC.
## 3 CNN Architectures
### VGG Network Architecture  
In 2014, researchers at Oxford's Visual Geometry Group introduced two novel architectures named VGG16 [42] and VGG19 [42]. VGG16 achieved a top five accuracy rate of 91.90% in the ImageNet competition in 2014. The VGG16 architecture has 138,355,752 parameters, five convolution blocks, and three dense layers. Each block contains some convolutional layers and then a max pool layer to decrease the block output size and remove the noise. The first two blocks have two convolutional layers each, and the last three blocks have three convolutional layers each. The size of the kernel that is used throughout this network has a stride of 1. After the five blocks, a flatten layer was added to convert the 3D vector of the blocks to a 1D vector to be inserted into the fully connected layers. The first two fully connected layers have 4096 neurons, and the last fully connected layer has 1000 neurons. After the fully connected layers, a softmax layer is inserted, and this is used to ensure that the probability summation of the output is one. The main difference between VGG16 and VGG19 is that VGG19 has 19 convolution layers instead of 16 convolution layers. The number of parameters increases from 138,357,544 to 143,667,240 because of additional layers. The authors argued that these additional layers make the architecture more robust and can learn more complex architectures.  
The main benefit of this network is its sequential blocks, where the sequential convolutional layers that are inserted after each other allow for a reduction of the amount of spatial information needed. The main drawback of this network is that the authors specify more weights for the classifier portion and not to the feature extraction portion. This considerably increases the number of parameters. The network's ImageNet weights are available in the Keras package.
## 3 Results and Discussions
### Numerical Analysis of Cooling Channel
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Cooling Oil** & \begin{tabular}{c} **Front Bearing Average** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & \begin{tabular}{c} **Motor Average** \\ **Temperature (\({}^{\circ}\)C)** \\ \end{tabular} & \
\begin{tabular}{c} **Channel Pressure** \\ **Drop (bar)** \\ \end{tabular} &
\begin{tabular}{c} **Spindle** \\ **Deformation (\(\upmu\)m)** \\ \end{tabular} \\ \hline VG10 & 61.2 & 50.9 & 3.35 & 124.6 \\ VG5 & 57.6 & 48.4 & 2.13 & 118.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation analysis results of different oil products. Table 3 illustrates the average temperatures, thermal deformations, and pressure drops with different cooling channel pattern combinations. After the results of combination 1 and combination 2 were compared, the helical type cooling channel was adopted at the motor, and the single-turn cooling channel was adopted at the front bearing. When the VG10 oil was applied at a flow of 12 L/min, the motor and front bearing cooling channel wall temperatures were relatively high, and the cooling channel wall temperature of the front bearing was higher than the cooling channel wall temperature of the motor by 10.3 \({}^{\circ}\)C. The heat generated at the front bearing had almost 4.7 times the copper loss and core loss of the motor because most heat concentrates at the front bearing. The deformation of the built-in spindle was 124.6 \(\mu\)m, and the internal pressure drop of the cooling channel was 3.35 bar. However, when the oil type was changed to VG5, the deformation of the built-in spindle was 118.3 \(\upmu\)m (difference is 6.3 \(\upmu\)m), which was lower than for VG10, and the pressure drop inside the cooling channel was reduced by 1.22 bar. The results show that the heat transfer coefficient and wall temperature inside the cooling channel can be improved and the thermal deformation can be reduced by selecting the suitable oil type. To improve the cooling efficiency of the front bearing, the design of the front bearing cooling channel was modified to the paralleling type cooling channel, and the front bearing cooling channel wall temperature of each motor cooling channel type was reduced by 14-15.2 \({}^{\circ}\)C. Therefore, the cooling efficiency of the front bearing can be increased effectively by the design. In terms of the pressure drop, after the front bearing of the cooling channel was changed to the paralleling type design, the pressure drop increased due to a longer cooling channel of the front bearing. The cooling channel of the front bearing was designed with the paralleling type design; the temperature of front bearing can be reduced effectively. Therefore, the paralleling type cooling channel was employed for the front bearing in subsequent simulation analysis. Figure 8: Simulation results of spindle thermal deformation and cooling channel pressure drop.
# Freeze-Thaw Pre-Treatment of Cassava Tubers to Improve Efficiency of Mechanical Peeling
###### Abstract  
The effect of a freeze-thaw pre-treatment (FTP) on the peeling process of cassava tubers was investigated in this study. The length and weight of the cassava tubers varied from 200 to 280 mm and 500 to 900 g, respectively. A prototype abrasive cassava peeling machine was used. The operational parameters were the rotational speed of the brushes (550-1150 rpm), peeling time (1-5 min), thawing temperature (50-90 \({}^{\circ}\)C), and incubation time of the thawing treatment (0-120 s). Response surface methodology was applied to optimize FTP to improve the peeling process of cassava tubers. Peeled surface area and peel loss were measured as the responses. Results revealed that the peeled surface area and peel loss were significantly influenced by the rotational speed of the brushes, peeling time, and the incubation time of the thawing treatment (\(p<0.05\)). Under optimal peeling conditions, with a rotational speed of 1000 rpm, a peeling time of 3.4 min, a thawing temperature of 59 \({}^{\circ}\)C, and an incubation time of 90 s, the peeled surface and the peel loss were approximately 99.5 and 19%, respectively. The results show that the use of FTP can improve cassava peeling by softening the peels and increasing the peeled surface area.  
Keywords:freeze-thaw pre-treatment; mechanical peeling; peel loss; peeling efficiency; response surface method +
Footnote †: journal: Applied Sciences
## 5 Non-Uniform In-Depth Residual Stress Field of the Orthotropic Bridge Deck
### Residual Stress Distribution of the Orthotropic Steel Deck
This compressive zone is smaller in size compared to the one from the experimental measurements and the value is 6% larger. Finally, there exists a tensile zone along the rest of the stiffener with a value of 32% of the material's yield strength. This is 19% larger compared to the experimental data. For the longitudinal direction it can be concluded that similar residual stress zones are present for both the FEM and experimental results. The compressive residual stresses remain the same within a margin of approximately 10%. The tensile residual stresses are in general 20% larger for the FEM results. In the transverse direction, there are three different zones that can be recognized on top of the deck plate. A tensile zone of 56% of the yield strength in between the welded webs of the stiffener followed by a compressive zone of 23% of the yield strength near the welding region and again a tensile zone of 53% of the material's yield strength. The tensile residual stresses are respectively 28% and 35% larger for the FEM results. The compressive residual stress values are 19% smaller. On the bottom side of the deck plate there is a tensile residual stress zone with a value of 88% of the yield strength. This results in residual stresses that are 18% larger compared to the experimental results. For the longitudinal stiffener, a totally different residual stress behavior is noticed for the FEM results. The results from the experimental data show tensile residual stresses equal to 69% of the yield strength along the entire stiffener. However, the FEM results show a tensile yield zone near the weld followed by a small compressive zone where the residual stress is close to zero and again a tensile stress zone of 80% of the yield strength. For the transverse direction, similar residual stress zones are recognized for the deck plate. The longitudinal stiffener shows a different residual stress behavior. In general, it can be concluded that the tensile residual stress values are larger for the FEM results compared to the experimental results and the compressive residual stresses are smaller. The finite element modeling of the hole-drilling method for the orthotropic steel deck gives the effect of the residual stresses including a more realistic material model. This results in larger tensile residual stresses and lower compressive stresses. Therefore, it can be concluded that the experimental results from the hole-drilling method calculated according the ASTM standard underestimate the tensile residual stresses and overestimate the compressive residual stresses. Since larger tensile residual stresses are more harmful for the fatigue of a welded bridge component, it is better to include the real material properties with elastic-plastic behavior. In this case, the verification of the hole-drilling results with finite element modeling is very valuable.