esa-sceva/satcom-chunk-collection huggingface dataset:
# Discrimination of Earthquake-Induced Building Destruction from Space Using a Pretrained CNN Model
## 4 Experimental Results and Discussion  
### Pretrained VGGNet Model for Collapsed Building Detection  
For the VGGNet structure, we slightly modified the layers after convolutional layers. The flatten layer was replaced by a global average pooling layer to reduce the total number of parameters in the model. Correspondingly, the number of neurons in the dense layer was reduced to 64. As the target is to classify collapsed and non-collapsed buildings, the softmax layer was replaced by a new layer with two neurons. The total number of network weights is 1.47 million. More details about VGGNet can be found in [18]. In this study, pretrained CNN model was fine-tuned using the target dataset and the model trained from scratch was also obtained using the same dataset. There are 1789 buildings utilized in the present study, including 716 and 1073 for testing and training, respectively. The results were shown in Table 1. It is obvious that the pretrained VGGNet model outperformed the VGGNet model with improvements of overall accuracy (OA) values from 83.38% to 85.19% and Kappa values from 60.69% to 67.14%. Even though VGGNet model achieved a higher value of OA, nearly half-collapsed buildings were misclassified as non-collapsed ones, which could be partly caused by that the target training dataset was too small for training VGGNet model since deep CNN requires a large amount of training dataset to obtain a satisfied classifier. On the contrary, the collapsed and non-collapsed buildings were relatively well identified by the pretrained VGGNet model, which indicated that pretrained CNNs can perform satisfactorily under a limited dataset. The pretrained weights have been proved to outperform randomly initialized weights [17; 29; 30]. Neural network models trained from scratch were compared to pretrained models fine-tuned by target dataset in [31], and the results indicated that fine-tuned pretrained models achieved better performance, which corresponds to the results obtained in the present study.
## 3 Analysis of Test Results  
### Analysis of Mechanical Characteristics  
Mechanical characteristics according to the alumina content added to the polymers of PBT and PA6 were analyzed. Table 2 lists the results of the test on the mechanical characteristics. It was found  
\begin{table}
\begin{tabular}{c c c} \hline \multirow{2}{*}{**Sample Designation**} & \multicolumn{2}{c}{**Composition (wt.\%)**} \\ \cline{2-3}  & **PBT** & **PA6** & **Al2O3** \\ \hline PBT (0) & 100 & 0 \\ PBT (60) & 40 & 60 \\ PA6 (0) & & 100 \\ PA6 (50) & & 50 10 \\ PA6 (60) & & 40 60 \\ PA6 (70) & & 30 & 70 \\ \hline \end{tabular}
\end{table}
Table 1: Content of PBT, PA6, and Al2O3 used for the preparation of polymer blends.  
that both PBT and PA6 exhibited lowered performances when alumina was added compared to the mechanical strengths without alumina. For PBT, when 60% alumina was included, the tensile strength sharply decreased, making it impossible to perform an experiment. It was also not possible to properly measure the elongation. In the case of the flexural strength, the result was only half that of PBT without alumina. Moreover, the impact strength test was not possible in this study because the impact strength specimen tended to be damaged while it was fabricated. When the mechanical strengths were analyzed for PA6 mixed with alumina, the tensile strengths were slightly lower compared to that of PA6 without alumina, and the value showed a tendency to decrease as the alumina content increased. When the content increased from 60% to 70%, however, there was no significant difference in tensile strength. In the case of the elongation, however, the value continuously decreased as the alumina content increased. Both flexural and impact strengths were found to decrease as the alumina content increased.  
The addition of a rigid filler restricts the chain mobility of polymer molecules; this may lead to the formation of micro-cracks in the composites. Furthermore, increased stress concentration at the ends of the filler is another reason for crack formation in the matrix. It is known that, when the extent of cracks in the specimen reaches a critical level, especially, in matrix surrounding filler, the matrix cannot resist applied load and then cracks initiate in those regions [32; 33; 34; 35]. PBT and PA6 revealed different mechanical strength properties when alumina was added. The addition of alumina to PBT was significantly detrimental to the mechanical strength of PBT; further, it was difficult to even fabricate specimens by the addition of alumina. Conversely, in the case of PA6, the addition of alumina led to an extreme decrease in the elongation, but no significant degradation of other mechanical properties was observed. Therefore, in terms of mechanical strength, the addition of alumina to PA6 would evidently be more suitable for the production of injection-molded products compared to PBT.
###### Abstract  
This study investigates how structural dynamic characteristics affect the response to high-frequency dominant seismic excitations, using a 3D numerical analysis considering soil-structure interaction (SSI). For this purpose, an SSI analysis was conducted using the finite element analysis software LS-DYNA, incorporating four representative Korean geotechnical characteristics and the 2016 Gyeongju earthquake, characterized by dominant high-frequency components. A comparison was conducted between a fixed-end model without considering SSI and an embedded model with SSI for a high-rise structure (40 stories) with low natural frequencies, and a low-rise structure (5 stories) with high natural frequencies. The analysis focused on key dynamic responses, including the natural frequency, frequency of maximum response, and maximum relative displacement of the structures, to identify differences in the SSI effect based on the structures' dynamic characteristics and the soil types. The analysis generally revealed that the SSI effect lowers the natural frequencies of structures and increases the damping effect. It was also found that depending on the match between the dominant frequency range of the seismic excitations and the range of the structure's natural frequencies, larger dynamic responses were calculated when SSI was considered, suggesting that it may be necessary to consider SSI for conservative design results.  
Keywords:soil-structure interaction (SSI); 3D finite element method (3D FEM); high-frequency seismic excitation; natural frequency +
Footnote †: journal: Applied sciences
## 3 Model and Methods
### Multi-Granularity Module
The dataset contains 385 identity video images taken by camera A and 749 identity video images taken by camera B; only 200 identities were captured by both cameras. As the acquisition environment of PRID-2011 was a relatively spacious outdoor street scene, the background of its person images is simple and clean and there is less person occlusion than in the other two datasets. To evaluate the re-identification performance of APMG network, we use the cumulative matching characteristic (CMC) curve and mean average precision (mAP) evaluation indicators. When using the MARS dataset, we followed the default training and testing groupings of the dataset and used the CMC curve and mAP to evaluate the model performance. When using iLIDS-VID and PRID-2011 datasets, we randomly divided the dataset into two subsets for training and testing groups. The experiment was repeated 10 times, and the CMC curve (representing the average result of 10 experiments and for which we took Rank-1 and Rank-5) was used to evaluate the model performance.
## 3 Simulation Results and Discussions
### RIN Spectra of \(LD\) for Various States
In the chaos regime, when the LEF \(\alpha=3\) and the SEF \(C=10^{-4}\), the peak intensity of the RIN with OFB is enhanced to higher values (almost two orders of magnitude) (dashed line) and shifted in the direction of decreasing frequency compared with the solitary laser RIN (solid line) at the relaxation frequency \(f_{r}\), as shown in Figure 5(a2). Figure 5(a2) exhibits a frequency shift between the RIN with OFB (\(f_{r}=4.14\) GHz) and the solitary laser RIN (\(f_{r}=5.9\) GHz). At low frequency, the RIN is enhanced to higher values (almost two orders of magnitude) than the laser without OFB noise and increases/decreases by increasing/decreasing the LEF/SEF, as shown in Figure 5(a2,a3,c2). This frequency shift found in the RIN at the relaxation oscillation frequency for both cases (solitary laser and laser with OFB) and due to the variation in the LEF and SEF is in agreement with the experimental and theoretical results mentioned in Refs. [29; 30; 31], which confirms our new results and expectations. From the results, under OFB in the weak region and when the LD is operating in the CW or PO region, we found that although the decrease/increase in the LEF/SEF causes an enhancement/reduction in the laser stability/noise, as presented in Figure 5, it causes considerable shifts in the lasing frequency and corrupts the LD efficiency in applications [32].
# Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization
## 5 Experimental Evaluation
### Data Representation
#### 5.3.2 TF-IDF Vectorizer  
Term Frequency-Inverse Document Frequency (TF-IDF) [63] is a representation that measures how relevant a word is to a document in a collection of documents. The term has two parts which are multiplied with each other. The first is the Term Frequency (TF), also called Term Score, which considers all the files in the dataset as a bag of words. The TF is calculated by dividing the number of word occurrences in a document by the total number of words in the same document. The second part is the Inverse Document Frequency (IDF) which is calculated by obtaining the exponential logarithm of the total number of documents in the dataset divided by the number of documents having a specified word _w._
###### Abstract  
In this paper, we propose an algorithm to remove movement noise from second derivative of photoplethysmography (SDPPG) signals. SDPPG is widely used in healthcare applications because of its easy and comfortable measurement. However, an SDPPG signal is vulnerable to movement, which degrades the signal. Degradation of SDPPG signal shapes can result in incorrect diagnosis. The proposed algorithm detects movement noise in a measurement signal using wavelet transform, and removes movement noise by selecting the best signal from among multiple signals measured at different locations. Experiment results show that the proposed algorithm outperforms the previous filter-based algorithm, and that movement noise with 30% time duration can be reduced by up to 70.89%.  
SDPPG; movement noise; wavelet transform; diversity combining Article
## 5 Empirical Results of Producer Services Agglomeration on Carbon Emission Reduction
### The Baseline Regression Result  
The estimated results of the effect of the agglomeration of producer service industry on carbon emissions are shown in Table 4. The (1) column is the ordinary panel estimation result, and the (2)-(5) columns are the Spatial Durbin model estimation results under different spatial weight matrices. The results show that the agglomeration of the producer service industry has a significant negative impact on carbon emissions, which means that the agglomeration of the producer service industry can bring about carbon emissions reduction.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Producer Service Industry Index** & \multicolumn{3}{c}{**Carbon Emissions**} \\ \hline  & **Moran’s I** & **Geary’s C** & & **Moran’s I** & **Geary’s C** \\ \hline
2010 & 0.073 & 0.440 & 2010 & 0.334 & 0.670 \\
2011 & 0.032 & 0.523 & 2011 & 0.335 & 0.707 \\
2012 & 0.060 & 0.519 & 2012 & 0.323 & 0.716 \\
2013 & 0.050 & 0.478 & 2013 & 0.340 & 0.694 \\
2014 & 0.058 & 0.467 & 2014 & 0.320 & 0.707 \\
2015 & 0.086 & 0.446 & 2015 & 0.313 & 0.703 \\
2016 & 0.107 & 0.430 & 2016 & 0.286 & 0.721 \\
2017 & 0.161 & 0.379 & 2017 & 0.287 & 0.724 \\
2018 & 0.168 & 0.376 & 2018 & 0.300 & 0.749\({}^{\ddagger}\) \\
2019 & 0.179 & 0.376 & 2019 & 0.276 & 0.799 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spatial correlations of producer service industry and carbon emissions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **Statistics** & \(\mathbf{p}\) \\ \hline Hausman & 52.18 & 0.000 \\ LM-Spatial error & 14.787 & 0.000 \\ Robust LM-Spatial error & 82.993 & 0.000 \\ LM-Spatial lag & 120.703 & 0.000 \\ Robust LM-Spatial lag & 188.909 & 0.000 \\ LR-Spatial error & 66.39 & 0.000 \\ LR-Spatial lag & 64.81 & 0.000 \\ LR-SDM-ind & 51.53 & 0.000 \\ LR-SDM-time & 303.52 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Space metering model selection test results.  
Specifically, the estimated coefficient of the ordinary panel is \(-0.180\), and the estimated coefficient of the Spatial Durbin model under different spatial weight matrices fluctuates around 0.2, and all models have passed different degrees of significance level tests.  
Taking the contiguity weight as an example, the estimated coefficient is \(-0.248\), which means that every 1% increase in the agglomeration level of the producer service industry will reduce carbon emissions by 0.248%. From the perspective of spatial lag terms, the estimated coefficients are also significantly negative, indicating that the agglomeration of the producer service industry can have a significant spatial spillover effect. The agglomeration of the producer service industry can not only produce a significant reduction in carbon emissions in the region, but also achieve reductions in the surrounding areas through various spillover effects. In addition, the spatial lag term of carbon emissions is significantly positive, which is consistent with the positive spatial correlation found in the foregoing calculations. Table 4 shows the baseline regression results.
## 5 Results and Discussion
### Initial Velocity of the Particles  
Usually, the structure in the process of cleaning particles plays a significant role in the weight mass. The airflow in the process of blocking particles must be able to move and lift the particles. Under vacuum conditions for the full removal of particles in the pickup head during road cleaner movement, the suction port allows the particles to be fully absorbed when the airflow rate is higher than the velocity at which the particles move. Figure 13 shows the effect of the change in the particle structure on the initial velocity of the particle. The calculation results are based on Equation (20), and the starting velocity of the particles by calculation was 4.3 m/s for particles with a size of 250 \(\upmu\)m, whereas during the experiment, the starting velocity of the particles was 5.8 m/s. As the airflow rate increased, the calculation rate of the speed at which the particles moved and the results of the experiment were relatively close to each other. The result of the starting velocity calculation of particles with a size of 2000 \(\upmu\)m was 12.2 m/s, compared with 12.5 m/s through experiments. The results showed that the difference between the simulation and experimental results with a particle size of 250 \(\upmu\)m was 25%, and with increasing particle size, the initial particle starting velocity was 5% for 1000 \(\upmu\)m particles and 2% for 2000 \(\upmu\)m particles. It can be seen that the experimental results for particle sizes from 250 \(\upmu\)m to 750 \(\upmu\)m were higher than the simulation results. However, when the particle size was greater than 750 \(\upmu\)m, the difference between the experimental and simulation results was very close. Crushed asphalt particles with a density of 2400 kg/m\({}^{3}\) were used in the calculations and experiments. In many cases, because the particles on the road surface are very diverse, it is important to study the effect of changes in the particle composition on the initial velocity. The effect of structural changes in the particles during the operation of road cleaners on the initial velocity of the particles is shown in Figure 14. Because the size of the particles on the road surface is not always small, when studying the size of the particles encountered in real conditions, it was found that the initial velocity of high-density particles is relatively high [10; 11]. The starting velocity of the removal of 3 mm sized wood particles was 7.4 m/s, while the starting velocity of sand particle removal was 12.3 m/s. The starting removal velocities of 15 mm particles was 29.5 m/s in clay particles, 31.7 m/s in asphalt particles, and 33 m/s, in granite particles, respectively. When we changed the particle size to 30 mm during the calculation, the particle starting velocity of the particles was 20.2 m/s for wood particles and the maximum velocity was 42.3 m/s for granite particles.  
Figure 13: Comparison of the particle starting velocity between calculated and experiment.
# Device-Free Indoor Location Estimation System Using Commodity Wireless LANs
## 2 Channel Sounding Using IEEE 802.11n Wireless LANs
### Developed System
#### 2.4.2 Back-To-Back Calibration  
The phase variation of the CSI occurs because of hardware imperfections such as the nonlinear distortion of the amplifiers, power control uncertainty, and phase ambiguity [22, 24], which should be removed to obtain the genuine propagation channel characteristics. However, because the six CSIs taken at consecutive WiFi channels are concatenated to extend the bandwidth, it is necessary to calibrate the inter-channel discontinuity in the amplitude and phase values.  
To remove the hardware imperfections contained in the CSI, we measure the system characteristics including the hardware imperfection in advance by back-to-back measurement. This measurement is conducted by connecting the transmit and receive antenna ports directly via an RF cable with known characteristics. We then obtain the channel transfer function by dividing the CSI by the system characteristics, which is called back-to-back calibration (B2B calibration). Here, CSI \(\hat{H}^{(k)}_{\text{CSI,B2B,;}i}\) is obtained by connecting the \(i\)th transmit and \(j\)th receive antenna ports directly with a cable and is performed for every antenna combination. The MIMO channel matrix can be obtained as:  
\[\hat{H}^{(k)}_{j,i}=\frac{\hat{H}^{(k)}_{\text{CSI,}j,i}/\sqrt{\text{G}_{\text {age},j}}}{\hat{H}^{(k)}_{\text{CSI,B2B,}j,i}/\sqrt{\text{G}_{\text{age},\text {B2B,}j}}}e^{\frac{2\pi}{\hbar}\hat{k}k}, \tag{11}\]  
Figure 5: Developed channel sounding system. (**a**) Hardware setup. (**b**) Software.  
where \(G_{\text{agc},j}\) denotes the gain of the AGC amplifier of the \(j\)th receiving branch. It is assumed that the values of the denominator in Equation (11) should usually be measured in advance (upon initial setup). In commodity WiFi devices, the AGC amplifier operates automatically according to the received signal level to maintain the signal power at a certain level; thus, the CSI values are scaled as in Equation (11). The gain of the AGC amplifier can be deduced by referring to the amplitude of the CSI and RSSI as:  
\[G_{\text{agc},j}=\frac{1}{P_{j}}10^{\frac{\text{RSSI}}{100}i}, \tag{12}\]  
where \(P_{j}=E\left[|y_{j}^{(k)}|^{2}\right]\).  
The B2B calibration allows the CSIs acquired on multiple channels to be connected continuously because the phase and amplitude should become zero and unit after calibration. However, a phase jump occurs between adjacent channels owing to the independent random offset of the CSI phase characteristics in every packet. This can be removed by calculating the inter-channel phase difference using overlapped sub-carriers in adjacent channels. The amplitude of each channel also fluctuates owing to the AGC gain uncertainty. Using the averaged values of the overlapped subcarriers, the amplitudes were concatenated smoothly. The calibration procedure is shown in Figure 6a,b.  
Figure 6: _Cont._
## 3 Data Analysis and Forecasting Process
### Data Pre-Processing
#### 3.2.1 Outlier Processing  
The occurrence of an outlier is due to unavoidable interference factors, resulting in errors and missing data in the raw dataset. Corresponding techniques are introduced in this section. For missing data, a load value is first judged in Equation (17), and the mean value of the front and rear data values is used once targeted as a missing outlier according to Equation (18). If the value is larger than the set threshold, the previous data is used instead with the same data type. As for the case where null is present for a certain type of data, the column data is deleted or completed with 0.  
when:  
\[max\Big{(}\Big{|}\frac{y_{dt}-y_{dt-1}}{y_{dt}}\Big{|}\Big{|}\frac{y_{dt}-y_{dt+ 1}}{y_{dt}}\Big{|}\Big{|}\leq\varepsilon_{a} \tag{17}\]  
where \(y_{dt}\) is the load value of the \(d^{th}\) day on time t, \(\varepsilon_{a}\) is the set threshold, the corresponding outlier data will be replaced by \(y_{dt}\) denoted below:  
\[y_{dt}=\frac{y_{dt-1}+y_{dt+1}}{2}. \tag{18}\]  
For the error data, a similar operation will be implemented: if the range of the data value exceeds the set threshold, the mean value of the previous and afterwards data values are used instead, being similar to the missing data.
# Influence of Rice Variety and Freezing on Flour Properties
## 3 Results and Discussion
### The Thermo-Mechanical Properties of Flour Samples from Raw and Frozen Rice

Table 6: Thermo-mechanical properties of rice flows obtained from raw and frozen Basmati and Arborio rice. Figure 1: Mixolab curves of flours from raw and frozen Basmati and Arborio rice. The effect of freezing on the starch gelatinization behavior is different for flours obtained from the two rice varieties. Thus, while for the Arborio rice flours (raw and frozen), a similar trend of the Mixolab curves was observed, describing similar starch behavior during heating-maximum gelatinization torque (C3), cooking stability (C3/C4), and breakdown (C3 \(-\) C4), in case of flours from Basmati rice variety the trend of the Mixolab curves was different (Figure 1). Thus, for the flour made from frozen Basmati rice, the torque continued to increase constantly after reaching the maximum gelatinization (C3) and temperature was maintained constant at 90 \({}^{\circ}\)C. Finally, when the temperature was decreased from 95 to 50 \({}^{\circ}\)C and starch retrogradation occurred, an inflection appears and curves tended to flatten. Practically, the decrease in torque after reaching the C3 values was not recorded, as it happens in a typically Mixolab curve. However, the dough prepared with flour from frozen Basmati rice had higher cooking stability (C3/C4), and higher (C5 \(-\) C4) and SR values, compared to the flour from raw Basmati rice, but the C3, C4, and C5 values were lower. In case of flour made from Arborio rice variety, one can see from Table 6 that C3, C4, and C5 torques of the dough sample prepared with flour from frozen rice are higher that of those from raw rice, and the trend of the Mixolab curves is similar (Figure 1). This difference in dough behavior during heating between the two varieties could be attributed to the fact that, in the case of Arborio variety, the amylose content increases in the frozen sample, as opposed to Basmati variety (Table 4). The common tendencies of the parameters for the two frozen flours, compared to the non-frozen samples, are the higher values of (C5 \(-\) C4) and (C3 \(-\) C4). According to [PERSON] et al. (2012b), starch retrogradation properties depend on rice variety, but the overall starch architecture, the structure of amylopectin and degree of polymerization had influence on retrogradation properties starch gel. The authors reported that rice amylose content influenced crystallinity degree of starch, and rice with high content of amylose present high retrogradation enthalpy and retrogradation degree.