esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Deep Dense Block-Based Model for Detecting Alzheimer's Disease
## 4 Results and Experiments
### Comparison with Related Works  
Morphometric-based studies have been limited to the literature. Research related to the application of deep-learning methods for the early detection of AD is scarce. Furthermore, existing studies have predominantly focused on VBM analyses, with only a few studies comparing TBM images. The effectiveness of the suggested model based on the Xception + PPC architecture was compared with that of other competing models. Many studies have utilized the same dataset for classification purposes; therefore, we selected the ADNI database for our research. Accuracy was used as the major parameter to evaluate the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Models** & **Accuracy** & **Precision** & **Sensitivity** & **F1-Score** & \begin{tabular}{c} **Training** \\ **Time (s)** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Time Per** \\ **Epoch (s)** \\ \end{tabular} &
\begin{tabular}{c} **Test Time** \\ **(s)** \\ \end{tabular} \\ \hline VGG-16 & 85.96\% & 84.08 & 85.49 & 84.55 & 2594.75 & 12,97 & 1.39 \\ (Baseline) & 87.44\% & 86.02 & 86.98 & 86.45 & 3174.44 & 15.87 & 4.77 \\ ResNet-50V2 (Baseline) & 87.93\% & 86.60 & 87.13 & 86.81 & 2749.96 & 13.74 & 5.52 \\ Xception (Baseline) & 89.16\% & 88.10 & 88.76 & 88.35 & 2982.52 & 14.91 & 4.72 \\ VGG-16 + PPC & 89.66\% & 87.94 & 89.04 & 88.14 & 3192.16 & 15.96 & 1.91 \\ VGG-19 + PPC & 91.63\% & 90.07 & 90.89 & 90.32 & 2844.81 & 14.22 & 4.88 \\ ResNet-50V2 + PPC & 93.35\% & 92.41 & 92.82 & 92.57 & 2668.44 & 13.34 & 3.38 \\ Xception + PPC & 95.81\% & 95.01 & 95.41 & 95.21 & 3130.86 & 15.65 & 5.04 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance evaluation of baseline models and proposed approaches.  
Figure 7: ROC curve from Xception + PPC model.  
outcomes of classification. Table 9 presents a comparison of the suggested model using the same dataset as other comparative studies in the literature, demonstrating its superior performance with a 95.81% accuracy rate in the three-class dataset. In addition, most studies have focused on binary classifications. To the best of our knowledge, no study has focused on the early diagnosis of AD using deep TL-based TBM analysis for multiclass classification. The proposed model exhibits the capability to effectively address a three-class problem.  
Despite utilizing a unified architecture, they achieved the highest accuracy rate of 87% using raw MRI data [32]. In this study, a higher success rate was achieved compared to methods that utilized raw datasets in parallel with other related studies. Similarly, it has been observed that morphometric methods achieved higher accuracy rates than raw MRI datasets [54; 55]. In [55], the VGGNet base architecture was used for VBM-based analysis, resulting in 96% accuracy. In this study, the accuracy rates of 85% and 87% were obtained using the baseline VGGNet16 and VGGNet19, respectively. This finding suggests that a VBM-based analysis using base models may yield more successful results than a TBM-based analysis.
## 2 Instrument Design
### Block Diagram
#### 2.1.1 Microwave Assembly
The two outputs of the splitter are then filtered by 4-Section band-pass filters (BP3a/b) with the center frequencies 1,407.5 MHz and 1,419.5 MHz, respectively each with a -3 dB bandwidth of 11 MHz. In this way, two slightly overlapping receiver channels within the protected band are created, which allow narrow-band RFI to be detected within the protected band. The corresponding lower side band (LSB) and the upper side band (USB) of the MA back-end are AC-coupled to the detectors through DC-blocks in order to remove any low-frequency internal RFI or DC-bias signals from ground loops or pick-up from the radiometer electronics. Summing up the specified losses (see Appendix) of the back-end components (attenuator (3 dB) + BP2 (1.22 dB) + splitter (0.4 dB) + one-to-one splitting into the LSB and the USB (3 dB) + BP3a/b (1.3 dB) + DC-block (0.15 dB) + five connecting cables (5\(\cdot\)0.1 dB)) yields the loss \(L_{\rm back}\)\(\approx\) 9.57 dB of each of the two frequency channels. The MA gain \(G_{\rm MA}\)\(\approx\) 69.01 dB is estimated as the difference between the gain of the two low-noise amplifiers (2\(\cdot\)40 dB) and the total loss \(L_{\rm MA}\) = \(L_{\rm front}\) + \(L_{\rm back}\) = 10.99 dB of the MA. Table 1 shows the typical noise temperatures applying at the MA inputs (column 1), the expected power levels \(P_{\rm front}\) at the output of the front-end (column 2), and the associated power levels \(P_{\rm PDA}\) (in units of dBm and \(\mu\)W) expected at the output of the back-end of the MA (columns 3 and 4). The selected MA inputs are: \(T_{\rm sky,\,in}\) expected for a sky measurement; \(T_{\rm ACS}\), \(T_{\rm RS}\), and \(T_{\rm HS}\) of the active cold source (ACS), the resistive source (RS), and the hot source (HS); and \(T_{\rm scene,min}\), \(T_{\rm scene,max}\) cover the range of land-surface brightness temperatures. The power levels \(P_{\rm front}\) are derived as the sum of the noise power associated with the radiometer residual noise \(T_{\rm RM\,0}\)\(\approx\) 125 K, plus the power due to the noise temperature applying at the MA input. Thereby, equation (2) is used with the bandwidth \(B\) = 22 MHz of the BP1. In units of dBm, the \(P_{\rm PDA}\) are:  
\[P_{\rm PDA}=P_{\rm front}+G_{\rm MA} \tag{3}\]
# The Real-Time Optimal Attitude Control of Tunnel Boring Machine Based on Reinforcement Learning
## 2 Data Review
### Data Analysis  
The statistical analysis was conducted on the overall preprocessed data used in the paper. The data included five GECs, namely 2, 3a, 3b, 4, and 5, and the ratio of each GEC is shown in Figure 2. The TBMAP representing parameters and attitude control parameters, namely TDA, TFA, HDTH, VDTH, DDSB, and DLTC, are the important parameters data to build the optimal attitude control policy, and their statistical distributions were analyzed, as shown in Figure 3. From the figure, it can be seen that these parameters as a whole are similar to the normal distribution. There were significant differences in the range of values between various parameters. So, normalization was needed to eliminate these differences when modeling with them as inputs.
## 2 Related Work
In order to overcome the above unaddressed issues, the current study proposes the MONWS algorithm to enhance the performance of scheduling in cloud computing and Table 2 summarizes the list of notations. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & \\ \cline{3-14}
**Ref.** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** & **Technique** \\ \hline
[25] & Modified HEFT & ✓ & - & - & - & - & - & - & - & - & - & Cloudsim \\ \hline
[26] & BDAS & - & - & - & ✓ & - & - & - & ✓ & - & - & - & Cloudsim \\ \hline \multirow{3}{*}{[27]} & MinIncreasInEnergy, & & & & & & & & & & & & \\  & NoldleMachineCTC, & & & & & & & & & & & & \\  & MaxUtilECTCandNol- & - & ✓ & - & - & - & - & - & - & ✓ & - & Cloudsim \\  & delMachineMaxUtil & & & & & & & & & & & & \\ \hline
[22] & NBWS & - & - & - & ✓ & - & - & - & - & - & - & Cloudsim \\ \hline
[28] & BHGALO-GOA & ✓ & - & ✓ & - & - & - & - & - & - & - & Workflowsim \\ \hline
[13] & DAGMap & - & - & - & - & - & - & ✓ & - & ✓ & - & Grid \\ \hline
[29] & NMMWS & ✓ & - & - & - & ✓ & - & - & - & - & MATLAB \\ \hline
[19] & MW-HBDCS & - & - & - & ✓ & - & ✓ & - & - & - & - & SimGrid \\ \hline
[23] & FDHEFT & - & - & ✓ & ✓ & - & - & - & - & - & - & Realtime \\ \hline
[21] & BDHEFT & - & - & ✓ & - & - & - & - & - & - & - & Cloudsim \\ \hline
[30] & Resource ranking and & - & - & - & - & - & - & - & ✓ & - & - & Cloudsim \\  & partitioning & & & & & & & & & & & \\ \hline
[31] & BB-BC & - & - & ✓ & - & - & - & ✓ & - & - & - & Cloudsim \\ \hline
[32] & PEFT & - & - & - & - & - & - & - & - & - & ✓ & Workflowsim \\ \hline
[33] & MCDM & ✓ & - & ✓ & - & - & - & - & - & - & - & Realtime \\ \hline
[34] & HBCWSP & ✓ & - & - & ✓ & - & - & - & - & - & - & Realtime \\ \hline
[35] & HEFT-ACO & ✓ & - & ✓ & - & - & - & - & - & - & Workflowsim \\ \hline
[36] & EM-WOA & - & ✓ & - & ✓ & - & - & - & - & - & cloudsim \\ \hline
[37] & NRBWS & ✓ & - & - & ✓ & - & - & - & - & - & Cloudsim \\ \hline \hline \end{tabular} Note: ✓ indicates considered by author and—indicates not considered by authors.  
\end{table} 
Table 1: Summary of workflow scheduling algorithms for cloud computing.
## 2 Materials and Methods
### Analytical Instrumentation  
For the evaluation of organic matter release by the biosorbent, the UV-Vis spectroscopy technique was used, and spectra in the range of 200-500 nm were recorded using a [PERSON] (Dusseldorf, Germany) Model UV 210 PC spectrophotometer, with quartz cells of 1 cm path length and ultrapure water as the reference for the baseline.  
The quantification of the metals zinc, copper, and lead was carried out with the Agilent 7700 ICP-MS equipment (Santa Clara, CA, USA) (with a collision cell), using the elements germanium, rhodium, and iridium as internal standards. The calibration was established with standard solutions using an independent standard. Quantification limits, corresponding to ten times the residual standard deviation of the linear regression divided by the sensitivity of the method, were 1.0 \(\upmu\)g/L for zinc, 0.10 \(\upmu\)g/L for copper, and 0.10 \(\upmu\)g/L for lead.  
To evaluate the morphological and chemical characteristics of the white bean peels before and after the sorption experiments, the techniques of FTIR-ATR, SEM, and EDX were applied to both sides of the biomaterial. The FTIR-ATR analyses were performed using a PerkinElmer (Waltham, MA, USA) Spectrum BX FTIR instrument, and to acquire transmittance spectra in a range of 4000-550 cm\({}^{-1}\), 64 scans were averaged with a resolution of 4 cm\({}^{-1}\) using air for the background spectrum. For the SEM and EDS analyses, a scanning electron microscope Hitachi SU-70 (Tokyo, Japan) operating at an accelerating voltage of 15 kV and an EDS Bruker (Berlin, Germany) were utilized, respectively. For these latest analyses, the peels used resulted from the maximum contact time with the solution (6 h)for each pH evaluated (4.0, 5.6, and 7.0) and for the higher initial concentration tested (500 \(\upmu\)g/L), in order to amplify the sorption behavior of elements on the peel.
# Automatic Matching of Multi-Source Satellite Images: A Case Study on ZY-1-02C and ETM+
## 3 Experiments and Results  
### Description of Test Data  
To evaluate the performance of the proposed approach, three datasets from ZY-1-02C and DOM of ETM+ were used in the experiments. The first experiment evaluated the applicability of the matching method. A PAN image of ZY-1-02C was down-sampled and matched with the original image. The matching results were then evaluated. Dataset 1, for an urban area, was used. The second experiment evaluated the accuracy of AMSIM between the ZY-1-02C image and the DOM of ETM+. Datasets 2 and 3, which mainly featured mountainous terrain in Northeast China, were used. The DOM of ETM+ was corrected via scan line correction using UTM-WGS84, which was obtained from the website of the USGS, Chinese Academy of Sciences. In the experiment, the ZY-1-02C image was a PAN image, and the ETM+ image was a Band 8 (PAN) image. The datasets are described in Table 1.
## 3 Results and Discussion
### Effect of Thermal-Bonding Temperature on Thermal Conductivity  
Figure 13 shows thermal conductivity results at 100 \({}^{\circ}\)C and 150 \({}^{\circ}\)C after composites were thermally bonded at different temperatures. As thermal-bonded temperature increases, thermal conductivity at 100 \({}^{\circ}\)C and at 150 \({}^{\circ}\)C firstly decreases and then increases. This is due to the number and size of air voids reduced air convection in composites. After thermal shrinkage and bonding, melting LPET fibers can lock some static air in the LPET nonwoven and interspace between basalt fabrics,  
Figure 11: SEM observations of knife failure planes when composites were thermal-bonded at **(a)** 100 \({}^{\circ}\)C; (b) 120 \({}^{\circ}\)C; (c) 140 \({}^{\circ}\)C; and (d) 160 \({}^{\circ}\)C. The red circles show thermal-bonding region.  
Figure 12: Yarm pull-out force as function of crosshead displacement for **(a)** pure basalt fabric and **(b)** sandwich composites thermal-bonded at varying temperature.  
which decreased the thermal convection. Meanwhile, static air had the lowest thermal conductivity \(K_{a}\), 0.0243 W/(m\(\cdot\)K), and the larger air volume fraction \(V_{a}\) results in lower thermal conductivity \(K_{m}\) in accordance with simple mix principle as \(K_{m}\)= \(V_{f}\)/\(K_{f}\) + \(V_{a}\)\(K_{a}\) (where \(V_{f}\) is fiber volume fraction, \(K_{f}\) is fiber thermal conductivity). This finding corresponds to the results indicated by [PERSON] et al. that air convection decreased with void size in the structure. Thermal conductivities of resultant composites were all below 0.0250 W/(m\(\cdot\)K) at testing temperature of 100 \({}^{\circ}\)C, much smaller than that of PET-LPET nonwoven indicated in the above-mentioned study [27]. Comparatively, increasing testing temperature to 150 \({}^{\circ}\)C, thermal conductivity increased to 0.0197 W/(m\(\cdot\)K) when composites were thermally bonded at 140 \({}^{\circ}\)C which due to higher thermal conductivity of fibers [28].
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 4 Results and Discussion
### Accuracy of Forest Fire Detection  
We applied the STM method and the contextual algorithm to the Yinanhe forest fire detection. The ground truth data discussed in Section 2.3 were used to evaluate the fire detection result. In the STM method, the size of window was \(21\times 21\). In our comparison, we used a series of contextual and absolute-threshold tests to detect the active fire pixels in every IRS image. The optimized selection of the valid neighbors and the window size was implemented as described in [24]. For the sake of comparison, the contextual algorithm used the same cloud masking method proposed in Section 3.2.2.  
The maps (Figure 13) below show the STM method and contextual algorithm results. We used the ground truth data as references to compare the accuracies of the STM method results with those of the contextual algorithm results (Table 2). The commission errors and omission errors were calculated for each image.  
Figure 12: Frequency histograms of the background BT predictions and the actual BTs measurements from band 7 (**a**) and band 8 (**b**). “BT” denotes the actual background BT measurements, “ST” denotes the background BTs predicted using STM, and “MF” denotes the background BTs predicted by averaging the neighboring pixels.  
Figure 13: Ground reference data (**i**) and forest fire detection results by contextual algorithm (**ii**) and STM (**iii**) for the days: 28 April 2009 (**a**); 29 April 2009 (**b**); 3 May 2009 (**c**); and 11 May 2009 (**d**).  
Herein, the commission error is the fraction of the wrongly detected fire pixels with regard to the extracted fire pixels, and the omission error is the fraction of fire pixels that were not detected with regard to the real fire pixels. The commission error is the inverse of the user's accuracy, whereas the omission error is the inverse of the producer's accuracy. According to Table 2, the STM method omission errors were obviously lower than those of the contextual algorithm. However, the STM method commission errors were slightly higher than those of the contextual method because the STM method detected more fire pixels. The overall omission error was 5.56%, which means that when the STM method was used, 3925 of 4158 fire pixels were correctly detected, at the same time, the overall commission error was 9.91%, which means that 399 \"normal\" pixels were wrongly detected as forest fire pixels. In reality, the detection result was satisfactory for the HJ-IRS data.  
Taking into consideration the fitting quality and detection accuracy, the STM method as proposed in this study performed better than the contextual fire detection algorithm, which was confirmed in the Yinanhe forest fire detection example. As introduced in Section 3.2.4, the good detection results were primarily due to the following reasons: (i) a more accurate background intensity prediction; and (ii) the integration of the spatial contextual information and temporal information.
# Algorithm for Option Number Selection in Stochastic Paired Comparison Models
## 4 Simulation Results
### The Cases \(s=2\) and \(s+2=4\)  
The two-option and four-option models are compared in this subsection. Table 1 shows an example for the simulation results of the two models. We present a case in Table 1 where the probabilities of an object falling into the options available are approximately equal. In the presented situation, there are eight objects to compare, their expectations are between 0 and 2, and \(d=1.35\). In the case of the four-option model, the'slightly better' and'much better' options can be distinguished: a'slightly worse' or'much worse' decision is a'slightly better' or'much better' decision from the perspective of the other object. In this case, the ratio of'much better' decisions to all decisions was 0.5186, while the ratio of'slightly better' was 0.4814.  
The first column in Table 1 shows the number of comparisons denoted by \(|A|\). The second column shows the number of cases in which we were able to use both models. The third column shows the number of cases for which we were only able to evaluate objects using the four-option model and the fourth column shows the number of cases for which we were not able to make an evaluation using either model. The fifth and sixth columns contain the average sum of the squared differences between the exact and the estimated expectations found by the two-option model and the four-option model, respectively. The quantities  
\[e^{(s*)}=\sum_{i=1}^{n}(\widehat{m}_{i}^{(s*)}-m_{i})^{2} \tag{19}\]  
measure how close the estimated expected values are to the exact expected values in the model allowing \(s*\) options. They are zero if, and only if, the estimated parameters coincide with the exact parameters. If we take the average of these values, we get a measure of the model's accuracy, denoted by \(\overline{e^{(s*)}}\).  
The seventh (last) column shows their relative relation, which is defined as follows:  
\[R^{(2),(4)}=\overline{\frac{e^{(2)}-\overline{e^{(4)}}}{\overline{e^{(4)}}}}. \tag{20}\]  
In these formulas, as previously, the upper index refers to the option number of the model used for the evaluation. As the quantities (19) are non-negative, the sign of (20) characterizes which model is better: if (20) is positive, then the four-option model is better; if it is negative, then the two-option model is. It can clearly be seen that in the case of few comparisons--i.e., when the number of comparisons conducted is less than 21--the two-option model is able to approximate the exact expectations more accurately. Meanwhile, in the case of more comparisons, the more complex four-option model performs better. It can also clearly  
This phenomenon can be explained as follows: if there are few comparisons, randomness plays a significant role. The more options there are, the greater the variability. As the number of comparisons increases, the generated data increasingly follow the probabilities derived by the model. The parameters become easier to estimate accurately. Models with more options contain more information, allowing for more precise estimates.
## 2 Materials and Methods  
### Pre-Processing of GPR Data  
To prepare GPR signals well for further EEMD analysis, three significant steps are utilized to pre-process original GPR data: time-depth conversion, 'de-wow' and amplitude gain.  
Firstly, the time-depth conversion is designed to extract location information from the recorded time series. According to propagation velocity invariant assumption and two-way travelling time (transmission and reflection) of electromagnetic waves in the subterranean area, the recorded depth of interfaces can be determined [25]\[Z=\frac{vt}{2}=\frac{ct}{2\sqrt{\mu_{r}\varepsilon_{r}}} \tag{1}\]  
where, \(v\) is the propagation velocity of electromagnetic waves, \(t\) is the two-way travelling time, \(c\) represents the propagation velocity in the vacuum, \(\mu_{r}\) is the relative magnetic permeability, and \(\varepsilon_{r}\) is the relative permittivity.  
Secondly, 'de-wow' processing is utilized to eliminate low-frequency distortions resulting from the inductive coupling effect or early saturation [25]. An effective approach with good practice to handle the 'wow' issue is simply removing the average amplitude from the signals [26]:  
\[A(Z)=a(Z)-\frac{1}{N}\sum_{k=1}^{N}a(Z_{k}) \tag{2}\]  
where, \(N\) is the number of discrete signal points, \(a(Z_{k})\) represents the original signal amplitude, and \(A(Z)\) represents the processed signal.  
Thirdly, amplitude gain is utilized to amplify the signals against energy loss including propagation attenuation and geological spreading loss [25]. Considering both exponential attenuation and evenly distributed rock fissures, radar signals can be recovered to realistic levels by multiplying Equation (3) [16].  
\[F(Z)=e^{2\alpha(Z-Z_{0})}+2\varphi(Z-Z_{0}) \tag{3}\]  
where, \(\alpha\) is the exponential attenuation parameter, \(\varphi\) is the geological spreading loss parameter, and \(Z_{0}\) is the starting depth for amplitude gain.
## 3 Results
### Spatial Characteristics of NPP and NEP
#### 3.2.2 Spatial Characteristics of NEP  
The temporal and spatial distribution of NEP in the HKH region was determined by estimating NPP and soil respiration. A positive value of NEP indicates the area is a carbon sink, and a negative value indicates a carbon source [59]. From the perspective of spatial distribution characteristics (Figure 4a), the interannual NEP accumulation in the HKH region from 2001 to 2018 showed a significant upward trend from west to east, which was consistent with the trend of NPP. The grassland ecosystems in the HKH region showed a weak carbon sink, with an average NEP of 42 \(\pm\) 197 gC\(\cdot\)m\({}^{-2}\), and the total net C sequestration was approximately 0.077 Pg C. In the past 18 years, the average amount of C sequestered by grassland ecosystems in the C sink area was 214.4 gC\(\cdot\)m\({}^{-2}\), and the total amount of carbon sequestered was approximately 0.187 Pg C. Carbon storage areas were mainly distributed in northern India, Nepal, and the southeastern Tibetan Plateau, accounting for approximately 52.2% of the total grassland area. At the same time, the average C emissions of grassland ecosystems in the carbon source sector was 116 gC\(\cdot\)m\({}^{-2}\), and the total C emissions were approximately 0.11 Pg C. The regions of carbon release were mainly concentrated in Afghanistan's Hindu Kush Mountain and the western part of the Tibetan Plateau, accounting for about 47.8% of the total grassland area. Judging from the difference in NEP between 2018 and 2001 (Figure 4b), the carbon sequestration capacity of grassland in 2018 was stronger than in 2001. Regions with large fluctuations in NEP values were mainly distributed in the northeastern part of the Qinghai-Tibet Plateau, with an increase of more than 100 gC\(\cdot\)m\({}^{-2}\), and the NEP values in most regions fluctuated within plus or minus 100 gC\(\cdot\)m\({}^{-2}\).