esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Novel Preformed Particle Gel System for Conformance Control in Mature Oil Reservoirs
## 3 Conclusions  
We introduced a novel polysaccharide-based material as a PPG, namely carboxymethyl cellulose-grafted crosslinked polyacrylamide (CMC/PAMBA). This material was synthesized through a microwave-assisted grafting copolymerization process, utilizing APS as the initiator and MBA as the crosslinker. The comprehensive characterization of the grafted material employed various techniques including Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), rheology, swelling measurements, and core flooding tests in an open fracture model. The key conclusions drawn from our investigations are as follows:  
FTIR analysis provided evidence of successful grafting reactions occurring between the hydroxyl groups of CMC and the vinyl groups of both AM and MBA.  
SEM images corroborated the grafting copolymerization process, revealing the formation of micropores responsible for the material's absorbency.  
The equilibrium swelling ratio of the CMC/PAMBA PPG shows less sensitivity to salinity and pH value, while higher temperatures result in faster swelling.  
Core flooding experiments demonstrate the effective plugging efficiency of the CMC/PAMBA PPG in open fractured models, with a minor impact of gel injection flow rate on stable pressure.  
Figure 19: CMC/PAMBA PPG after post-water flood; smaller particles (**A**) showed better packing than larger particles (**B**).  
Small particle sizes of CMC/PAMBA PPG exhibited better packing and less dehydration, resulting in stable plugging.  
Future developments suggested for this study include improving the plugging efficiency performance at higher post-water injection rates by adding a self-healing property to the carboxymethyl cellulose-grafted crosslinker polyacrylamide PPG, as this property will help in forming a strong, coherent bulk gel within the fracture to resist higher pressure gradients.
# Hammering Test for Tile Wall Using Deep Learning
## 3 Non-Destructive Test for Adhesive-Applied Tile Wall
### Test Specimen
#### 3.3.2 Recording Sound  
Sounds were captured by rubbing the surface of the tile with a hitting rod equipped with a sphere that had a diameter of 2 cm, as illustrated in Figure 4. To ensure consistency, a smartphone was positioned at a fixed distance from the tile during the recording sessions. In total, 12 recordings were made for each specimen, with each recording lasting approximately 30 s. Following the recordings, the audio files were extracted from the corresponding videos and converted to a mono format for ease of analysis. Additionally, any silent sections within the recordings were meticulously removed to enhance the quality of the sound data.  
The accurate assessment of the bonding condition for each individual tile is vital when inspecting adhesive-laminated tile walls. Therefore, it was necessary to isolate the sounds associated with each tile from the continuous friction noises that occurred during the inspection process. This isolation was achieved through effective segmentation techniques, which proved to be a fundamental aspect of the study. By segmenting the sounds, researchers were able to focus on the specific acoustic signatures of each tile, allowing for a more detailed and precise evaluation of their bonding conditions. The audio data were segmented using Python, adhering to the outlined steps detailed below. This process involved several methodical stages to ensure accurate division and analysis of the audio files. Each step was carefully executed to maintain the integrity of the original data while facilitating further examination and processing. Below are the steps we followed in detail.  
Load the audio data using the librosa library [23]  
Analyze the audio waveform to define the segment length \(L\) and the overlap rate \(O\)  
Proceed with the segmentation. Each segment \(Si\), where \(i\) represents the \(i\)-th segment, is generated according to the formula below. Here, \"audio\" refers to the audio data, and \"\(n\)\" is the starting sample index for the segmentation.  
\[S_{i}=\text{audio}[n:n+L],\quad n=i\times(L-L\times O)\]  
Each sound sample is stored as a separate file to facilitate analysis by artificial intelligence systems. This approach allows for better organization and processing of the audio data, enabling AI to perform more effective evaluation and learning.  
Figure 11: Procedure of experiment.  
Table 1 presents the number of sampled sounds for each adhesion rate obtained using the segmentation method described above.
## 5 Results
### Classification Algorithms for DUI
#### 5.3.2 Random Forest  
Table 3 presents the results of the random forest model. Similar to logistic regression, accuracy improves when multiple sensor sources are fused. However, as with the previous model, the training accuracy exceeds the test accuracy. In most scenarios, the random forest model achieves 100% accuracy on the training data, indicating significant overfitting and noticeably poorer performance on the test data. Despite this, the test accuracy remains comparable to that of logistic regression. Due to the inherent randomness of the random forest algorithm, the results are not fully reproducible, with slight variations occurring in each run. Nevertheless, these variations are minimal, and the overall accuracy remains consistent with the values shown in Table 3.  
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Data** & **Scenario** & **Accuracy\_Train** & **Accuracy\_Test** & **F1\_Value** & **Precision** & **Recall** \\ \hline Vehicle & City & 67.38 & 56.67 & 55.17 & 57.14 & 53.33 \\ \hline Camera & City & 70.83 & 58.33 & 56.14 & 59.26 & 53.33 \\ \hline Fusion\_Data & City & 83.21 & 56.67 & 56.67 & 56.67 & 56.67 \\ \hline Fusion\_Interpretation & City & 78.81 & 60.00 & 60.00 & 60.00 & 60.00 \\ \hline Vehicle & Rural & 66.79 & 55.00 & 55.74 & 54.84 & 56.67 \\ \hline Camera & Rural & 68.69 & 55.00 & 54.24 & 55.17 & 53.33 \\ \hline Fusion\_Data & Rural & 75.00 & 51.67 & 47.27 & 52.00 & 43.33 \\ \hline Fusion\_Interpretation & Rural & 70.36 & 50.00 & 50.00 & 50.00 & 50.00 \\ \hline Vehicle & Highway & 65.71 & 56.67 & 53.57 & 57.69 & 50.00 \\ \hline Camera & Highway & 62.38 & 51.67 & 50.85 & 51.72 & 50.00 \\ \hline Fusion\_Data & Highway & 82.86 & 61.67 & 62.30 & 61.29 & 63.33 \\ \hline Fusion\_Interpretation & Highway & 74.40 & 58.33 & 60.32 & 57.58 & 63.33 \\ \hline Vehicle & Complete Track & 69.40 & 60.00 & 60.00 & 60.00 & 60.00 \\ \hline Camera & Complete Track & 78.69 & 68.33 & 68.85 & 67.74 & 70.00 \\ \hline Fusion\_Data & Complete Track & 96.79 & 65.00 & 63.16 & 66.67 & 60.00 \\ \hline Fusion\_Interpretation & Complete Track & 82.98 & 66.67 & 66.67 & 66.67 & 66.67 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model performance of logistic regression.  
Figure 5: Confusion matrix of logistic regression: complete track with camera data.  
In the best-case scenario on the country road, the random forest model achieves an accuracy of over 70% (see Figure 6). It also attains precision and recall values of 75% and 70%, respectively. These results indicate both a high true positive rate and a strong true negative rate, reflecting the model's ability to accurately classify both drunk and sober drivers.
## 5 Discussion
### Interpretation of the Findings Associated with the Store-and-Forward Dataset
Consequently, this research posits that its integration into the realm of network security and intrusion detection would be a beneficial contribution.
## 2 The Features of FM-OFDM Communications Scheme
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & \\ \hline \(\gamma\) & 2 & 1.5 & 1.3 & 1.1 & 0.9 & 0.7 & 0.5 & 0.3 & 0.1 & 0.05 & 0.02 & 0.01 & \(5\times 10^{-3}\) & \(10^{-3}\) & \(10^{-4}\) \\ BER & 0.1768 & 0.1697 & 0.1626 & 0.1594 & 0.1476 & 0.1239 & 0.1 & 0.096 & 0.1342 & 0.1436 & 0.146 & 0.1515 & 0.15 & 0.1531 & 0.1539 \\ \hline \multicolumn{11}{c}{\(H(\omega)=1+0.9^{-}e^{-\
u\times 11.25\times 10^{-3}}\)} \\ \hline \(\gamma\) & 2 & 1.5 & 1.3 & 1.1 & 0.9 & 0.7 & 0.5 & 0.3 & 0.1 & 0.05 & 0.02 & 0.01 & \(5\times 10^{-3}\) & \(10^{-3}\) & \(10^{-4}\) \\ BER & 0.2147 & 0.2123 & 0.2013 & 0.1823 & 0.1563 & 0.1279 & 0.089 & 0.101 & 0.1949 & 0.1965 & 0.1965 & 0.1965 & 0.1965 & 0.1965 \\ \hline \end{tabular}
\end{table} 
Table 1: BER values for FM-OFDM-QPSK depending on the saturation parameter \(\gamma\).
# Fuzzy Based Prediction Model for Air Quality Monitoring for Kampala City in East Africa
## 2 Materials and Methods
### Defining the Input Variables and Fuzzyfication of the Values
#### 2.2.1 Selection of Membership Functions  
A membership function (MF) is a function that specifies the degree to which a given input belongs to a set [25]. The output of a membership function is also known as the degree of the membership function, where its value is always limited to between 0 and 1. Membership functions are used in the fuzzification and defuzzification processes to map the non-fuzzy input values to fuzzy linguistic terms and vice versa.  
The Mamdani Fuzzy Logic Toolbox has many inbuilt membership functions.  
In this paper, the triangular membership function, known as (trimf), is applied in the design of the proposed fuzzy based prediction model. The triangular membership function is computationally efficient and is used to normalize crisp inputs.  
The Mamdani Fuzzy Inference System is suitable for designing AQI prediction models, as both the inputs and outputs of the Fuzzy Inference Systems are represented by the values of linguistic variables [26]. In order to transform crisp input values into fuzzy values, the membership function for each input is determined.  
The corresponding fuzzy membership values in this paper are defined as follows:  
The intensity of nitrogen dioxide (\(\Delta\)NO\({}_{2}\)) = Low, Medium and High  
The intensity of sulphur dioxide (\(\Delta\)SO\({}_{2}\)) = Low, Medium and High  
The intensity of particulate matter 2.5 (\(\Delta\)PM\({}_{2.5}\)) = Low, Medium and High  
The KAQI is defined and estimated as the output by the following membership values: good, moderate, sensitive, unhealthy, very unhealthy, hazardous.
## 2 Mathematical Model of Aerostatic Bearing  
### Structure of Aerostatic Bearing  
The structure of the aerostatic bearing is shown in Figure 1. Aerostatic bearing adopts double row orifice structure; the key parameters of the structure are bearing length \(L\), journal diameter \(D\), distance from the center of the orifice to the outlet \(l\), orifice diameter \(d\), gas film thickness \(h\), eccentricity \(e\), gas supply pressure \(P_{s}\), and atmospheric pressure \(P_{a}\). When the journal is not eccentric, the gas film thickness is consistent at any circumferential position, which is recorded as the average gas film thickness \(h_{\text{zw}}\).
## 3 Results and Discussions
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & \(\leq\)**0.02 m** & \(\leq\)**0.05 m** & \(\leq\)**0.10 m** & \(\leq\)**0.15 m** & \(\leq\)**0.20 m** & \(\leq\)**0.50 m** \\ \hline KNGeoid13 & 352 (29.8\%) & 764 (64.6\%) & 1047 (88.6\%) & 1107 (93.7\%) & 1128 (95.4\%) & 1174 (99.3\%) \\ KNGeoid14 & 471 (39.8\%) & 899 (76.1\%) & 1079 (91.3\%) & 1119 (94.7\%) & 1133 (95.9\%) & 1173 (99.2\%) \\ KNGeoid18 & 549 (46.4\%) & 950 (80.4\%) & 1108 (93.7\%) & 1131 (95.7\%) & 1141 (96.5\%) & 1173 (99.2\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Percentage of the 1182 UCPs whose absolute values of differences between the geoid heights derived from the GNSS/leveling and those derived from the three KNGeoid models. Figure 6: Residual surface maps between the GNSS/leveling-derived geoid heights (\(N_{GNSS/leveling}\)) on the 1182 UCPs and those (\(N_{KNGeoid}\)) derived from the three KNGeoid models. (**a**) KNGeoid13; (**b**) KNGeoid14; and (**c**) KNGeoid18. The red dots represent the locations of the 1182 GNSS/leveling UCPs.
## 5 Experiment Results and Analysis  
To reduce the influence of noise, we employed wavelength modulation photoacoustic spectroscopy in the experiments. The modulation signal to the laser, which consists of a high-frequency modulated sine wave and a low-frequency sawtooth wave signal, was generated by the data acquisition card (USB-6211, National Instrument, Austin, TX, USA). The sawtooth wave signal with a frequency of 0.1 Hz was used to slowly change the laser wavelength to cover the absorption line of methane. The frequency of the high-frequency modulated sine wave was determined by the resonant frequency of the resonator. To obtain the second-harmonic signal, the resonant frequency of the resonator was set as 2280 Hz. Meanwhile, the sine wave was also sent to the phase-locked amplifier (LIA-BVD-150-L, Femto, Berlin, Germany) as a reference signal. The sound signal was collected by the microphone and demodulated through the phase-locked amplifier, and then the demodulated second-harmonic signal was sent to the computer through the data acquisition card.
## 6 Open Challenges, Future Prospects and Research Directions
### Adaptive Aperture Configurations  
Innovations in AO and dynamic aperture configurations also hold significant potential for real-time performance enhancement. AO systems, which use DMs and other technologies to adjust the optical path in real-time, can be integrated with aperture averaging to dynamically counteract turbulence effects. This real-time adjustment capability allows the FSO system to maintain high performance under varying environmental conditions. Thus, dynamic aperture configurations, where the aperture size, shape and/or arrangement can be altered on-the-fly, can also adapt to changing turbulence conditions. This adaptability will help maintain signal integrity and reduce errors in data transmission.
# A Survey of of Side-Channel Attacks and Mitigation for Processor Interconnects
## 4 Interconnect Contention Side Channel
### CPU Cache Architecture  
Modern CPU caches feature a multi-level structure. Taking the most common Intel x86 microarchitecture cache architecture as an example, CPU caches are divided into L1, L2 (present in most microarchitectures), and L3 (often referred to as the last-level cache or LLC). L1 and L2 caches are typically referred to as private caches, integrated with each CPU core on the processor, thus providing very fast access (4 to 12 cycles) but with smaller capacities, primarily used to store local data and instructions. In contrast, the LLC, compared to private caches, is slower (40 to 60 cycles) but has a larger capacity and is shared among the cores. Starting from the Nehalem-EX [57] architecture, the LLC has been divided into equally sized LLC slices, where each LLC slice is typically integrated with a core set from an interconnect perspective.
# A Novel STDM Watermarking Using Visual Saliency-Based JND Model
## 5 Experimental Results and Analysis  
To evaluate the performance of our proposed scheme, we used standard images with dimensions of \(256\times 256\) from the USC-SIPI image database [33]. A random binary message of length 1024 bits was embedded into each image. Specifically, we selected the four to ten DCT coefficients (in zig-zag-scanned order after the eight by eight block DCT on each image) to form the host vector and embedded one bit in it. The bit error rate (BER) was computed for comparison purposes.  
The experiments were conducted to compare the performance of the proposed scheme and other proposed STDM improvements, termed as STDM-RW [7], STDM-AdpWM [12], STDM-RDMWm [13], and LSTDM-WM [34]. Meanwhile, three kinds of attacks (Gaussian noise with mean zero variance ranging from 0 to 15; JPEG compression, where the JPEG quality factor varies from 20 to 100; and volumetric scaling attacks that can reduce the image intensities as scaling factor varies from 0.1 to 1.5) were used to verify the performance of the proposed models.
###### Abstract  
The remote sensing of solar-induced chlorophyll fluorescence (SIF) has attracted considerable attention as a new monitor of vegetation photosynthesis. Previous studies have revealed the close correlation between SIF and terrestrial gross primary productivity (GPP), and have used SIF to estimate vegetation GPP. This study investigated the relationship between the Orbiting Carbon Observatory-2 (OCO-2) SIF products at two retrieval bands (SIF757, SIF771) and the autumn crop production in China during the summer of 2015 on different timescales. Subsequently, we evaluated the performance to estimate the autumn crop production of 2016 by using the optimal model developed in 2015. In addition, the OCO-2 SIF was compared with the moderate resolution imaging spectroradiometer (MODIS) vegetation indices (VIs) (normalized difference vegetation index, NDVI; enhanced vegetation index, EVI) for predicting the crop production. All the remotely sensed products exhibited the strongest correlation with autumn crop production in July. The OCO-2 SIF757 estimated autumn crop production best (R\({}^{2}\) = 0.678, \(p\) < 0.01; RMSE = 748.901 ten kilotons; MAE = 567.629 ten kilotons). SIF monitored the crop dynamics better than VIs, although the performances of VIs were similar to SIF. The estimation accuracy was limited by the spatial resolution and discreteness of the OCO-2 SIF products. Our findings demonstrate that SIF is a feasible approach for the crop production estimation and is not inferior to VIs, and suggest that accurate autumn crop production forecasts while using the SIF-based model can be obtained one to two months before the harvest. Furthermore, the proposed method can be widely applied with the development of satellite-based SIF observation technology.  
solar-induced chlorophyll fluorescence; OCO-2; EVI; NDVI; crop production 15 May 2019; Accepted: 17 July 2019; Published: 19 July 2019
# Research on Thermal and Heat Insulation Properties of Aerogel Heat-Insulating Reflective Coatings
## 3 Data Analysis
### Temperature of the Measuring Point for Working Condition 2  
The temperature of the test specimen was lower than the temperature of the test specimen in Condition 1, and the temperature of the test specimen was lower than that of the test specimen in Condition 2. By comparing Figure 5a-c, we can see that during the heating stabilization stage, the surface temperature of the XPS board was 36 \({}^{\circ}\)C at the heat source side of the test piece, which was much higher than the surface temperature of the other two test pieces. This suggests that the XPS board's surface absorbs more heat. The surface temperature of the hot end was the lowest as the aerogel coatings reflected most of the heat. Figure 5d shows the air temperature curves in the cold box for the three specimens and shows that the fastest temperature rise was for the blank board, followed by the aerogel board, and the slowest was for the XPS board. The temperatures of the XPS board and the aerogel board were 24 \({}^{\circ}\)C and 24.2 \({}^{\circ}\)C, respectively, during the stabilization phase. From the heat insulation point of view, 1 mm aerogel coatings can achieve the same effect as a 2 cm XPS insulation board. The slopes of the temperature curves of the three specimens with a temperature of 22.5 \({}^{\circ}\)C in the cooling stage were taken for comparison due to the different initial cooling temperatures. When the temperature dropped to 25 \({}^{\circ}\)C,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Materials**} & **Thickness** & \multicolumn{2}{c}{**Thermal**} & \multirow{2}{*}{**Manufacturers**} \\  & **mm** & **Conductivity** & & \\  & & **W/(m\(\cdot\)K)** & & \\ \hline \hline \multirow{2}{*}{Plasterboard} & \multirow{2}{*}{9.7} & \multirow{2}{*}{0.120} & Hefei Youtai Building Materials Co. \\ \cline{1-1} \cline{4-4} \multirow{2}{*}{Aerrogel coatings} & \multirow{2}{*}{1.1} & \multirow{2}{*}{0.066} & Hefei, China \\  & & & Laboratory homemade \\ \cline{1-1} \cline{4-4} \multirow{2}{*}{XPS insulation panels} & \multirow{2}{*}{19} & \multirow{2}{*}{0.037} & Nanjing Ogle Energy Saving and \\  & & & Environmental Protection Technology \\ \cline{1-1} \cline{4-4} \multirow{2}{*}{White coating} & \multirow{2}{*}{1} & \multirow{2}{*}{0.117} & Anhui Xin Peng New Material Co. \\ \cline{1-1} \cline{4-4}  & & & Anqing, China \\ \hline \hline \end{tabular}
\end{table}
Table 1: Table of test piece material parameters.  
the slopes of the curves were \(-0.065\), \(-0.053\), and \(-0.034\) for the blank, aerogel, and XPS board, respectively. This shows that the XPS board has better thermal insulation properties than the aerogel board.
## 3 Results and Discussion
### Estimation of Photodegradation Performance
Scavengers were added to the methylene blue degradation solution to detect the reactive oxygen species (ROS) produced during the photocatalytic activity. For species such as h\({}^{+}\), O\({}^{2}\)\({}^{\bullet-}\), and \({}^{\bullet}\)OH radicals, we used triethanolamine (TEOA), benzoquinone (BQ), and isopropyl alcohol (IPA) [53, 54]. As shown in Figure 7a, the MB dye degradation clearly decreased the percentage of degradation efficiency with the addition of TEOA (68.5%), IPA (38.2%) and BQ (50.3%) scavengers. The photocatalytic reactions are crucial to the degradation rates of MB, and the primary active species is \({}^{\bullet}\)OH radicals. This is in accordance with the suggested mechanism for the photocatalytic degradation of DWZG shown in Figure 8. In the diagram,  
Figure 6: Photodegradation of MB solution under different conditions (**a**) different catalysts (catalysts: 30 mg, MB conc. : 1 \(\times\) 10\({}^{-5}\) M), (**b**) absorption spectrum of degradation of MB dye, (**c**) different dosage of DWZG (MB conc. : 1 \(\times\) 10\({}^{-5}\)) and (**d**) different concentration of MB (DWZG: 30 mg). the positive holes leave the valence band (VB), and the electrons go into the conduction band (CB) after being activated by the light of ZnO/GO. Electron-hole recombination occurs when electrons from Dy\({}_{2}\)WO\({}_{6}\) in CB react with oxygen in the water, creating superoxide radical anion (O2*-) [42,55]. The hydroxyl radical (*OH) is produced in VB when holes combine with water molecules. Nevertheless, the coupled h+ were making it harder for the water molecule to produce *OH radicals in an indirect manner [55]. In conclusion, the MB photodegradation was caused by the reactive species *OH and O2*- radicals. The residual colloidal catalyst was filtered, dried and reused for the degradation of MB dye after the first run was completed. Under six iterations of photodegradation, the collective catalyst was continuously engaged in the reaction. Figure 7b shows that the MB dye can be recycled. In the reusability experiment, the effectiveness of degradation somewhat decreases as the number of counts per run increases (98.2% to 87.91%). Nonetheless, the effectiveness of MB degradation by DWZG plateaued after the sixth cycle. The data indicates that the catalyst is mechanically stable [56]. During the photocatalytic process, the GO content of Dy\({}_{2}\)WO\({}_{6}\)-ZnO/GO photocatalysts can undergo a chemical transformation, changing from an oxygenated to a reduced state [57, 58, 59, 60].