esa-sceva/satcom-chunk-collection huggingface dataset:
# FTGM: Fast Transformer-Based Global Matching for Particle Image Velocimetry
## 1 Introduction
This development has spurred the emergence of numerous improved PIV estimation methods based on this architecture [25, 26, 27], collectively termed RAFT-PIV. RAFT continues to be recognized as the most advanced deep optical flow architecture for PIV [28]. The application of deep optical flow models in PIV has subsequently expanded into a broader range of domains [29, 30, 31, 32, 33, 34, 35, 36]. Nonetheless, these methods involve extensive iterative refinement processes, which result in a linear increase in inference time with the number of iterations. There remains considerable potential for enhancing computational efficiency and precision. In this paper, we propose a Fast Transformer-based Global Matching (FTGM) method for PIV estimation, which redefines the optical flow estimation as a global matching problem (Figure 1). FTGM provides a fresh perspective on PIV estimation. In summary, our major contributions can be summarized as follows:  
1. We apply the global matching approach to PIV estimation, thereby refining the learning-based flow regression methods. This approach allows for a simpler PIV estimation pipeline while achieving higher precision in displacement estimation and greater computational efficiency. 2. Given the intrinsic differences between PIV datasets and optical flow datasets, we have tailored a Transformer incorporating self-attention, cross-attention, and a feed-forward network specifically for enhancing the features of PIV images, thereby obtaining high-quality discriminative features essential for accurate matching. 3. We further introduce a refinement step that utilizes higher-resolution features to improve estimation accuracy. This process can reuse the FTGM framework for residual flow estimation, thereby reducing network complexity and parameters.
# An Artificial Neural Network Autoencoder for Insider Cyber Security Threat Detection
## 1 Introduction  
Possible attacks that are characterized as cyber security threats can impact a particular entity of an organization or a whole organization and can be categorized as external or insider attacks. External attacks propagate from an outside perimeter of the organization network and can occur in different forms, such as distributed denial of service (DDOS), phishing, etc. Otherwise, insider attacks originate from the inside of the organization's network, which means that the attacker(s) have already gained some level of access to the network.  
The frequent occurrence of cyberattacks and threats points to the importance of maintaining and enhancing an organization's cyber security. To defend against external attacks such as distributed denial of service (DDoS), session hijacking, etc., organizations generally utilize several security tools, including firewalls, intrusion detection systems (IDS), intrusion prevention systems (IPS), and security gateways.  
However, cyber security attacks originating from the organization, such as insider attacks, are especially challenging for detection, since threat detection of such attacks requires more sophisticated and complex threat detection processes. A report issued by the International Business Machines (IBM) company indicates that the average time it takes to detect a software vulnerability discovered by attackers before the vendor has become aware of it (zero-day attack) is 287 days [1]. Identifying a threat at the early stages will limit the propagation of a threat in an organization, since the longer the threat exists, the more damage it causes by propagating through the network and compromising more devices. This indicates the severity and extent of the damage that can be caused by insider security attacks. Insider cyber security attacks can be classified into three types, which include insider threat attacks, security information and event management (SIEM) attacks, and user behavior or user and entity behavior analysis (UBA/UEBA) attacks.
## 4 Conclusions  
In this paper, eight kinds of quartz sands with different grain sizes were used to measure relaxation time and permeability using the field time-domain induced polarization experiment system and the Darcy flow experiment system. According to the experimental data, the following conclusions can be derived:  
1. Relaxation time and permeability increase with an increase in quartz sand grain size, and the increasing trend gradually accelerates, which has a significant positive correlation.
2. By using the grain size of a water-bearing sand sample as the intermediate quantity, the relationship curve between permeability and relaxation time within the same grain size can be formed through a mathematical fitting. Furthermore, the power function equation describing the correlation between relaxation time and permeability is obtained.
3. With regard to the relationship between permeability and relaxation time, the data in Figure 8 provide theoretical support for predicting the behavior of water bodies via the tunnel-induced polarization method, and it is even possible to improve the accuracy of predictions.  
In this paper, the relationship was obtained from the quartz sands experiment. However, these methods may not be applicable in all geological conditions, and the coefficients may vary in different experimental conditions.  
Conceptualization, Z.M. and L.N.; methodology, Z.M.; validation, L.N. and N.L.; formal analysis, X.Y. and Z.D.; investigation, J.S.; resources, X.X. and K.W.; data curation, X.X.; writing--original draft preparation, Z.M.; writing--review and editing, Z.D.; visualization, Z.M.; supervision, L.N. and Z.M.; project administration, L.N.; funding acquisition, X.X. and K.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science & Technology Program of Department of Transport of Shandong Province, grant number 2019B47_2.  
Figure 8: Relationship between relaxation time and permeability.
## 3 IoT Applications
### Smart City  
A smart city is a technologically advanced metropolitan region that collects information using different electronic techniques, voice recognition technologies, and sensors. The data is utilized to successfully handle assets, services, and programs; in turn, the information is being used to function smoothly throughout the city. Data obtained from community members, equipment, structures, and assets are processed and analyzed to track and maintain road and transport infrastructures, energy plants, utilities, water system connectivity, waste management, preventing crime, data management, education institutions, libraries, healthcare facilities, and other community programs. A smart city is a collection of different sensors and equipment to monitor, report, and process to manage the resources of the infrastructure effectively. Using the information gathered from the wireless sensors, the system will learn and make decisions to provide beneficial outcomes to the people. Compared to the healthcare, water supplies, and environment surveillance in the current urban areas, a smart city will be able to connect the citizens and the required services in a better way [45,46]. It is necessary to protect both the residents' privacy and the information system's integrity. Sensitive information is being gathered by sensing devices, which makes it vulnerable to cyberattacks.
# Fabrication of Partially Etched Polystyrene Nanoparticles
## 3 Results
### Partially Ectbed PS NPs  
Chemical etching was used to produce partially etched particles, as illustrated in Figures 1b and 3a. During the growth process of PS particles, the addition of DVB at a designated time \(t_{DVB}\) led to the formation of crosslinked PS-co-DVB copolymer chains mainly from the outer layer of the particles. Thus, the crosslinking reaction primarily occurred in the outer layer, leaving the core region of the particles relatively non-crosslinked. In addition, we expected that the delayed DVB addition partially formed weakly crosslinked regions even in the outer layer. When these partially crosslinked PS NPs were dispersed in an organic solvent that could dissolve PS linear chains, only the non- or weakly crosslinked portions of the particles were locally dissolved, resulting in a partially etched morphology.  
The particle shape was adjusted by tuning \(t_{DVB}\), as shown in Figure 3b,c. When DVB was added immediately after the initiator addition (\(t_{DVB}=0\) min), the particle shape remained intact after dispersing them in toluene, demonstrating that these PS NPs were completely crosslinked to form PS-co-DVB copolymer. When \(t_{DVB}\) increased to 35 min and 70 min, the resulting particles' surface was partially etched after solvent treatment. As \(t_{DVB}\) further increased to 140 min, the non- or weakly crosslinked portions that could be chemically etched increased, resulting in a more significant degree of structural roughness. When DVB was added after the completion of polymerization (i.e., \(t_{DVB}=300\) min), the crosslinked regions of the PS NPs were minimally formed, resulting in unstable particles in toluene that formed aggregated clumps.  
Figure 2: SEM images of PS NPs. Different surfactants were added: SDS (**a**,**b**) and CTAB (**c**,**d**). The top and bottom rows indicate non-crosslinked PS NPs and partially crosslinked PS NPs at \(t_{DVD}=70\) min, respectively.  
Figure 3: Formation of partially etched PS NPs. **(a)** Schematic for illustrating the prospective mechanism. (**b**,**c**) SEM images showing morphological changes before (**b**) and after (**c**) chemical etching with toluene depending on \(t_{DVB}\) (SDS was used). Insets in panel c indicate the corresponding magnified images.  
The morphological changes in partially crosslinked PS NPs (\(t_{DVB}=70\) min) were further evaluated by treating them with different organic solvents. The study used SDS and CTAB surfactants in the polymerization reaction. When examining the PS NPs polymerized in the presence of SDS (Figure 4a-c), the use of THF and chloroform produced a similar morphology compared to toluene. In contrast, the PS NPs polymerized with CTAB were more vulnerable to solvent treatment (Figure 4d-f). When the particles were dispersed in THF, chloroform, or toluene, they formed aggregates and fused together, indicating that an inefficient crosslinking reaction occurred for \(t_{DVB}=70\) min.
## 5 Numerical Examples
### The 15-Bar Planar Truss
The minimum and maximum stresses lie within the feasible range of [-25, 25] ksi. As seen in Figure 5, the stress values in almost all members closely approach the tensile or compressive strength limits. Figure 6 presents the initial and optimized geometries, where the initial configuration is shown with dashed grey lines and the solid, colored lines represent the optimized layout. For enhanced clarity, the member between nodes 4 and 8 has been zoomed in on and included as an additional feature in the graph. After optimization using NCSBO, joint 4, which was initially positioned at (360.0, 120.0), shifts to (360.0, 51.0915), while joint 8, which was initially at (360.0, 0.0), relocates to (360.0, 51.2169). In the optimized geometry, member 9 experiences a substantial reduction in length, contributing directly to the overall weight reduction of the structure. Additionally, Figure 6 presents a color map illustrating the status of element stresses relative to their allowable stress limits. Figure 5: Comparison of member stresses with allowable limits at the optimized design of the 15-bar truss by (**a**) CSBO and (**b**) NCSBO. Figure 7 illustrates the variation in weight values with respect to the number of analyses conducted using both the NCSBO and standard CSBO methods. A comparison of the convergence behavior of the two methods reveals that NCSBO exhibits a more rapid decrease in the initial exploration phase compared to CSBO.
## 4 Conclusions  
This study confirmed the characteristic absorption peaks of CTBN and bitumen by using FTIR spectroscopy. The I\({}_{\rm{CTBN}}\) was calculated semi-quantitatively and one-way ANOVA was performed to investigate the reliability of I\({}_{\rm{CTBN}}\). By establishing the standard curve between I\({}_{\rm{CTBN}}\) and the CTBN content, the blending degree of virgin and RAP binders at different locations of RAM were determined quantitatively. Based on the experimental results and analysis, the following findings were obtained:  
The CTBN content has a significant effect on I\({}_{\rm{CTBN}}\). Moreover, a linear relationship exists between the CTBN content and I\({}_{\rm{CTBN}}\) which could be used to quantitatively investigate the CTBN contents at different locations of RAP in RAM and to explore the blending degree of virgin and RAP binders in RAM.  
It was shown that virgin and RAP binders blend within the different layers of RAP in RAM with high RAP contents, but the blending degree is not uniform; rather, the blending degree decreases with an increase in the RAP film depth. Furthermore, the virgin bitumen percentage in the outer RAP layer mostly reaches 90%, which was higher than that in the case of complete blending (55.88%), and the RAP binder completely blends with the virgin bitumen. Meanwhile, the virgin bitumen percentages in the inner RAP layer are much lower than 55.88%, while the blending degree of virgin and RAP binders in the inner layer is less than half of that in the case of complete blending.  
Conceptualization, Z.Y. and W.X.; Data curation, G.Z. and X.W.; Formal analysis, Z.Y.; Funding acquisition, W.X.; Investigation, Z.Y.; Methodology, Z.Y.; Project administration, Z.Y.; Resources, W.X.; Software, G.Z. and J.W.; Supervision, W.X.; Validation, W.X.; Visualization, X.W. and J.W.; Writing--original draft, Z.Y.; Writing - review & editing, H.Y. and W.X.  
This research received no external funding.  
The authors express their sincere gratitude to [PERSON] and [PERSON] from South China University of Technology for their technical support and assistance in experimental activities and data analysis.  
The authors declare no conflicts of interest.
## 4 Numerical Modeling of Da'anshan Landslide
### Numerical Simulation Results and Analysis
#### 4.3.1 Flow Height Characters of Simulation (I)  
The distributions of the simulated flow heights at different times are presented in Figure 9 (The blue dashed line represents the extent of the avalanche impact area; the green dashed lines represent the profile line used for flow height extraction in Figure 4a; and C1 and C2 mark two locally convex terrains). At 2.0 s (Figure 9a), the detached rock mass slides down a very short distance, and the sliding body almost maintains its original shape. The front of the landslide has slid onto the pavement. At 4.0 s (Figure 9b), the landslide continued to slide downhill. The coverage area of the landslide did not clearly change, and the front of the landslide slid across the pavement and moved a very small distance in the downhill direction. The position of the landslide front is consistent with the image extracted from the video (Figure 8f). After 4.0 s, the dynamics of the landslide are controlled by the depth-averaged model introduced in Section 2.2. At 6.0 s (Figure 9c), the impacted area of the avalanche body spreads out significantly. By comparing the magnitude of the velocity represented by the arrow line length in Figure 9, it can be found that the speed at this time was significantly greater than that before 4 s. This reflects the phenomenon of rapid expansion of the landslide after disintegration (Figure 8g,h). At 8 s (Figure 9d), the landslide continues to spread downhill at a high speed. At 12 s (Figure 9e), the landslide bypasses the locally convex terrain and slides downhill. The front of the landslide reaches the foot of the slope. At 20 s (Figure 9f), almost all unstable soils have runout from the source area, and a pile of soil with a length of about 66 m has remained on the pavement.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Symbol** & **Parameter** & **Unit** & **Value** \\ \hline \(\rho\) & Bulk density & Kg/m\({}^{3}\) & 2500 \\ \(\varphi\) & Internal friction angle & Degree & 38 \\ \(\delta\) & Basal friction angle & Degree & 30 \\ \(c\) & Cohesion stress & KPa & 10 \\ \(\xi\) & Turbulent coefficient & m/s\({}^{2}\) & 600 \\ \(CE\) & Entrainment coefficient & - & 10–6.2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main model parameters used in the simulations.  
The impacted area in the flat area at the slope foot is close to the actual final accumulation range (marked by the blue dashed lines in Figure 9). The global maximum avalanching velocity obtained from numerical simulation (I) is about 24.1 m/s. The peak velocity of the landslide determined via the analysis of on-site video is 26.1 m/s, which is slightly higher than the numerical simulation results.
## 3 Results and Discussion
### pH Value  
The results show that the packaging and storage condition had a highly significant (\(p<0.001\)) influence on the pH values of the tomatoes. Unpackaged (control) tomatoes had the highest increase in pH value from 4.20 to 4.60 under ambient storage, while the EPS Tray + PVC wrap under cold storage had the lowest increase in pH value from 4.20 to 4.44. Similar results were reported by [28] where packaged tomato fruits had the lowest pH values, compared to unpackaged tomato fruits. According to [23] the lower pH values in packaged tomatoes can be attributed to the relatively reduced respiration rate which results from the changes brought about by the packaging environment. The difference shows that respiration occurs much faster in unpackaged tomatoes than in packaged treatments. Generally, the normal tomato ripening process causes the pH to rise as the fruit converts acids to sugars [29]. The higher pH values of tomatoes under ambient storage conditions could be attributed to the faster conversion rate of acids to sugar, due to the faster respiration rate experienced by tomatoes during ripening [29]. Ref. [18] reported that higher temperature conditions cause a faster respiration rate which results in a loss of acidity. The pH values show a significant increase as the storage time progressed, from 4.20 to 4.46 between Day 0 and Day 28, respectively. Similar observations have been reported by [30; 31] where the pH continually increased with the storage time. Ref. [32] explained that the pH value of tomatoes increases with the storage time due to ripening and respiration. The pH values ranged from 4.20 to 4.60 over the 28-day storage period, as presented in Table 3. Ref. [33] found a similar pH range, from 4.15 to 4.55 over eight days of storage.
# Investigation of Crosstalk between Data Signals and Feed Light in Power-over-Fiber
## 5 Conclusions  
We experimentally investigated and evaluated the crosstalk between data signals and feed light in PWoF using MMFs. We compared and evaluated the intensity noise spectra of RFLs with different laser powers and transmission distances. We showed the longitudinal mode beating and mode partition noise of the RFL are representative intensity noises, which strongly depend on the laser output power and fiber transmission distance. Furthermore, we experimentally observed that the intensity noise component was transferred to the data  
Figure 10: Power penalties to back-to-back signal of the data signal transmitted to the 2 km and 4 km MMF transmissions while varying laser output power characteristics of digital data signal transmitted into 4 km MMF for various laser output powers.  
Figure 9: BER characteristics of digital data signal transmitted into 4 km MMF for various laser output powers. Insets show eye-patterns of transmitted data signal when laser output powers were (**a**) 0 W and (**b**) 10 W, respectively.  
signal light by simultaneous transmission with the data signal light. We also quantitatively evaluated the signal degradation in terms of EVM and BER measurements and found that it was consistent with the dependence of these intensity noises on the laser output power and fiber transmission distance. The obtained results will be useful in the study of feed light sources and fibers required to suppress crosstalk when simultaneously transmitting data signals and feed light in PWoF using MMFs in the future.  
Conceptualization and methodology, M.M. and H.K.; investigation, A.I. and Y.K.; data curation, A.I. and Y.K.; writing--original draft preparation, M.M.; writing--review and editing, M.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Data and Methods
### Methods
#### Validation of Atmospheric Correction  
In this study, the DSF atmospheric correction method applied to Pleiades data recorded over the GE was validated by a step-by-step comparison with already-validated satellite products and field measurements. For that purpose, Pleiades products were compared to (i) spectral relationships obtained from previous field measurements (see Section 3.1), (ii) already-validated satellite products (Section 3.1) and (iii) simultaneous field measurements of turbidity (Section 3.2).  
Water reflectances from Pleiades were compared with same-day imagery from Landsat8-OLI and MODIS-Aqua/Terra recorded over a specific test site. Cloud-free satellite images were collected on 7 April 2017 within a \(\pm\) 3 h time window, and imagery from the different sensors was reprojected onto the same grid, by resampling Pleiades data to a 30 m and 250 m spatial resolutions, using mean averaging. The green, red, and NIR reflectances from the different sensors were spectrally adjusted to the Pleiades spectral bands. In situ hyperspectral water reflectance measurements made in the GE in 2012 and 2013 [42; 43] and the relative spectral responses (RSRs) of Pleiades-1B, OLI, MODIS-Aqua and MODIS-Terra sensors were used for this adjustment (Figure 3). The reflectance in the considered satellite spectral band was computed as  
\[\rho_{s}=\int_{\lambda 1}^{\lambda 2}\rho_{\lambda}\times RSR(\lambda)d\lambda/ \int_{\lambda 1}^{\lambda 2}RSR(\lambda)d\lambda \tag{3}\]  
where \(\rho_{s}\) is the band specific reflectance, \(\rho_{\lambda}\) is the hyperspectral water reflectance in the GE, \(\lambda_{1}\)-\(\lambda_{2}\) are the wavelengths defining the spectral range.  
Linear relationships were established between the water reflectance in OLI and MODIS bands and in the corresponding Pleiades band (Table 3).  
Pleiades-derived turbidity values in the \(2\times 5\) pixel box around the Pauillac autonomous station were compared to simultaneous in situ measurements.
# Design and Analysis on Decoupling Techniques for MIMO Wireless Systems in 5G Applications
## 5 Additional Effects of Decoupling Techniques
### Analysis of Internal Decoupling
Although the polarization diversity is sufficient for isolation enhancement, the surface current cannot be suppressed. Therefore, only selecting internal decoupling is not enough for MIMO systems. It can be seen that the solutions of internal decoupling are more complicated compared to external decoupling in order to achieve the preferred decoupling effect in the application. The implementation of the diversity function on the common antenna element is the innovation point of this technique, so its antenna module with abundant components. However, the MIMO systems using internal decoupling have excellent performance in terms of integrity and compactness, which shows it is possible to develop in the MIMO decoupling field.