esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Photosensitizers  
The clinical systemic use of methylene blue in humans has been examined for more than 100 years with outstanding safety records [12]. Indeed, in aPDT dental applications, it is considered the most studied photosensitizer presenting positive treatment outcomes [11]. It is both a cationic and hydrophilic compound, i.e., an amphipathic species, of a low molecular mass. In view of its positive charge, it can bind both to the lipopolysaccharide-saccharides present at the outer membrane of Gram-negative bacteria and to the teichuronic acid residues of the outer membrane of Gram-positive bacteria [8; 11]. Its maximal absorption band (\(\lambda_{\text{max.}}\) value) is located at 660-665 nm [6; 27].  
Toluidine blue has received clinical approval to be used as an adjunct in dentistry [12]. It is a low-molecular-mass cationic agent, with amphipathic characteristics in view of its hydrophilic portion. As documented above regarding its cationic charge, it can bind both to Gram-positive and Gram-negative bacteria [8; 11]. Its peak absorption band is centred at a wavelength of 635 nm [36].  
Regarding indocyanine green (ICG), its use for medical applications has been approved by the United States Food and Drug Administration (FDA) [9]. Its absorbance can be critically affected by the dissolving medium, its concentration and binding equilibria to plasma proteins [37; 38]. However, its self-organization through van der Waals forces and hydrophobic interactions can lead to the generation of aggregates, which may result in dramatic changes in its optical properties [39]. Its peak absorption spectral band is located at 780-810 nm [37; 39], and its respective fluorescence maximum is at 820 nm [39]. Furthermore, ICG presents a special characteristic concerning its mechanism of action, which is predominately based on photothermal (80%) rather than photochemical (20%) phenomena [40]. Specifically, at concentrations below 30 \(\upmu\)M (equivalent to 0.023 mg/mL), the treatment outcome will solely depend on \({}^{1}\)O\({}_{2}\) generation, while at higher values (>0.023 mg/mL), both photodynamic and photothermal effects will take place [41].  
As for curcumin, it has been shown to exhibit therapeutic properties, including anti-inflammatory, photosensitization, chemopreventative and chemotherapeutic effects. Notably, as a natural product, it represents a biologically safe substance even at high doses of up to 12 g/kg per day. It is activated by blue wavelengths, including a peak absorption within the 420-435 nm range [42; 43].  
A special reference should be made of the methylene blue derivative (Photolase) photosensitizer. For this agent, the manufacturer declares that a presumably chemical modification to the original authentic molecule can lead to a \"long-waved flank\", which results in optical activation at a wavelength of 810 nm [29]. This process may be attributed to the bathochromic shift of methylene blue, where the \(\lambda_{\text{max}}\) value shifts to longer wavelengths, since a decreased energy gap between the ground and excited states of the photosensitizer is observed [44].
## 3 Proposed Solution
### ONOS Intent-Leveraged Networking Control  
The intent framework of ONOS is a subsystem that allows applications to specify their networking control requirements based on policy-based directives, called intents [2]. Intent-based networking (IBN) is defined as a flexible, agile, and simplified network configuration with minimal external intervention. Similarly, ONOS core accepts and translates the intent specifications into installable intents, which are allowed to be added to ONOS dynamically at run-time. ONOS intent is an immutable model object that describes an application's request to control the network's behavior. These intents typically encompass:  
* Network resource: links and ports;
* Constraints: bandwidth, optical frequency, and link type;
* Criteria: packet header fields/patterns that are implemented as _TrafficSelector_;
* Instructions: header field modifications or specific output ports that are implemented as _TrafficTreatment_.  
We design an ONOS intent-leveraged networking control application for applying a recommended policy for a specific type of flow received from the flow-centric visibility through ONOS intent specification, as depicted in Figure 5. For a flow with forward policy, it keeps a related intent configuration with the proper device and ingress/egress port number where the flow is received/sent. A redirect policy is applied to point-to-point intent with a specific ingress port where small number of flows are coming from, and an egress port to send flows for further inspection by network IDS to detect attacks based on the signatures, or temporary storage for a short time period. Then, a blocking policy is applied to point-to-point intent with specialized instructions in the intent's TrafficTreatment as a drop action.  
Figure 4: Design of flow-centric visibility solution.  
A critical part of our design is an intent-leveraged application that needs to be implemented as part of the ONOS SDN controller. This application operates with the flow-centric visibility solution, interacting with it to receive queries and input. Specifically, it interfaces with flow-centric visibility to access information regarding specific source subnet addresses and their corresponding recommended policies. This interaction enables the intent-leveraged application to effectively translate these recommendations into actionable intents within the SDN controller environment. Through this seamless integration, the ONOS controller dynamically adjusts network configurations and policies based on the insights provided by the flow-centric visibility, thereby enhancing overall network management and control.
## 3 Materials and Methods
### Equations
#### 3.2.5 Total Disruption Time (TDT)  
When a given machine part fails or wears out, it must be replaced, thus activating BPE's spare part procurement process. Each stage of this process entails a series of tasks, each of which take specific time to be accomplished. Consequently, the affected production line is stopped until the damaged part is successfully replaced in the machine. Unfortunately, production disruptions can severely affect downstream activities. To calculate total disruption time, we propose Equation (5):  
\[TDT=\sum TSR+PRD+PR+OAD+OS_{SP}+LT+RT, \tag{5}\]  
where \(TSR\) refers to the review of the part's technical specifications, performed by material inspectors. If the spare part needed is on-site, the production line will be stopped only during the time it takes operators to replace the old part with the new part. On the other hand, if the spare part must be purchased, its technical specifications must be reported to the procurement department (\(PRD\)) by filling out a purchase request form. Also, in Equation (5), \(PR\) stands for the proposal request and comprises from the moment the company asks proposals to suppliers to the moment the department requesting the spare part (\(OAD\)) approves a proposal. Once the proposal is approved, the purchase order is sent to the supplier (\(OSSP\)), and lead time (\(LT\)) begins. Once the spare part arrives at BPE, repair time (\(RT\)) initiates. Finally, \(TDT\) is the sum of all the aforementioned time intervals.
## 2 Numerical Model Construction  
### Three-Dimensional Shape Recognition of Rockfill Materials  
The appearance, grading, densification, shape, and size of stacked stone particles control rockfill materials' strength and deformation characteristics. In this section, the 3D laser scanning data of the block stone particles of the rockfill material are used to analyze the particles' shape characteristics and establish 3D microscopic characterization and reconstruction methods for the particles.  
Blue light scanning technology, recognized for its advanced, fully automatic, and high-precision) three-dimensional scanning capabilities, is used for this purpose [30]. The SENSE-DS2+ (KANGKE 3D) (Guangzhou Concord Information Technology Co., Guangzhou, China) blue light scanner is equipped with a light emitting diode (LED) cold light source with a wavelength of 450 mm or less, strong anti-interference, high brightness, low power consumption, and fast response, with an accuracy of 0.05 mm. The scanned block shape is more ideal. The stacked stone specimen is placed on an automatic rotating table during scanning, and the surface point cloud data of the granular specimen are acquired by scanning, as illustrated in Figure 1a. The particle surface point cloud in Figure 1c comprises nearly one million data points directly used for discrete element modeling. This approach poses challenges in locating contact points between particles due to the high data density. To address this, the particle surface point cloud data are imported into the three-dimensional reverse engineering software Geomagic Wrap (2021) to reduce the data noise, creating a smooth triangular surface piece to obtain a smooth closed surface that approximates the real geometric boundary of the particle specimen (Figure 1d). Finally, the closed surface is imported into the discrete element software PFC3D (6.0) through a self-programmed program, as shown in Figure 1e. This comprehensive process enables detailed analysis and modeling of the shape characteristics of stacked stone particles to understand their mechanical properties further.
# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 3 Proposed Method
### Loss Function
Based on the partitioning scheme, the original model calculates the adjacency matrix \(A_{k}^{i,j}\) for the GCN used in prediction, as shown in Equation (10):  
\[A_{k}^{i,j}=A^{i,j}.Z^{j,k} \tag{10}\]  
By merging the region attributes based on the POI features, we obtain the adjacency matrix \(A_{k}^{i,j}\) that captures the similar patterns of city functionalities, taking into account the impact of complex urban functional structures on population flow. This adjacency matrix is derived using Equations (9) and (10), and it enables the model to achieve better performance by considering the influence of urban functional structures on population movement. Equation (11) illustrates how the original model uses the cross-entropy loss on labeled nodes as the loss function for semi-supervised learning:  
\[L_{s}=-\sum\limits_{v_{i}\in V_{L}}\frac{\ln\left(Z^{i,\phi_{i}}\right)}{\left| V_{L}^{\phi_{i}}\right|} \tag{11}\]  
where \(\phi_{i}\in\{1,\ldots,K\}\) represents the partition of node \(v_{i}\), \(V_{L}\) is the set of all labeled nodes, and the subset \(V_{L}^{\phi_{i}}\) contains all labeled nodes within partition \(\phi_{i}\). Since the sizes of each partition are not uniform, the term \(\frac{1}{2}\left|V_{L}^{\phi_{i}}\right|\) is used as a normalization factor. We combined the above two training tasks and defined a multi-task learning loss function as in Equation (12):  
\[L=L_{Huber}+\beta L_{s} \tag{12}\]  
where the Huber loss function, which calculates the model's error in predicting node flow, is represented by the first term \(L_{Huber}\). The cross-entropy loss function for semi-supervised node classification is the second term, \(L_{s}\), which the error of the model in predicting node flow categories. \(\beta\) is the weight that balances these two loss functions. The model is adjusted to produce reliable output results by taking advantage of the association between population flow patterns and urban area structure. The Huber loss function Is suitable for regression problems, while the cross-entropy loss function is commonly used for classification problems. In the task of predictingpopulation flow, the flow of nodes is typically influenced by various factors such as weather, time, and geographic location. These factors can result in complex variations in node flow, making it challenging to accurately capture them using a single loss function. By incorporating both the Huber loss and the cross-entropy loss, the model can take into account the diverse nature of node flow changes more comprehensively. This enables more accurate prediction of population flow, enhancing the model's performance and robustness in handling the complexities of the task.
# Semi-Analytical Planetary Landing Guidance with Constraint Equations Using Model Predictive Control
###### Abstract  
With the deepening of planetary exploration, rapid decision making and descent trajectory planning capabilities are needed to cope with uncertain environmental disturbances and possible faults during planetary landings. In this article, a novel decoupling method is adopted, and the analytical three-dimensional constraint equations are derived and solved, ensuring real-time guidance computation. The three-dimensional motion modes and thrust profiles are analyzed and determined based on [PERSON]'s minimum principle, and a supporting semi-analytical reachability judgment method is presented, which can also be used to determine controllability. The algorithm is embedded in the model predictive control (MPC) framework, and several techniques are adopted to enhance stability and robustness, including thrust averaging, thrust correction after ignition, thrust reservation, and open-loop terminal guidance. Numerical simulations demonstrate that the proposed algorithm can guarantee real-time trajectory generation and meanwhile maintain considerable optimality. In addition, the MPC simulation shows that the algorithm can maintain a good accuracy under external disturbances.  
planetary soft landing powered decent guidance; decoupling method; real-time control; reachability determination Article
# Damping and Natural Period Evaluation of Tall RC Buildings Using Full-Scale Data in Korea
## 3 Empirical Formulae for Damping Ratio of RC Buildings for Wind Design
### Frequency- and Amplitude-Dependent Damping Ratio
#### 3.2.5 [PERSON] [8] proposed the following empirical formula that depends only on the height of the building and excludes the top drift effect:  
\[\zeta=0.007238f_{1}+\frac{0.007026}{f_{1}} \tag{18}\]  
Because the fundamental mode frequency \(f_{1}\) is a reciprocal of the fundamental mode period \(T\), and \(T\) is related with to building height, as given in Equation (5), it can be concluded that the damping ratio in Equation (18) is a function of the building height. Notably, Equation (18) yields a very large damping ratio for tall buildings whose natural frequencies are very low because Equation (18) is based on Rayleigh damping.
# Automated Docking System for LNG Loading Arm Based on Machine Vision and Multi-Sensor Fusion
## 2 Design of Docking Logic and Kinematic Analysis for LNG Loading Arm
### Decomposition of LNG Loading-Arm Docking Tasks
#### 2.1.3 Fine Docking Phase  
The fine docking phase focuses on achieving alignment between the loading arm and the target flange by iteratively refining position and posture based on feedback until the docking requirements are met:  
* Relative position calculation: By integrating vision data and ultrasonic sensor measurements, the system determines the relative position of the target flange in the x, y, and z directions;
* Precise posture adjustment: The system dynamically fine-tunes the arm's posture based on relative position feedback to align the flange with the pipe accurately;
* Docking verification: The system continuously monitors the docking status and performs iterative adjustments until the desired accuracy is achieved.  
By implementing this two-phase control strategy, the system prioritizes rapid target approach during the coarse docking phase and focuses on precise alignment during the fine docking phase, ensuring safe and efficient automated docking in complex marine environments.
# Effect of Excitation Beam Divergence on the Goos-Hanchen Shift Enhanced by Bloch Surface Waves
## 2 Theory and Analysis  
The stationary phase theory proposed by [PERSON] in 1948 [PERSON] (1948) explains that the GH shift is the summation of the contribution for different spatial frequencies experiencing different phase changes in a bounded beam. The following equation is still widely used in predicting the relation between the GH shift and the phase change during reflection for a quasi-collimated beam:  
\[\mathrm{D}=-\frac{\lambda}{2\pi}\frac{d\phi}{d\theta} \tag{1}\]  
where D is the GH shift, \(\lambda\) is the wavelength, \(\phi\) is the phase of the reflection coefficient, and \(\theta\) is the incident angle, respectively. The schematic diagram of the GH shift is shown in Figure 1.  
However, for a bounded beam, a more accurate GH shift can be estimated using diffraction theory [9; 17]. For a Gaussian beam, the incident optical field distribution with the beam-waist radius \(w_{0}\) is given by:  
\[E_{i}(x_{i},0)=\exp[-(x_{i}/w_{0})^{2}]/\sqrt{\pi}w_{0} \tag{2}\]  
According to the diffraction theory, the optical field distribution for observation can be derived as [9]:  
\[E_{Br}(x_{r},0)=\frac{1}{2\pi\cos\theta}\int\limits_{-\infty}^{+\infty}r(k_{x })\exp[-(\frac{k_{x}-k\sin\theta}{2\cos\theta}\omega_{0})^{2}]\exp[ix_{r}( \frac{k_{x}-k\sin\theta}{\cos\theta})]dk_{x} \tag{3}\]  
where \(r(k_{x})\) is the complex reflective function associated with incident angle, \(\theta\) is the incident angle, \(k\) is the wavenumber in the incident medium, \(k_{x}\) and \(k_{y}\) are the wavenumbers in \(x\) and \(y\) direction respectively, and \(k_{x}{}^{2}+k_{y}{}^{2}=k^{2}\), \(\sigma=(k_{x}-ksin\theta)/(kcos\theta)\) represents the divergence of the \(k_{x}\) vector to the central incident wavenumber \(ksin\theta\).  
According to Equation (3), it is shown that for the same reflection surface (the same \(r\)), the reflected optical distribution, i.e., the shape of the reflected beam, depends on the divergence of the excitation beam, which corresponds to the waist radius of the incident Gaussian beam. As such, the GH shift calculated through the reflected optical distribution depends on the waist radius of the incident beam.
# Underwater 2D Image Acquisition Using Sequential Striping Illumination
###### Abstract  
Structured lighting techniques have increasingly been employed in underwater imaging, where scattering effects cannot be ignored. This paper presents an approach to underwater image recovery using structured light as a scanning mode. The method tackles both the forward scattering and back scattering problems. By integrating each of the sequentially striping illuminated frame images, we generate a synthesized image that can be modeled on the convolution of the surface albedo and the illumination function. Thus, image acquisition is issued as a problem of image recovery by deconvolution. The convolutional model has the advantage of integrating the forward scattering light into a recovered image so as to eliminate image blur. Notably, the removal of the back scattered light from each frame image can be easily realized by a virtual aperture to limit the field of view; the same principle as of the synchronous scanning systems in underwater imaging. Herein, the implementation of the proposed approach is described, and the results of the underwater experiments are presented.  
Keywords:structured light; scanning imaging; back scattering removal; synthesized image; image recovery +
Footnote †: journal: Applied Sciences
## Experimental Results
### The Impact of Topography  
The deployment environment can have an important impact on a radio link. In our case, the shape of the road, networks nearby and the weather were some environment factors that might have influenced the wireless link properties. We therefore performed experiments in another environment in addition to the relatively flat area used thus far. In this second environment, the nodes were deployed on a hill with a steep slope. In this deployment, the sensors were configured to use channel number 26 and 0 dBm as output power for transmissions. The results presented in this section are for an application payload of 82 bytes.  
Figure 13 shows the CDF of PRR and RSSI for both deployment areas. Based on these results, we observed links with very poor quality for the hill deployment (labeled \"Hill\") compared to links on a flat area (labeled \"Flat\"), whether we considered the PRR or the RSSI metrics.  
The results presented in Figure 14 complement these findings. Indeed, while we observed 81.5% of good links on a flat area, we observed 70% of poor links on the hill deployment. As in previous sections, the majority of links in the network were either good or bad with few intermediate links. Table 4 presents the links asymmetry parameters for the two different environments: the link asymmetry was also much more pronounced on the hill than on the flat area, with more than 45% unidirectional links. Our tests did not show a significant difference between the PRR and the RSSI values of uphill and downhill links, meaning that the radiation pattern of the CC2420 module antenna was not the reason for these results. We believe that the shape of the deployment environment was the main cause for this poor link quality, resulting in more signal reflections.  
Figure 13: CDF of PRR and RSSI for links with distances 3 m and 6 m, as well as for all the links in the network in the case of a hill and a flat area deployment.
## 5 Conclusions  
This study conducted exploratory research on monitoring the surface deformation of the Great Wall corridor in Shanxi Province, China, from March 2017 to January 2022. The study employed 161 scenes of ascending orbit images from the Sentinel-1A satellite and utilized the SBAS-InSAR technique. GACOS atmospheric phase correction and Goldstein filtering were also applied to derive the annual deformation velocity field of the 896.53 km Great Wall corridor in Shanxi Province. The findings of this research indicate that over the five-year period from 2017 to 2022, various sections of the Great Wall in Shanxi Province experienced significant deformation attributed to both natural and anthropogenic factors. The analysis of the stability classification map of the Great Wall corridor in Shanxi Province revealed that 75.8% of the corridor exhibited relative stability, while 24.2% showed notable deformation (with an absolute annual deformation rate exceeding 10 mm/y). These findings provide quantitative monitoring data and present a novel monitoring approach for identifying deformation hazards, locating target areas, and implementing corrective repairs to preserve the architectural heritage of the Great Wall in Shanxi Province. Furthermore, the research results demonstrate the effectiveness of the SBAS-InSAR technique based on coherent points for monitoring the surface deformation of large-scale linear cultural heritage. This approach can be extended to the macro-monitoring and dynamic assessment of other large-scale linear cultural heritage sites worldwide, such as the Roman Wall, city walls, and canal embankments.  
Figure 14: Thematic classification map of deformation stability along the Great Wall Corridor in Shanxi Province.  
**Author Contributions:** Conceptualization, Q.L. and K.C.; methodology, Q.L. and X.W.; software, Q.L., X.W. and K.C.; validation, Q.L., X.W., K.C., J.Z. and Z.Y.; formal analysis, Q.L., X.W. and K.C.; investigation, Q.L., X.W. and K.C.; resources, Q.L.; data curation, Q.L.; writing--original draft preparation, Q.L., X.W. and K.C.; writing--review and editing, Q.L., X.W. and K.C.; visualization, X.W.; supervision, Q.L., X.W. and K.C.; project administration, Q.L.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Natural Science Foundation of Shandong Province, grant numbers ZR2021 MD096 and ZR2021 MD082.  
Not applicable.  
**Data Availability Statement:** The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.