esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Method
### Random Forest Classifier
Moreover, the water related spectral indices are proposed as the combination of shortwave infrared and near infrared or visible spectral regions [45]. Due to the band positions of the HJ satellite data, we incorporated a normalized difference water index as shown in formula (3):  
\[\mathrm{NDWI}=\frac{\rho(\mathrm{Green})-\rho(\mathrm{NIR})}{\rho(\mathrm{ Green})+\rho(\mathrm{NIR})} \tag{3}\]  
where \(\rho(\mathrm{Green})\) and \(\rho(\mathrm{NIR})\) refer to the reflectance of the green band (Band-2) and near infrared band (Band-4), respectively. Meanwhile, to improve the separability of different land cover types, the topographical parameters were also added here including DEM, slope, and aspect. The DEM, with a spatial resolution of 30 m, was derived from the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) products [11]. According to visual interpretation through high resolution unmanned aerial vehicle photos [30], five classes were chosen for the entire image as follows, water, built-up, woodland, cropland, and bare soil. The selection procedure of training and testing samples is of great significance in supervised image classification. According to previous studies [29,46], there are several selection strategies including single pixel selection, similar contiguous pixels through seeding, and polygon-block selection. In this study, a polygon-block sampling procedure was utilized according to [PERSON] _et al._[46]. All the training and sampling were selected by visualizing and digitizing small polygons or blocks of pixels, and it was assumed all the pixels within each polygon belong to the same land cover category [46]. Usually there were two to six pixels in a polygon utilized in this research. Besides, all the training and testing samples were derived from the remote sensing image based on high resolution areal image, Google Earth history image and _a priori_ knowledge. Each land cover type had 200 training points to train the Random Forest classifier in order to avoid any under- or over-estimation of the spectral patterns. The trained Random Forest was then utilized to classify the input images. Next, the non-water classes (woodland, cropland, built up area, and bare soil) were merged into the non-flooded class to simplify the problem while the flooded class was extracted by subtracting the persistent water. The \"persistent water\" was derived from the \"water\" class of the classification result of the pre-flood image (2013.09.10). Because the \"real\" flooded area and the persistent water were both included in the \"water\" class of the classification result of the flooding image (2013.10.11), the persistent water was then utilized to separate the \"real\" flooded areas.
# Investigating the Physical Mechanisms of Quicksand Using a Custom-Designed Experimental Apparatus
## 4 Discussion
### Reorganization Stage:  
When subjected to seismic activity or rising groundwater levels, water infiltrates through the pores between solid particles in sandy soil, dissolving crystalline salts. Within the experimental variable range, the seepage discharge is primarily controlled by the  
Figure 6: Sensor pressure values at Bottom height positions of quartz sand samples: (**a**) Specimen 1# (d\({}_{50}\) = 2.010 mm); (**b**) Specimen 2# (d\({}_{50}\) = 1.590 mm); (**c**) Specim 3# (d\({}_{50}\) = 1.150 mm); (**d**) Specimen 4# (d\({}_{50}\) = 0.710 mm); (**e**) Specim 5# (d\({}_{50}\) = 0.430 mm).  
water supply unit (Figure 2). The influx of water triggers spatial reorganization of solid particles, altering porosity and increasing seepage discharge (Figure 3). This process disrupts the stable consolidation state, transitioning the quartz sand specimen into a steady seepage network. Under sustained water infiltration and within specific ranges of gradation and hydrodynamic velocity, sandy soil may exhibit transient quicksand behavior, eventually re-establishing a consolidated seepage network and entering a pseudo-\"self-consolidation\" stage.  
Quicksand Stage:  
As pore water pressure progressively increases, the consolidated seepage network undergoes destabilization, reverting sandy soil to the reorganization stage. This marks the beginning of a cyclic interplay between the self-consolidation and reorganization stages, which remains confined within specific operational limits (Figures 5 and 6). Complete quicksand initiation is contingent upon the simultaneous fulfillment of three critical conditions: a defined depth range, specific gradation characteristics, and adequate hydrodynamic pressure (Figure 4). Notably, in loose sandy soils, the hydrodynamic pressure required to trigger initial quicksand may fall below theoretical thresholds, whereas in dense sands, it often exceeds these values [24]. In this experiment, the quartz sand exhibited a moderate relative density, and the hydrodynamic pressure necessary for quicksand initiation aligned closely with theoretical predictions, demonstrating the precision of the experimental design.  
The occurrence of quicksand is primarily characterized by reductions in viscosity and internal friction angle [25]. Engineering structures located within or near quicksand-prone sandy soil layers are subject to varying degrees of impact. In summary, the self-developed apparatus successfully reveals the staged evolution and physical mechanisms of quartz quicksand under controlled hydrodynamic conditions. However, the current system remains limited by its small scale, inability to simulate seismic-induced pore pressure buildup, and lack of visual monitoring, all of which constrain its capacity to fully replicate, monitor, and interpret the complex in-situ mechanisms of quicksand initiation and development. Preventing quicksand initiation and mitigating its effects on engineering projects remain critical scientific challenges requiring ongoing exploration [26].
## 4 Materials and Methods
### Soil Metagenomic Sequencing and Analysis  
Soil DNA was extracted following the manufacturer's instruction for the PowerSoil DNA Isolation Kit (MoBio, Carlsbad, CA, USA). The concentration and integrity of the isolated DNA was estimated using a Qubit 3.0 Fluorometer (Thermo Fisher, Waltham, MA, USA) and 1% agarose gel electrophoresis, respectively.  
DNA library (300-400 bp DNA) was constructed and sequenced on the DNBSEQ platform (MGI Tech, Shenzhen, China). Numbers of sequencing reads were showed in Supplementary Table S8. The raw data were quality-controlled using SOAPnuke (v.2.2.1) [51]. Afterwards, the clean data were assembled de novo using MEGAHIT (v.1.1.3) software [52]. Genes were predicted over contigs (>500 bp) using MetaGeneMark [53], and predicted genes with \(\geq\)95% identity and \(\geq\)90% coverage were clustered using CD-HIT to remove the redundant genes [54]. Salmon (v.1.3.0) software was used for quantification to obtain the standardized gene abundance [55]. The protein sequences of genes were aligned against the public database using DIAMOND blastp with an E value cutoff of 1 \(\times\) 10\({}^{-5}\)[56]. Moreover, MEGAN (version 5) was used for taxonomic annotations of each sample according to the National Center for Biotechnology Information (NCBI) taxonomy [57]. The species abundance in the sample was calculated by aggregating the abundance of gene annotated by the same species.
## 4 Results and Discussion of Explainable AI (XAI) for VGG19 Model
### Comparison of LIME and Grad-CAM Explanations for VGG19
#### 4.1.1 Correctly Classified Instances  
For correctly classified instances, both LIME and Grad-CAM highlight relevant regions that contribute to the VGG19 model's predictions. Figures 5 and 11 illustrate the explanations for the same set of correctly classified images. LIME focuses on specific features and areas in the images, showing which parts of the drilled holes most influenced the classification as \"Green\", \"Yellow\", or \"Red\". Grad-CAM highlights broader regions within the images that are important for the model's decisions. It provides a more holistic view of the areas contributing to the classification.  
Both techniques show a high degree of agreement in identifying the critical regions for classification. However, LIME's explanations are more granular, focusing on smaller image patches, while Grad-CAM provides a more general overview of the important areas.
## 5 Parametric Study Based on the Numerical Model  
### Effect of the Sensors  
Figure 19 shows the evolution of the descriptors extracted from numerical signals with several sensor effects (picoHF, WD, micro80, nano30, R15). While the medium and the excitation source are the same, significant differences are observed in the time and frequency domain results.  
The aperture effect consists of the progressive elimination of higher frequency wavelengths. The frequency response of the AE sensor strongly influences the characteristics of the output signal. This result shows a strong influence of the type of sensor, which controls the frequency content such as frequency centroid or peak frequency (two common features used). In this context, it is difficult to develop generalized pattern recognition methods using the resonant type AE sensors. It is further noted that the R15 sensor is active in a smaller frequency range than that of the WD, hence energy distribution with frequency seems to match with the sensor properties near the source. The peak amplitude is also strongly affected by the sensor. With the numerical approach, it may be possible to enlarge the library with the acoustic signature obtained with different sensors.  
Figure 18: Simulated dispersion curves (wavenumber k versus frequency f) via 2 DFFT of signals recorded along a straight line for a fiber break at the position P2 (**a**) on the lateral side of the specimen v(y) (**b**) on the top surface v(z) of computational specimen (e = 2.8 mm and z = 0.9 mm).
# Efficient Key Exchange Using Identity-Based Encryption in Multipath TCP Environment
## 5 Experimental Evaluation of Proposed Model  
The proposed scheme is tested with MPTCP using the Linux kernel implementation of MPTCP. The Oracle VirtualBoxes are used to set up the scenario of the proposed scheme by creating two virtual machines (VMs), client and server, as shown in Figure 13. The client VM and server VM are configured with the Linux kernel implementation of MPTCP. Here, the PKG is configured on the host machine. In order to connect PKG with any of the hosts with MPTCP, the tap interfaces are used.  
The IBE requires PKG for generating system parameters and distributing private keys on the basis of the ID of the host. The key role of PKG is to configure the system parameters and master share, which can be used during the encryption and decryption of the keys shared during the initial handshake of the MPTCP. The proposed scheme uses IBE for encrypting the data without communicating keys and with a communicating host. Here, PKG plays a significant role in authenticating the users and sharing the master private key to generate the private key using identity. Our proposed model uses ECC for the generation of session keys, and each communication will take place by digital signature for authentication.  
Figure 12: Secure key exchange during a three-way handshake using IBE.
# Sparse Representing Denoising of Hyperspectral Data for Water Color Remote Sensing
## 4 Discussion
### Influence of Denoising to \(C_{\text{chla}}\) Estimation Models  
The final goal of denoising is the quantitative estimation of the water quality parameters. We take \(C_{\text{chla}}\) as the example to evaluate the effectiveness of the SR denoising method in practice. Limited by the amount of the field samples and measurements (Table 1), we cannot refine the estimation models and evaluate the estimation result directly by comparing it with the in situ measured \(C_{\text{chla}}\). Therefore, the correlation coefficients between the popular spectral indices and \(C_{\text{chla}}\) were selected to complete this assessment.  
The five popular spectral indices are the band ratio parameter [54,55], normalized difference chlorophyll-a index (NDCI) [56], three-band parameter [12], four-band parameter [57], and enhanced three-band parameter [58]. Their expressions are listed in Table 2. In our dataset, the three-band index exhibited the highest correlation with \(C_{\text{chla}}\). It is followed by the enhanced three-band and band ratio indices. The correlation coefficients of the three-band and four-band indices were slightly lower than the other three indices. For all of the five indices, the denoised \(R_{\text{rs}}\) spectra showed better correlation coefficients compared with the original \(R_{\text{rs}}\). The improvements are not obvious, because the main wavelengths of the indices are located in the low-noise ranges (Figure 7), where the SR method kept most of the original signals.
## 3 The Proposed DRL + GNN-Based Handover Scheme
### MPNN-DQN Based Handover Scheme  
This paper proposes a DQN + MPNN architecture for inter-satellite handover, aiming to address the satellite handover decision problem encountered by grid users. This architecture represents handover users as intelligent agents and reformulates the satellite handover decision problem into a multi-criteria optimization problem. The corresponding set of decision strategies is derived by maximizing the long-term cumulative expected reward function. Throughout the learning iterations of the MPNN-DQN algorithm, the agents receive graph-structured state observations from the environment at various time steps. The GNN constructs a graph representation in which the directed edges of the graph represent graph entities. The topology of the GNN graph is invariant, since the state space size is predefined, ensuring it maintains a consistent length across different topology sizes. Using the graph structure, an iterative message passing algorithm operates between the states of the edges. The output of this algorithm is aggregated into a global state that encodes the graph topology information. Subsequently, the output is processed by the DNN to estimate the q-value. Through comparing the q-value estimates of a set of actions in the current state, the agent can select the most efficient action. The overall framework of the satellite handover decision algorithm designed in this article is shown in Figure 7.  
Figure 7: The overall framework of satellite handover decision algorithm. The specific implementation flow of the handover algorithm framework is illustrated in Algorithm 2. Initially, we initialize the environment and the DRL intelligence, set the reward to zero, create an experience replay buffer, etc., and set the end-of-training flag, DONE, to false. Subsequently, we enter a while loop, where the DRL intelligence learns by interacting with the environment until the most suitable handover paths are discovered for each time slot within the communication duration. For each time slot in the satellite handover graph topology, we calculate the q-value of each state-action pair after obtaining all possible handover paths from the virtual start node. Subsequently, we transition to the new satellite network topology state \(s^{\prime}\) for the next time slot, obtain the reward r and the completion marker DONE value according to the reward function, and store the state, action, etc., in the experience replay buffer M. These stored memories are subsequently utilized to train the GNN model every 15 iterations. 1. Initialization  
(1) Low Earth orbit satellite network environment: Satellite handover graph topology, transmission delay, data rate, remaining service time, etc. (2) GNN Parameters: Model Parameters w  
(3) DQN intelligent agent: Set the reward at 0, the estimated network parameter for Q is \(\theta\), and the target network parameter \(\theta^{-}\) for Q is equal to \(\theta\); Empty the experience replay pool; Set the end of training to indicate that DONE is false;  
1.
# Collaborative Use of a Shared System Interface:
## 4 Methodology
### Measures  
As we are interested in where dyads look in the system interface, and since all E-Prime displays were in full-screen mode, we considered gaze location in the orthonormal plane represented by the computer screen. As suggested previously, dyad GC is high when the distance between gaze locations is low, and vice versa. We measured dyad GC in both studies as the opposite (i.e., multiplied by \(-1\)) average Euclidian distance between gaze locations of the two dyad members at all fixation points. The fixation data of both users were synchronous in time, allowing for direct comparison. Similar measures based on Euclidian distance but in context of asynchronous gaze recording have been reported to be valid measures [47; 48; 49].  
In Study 1, in order to get a more comprehensive assessment of the distribution of distance between dyad gaze locations (see time series of distances in Figure 5), other statistics of Euclidian distance between dyad gazes were planned for analysis, including minimum, standard deviation, 1 st quartile, 2 nd quartile (i.e., the median), 3 rd quartile, and maximum. These analyses would reveal whether the difference between convergent and divergent gaze behavior detected by our dyad GC index is supported throughout the gaze distance data distribution, from lower (lower GC) to higher (higher GC) data points.  
In Study 2, two other constructs were measured: dyad task performance, and cognitive load. We operationalized dyad task performance to be associated with higher accuracy and shorter response time. Accuracy was scored according to the following: 1 point for incorrect responses, 2 for unknown answers, and 3 for correct responses. This grading was in line with the nature of the experimental task. As every image pair had a single subtle difference and dyads had unlimited time for double-checking, it was more expected that user dyads would either find the right difference or abandon the trial without finding the image difference. Hence, incorrect response was the most penalizing answer. The sum of points for all trials in Study 2 determined a dyad's Accuracy Score. Moreover, the total time taken by a dyad to analyze the stimuli for each trial before responding was deemed as the Trial Completion Time. Dyad task performance was measured as follows:  
Task performance = {Accuracy Score}/[Trial Completion Time]Meanwhile, cognitive load was indexed via pupillary dilation, a common strategy for human-computer-interface studies in the IT literature [22; 50; 51]. Specifically, cognitive load for an individual user was determined as the increase (or decrease) in pupil diameter during the task, that is, pupil diameter recorded during the main experimental task minus baseline recorded before the start of the experiment. Dyad cognitive load was determined as the mean cognitive load between the two users in the dyad. This operationalization of dyad cognitive load is in line with past studies of team-level cognitive load (e.g., [PERSON] [52]; [PERSON] and [PERSON] [53]; [PERSON] et al. [54]).
## 5 Simulation and Analysis
### Case Study 2  
To demonstrate the frequency modulation effect of the ESS, the simulation is performed with AGFTSMC in two cases: with and without ESS in the system. First, the large frequency disturbance of area 1 and area 2 is set to 0.02 p.u at \(t=5\) s. Moreover, continuous random disturbances of area 1 and area 2, with the upper limit set to 0.002 p.u, are introduced, occurring at \(t=5\) s \(\sim 15\) s. The simulation results are presented in Figure 4a,b. A quantitative comparison of the simulation results is depicted in Table 5.  
Figure 4: Results of Case 2. (**a**) Frequency deviations of area 1 with and without ESS under AGFTSMC. (**b**) Frequency deviations of area 2 with and without ESS under AGFTSMC.  
From Figure 4a,b and Table 5, compared with the case without ESS in the system, the maximum frequency deviation of the system with ESS is \(-0.297\) Hz, which is \(37.78\%\) smaller than that of the system without ESS. The stabilization time of the system with ESS is roughly \(17.50\) s, about \(31.75\%\) shorter than that of the system without ESS. As shown from Figure 4a,b, the maximum frequency deviation and the response time of the system can be reduced significantly when ESS is applied. Thus, the dynamic compensation capability of ESS is verified.
###### Abstract  
Oropharyngeal squamous cell carcinoma (OPSCC) is a subset of head and neck cancers that can arise due to human papillomavirus (HPV) infection. We designed a retrospective analysis to determine differences in outcomes of OPSCC patients treated at City of Hope (COH) Cancer Center's main campus versus selected satellite sites with COH-associated faculty and facilities. Patients diagnosed with OPSCC and treated with concurrent chemoradiation therapy (\(n=94\)) were identified and included in the study. Patients underwent treatment at the COH main campus site (\(n=50\)) or satellite sites (\(n=44\)). The majority of patients were Caucasian, male, and diagnosed with p16 positive stage IV locally advanced OPSCC by AJCC 7 th edition. Most patients completed their prescribed cumulative radiation therapy dose and had a complete response to treatment. No significant difference in overall survival and progression-free survival was observed between the main campus and the satellite sites. Our study demonstrates successful treatment completion rates as well as comparable recurrence rates between the main campus and COH-associated satellite sites. A trend toward significant difference in feeding tube dependency at 6-months was observed. Differences in feeding tube placement and dependency rates could be addressed by the establishment of on-site supportive services in satellite sites.  
oropharyngeal cancer; concurrent chemoradiation therapy; human papillomavirus; feeding tube dependency 2020 9 3083; doi:10.3390/jcm9103083
# On Data Leakage Prevention Maturity: Adapting the C2M2 Framework
## 2 Materials and Methods
### Problem  
In the realm of DLP, while there is a substantial body of research into DLP solutions, product comparisons, and cyber security maturity models [15], a conspicuous gap is evident in the literature: the lack of a structured DLP maturity model. This gap in both academic discourse and practical guidelines has led us to the development of a bespoke DLP Maturity Model, aimed at providing a structured approach to DLP implementation and management. This facilitates a systematic and holistic assessment of an organization's DLP capabilities, enabling the identification of areas for enhancement and strategic investment.  
While it is true that skilled and experienced personnel could manage several DLP measures efficiently through ad hoc practices, it is worth noting a caveat. This approach, although less resource-intensive, might not align with the structured, documented processes typically favored by regulatory bodies. Regulators often emphasize the need for structured, replicable procedures and clear documentation for compliance purposes. Hence, while an informal method might seem advantageous in terms of effort and resource allocation, its alignment with regulatory standards and compliance needs is an aspect that warrants careful consideration.  
It is therefore important to understand that DLP serves not merely as a technical safeguard but also as a financial instrument, facilitating compliance and consequently averting regulatory penalties. This is particularly interesting for participants in highly regulated markets undergoing consolidation, such as the European banking sector [16]. While regulators increasingly mandate DLP requirements, to our knowledge, there exists no official framework or process dedicated to achieving a high level of DLP maturity. Often, DLP is integrated as part of a broader security solution, which, while generally beneficial, lacks detailed guidance on developing a step-by-step holistic DLP strategy. The approach by [PERSON] et al. [17], though one of the few cited methodologies, primarily focuses on technical solutions, leaving a gap in strategic DLP planning and implementation.