esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussions
It is the dividing line between recoverable and unrecoverable deformation, with important engineering value. Rock dilatancy is caused by the generation of internal micro cracks or the movement of crystals. The dilatancy boundary stress is obtained by the peak of the axial stress-strain curve, and this stress is 60-80% of the rock strength. In this study, the dilatancy of rock salt occurred in the brittle-ductile transition stage, and it was inferred that this dilatancy phenomenon resulted in the motion of salt crystal slip and not the generation of cracks, for the ultrasonic wave velocity did not drop, but it remained stable. When the confining stresses of the rock were 5, 10, 15, and 20 MPa, the crack initiation thresholds were 59.76%, 67.40%, 67.51%, and 63.06% of the short-term strength of the rock salt, respectively; and, the dilatancy boundary stresses were 80.47%, 80.11%, 77.66%, and 72.97% of the short-term strength of the rock salt, respectively. According to these data, it is inferred that the stress interval between the crack initiation threshold and the dilatancy boundary is short, and that the dilatancy will happen soon after the linear elastic deformation stage. Subsequently, the volume strain curve will decrease after dilatancy, and the stress when the volumetric strain is \"0\" is defined as the short-term strength of rock salt. The short-term strength is a particularly important boundary of the brittle-ductile transition stage and the plastic deformation stage. When the axial stress exceeds the short-term strength, the damage of rock salt will dramatically develop, as seen in the decline of ultrasonic wave velocity and the rising of AE events. The rock salt enters a stage of accelerated plastic deformation, with uncontrollable crack development. The internal structure of the rock has undergone considerable changes, which is extremely detrimental to the sealing and stability of underground gas storage. When the confining stress changed from 5 to 20 MPa, the short-term strength of rock salt increased from 16.9 to 22.2 MPa. It is clear that the confining stress has a certain influence on the short-term strength of rock salt, yet the effect is not extraordinarily strong. In this study, the damage ultimate stress was determined by the peak of the AE energy release of the rock salt. It is seen as the ultimate strength of the rock, like the triaxial compressive strength of hard rock. When the load on the rock exceeds this value, the rock will break and the underground structure will not be stable.
## 4 Discussion
### Influence of the Explanatory Input Variables  
Figure 10 summarizes the relative importance of the explanatory input variables of the modeling approach, based on the results obtained for the 30 m radius buffer. At this intra-plot spatial scale, the four components of roughness (i.e., hrms and lc measured in the perpendicular and parallel direction) have a more pronounced relative importance than other surface descriptors (TSM or texture), whatever the considered satellite configuration (with a relative importance of 31.4% in the X-band, \(\sim\)37.0% and 39.3% for the co- and cross-polarization states in the C-band). Top soil moisture is the second most important variable, with 27.9% in the X-band and between 30.2 and 31.7% in the C-band (depending on the polarization state). The texture's variables only show a limited importance (reaching a maximum of 15.3% for cross-polarization states in the C-band), with the relative influence of clay content being always greater than that of the sand content.  
These results show that the influences of the soil descriptors used in the random forest algorithm are consistent with their influences in empirical, semi-empirical, and physical-based models [6; 8; 9; 10; 11; 12; 32; 33; 32], as reflected in the higher influence of TSM and surface roughness on the estimation of backscattering coefficients, and the more limited impact  
Figure 10: Relative importance of the input variables of RF algorithms for the X- and C-bands with the 3 polarization states: HH, VV, and VH, for buffer with a radius of 30 m.  
Figure 9: Evolution of the values of radiometric resolution with the buffer’s size for the three studied SAR modes (full quad-polarization—FQP, Stripmap—SM, and Spotlight—SL modes).  
of texture. The influence of the incidence angle is minimal regarding the cross-polarized satellite signal (13.7% for VH), which is lower than the relative importance observed for co-polarized C-band signals (20.2 and 21.2% for HH and VV, respectively). Such a difference between the polarization states has been already pointed out in previous studies, showing a lower angular sensitivity of signals acquired in cross-polarization [36]. Finally, the influence of the incidence angle is higher in the X-band (reaching 33%) compared to the signals acquired with the same polarization state in the C-band (relative importance close to 20% for HH). Such difference between frequencies may be related to the range of variation of the incidence angle of the satellite images used (\(\Delta\theta\) in C-band = 16\({}^{\circ}\), \(\Delta\theta\) in X-band = 26\({}^{\circ}\)).
## 4 Results and Analysis
### Model Comparisons with Related Works  
The accuracy of the models is shown in Figure 4. [PERSON] et al. refrained from employing the XGB and HGB models in their investigation. The ensemble model surpasses the other models in terms of accuracy, as evidenced by Figure 4. The accuracy of the RF, XGB, and HGB models is significantly higher than that of the other models, with values of 99.76%, 99.75%, and 99.69%, respectively. In contrast, the accuracy of these three models is enhanced to 99.80% when the voting ensemble approach is implemented, surpassing that of all other models. Table 7 displays all of the model classification reports, demonstrating that the performance of all the models was enhanced by our approach. The precision of the LR model increases from 0.63 to 0.70 when trained using our approach, resulting in the greatest improvement. The precision, recall, and F1 score of the other models also showed slight improvements.  
Above all, the ensemble model has a precision of 1, a recall of 0.99, and an F1 score of 0.99. Conversely, we observed that the LR and GNB models exhibit the lowest level of accuracy in both of the experiments.  
Figure 4: Comparison of accuracy of models from this study and a related study for dataset 1 [14].
# A Robust Routing Protocol in Cognitive Unmanned Aerial Vehicular Networks
## 4 Proposed Routing Protocol  
This section outlines the development of the proposed routing protocol, including proposed clustering strategy, neighboring UAV discovery process, cluster formation, central node resolution factor, central node discovery process and delay awareness of the proposed protocol. The reactive protocol considers factors like distance, speed, direction, network quality, and reliability to enhance rapid and dependable data exchange. The proposed routing protocol involves broadcasting Route Request (RREQ) messages to discover routes, with neighboring UAVs replying with Route Reply (RREP) messages containing path delay, reliability factor (RF), and residual energy (RE). UAVs evaluate these repiles and select the most reliable paths based on the Central Node Resolution Factor (CNRF), electing Central Nodes (CNs) accordingly. Data packets are routed through these CNs for stability and efficiency, and alternative routes are used if a link fails. The protocol dynamically adjusts to network changes, continuously monitoring link quality and energy levels for optimal performance.
# Shading by Overhang PV Collectors
## 3 Results
### Incident Radiation on Overhang PV Collectors
#### 3.2.1 Single Overhang Collector  
The global solar irradiance \(G_{\varepsilon}\), in (W/m\({}^{2}\)), incident on an inclined overhang PV collector with an angle \(\varepsilon\) consists of the direct beam \(G_{b}\), diffuse irradiance on a horizontal plane \(G_{dh}\), reflected irradiance from the wall above the overhang, and the sky and wall view factors:  
\[G_{\varepsilon}=G_{b}\cdot\cos\theta+F_{H\to sky}\cdot G_{dh}+ref_{wall} \cdot F_{H\to wall}\cdot G_{g} \tag{31}\]  
where \(\theta\) is the angle between the solar beam and the normal to the collector given by [16]:  
\[\cos\theta=\cos\beta\sin\alpha+\sin\beta\cos\alpha\cos(\gamma_{s}-\gamma_{c}) \tag{32}\]  
The angles involved in solar calculation are shown in Figure 14.  
\(\alpha\) is the sun elevation angle; \(\beta\) is the collector inclination angle with respect to horizontal plane, \(\beta=90-\varepsilon\) (\(\varepsilon\) is the collector angle with respect to the wall) and \(\gamma=\gamma_{s}-\gamma_{c}\) is the difference between the sun and collector azimuths with respect to south. \(G_{g}\) is the global incident irradiance on the wall;  
Figure 12: Daily variation of percentage shaded area on the bottom collector on April (...) and June (...) 21 st, \(H=0.941m,D=1.0,\,2.0,\,3.0\,m\).  
Figure 13: Daily variation of percentage shaded area on the ground on April (...) and June (...) 21 st, \(H=5.0\,m,D=\,3.0,\,5.0\,m\).  
\(F_{H\to sky}\) is the view factor of the surface \(H\) to sky; \(F_{H\to null}\) is the view factor of the surface \(H\) to wall; \(ref^{null}\) is the reflectance of the wall above the collector.  
The collector is open to sky therefore the view factor to sky of a single collector is [17]:  
\[F_{H\to sky}=(1+cos\beta)/2=(1+\sin\varepsilon)/2 \tag{33}\]  
and view factor of the collector to the wall is given by:  
\[F_{H\to null}=(1-\cos\beta)/2=(1-\sin\varepsilon)/2 \tag{34}\]  
The reflected irradiance, \(ref^{null}\cdot F_{H\to null}\cdot G_{g}\), of the wall on the collector is small with respect to the beam and diffuse irradiance. For \(\varepsilon=60^{\circ}\), for example,\(F_{H\to null}^{f}=0.067\) and multiplying by a high reflectance coefficient of the wall would result only in about 2% of the global irradiance \(G_{\varepsilon}\).  
The yearly direct beam incident irradiation on the top collector, \(q_{b}^{top}\), per unit area, is given in Wh/m\({}^{2}\) by:  
\[q_{b}^{top}=\sum_{n=1}^{n=365}\sum_{T_{R}}^{T_{S}}G_{b}\cos\theta\Delta T \tag{35}\]  
and the diffuse incident radiation \(q_{d}^{top}\), per unit area, in Wh/m\({}^{2}\) is given (see Equation (33)) by:  
\[q_{d}^{top}=(\frac{1+\sin\varepsilon}{2})\times\sum_{n=1}^{n=365}\sum_{T_{S }}^{T_{S}}G_{dh}\Delta T \tag{36}\]  
where \(\Delta\)T is the summation time interval (for solar data sampled every hour \(\Delta\)T = 1) from sun rise \(T_{R}\) to sunset \(T_{S}\) on the collector for the beam irradiance, and from sun rise \(T_{SR}\) to sun set \(T_{SS}\) for the diffuse irradiance. The other summation is from January 1 (n = 1) to December 31 (n = 365).
# A Survey of GIS and IoT Integration: Applications and Architecture
## 4 Integrated IoT and GIS Architectures
### GIS and IoT Integration Architecture
#### 4.3.2 GIS in Application Layer  
GIS can provide its capabilities within the Web and mobile services which can be utilized in the Application layer of IoT architecture. With the development of web technologies, many services are provided for users without many locational restrictions [184]. Nowadays the capabilities of GIS including the visualization of spatial data and its processing tools can be accessible for users through web services [185]. Therefore, many organizations are inclined to use the Web version of GIS to simultaneously benefit from the advantages of Web technology and GIS [186]. In [29; 111; 173] Web GIS is used to provide the geovisualization of spatial data.  
Mobile GIS is another solution to adopt GIS services in mobile devices [187]. This form of the GIS would help users to tackle the problems requiring real-time field mapping and positioning information [188]. Most of the functionalities of GIS discussed earlier could be brought up in Mobile GIS, therefore, users can take advantage of their portable GIS devices. The prominent components of a Mobile GIS are hardware, software, wireless communication, and GPS which also exist in IoT. The studies [27; 31; 170] use Mobile GIS in their proposed systems.
# Gaze Estimation Based on a Multi-Stream Adaptive Feature Fusion Network
## 4 Experiments
### Settings
Thus, selecting Dilated-Net as the baseline model not only provides an effective performance comparison but also enables a deeper analysis of the advantages and improvements of MSMI-Net in feature representation, information fusion, and angular adaptability. Dataset Processing: In this study, based on the review published by [PERSON] et al. [42], the EyeDiap, MPIIFaceGaze, and Gaze360 datasets were processed in a unified manner. For the EyeDiap video dataset, we extracted one image every 15 frames from the VGA resolution videos as potential gaze images to ensure consistency in the number of images across different videos. Additionally, we performed pixel value normalization on the extracted images, adjusting their range to [0, 1], to facilitate subsequent computational processing. When processing the MPIIFaceGaze and Gaze360 datasets, we noted that each eye image had a corresponding facial image. Therefore, we first normalized the full-face images to facilitate the cropping of eye images from them. For both the EyeDiap and MPIIFaceGaze datasets, we used the same evaluation strategy as in previous studies, namely leave-one-subject-out cross-validation, and computed the average results to assess the performance of gaze estimation. For Gaze360, we directly performed evaluation on the test set. This approach ensures the accuracy and reliability of the evaluation results and makes our research findings comparable to other methods in the existing literature. Training Details: The proposed method was implemented using the PyTorch framework, version Torch-1.8.1. The experiments were run on a Windows 10 operating system with hardware consisting of an Intel Core i5-14600 KF [EMAIL_ADDRESS] GHz and an NVIDIA RTX 4080 GPU (16,384 MiB of VRAM). During the training phase, the model's input data consisted of three parts: left eye image, right eye image, and facial image. The original left and right eye images had a size of 60 \(\times\) 36, while the facial images had a size of 224 \(\times\) 224. To standardize the input image sizes and ensure a fair comparison with the baseline method, all input images were cropped to 96 \(\times\) 96 pixels. Additionally, the batch size for each input to the network during training was set to 64 to improve training efficiency. For different dataset sizes, we designed training strategies with 60, 100, and 200 epochs to ensure the model could learn effectively under different data conditions. The loss function used was L1 loss to measure the difference between predicted and true values, and the Adam optimizer [43] was employed for parameter updates, with the optimizer's settings left at their default configuration. The model's initial learning rate was set to 0.001, and it decayed to one-tenth of the original value every 30 epochs to accelerate convergence and improve model stability.
## 3 Results and Discussions
### Interfacial Activity of JNP-bPEI Homologous Series  
The interfacial activity of JNP-bPEI at the heptane/water interface was determined using the pendant drop method (OCA 25, DataPhysics). A droplet of an aqueous suspension of JNPs was formed in n-heptane and the curvature of the droplet fitted to the Young-Laplace equation. The interfacial tension (IFT) of a pure heptane/water interface at 25 \({}^{\circ}\)C is \(\gamma\) = 50.71 mN\(\cdot\)m\({}^{-1}\)[17]. In the presence of the JNPs-bPEI, the IFT decreases vs. time due to interfacial adsorption. In Figure S6 it is shown that the interfacial activity, as judged by the lowest values of the IFT reached in the plateau, is significantly larger at lower pH values, which can be explained by the protonation of the bPEI grafted on the second Janus lobe. With the protonation of the bPEI, the surface of the second Janus lobe becomes more polar and produces a good amphiphilic contrast to the less polar PS lobe.  
Furthermore, we investigated the influence of the Janus lobe ratio on the interfacial activity of the JNP-bPEI. The influence of the lobe size ratio on the interfacial activity of the particles is demonstrated in Figure 1. The IFT of all suspensions starts out \(\pm\)1 mN\(\cdot\)m\({}^{-1}\) of the pristine heptane/water IFT value and then slowly declines as the particles reach the interface. This also indicates the absence of fast-acting molecular surfactants. It is apparent that increasing the size of the hydrophilic bPEI-modified P(3-TSPM) lobe increases the interfacial activity of the particles (compare curves A, B and C in Figure 1). A similar observation has been reported for similar JNPs by [PERSON] et al. [10; 18] and was attributed to the hydrophilic-lyophilic balance of the JNPs, which is also called the Janus balance [19].  
Furthermore, the IFT is greatly affected by JNP-bPEI concentration, with a minimum value of \(\approx\) 33 mN/m for a concentration of 300 mg/mL, see Figure S7.
## 3 Results
### Sentinel-1 SAR-based Flood Mapping
#### 3.1.1 The Tax Day Storm and Hurricane Harvey Flood Events
For instance, the mean pre-event coherence value of the pixels close to the banks of Cypress Creek (e.g., the outskirts of Cypress City in Harris county located 26 miles northwest of downtown Houston (yellow box in Figure 3)) amounted to 0.99; however, the 2016 and 2017 flood events prompted significant changes in the surface conditions, as noted by a substantial decline in the co-event coherence value (mean: 0.31). Similarly, the Bear Creek area (pink box in Figure 3) was also significantly impacted by the two flood events, as indicated by the substantial reduction in the coherence values of the pixels in the area following the events (mean pre-event coherence = 0.97; mean co-event coherence = 0.34). Two areas were selected here (black dotted lines in Figure 3) to demonstrate the impact of flooding in areas proximal to the two rivers in the study area. Extensive flooding has been noted in the Lower Brazos watershed (black dotted line near Brazos River in Figure 3), including the city of Rosenburg (Fort Bend county) and the wide agricultural fields lying proximal (on the bank) to the course of Brazos River (Figures 1b and 3). The pixels in this part of the study area exhibited more than a 61% decrease in coherence values compared to the values before the severe weather events (mean pre-event coherence = 0.96; mean post-event coherence = 0.38) that is attributed herein to the flooding events (Figure 3). Similarly, the outskirts of Humble, located along Interstate 69 within the West Fork San Jacinto watershed (black dotted line in the upstream part of San Jacinto River in Figure 3) and situated adjacent to the course of San Jacinto River, experienced recurrent flooding, as noted by the vast decline (up to 70%) in the coherence pixels following the two major events--although the impact is much more pronounced in the case of Hurricane Harvey than the 2016 Tax Day storm (mean pre-event coherence = 0.98; mean co-event coherence = 0.30). Given that parts of the study area, including the banks/proximal areas to the major rivers and the small creeksand streams, are dominated by dense vegetation, an attempt to validate the CCD-based assessment was undertaken to ascertain whether the cause for the observed decline in coherence emanated from the surface changes attributed to flooding and not from other natural or anthropogenically driven alterations (discussed in Section 3.2). Recurrent flooding was noted in the northeastern part of the investigated area, including parts (south) of the George Bush Intercontinental Airport (shown in a red box in Figure 3; mean pre-event coherence = 0.98; mean co-event coherence = 0.40). Likewise, the pixels of the outskirts of Baytown city located near the Galveston Bay area (east of Houston city; Figure 3) also demonstrated a similar pattern of coherence decline (mean pre-event coherence = 0.96; mean post-event coherence = 0.38).
# Optical Studies in Red/NIR Persistent Luminescent Cr-Doped Zinc Gallogermanate (ZGGO:Cr)
## 2 Materials and Methods
### Pellets Synthesis  
The Zn\({}_{1+\mathrm{x}}\)Ga\({}_{2-2\mathrm{x}}\)Ge\({}_{\mathrm{x}}\)O\({}_{4}\):Cr doped with 0.5% molar of chromium, x = 0 and x = 0.1, hereafter designated as ZGO:Cr and ZGGO:Cr, respectively, were synthesised via a conventional solid-state reaction with mixtures of appropriate mole ratios of 1:2:0 and 1.1:1.8:0.1 for Zn:Ga:Ge in the ZGO:Cr and ZGGO:Cr, respectively. In the case of the chromium dopant, a stoichiometric ratio of \(Zn/(Ga+Cr)=0.495\) (0.5 mol%) was used for both alloys, as mentioned in [11]. The synthesis method was based on the work of [PERSON] et al. [13], in which the synthesis of ZGGO:Cr with \(x=0.1\) was optimised.  
The precursors were mixed in an agate ball mill with ethanol for 8 h. This mixture was dried in the oven for 7 h at 70 \({}^{\circ}\)C to evaporate all the ethanol. Afterwards, the powder was mixed and pressed into pellets with a 13 mm diameter. The pellets were then fired at 900 \({}^{\circ}\)C for 2 h and then grounded and reshaped into pellets with a 1.5 t uniaxial press. These pellets suffered a sintering treatment at 1350 \({}^{\circ}\)C for 36 h in a ThermoLab oven (heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\)). Additionally, a non-intentionally doped pellet with a molar fraction of x = 0.1 was produced and subjected to the same thermal treatments to evaluate the absorption properties of the matrix, for comparison purposes.  
For the case of ZGO:Cr, the mixture of the three precursors with ethanol was prepared in a planetary mill for 2 h and then left to dry at RT. The pellets were produced by employing a 13 mm diameter mould with a 1.5 t uniaxial press. The pellets were then sintered at 1300 \({}^{\circ}\)C (heating rate of 5 \({}^{\circ}\)C min\({}^{-1}\)) for 6 h [11].
# The Application of Microplasma in the Terahertz Field: A Review
## 3 Challenges  
### THz Radiation from Laser-Induced Microplasma  
Regarding the mechanism of THz waves emitted from laser-induced microplasma, a photocurrent theory proposed by [PERSON]'s team is widely accepted [68]. They believed that the free electrons released by the ionization of gas molecules are driven by the asymmetric laser field, resulting in a fast transient current and emitting THz wave. On the other hand, [PERSON] et al. found that the THz radiation may originate from the plasma oscillation or photoionization, where the THz radiation intensity is dependent on the plasma skin depth and propagation length [69; 70]. To understand the mechanism of the THz radiation from laser-induced microplasma, [PERSON] et al. and [PERSON] et al. made great efforts on the time-domain diagnostics and effective regulation of the terahertz radiation spectrum by the particle-in-cell (PIC) model [34; 35; 71; 72; 73; 74; 75]. Nevertheless, it is still unclear which mechanism of THz radiation is dominant in laser-induced microplasma. Moreover, the energy efficiency of the THz sources based on laser-induced microplasma is requires further improvement for THz applications in 6G communications and explosives detections.
# Asynchronous Partial Gaussian Approximation Detection Algorithm for Uplink-Grouped MIMO-SCMA System
## 2 System Model  
In this paper, the uplink MIMO-SCMA system is constructed with one base station, \(K\) frequency resources and \(L\) users. Each user is equipped with one antenna and the base station has \(R\) antennas \(\gamma_{r}\), \(r=1\cdots R\). The distance from each user to the base station is \(d_{l}(1\leq l\leq L)\). In this system, users are located in various directions and distances and send their information at any time. Users within the coverage of the base station are divided into different groups according to different delays.