esa-sceva/satcom-chunk-collection huggingface dataset:
# Formation Kinetics and Morphology Characteristics of Natural Gas Hydrates in Sandstone Fractures
## 3 Results and Discussion
### Hydrate Formation
#### 3.1.2 Effect of Fracture Shape  
Figure 9 illustrates a comparison of the hydrate induction time and total formation duration in Type I and Type X fractures. As shown in Figure 9a, under identical conditions, the hydrate induction time in Type X fractures is typically shorter than that in Type I  
Figure 8: Micrographs of CH\({}_{4}\) hydrate morphology within Type I fractures: (**A1,A2**): 3 mm; (**A3,A4**): 6 mm; (**A5,A6**): 10 mm.  
Figure 7: Observation images of hydrate growth under different fracture widths.  
fractures. This may be attributed to the relatively simple gas and liquid phase flow in Type I fractures, where the flow direction remains constant. In contrast, Type X fractures, with their relatively unique geometric structure and a greater number of gas flow pathways, exhibit more complex fluid dynamics. This may involve potential vortices or changes in the direction of the flow. These irregular variations facilitate the mixing of gas and liquid molecules, thereby increasing the gas-liquid contact area and ultimately promoting the hydrate induction process. Furthermore, as shown in Figure 9b, the duration of hydrate growth in Type X fractures exceeds that in Type I fractures. When calculating the final quantity of hydrate formation, as shown in Figure 10, it is evident that as the hydrate formation time increases, the final quantity of hydrate formation typically increases. This further highlights the role of the complex flow patterns within Type X fractures in promoting hydrate growth.  
Interestingly, the growth trajectory and morphological changes of hydrates in Type X fractures also differ from those in Type I fractures. As shown in Figure 11, in Type I fractures, hydrate formation typically spreads progressively from the bottom to the top. In contrast, hydrates in Type X fractures diffuse toward the center from multiple directions. This is because, in Type I fractures, the regular fracture geometry results in a unidirectional flow of fluid, causing the gas-liquid interface to expand in a relatively linear manner. Consequently, hydrates extend from the bottom upward, forming a layered hydrate structure. Conversely, in Type X fractures, the relatively complex geometric structure and the presence of intersecting regions in the middle result in gas entering the fracture from various directions. The complex fluid motion and water molecule diffusion patterns cause the gas-liquid contact interface to expand in multiple directions, leading to a more intricate hydrate growth trajectory.  
Figure 9: _Cont._  
Figure 10: CH\({}_{4}\) gas consumption curves during the hydrate formation process under different fracture shapes.  
Figure 9: Different fracture shape: (**a**) hydrate induction time; (**b**) total formation duration.
# Video Captioning Based on Channel Soft Attention and Semantic Reconstruct
## 4 Experimental Evaluation  
Our model is evaluated by using two benchmark datasets of video description, including the MSVD (Microsoft Video Description) dataset [12] and MSR-VTT (MSR-Video To Text) dataset [13]. We briefly introduce these datasets and the evaluation metrics in our experiments first, then show the experimental details and results.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 2 Research Methodology
### Performance Analysis Criteria:  
We analysed each technology's throughput and network bandwidth to determine its capacity to handle data traffic efficiently. Additionally, we assessed the latency and packet processing delay to understand the responsiveness of the network infrastructure. Furthermore, we investigated each technology's scalability to accommodate changes in network size and traffic load dynamically.  
Within the NFV framework, we analysed the efficiency of resource utilisation and assessed the performance of VNFs by examining throughput and latency. Moreover, we examined NFV's elasticity and dynamic scaling capabilities to deploy and manage VNF instances flexibly according to demand. Additionally, we scrutinised the efficiency of service chaining through multiple VNFs to ensure optimal traffic routing and processing.  
For CASB, we assessed the impact of inspection and enforcement mechanisms on application latency to minimise performance overhead. Furthermore, we analysed the throughput capacity of CASB solutions to handle large traffic volumes effectively. Finally, we examined the scalability of CASB solutions to support increasing numbers of users and cloud applications without compromising performance.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 3 Model of an ASK-Type AQC System
### Description of Received Quantum State
#### 3.1.2 TSVS with Kraus Representation  
In the TSVS case, a Kraus representation is used to express the attenuation channel. This Kraus representation allows us to describe the relationship between the transmitted and received quantum states, and can be expressed without use of an external system, unlike the Stinespring representation. If the transmitted and received quantum states are \(\rho^{(\mathrm{in})}\) and \(\rho^{(\mathrm{out})}\), respectively, then the Kraus representation of the attenuation channel is given as follows:  
\[\rho^{(\mathrm{out})}=\sum_{k=0}^{\infty}\mathbb{E}^{(\eta)}_{k}\rho^{(\mathrm{ in})}\mathbb{E}^{(\eta)}_{k}, \tag{19}\]  
where  
\[\mathbb{E}^{(\eta)}_{k}=\sum_{n}\sqrt{\left(\begin{array}{c}n\\ k\end{array}\right)}\sqrt{\eta^{n-k}(1-\eta)^{k}}|n-k\rangle\langle n| \tag{20}\]  
is the Kraus operator [21] for the attenuation channel with respect to the energy transmissivity \(\eta\).  
For the case where \(\mathsf{b}\in\{0,1\}\), we now consider the received quantum states \(\Psi^{(\mathsf{b})}_{\mathrm{SA}}\) when the binary information recorded using the reflectivities \(R_{\mathsf{b}}\) is denoted by \"0\" and \"1\". The received quantum state \(\psi^{(\mathsf{b})}_{\mathrm{SA}}\) can then be represented by  
\[\psi^{(\mathsf{b})}_{\mathrm{SA}}=\sum_{k=0}^{\infty}\Big{\{}\big{(}(\mathbb{ E}^{(R_{\mathsf{b}})}_{k})_{\mathrm{S}}\otimes\mathbb{I}_{\mathrm{A}}\big{)}| \psi\rangle_{\mathrm{SA}}\langle\psi|\Big{\{}\big{(}(\mathbb{E}^{(R_{\mathsf{ b}})}_{k})_{\mathrm{S}}\otimes\mathbb{I}_{\mathrm{A}}\big{\}}^{\dagger}. \tag{21}\]
## 2 Prediction of Penetration Depth Using the Finite Cavity Pressure Method  
### Cavity Pressure Method  
Analytic calculation methods for penetration depth began with [PERSON] et al. [16]. Spherical cavity expansion models approximate a two-dimensional target response with equations derived from cavity expansion analyses. A schematic figure of the cavity pressure at the tip is shown in Figure 1. A spherical cavity expanded from the initial radius with a constant velocity V. As the spherical cavity expands, response regions were developed following plastic and elastic response regions [9; 10]. The distribution of the cavity pressure is shown in Figure 2. The plastic region starts at the radius of b, and the elastic region begins at the radius of d. Cavity pressure was employed as an equation of penetration equations. From the equations of momentum and mass conservation with spherical symmetry, the pressure distribution can be calculated.  
The cavity pressure model depends on the target material property. Therefore, an appropriate cavity pressure model should be used. In this work, the target material was a 6061-T6511 aluminum plate. For this material, the compressible cavity pressure model considering strain hardening and strain rate dependency can precisely predict the response of the material [10]. Instead of using complex equations, the cavity pressure model was approximated with a second-order equation [10]. From the least square fit to the calculated pressure distribution with respect to the particle velocity (v), a normalized equation in Equation (1) was obtained.  
\[\frac{\sigma_{r}}{Y}=A+B(\sqrt{\frac{\rho_{0}}{Y}}v)+C(\sqrt{\frac{\rho_{0}}{ Y}}v)^{2} \tag{1}\]  
In Equation (1), v is the expansion speed of the cavity; Y is the yield stress of the target material; \(\rho_{0}\) is the density of the material; A, B, and C are fitting coefficients. According to the material property and material model, the values of coefficients are varying. The value of coefficients of A, B, and C for a 6061-T6511 aluminum considering strain hardening and strain rate dependency are 5.039, 0.983, and 0.940, respectively. This model is suitable for 300 m/s to 1200 m/s [10].
###### Abstract  
Implant stability is relevant for the correct osseointegration and long-term success of dental implant treatments. The aim of this study has been to evaluate the influence of implant dimensions and position on primary and secondary stability of implants placed in maxilla using resonance frequency analysis. Thirty-one healthy patients who underwent dental implant placement were enrolled for the study. A total of 70 O osseoSpeed TX (Astra Tech Implant System--Dentsply Implants; Molndal, Sweden) implants were placed. All implants have been placed according to a conventional two-stage surgical procedure according to the manufacturer instructions. Bone quality and implant stability quotient were recorded. Mean implant stability quotient (ISQ) at baseline (ISQI) was statistically significant lower compared to 3-months post-implant placement (ISQ2) (\(p<0.05\)). Initial implant stability was significantly higher with 4 mm diameter implants with respect to 3.5 mm. No differences were observed within maxilla regions. Implant length, diameter and maxillary regions have an influence on primary stability.  
dental implants; osseointegration; resonance frequency analysis +
Footnote †: journal: Applied Sciences
# Compact Broadband Antenna with Vicsek Fractal Slots for WLAN and WiMAX Applications
## 1 Introduction
According to [20], a 4 \(\times\) 4 MIMO antenna with dual operating bands was modelled, with four elements positioned on both sides of the dielectric material in the proposed MIMO antenna. Each element had a rectangular open-loop resonator inserted into the ground plane. The antenna had bandwidths of 0.08 GHz and 0.17 GHz at 2.46 and 3.5, respectively. WLAN (2.4-2.485 GHz) and WiMAX (3.4-3.6 GHz) bandwidth requirements were met. The design in [21] was composed of two layers, one layer comprising an E-shaped patch and the second comprising a U-slot. The proposed antenna covered the WLAN at 2.4 GHz and the WiMAX at 3.5 GHz. [PERSON], in [22], designed a Y-shaped dielectric resonator antenna with size of 120 \(\times\) 70 mm\({}^{2}\); it presented two bands from 2.401 GHz to 2.495 GHz and from 3.4 GHz to 3.69 GHz which shielded WLAN and WiMAX bands, respectively. In all the above analyses, it can be observed that these antennas had complex structures with large areas, or they do not cover all the bands of WLAN and WiMAX applications. This work is related to the design of a broadband octagonal microstrip patch antenna with Vicsek fractal slots for WLAN and WiMAX applications. The investigated antenna offers a broadband of 4.22 GHz from 2.48 GHz to 6.7 GHz with two resonant frequencies at 3.6 GHz and 5.3 GHz which shield both the WLAN and WiMAX bands. The broadband characteristic is realized by using the fractal slots and partial ground plane. The designed antenna has good gains of 2.78 dBi and 5.32 dBi, an omnidirectional radiation pattern in both the E-plane and the H-plane and high efficiency of 88.5% and 84.6% at the resonant frequencies. In the first section of this paper, the antenna geometry and the design evolution procedure are described. A parametric analysis of critical dimensions is presented in Section 2. The discussion of the simulated and experimental results is elaborated upon in Section 3. The conclusion of this work is given in the last section.
## 4 Findings
### Other Works
#### 4.7.3 Edge Computing  
A study addressed the access control issue in edge computing applications called TrustChain [90]. TrustChain aims to eliminate centralized processing delays and privacy problems while preserving IoT networks' resources. TrustChain is based on the following characteristics: interoperability, compatibility with different models, enhanced scalability and privacy, reduced delay and operation with an efficient mining scheme. This model describes various services offered using this model, including trust services (validator management, trust management, auditing and accountability, and trust data repository), blockchain services (consensus management, distributed ledger, ledger storage, P2P protocol, cryptographic services, and IPFS storage), smart contract services (registry, secure container, and life cycle), membership services (registration, ledger identities, and resource identities), and policy services (privacy, access control, and consent management).
# Analysis of Cryptographic Algorithms to Improve Cybersecurity in the Industrial Electrical Sector
## 3 Cybersecurity in the IEC61850 Standard
### Vulnerabilities in the GOOSE Protocol  
Cyber attacks on GOOSE messaging can cause catastrophic problems within an electrical substation, for example, irreparable damage to field equipment such as switches or disconnectors. GOOSE messaging works at layer 2 of the OSI model and there are several known attacks for this layer in particular, such as STP attacks, MAC spoofing, CAM table overflows, and VLAN hopping. Below are some attacks that can be performed on GOOSE messaging:  
* stNum attack: After inspecting GOOSE messages, malware proceeds to send a GOOSE message with a high number of stNum to the subscribing IEDs. They will think that an event has occurred in the substation and will proceed to process the false GOOSE message.
* GOOSE flood attack: Once the malware manages to obtain a GOOSE frame, it floods the network by sending in a very short period the same GOOSE frame that it obtained, but with the APDU field modified and filled in such a way as to reach the maximum supported by the GOOSE frame (approximately 1500 bytes). This attack has the purpose of compromising the processing capacity of the IEDs and the network.  
* Semantic spoofing GOOSE attack: After encountering a GOOSE message on the network, reproducing the original message, but with false information, the malware checks and manipulates the boolean (for example, the state of a switch) or analog information of the APDU. The malware also simulates message transition mechanics by incrementing stNum and zeroing sqNum. The intention is to deceive the IED through a false message.
* Goose replay attack: It uses the injection of an old but real GOOSE message from the substation having an event, for example, an overcurrent fault, through some open port. This open port could be an unconfigured switch port or a test port with access to all network traffic.  
The GOOSE flood attack was performed by [6; 12], and the stNum attack and semantic spoofing GOOSE attack by [12], where all attacks on GOOSE messaging were successfully performed. More details of the semantic spoofing GOOSE attack can be reviewed in [9].
## 4 Experiment Results
### Mean Square Error  
In the experiment, a number of different equalization methods are investigated to evaluate the performance of the proposed VDSR channel estimation system. OCDM MMSE and OCDM ZF represent channel equalization using the channel state information obtained by MMSE and ZF channel estimation algorithms for OCDM communications, respectively. Similarly, OFDM LMMSE and OFDM LS represent channel equalization using the channel state information obtained by LMMSE and LS channel estimation algorithms for OFDM communications, respectively. OFDM DL and OCDM DL represent the equalization of the channel estimated by the enhanced super resolution model VDSR for OFDM and OCDM, respectively.  
As shown in Figure 15, the trends in OFDM and OCDM in the shallow water acoustic channel at a distance of \(800\) m are similar, where MSE decreases with SNR increases. The lower bound of MSE to OFDM is \(-6\) dB whereas OCDM is \(-8\) dB. In high SNR regions OCDM outperforms OFDM, which is consistent with the theoretical predictions. However, when SNR is below \(7.5\) dB (i.e., low SNR region), the MSE of OCDM is higher than OFDM due to a frequency point estimation error whereby the OCDM modulation scheme based on Chirp subcarriers results in data detection errors across all subcarriers, whereas OFDM only incurs detection errors at specific data points. By applying deep learning, it is observed that MSE is significantly reduced in both OFDM and OCDM cases. In OFDM cases, it can be reduced to \(-8\) dB. Particularly noteworthy is the case of OCDM, where it can be reduced to \(-12\) dB. The combination of OCDM and VSDR can reduce the MSE from \(-8\) dB to \(-12\) dB. This indicates that the enhanced VDSR model -based channel estimation neural network trained by joint SNR training exhibits high versatility, enabling it to handle noise of varying intensities.  
Figure 14: Normalized channel impulse response.  
The mean square error measurement at 300 m and 3000 m are shown in Figures 16 and 17, respectively. It can be seen from the three sets of channel models that the lower limit of MSE for OCDM is lower than that for the OFDM system, and using deep learning can reduce the lower limit of MSE for both OFDM and OCDM.  
Figure 16: Mean square error measurement in traditional and deep learning approaches for OFDM and OCDM at 300 m distance.  
Figure 15: Mean square error measurement in traditional and deep learning approaches for OFDM and OCDM at 800 m distance.
## 1 Introduction
consider the electrical conductor as a leaking capacitor due to the interchange of ions between the mineral and fluid [25; 27; 28]. Thus, the polarization of charges within the conductive material never results in the form of a dielectric, but in an overall decrease in the amplitude of the polarization response and a shift of the polarization peak towards lower frequencies [25; 27; 28]. At the field scale, [PERSON] et al. investigated the SIP response of graphitic black schists of a Himalayan shear zone (in Nepal) [17]. They observed a high polarization response (around 1 Hz parallel to the foliation) and concluded that the black schist is characterized by high anisotropy. [PERSON] et al. proposed a linear model to estimate the volume fraction of graphite, applying the model proposed by [PERSON] et al. and found that the conductivity of the background material controls the conductivity of the material, and the chargeability depends linearly on the volumetric graphite content [16; 26; 31]. Nonetheless, [PERSON] and [PERSON] found that besides the volumetric concentration of the graphite, the fluid chemistry (pore fluid salinity and pH) also controls the polarization response [15]. In this study, we aim to evaluate the applicability of the SIP method to quantify the remaining graphite content in the mining tailings of a former graphite quarry. The graphite deposit has been reported from geological observations between two faults, as depicted in Figure 1a, and the graphite-rich formation is expected to continue under the subsurface towards the South-West direction. However, no drilling is available to justify such a hypothesis. Hence, we propose an alternative methodology for the application (i.e., calibration) of existing petrophysical models towards the quantitative interpretation of SIP imaging results. We compared SIP signatures obtained in the field and laboratory to develop a semi-empirical model linking the graphite content with the amplitude of the polarization effect. Here, laboratory measurements are conducted in rock plugs (to avoid changes in the geometry of the pore scale), followed by measurements in ground samples, where we evaluate the frequency dependence of IP readings by changing the texture of the material. Chemical analysis of the ground samples was conducted to obtain the graphite content. Hence, our objective is to interpret the SIP imaging results regarding graphite content and its textural properties.
## 2 Materials and Methods
### Method
#### 2.3.2 SCOPE Model  
Theoretically, the atmospheric CO\({}_{2}\) concentration could be an important factor affecting photosynthesis of vegetation, and many studies have shown that CO\({}_{2}\) has a fertilizing effect on most vegetation [28; 34; 35]. In this study, SCOPE model (Soil-Canopy Observation of Photosynthesis and Energy) is used to explore the mechanism of how CO\({}_{2}\) affects GPP estimation. SCOPE model is a vertical (1-D), integrated, radiative transfer and energy balance model [36], which can be applied to the quantitative study of canopy fluorescence spectrum and net photosynthetic of the canopy. SCOPE model consists of two modules: Fluspect, which is used to simulate the radiation transmission of reflected light, transmitted light and fluorescence inside the blade, and Biochemical module, which is used to calculated the photosynthetic rate and the ratio of absorbing radiation to fluorescence in C3 and C4 plants [37].  
In this study. Firstly, we used the SCOPE model to simulate GPP with a varying SIF and different settings of atmospheric CO\({}_{2}\) concentrations. Then, we can analyze the effect of different CO\({}_{2}\) concentrations on the linear relationship of SIF-GPP. The required input parameters of the SCOPE model include meteorological data (incoming shortwave and long-wave radiation, air temperature, humidity, wind speed and CO\({}_{2}\) concentration), leaf area index(LAI), leaf angle distribution, leaf chlorophyll content(C\({}_{\text{ab}}\)), stomatal conductance parameter (m), maximum carboxylation capacity(V\({}_{\text{cmo}}\)) and so on. In order to explore the effect of the CO\({}_{2}\) concentrations on daily variation characteristics of vegetation canopy GPP and SIF, the model parameters were set as follows: (1) Biochemical parameters: Set V\({}_{\text{cmo}}\) to 90 according to [PERSON]'s work [38]. When C3 plants have enough water m(Ball-Berry stomatal conductance parameter) should be set to 9 [39]. Dark respiration (Rdparam) was set to 0, so that the net photosynthesis of canopy output is equivalent to GPP [40]. (2) The meteorological data, which spans from 8:00 am to 18:00 pm on April 14, 2015, with a time resolution of half an hour, were acquired from US-Syr flux towers. (3) Finally, the atmospheric CO\({}_{2}\) concentration was set to 8 levels, namely, 300, 350, 375, 400, 425, 450, 475 and 500 ppm. The remaining input parameters were set as defaults of the SCOPE model.