esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Analysis of Shale Cuttings Migration in Horizontal Wells
## 3 Experimental Results  
### Experimental Phenomenon  
#### 3.1.1 Migration Process  
Figure 7 illustrates the trajectory and mechanism of cuttings bed migration in detail. In the initial phase, as shown in Figure 7a,b, the cuttings begin to move through the annulus, leading to the formation of one or more localized cuttings beds. This early stage is characterized by instability, where the fluid shear forces exerted on the cuttings are insufficient to induce coherent flow. As a result, the cuttings accumulate in a disorganized manner without forming a unified bed.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multicolumn{4}{c}{**Size (mm)**} & \multirow{2}{*}{**Density (g/cm\({}^{3}\))**} \\ \cline{2-4}  & **Length** & **Width** & **Height** & \\ \hline
2 & 2 & 1 & 1 & 2.7 \\
3 & 1 & 1 & 0.5 & 2.7 \\
4 & 4 & 4 & 4 & 2.7 \\
5 & 2.5 & 2.5 & 2.5 & 2.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Shale cuttings size.  
Figure 6: Cuttings shape diagram. (**a**) unmilled cuttings; (**b**) milled cuttings.  
Figure 7: Cuttings bed migration process ((**a**,**b**) Early phase of cuttings migration with localized bed formation. (**c**,**d**) Cuttings accumulate, forming longer beds. (**e**–**g**) Final stage, where beds elongate but remain low).  
As the migration progresses, the cuttings accumulate to form a more extensive and elongated bed, as depicted in Figure 7c,d. At this stage, the cuttings progressively cover a larger portion of the annulus, resulting in greater overall accumulation. This behavior suggests that the cuttings exhibit significant migratory capacity, driven by the drilling fluid, which enables their movement over extended distances.  
In the expanding section of the annulus, the leading edge of the cuttings bed accelerates, resulting in a reduction in the bed's length. This acceleration can primarily be attributed to the narrowing of the annular flow channel, which increases the fluid velocity. Consequently, the enhanced flow exerts greater dynamic forces on the cuttings, accelerating their migration and contributing to the observed shortening of the bed. Upon passing through the expanding section, the migration velocity of the cuttings bed reaches its peak. Subsequently, the cuttings begin to accumulate again, ultimately forming elongated but low-height beds, as shown in Figure 7e-g.
# Fast 3D Transmission Tower Detection Based on Virtual Views
## 3 Methodology
### Virtual View Generation Based on 2D Splatting
When the 3D Gaussian point \(G(X)\) is projected onto the virtual view via the parallel projection matrix, \(P\), corresponding to the side view, it results in a 2D Gaussian point \(g(x)\); the mathematical expression is as follows:  
\[g(x)=e^{-\frac{1}{2}(x-\mu^{\prime})^{T}\sum^{\prime-1}(x-\mu^{\prime})} \tag{2}\]  
where \(\mu^{\prime}\) denotes the 2D coordinates obtained by projecting \(\mu\), that is, \(\mu^{\prime}=P\mu\), while \(\sum^{\prime}\) is the 2D covariance matrix, which is obtained as follows:  
\[\sum^{\prime}=JP\sum P^{T}J^{T} \tag{3}\]  
Figure 2: Comparison of two projection strategies on the TT. where \(J\) is the Jacobian matrix of \(P\). Following the projection of all 3D Gaussian points onto a 2D plane via the projection model \(P\), the subsequent procedure entails rendering the virtual view by utilizing the projected geometric and color attributes. The process initiates with the sorting of the projected 2D Gaussian points based on the depth information of each point. The color of a point \(x\) within the virtual image is computed as the weighted sum of the colors of all 2D Gaussian points at that location:  
\[ \begin{split}\mathbf{C}&=\sum_{i=1}^{N}\mathbf{c} _{i}\mathbf{a}_{i}^{\prime}\prod_{j=1}^{i-1}\left(1-\alpha_{j}^{\prime}\right) \\ \alpha_{i}^{\prime}&=\alpha_{i}\cdot exp\left(- \frac{1}{2}\left(x-\mu_{i}^{\prime}\right)^{T}\Sigma_{i}{}^{\prime-1}\left(x- \mu_{i}^{\prime}\right)\right)\end{split}  \tag{4}\]  
where \((\mathbf{c}_{i},\alpha_{i})\) represents the color (directly provided by LiDAR data) and opacity (with a default value of 1) of the 2D Gaussian point \(i\), while \((\mu_{i}^{\prime},\Sigma_{i}^{\prime})\) are the Gaussian parameters of \(i\), with \(\alpha_{i}^{\prime}\) indicating the opacity of Gaussian point \(i\) at the image pixel \(x\). It is noteworthy that since the entire view rendering process is based on an individual point, it is very easy to parallelize and accelerate, which can greatly reduce the virtual view generation time. Figure 3 shows a portion of the virtual view covering a TT.
## 3 The Proposed Algorithm
### Parameter Selection Criteria  
In our proposed ego-motion estimation approach, the selection of parameters plays a crucial role in ensuring both computational efficiency and high estimation accuracy. One of the key considerations when choosing parameters is the compatibility with the GPU architecture, which is optimized for processing data in powers of two. This approach maximizes the parallel processing capabilities of modern GPUs, ensuring efficient memory management and fast execution of kernel functions.  
We specifically recommend selecting parameters such as the number of candidates or the resolution of image patches in powers of two, such as 32, 64, or 128. This choice stems from the fact that GPU architectures handle memory and data processing more efficiently when data structures are aligned to power-of-two sizes. When parameters are selected in powers of two, memory access becomes more optimized, improving memory coalescing and reducing fragmentation. This, in turn, improves the overall performance of the genetic algorithm running on the GPU.  
For example, when defining the number of population candidates in the genetic algorithm, selecting values such as 32 or 64 ensures that memory operations are streamlined.  
This alignment improves kernel execution by facilitating better load balance, reducing memory latency, and minimizing the overhead caused by non-aligned memory accesses. Additionally, selecting powers of two helps to optimize the memory hierarchy, particularly with shared memory on GPUs. This optimization ensures that the algorithm can make efficient use of GPU resources, thus reducing the time required for each iteration.  
By adhering to these power-of-two values for parameter selection, we achieve both higher processing speeds and more efficient utilization of the available resources, enabling real-time performance for ego-motion estimation in autonomous vehicle systems. These choices align with industry best practices for high-performance parallel computing on GPUs.
# Privacy-Preserving E-Voting System Supporting Score Voting Using Blockchain
## 3 Background
### Zero-Knowledge Proof of Set Membership  
A set membership proof enables a prover to prove that a secret value lies within a given public set. This proof is done in a zero-knowledge manner such that no information about the value is made public. This proves particularly useful in e-voting, particularly score-based since a voter is required to prove _(a)_ that their vote belongs to the set of approved candidates; _(b)_ that the score they give to a candidate falls within the valid range (a set); and _(c)_ that they are indeed a member of the set of registered voters eligible to vote in the active election. Using the Camenisch and Stadler [31] notation for proofs of knowledge (PoK):  
\[PK(\delta,\gamma):Y=g^{\delta}h^{\gamma}\wedge(\gamma\in\phi)\]  
where \(Y=g^{\delta}h^{\gamma}\) is a Pedersen commitment of the integer _more symbols_ using randomness \(\gamma\). This proof convinces the verifier that the secret committed in \(Y\) lies in the set \(\phi\) without explicitly revealing \(\phi\) in the proof. The set \(\phi\) can be an input that is common both to the verifier and the prover. The proof can also be made non-interactive by implementing with the Fiat-Shamir heuristic.  
This type of proof has three security guarantees: _(i) Soundness._ An honest verifier cannot be convinced by any prover if that prover did not compute the results correctly. _(ii) Completeness._ The honest prover will always convince an honest verifier that a true statement is indeed valid. _(iii) Zero-knowledge._ The verifier learns nothing during the proof distribution other than the fact a statement is true (or false).
## 2 Methodology
### Grid Convergence Study  
The selection of a proper mesh is very important in numerical studies. On one hand, choosing coarse mesh can result in a reduction in the accuracy of results, while on the other hand, choosing a very fine mesh can increase the time and cost of the simulation. Due to this reason, a grid convergence/mesh independent study was performed to find the most appropriate mesh size to be used in this study. For the grid convergence study, three different sizes of mesh were chosen, namely coarse, medium, and fine. The size of the coarse, medium, and fine meshes were 0.008, 0.005, and 0.004 m, respectively. During simulation, liquid and gas superficial velocities were kept at 2.8 m/s and 3.2 m/s. Dynamic pressure at location 1.33 m downstream was chosen as a reference point to quantify dynamic pressure for all mesh sizes.  
The Grid Convergence Index (GCI) can be used to check if the results are in the asymptotic range of convergence, and it can be calculated using Equation (12). Numerical  
Figure 1: Pipe geometry.  
Figure 2: Hexahedral mesh. (**a**) Horizontal view, and (**b**) side view showing the inlet.  
solution is closer to actual solution when grid resolution approaches zero. GCI indicates how much can a solution improves with more refinement of the mesh. A smaller GCI value shows that the use of a corresponding mesh size results in solutions that are well within range of the true numerical value.  
\[\text{GCI}=\frac{F|\varepsilon|}{(r^{p}-1)} \tag{12}\]  
\[\varepsilon=\frac{f_{2}-f_{1}}{f_{1}} \tag{13}\]  
\[p=\frac{\ln\left(\frac{f_{2}-f_{2}}{f_{2}-f_{1}}\right)}{\ln(r)} \tag{14}\]  
where \(F\), \(\varepsilon\), \(p\), and \(f\) stands for factor of safety, relative error, order of convergence, and selected variable, respectively. Details regarding mesh size and dynamic pressure are shown in Table 1. Extrapolated value indicates the highest number of mesh, and it can be used to represent the true solution.  
Figure 3 shows the variation in average dynamic pressure readings at the selected measurement points for different meshes. When the mesh is improved from coarse to medium, the difference between the readings is high. The GCI value for this transition is 2.2%, which is not favorable and coarse mesh is not recommended. In the case of transition from medium mesh to fine mesh, the corresponding reading did not vary a lot and GCI value is 0.5%. Hence by using fine mesh, the number of elements are increased, which will increase the simulation time and cost, while not improving the results significantly. Due to this reason, medium mesh is chosen with the number of elements equal to 305,254.
## 2 Biogas Cleaning and Upgrading  
Depending on the final utilization of biogas, its composition must be adjusted accordingly. It is necessary to clean and upgrade biogas when biomethane is desired. Cleaning and upgrading are usually carried out separately, but sometimes, they are accomplished simultaneously for certain pollutants.  
H\({}_{2}\)S is formed during the microbiological reduction of compounds that contain sulfur, and it can lead to corrosion problems [49]. The existing methods for H\({}_{2}\)S removal are as follows: in situ precipitation [50], adsorption (using iron oxides/hydroxides or activated carbon) [51], physical and chemical absorption [51], membrane separation (MS) [50], biofiltration [51], in situ microaerobic removal [52], micro-algae-based removal [52], and the combined biological method [53]. The emerging technologies for H\({}_{2}\)S removal are FlashH\({}_{2}\)S [54] and a novel electrochemical process [55].  
Oxygen and nitrogen are not detected in a properly conducted AD process [56]. The proportions of impurities vary, depending on the substrate and digestion conditions, and they are removed through adsorption or MS [50]. Water and ammonia are removed via gas cooling, compression, adsorption, or absorption [49]. Siloxanes and Volatile Methylsiloxanes (VMSs) are removed via commercially available techniques such as adsorption [57], absorption, or cryogenic separation (CS) [52], while biological methods are in the preliminary research stage [53]. Particles can be removed from biogas through mechanical filters (installed at the biogas outlet of the digester and before the biogas enters the upgrading unit) [40; 49]. However, due to the humidity content of biogas, wet dedusting systems, such as electrostatic precipitators and wet scrubbers, are primarily employed for particle removal [58]. Halogenated compounds are usually removed simultaneously with CO\({}_{2}\)[59].  
Biogas upgrading involves refining biogas by reducing or removing the CO\({}_{2}\) from biogas (although other compounds can be separated, too) to obtain biomethane with the needed quality for its utilization as a renewable substitute for NG [51]. The existing biogas upgrading technologies are divided into developed technologies that are commercially available and established and emerging technologies that are still at a laboratory or pilot scale. The processes used for biogas upgrading can be classified according to the separation methods: phase addition, a solid agent, a barrier, phase creation, and a force field or gradient [60].
## 3 Results and Discussion
### Advanced Energy Management Systems and Control Architectures
#### 3.1.3 Demand Response and Predictive Control Strategies  
Active demand management has become a central focus in microgrid operation, promoting user participation in optimizing energy consumption [39; 63]. demand response (DR) programs enable flexible adjustment of consumption patterns, contributing to the balance of supply and demand in systems with high RES penetration [64; 65; 66]. The application of model predictive control (MPC) is frequently identified as an effective approach for implementing predictive control strategies [67; 68]. These models forecast system behavior and optimize energy dispatch decisions while considering technical constraints, economic factors, and user comfort requirements [69; 70; 71].  
However, the lack of high-quality datasets and concerns over user data privacy present significant barriers to the effective deployment of these strategies [72; 73; 74]. The use of deep learning and neural network models is gaining attention for enhancing prediction accuracy, although these methods require substantial data resources and computational capacity [75; 76; 77].
## 4 Encrypted SFC Architecture
### The Authentication Centre (AuC)  
The EFs are controlled by an Authentication centre that distributes the encryption keys and the integrity keys. Due to the non-bidirectional NSH communication channel between EFs [2], an IPSec IKE channel is not possible to establish on the data plane. Hence, we adopt the aforementioned SD-SA application from our previous work [7] in order to replace IKE in IPsec. In summary, this application includes the following functionality. The initial step is to pre-configure an authentication key for every EF during EF instantiation. Second, all EFs establish a secure channel to the AuC. Third, the AuC sends the integrity and confidentiality key to the encryption function over the authenticated and secured RESTconf channel. This last step is a periodic event that is repeated for every key change. An important requirement for this concept to work is that all EFs are connected to one common AuC. This also requires a shared control plane VPN between all data centre tenants. This control plane VPN is established by using site-to-site IPsec VPN tunnels between the data centres.
## 2 Background
### Security Analysis and Threat Modelling
#### 2.2.1 STRIDE Threat Model  
One of the most common threat modelling techniques is the STRIDE method, which was developed by [PERSON] and [PERSON] in 1999 at Microsoft [29]. In this method, threats to the system are considered in six categories, each violating one of the properties necessary for the secure functioning of the system [30]:  
* Spoofing refers to an attempt by an attacker to impersonate an approved user or system element and use their identity to access information or services. Therefore, secure system _authentication_ may be compromised.
* Tampering relates to the malicious alteration or deletion of data, which violates the _integrity_ property of the system.
* Repudiation means an attacker is able to deny their actions, which leads to a violation of the _non-repudiation_ of the system.
* Information Disclosure involves unauthorised disclosure of information, compromising _confidentiality_.
* Denial of Service threat utilisation violates the _availability_ of the system for approved users.
* Elevation of Privilege refers to granting users access to services, information, or system elements to which they should not have access due to their role restrictions. During the exploitation of this threat, the secure _authorisation_ of the system is compromised.  
The application of STRIDE consists of the following five steps, as also depicted in Figure 1.  
1. _System Decomposition_. To begin with, it is necessary to split the analysed system into logical or structural components. It can be processes occurring in the system or in which the system participates, physical elements of the system, or ones that communicate with the system.
2. _Data Flow Diagram (DFD)_. Next, for each system component defined in Step I, it is necessary to create a DFD that reflects its functionality. Some of the basic DFD elements include external entities, process, data flows, and data stores.
3. _Threat Identification_. During this step, for each system element, possible STRIDE threats should be identified.
4. _Vulnerability Identification_. Further, it is required for each previously identified 'per-element-threat' pair to determine the vulnerabilities of elements that can be used to enable the threat.
5. _Mitigation Strategy Planning_. Finally, for each vulnerability, strategies for mitigation should be proposed/developed.  
Figure 1: STRIDE Steps.
# Recent Advances and Insights into Bromelain Processing, Pharmacokinetics and Therapeutic Uses
## 3 Extraction and Purification Methods
### Aqueous two-phase systems  
Aqueous two-phase systems are a low-cost, environmentally friendly, rapid and scalable technique for the extraction and purification of many compounds, and involves a reusable polymer and a salt, or two reusable polymers [36]. In general, this approach separates proteins and prevents denaturation while removing undesirable contaminants, such as pigments and polysaccharides [37]. However, the structure of bromelain means that few organic solvents are well tolerated by the enzymes in the extract [38]. Using this method [PERSON] et al. have obtained pure bromelain with significant protease activity (stem: 4.0 units/mg; fruit: 3.6 units/mg) when a mix of polyethylene glycol (PEG) (\(8\%\,w/w\)) and ammonium sulphate (15% \(w/w\)) was used [39]. Similar results have been obtained using a PEG/potassium phosphate system [27].  
[PERSON] et al. have used biphasic systems, containing PEG and salts at different concentrations, to test the purification of bromelain from pineapple peel. The highest enzymatic activity and yield was obtained with PEG-3000 (15% \(w/w\)) and magnesium sulphate (20%) in water (enzyme activity: 5.23 units/mg) [40].  
Finally, an aqueous two-phase system used to extract and purify bromelain with polyphenol oxidase provided a four-fold concentration from the starting material [27].
## 1 Introduction
The IRS's ability to reshape the wireless environment passively allows networks to adaptively steer signals, making it possible to satisfy individual QoS needs. For example, IRS can help sustain high SINR levels and reliable links even under non-line-of-sight (NLoS) conditions. Nevertheless, consistent QoS in IRS-assisted systems demands advanced solutions in beamforming, real-time reconfiguration, and optimisation that can address issues such as user mobility, fading, and resource allocation. Ultimately, service providers must strike a balance between maintaining QoS and optimising costs and energy consumption, positioning IRS as a transformative--yet technically demanding--tool for future 6G networks [9; 10]. From the above discussion, it is evident that while some attempt has been made to improve achievable rate in an IRS-enabled system, a lot of other complexities remain unresolved and required further exploration and analytical solutions to these complex optimisation problems. In this work, the achievable rate at the user in an IRS-enabled wireless system is maximised, subject to each individual user's SINR constraints as well as the total transmit power constraint at the AP. This non-convex problem solved by optimally solving a power minimisation problem which achieved superior performance compared to benchmark systems. The main contributions of this paper can be summarised as:  
* To address a highly non-convex rate maximisation problem under QoS guarantees and total power constraints. * To establish the solution equivalence between the rate maximisation and the corresponding power minimisation problems. * To solve the power minimisation problem optimally. * To demonstrate the effectiveness of the proposed solution approach through numerical simulations. * To show the effect of IRS location on the performance of the system in simulation results. The rest of this article is organised as follows: In Section 2, related works are discussed. Section 3 presents an IRS-enabled wireless communication system model. Then the rate maximisation problem is formulated based on the proposed model. For this, alternating optimisation algorithm is described in Section 4. Section 5 provides thorough numerical simulations and rigorous result analysis. Finally, in Section 6, the conclusions and future research ideas are presented.