esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of the Mining Dimensions on the Stability of Backfilled Pier-Columns
## 4 Physical Experiments  
This investigation explored the impact of the mining dimensions on the stability of the backfilled pier-columns. A plane stress model was used to conduct the physical experiments. The length, height and width of the model were 250 cm, 200 cm and 20 cm respectively. These experiments monitored the variation pattern of the vertical stress in the backfilled pier-column.  
In accordance with the overlying strata of borehole 8-15, the stratification of the strata has been simplified in the model. Backfilled pier-columns, which had a length of 7.5 cm, width of 7.5 cm, and height of 2 cm or 4 cm were arranged along the width with a row spacing of 12 cm between them. The pier-columns are numbered 1 to 10 from left to right; their exact positions are shown in Figure 7.  
The pressure cell was embedded in the prefabricated pier-column. In addition, the data logger was used to collect the stress data in the experiment. The process of the mining and backfilling is illustrated in Figure 8a,b.  
Figure 6: Distribution of the stress and elastic zone with a mining thickness of 1 m and a variable mining width: (**a**) vertical stress of 160 m width; (**b**) elastic zone of 160 m width; (**c**) vertical stress of 240 m width; (**d**) elastic zone of 240 m width.  
Figure 7: The positions of the pier-columns.  
In the process of mining the physical model with a mining thickness of 2 cm, no major failure occurred in the pier-column, and the overlying strata were well supported and controlled. The experimental results of the backfilled pier-column are shown in Figure 9a. In the process of mining the physical model with a mining thickness of 4 cm, apparent fractures were found in the pier-column. The residual strength that occurred after the failure of the pier-column still provided strong support to the roof. Cracks appeared in the immediate roof, whereas no cracks or fractures were found in the other strata. The experimental results are shown in Figure 9.  
In the two physical models with different mining thicknesses, the stress of the pier-column was monitored from the point of setting the first pier-column to the completion of the experiment. In addition, the stress monitoring curves of No. 1, No. 3, and No. 5 pier-columns were compared and analyzed, as shown in Figure 10.  
By comparing the stress of the pier-column under the different mining thickness conditions, it can be observed that when the mining thickness of the working face increased, the stress of the pier-column increased significantly. This shows that the mining thickness has a greater impact on the stress of the pier-column. Based on these results, there is a negative correlation between them. When the stress exceeds the strength of the pier-column, the pier-column will undergo plastic failure.  
Figure 8: Physical experiments: (**a**) stress cell in different thickness backfilled pier-columns; (**b**) data logger.  
Figure 9: View of the bearing performance of backfilled pier-column with a different mining thickness: (**a**) 2 cm; (**b**) 4 cm.
## 3 Experimental Research Set Up  
### Optical Cavity Boundary Layer Control Test  
The experimental study was conducted on a certain type of chemical reaction experimental platform, and the upper and lower wall panels of the optical cavity were designed to be removed to replace the experimental parts, as shown in Figure 3. In the experimental study, three different boundary layer control experiments were designed, that is, three different types of upper and lower wall panels of the optical cavity: a slotted plate, a mainstream ejection slot, and an orifice plate.  
The slotted plate test piece is shown in Figure 3a. The opening angle of the slotted plate is equal to that of the optical cavity. The upper and lower slotted plates are arranged in equally spaced slots. As illustrated in Figure 3b, a joint was added to the back edge of the slotted plate. As shown in Figure 3c, the spacing between any two adjacent offices was equal. The control effects of different test pieces on the boundary layer were experimentally compared.  
In an experimental study on the boundary layer control method, vacuum and mainstream ejection systems were used to control the optical cavity boundary layer using the  
Figure 3: Cavity structure and test pieces. (**a**) is the slotted plate test piece; (**b**) is the mainstream ejected plated with the upper and lower slotted plates are arranged in equally spaced slots; (**c**) is the orifice plate with the spacing between any two adjacent offices was equal.  
boundary layer suction method. The key parameter was the amount of air pumped. The boundary layer cannot be effectively controlled when the amount of pumped air is small. When the pumping volume is large, the flow field parameters of the main air are affected. In this study, the thickness of the boundary layer on the upper and lower walls of the optical cavity was estimated based on a developed formula for the thickness of the compressible plate flow boundary layer. The pumping capacity was controlled by the throttle orifice plate installed in the vacuum pipe, and throttle orifice plates with different opening rates should have different pumping capacities. Subsequently, the area of the opening hole or slot was determined based on the parameters of the pumping volume and throttling area.  
The flow near the vent wall (opening and slot walls) was extremely complicated. When the shock wave generated by the supersonic flow reached the wall at the opening or slot, the airflow entered the stationary chamber with almost no pressure drop; the local pressure dropped, and the shock wave was reflected as an expansion wave. However, on a real wall, the shockwave reflection is still a shockwave. The reflected shock and expansion waves must pass through a mixing zone to cancel each other. Therefore, it is essential for the ventilation area to be suitable, and the arrangement of holes or slots should be compact. That is, under a certain opening/closing ratio, the aperture or slot width should be as small as possible [18].
# EAOA: Energy-Aware Grid-Based 3D-Obstacle Avoidance in Coverage Path Planning for UAVs
## 4 Evaluation Metrics and Results
### Evaluation Metrics
#### 4.1.1 Turning Angle  
The power consumption is affected by the turn rate [32]. How many turns are needed during the whole flight mission is a main concern. Assume that the drone is coming from vertex \(v_{1}\) to \(v_{2}\) and is going to make a turn at \(v_{2}\) to head later to \(v_{3}\) (see Figure 9). The turning angle is computed as follows [14]:  
\[\Phi_{123}=\pi-\cos^{-1}(\angle v_{1}v_{2}v_{3}) \tag{9}\]  
Figure 9: Graph representation: red points are vertices and blue lines represent edges.  
where \(\cos^{-1}(\angle v_{1}v_{2}v_{3})\) is computed from the law of cosines in \(\triangle v_{1}v_{2}v_{3}\). Hence, the angle can be found from the Euclidean distance between vertices as follows:  
\[\Phi_{123}=\pi-\cos^{-1}\left[\frac{(e_{12}{}^{2}+e_{23}{}^{2}-e_{31}{}^{2})}{2e _{12}e_{23}}\right] \tag{10}\]  
where \(e_{ij}=d(v_{i},v_{j})\) is the Euclidean distance between two vertices \(v_{i}\) and \(v_{j}\).
## 4 Assumption Involved in CS-SR
### Modeling Variables Used in CS-SR  
The variables used in the CS-SR method are the requirements taken from the user in the pre-defined range (with the minimum and the maximum) QoS attributes [23]. The priority of the QoS attributes requested is also given in numeric form.  
The number of cloud users is shown in Equation (1):  
\[U=<u_{1},\ u_{2},\ \ldots,\ u_{q},\ \ldots,\ u_{Q}>,\ U\in[1,Q], \tag{1}\]  
Figure 2: Decision-making system.  
where \(U\) is the set containing the number of users in which \(u_{Q}\) is one of the users from the is the total number of users \(Q\) requesting the cloud services.  
The requirements raised by one of the cloud users are shown in Equation (2):  
\[U_{r}=\;<u_{1[min,max]},\;u_{2[min,max]},\;\ldots,\;u_{r[min,max]},\;\ldots,\;M> U_{r}\in[1,M], \tag{2}\]  
where \(U_{r}\) refers to the requirements of the user in the given range [min, max]. The \(u_{qr[min,max]}\) represents the qth user requirements for the rth attribute taken in range, and \(M\) represents the total number of attributes. The QoS priority given by the cloud user is shown in Equation (3).  
\[P_{u}=\;<p_{1},\;p_{2},\;\ldots,\;p_{i},\;\ldots,\;p_{M}> P_{u}\in[1,M]. \tag{3}\]  
The priority is taken from the user for each attribute because some attributes are more critical to the user. The attributes with the lowest priority are considered more important than others.  
The total number of services is N stored in the service set S, and S\({}_{\text{j}}\) is one of the services from the total services from 1 to N. The service S\({}_{\text{j}}\) also has QoS attributes associated with the service, and there can be total M QoS attributes for the service S\({}_{\text{j}}\). The QoS attribute q\({}_{\text{m}}\) is one of the QoS attributes for service S\({}_{\text{j}}\). The cloud services and their QoS attributes offered by the provider are shown in Equations (4) and (5):  
\[S=\big{\{}s_{1},\;s_{2},\;\ldots,\;s_{j},\;\ldots,\;s_{N}\big{\}},\;j\in[1,N], \tag{4}\]  
\[s_{j}=\;<q_{1},\;q_{2},\;\ldots,\;q_{m},\;\ldots,\;q_{M}>,\;j\in[1,M], \tag{5}\]  
The candidate set C.S. containing the list of candidate services received after the filtration step, and the feedback associated with each candidate service is shown in Equation (6). \(s_{j}q_{jffj}\) represents the jth service with the q\({}_{\text{m}}\) QoS attribute, and F\({}_{\text{j}}\) is the feedback for the service taken from an experienced user.  
\[CS=\big{\{}s_{1}q_{1}f_{1},\;s_{2}q_{2}/2,\;\ldots,\;s_{j}q_{mfj},\;\ldots,s_{ N}q_{MfN}\big{\}}\;j\in[1,M], \tag{6}\]
## 2 Antenna Design
### Parameter Analysis of the CP Element
To sum up, during the design stage of the proposed element, we can first optimize the parameters of the ME dipole to minimize the AR value in the lower frequency band. According to the design guideline in [36], the basic parameters of an ME dipole can be obtained. Next, we can optimize the parameters of the vertical metal plates to further expand the AR in the higher frequency range. Finally, the ME dipole parameters can be optimized in a small range to ensure that the AR fluctuation across the entire frequency band is minimal.
## 4 Numerical Simulation for the Highway Subgrade Stability  
### Numerical Model  
To verify the theoretical results, a numerical simulation was carried out to study the subgrade stability in this paper. The numerical model was established using Flac3D. As shown in Figure 6, the numerical model consists of four parts. The upper part is the subgrade; the middle part is the soft soil; the lower part is the sandy soil. There are two ELBs at the foot of the subgrade. The width of the subgrade top is 33 m; the height of the subgrade is 6 m; the subgrade slope ratio is 1:1.5; the heights of soft soil and sandy soils are 20 m and 10 m, respectively; the length of soft soil and sandy soil is 91 m. The width of the model is 1 m.  
In the model, the hexahedral mesh was used for establishing all layers. Mesh sizes are 1.00 m, 1.00 m, and 1.00 m, respectively, in the X, Y, and Z directions for the soft soil and sandy soil layer. There are 91 mesh elements in the X direction, 1 mesh element in the Y direction, and 30 mesh elements in the Z direction. There are a total of 2730 mesh elements for the soft soil and sandy soil layer. At the subgrade bottom, mesh sizes are also 1 m in the X and Y directions, respectively. At the subgrade top, the total number of mesh elements is 306, which is the same as at the subgrade bottom.  
As shown in Figure 6, the X direction displacement is fixed on the left and right boundaries. The Y direction displacement is fixed on the front and back boundaries. The X Y, and Z direction displacements are fixed on the bottom boundary. The other boundaries of the model are free. The Mohr-Coulomb constitutive model was adopted for every layer.  
The physical and mechanical parameters of the model can be found with a laboratory test. The parameters of the ELB are the same as those of the subgrade. The parameters for all layers are provided in Table 1.
## 3 Experimental Process and Results
The evaporation rate of fuels is positively correlated with their equilibrium vapor pressures [27]. The equilibrium vapor pressures of the three fuels are presented in Table 3. It can be seen that, under the same conditions, ethanol has the highest evaporation rate, while n-dodecane has the lowest. Therefore, under the same conditions, ethanol has a lower ignition temperature, while n-dodecane has a higher ignition temperature. This is consistent with the scatter plot of the experimental results for the three fuels presented in this paper. Figure 10: Parameter distribution curves during fuel droplet evaporation [10]. \(T_{w}\): temperature at the droplet surface; \(T^{*}\): temperature at the reactive zone’s inner boundary; \(T_{\infty}\): ambient temperature; \(Y_{f,w}\): the concentration of fuel gas at the droplet surface; \(Y_{f}^{*}\): the concentration of fuel gas at the reactive zone’s inner boundary; \(Y_{ox,\infty}^{*}\): the concentration of oxygen at the reactive zone’s inner boundary; \(Y_{ox,\infty}\): ambient oxygen concentration; \(r_{w}\): the radius of the droplet surface; \(r_{1}\): the radius of the reaction zone’s thin layer. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Ethanol** & **N-Heptane** & **N-Dodecane** \\ \hline Boiling Point/K & 351 & 371.5 & 489.2 \\ Saturation Vapor Pressure/kPa & 7.97 & 6.36 & 0.15 \\ Latent Heat of Evaporation at Boiling & & & \\ Point/kJ\(\cdot\)kg\({}^{-1}\) & 836.9 & 316.4 & 308.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Physical parameters of three liquid fuels at a temperature of 298 K and a pressure of 1.00 kPa [28].
## 6 Data Analysis
### Techniques Based on Authenticity Requirements:
#### 6.2.2 Techniques Based on More Than One Security and Privacy Requirement  
Some techniques have satisfied more than one of the requirements, as seen in Figure 8, which shows the classification of policy technologies that consider multiple requirements. For example, some technologies fulfil multiple security and privacy requirements, according to previous studies. Firstly, some methods address both confidentiality and authentication. Many businesses have the necessary security access for BYOD to wireless networks. The network enterprise delivers WPA2 based on the 802.1X standard [46; 47]. It is insufficient and should be strengthened. [PERSON] and [PERSON] proposed encryption-based authentication in [99]. They proposed employing near field communication multi-factor authentication (NFC) for secure authentication in combination with hybrid cryptosystems. [PERSON] et al. [100] proposed an authentication scheme for cloud data in IoT/BYOD. Participants can employ ciphertext policy attribute-based encryption to construct fine-grained access control rules (CP-ABE). The approach uses hybrid cloud infrastructure to offload expensive CP-ABE activities while protecting privacy. This way, encryption and optimization methods protect critical data at the item level, preventing leakage. Secondly, some procedures fulfil the criteria for confidentiality and detection of attacks. [PERSON] et al. [103] employed a new model combining anomaly detection, tracing, and revocation procedures. Partially ordered hierarchical encryption (PHE) is a novel threshold public key-based cryptosystem that implements a partial-order key hierarchy similar to RBAC roles. Tracking and large service interruptions are critical security measures. Thirdly, some techniques combine authentication, attack detection and confidentiality requirements. For instance, the technique proposed by [PERSON] et al. [102] can enable SDN-based authentication in a BYOD setting using OpenFlo-based infrastructure. It uses SDN characteristics to create virtual networks for monitoring dynamically selected BYOD users. By employing the portal and two-factor authentication, the user is provided with a safe and convenient means for enabling access to critical applications only and barring unauthorised requests, thereby limiting the services that a potentially attacking device can access. Changes to switching streaming rules may be deployed quickly, ensuring that users always have the most up-to-date access to the network with attack predictions. Three security and privacy requirements have been fulfilled: the authentication of BYOD devices used two-factor authentication, attack detection was achieved via continuous risk monitoring, and confidentiality was based on virtual networks. Table 11 depicts those techniques that have employed more than one security and privacy requirement in previous studies in the field.
## 5 Incremental Dynamic Analysis (IDA)
### Modelling in OpenSees
**Analysis:**: Sets up the solver for the system of equations, defines boundary conditions, numbering of equations, degrees of freedom, convergence tests, and algorithms used for solving nonlinear systems. OpenSees allows performing a fibre modelling of the sections of the structural elements. It facilitates fibre modelling of structural element sections, allowing for nonlinear analyses by assigning constitutive properties to each fibre. This approach enables non-elastic behaviour within the materials, as each fibre represents a uniaxial behaviour. The structural element is divided into control sections, employing distributed plasticity, contrasting with concentrated plasticity, which focuses on plastic hinges. Significant variations in element response occur based on the type of element modelling used, but the effective approximation of structural behaviour is achievable with proper mesh handling. The fibre section is defined using the wide flange command, directly assigning the main section dimensions (Figure 12a). Elements are considered force-based. A 'Steel 02' Giuffre-Menegotto-Pinto uniaxial material, representative of S355 steel grade, was utilised [38; 39]. Figure 12b depicts the comparison between the static pushover curves obtained from SAP2000 and OpenSees for Case Study 1. Notably, the SAP2000 model employs lumped plasticity, utilizing P-Hinge properties. In contrast, Opensees replicates the structure's behaviour using distributed plasticity without incorporating any material hardening.
# A Computational Platform for Automatic Signal Processing for Bender Element Sensors +
## 3 Description of the GeoHyTE Platform
### Computational Architecture  
GeoHyTE implements the process described in Section 3.1.3 using the hybrid-Trefftz finite elements presented in Section 3.1.2 to construct the numerical models. GeoHyTE is entirely implemented in Matlab and features intuitive Graphical User Interfaces (GUIs) for the interpretation of the output signal, definition of the finite element model, and during the entire execution and post-processing. The choice of Matlab is motivated by the availability of highly efficient procedures for dealing with multi-dimensional arrays and solving large algebraic systems, which ideally suit finite element implementations. Moreover, Matlab offers various deployment options, including as a standalone executable, without requiring access to proprietary components.
# An Affordable Fast Early Warning System for Edge Computing in Assembly Line
## 4 Results and Discussions
### Fault Model Performance  
The proposed hybrid fault model was compared with several current classification models, as shown in Table 5. The proposed model outperformed all other considered models significantly for all of r, f, p, and a, providing 98.64% prediction accuracy.  
Figure 9: Edge device performance in terms of response time and CPU usage for different storing periodicity. Scenario details are provided in the main text above.  
The proposed hybrid fault model combining DBSCAN, SMOTE, and RF provided improved classification accuracy. We investigated DBSCAN and SMOTE impacts for classification models, as shown in Figure 10. Applying DBSCAN and SMOTE improved classification accuracy for all models except logistic regression (LR), with 1.07% average improvement.  
The generated hybrid fault model was installed onto the edge devices to provide fault prediction. Input gyroscope, accelerometer, temperature, humidity, ambient light, and air quality sensor data were then handled, processed, and analyzed in the edge device without requiring network communication with the cloud server, minimizing network costs, while simultaneously improving data processing and analysis speeds. The proposed AFEWS will help improve decision-making, as well as reduce or prevent unexpected losses caused by early stage faults.
# An Overview of Nanofiltration and Nanoadsorption Technologies to Emerging Pollutants Treatment
## 3 Nanotechnological Approaches to the Treatment of Emerging Pollutants
### Nanoadsorbents
#### 3.2.1 Carbon-Based nanoadsorbents  
Carbon-based nanomaterials have been widely studied as adsorbents for water treatment. These nanoadsorbents have an ordered structure, high porosity, homogeneous pore size distribution, high specific surface, chemical and thermal stability, and non-toxicity [56]. These characteristics are favorable for their application in the remediation of contaminants. Moreover, the surface of these materials can be modified, converting them into functional materials to increase the ability to remove different contaminants. The adsorption mechanism of carbon-based nanomaterials mainly involves \(\pi\)-\(\pi\) interactions, hydrogen bonds, and electrostatic interactions [54,55, 56]. \(\pi\)-\(\pi\) bonds occur between compounds that accept \(\pi\) electrons and \(\pi\) electron-rich regions on the surface. Electrostatic interactions occur when the pH of the solution is different from the isoelectric point of carbon-basednanoadsorbents. In this way, the material's surface acquires a positive or negative charge, interacting with the contaminant containing electrical charges [59]. Adsorption involving hydrophobic bonds occurs mainly when contaminants are in their molecular state and the nanoadsorbents charge is zero [63; 64]. The presence of oxygen-containing functional groups in adsorbents also favors the adsorption phenomenon [65; 66]. Hydrogen bonds also participate in adsorption processes and can be formed between the benzene ring on the surface of carbon nanotubes and organic compounds with functional groups containing oxygen (bivalent compounds) [67]. The main carbon-based nanoadsorbents reported are carbon nanotubes and graphene oxide.  
The adsorbent properties of carbon nanotubes can be modified by covalent or noncovalent functionalization [54; 56; 68; 69]. Another modification of carbon nanotubes is the treatment with ionic liquids, which increases the hydrophilicity of these nanoadsorbents and their dispersion in the aqueous phase and interaction with contaminants [56]. Carbon nanotubes demonstrated adsorbent potential for removing emerging contaminants such as antibiotics, hormones, and pesticides (Table 2).  
Graphene oxide has excellent mechanical, thermal, and electrical properties and chemical stability [55]. Functional groups containing oxygen are introduced in the oxidation of graphene and provide interactions with different inorganic and organic pollutants, thus improving the removal efficiency of this adsorbent [56]. Another technique investigated to increase the adsorption capacity is the reduction of graphene oxide, which improves surface properties and hydrophobicity [55; 56]. Graphene oxide is an efficient adsorbent of several compounds, such as aromatic compounds (phenanthrene and biphenyl), dyes, hormones, and pharmaceutical products [55; 70; 71; 72].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline