esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling and Numerical Computation of the Longitudinal Non-Linear Dynamics of High-Speed Elevators
## 2 Elevator System Dynamics Modeling
### Linear Subsystem
The car and frame subsystem and counterweight subsystem are simple linear substructure models with selected degrees of freedom. [PERSON]'s second law is used to construct a dynamic model. \(x_{3},x_{4},x_{5}\) are the three degrees of freedom of the car and frame subsystem, and the car and frame are connected to the traction wire rope on the side of the car through the node above the frame, and the displacement of this node is recorded as \(x_{3}\). \(x_{4}\) is the displacement of the car. The bottom node of the frame is directly connected to the tensioning wire rope, and the displacement of this node is recorded as \(x_{5}\). This paper solely focuses on vertical vibrations, specifically addressing the vibration isolation rubber located at the bottom of the car, while disregarding the left and right vibration isolation rubber. The developed kinetic model is depicted in Figure 5.  
Figure 5: Car and frame subsystem. In Figure 5, \(m_{3}\) is the mass of the car frame, \(m_{4}\) is the mass of the car, \(k_{3},c_{3}\) is the stiffness and damping of the rope head spring, and \(k_{4},c_{4}\) is the stiffness and damping of the vibration isolation rubber at the bottom of the car. According to [PERSON]'s second law, to obtain the differential equation of the car and frame subsystem:  
\[ \begin{array}{l}m_{3}\ddot{x}_{3}+c_{3}\big{(}\dot{x}_{3}-\dot{x}_{4}\big{)}+k _{3}(x_{3}-x_{4})=0\\ m_{4}\ddot{x}_{4}+c_{4}\big{(}\dot{x}_{4}-\dot{x}_{5}\big{)}+c_{3}\big{(}\dot{x}_ {4}-\dot{x}_{3}\big{)}+k_{4}(x_{4}-x_{5})+k_{3}(x_{4}-x_{3})=0\\ c_{3}\big{(}x_{5}-\dot{x}_{4}\big{)}+k_{4}(x_{5}-x_{4})=0\end{array}  \tag{17}\]  
Solve the contributing mass matrix of the car and frame subsystem as:  
\[[M_{3}]= \begin{pmatrix}m_{3}&\\ &m_{4}&\\ &&0\end{pmatrix}  \tag{18}\]  
The contributing stiffness matrix is:  
\[[K_{3}]= \begin{pmatrix}k_{3}&-k_{3}&0\\ -k_{3}&k_{3}+k_{4}&-k_{4}\\ 0&-k_{4}&k_{4}\end{pmatrix}  \tag{19}\]  
The contribution damping matrix is:  
\[[C_{3}]= \begin{pmatrix}c_{3}&-c_{3}&0\\ -c_{3}&c_{3}+c_{4}&-c_{4}\\ 0&-c_{4}&c_{4}\end{pmatrix}  \tag{20}\]  
The counterweight subsystem mirrors the car and frame subsystem. It involves selecting the connection point of the rope head spring on the counterweight with the traction rope, along with the counterweight displacement, as degrees of freedom to construct the model. [PERSON]'s second law is utilized to obtain the contributing mass matrix \([M_{4}]\), contributing stiffness matrix \([K_{4}]\), and contributing damping matrix \([C_{4}]\) of the counterweight subsystem.
## 2 Materials and Methods
### Batch Co-Digestion of FW and MA at Various Ratios  
The batch co-digestion tests were carried out in a self-designed reactor (1000 mL) with an effective working volume of 800 mL. The ratio of inoculum and substrate was 2:1 based on VS; the mixed ratios of FW/MA (based on VS) were 0:1, 1:3, 1:1, 3:1, 1:0, and the total substrate VS mass was maintained at 6.49 g. All the tests were conducted in triplicate, and the blank group was used to account for endogenous methane. The initial pH value for each reactor was adjusted to 7.0 using a 1 mol/L hydrochloric acid and sodium hydroxide solution. Next, reactors were flushed with nitrogen gas (\(>\)99.99%, 1 L/min) for 5 min to create anoxic conditions. After, all reactors were tightly sealed using a rubber stopper with two ports that were both connected to a three-way stopcock. One port was used for biogas sampling and the other port was connected to an aluminum gas bag for biogas collection. Finally, all reactors were placed in thermostatic water bath shakers set to maintain a temperature of 35 \(\pm\) 1 \({}^{\circ}\)C with a shaking speed of 120 rpm. The batch experiments were run for 35 days until the biogas production was \(<\)2% of the accumulated biogas produced, suggesting that biodegradation was completed [11]. The daily and cumulative biogas production with different mixing ratios were expressed at standard pressure and temperature.
## 4 Application of UWSN
### Monitoring
#### 4.1.1 Underwater Exploration  
The ocean is an untapped place for finding natural resources such as oil and gas, and UWSN helps in exploring it [79]. The oil and gas fields are connected via pipes of the large structure, UWSN is also used in monitoring the health of these pipes and ensures the safe transition from the depth of the sea to land.
# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 2 Materials and Methods  
### Study Area  
The location of this study is within the Wet Tropics and Atherton Tabledands, located approximately 1200 km northwest of Brisbane, Australia (Figure 1). The region of 2.7 million hectares, includes the Wet Tropics World Heritage Area, and is adjacent to the Great Barrier Reef World Heritage Area.  
In 2015, QLUMP reported the major secondary land uses within the project area to be: Nature Conservation (37.5%); Grazing (31.52%); Other Minimal Use (8.2%); and Cropping (7.2%). There were 14,533 hectares (0.65%) of banana plantations mapped.
## 2 Numerical Modeling
### Numerical Method  
The numerical solver was based on the finite volume method. The governing equations written in a Cartesian frame were solved in 3D in their unsteady formulation through ANSYS Fluent 18.2. The phase change process is simulated by the enthalpy-porosity method described in Section 2.2. The SIMPLE algorithm is used to overcome the velocity-pressure coupling. The spatial discretization for both momentum and energy equations corresponds to the QUICK scheme with a first-order implicit scheme for the temporal discretization. The least-square cell-based is applied for gradient spatial discretization. The continuity, velocity, and energy residuals are set to 10\({}^{-3}\), 10\({}^{-3}\), and 10\({}^{-6}\), respectively. Values of under-relaxation factors are fixed to 0.3 for pressure, 1 for density, body forces and energy, 0.7 for the momentum, and finally 0.9 for the liquid fraction update.
## 2 Synthetic Aperture Radar Interferometric Processing  
### Acquisition of SAR Images  
Normally, there are three ways to acquire Synthetic Aperture Radar (SAR) images, according to the number of antenna mounted on the platform and the orientation of the formed baseline [3, 16]: repeat-pass, across-track and along-track. There is no difference on the procedure of InSAR image co-registration regardless of the interferometric way the images are acquired.  
In order to understand the principle of SAR image acquisition, the general InSAR imaging geometry is illustrated in Figure 1. Two antennas \(S_{1}\) and \(S_{2}\) on ideally parallel flight paths are separated by a baseline \(B\). The slant range from antenna \(S_{1}\) to an illuminated point on the ground is \(r_{1}\); \(r_{2}\) is the slant range from antenna \(S_{2}\) to the same point. With the looking angle \(\theta\), the angle of baseline with respect to horizontal line \(\alpha\) and the flying height \(H\), the imaging geometry of InSAR is fixed.  
The height can be calculated by the following equations:  
\[\sin(\ \theta\ -\ \alpha\ )\ =\ \frac{{r_{1}}^{2}\ -\ r_{2}^{2}\ +\ B^{2}}{2\ r_{1}B}\ \approx\ \frac{-\ \lambda\varphi\ _{\ \where \(H\) is the antenna \(s_{1}\) is the height above the reference plane and \(h\) is the target height. \(r_{1}^{\prime}\) and \(r_{2}^{\prime}\) are the slant ranges from antennae to the point on the ground, respectively. \(\theta\) is the looking angle. \(a\) is the angle of baseline with respect to horizontal line. \(\varphi_{unwrapped}\) is the unwrapped interferometric phase. \(B\) is the baseline. \(\lambda\) is the wavelength of radar signal. Therefore, the DEM data can be derived from radar interferogram.
## 2 Datasets and Algorithms
### Generated Images
#### 2.2.3 Histogram of Oriented Gradients (HOG) Image  
Another helpful technique for analyzing images such as spectrograms is the histogram of oriented gradients (HOG) [30; 31; 32]. Introduced in 2005 [33], the HOG captures the overall shape and form within an image by looking at the direction and intensity of changes (gradients) in small image regions. As the spectrogram represents frequencies at different time intervals, the HOG helps us understand the shapes formed by these frequencies. It does this by dividing the spectrogram into small grids and analyzing the direction of the gradients (steepness) within each grid. By looking at the distribution of these gradient directions, the HOG builds a kind of fingerprint for the local shapes in the spectrogram.  
Figure 5 (top row) showcases the ECG signals and respective spectrogram images (second row) for various ECG signals to visually analyze their frequency content. The third row of Figure 5 shows examples of HOG images derived from their respective spectrograms. These HOG images highlight the distinct shapes present in the frequency patterns of the ECG signals. The bottom row of Figure 5 showcases examples of LBP images derived from their respective spectrograms, highlighting their textural variations.  
Figure 4: LBP image generation using \(3\times 3\) neighborhood.  
Figure 5: Examples of the generated images from three ECG signals: the ECG signals (**top row**), the spectrogram images (**second row**), their corresponding HOG images (**third row**), and their corresponding LBP images (**bottom row**).
# Fuzzy-Based Dynamic Time Slot Allocation for Wireless Body Area Networks
## 2 Related Works
These protocols were designed for collision-free and interference avoiding communications. The TSCH and its variants need centralized coordination technique for time-frequency slotting in networks. It resulted in slow convergence to the steady state during mobility. Hence, Dt-SCS was introduced with a decentralized concept based on the coupling of distributed synchronization and desynchronization mechanisms. All the existing aforementioned approaches mainly concentrated on any one of the QoS aspects at a time, whereas a combined set of QoS parameter optimization is necessary for WBAN medical applications. Additionally, most of the MAC protocols based on the IEEE 802.15.4 standard concentrated on any one of the MAC aspects for the protocol design. Most of the schemes used data traffic and traffic priority for the analysis. Also, the developed protocols attained their objectives by adjusting the CAP/CFP in the superframe structure, which has its own limitations in terms of bandwidth and number of devices used. The comparative survey of different routing protocols for WBAN medical applications is summarized in [39].
# Priority Pricing for Efficient Resource Usage of Mobile Internet Access
## 5 Pricing Optimality Analysis
### Example 1
Figure 9: The impact of utility functions on pruning efficiency  
Figure 11 illustrates that the system does not necessarily converge to a stable state if the price vector belongs to the unpruned zone. Rule 4 determines the range of \(P_{2}\) for a given \(P_{1}\). In the case of \(P_{1}=9.0\), \(P_{2}\) below 6.5 always converges on a scenario that all jobs choose level 2 so that it can be pruned (see the straight line in Figure 11). However, the system oscillates for \(P_{2}\) above 6.5, and the system objective cannot be trivially computed (see the rugged line in Figure 11). My scheme applies the iterative-deepening search on the unpruned region and obtains the best price vector of \(\{P_{1}=8.90\), \(P_{2}=8.00\}\). The corresponding expected profit is 35.04. The parameters for the iterative-deepening search were set as \(G=10\), \(D=5\), \(\Delta_{\max}=\infty\), and \(\Omega=0\). The equilibrium methods utilized about 83% of the network capacity because of the oscillation, but the proposed method let users fully utilize the capacity in this example.
## 1 Introduction
The idea of this method refers to the calculation of features by appropriate network maps/filters and to the adaptation of the corresponding ones to the received input data. Article [23] presents the architecture of a 2-D CNN network that was prepared to recognize changes in MBN spectrograms obtained for grain-oriented electrical steel sheets and classify them considering their properties and state. An analysis of the accuracy of the neural network in recognizing different types of grain-oriented electrical steel sheets including conventional and high magnetic permeability and additionally subjected to various surface engineering methods was undertaken. In [40], the 1-D CNN network based on the ResNet-18 CNN (a residual network) was used for classification  
Figure 2: Time–frequency representation space depending on the size of the window. of sheet-metal blanking processes. Furthermore, paper [41] employs a 1-D CNN with an inception module named InceptionTime to classify MBN signals obtained from materials with different hardness levels. The enormous possibilities and exceptional efficiency of deep learning methods result in an increasingly growing area of their application, including the analysis of MBN signals as well. The accuracy of analysis methods using various deep structures has recently been presented in several articles. Comparison of the effectiveness of methods based on the classical approach with deep methods also indicates a higher accuracy of solving complex diagnostic problems of the latter ones. However, just as in the case of classical methods, the problem is the selection of the right set of signal features and training the method or finding the appropriate multi-parametric connection; in the case of deep learning methods, attention should be paid to the issue of data preprocessing. The question arises about the proper resolution of data used as input data for the network. Therefore, while previous studies have focused on testing the applicability of deep learning techniques in MBN signal analysis, there is a gap in understanding how specific computational parameters of time-frequency transformation affect the deep network model performance. Therefore, this article investigates the impact of window size changes on the classification accuracy by a 2-D CNN network. In the following sections, we present the 2-D CNN architecture used as a tool to examine the effect of window size on accuracy.
# A Next-Generation Core Network Architecture for Mobile Networks
## 6 Re-Direction
### Scenario with eNodeB  
Figures 8 and 9 show the high-level components of an SN and VCN, respectively, when using an eNodeB. In particular, we show the IP addresses relevant for UE operations and mobility. As mentioned earlier, when a UE attaches to the network, its eNodeB will identify which MME to talk to. The MME will then tell the eNodeB which S-GW and P-GW to talk to. Tunnels are then established between UE and P-GW and between UE and IMS (i.e., SIP proxy or P-CSCF). Such tunnels are also known as EPS bearer and IMS bearer, respectively. According to 3 GPP terminology, a _bearer_ is a tunnel with an associated Quality of Service (QoS), and it may have either a short or long lifetime. As it can be evinced  
Figure 15: VoLTE call initiation with eeNodeB.  
by Figure 16, there are two types of bearers: default bearers and dedicated bearers. A default EPS bearer is created between UE and P-GW when the UE attaches to the network for the first time and remains as long as UE is attached. At the same time or as soon as the UE performs an SIP registration, a default IMS bearer is also created (for VoLTE service). Each default bearer has its dedicated IP address. When a VoLTE call is initiated, the default IMS bearer is used for the SIP signaling, while a new dedicated bearers with higher QoS are set-up for voice and/or video data traffic. One or multiple dedicated bearers, bundled together, are encapsulated (tunneled) on top of a default bearer, without requiring new dedicated IP addresses. Default bearers are established and switched through the core network by means of dedicated MPLS flows. For this reason, when a UE moves from an SN to a VCN, the EPS bearer, IMS bearer, and MPLS routing (MPLS flow tables) must be updated. In the eNodeB case, the MPLS routing is automatically updated as a consequence of the bearer update--this, however, is not true for the eeNodeB case, as will be discussed later.
# Transforming Petrochemical Processes: Cutting-Edge Advances in Kaolin Catalyst Fabrication
## 6 Unlocking the Potential: Advanced Applications of Kaolin in Industry
### Plastic Industry  
Kaolin-based materials are widely utilized as fillers in the plastics industry due to their various beneficial properties. Kaolin enhances the surface finish of plastics, mitigates cracking and shrinkage during curing, and conceals the fiber pattern in fiberglass-reinforced composites [100]. Additionally, kaolin contributes to improved thermal stability, high impact strength, and resistance to both chemical action and weathering [101; 102]. It also aids in regulating the flow properties of plastic materials. The concentration of kaolin fillers in plastic formulations can range from 15% to as high as 60%.  
A primary application of kaolin is in polyvinyl chloride (PVC) coatings for wire and cable insulation [103]. Calcined kaolin, along with silane-treated kaolin, is employed to enhance electrical resistance and reduce costs. Calcination, which is typically performed at approximately 1000 \({}^{\circ}\)C, lowers the surface energy of kaolin, imparting some degree of hydrophobicity and making it a preferred filler for PVC. Surface modification with silanes further increases this hydrophobicity, optimizing the performance of kaolin in electrical insulation. The effectiveness of kaolin as a filler is highly influenced by particle size, with finer particles generally offering better reinforcement of physical properties across various polymers. Enhanced strength and impact resistance in plastics, such as polypropylene and PVC, can be achieved through the use of fine kaolin particles combined with coupling agents that facilitate chemical bonding between the filler and the polymer matrix [104]. Silane treatments improve the dispersion of kaolin and its interaction with the polymer [92]. Furthermore, the thin plate-like shape of kaolinite benefits certain polymers by improving the flexural modulus, dimensional stability, surface smoothness, and barrier properties [105].  
Recent developments continue to emphasize the advantages of kaolin in plastics, with ongoing research focusing on optimizing its properties for enhanced performance in diverse plastic applications. The versatile role of kaolin in improving the physical characteristics and cost efficiency of plastics underscores its value as a critical filler material in the industry. For example, the _DuPont_ company incorporates kaolin in the manufacturing of certain plastic products to enhance stiffness and thermal stability.
## 4 A Privacy-Aware and Secure Decentralized Framework
### Routing and Sink Placement Optimization
Regarding the solution, we separate different cases connected to the set of sink nodes that need to know the collected information. The feasibility of the optimization problem depends on the information spread regarding to the sink node level of the network and the user requirements. However, the different cases regarding the feasibility of the problem can be defined in weak and strong routing systems. It is also important to note that different objective functions can be defined regarding the sink placement optimization. In the final system, many factors can be defined by the user, for example, minimizing the largest distance between the sensor and sink nodes, maximizing the number of sink nodes that are reached by the collected data, minimizing the sum of distances, etc. The functions can be used in different ways: a linear combination as a single objective function, bilevel optimization, or multiobjective optimization (e.g., as Pareto optimum). We conclude the section, for illustrative purposes, with a more concrete solution proposal to the problem. Our network consists of \(n\) nodes that we model as vertices \(V\). In the process of designating the sink nodes, we compute the distance matrix \(D=[d_{s,d}]\) between all source nodes \(s\) and all destination nodes \(d\). In order to define the final optimization problem, for each node \(s\in V\), we define the distance to the closest sink node \(d_{s}\). This yields the definition of the optimization problem, which minimizes:  
\[\min_{\text{possible sink placements}}\sum_{s\in V}d_{s}\enspace. \tag{1}\]  
In detail, the solution finds \(k\) sink nodes or gateways in the following phases: \begin{tabular}{l l} Phase 1: & collect the information about the network at one node; \\ Phase 2: & compute the distance matrix \(D\); \\ Phase 3: & build an MILP (Mixed Integer Linear Program) to solve the optimization problem from Equation (1); \\ Phase 4: & solve MILP; \\ Phase 5: & distribute solution. \\ \end{tabular}   
The practical implementation of Phase 2: takes \(O(n^{2}\log^{2}n)\) time and the construction of MILP in Phase 3: can also be performed in polynomial time. However, the optimization problem in Equation (1) is, as already mentioned, NP-complete. Therefore, the MILP solution gives us only an approximate solution. Moreover, the presented solution is centralized and requires further changes, in particular related to parallelization, which, consequently, improves the efficiency and time complexity. For example, replacing MILP with an ant-colony metaheuristics gives a very practical parallel solution [71].
# A Tiered Control Plane Model for Service Function Chaining Isolation
## 5 Protocol and Interfaces
### Control Plane Interfaces
#### 5.2.5 Key Management Service Interfaces  
The KMS server implements a protocol that can provide an IPsec Security Association (SA) between two VNFs running encrypting services. The protocol defines a trusted KMS server with two random endpoints (instantiated encrypting VNFs) as the client and the server, where the KMS instructs the VNFs to establish an SA. For the initializing phase, the KMS server and VNFs utilize a Public Key Infrastructure (PKI) to establish a connection between each other, which the Kerberized Internet Negotiation of Keys (KINK) protocol is referring to as PKINIT [23]. Hence, certificates are issued for peers by the use of public and private keys instead of using passwords. The public keys are distributed over BGP and secured by secure origin BGP (soBGP) [24]. The KMS service provides services such as ticket granting to ensure the integrity of messages to the server. To ensure a two-way authorization, an additional Preshared Key (PSK) authentication is added (Figure 19) to the protocol. The PSK is pre-provisioned by the orchestration layer. The KMS server protocol follows the same procedure for Tiers 1-3 authentications, where the \"user identity\" is the only difference (see Section 4.2.4).  
An additional feature to the authentication and key negotiation protocol is the capability to inform the endpoint about the IP-address of the remote endpoint. After authentication of the endpoints, the remote endpoint address together with a new dynamic shared key is offered to the endpoints by the KMS server. Next, the endpoints establish a direct link between for the SA negotiations. Furthermore, implementation guidelines about the KMS protocol are given in Section 6.  
Figure 18: BGP announcement Tier 3.  
In this architecture, the KMS server is simplified to only include one instance. In real life deployment, the number of KMS instances should reflect the number of control plane tiers. In summary, the KMS server holds two service interfaces:  
* An authentication protocol interface used by the encryption services and the VPN gateway,
* A management interface to maintain the \"user\" identities (Section 4.2.4) and their corresponding PSKs.