esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Methodology
### Unified Hypergraph Representation Learning Network  
In this section, we propose a novel approach to learn disentangled representations of location prediction from three distinct hypergraphs, employing advanced aggregation and propagation methods within hypergraph convolutional networks (HGCNs) [23; 37]. These networks aim to capture the complex dependencies between users and location predictions through multiple views.  
Before encoding, given the user set \(U\) and location set \(L\), we initialize the user embeddings \(\mathbf{U}=\text{Embed}(U)=\{\vec{u}_{1},\vec{u}_{2},\cdots,\vec{u}_{|U|}\} \in\mathbb{R}^{|U|\times d}\) and location embeddings \(\mathbf{L}=\text{Embed}(L)=\{\vec{l}_{1},\vec{l}_{2},\cdots,\vec{l}_{|L|}\} \in\mathbb{R}^{|L|\times d}\) via a look-up table, where \(\text{Embed}(\cdot)\) stands for a embedded function designed by stacking multiple full connection layers, \(d\) denotes the embedding dimension, \(|U|\) is the number of users, and \(|L|\) represents the number of locations. Each element \(\vec{u}_{i}\in\mathbb{R}^{d}\) and \(\vec{l}_{i}\in\mathbb{R}^{d}\). The embedding matrices \(\mathbf{U}\) and \(\mathbf{L}\) are updated during training using gradient-based optimization.  
These embeddings are propagated through the hypergraph convolution network in subsequent steps, where the aggregation function considers interactions between users and location predictions across multiple views, enhancing their feature representations. The propagation process of traditional hypergraph learning involves an iterative refinement of embeddings, computed as follows:  
\[h^{(k+1)}=\sigma\Big{(}\sum_{e\in\mathcal{E}}A_{e}h_{e}^{(k)}\Big{)}, \tag{12}\]  
where \(A_{e}\) is the adjacency matrix corresponding to hyperedge \(e\), \(h_{e}^{(k)}\) is the embedding at the \(k\)-th layer for the hyperedge \(e\), and \(\sigma(\cdot)\) denotes a nonlinear activation function, such as ReLU.  
However, the traditional hypergraph learning only considers the feature aggregation of adjacent nodes within a hyperedge and ignore the interaction of non-adjacent nodes across different hyperedges.  
In this paper, we will focus on the interaction between non-adjacent nodes across different hyperedges and propose a disentangled hypergraph representation learning network. It selectively emphasizes more informative connections between nodes across different hyperedges and computes weighted sums of neighboring node embeddings, dynamically adjusting weights based on the relative importance of each connection.  
Specifically, the proposed unified hypergraph representation learning network incorporates a two-step information propagation scheme to capture high-order locations iteratively. This scheme is based on a node-hyperedge-node propagation model, where hyperedges act as intermediaries for node aggregation within the hypergraph and for propagating information across the hyperedges. The propagation process is composed of two main operations: (a) within-hyperedge feature aggregation and (b) across-hyperedge feature propagation.
## 5 An SOA-Based Platform Example and Case Study Results
### Additional Details on the Results from the Smart Recycling AB Case  
Smart Recycling AB has made its own cloud service/IoT platform, based on ideas and concepts from the Arrowhead Framework, and uses it, for instance, to build the service of measuring and trending the filling level of recycling containers for glass, metals, plastics, and paper, spanning from the sensors deployed, communications, security, data collection and analysis, and visualization of data and results. The visualization provides decision-making support to operators responsible for emptying the recycling containers before they become overfilled or to not empty them until properly filled up. Data mining employed enables operators to further understand the filling variations/patterns during a year when there is a frequent need or less frequent need to empty the containers. Some lessons learned from the development are that the strict rule to define service contracts/definitions combined with faster development has made the company competitive. In addition, the fleet management functionality, the capacity to manage mass-deployed sensors over vast geographical areas, continuous improvements to remove complexity and build in everything necessary into the sensor package, have streamlined it all further. Some of the issues overcome were related to: efficient infrastructure, getting the mobile communications to work on a long-term basis (low battery consumption at end points), to find new skilled employees, and to develop existing employees to acquire the required skills and competencies. Additional tough challenges to overcome were to redefine the work processes (from traditional development and business development to agile) and to set up service/support for national/international coverage along with finding a sustainable business model.
## 2 Numerical Calculation Method for Telecommunication Towers
### Numerical Calculation Method and Boundary Conditions
The transport equations for the SST \(k\)-\(\omega\) model are given by  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho kv_{i})}{\partial x_{i}}= \frac{\partial}{\partial x_{j}}\Bigg{(}T_{k}\frac{\partial k}{\partial x_{j}} \Bigg{)}+G_{k}-Y_{k}+S_{k} \tag{9}\]  
\[\frac{\partial(\rho\omega)}{\partial t}+\frac{\partial(\rho\omega v_{j})}{ \partial x_{j}}=\frac{\partial}{\partial x_{j}}\Bigg{(}\Gamma_{\omega}\frac{ \partial\omega}{\partial x_{j}}\Bigg{)}+G_{\omega}-Y_{\omega}+D_{\omega}+S_{\omega} \tag{10}\]  
where \(k\) is the turbulent kinetic energy; \(\omega\) is the specific dissipation rate; \(\rho\) is the fluid density; \(t\) is the time; \(V\) is the instantaneous velocity; \(p\) is the pressure; \(\tau\) is the stress tensor; \(x\) is the Cartesian coordinates; \(G_{k}\) is the generation of turbulent kinetic energy due to the mean velocity gradients; \(G_{\omega}\) is the generation of \(\omega\); \(T_{k}\) is the effective diffusivity of \(k\); \(\Gamma_{\omega}\) is the effective diffusivity of \(\omega\); \(Y_{k}\) and \(Y_{\omega}\) represent the dissipation of \(k\) and \(\omega\) due to turbulence, respectively; \(D_{\omega}\) is the cross-diffusion term; \(S_{k}\) and \(S_{\omega}\) are user-defined source terms. The inlet employs a velocity-inflow boundary condition (Velocity-inlet), simulating a Class B wind field in the calculations, represented by an exponential law for the average wind profile:  
\[U(z)=U_{10}\Big{(}\frac{z}{10}\Big{)}^{\alpha} \tag{11}\]  
where \(U_{10}\) is the average inflow wind speed at a height of 10 m, \(z\) represents the height above ground, and \(\alpha\) is the ground roughness index. According to China's \"Load Code for the Design of Building Structures\" (GB50009-2012) [31], the \(\alpha\) value for Class B wind fields is 0.15. The turbulent kinetic energy and dissipation rate at the inflow plane are expressed as follows:  
\[k(z)=\frac{3}{2}\cdot\left[U(z)\cdot I_{u}(z)\right]^{2} \tag{12}\]  
\[\varepsilon(z)=C_{\mu}^{3/4}\cdot\frac{k^{2/3}(z)}{K\cdot z} \tag{13}\]  
where \(C_{\mu}\) is the model constant, valued at 0.09; \(K\) is the Karman constant, valued at 0.42; \(I_{\rm u}(z)\) is the turbulent intensity of the incoming flow at height \(z\), expressed as suggested in China's \"Load Code for the Design of Building Structures\" (GB50009-2012) [31]:  
\[I_{u}(z)=I_{10}\cdot\Big{(}\frac{z}{10}\Big{)}^{-\alpha} \tag{14}\]  
where the class B wind field \(I_{10}=0.14\). The outlet employed a free outflow boundary condition (Outflow), while the lateral and top boundaries of the flow field used a free slip wall boundary condition (Free Slip Wall); all other boundaries were subjected to a no-slip wall boundary condition (No Slip Wall).
## 4 HCS Based on Active Disturbance Rejection  
The HCS of the MAR is a coupled system. In view of this, this paper utilizes the characteristics of the ADRC, a system that has low requirements for modeling accuracy and can effectively compensate for comprehensive disturbances, to solve the decoupling problem in the horizontal system of the MAR. The ADRC regards the coupling between various degrees of freedom (\(y_{b}\) and \(\psi_{z}\)) as disturbances, observes them through an ESO, and compensates for their combined effects (i.e., the total disturbance of the system), thereby achieving decoupling control. Essentially, this compensation is a means of anti-disturbance. This control method has low requirements for the model and is easy to implement, which can effectively solve the problems of decoupling and disturbance suppression in horizontal control.  
The ADRC controller of the HCS is shown in Figure 4. It mainly consists of a tracking differentiator (TD), an extended state observer (ESO), and a nonlinear state error feedback control law (NLSEF).
# Broadband Dual-Polarized 2 \(\times\) 2 MIMO Antenna for a 5G Wireless Communication System
## 2 Single Antenna Element  
Figure 1 shows the CMA used in this study. The antenna had dimensions 80 mm \(\times\) 80 mm \(\times\) 33 mm and comprised driven circular and parasitic circular radiators manufactured on a 0.8 mm thick FR4 substrate (\(\varepsilon_{r}\) = 4.4, tan \(\delta\) = 0.02) and a ground plane. The driven radiator (R1), having a diameter of 32 mm (\(\approx\)\(\lambda/2\) at 2.2 GHz), was used to determine the resonant frequency, and the driven element was the impedance matched with the gap between the dielectric and driven element. Figure 2 shows the simulated and measured values of the voltage standing wave ratio (VSWR) based on the distance between the driven element and the dielectric (D1). An EM simulation tool Ansys HFSS was used to simulate. It was observed that the smaller the gap between the driven element and ground plane, the better the impedance match. The parasitic radiator (R2), having a diameter of 10 mm (\(\approx\)\(\lambda/2\) at 7 GHz), was used to analyze the wide impedance bandwidth. Figure 3 shows the simulated and measured CMA \(S_{11}\) values in the presence and absence of the parasitic element.  
Figure 4 shows the three different versions of the MRDA. Figure 4a shows the first version of the MRDA. It has no notch at the edge of radiator. Figure 4b,c show the second and final versions of the MRDA. The final version of the MRDA achieved a wide impedance bandwidth owing to an added slot at the edge of radiators. Figure 5 shows the \(S_{11}\) value of  
Figure 1: Overall dimensions of a circular monopole antenna (CMA).  
Figure 3: Simulated and measured CMA return loss with and without parasitic elements.  
Figure 2: Simulated and measured voltage standing wave ratio (VSWR) according to the distance between the driven element and dielectric (D1).  
the first, second, and final versions of the MRDA. It shows that the final version of MRDA has the widest impedance bandwidth of 142.77% (6.23-1.04 GHz).  
Figure 4: Three different versions of MRDA (modified rectangular dipole antennas): (**a**) version1, (**b**) version2, and (**c**) final version.  
Figure 5: Simulated \(S_{11}\) values of three different versions of MRDA.  
Figure 6: Overall dimensions of MRDA.  
Figure 7 shows the simulated and measured VSWR values based on the distance between the radiators in the presence and absence of the ground plane. It was observed that the antenna exhibits the widest impedance bandwidth in the absence of the ground plane. However, the ground plane, which acts as a reflector, was introduced to improve the gain of the MRDA.
# A Systematic Literature Review of LoRaWAN: Sensors and Applications
## 3 Results
### Applications of LoRaWAN
#### 3.1.2 Environmental Monitoring
In [16], WaterGrid-Sense is used, which is an intelligent interface platform; this platform has an integrated LoRa module that uses class A LoRaWAN protocol and has two sensor slots: one for a pressure sensor and the other for a pulse sensor. This intelligent platform was used for the management of a real-time intelligent water system, which was implemented at the Council for Scientific and Industrial Research (CSIR) campus. Another work is [21], in which a device was created to help monitor water quality in real time and identify abnormalities through a phone application. The device was created with the purpose of helping fish farmers; the device has different water sensors such as pH, water level, temperature, dissolved oxygen, total dissolved solids, oxidation, and turbidity sensors. The anomalies detected by the sensors activate different actuators such as the aerator, water filter, peristaltic pump, water pump, fish feeder, and heater to solve the problem. On the other hand, in [4], a comparison of LoRaWAN, Sig-Fox, and NB-IoT technologies is made; for this, a smart water grid is simulated using the NS-3 simulator. In this work, several problems in the water infrastructure were detected through the use of sensors; an example of these problems was the pipe leaks. With the results of the simulation, the conclusion was reached that NB-IoT has better scalability compared to the other two technologies.
## 3 Results
### Physicochemical Changes: pH Value  
Our findings in Figure 2c shows that the same trend of TSS/TA and pH were observed in non-impregnated and all impregnated leaves. Throughout 7 days of storage time, the pH values of non-impregnated and SA impregnated leaves were decreased but increased gradually in GABA and sucrose impregnated leaves. However, there is no significant difference in pH value throughout 7 days of storage of all non-impregnated and impregnated leaves.  
Figure 2: (**a**) Total soluble solid content in non-impregnated leaves (\(\Circle\)) and leaves vacuum impregnated with 2 mM SA (\(\blacktriangle\)), 5 mM GABA (\(\blacksquare\)) and 0.3 M sucrose (\(\blacklozenge\)) during 7 days at 4 \({}^{\circ}\)C. The first point of the impregnated leaves represent the total soluble solid content 20 min after VI. (**b**) Titratable acid content in non-impregnated leaves (\(\Circle\)) and leaves vacuum impregnated with 2 mM SA (\(\blacktriangle\)), 5 mM GABA (\(\blacksquare\)) and 0.3 M sucrose (\(\blacklozenge\)) during 7 days at 4 \({}^{\circ}\)C. The first point of the impregnated leaves represent the titratable acid content 20 min after VI. (**c**) pH value of non-impregnated leaves (\(\Circle\)) and leaves vacuum impregnated with 2 mM SA (\(\blacktriangle\)), 5 mM GABA (\(\blacksquare\)) and 0.3 M sucrose (\(\blacklozenge\)) during 7 days at 4 \({}^{\circ}\)C. The first point of the impregnated leaves represent the pH value of leaves 20 min after VI. Bars represent \(\pm\) S.E.
# Formal Modeling and Improvement in the Random Path Routing Network Scheme Using Colored Petri Nets
## 4 Experimental Results
### Verification and Validation
#### 4.1.2 Dead Marking  
Verification of the model depends upon the dead markings of the model. In our model, there are three dead markings (21, 35, and 28). Additionally, all three of these nodes are end nodes called sink nodes. This property shows that our model is reaching the endpoint of the network.
## 3 Methodology
### PHR-NFT System Workflow  
The workflow of the PHR-NFT system (see Figure 1) begins with the registration of healthcare organizations by the MoH on the blockchain network. The hospital (H) works with the MoH to safely log all transactions on the blockchain, including prescriptions and test results. The MoH confirms the recorded information using the patient's Emirates ID to guarantee data authenticity and accuracy. The MoH provides the patient with a non-fungible certificate (NFC) that serves as a representation of their certified medical records after successful verification. Hospitals worldwide can accept this NFC, which acts as a digital token for the patient's approved medical data and has built-in authentication to ensure dependability. With the patient's consent, authorized healthcare practitioners can safely access the patient's records via the blockchain. The blockchain is used to build and store smart contracts, which define conditions such as the requirement for patient consent before document access is granted. Each institution is limited to tracking and accessing patient records to protect privacy and security. Doctors can quickly access pertinent information for proper care by accessing the patient's records during appointments and a prearranged follow-up period. Moreover, the patient controls their data sharing by allowing other insurance companies access to their medical records from the blockchain only with express authorization. This process guarantees safe and effective patient data administration, with strict privacy regulations and simplified access for insurance companies and healthcare providers.
## 2 Model and Methods
### Power Shifting to Access Attractors or Windows
#### 2.3.4 Interim Parameter (Gain) Shift  
The interim parameter (gain-)shift functions in the following way: Start on a limit cycle and abruptly change the value of the gain for a certain amount of time and then shift it back to its original value. This may result in accessing a different attractor. For our study, we select the gain parameter, because it is convenient and works well as an experimental adjustable. To interim gain shift, we begin at the peak electric field output as the initial condition at _t = 0_ and allow the system to evolve along the limit cycle until we reach the desired TAP. At that point, we slightly change the gain from its original value to its new value. After an arbitrary _t = 10,000_\(\tau\) we shift the gain back to its original value and record the trajectory's evolution to the resultant attractor. For this study we chose to shift through AIII's domain; we record different TAP tranches on the limit cycle and determine the statistical distribution resulting from the gain shift. This gives the percentage chance the system leaves its original (base) attractor. See Section 3.3 for results.  
Figure 4: The axes are Re \(\mid\)X\(\mid\), Im \(\mid\)X\(\mid\), and D. The magenta \(x\) marks the power shift location, the base attractor AVL. (**a**) Down shift from AVI (in green) at \(Y_{0}\) = 1.96 to AV (in blue) at \(Y_{PS}\) = 1.84. (**b**) Down shift from AVI (in green) at \(Y_{0}\) = 1.96 to AII (in blue) at \(Y_{PS}\) = 1.84.
# RPREC: A Radar Plot Recognition Algorithm Based on Adaptive Evidence Classification
## 3 Proposed Method
### Design of a Neural Network Classifier  
In the RPREC, the recognition framework \(\Omega=\{C_{o},C_{n},\Theta\}\) for target, clutter, and uncertainty plots is first constructed, which is different from the binary recognition rule of either target or clutter in traditional methods. Here, \(C_{o}\) represents the category of the real target plots, \(C_{n}\) represents the category of clutter, and \(\Theta\) represents the category of uncertainty. The mathematical relationship between them is: \(C_{o}\cap C_{n}=\varnothing\), \(C_{o}\cap\Theta=C_{o}\), and \(C_{n}\cap\Theta=C_{n}\).  
As shown in Figure 2, a fully connected neural network has been designed, which can continuously optimize as the confidence function of the plots' category is iteratively updated. The specific network parameters are set as follows:  
Figure 1: The flowchart of evidence classification.  
Figure 2: The fully connected network structure designed in the RPREC.  
Input layer: The network input is the collected radar plot data, including target, clutter, and uncertainty. Before being input into the network classifier for category recognition, all plots are modeled using feature vectors recommended in [53]. The mathematical representation is \(F=[R,E,Rw,Aw,Ma,Aa]\). Here, \(R\) represents the distance from the target to the radar, \(E\) represents the altitude information, \(Rw\) represents the size of the target in the distance, \(Aw\) represents the size of the target in the azimuth, \(Ma\) represents the maximum amplitude of all participating condensation echoes, and \(Ma\) represents the average amplitude of all participating condensation echoes.  
Therefore, the number of nodes in this network input layer is set to 6, which is consistent with the input feature dimension.  
Output layer: The network output represents the category membership of plots belonging to \(C_{o}\), \(C_{n}\), and \(\Theta\) under the confidence recognition framework \(\Omega\). It represents the confidence level of radar plots belonging to target, clutter, and uncertainty, respectively. Its mathematical representation is \(\mu=[\mu_{C_{o}},\mu_{C_{n}},\mu_{\Theta}]\); therefore, the number of output nodes is set to 3.  
Hidden layer: The design of network hidden layers is usually related to the size and distribution characteristics of the dataset. After some preliminary experiments, setting the hidden layer to 30 layers with 50 nodes per layer and using the sigmoid function for nonlinear processing has the best effect.
## 5 Evaluation
### Network Evaluation
#### 5.3.2 Communication Channels Establish Evaluation  
In this section, we evaluate two communication channel establishment schemes. As in the previous section, we randomly select five nodes as the bootstrap nodes and hard-code the bootstrap nodes into the control nodes. A total of 7000 nodes are used for the experiments and 30% of them, 2100 nodes, are randomly selected as unreachable nodes and the remaining 4900 nodes are used as reachable nodes, and the bootstrap nodes are included in the reachable nodes. The reachable nodes and unreachable nodes are randomly distributed among all nodes. The parameter N for unreachable nodes to access the network is taken as 1. We construct DHT stable overlay networks of 1000, 2000, 3000,..., 7000 nodes respectively. Then, we verify the success rate of the two schemes in different network size  
Figure 19: Lookup success rate of reachable and unreachable nodes with different number of long connections N.  
cases by selecting not less than 10 unreachable nodes and not less than 5 unreachable nodes randomly selected for each reachable node respectively. The transmission channels were established between them using direct connection, relay connection, and direct connection three times, and the success rates of the two schemes in establishing communication channels based on the success of DHT lookup were counted, as shown in Figure 20.  
In the figure we can see that the relaying scheme is not greatly affected by the network size and the success rate is always close to 100%. The success rate of the direct connection scheme decreases slightly as the network size increases, but the three times attempts of the direct connection scheme significantly increase the success rate of communication channel establishment.  
The relay solution is less affected by the network condition because it does not need to establish a direct transmission channel for network packets, and the success rate is fluctuating without a decreasing trend. The success rate of the direct connection scheme gradually decreases with the increase of network size because it needs to establish mapping relationships on NAT devices or firewalls, and the increase of network size will reduce the success rate of this link. However, direct connection three times will increase the success rate of establishing mapping relationship, so it makes the success rate of the scheme increase significantly. Overall, the success rates of the direct connect three times and the third point relay scheme are basically the same, but the direct connect three times decreases slightly when the network size increases, which is also within the available range. Therefore, in use, it is still necessary to give priority to the direct connection scheme, and if the connection is not successful because of the network access method, the relay scheme is used, which will minimize the overall network resource consumption.