esa-sceva/satcom-chunk-collection huggingface dataset:
# An Evaluation of Orbital Angular Momentum Multiplexing Technology
## 2 Background and Theoretical Performance Evaluation  
### OAM Generation Using a Uniform Circular Array  
Studies regarding OAM multiplexing in the wireless communication field are categorized into antenna design and beam generation, end-to-end experiments, signal processing methods, and system studies for topics such as capacity analysis and link budget. Among these, we mainly focus on antenna design in this subsection. Various types of antenna designs have been reported using helicoidally deformed parabolic antennas [8], spiral phase plates (SPP) [2; 3; 9], holographic plates [10], elaborately tuned planer SPPs [11], and other components [12; 13]. In our latest research, we focused on OAM generation using UCAs [14; 15; 16]. Figure 1 shows OAM mode generation by using UCAs. The phase of each antenna is shifted in accordance with an OAM mode. The transmitted signal from each antenna can be written in vector form as  
\[\mathbf{x}=\left[1,e^{j\frac{2\pi L}{N}},\cdots,e^{j\frac{2\pi(N-1)}{N}}\right]^{T}, \tag{1}\]  
where \(L\) is the OAM mode number and \(N\) is the number of radiating antennas in the transmitting UCA. By using a UCA, the OAM state number \(L\) is limited by the number of transmitting antennas as \(|L|<N/2\).  
Since the diffraction pattern of the UCA can be approximated by the Bessel beam [17], the field distribution of the beam with the OAM mode L is often expressed in the Bessel beam equation as  
\[v_{L}(r,\theta,z)=\frac{\lambda\exp[(2\pi i/\lambda)\sqrt{r^{2}+z^{2}}]}{4\pi \sqrt{r^{2}+z^{2}}}\cdot i^{-L}\exp[iL\theta]\cdot J_{L}\left(\frac{2\pi rD}{ \lambda\sqrt{r^{2}+z^{2}}}\right), \tag{2}\]  
where \(J_{L}(\cdot)\), \(\lambda\), and D, respectively, denote the Lth order Bessel function of the first kind, the wavelength of the carrier frequency, and the radius of the transmitting UCA. Equation (2) is represented in cylindrical coordinates, where \(r\) and \(\theta\) are, respectively, radius and azimuthal angle at the Rx plane that is vertical to the beam propagation direction. \(z\) is the distance between the centers of the Tx (transmission) and Rx UCAs. Here, let us discuss a comparison between OAM and MIMO. OAM is a specific implementation of MIMO with circular antenna arrays. The difference is that an OAM beam can be achieved virtually and practically in accordance with an OAM state, so it is possible to design a mechanism to manipulate that beam specifically to achieve a specific type of communication. The difference from the conventional MIMO and OAM multiplexing is as follow. To obtain a full rank matrix, usually an NLOS (non-line-of-sight) multipath and a rich scattering environment are assumed in the conventional MIMO. In LOS (line-of-sight) environment cases, digital signal processing such as precoding at the transmitter may be required for the conventional MIMO case to obtain a full rank matrix. On the other hand, OAM does not need digital signal processing at the transmitter to obtain the full rank matrix.
## 3 Experimental Setup
### Software Implementation
#### 3.2.3 Peak Shaving Implementation  
To reduce power consumption, the system relies on predicted values. Users can define peak shaving targets, which in this study are set at 2.5 kW for summer and 4.5 kW for winter. If predicted power consumption exceeds the set threshold, the system automatically discharges power from external distributed energy resources such as the R-ESS or V2G charger to reduce the peak load. This proactive approach ensures that the building's power consumption stays within the specified limits, minimizing energy costs and improving grid stability.
## 3 Results
### Assessment of the Proposed Models
In addition, [PERSON] et al. [98] developed a hybrid model of PSO and k-nearest neighbors (KNN) to estimate the blast-induced PPV. The database included 152 blasting events. Three different kernel functions were used to develop the KNN models. They used two input variables, including MC and D, for predicting PPV. The results of the PSO-KNN models were compared with RF and support vector regression (SVR) in terms of \(R^{2}\), RMSE, and MAE. The results of this study show that the \(R^{2}\) values of PSO-KNN (training = 0.982, testing = 0.977) was higher than RF (training = 0.996, testing = 0.953) and SVR (training = 0.973, testing = 0.944) models for both training and testing datasets. The difference between our study and the study by [PERSON] et al. [98] is based on three aspects: (1) different input parameters, (2) different case study and type of rock mass and region, and (3) different predictive techniques. According to the above discussion, the methods presented in this study have a high level of originality and novelty in the field of blasting environmental issues. Figure 6: Gain chart for comparing the accuracy of the developed models.
## 2 Existing Expected Threat (xT) Models Theory
### Lack of Realism in Expected Threat (xT) Models
#### 2.3.3. Simplistic Ball Transition Probabilities  
The calculation of \(T_{i,j\to q^{\prime},i^{\prime}}\) (representing the probability of moving the ball from the grid cell \((i,j)\) to the grid cell \((i^{\prime},j^{\prime})\)) in the existing xT models is grounded on the premise that the probability of a player relocating the ball from one zone to another is exclusively determined by the historical frequency of actions transitioning from one zone to another. Yet, in practical scenarios, numerous other variables come into play, prominently including the spatial locations and orientation of players on the pitch, with the objective of optimizing the threat magnitude. To elucidate further, based on a player's specific grid location, their intent often gravitates towards directing the ball to zones exhibiting elevated xG values or zones expected to yield augmented xG values in subsequent actions.
# Application of Tucker Decomposition in Temperature Distribution Reconstruction
## 4 Simulation Experiment
### Prior Data and Test Data Acquisition  
As mentioned in the previous section, the established numerical simulation model can simulate the methane combustion process, and the calculated temperature distribution data is close to the actual value and can reflect the distribution of the actual combustion temperature. Therefore, this model can be applied to build prior datasets as well as the test dataset for the temperature distribution reconstruction. Considering the turbulence intensity and the range of the prior dataset, the boundary conditions for methane combustion simulation calculation are set as shown in Table 14.  
In order to construct the temperature distribution reconstruction calculation samples, five test cases were set up. The boundary conditions of the test condition are different from the prior condition, and the boundary condition of the test condition 5 is set outside the range of the boundary condition of the prior condition to test the robustness of the temperature distribution reconstruction algorithm. The boundary condition settings of the test conditions are shown in Table 2.
# Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
## 3 Green Radio-Frequency Identification  
The RFID system consists of RFID tags with a unique identifier electronic product code (EPC), an RFID reader, and middleware [95]. For an object to be tracked by an RFID enabled system, RFID tags must be appended to the target object. The design nomenclature of RFID tags consists of a small microchip linked to an antenna. As with other wireless devices, the antenna is responsible for transmitting and receiving of the radio signals. It is not necessary compulsory that RFID tags must be direct line of sight with the RFID reader. RFID tags can be read utilizing non-line of sight technology. Boasted by the memory capacity, the EPC and other valuable data can be read and traced using RFID readers effortlessly. An RFID reader can be considered as the base station (BS) or access point of the system and it is responsible for energizing, sending data and commands to connect RFID tags attached on a fixed or mobile object [96]. Meanwhile, RFID systems is characterized as having very low (i.e., a few meters) RF coverage. Some of the notable spectrum bandwidths of interest are very low frequencies (VLF) at 124-135 kHz up to ultrahigh frequencies (UHF) at 860-960 MHz [16].  
RFID tags can be classified into two types in terms of power source: (i) Active RFID tags have a local power source (battery) and operate hundreds of meters from the RFID reader. (ii) Passive RFID tags are without batteries and collect energy from a nearby RFID reader's interrogating radio waves by means of the principle of induction. Thus, the EE is a key requirement for the wider acceptanceof the active RFID systems that utilize battery-constrained tags. Additionally, active RFID is less advantageous than passive RFID in terms of its tag cost and size, but more advantageous in terms of sensing distance, sensing rate, and stability [16; 97]. Semi-passive tags are a third variant of RFID, and they are equipped with batteries; however, these batteries are not an integral part of RF transmission. Hence, they are not considered active RFID tags.
## 3 System Design
### Control System Hardware Framework  
The harvester's feeding volume-traveling speed coupling system consists of a sensing module, a control unit, an electric actuation module, and a power supply module. The control principle is that the sensor module detects the torque and speed of the tilting conveyor's power shaft to calculate the real-time feeding volume. It obtains the deviation value \(e(t)\) by comparing the calculated value \(c(t)\) to the ideal feeding volume \(r(t)\) provided by the system. The fuzzy PID controller receives and processes the error signal, and the corresponding control signal is output. The signal is multi-stage amplified by the drive module to control the stepping electric actuator action, adjusting the harvester's stopper position, i.e., controlling the traveling speed toward deviation elimination until the deviation tends to zero, thus realizing the feeding volume-traveling speed self-adaptation. The hardware framework of the control system was designed as shown in Figure 5.  
The control unit is responsible for communication management, analyzing all information and implementing logical operations. The sensing module detects parameters such as the torque and rotation speed of the tilt conveyor's power shaft during harvesting operations. The power supply module provides power to all modules.
# Nozzle with a Feedback Channel for Agricultural Drones
## 2 Proposed Design of Agricultural Drone Nozzle  
The fluidic oscillator can also be found in previous studies, and a number of visualization studies have been conducted to find out the flow characteristics of fluid vibrators. In this study, a new nozzle was designed, using the flow characteristics of a fluid vibrator [22; 23; 24; 25]. A nozzle with a feedback channel generates an oscillating jet flow through the interaction of the feedback block and the chamber with the inner block, without additional external equipment. This oscillation can generate a uniform spraying, compared to a conventional nozzle. When fluid is supplied into the feedback channel, a Coanda effect occurs in which the airflow ejected in close proximity to a wall of the feedback chamber adheres to the wall. The nozzle with a feedback channel uses this effect to generate a cross-flow through the feedback flow path inside the nozzle, thereby causing the fluid to vibrate at the outlet.  
Fan-shaped commercial nozzles are generally used in agricultural drones, owing to their wide spray width, but the nozzles provide low uniformity. To overcome this problem, we tried to improve the spraying uniformity of agricultural drones by the oscillating characteristic of the nozzle with a feedback channel. Figure 1 shows the expected spraying distributions with the conventional and the feedback channel nozzles. Figure 2 shows the proposed design for an agricultural drone nozzle that can generate periodic oscillation and improve the spraying uniformity. In general, fluid oscillation effectively occurs when a fluid is discharged into a space filled with the same phase fluid. By contrast, when a fluid is ejected on to a different phase, the oscillation is reduced, and the formation of atomized droplets becomes difficult. Therefore, in this study, in contrast to a conventional feedback channel nozzle, a certain amount of air is injected through the main inlet, and water is introduced through an additional channel installed at the outlet neck. The size of the feedback channel nozzle was 70 mm in length and 30 mm in width for application to small agricultural drones, the outlet height was 10 mm, and the nozzle height was 2 mm. The proposed nozzle was built, using digital light processing 3D printing to evaluate the spraying performance.
## 2 Materials and Methods
### Characterisation
#### 2.4.5 Fourier Transform Infrared (FTIR) Spectrometry  
The functional group modifications of the MOFs, both before and after chemical exposure, were analysed using a Perkin Elmer Spectrum 100 FTIR spectrometer (Buckinghamshire, UK). The FTIR instrument was equipped with universal Attenuated Total Reflectance sampling accessories. For each sample, sixteen scans were performed and then averaged to obtain reliable results. The spectral range covered was from 4000 cm\({}^{-1}\) to 650 cm\({}^{-1}\), with a resolution of 4 cm\({}^{-1}\). This FTIR analysis allowed for the identification and characterisation of the functional groups present in the MOFs and provided insights into any changes that occurred after the drug loading.
# Cooperative Spectrum Sensing Based on Convolutional Neural Networks
## 3 System Model  
### Sensing Scenario  
As shown in Figure 1, each SU directly transmits the local sensing information to the FC, and the FC makes the final decision and then sends it to each SU for the centralized CSS. The details of centralized CSS are as follows:  
1. The energy vector of each sensing node is obtained by the sampling and signal processing.
2. The obtained energy vector of each node is sent to FC over the reporting channel, where the reporting channel obeys the Rayleigh distribution.
3. After the reporting channel, a two-dimensional matrix is obtained with the energy vector of each sensing node. Then, the mean value of the covariance of the two-dimensional matrix is removed by each matrix element. Finally, the updated two-dimensional matrix is input to the CNN module for the final decision.
4. The final decision result at FC is sent to each local sensing node.
# Deep Learning-Enhanced Spectrogram Analysis for
## 1 Introduction
#### Key Contributions  
This research presents the following major contributions:  
* The first deep learning framework for anatomical region classification of biomedical spectrograms ensured interpretability and clinical relevance.
* The fine-tuned ResNet50 model was adapted for grayscale spectrogram input, effectively capturing spectral and spatial features of palpation and percussion signals.
* The achieved high classification accuracy (93.37%) surpassed that of conventional models and demonstrated robustness in anatomical region-based biomedical classification.  
Figure 1: Workflow of the proposed deep learning-based biomedical signal classification pipeline. The figure illustrates the complete process from raw signal input to final classification into anatomical regions.  
* A comprehensive preprocessing pipeline incorporating spectrogram normalization, augmentation, and optimized feature extraction ensures an improved model generalization and robustness.  
Although the ResNet50 architecture itself is not newly developed, its application in this context represents a distinct use case within biomedical signal classification. Region-based classification of biomedical signals offers a critical advancement in clinical workflows, particularly in non-invasive gastrointestinal and abdominal assessments. By identifying the anatomical origin of sound-based signals, the proposed method can support spatially guided diagnosis, such as the detection of abnormal resonance, bowel sound shifts, or localized abnormalities, which are often missed in binary disease classification models. This allows for more precise, region-aware clinical interpretation in both remote and in-person diagnostic settings.  
By bridging the gap between raw biomedical signal acquisition and interpretable clinical outputs, this study contributes to the advancement of non-invasive diagnostic techniques, paving the way for real-world clinical applications [12].
## 3 Numerical Computing Model
### Model Establishment
#### 3.1.2 Computational Parameters  
The Mohr-Coulomb model is used to represent the behavior of surrounding rock II. Based on the guidelines outlined in the _Design Rules for Highway Tunnel_ (JTG/T D70-2010) [23], the parameters for surrounding rock II are specified as follows: the density of surrounding rock II is 2500 kg/m\({}^{3}\), with an elastic modulus of 20 GPa, Poisson's ratio of 0.25, cohesion of 1.5 MPa, and an internal friction angle of 50\({}^{\circ}\). The tunnel lining is modeled using a linear elastic constitutive relation. The lining material is C30 reinforced concrete, and the calculation parameters are based on the Code for _Designing Concrete Structures_ (GB 50010-2010) [24]. Specifically, the elastic modulus of the lining is set to 30 GPa, and Poisson's ratio is 0.2.  
In vibration analysis, a proportional damping model is widely used to capture the damping behavior of vibrating structures. The popularity of this model is due to its simplicity in implementation, de-coupling of the governing equations, and control over the tuning of the mass and stiffness proportional constants, a and b, respectively. Many FE analysis codes implement the Rayleigh damping model [25]:  
\[[C]=\alpha[M]+\beta[K] \tag{2}\]  
The damping matrix \([C]\), mass matrix \([M]\), and stiffness matrix \([K]\) are defined, with \(\alpha\) and \(\beta\) representing the mass damping and stiffness damping proportionality constants, respectively. The values of \(\alpha\) and \(\beta\) can be determined according to the publication:  
\[\xi_{\mathrm{n}}=\frac{\alpha}{2\omega_{n}}+\frac{\beta\omega_{n}}{2} \tag{3}\]  
In the formula, \(\xi_{\mathrm{n}}\) represents the modal damping ratio of the N-th mode form, and \(\omega_{n}\) represents the frequency of the N-th mode.  
With a modal analysis, the first two fundamental frequencies, \(\omega_{1}\) and \(\omega_{2}\), can be obtained. The values of \(\alpha\) and \(\beta\) can be determined using \(\xi_{1}\) and \(\xi_{2}\), typically set at 0.05, obtained through modal analysis. The modal analysis yielded the first and second mode frequencies of the surrounding rock as \(\omega_{1}=15.758\) and \(\omega_{2}=20.412\), respectively. Based on these results, the Rayleigh damping coefficients were determined as \(\alpha=0.8893\) and \(\beta=0.0028\).
# Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
###### Abstract  
In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By \"rational\", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSGs, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.  
subgoal graphs; reinforcement learning; hierarchical path planning; uncertain environments; mobile robots +
Footnote †: journal: Applied Sciences
## 3 Controller Design and Stability Analysis
To verify the effectiveness of the designed controller, we simulated a running process with several operation conditions, which contains two acceleration phases, four cruising phases, and two deceleration phases, as depicted in \[\dot{s}=\ddot{e}+\hat{\delta}_{1}e+\hat{\delta}_{1}\dot{e}+\hat{\delta}_{2} \int_{0}^{t}e(\tau)d\tau+\hat{\delta}_{2}e. \tag{14}\]8 Parameters of the designed controller were selected as: \(k=0.01\), \(\Gamma_{1}=\text{diag}[0.01,0.01,0.01]\), \(k_{3}=1\),000,000, \(\Gamma_{2}=\text{diag}[5,5,0.00001,5]\), \(l_{4}=0.0000
# A Secure Blockchain-Based Authentication and Key Agreement Scheme for 3 GPP 5G Networks
## 3 System and Security Model
### Security Model  
Our security model is also displayed in Figure 1. It is assumed that the 5G wireless channels follow the Dolev-Yao model [30], which assumes that there could be some neighboring active and passive attackers. Passive attackers eavesdrop and interpret the messages sent from both UEs and gNBs. Then, they can analyze the intercepted data to figure out the messages sent from both parties. Active attackers not only eavesdrop on the wireless channels, but also modify the intercepted messages, replay them, or even fabricate new messages to impersonate legal UEs or gNBs to disrupt the network. All these attackers are labeled as \"Dolev-Yao (DY) Attackers\" in Figure 1. Moreover, although it is assumed that the communication channels within 5 GC are trustworthy, some accidents or misconfiguration could happen to the functional entities. For example, information in the data repositories of the 5 GC functional entities could be leaked in some rare cases. In this scenario, these passive attackers would try to use the exposed permanent keys to decrypt previous communication data. Additionally, it is possible that there is compromised botnet UE in the wireless network. UE could launch DoS and DDoS attacks at any scheduled time, attempting to flood or paralyze the authentication-related functional entities in the 5 GC.  
Figure 1: System and security model.  
Due to all the assumptions above, a desired 5G authentication protocol should provide security functionalities including device anonymity, mutual authentication, secure data transmission, and session key perfect forward secrecy. Moreover, it should be able to prevent active attacks such as impersonation, linkability attacks [5], replay attacks, man-in-the-middle (MITM) attacks, and DoS attacks. For passive attacks, there could be eavesdropping and location tracking attacks. These attacks must also be deterred.