esa-sceva/satcom-chunk-collection huggingface dataset:
# FL-SDUAN: A Fuzzy Logic-Based Routing Scheme for Software-Defined Underwater Acoustic Networks
## 2 Related Work
### Summary  
The above works aimed to design software-defined system architectures that run fewer routing algorithms in the control plane. Fuzzy logic is suitable for solving routing problems in dynamic underwater environments because it can handle uncertainty and give optimal solutions. Most studies use fuzzy logic to optimize residual energy, hop distance, link quality, and deployment density. However, it also faces the following challenges. On the one hand, the extended fuzzy logic-based schemes are mainly based on the cooperative communication of CHs. Therefore, the energy of CHs will be depleted quickly. Frequent CH elections can affect the stability of clusters and lead to constant changes in network topology. On the other hand, many fuzzy logic-based schemes assume a uniform distribution of underwater nodes, which differs significantly from the actual deployment. In underwater acoustic networks, DNs move due to ocean currents. Consequently, the possibility ofdense and sparse node types is high. Unfortunately, there are relatively few fuzzy routing schemes involving these two states.
## 3 Joint Cooperative Spectrum-Sharing Scheme
### Utility of \(DT_{n}\)  
During phase 2, \(DT_{n}\) has to consume a part of the power, namely \(P_{mn}^{B}=(1-\alpha_{mn})P_{n}\), to forward the \(E\!U_{m}\)'s data, in exchange of the opportunity to access the cellular spectrum for D2D communication. Hence, the utility of the \(DT_{n}\) may be expressed as:  
\[U_{mn}^{D2D}(\tau_{mn},\alpha_{mn})= R_{mn}^{D2D}-\frac{P_{mn}^{B}}{P_{n}}\] \[= (1-\tau_{mn})\left[\log_{2}(1+SINR_{D2D})\right]-\frac{P_{mn}^{B} }{P_{n}}\] \[= (1-\tau_{mn})\left\{\log_{2}\left[1+\frac{h_{mn}\alpha_{mn}P_{n }}{bh_{nn}(1-\alpha_{mn})P_{n}+N_{0}}\right]\right\}-(1-\alpha_{mn}) \tag{12}\]  
where \(R_{mn}^{D2D}\) is the achievable transmit rate of the D2D link by accessing the the cellular spectrum and \(\frac{P_{mn}^{B}}{P_{n}}\) is the loss of \(DT_{n}\).
# Risk-Aware Wireless Positioning in
## 3 Problem Formulation
### System Model  
Our system model is depicted in Figure 4. There are \(K\) BSs and a single user in a certain area comprising a safe zone and an unsafe zone. The user is moving around the safe zone and the BSs trace the user position to prevent the user from entering the unsafe zone. To help the BS monitoring, the user periodically broadcasts a positioning signal. Thus, the BSs can determine the user position from the received signal strengths (RSSs). For example, there can be a user swimming on the water with a wireless device in a life jacket. In this case, the BSs need to monitor the user position for safety and the user's mobile device broadcasts the positioning signal for the BS's monitoring.  
In our proposed positioning scheme, the positioning signal frequency changes according to the user position. Our proposed scheme is based on the fingerprinting scheme, which we explained in Section 2. For the fingerprinting scheme, we divided the whole area into \(N\) Voronoi regions. Once the BSs locate the cell in which the user belongs (i.e., the user's Voronoi region) from the RSS, the BSs notify the user of the new positioning signal broadcasting frequency. We denote by \(f_{i}\) the user's positioning signal frequency when the user is in cell \(i\).
## 8 SCOH Transceiver Structures
### Optical Field Representation
#### 8.1.1 Jones Space Representation  
In the SV-DD schemes, the polarization diversity feature in the fiber channel is exploited by using the Stokes space signal representations to support multidimensional polarization modulations. Based on this, the (electrical/optical) SE and the transmission reach can be relatively enhanced [11; 13]. Likewise, in the COH detection scheme, the polarization state acquisition is normally realized in the Jones space. Therefore, at the transmitter, the signal Jones representation of an optical field with the Jones vector, \(\vec{E}\), can be expressed as [8; 140]:  
\[\vec{E}=\left[\begin{array}{c}E_{\delta}\\ E_{\delta}\end{array}\right]=\left[\begin{array}{c}E_{x}e^{i\phi_{x}}\\ E_{y}e^{i\phi_{y}}\end{array}\right]=e^{i\phi_{y}}\left[\begin{array}{c}E_{ x}\\ E_{y}e^{i(\phi_{y}-\phi_{x})}\end{array}\right] \tag{41}\]  
where \(E_{\delta}\) and \(E_{\delta}\) are complex valued, \(E_{x}\) and \(E_{y}\) represent signed real values with phases \(\phi_{x}\) and \(\phi_{y}\), respectively, and \((\cdot)^{*}\) denotes complex conjugation.
## 5 Dynamic Analysis
### Simulation Results
#### 5.3.1 Test Case 1--1-Step Profile  
A 1-step burr profile with a constant height (\(h=5\) mm, see solid line in Figure 10 (top)) is used as an input for the dynamic simulation. The burr height is sufficiently high to make the projection of the cutting force along the sliding direction exceed the elastic force threshold of the mechanism (dashed line in Figure 10). The simulation results, i.e., the position and velocity of the grinding wheel support in function of time, are presented in Figure 10 (middle and bottom). From the analysis of Figure 10, it can be noticed that the grinding wheel support moves backwards (positive values of \(x\)) thanks to the compliance of the mechanism; moreover, an equilibrium condition is achieved (after a transient of about 0.25 s with a small overshoot) between the cutting force projection and the elastic reaction force and, therefore, a cut profile with constant height is obtained. Therefore, the simulation results for this test case are in line with what was expected in this case (Condition 2 of Section 5.3). The burr height, initially equal to 5 mm, after processing is about 2.5 mm. This means that a second cutting cycle is necessary to completely remove the burr. The maximum (normal) deburring force reduces to 148 N from the nominal value of 300 N (corresponding to the nominal height of 5 mm), i.e., roughly a 50% (automatic) force reduction is obtained, which is related to the 50% reduction of the cutting height (2.5 instead of 5 mm).
## 3 Experimental Scheme  
### System Description  
Circular polarization can be achieved by setting a 90\({}^{\circ}\) phase difference between H and V polarizations [23]. To introduce this concept, we employed diagonal dual LP-horn antennas with a phase shifter for transmitting and receiving the CP-signal. The vector network analyzer and rotational positioner controlled by the positioner controller were utilized to construct the whole CP-radar system. The experimental geometry illustrated in Figure 3 was adopted, and the rice measurements were conducted within an anechoic chamber to achieve a fully controlled environment. The incidence angle was set to 70\({}^{\circ}\), and the operational frequency was adjusted to 4.5-7.5 GHz (bandwidth is 3 GHz) in C-band. An angular span of 0\({}^{\circ}\) to 359\({}^{\circ}\) was used to investigate the rice backscattering from all azimuth angles. Therefore, quasi-monostatic antennas always face the target direction.  
Before starting experiments, the measurement of the AR parameter of the antennas was needed in order to check the deviation from circular polarization. Theoretically, 0 dB of AR shows ideal circular polarization whereas an infinite value corresponds to ideal linear polarization [23]. An AR value of less than 3 dB is generally considered as an acceptable value for most applications [24], and we follow this definition in this paper. The AR of our CP-antennas was measured inside the anechoic chamber and was found to be under 2.5 dB over the entire operational frequency bandwidth which means that our antennas achieve good circular polarization purity within the investigated bandwidth.
# Shock Initiation of a Satellite Tank under Debris Hypervelocity Impact
## 3 Initiation Criterion for the Liquid Hydrazine Tank  
According to Walker-Wasley criterion, whether the debris hypervelocity impact can detonate liquid hydrazine depends on the impact shock wave pressure \(P\), pressure duration \(t\), and critical initiation energy \(E_{c}\) which is determined experimentally.
## 4 Experimental Results
### Memory Consumption Analysis  
In Figure 6, Salsa demonstrates the largest memory footprint, followed by Grain-128a, while Sosemanuk exhibits the smallest footprint, followed by Grain-v1. Mickey-v1 and Trivium have relatively moderate memory footprints.  
In Figure 7, Sosemanuk displays the highest code size, significantly differing from the other selected LWSCs, with Grain-128a following behind. Grain-v1 has the smallest code size, followed by Salsa, Trivium, and Mickey.  
Figure 4: Throughput of selected LWSCs.  
Figure 5: Processor Cycles per Byte (CpB) of selected LWSCs.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Future Directions and Open Research Questions
#### 4.5.1 Energy-Harvesting Technologies  
Given the necessity for future IoT devices to be more sustainable, energy-harvesting technologies will be a pivotal area of focus. Energy-harvesting technology is an environmentally friendly and innovative solution that extends the working life of sensors and, in some scenarios, can even completely replace traditional battery-powered methods. By optimizing the energy consumption and reducing the network maintenance costs, energy-harvesting technology offers both economic benefits and practical advantages [23]. Researchers will investigate methods to harness energy sources in the environment, such as solar, wind, and thermal energy, to deliver long-term stable power to devices. Emerging energy-harvesting materials, efficient conversion technologies, and strategies for integration with IoT devices to reduce the dependence on conventional batteries will also garner increased research attention.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture
### Traditional Classifier Experiments
#### 4.2.1 Support Vector Machines (SVM)  
A multi-class support vector machine (SVM) classifier was trained by applying the linear function. As the SVM is considered to be simple as well as competent for machine learning algorithm computation, it was applied for pattern recognition and classification issues. Since the training data were limited, the classification performance was quite efficient when compared to other classifiers. Hence, in the current research, the speech data were classified using the support vector machine, and the following parameters were used.  
* SVC: C = 1.0
* Kernel = linear
* decision_function_shape = ovr
## 3 Results and Discussion
### Specimen Experiment
#### 3.1.3 Microstructure Properties  
Samples A-3, B-1 and C-1 were selected for the specific pore structure tests based on the SEM analysis. The pore structure results are shown in Table 7. The specific surface area of sample C-1 is the largest, which is three times larger than that of sample A-3 and twice as large as that of sample B-1. The pore structure characteristics of CHM-WSE specimens are shown in Table 8. In the pore size distribution of sample C-1, the number of pores in the 10-50 nm range is the largest, the specific surface area and the average pore size are the largest, showing a good pore structure distribution. Most pores of sample A-3 are micropores. Most pores are less than 10 nm, giving the smallest specific surface area and pore volume. Sample B-1 has mostly micropores and macropores, and larger specific surface area. According to the Kelvin capillary condensation theory, relative pressure of adsorbate during capillary condensation is related to pore diameter. The larger pore size, the greater the relative pressure required for the occurrence of capillary condensation. At the same time, the pore volume decreases along with the pore size. Above all, the sample C-1 sample has more adsorption sites and space to accommodate the adsorbent.  
Figure 6: SEM pictures.  
The results of nitrogen adsorption/desorption tests (Figure 7) indicate that all the three kinds of sample show a hysteresis loop during the adsorption and desorption processes. The nitrogen adsorption isotherms are all Type IV. Sample A-3 and sample B-1 have a smaller adsorption capacity. Sample A-3 has a maximum hysteresis loop and a poor regulation performance. The adsorption capacity of sample C-1 is five times larger than that of the previous two, which has a smaller hysteresis loop, and a better regulation performance.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mortar Samples**} & \multicolumn{3}{c}{**Distribution of the Pore Diameter (nm) (\%)**} \\ \cline{2-4}  & **<6** & **6–8** & **8–10** & **10–12** & **12–16** & **16–20** & **20–50** & **>50** \\ \hline A-3 & 22.81 & 18.65 & 6.70 & 7.35 & 7.97 & 9.27 & 12.07 & 15.18 \\ B-1 & 27.27 & 8.97 & 2.42 & 2.51 & 2.93 & 9.26 & 12.04 & 34.60 \\ C-1 & 15.75 & 13.17 & 5.86 & 6.82 & 10.18 & 13.70 & 16.91 & 17.61 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Distribution of the pore diameter.  
Figure 7: The adsorption-desorption isotherms of specimens.
## 4 Simulations and Results
The use of an ultimate beamwidth according to the pointing error will save power, and the reserved power enables transmitting signals using a high-order modulation technique. The adaptive modulation according to received power enhances the data rate, with the power secured using the narrow beam for a high modulation order. Figure 8: BER and SNR comparison of fixed and adaptive beams according to the strength of ground turbulence. Figure 9: BER and SNR comparison of fixed and adaptive beams using power allocation to enhance the SNR.
# An In-Depth Review on Blockchain Simulators for IoT Environments
## 4 A Systematic Review of Blockchain Simulators in IoT
### Comparative Analysis
#### 4.3.1 Hyperledger Fabric  
Hyperledger Fabric is a simulator that simulates only the network layer and its native language is Java. The reason this simulator was selected is its high popularity and relative ease when it came to finding articles. The main components of the Hyperledger Fabric architecture are peer nodes, ordering nodes, and client applications with identities of components being generated from certificate authorities. It is well suited to simulate blockchain networks that are supported by smart contracts [39]. However, some downsides include not being able to simulate traditional blockchain consensuses such as proof based or PBFT.  
In terms of simulating blockchain within IoT environments, it has been tested with IoT for material and product tracking in smart manufacturing, IoT data management, IoT edge device security, and IoT fish farm platform. Despite not having any mainstream  
Figure 5: The number of blockchain layers supported by the 15 simulators versus the blockchain layers.  
consensus, Hyperledger Fabric is a relatively popular simulator. In terms of material and product tracking, the simulator was used to build a blockchain network in which different numbers of devices were simulated and one device was simulated to send ten concurrent transactions [33]. Within the IoT data management simulation, the simulator was used as the blockchain network with five nodes deployed on Vultr VPS connecting to this blockchain network directly [40]. Transactions were tested between the network-to-peer and peer-to-peer, with nodes receiving data from Hyperledger Fabric clients. In the IoT edge security devices, the simulator was implemented as permissioned blockchain to secure the edge computing device by employing a local authentication process. It has been stated that Hyperledger was used due to its lower processing complexity [41]. Lastly, in the final simulation of implementing a secure fish farm platform, the infrastructure of the blockchain network was built on the simulator. The network consisted of four peer nodes and one order node along with a smart contract that was deployed to all peers [42]. The research team used Hyperledger Fabric to collect and graph data to get a better understanding on how their network should run. For example, [PERSON] et al. graphed the effect of the send rate on the blockchain network transaction throughput [42] to calculate the optimal transaction throughput for their network. By referring to Figure 6, they determined the optimal rate for transaction throughput to be 1100 tps since the average throughput drops after that. This is critical information to know about their network to ensure that it is running optimally.
# Differential Relapse of Proximal and Distal Segments after Mandibular Setback Surgery
## 2 Materials and Methods  
### Subjects  
This retrospective study was reviewed and approved by the Institutional Review Board (IRB) of Chosun University Dental Hospital (IRB approval number CUDH IRB 2301 003). Among the patients who visited the Department of Orthodontics at Chosun University Dental Hospital from 2013 to 2020, 211 patients were diagnosed with mandibular propagnathism and underwent orthognathic surgery at the Department of Oral and Max-illofacial Surgery at our hospital. Thirty patients met the inclusion criteria. The inclusion criteria were as follows:  
(1) Skeletal Class III malocclusion with mandibular propagnathism; (2) single jaw surgery with bilateral sagittal split ramus osteotomy (BSSRO) mandibular setback or double jaw surgery including BSSRO and Le Fort I osteotomy; (3) availability of cone-beam computed tomography (CBCT) scans at the time points before treatment (T0), within one month of surgery (T1), and after orthodontic treatment (T2).  
The exclusion criteria were as follows:  
(1) Presence of congenital anomalies, including cleft lip and palate; (2) degenerative disease of the temporomandibular joint; (3) an anterior open bite of 1.2 mm or more at pretreatment; (4) an open bite of a second molar of 1.2 mm or more at postsurgery CBCT; (5) a history of previous maxillofacial surgery, trauma, or infection in the maxillofacial region; (6) severe mandibular chin deviation greater than 5 mm from the facial midline.  
According to the above criteria, 181 patients were excluded.  
A splint was used to ensure a stable mandibular position during surgery, and semi-rigid fixation of the bone segments was performed using mini-plates and screws after orthognathic surgery. To ensure the adequate strength of the surgical splint, the surgical bite can be opened to accommodate the necessary thickness of the surgical splint. However, this may induce autorotation of the mandible upon splint removal, which must be distinguished from mandibular rotation caused by relapse. In the present study, the postoperative occlusion of the maxillary second molars ranged from complete contact (0 mm) to an open occlusion of up to 1.2 mm, with a mean value of 0.3 \(\pm\) 0.4 mm.  
The 30 patients selected had a mean age of 21.9 \(\pm\) 4.3 years. The duration of presurgical orthodontic treatment was 10.7 \(\pm\) 7.4 months, and the duration of postsurgical orthodontic treatment was 18.3 \(\pm\) 10.9 months. There were 15 patients each who underwent isolated single mandibular setback surgery and bimaxillary surgery. The mean amount of mandibular setback, measured from the pogonion (Pog), was 13.6 \(\pm\) 5.3 mm.
# Numerical Algorithm for Dynamic Impedance of Bridge Pile-Group Foundation and Its Validation
## 2 Project-Oriented Numerical Algorithm for Impedance of Pile-Group Foundation
### Refined FE Model of the Cap-Pile Group-Surrounding Soil System  
The pile-group foundation of the bridge can be regarded as a dynamic interaction system composed of the pile cap, pile group, and layered soil, the pile group effect of which is prominent. Therefore, to better consider the influence of the pile cap, pile layout, pile spacing, layered soil, soil properties, and other factors on the dynamic performance of pile-group foundations, a three-dimensional analysis model is necessary.  
Based on the finite element method, which is widely recognized and mastered by structural engineers, a refined FE model of the cap-pile group-surrounding soil system is established, as shown in Figure 1. Here, the ANSYS software [34] is used for establishing the model, in which the cap, piles, and soil medium are all modelled with Solid45 elements. The connection between the cap and the piles, and that between the piles and the soil, are realized by master-slave nodes or constraint equations. It is assumed that the interface between the piles and the surrounding soil is always in an ideal bonding state.
# Dynamic Mixed Reality Assembly Guidance Using Optical Recognition Methods
## 5 Results
### Time Measurements  
Time measurements were collected for each step and sub-step. First, from an experienced user doing the assembly procedure 10 times without any guidance. The same user then used the MR application for guidance, completing the assembly procedure 10 times. These first two steps were planned to have a reference point to compare with the time registered for the application. Being this a simple product with easy to perform actions, we expected the times for assembling by the experienced user to be low, whereas the times collected when using MR, we expected to be higher, as they include the instructions and animations within the app. Lastly, 10 inexperienced subjects completed the assembly procedure using the MR application one time each. The average times for each assembly step can be seen in Figure 15. The inexperienced users took a significantly longer time to complete each step compared to the experienced user, with or without MR guidance.  
Figure 14: Component placement validation results for experienced and inexperienced users.  
Figure 16 shows the average total time from both the experienced user and inexperienced users when using the MR application. The total time is split into component selection time, placement time, validation time, and corrections time. Components placement took the longest time for all users. This represents the time frame spanning from completing component selection to the start of the validation. For the experienced user no correction time is available since they committed no placement errors and the application always validated correctly.