esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 2 Related Work
### Research Gap  
We summarize the related works in Table 1. Most previous works have focused on improving the performance of energy harvesting based on the demands of task computation or data transmission. However, these works do not attempt to improve the performance of data transmission by arranging the charging completion time of charged devices. We are thus motivated to reduce charging time and improve the performance of data transmission by minimizing the energy overflow.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Beamforming** & **Battery** & **Research Goal** \\ \hline
[5] & None & No & Minimizing energy consumption with single \\  & & & WET source \\ \hline
[7] & None & Yes & Optimizing sectors for single WET source \\ \hline
[15] & None & Yes & Optimizing directions and phases of multiple \\  & & & WET sources \\ \hline
[20; 22] & None & Yes & Optimizing WET time period for computation Tasks \\ \hline
[23] & None & Yes & Predicting WET for computation tasks \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of previous works.
# Quality Enhancement of MPEG-H 3 DA Binaural Rendering Using a Spectral Compensation Technique
## 4 Implementation and Complexity
### Subject Evaluation  
A MUSHRA (multiple stimuli with hidden reference and anchor) [20] test was conducted in a 3 DoF rendering situation. We used the official 22.2-channel layout test materials, and seven signals included in MPEG-H 3 DA call for proposal (CfP) [21]. They include recorded and synthesized 22.2-channel signals as explained in the Appendix. All binaural signals were generated using the DFE MIT HRTF [14] with normal pinnae. In the simulation of the 3 DoF rendering, each channel of the test materials was regarded as an object signal located at the corresponding virtual loudspeaker position. The yaw, pitch, and roll angles of the simulated head rotation were 15\({}^{\circ}\), 0\({}^{\circ}\) and 10\({}^{\circ}\), respectively.  
In the MUSHRA test, five systems were evaluated: the original MPEG-H 3 DA 3 DoF rendering system (\"MPEG-H 3 DA BR\"), the same system employing the proposed algorithm (\"MPEG-H 3 DA BR w/ Proposed\"), a hidden reference (\"Ref\"), and two anchors (\"Ancl\" and \"Anc2\"). The hidden reference was obtained by filtering the object signal using the actual HRTF nearest to the panning direction. The two anchors were 3.5 kHz and 7 kHz low-pass filtered versions of the hidden reference. Fifteen experienced subjects aged from 23 to 45 participated in the test.  
The test results are plotted in Figure 7. We can see that the proposed algorithm significantly improves the subjective quality for all the items. The averaged MUSHRA scores of \"MPEG-H BR\" and \"MPEG-H BR w/ Proposed\" are 66.1 and 87.1, respectively, with a statistically significant difference by a large margin. After the experiments, we also collected some feedback about the proposed system. Many participants reported that dullness and coloration problems were greatly alleviated by the proposed algorithm.
# LIF-M: A Manifold-Based Approach for 3D Robot Localization in Unstructured Environments
## 4 Multi-Sensor Data Fusion Framework
### Manifold-Based Unscented Kalman Filter  
The EKF framework typically relies on Gaussian assumptions and Taylor expansion to linearize models, thereby enabling derivation of the system's probability distribution. On the other hand, the UKF approaches the problem by simulating a set of points to approximate the system's underlying probability distribution. This addresses the issue faced in higher-order systems when using EKF, where the geometric expansion of Jacobian matrices leads to excessive utilization of computational resources.  
To tackle this challenge, transforming the sigma points (the point set required for attitude estimation) generated by UKF from Euclidean space to a Riemannian manifold simplifies the calculation of more accurate robot poses. Performing the UT transformation within the manifold exploits the advantages of Lie groups while disregarding related lower-order terms, consequently reducing the overall system error. This technique contributes to more precise state estimation for the robot.
# A Knowledge Graph-Based Consistency Detection Method for Network Security Policies
## 7 Experiment and Analysis  
To verify the effectiveness of the policy consistency detection method in this article, simulation experiments were conducted based on the university dataset [23]. This dataset is often used to generate Attribute-Based Access Control (ABAC) policies, covering 43 categories, 8000 access control logs for allowed categories, and 8000 access control logs for prohibited categories. There are two advantages to using this dataset for experiments:  
* The method for generating a four-layer knowledge graph in this experiment is as follows: the log data used to generate the ABAC policy dataset are highly formalized,and a simple regular expression method can be used to input the policy entities and attributes of these logs into the knowledge graph, forming a simple network information layer graph. The access control problem is relatively simple, so there is no need to design a logical dependency layer graph in this experiment. The security knowledge layer graph only needs to record the attribute information of the 43 categories involved in the policy entity, without worrying about the specific meaning of the attributes. The ABAC policy has the characteristics of decision trees, which can be used to write automated Python scripts to organize and rewrite the judgment logic into a BMSL like language with cypher syntax, quickly generating detection logic layer diagrams.
* This dataset belongs to the classic dataset for generating ABAC policy problems and is suitable for various algorithms that automatically generate ABAC policies. Therefore, in this experiment, these algorithms can be used to generate a variety of ABAC policies, which have different attribute scales, policy scales, and access control sets for attribute ratings, making it convenient to design various simulation experiments.  
This article designs two experiments to evaluate the effectiveness of policy consistency detection, namely the attribute scale and detection rate experiment, and the policy attribute importance experiment.
# Research on Compression Behavior of Square Thin-Walled CFST Columns with Steel-Bar Stiffeners
## 3 Test Results
### Load-Displacement Relationship  
The load-displacement relationship curves of the six column specimens are shown in Figure 6. Three stages were observed in Figure 6. In the first stage, the displacement was small and proportionally increased with the load. Columns were still in the range of elasticity under the test load. However, in the second stage, the value of loads began to decrease, with displacement increasing after achieving the ultimate load. The higher the ultimate load was, the more rapidly the load decreased. When it came to the ultimate load, the displacement of C3 was the biggest among all the specimens, 10.1 mm, and the displacement of C6 was the smallest, 4.6 mm. In the last stage, the load tended to stabilize or increase a little, while displacement increased rapidly leading to the final damage of the columns. It has been observed that the displacement of the columns ranged from 20 to 40 mm as the loads stabilized.  
The ultimate load of C2, with a value of 802 kN, was significantly larger as compared to C1. Columns with stiffeners, meanwhile, achieved an even larger ultimate load than C2, ranging from  
Figure 5: Failure modes of specimens.  
Figure 6: Load–displacement relationship curves of column specimens. (**a**) C1; (**b**) C2; (**c**) C3; (**d**) C4; (**e**) C5; (**f**) C6.  
1100 to 1353 kN. It can also be observed that C6, with steel-bar stiffeners outside the tube, reached the highest ultimate load among the columns with a value of 1353 kN. It can be concluded that the ultimate load and maximum displacement of the concrete-filled columns with stiffeners were much larger than the columns without any stiffeners, which proved that the stiffeners were very useful in improving the mechanical performance of the square thin-walled CFST columns.
## 3 Results
### Tensile Properties  
Based on the static tensile test data, representative tensile stress-strain curves for gradient hardness and different molding methods are shown in Figure 5 below. All presented tensile curves were based on the median sample from a total of six samples. As observed from the images, the stress-strain curves generated from the uniaxial tensile tests of all materials exhibited two distinct phases. Phase A was a linear phase where the implant had a relatively high elastic modulus, while Phase B was a non-linear phase within the strain range of 15-350%, during which the elastic modulus dropped before gradually stiffening. This behavior was characteristic of classic elastomers and aligned with the expected trend for elastic materials. Furthermore, regardless of the molding method (IM, CM, or 3D), as the material hardness increased from 75A to 55D, the stress values corresponding to the same strain increased. That is, under the same strain conditions, the higher the material hardness, the higher the elastic modulus of the material. For 75A and 85A hardness levels, the elastic modulus of the material processed by IM was greater than that of CM and 3D molding methods. At 95A and 55D hardness levels, the elastic moduli of the three different molding methods were comparable. For the second phase of deformation, under the same strain conditions, the stress values tended to be higher for CM than for 3D and IM.  
The following Figure 6 presents the stress levels of PCUs with gradient hardness under three different molding methods at various elongation rates. For all materials, as the elongation rate increased, the stress levels of PCUs with gradient hardness exhibited a consistent upward trend. Among the three molding methods, there was no significant difference in the stress levels of PCUs of the same hardness. At 100% and 200% elongation rates, the stress levels of PCU materials with hardness of 75A, 85A, and 95A under the IM molding method were 3.94 MPa, 4.01 MPa, and 6.31 MPa, and 6.48 MPa, 7.13 MPa, and 12.15 MPa, respectively, all higher than those under the CM and 3D molding methods. For the hardness of 55D, the stress levels were lower than the other two molding methods. At a 300% elongation rate, the stress levels of PCU materials under the CM molding method  
Figure 4: FTIR spectra of PCU samples via different molding methods: (**a**) IM; (**b**) CM; (**c**) 3D.  
were higher than those under IM and 3D molding. Furthermore, the PCU materials with a hardness of 55D under CM and IM molding did not reach 300% elongation before reaching their tensile limit and fracturing.
## 2 Energy-Aware Multi-UAV Dispatch Algorithm (EAMUD)
### The Energy Model of UAV Battery  
#### 2.2.1.  
Redranging of the UAV Battery  
Whenever a UAV stays on the dock of GCS, GCS continues charging this UAV until its battery has been fully charged or it has been dispatched by GCS. Assume the battery capacity of the UAV is denoted as \(E^{Max}\) in Joule (\(J\)). The maximum time, i.e., \(T^{Char}\), in hours to fully charge the UAV battery is formulated as follows:  
\[T^{char}=\frac{E^{Max}\times(1+\alpha_{loss})}{I_{char}\times V_{char}} \tag{1}\]  
where \(I_{char}\) is the charge current in \(Amp\), \(V_{char}\) is the current voltage in \(V\) and \(\alpha_{loss}\) is the efficiency loss coefficient, e.g., 10%.
###### Abstract  
Recurrent peroneal tendon dislocation is a rare and often underrecognized condition. It may result from acute injuries, more commonly seen in athletes, or from untreated ankle sprains leading to chronic ankle instability. The aim of this study is to retrospectively evaluate short- and medium-term functional outcomes of the surgical technique involving deepening of the retromalleolar groove for the management of recurrent peroneal tendon dislocation. Nine patients, one of whom had bilateral dislocation, were enrolled in this study. The study group consisted of six males and three females, with a mean age of 31.2 years. CT scans were used to measure the depth of the neo-grooves, while the EFAS score (European Foot and Ankle Society Score) and AOFAS score (American Orthopedic Foot and Ankle Score) were used to assess functional results. The medium follow-up period was 4.8 years. CT scan data revealed a mean increase of 4.69 mm in the depth of the neo-retromalleolar groove. AOFAS scores improved from a mean preoperative value of 74.4 to 86.9 after surgery, and EFAS scores increased from a mean preoperative score of 19.7 to 31. Statistical analysis of clinical scores yielded significant results with a \(p\)-value \(<\) 0.005. All patients returned to their previous sports activities within an average time of 7.7 months, reporting no pain or limitations. There were no recurrences observed. The retromalleolar groove deepening technique demonstrated excellent results in the treatment of recurrent peroneal tendon dislocation, evidenced by significant improvements in clinical scores, functional recovery, successful return to sports, and high patient satisfaction.  
peroneal tendons; dislocation; groove deepening; surgical treatment; sport Article
## 3 Results and Discussion
where \(p\) is the pressure, \(V\) is the volume, and \(n\) is the amount of substance of the gas, respectively, which can be expressed as:  
\[n=\frac{N}{N_{A}} \tag{9}\]  
where \(N\) is the number of gas molecules and \(N_{A}\) is the Avogadro constant. As above, we find for cases with 10%, 75%, and 90% relative humidity, the relevant water content is 0.245%, 1.866%, and 2.248%, respectively. As for the case with 1.0% water content analyzed above, the relative humidity was 40%. In order to keep the total atmospheric density constant in all cases (\(\rho_{N_{2}}+\rho_{O_{2}}+\rho_{H_{2}O}=\rho_{at}\)), the components of dry air were reduced proportionally. Figure 6 shows the on-axis intensity and electron density along propagation distance for the five different humidity values. In the simulation, we adopted I\({}_{0}\) = 2.5 \(\times\) 10\({}^{15}\) W/m\({}^{2}\), \(\tau_{\rm p}\) = 160 fs, and r = 1 mm. We can clearly see that the atmospheric humidity levels do influence the overall filament dynamics. As the content of water vapor increases, the initial filamentation position slightly increases. The on-axis intensity experienced obviously decreases for 0% humidity to 40% humidity. The evolution of on-axis intensity in 75% humidity condition is very close to the 90% humidity condition. The differences in evolution of on-axis intensity along z decrease gradually from low humidity to high humidity. Analogously, the peak of on-axis electron density increases rapidly from low humidity to high humidity and the trends of increasing gradually decreases as the water content increases. Figure 7 shows the total number of electrons ionized from H\({}_{2}\)O and the overall energy of the wave packet as a function of propagation distance z. As presented in Figure 7a, the number of electrons increases with the water content but the trend of increase gradually decreases as the humidity increases. In other words, the density of electrons tends to saturate in high humidity conditions. This saturation is attributed to the nonlinear energy loss in the multiphoton gas ionization by [PERSON] [17]. The differences in nonlinear energy loss can be clearly seen in Figure 7b. The deposited energy of filamentation increases with the humidity increases but the growth quantity takes on reducing trends in the higher humidity conditions. We also simulated the femtosecond filamentation with initial peak intensity of I\({}_{0}\) = 1.0 \(\times\) 10\({}^{15}\) W/m\({}^{2}\) and I\({}_{0}\) = 5.0 \(\times\) 10\({}^{15}\) W/m\({}^{2}\). We find the conclusions are identical to those above and the results are not plotted again. Figure 6: Evolution of the on-axis (**a**) intensity and (**b**) electron density along propagation distance z in different humidity conditions. Red solid lines: 0% humidity; purple solid lines, 10% humidity; blue solid lines, 40% humidity; green solid lines, 75% humidity; light green solid lines, 90% humidity.
# Enhancing Target Speech Based on Nonlinear Soft Masking Using a Single Acoustic Vector Sensor
## 2 Problem Formulation  
In this section, the sparsity of speech in the time-frequency (TF) domain is discussed first. Then, the AVS data model and the corresponding inter-sensor data ratio (ISDR) models are presented for completeness, which was developed by our team in a previous work [13]. After that, the derivation of monotone functions between ISDRs and the DOA is given. Finally, the nonlinear soft TF mask estimation algorithm is derived specifically.
# Low-Cost Assessment Method for Existing Adjacent Beam Bridges
## 5 Case Study
### Lateral Load Distribution Performance Rating Number LDN of the Three Bridges  
It can be seen from the introduction in Section 5.1 that the assessment grades of the superstructure of the three bridges are all D. All three bridges show exfoliation of concrete and reinforcement corrosion. The grouted joints of each bridge are damaged to various degrees.  
In order to establish an appropriate maintenance sequence, this paper uses the method proposed in Section 3 to calculate the lateral load distribution performance rating number \(LDN\) of the three bridges.  
The importance coefficient of the three bridges is \(S=1.1\). The bending and torsional parameter of the three bridges is \(\gamma\leq 0.25\), so \(N_{\gamma}=5\). There are no transverse ties for the three bridges, so Equation (18a) is selected for the calculation.  
According to the visual inspection results:  
1. For the 13 m span of Changhong Tunnel K1 + 812 Bridge, the damage degree \(D_{n}\) of the damaged joints is \(D_{n}=3\), where \(n=1,\ 2,\ 4,\ 6,\ 7,\ 8\), and the other joints are intact with \(D_{n}=0.\); for the 10 m span, \(D_{n}\) of the damaged joints is \(D_{n}=3\), where \(n=1,2,3,7,8\), and the other joints are intact with \(D_{n}=0\);
2. For Baidong No. 2 Bridge, the damage degree \(D_{n}\) of the damaged joints is \(D_{n}=3\), where \(n=4,\ 6,\ 9,\ 10,\ 11,\ 12,\ 14,\ 15\), and the others are intact with \(D_{n}=0\);
3. For Yunhe Bridge, the damage degree \(D_{n}\) of all joints is \(D_{n}=1\), where \(n=1\) to 13.  
Before calculating the lateral load distribution performance rating number \(LDN\) of the three bridges, Equation (19a) was used to calculate the variation in the LLDF \(VA_{i}\) of each beam of the three bridges. The results are shown in Table 5a-c. For Changhong Tunnel K1 + 812 Bridge, due to the lack of truck testing data that could be compared, only \(VA_{i}\) of the 13 m span is displayed in Table 5a.  
Figure 5: (**a**) Arrangement of numbers of beams and joints of Yunhe Bridge. (**b**) Location of damaged joints of Yunhe Bridge.  
It can be seen from Table 6 that the lateral load distribution performance rating number \(LDN\) of Baidong No. 2 Bridge is the largest among the three bridges, which means that the variation in the LLDFs of the beams is the largest, and that the degradation of the load transmission performance of the bridge is the worst. Next is Changhong Tunnel K1 + 812 Bridge. Yunhe Bridge has the least degradation in the load transmission performance.  
Therefore, the sequencing in bridge maintenance from first to last should be Baidong No. 2 Bridge, Changhong Tunnel K1 + 812 Bridge, and Yunhe Bridge.
# Performance Evaluation of Multi-UAV Network Applied to Scanning Rocket Impact Area +
## 6 Results  
### Antenna Modeling and Characterization  
This section aims to define a position to place the XBee module in the UAV so that the antenna lobe has minimal deformation caused by the aircraft structure as stated in Reference [52] where it is effectively shown that the aircraft structure can cause normal log shading. This effect can be harmful to communication and has been minimized by using 4 nec2 software [53] to determine the location of the XBee module antenna in the UAV. The antenna model and the definition of its position in the aircraft are discussed in this section.  
During the flight, the XBee device was attached to the aircraft structure. One of the main concerns was to ensure that the UAV would be able to send data regardless of the direction it was flying to. For this purpose, the antenna was positioned so that the internal and external metallic structures of the aircraft could not interfere with the antenna radiation lobe. The XBee antenna is omnidirectional with an approximate gain of 2 dBi.  
In order to analyze the structure in 2D and 3D view from the antenna, 4 nec2 software [53] was used. This software is capable of comparing distinct radiation patterns of distance field, as well as verifying the voltage standing wave ratio (VSWR) and gain through frequency sweeping. A filament antenna was created with the same characteristics obtained from the XBee datasheet to simulate the metallic structure used by the aircraft. The antenna was positioned at different places of the UAV to reach a minimum interference.  
It can be stated that keeping the antenna at a distance greater than or equal to \(\lambda/4\) from the metallic structure leads to good results since low interference is obtained as seen in Figures 15 and 16. Figure 15 shows that the vertical lobe presents omnidirectional irradiation with a maximum edge gain of 1.74 dBi and a half-power beamwidth with 90\({}^{\circ}\). The green lines represent the half-power beamwidth and the red line indicates the position of maximum gain of the antenna. Considering the XBee antenna, the half-power beamwidth is around 85\({}^{\circ}\)[54].  
Figure 16 represents the VSWR coefficient, whose ideal value is close to unit. If this condition occurs, the impedance match between the transceiver and antenna will be more efficient at the maximum rated power. Therefore the antenna operating frequency will not change with the distance when compared with the datasheet specification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **BS** & **HUB** & **ED** \\ \hline ZigBee Function & Coordinator & Router & End device \\ Distance from BS peer-to-peer & - & - & 700–800 m \\ Distance from BS with hop & - & 400–600 m & 900–1100 m \\ Baud Rate & 38,800 bps & 115,200 bps & 38,800 bps \\ Sent Bytes & - & - & 128,000 \\ Sent Packets & - & - & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Scenario 2: Configuration and position nodes.
# Networked Roadside Perception Units for Autonomous Driving
## 3 Design and Implementation of the RSPU System
### Implementation of AutoC2X
#### 3.2.1 Localization  
Autoware and OpenC2X employ scan matching and GPS for localization. Put differently, the function of localization is duplicated in the host and subsequently the router in the ITS station. The localization function of Autoware is employed throughout the ITS station in the planned system due to the high localization accuracy of scan matching, which is generally less than 10 cm. Furthermore, in comparison to GPS, scan matching is found to be more robust in the urban environment. Thus,  
Figure 3: AutoC2X overview.  
in Autoware, the ego-vehicle location is published in the topic of /ndt_pose in the developed system, before being transmitted to OpenC2X in the aftermath of coordinate transformation. Rather than the GPS coordinates, OpenC2X makes use of the received position.