esa-sceva/satcom-chunk-collection huggingface dataset:
# A Spatial Compounding Method for Non-Delayed Sequential Beamforming
## 5 Conclusions  
In this work, we propose two spatial compounding methods for a sequential beamforming algorithm named non-delay sequential beamforming (NDSB). Pressure field analysis indicates different sub-images can be beamformed under different transmit configurations. The summation of these sub-images produces a compound final image. In vitro and in vivo studies are carried out to quantify the proposed compounding methods. The results show the proposed compounding methods significantly improve the target detectability: in vitro results confirm a 13.0-dB better CR and 4.0-dB better CNR compared with NDSB. In vivo imaging of cross-section jugular vein reveals 10.9-dB and 6.0-dB improvement in CR and CNR using the proposed method. For the longitudinal-section carotid artery, the compounding method provides up to 8.0-dB and 4.5-dB improvement in CR and CNR, respectively.  
Conceptualization, L.W. and S.L.; methodology, S.L.; software, S.L.; validation, S.L.; resources, L.W.; writing, L.W. and S.L. Both authors have read and agreed to the published version of the manuscript. This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board of City University of Hong Kong. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data that support the findings of this study are available on request from the corresponding author.  
Figure 9: Comparison of transmit F# to (**a**) lateral resolution and (**b**) image contrast (CR, CNR).  
**Acknowledgments:** This work is partially supported by the National Natural Science Foundation of China (NSFC) (81627805, 61805102); Research Grants Council of the Hong Kong Special Administrative Region (11215817).  
**Conflicts of Interest:** The authors declare that they have no conflict of interest.
## 3 Results
### Anti-Disturbance Performance Test  
A good control algorithm should not only have a good tracking performance, but also have a strong anti-disturbance ability. By having these properties, it can be applied to control a complex system such as wastewater treatment process to achieve a precise control effect. To further verify the anti-disturbance ability of the RBFNNPID algorithm, we used the data collected in rain and storm weather to simulate the algorithm. Different weather condition can be looked as different disturbances in the influent. The results are shown in Figures 8 and 9.  
Figure 7: Comparison results of the tracking performance (changing set point of DO): **(a)** DO concentration in the fifth tank; **(b)** Dynamic changes of the manipulated variable \(K_{\text{L}}\)a5; **(c)** Dynamic adaptive adjustments of the three parameters \(kp\), \(ki\), \(kd\) of RBFNNPID algorithm.  
Figure 8: Comparison results of the anti-disturbance performance (rain weather): (**a**) Influent flow rate (days 7 to 14); (**b**) DO concentration in the fifth tank; (**c**) Dynamic changes of the manipulated variable \(K_{l}\)a\({}_{5}\); (**d**) Dynamic adaptive adjustments of the three parameters _kp_, _ki_, _kd_ of RBFNNPID algorithm.  
Figure 9: Comparison results of the anti-disturbance performance (storm weather): (**a**) Influent flow rate (days 7 to 14); (**b**) DO concentration in the fifth tank; (**c**) Dynamic changes of the manipulated variable \(K_{l}\)a\({}_{5}\); (**d**) Dynamic adaptive adjustments of the three parameters _kp_, _ki_, _kd_ of RBFNNPID algorithm.
A simplified 3D FEM model was used in [8] to study the effect of clearance on contact stresses and the kinematics of a polymer-composite sliding bearing. Article [9] was a numerical study using the adaptive method of modeling the wear of a bearing made of a laminated polymer composite but it did not provide estimate the durability. Here, local wear was modeled according to the Archard abrasive/adhesive wear law with a linear dependence of wear on contact pressure and sliding speed. However, Ref. [10] indicated that at friction coefficients above 0.3, which is typical for dry friction pairs, the dominant type of polymer wear is the frictional fatigue mechanism under the influence of specific friction forces. Adhesive wear can then be a secondary concomitant process. A sign of adhesive wear as the main process is the transfer of a thin adhesive film of the polymeric material to a metal counterbody. In the literature, more attention is paid to experimental studies of mechanical bearings [11; 12; 13; 14; 15; 16; 17], for example, the works of [7; 9]. Paper [11] reports on the tribological behavior of various bearings with polymeric bushings made of polyacetal (POM-Delrin), cast polyamide PA6, etc., under dry friction. Ref. [12] is devoted to the study and evaluation of the wear resistance of bearings based on PE (polyethylene), PA, POM, PTFE, and bakelite polymers. The effect of the sliding speed, pressure, and temperature on the friction and wear of PA66 bearings and PA66+18 PTFE and PA66+20 GF+25 PTFE composites was studied at room temperature in [13]. A tribological analysis of the dry friction behavior of PA6 in a connecting rod plain bearing was performed in [14]. Study [15] evaluated polymeric plain bearings with PET and UHMWPE bushings and an Inconel stainless steel shaft under dry friction and with water lubrication. The effect of radial clearance on the coefficient of friction, mass wear, and temperature of plain bearings with a sleeve made of the NORDEN Marine 605 composite and a shaft made of AISI 316 steel was studied in [16]. Two modes of friction in the bearings were used--dry and with solid lubrication (PTFE). In [17], the wear of a sliding bearing with PEEK bushings and PEEK+30 CF, PEEK+30 GF, and PEEK+10 CF+10 CF+10 PTFE composites was studied. The authors have solved the problem of calculating the maximum contact pressures, wear, and durability of mechanical bearings by reasonably modifying the developed method of the elasticity theory for the study of metal bearings [18; 19; 20; 21; 22]. The developed method has been presented in [23], and the results of studies using it are presented in [24; 25; 26; 27; 28]. Below are the results for MP bearings with bushings made of the polyamides PA6, PA6+30 GF, PA6+MoS2, and PA6+oil; polytetrafluoroethylenes PTFE+10C+10 CF and PTFE+15C+5 MoS2; polyethylene terephthalates PET and PET+20 PTFE; and polyetheretherketones PEEK and PEEK+30 GF.
## 3 Data and Scenarios
### Data Process Procedures
Additionally, during post-processing, the pixel coordinates were transformed into geographical coordinates. We first identified eight landmark points around the road from nearby structures. Using the camera's intrinsic parameters, we then determined the relative positions between these points. Finally, we applied a homography transformation algorithm to accurately convert the pixel coordinates into geographic coordinates. Due to occlusions caused by signage or gantries on the highway, some trajectories were segmented into multiple parts, each assigned to different vehicle IDs. These interruptions could lead to fragmented trajectory data, potentially affecting the accuracy of vehicle movement analysis. To address this issue, we manually searched for incomplete trajectory pairs and spliced them into continuous trajectories to ensure data completeness. While these steps improved the overall accuracy, it is important to note that some residual inaccuracies could still occur, particularly in sections where visual obstructions or extreme weather conditions were present. For errors that could not be fully corrected through automated processes, we conducted manual adjustments by visually inspecting the affected trajectory segments and refining the data where necessary. This combination of automated and manual correction ensured a high level of precision, although minor inaccuracies may remain. These limitations are unlikely to significantly affect the broader trends in merging behavior but could introduce slight errors in micro-level analyses, such as detailed interactions between vehicles at specific points in time. To further enhance accuracy, additional information such as vehicle speed, lane ID, and the positions of surrounding vehicles was attached to each vehicle. This was achieved by transforming the geographical coordinates into driving coordinates, represented by the horizontal driving distance along the central lane lines and the vertical distance from the central lane lines. This study collects the merging behavior data from the trajectory in the wzone dataset for the first step. Statistics of vehicle merging behaviors are shown in Figure 2. The merging maneuver starts at a distance of \(L_{m}\) in front of the taper area with speed \(v_{Ms}\). At that frame, the target leader, follower, and the merging vehicle's accepted gap are recorded. \(v_{f}\) and \(v_{l}\) represent the speed of follower and leader, respectively. When the merging vehicle crosses the line, the speed of the ego vehicle is \(v_{Mc}\). Finally, the vehicle finishes merging with a speed \(v_{Mf}\). And the distance and time duration of the merging maneuver are denoted as \(D_{M}\) and \(T_{M}\).
# Power Management Strategy for an Autonomous DC Microgrid
## 5 Economic Analysis
### PV and Load-Shedding Tariff Modelling  
Once PV panels have been installed, without considering periodic cleaning, power generation does not engender extra costs. However, the occurrence of PV shedding means that assets are not used efficiently. PV shedding is therefore penalized, and its cost is defined as in Equation (16).  
\[C_{PV_{-}S}=\frac{1}{3.6\times 10^{6}}\sum_{i=t_{0}}^{t_{F}}c_{PV_{-}S}\cdot p _{PV_{-}S}(i)\cdot\Delta t \tag{16}\]where \(c_{PV\_S}\) is the PV shedding energy tariff.  
Regarding load, it is supposed that non-critical appliances account for at most 20% of total power demand and that when the shortage of power supply from the electrochemical battery bank and PV source is more than this value, the DG will be started up. Nevertheless, non-critical appliance shedding does lead to inconvenience for end-users. Load-shedding is therefore penalized, and its cost defined as in Equation (17).  
\[C_{L\_S}=\frac{1}{3.6\times 10^{6}}\sum_{i=l_{0}}^{t_{F}}c_{L\_S}\cdot p_{L\_S}(i) \cdot\Delta t \tag{17}\]  
where \(c_{L\_S}\) is the load-shedding tariff.  
PV shedding and load-shedding tariff-calculations are not trivial and require specific statistical data that are not reported in the literature. Nonetheless, to highlight the penalties inflicted on these shedding actions, the PV shedding energy tariff is set at 0.7 \(\up  
This short economic analysis demonstrates that the DG duty-cycle operating mode is able to save fuel and reduce greenhouse gas emissions, and it is therefore more advantageous than the load-following mode. However, for a more efficient operating mode, the duty-cycle time period has to be estimated, either by optimization methods or based on experience (after a significant running period). Furthermore, this analysis can be considered as a preliminary sizing study for the autonomous DC microgrid. DG sizing depends strongly on solar irradiance potential, as well as on total load power and total critical power demand especially. Regarding PV power limitation, the electrochemical battery bank will be sized according to solar irradiance potential.
## 5 Simulation and Performance Analysis
### Evaluation Metrics  
In our experiments, we compare the performance of the above three methods regarding traffic access success rate, average network load, average end-to-end path delay, network capacity, and average running time.  
* Traffic access success rate: The ratio of the amount of traffic that meets the SFC constraint transmission path to the total amount of traffic can be calculated using the following formula: \[R_{suc}=\frac{N_{SFC}}{N_{total}}\] (27) where \(N_{SFC}\) represents the amount of traffic that meets the SFC constraint transmission path, and \(N_{total}\) represents the total amount of traffic.
* Average network load: The load average of all satellite and ground station nodes.
* Average end-to-end path delay: The average delay of all transmission paths that meet the SFC constraint.
* Network capacity: The sum of the service rates of all transmission paths that meet the SFC constraints.
* Average running time: The ratio of the time it takes for the algorithm to complete the calculation of all traffic transmission paths to the total amount of traffic.
## 3 Measured Temperature Field of Thin-Walled Concrete  
### Comparison of Horizontal and Vertical Temperature Fields  
To understand the distribution patterns of the temperature field of thin-walled concrete in different directions, the horizontal and vertical temperature fields of the concrete specimen were compared based on the measured data in the hottest month (August 2022) during the experiment process, as shown in Figure 3. During August 2022, the temperatures within each horizontal elevation all presented small differences: the average horizontal temperature differences at the bottom, middle, and top elevations were 0.31 \({}^{\circ}\)C, 0.08 \({}^{\circ}\)C, and 0.40 \({}^{\circ}\)C, respectively. The overall horizontal difference was 0.26 \({}^{\circ}\)C. Meanwhile, the reason for the higher temperature of T10 is that it is farther away from the vertical centerline, so it is easier to dissipate heat through lateral convection.  
As a comparison, the temperatures at the vertical centerline apparently had various change rules. In terms of the vertical distribution pattern of thin-walled concrete, the temperature at any elevation fluctuated in the form of a sine function, with the temperature of the top surface having the most drastic fluctuations (15 \({}^{\circ}\)C in the most extreme situation), while the temperature of the bottom surface was the mildest (4 \({}^{\circ}\)C in the most extreme situation). Meanwhile, the average vertical difference between the top and bottom surface was 2.93 \({}^{\circ}\)C, which is 11.3 times the horizontal temperature difference.  
The most prominent variation in the temperature field of the thin-walled concrete specimen was found in the vertical temperature, and the temperature field of the thin  
Figure 3: Comparison of horizontal and vertical temperature differences. (**a**) Horizontal temperature; (**b**) vertical temperature.  
-walled concrete specimen could be assumed to vary in one dimension along the thickness direction. This is consistent with other studies on thin-walled concrete structures. Therefore, this lateral thermal insulation material was verified to be effective in conducting equivalent experiments on thin-walled concrete temperature, and the following temperature analysis was mainly focused on its vertical temperature.  
In various design specifications of different countries for bridges or road pavements, such as Chinese JTG D60-2015 [25] and American AATHSO LRDF [28], the vertical temperature is often considered as the main thermal load. According to these specifications, the thin-walled concrete temperature is composed of the mean temperature (\(T_{\text{m}}\)) and gradient temperature (\(T_{\text{g}}\)), as shown in Figure 4. The mean temperature is the reason for uniform shrinkage or expansion of concrete structures, while the gradient temperature causes uneven bending or twisting. For the experiment in this study, \(T_{\text{m}}\) and \(T_{\text{g}}\) were calculated using Equation (1).  
\[\left\{\begin{array}{l}T_{\text{m}}=\sum\limits_{i=1}^{10}\left(T_{\text{i}} /10\right)\\ T_{\text{g}}=T_{\text{measured}}-T_{\text{m}}\end{array}\right. \tag{1}\]
## 2 Baseline Multichannel Acoustic Score Computation of a CSC BLSTM AM
### The Use of a CSC BLSTM AM  
To efficiently incorporate a BLSTM AM, our ASR system utilizes a CSC-based backpropagation through time (BPTT) and decoding [30]. That is, a CSC comprises an audio chunk of fixed length and its left/right chunks. The chunk size is relatively smaller than the input audio. The ASR system employing the CSC BLSTM AM can reduce the training time and decoding latency as the CSC-based approach regards a CSC as an isolated sequence.  
Let us assume the speech features of an overall input sequence of length \(\mathcal{T}_{tot}\), as shown in Figure 2a. As mentioned in Section 2.1.1, the decoder thread obtains a speech feature vector with length \(\mathcal{T}_{bat}\) + \(\mathcal{T}_{ext}\). The features of length \(\mathcal{T}_{bat}\) and those of length \(\mathcal{T}_{ext}\) are used as the features to be decoded at the time and its future context, which are drawn in yellow and in gray, respectively, in Figure 2b. As shown in Figure 2c, each speech feature vector is split into a set of chunks of length \(\mathcal{T}_{chunk}\). Then, CSC vectors are generated by appending the past features of length \(\mathcal{T}_{ext}\) and the future features of length \(\mathcal{T}_{ext}\) for each split chunk, as shown in Figure 2d. The length (\(\mathcal{T}_{win}\)) of a CSC vector and the number (\(\mathcal{N}_{seg}\)) of CSC vectors are defined in Equation (1) and Equation (2), respectively.  
\[\mathcal{T}_{win}=\mathcal{T}_{chunk}+2\times\mathcal{T}_{ext}, \tag{1}\]  
\[\mathcal{N}_{seg}=\mathcal{T}_{bat}/\mathcal{T}_{chunk}. \tag{2}\]  
For an efficient use of GPU parallelization, the CSC vectors are merged into a CSC matrix in the form of \(\mathcal{N}_{seg}\times\mathcal{T}_{win}\), as shown in Figure 2e. By using the CSC matrix, the acoustic scores are calculated using a CSC BLSTM AM. In this paper, \(\mathcal{T}_{chunk}\) and \(\mathcal{T}_{ext}\) are set as 20 and 40 frames, respectively. Therefore, \(\mathcal{T}_{win}\) and \(\mathcal{N}_{seg}\) are 100 frames and 10.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
###### Abstract  
Sensors for industrial and structural health monitoring are often in shielded and hard-to-reach places. Acoustic wireless power transfer (WPT) and piezoelectric backscatter enable batteryless sensors in such scenarios. Although the low efficiency of WPT demands power-conserving sensor nodes, backscatter communication, which consumes near-zero power, has not yet been combined with WPT. This study reviews the available approaches to acoustic WPT and active and passive acoustic through-metal communication. We design a batteryless and backscattering tag prototype from commercially available components. Analysis of the prototypes reveals that low-power hardware poses additional challenges for communication, i.e., unstable and inaccurate oscillators. Therefore, we implement a software-defined receiver using digital phase-locked loops (DPLLs) to mitigate the effects of oscillator instability. We show that DPLLs enable reliable backscatter communication with inaccurate clocks using simulation and real-world measurements. Our prototype achieves communication at 2 kBs\({}^{-1}\) over a distance of 3 m. Furthermore, during transmission, the prototype consumes less than 300 uW power. At the same time, over 4 mW of power is received through wireless transmission over a distance of 3 m with an efficiency of 2.8%.  
wireless power transfer; ultrasonics; backscatter communication +
Footnote †: journal: Sensors  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Nanosatellite Autonomous Navigation via Extreme Learning Machine Using Magnetometer Measurements
## 4 Dataset Generation
### Geomagnetic Field Modeling  
#### 4.3.1 IGRF Model  
The goal of this work was to test the behaviour of the EKF when the onboard implemented model of the geomagnetic field was lighter but less accurate than the model used to generate the collected measurements. For this reason, the EKF used a spherical harmonics expansion of the geomagnetic scalar potential \(V\) using the 13 th-Generation International Geomagnetic Reference Field (IGRF) coefficients [17] up to the thirteenth order,  
\[V(r,\theta,\phi,t)=R\sum_{n=1}^{N}\sum_{m=0}^{n}\left(\frac{R}{r}\right)^{n+1} [g_{n}^{m}(t)\cos(m\phi)+h_{n}^{m}(t)\sin(m\phi)]P_{n}^{m}(\cos\theta), \tag{27}\]  
where we have the following:  
* \(r\) is the distance from the Earth's center in km;
* \(\theta\) and \(\phi\) are the colatitude and longitude, respectively, in decimal degrees;
* \(t\) is the date and time expressed in decimal years;
* \(R\) is the Earth's mean radius in km;
* \(N=13\) is the order of truncation;
* \(g_{n}^{m}\) and \(h_{n}^{m}\) are the 13 th-Generation IGRF coefficients, finalized in December 2019 by the International Association of Geomagnetism and Aeronomy (IAGA);
* \(P_{n}^{m}\) are the Schmidt semi-normalized associated Legendre functions of the degree \(n\) and order \(m\).  
According to the model, in source-free regions above the surface of the Earth, the main field \(\mathbf{B}_{Earth}\) is given by the negative gradient of the scalar potential:  
\[\mathbf{B}_{Earth}=-\
abla V. \tag{28}\]
## 3 LPWAN Technologies and Design Goals
### LoRa Duty Cycling  
Duty cycling is heavily used in LPWAN devices to turn off the power-hungry transceiver through pre-arranged listening and transmission schedules [37]; duty cycling is used at the rate of 1% i.e., 36 s per hour. Duty cycling is also carried out due to limited spectrum availability, where devices have to coexist within a limited unlicensed band. Duty cycle limitation for Sigfox devices is around 0.1% to 1%, similar to LoRa. The duty cycle is guided by government and regional regulations; for instance, in Europe, under the regulations of the European Telecommunications Standards Institute (ETSI), limitation in the 868 MHz band is 1%. LoRa is more versatile and flexible, while Sigfox is a more stringent and simpler transmission technology while supporting applications with fixed data rates, such as smart metering and others. Duty cycling for Sigfox can be calculated using the formula as given in Equation (4).  
\[D=\frac{T_{ON}}{T_{Total}}*100\% \tag{4}\]  
where \(D\) represents duty cycle, \(T_{ON}\) is total transmission time, and \(T_{TOTAL}\) is total time in a day. Now, each transmission of a Sigfox device is of 6 s and taking the device transmits the maximum number of messages in a day, i.e., 140 messages per day, \(T_{ON}\) will be 140 messages \(\times\) 6 s per message, calculated as 840 s and \(T_{TOTAL}\) will be 86,400 s for a 24 h cycle. Thus, the duty cycle for a Sigfox device \(D_{Sigfox}\) can be calculated as following:  
\[D_{Sigfox}=\frac{840}{86400}*100\%=0.97\% \tag{5}\]