esa-sceva/satcom-chunk-collection huggingface dataset:
# A Dependable Localization Algorithm for Survivable Belt-Type Sensor Networks
## 3 Hop-Distance Estimation Correction
### Correcting the Accumulated Values
The higher the accuracy of the neighbor node selected by the neighbor of the node, the closer the final position estimate is to the real. According to the following two conclusions [36]: (I) the greater the connectivity difference between nodes, the greater the distance between neighbors, and (II), the greater the distance between neighbor nodes, the closer the communication radius is to the other nodes. When quantifying the difference characteristics of connectivity between network nodes, we could achieve the summation of the absolute values of the connectivity differences. For the unknown node \(I\) (target node) and its neighbor anchor node \(m\) to be located, the value of connectivity difference between the two can be expressed as:  
\[C_{im}=\sum_{i\
eq m}|h_{ij}-h_{mj}|. \tag{8}\]  
In the Equation (8), \(h_{ij}\) is defined as the hop count value of the shortest connection path between the fixed anchor \(j\) and the unknown node \(i\) which should be located; \(h_{mj}\) is defined as the shortest hops between the neighbor anchor node \(m\) and the anchor node \(j\). Thus, the neighbor distance of the maximum difference between the target node and undetermined target node \(i\) can be set as the communication radius of the node (\(R_{c}\)). The ratio of the node connectivity difference value \(C_{im}\) obtained by Equation (8) to the maximum difference value \(C_{iMAX}\) in the network is taken as the weight value. We can multiply the ratio with the node communication radius to obtain the actual distance \(d_{im}\) from the target node \(i\) to the \(m^{\text{th}}\) anchor node. \(d_{im}\) is calculated as follows:  
\[d_{im}=\sum_{i\
eq m}C_{im}\times\eta_{m}\times R_{c}/C_{iMAX}. \tag{9}\]  
We improve the method of estimating the hop count of the whole communication link, correct the distance value represented by each hop between the target node and the anchor node. The above work can more accurately represent and reflect the true connection of nodes in sensor networks. These jobs lay the key foundation for further reduction of positioning errors.
# Miniaturized Compact Reconfigurable Half-Mode SIW Phase Shifter with PIN Diodes
## 4 Conclusions  
A brand-new half-mode substrate integrated waveguide (HM-SIW) phase shifter is proposed here to cover 4.5 to 7 GHz frequencies. The structure is based on microstrip lines integrated into HM-SIW, PIN diodes, and radial stubs. It is optimized to achieve low insertion (\(<\)\(-\)2 dB) and reflection losses. By exploiting a half-mode SIW formation, it results in a smaller dimension than a typical SIW formation. A parametric study achieves high performance with multiple phase shifts, scattering parameters, and a miniaturized modular design. The primary design concept covers 0\({}^{\circ}\) to 60\({}^{\circ}\) phase shifts with 7 distinct states.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**References** & [20] & [24] & [25] & [26] & [27] & [28] & [29] & [30] & [35] & **This Work** \\ \hline \multirow{3}{*}{Technology} & Two & Change in & Change & Two & Control the & & Different & Change in & Change & Tunable & Parallel \\  & reconfigurable & the diameter & length & of & slots & a high & cross- & the & the & resonators & with & component \\  & filter & coupled & of posts & slots & location & dielectric & constant & elements & of posts & loading & varactor & with \\ \hline Frequency & & & & & & & & & & & \\  (GHz) & 1–1.3 & 10 & 10–  
By cascading two similar topologies without extra wave dissipation, the phase shift can reach 120\({}^{\circ}\). Furthermore, the observed phase deviation was less than 5\({}^{\circ}\) in the frequency band. The structure is innovatively designed on one substrate layer. This makes it compact, low-loss, and low-profile, so it can be easily combined with other modular microwave devices. It does not require extra wire binding for DC biasing the tunable element (PIN diode). This makes it attractive for millimeter-wave frequencies and applications such as sensing and antenna beamforming.  
Conceptualization, H.B. and F.D.W.; methodology, H.B. and F.D.W.; software, A.G., B.M. and F.D.W.; validation, H.B., A.G. and F.D.W.; resources, H.B. and L.T.; writing--original draft preparation, B.M. and F.D.W.; writing--review and editing, H.B. and A.G.; supervision, H.B. and L.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available.  
The authors declare no conflict of interest.
# Dynamic Parameter Identification of a Lower Extremity Exoskeleton Using RLS-PSO
###### Abstract  
The lower extremity exoskeleton is a device for auxiliary assistance of human movement. The interaction performance between the exoskeleton and the human is determined by the lower extremity exoskeleton's controller. The performance of the controller is affected by the accuracy of the dynamic equation. Therefore, it is necessary to study the dynamic parameter identification of lower extremity exoskeleton. The existing dynamic parameter identification algorithms for lower extremity exoskeletons are generally based on Least Square (LS). There are some internal drawbacks, such as complicated experimental processes and low identification accuracy. A dynamic parameter identification algorithm based on Particle Swarm Optimization (PSO) with search space defined by Recursive Least Square (RLS) is developed in this investigation. The developed algorithm is named RLS-PSO. By defining the search space of PSO, RLS-PSO not only avoids the convergence of identified parameters to the local minima, but also improves the identification accuracy of exoskeleton dynamic parameters. Under the same experimental conditions, the identification accuracy of RLS-PSO, PSO and LS was quantitatively compared and analyzed. The results demonstrated that the identification accuracy of RLS-PSO is higher than that of LS and PSO.  
dynamic parameter identification; exoskeleton; recursive least square; particle swarm optimization +
Footnote †: journal: Applied Sciences
## 3 Problem Formulation
### Mathematical Model of Plants and Feedback Controller  
First, we explain the outline of SANs. A SAN is generally a system that through a communication network integrates sensors and actuators to enable autonomous data sensing, processing, and actuation. Figure 1 shows an example of SANs. Sensors and actuators are located in a distributed way. Routers are frequently used. There are several technical issues depending on applications. In this paper, we focus on the reduction of redundant sensors/actuators as one of the technical issues. In other words, we do not focus on the technical issues related to communications such as delays.  
As a mathematical model of the plant in a SAN, consider the following discrete-time linear system:  
\[\begin{cases}x(k+1)=Ax(k)+Bu(k),\\ y(k)=Cx(k),\end{cases} \tag{9}\]  
where \(x(k)\in\mathcal{R}^{n}\) is the state of the system, \(u(k)\in\mathcal{R}^{m}\) is the control input which represents \(m\) actuators, \(y(k)\in\mathcal{R}^{p}\) is the measured output which represents \(p\) sensors, \(A\in\mathcal{R}^{n\times n}\), \(B\in\mathcal{R}^{n\times m}\), and \(C\in\mathcal{R}^{p\times n}\) are given matrices, and \(k\in\{0,1,2,\dots\}\) is the discrete time. We suppose that the control input and the measured output include redundant actuators and sensors, respectively.  
As a controller, we consider the following dynamic output-feedback controller:  
\[\begin{cases}\hat{x}(k+1)=A_{c}\hat{x}(k)+B_{c}y(k),\\ u(k)=C_{c}\hat{x}(k),\end{cases} \tag{10}\]  
where \(\hat{x}(k)\in\mathcal{R}^{n}\) is the state of the controller, and \(A_{c}\in\mathcal{R}^{n\times n}\), \(B_{c}\in\mathcal{R}^{n\times m}\), and \(C_{c}\in\mathcal{R}^{p\times n}\) are design parameters.
## 4 Conclusions  
In this study, the microstructure and performance evolution of Sn50 Pb49 Sb1/Cu solder joints exposed to \(\gamma\)-ray irradiation were investigated. \(\gamma\)-ray irradiation had little effect on the interfacial layer of solder joints, but the formation and expansion of micro-voids and micro-cracks were observed in the Pb-based solid solution during the irradiation process, which reduced the strength of the joint. Compared with the as-soldered joint, the pull force of the Sn50 Pb49 Sb1/Cu solder joint after 960 h irradiation decreased by 22%. In addition, \(\gamma\)-ray irradiation could not change the fracture type of the Sn50 Pb49 Sb1/Cu solder joint, however a slight decrement of ductility was observed after irradiation. The results of this paper are conducive to comprehending the reliability and sensitivity of solder joints under the cosmic radiation environment and provide references for the design and manufacture of future small satellites. As the actual service environment of satellites consists of cosmic radiation, extreme temperature variations and vibration, further investigations of the combined effect of the conditions mentioned above on the reliability of solder joints are necessary for the design of excellent solder alloys for use in deep space satellites.  
[PERSON] and [PERSON] conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed materials and analysis tools. [PERSON] wrote the paper.  
This research was funded by the National Natural Science Foundation of China, grant No. 51675269 and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Region** & **Sn** & **Pb** & **Sb** & **Cu** \\ \hline C & 64.49 & 33.91 & 0.96 & 0.64 \\ D & 59.88 & 38.56 & 1.08 & 0.48 \\ E & 61.6 & 36.82 & 0.87 & 0.71 \\ \hline \hline \end{tabular}
\end{table}
Table 1: EDS analysis of region C, D and E (at. %).  
Figure 8: Fracture morphology of the solder joint after pull tests at various irradiation times: (**a**) 0 h; (**b**) 480 h; (**c**) 960 h.
## 3 The Analysis
### Effect of Data Duplication  
In this section, we further extend the analysis to duplication transmission, which has been proposed as a fundamental enabler [9] for URLLC. The key concept of duplication transmission is to transmit the same information packet for several time either in time domain or in frequency domain. For time domain duplication, one URLLC information packet will be transmitted for several times in different time periods, which could improve the reliability but introduce extra delay, while for frequency domain duplication, several bandwidth resources will be used at the same time to transmit one URLLC information packet, which will improve the reliability without extra delay but occupy more spectrum resource. Note that hybrid duplication in both time domain and frequency domain is also possible. It is a tradeoff between time domain duplication and frequency domain duplication and will not be discussed in this work. The bandwidth allocation will be analyzed first, followed by discussions on average delay.
## 4 Traveling Salesman Problem (TSP)  
The traveling salesman problem (TSP) is a widely studied problem in computational mathematics that aims to find the shortest route between all the nodes, where each node is visited exactly once and is returned back to the origin node. It is an NP-hard problem, meaning that finding an efficient algorithm to solve it in polynomial time is computationally challenging. However, NP-hard problems may still be solvable using algorithms that have exponential time complexity or through heuristic or approximation methods. In Figure 1, we present an example graph illustrating a simple solution to the TSP problem.  
Mathematically, the TSP problem can be represented by the following formula, where \(n\) represents the number of nodes:  
\[\frac{(n-1)!}{2} \tag{1}\]  
For small instances of the problem, the optimal solution can be found by exhaustive search. However, as you increase the number of nodes, the problem size increases, and the time required to find an optimal solution increases dramatically. For instance, at 5 nodes, the number of possible paths is 12, while at 30 nodes, the number of paths increases exponentially to \(4\times 10^{30}\). Consequently, finding an optimal solution by exhaustively ex ploring all the possibilities becomes infeasible because of the immense computing cost and time required.  
To address the challenges posed by the TSP, one approach is to employ ant colony optimization (ACO). ACO aims to simplify the TSP by emulating the behavior of ants and their pheromone trails. ACO algorithms can efficiently explore the solution space and give us a near-optimal solution to the TSP problem and coverage in WSNs.
## 3 Results
### Experiment on Indoor Positioning Accuracy of Binocular Stereo Vision
Within the depth plane of 400 mm, the field of view in the \(X\)-axis direction was \(-140\) mm to 90 mm, and the field of view in the \(Y\)-axis direction was \(-120\) mm to 50 mm, with a total of 24 test points set accordingly. With the increase in the depth distance, the field of view was correspondingly expanded, and the number of spatial points set was correspondingly increased. When the depth distance \(Z\) was 550 mm, the field of view in the \(X\)-axis direction was \(-210\) mm to 140 mm, and the field of view in the \(Y\)-axis direction was \(-180\) mm to 120 mm. A total of 72 test points were set. The error statistic chart was plotted by inputting the data of these test points into Equation (7) for calculation, as shown in Figure 7. The average elapsed time of the identification and positioning algorithms in the test process as shown in Table 4.  
It can be seen from Figure 7 that the three-dimensional positioning accuracy of the binocular stereo vision system was relatively stable within the depth range of 400-550 mm. With the deepening of measurement depth, the visual field range increases correspondingly, the error increases gradually and the points with large errors increases. Within the depth range of 400-550 mm, the maximum comprehensive error was 4.06 mm, the minimum comprehensive error was 0.37 mm, and the average comprehensive error was 2.306 mm.
# Analysis of Earth Pressure Variation for Partial Displacement of Retaining Wall
###### Abstract  
Parts of the retaining wall might produce displacement under different load conditions. The moveable wall could impact the adjacent fixed wall, mainly reflecting on the variation of earth pressure and formation of the soil archive effect. This paper conducted the horizontal trap-door test to explore the variation of active earth pressure caused by partial displacement of the retaining wall. Different trap-door width and three displacement modes were addressed as the influence factors. The results indicated that the horizontal soil arching effect was generated after the active displacement of the trap-door and the soil pressure was redistributed. The distribution of lateral soil pressure was approximately an \"inverted bell\" curve. For trap-door widths of 20 cm, 30 cm, and 40 cm, a secondary soil arching effect appeared in the test. The relationship between lateral earth pressure and displacement was different with the traditional limited theory due to the influence of the soil arching effect. The variation curve of earth pressure corresponding to displacement could be divided into three stages. In addition, the distribution of earth pressure along the trap-door height was non-linear. Trap-door width can significantly influence the maximum earth pressure on the fixed wall and the range where pressure changes. Finally, the effect of load sharing was explored and found to be related with displacement and width of trap-door as well as the displacement mode.  
retaining wall; partial displacement; horizontal trap-door test; active earth pressure; soil arching effect; loading sharing ratio Article
## 2 Material and Methods
### Measurement Protocol  
The measurements took place in a separate room without disturbing noises and other persons being present. This meant that apart from the person to be measured and the person who performed the measurement, no one else was present in the premises. The measurements were taken barefoot whilst standing in a habitual double-legged stance. To exclude any possible visual influencing factors, all subjects wore a blindfold. Measurements were taken in the standing position because the postural data were recorded on the same subjects during the course of this study. Everyone first stood for 15 min before the measurements commenced in order to counteract orthostatic hypertension with a compensatory heart rate increase.  
Furthermore, all test persons used identical wireless headphones (Bose QC35; Friedrichsdorf, Germany), which have the option of switching off ambient noise via acoustic noise canceling (NC). The loudness of all acoustic stimuli were adjusted to equal level. To do this, the RMS (Root Mean Square) level over the entire length of the digital audio signal of the respective sounds was calculated and adjusted, respectively, to equal. The playback level was set to 70 dB(A).  
Pink noise was used as the noise signal. Pink noise decreases in amplitude with increasing frequency at 1/f. This is perceived as a noise in which the average person perceives all frequency ranges of the audible sound spectrum as being approximately equal volume [27]. A graphical representation of the first 10 s of the acoustic stimuli in the time and frequency domains is shown in Figure 1.  
This HR display was launched on the app after the appropriate activity level was selected. The HR was measured continuously throughout the performance, with documentation of the HR occurring within the three trials, each after 25 s of the 30 s measurement interval. Subsequently, the three values of HR were statistically averaged. Due to the previous stance of the selected PD patients, a longer measurement sequence was not possible based on pilot measurements. The following measurement conditions were recorded at constant volume, in a randomized order:  
* (A) without headphones (wHP)
* (B) with headphones without ambient noise canceling (Bose) (HP without noise canceling; HP_w_NC)
* (C) headphones with noise cancelation (HP_NC).
* (D) genre relaxation (Drifting Clouds--[PERSON]; 117 bpm, 800-1300 Hz) with NC
* (E) genre classical music (Carmina Burana- [PERSON]; 139 bpm, 1400-1800 Hz) with NC
# Effect of Whey Protein Changes on Milk Flavor and Sensory Characteristics During Heating
## 3 Results and Discussion
### Protein Profile and Structural Changes
#### 3.1.2 Denaturation Rate of the Active Milk Proteins  
HPLC was used to analyze the milk protein components due to its high resolution, accurate, and repeatable results [33]. The whey protein profile showed that \(\beta\)-LG (50%) and \(\alpha\)-LA (20%) represented the primary whey proteins in the milk, with a small amount of LF (4%) [34]. Figure 1C shows the denaturation percentage of \(\beta\)-LG, \(\alpha\)-LA, and LF after different heat treatments. Heat treatment at temperatures above 60 \({}^{\circ}\)C caused whey protein denaturation, mainly \(\beta\)-LG and \(\alpha\)-LA [34]. At 60 \({}^{\circ}\)C, the LF denaturation degree exceeded that of \(\beta\)-LG and \(\alpha\)-LA, indicating that it displayed higher thermal sensitivity. This was because the LF contained 17 intramolecular disulfide bonds and no free thiol groups, causing thermal denaturation. Moreover, LF exhibited two denaturation temperatures at a neutral pH: 60.4 \({}^{\circ}\)C for lobe N and 89.1 \({}^{\circ}\)C for lobe C. The temperature sensitivity of the two lobes differed since the C-lobe is denser than the N-lobe [35]. A heat treatment temperature of 75 \({}^{\circ}\)C increased the thermal denaturation percentages of the three whey proteins, with almost complete denaturation evident at 100 \({}^{\circ}\)C.
# Horizontal Wind Effect on the Aerodynamic Performance of Coaxial Tri-Rotor MAV
## 2 Theoretical Analysis
### Flow Field Model  
Flow model of the coaxial Tri-rotor MAV considering the horizontal wind is shown in Figure 2.  
Figure 1: Structure of coaxial Tri-rotor micro air vehicle (MAV).  
In the Figure 2, it can be observed that the flow field of the coaxial Tri-rotor system will shift along the incoming flow direction in a horizontal wind. Compared with no wind effect, it can be found that the downwash flows are coupled with each other besides the interference between upper and lower rotor of coaxial rotors. In this case, the wake vortices of the front rotors directly affect the flow of the rear rotors. In this case, the wind may aggravate the aerodynamic interference among rotors with varied power consumption accordingly.  
In the natural environment, the wind speed is usually less than 5.0 m/s. Furthermore, the light breeze (1.6-3.3 m/s) and the gentle breeze (3.4-5.4 m/s) frequently appear in the natural environment. The average values of the wind, 2.5 m/s and 4 m/s are selected as the horizontal incoming wind speed. Therefore, it is conducive to study the influence of the horizontal wind on the aerodynamic performance of the rotor system. In the meanwhile, the situation of the horizontal wind at 0 m/s is also taken as the comparison to analyze the effect of the horizontal wind.
# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 3 Results and Discussion  
### Model Training Results  
There are totally 950 images in the dataset of the frame selection in this work, including 600, 150, and 200 ones in the training, validation, and test sets, respectively. The training and validation sets were put together into the model training. After the training process, the accuracy rates (also known as the average precision, AP) of the validation set and the test set are 92.63% and 86.23%, respectively, which implies that this work is of high practicability. The object detection results of the partial training set are shown in Figure 19.