esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In this paper, an SR-ODBR-DCSK scheme is established to improve the energy efficiency and data transmission rate of the differential chaotic shift keying system. Based on the system of SR-DCSK, the modulation technology generates two orthogonal chaotic signals by using the orthogonal characteristics of the chaotic signal acting on the Walsh function and modulates the continuous two-bit data information respectively to realize the simultaneous transmission of two signals so as to improve the transmission rate twice on the basis of the original channel. Furthermore, the influence of four typical discrete chaotic mappings on the system is explored, and the simulation is carried out from three aspects replication number \(P\), chaotic mapping type, and semi-spread spectrum factor. The simulation shows that the error performance is better when the number of reference signal replication \(P\) is 2. Four typical discrete chaotic maps have little effect on system performance under the same conditions. Different numbers of semi-spread spectrum factors will result in different BER performances. At the same time, the SR-ODBR-DCSK system is compared with the ODBR-DCSK system, SR-DCSK system, and DCSK system. It is found that the SR-ODBR-DCSK system has better error performance and high energy efficiency under the same conditions. Currently, we have studied the software simulation of the SR-ODBR-DCSK scheme, and in the future, we hope to implement the work of chaotic communication signal generation and modulation scheme using the FPGA hardware platform. SR-ODBR-DCSK system is promising and demonstrates great potential for the future of high-data-rate communication systems.  
Conceptualization, Y.F.; data curation, T.Z.; investigation, Q.J.; methodology, T.S.; writing--original draft, T.S. and F.L. All authors have read and agreed to the published version of the manuscript.  
This work is supported by grants from the National Natural Science Foundation of China (grant no. 61971291).  
The authors declare no conflict of interest.
## 3 Preliminaries
### TDMA (Time Division Multiple Access)  
TDMA is a channel access method for shared media networks that allows multiple users to share the same frequency channel by dividing the signal into different slots [24; 25; 26]. Time is divided into equal time slices and a time slice is divided into different time slots. Each node can be allocated to different time slots with a different serial number. The node only transmits data when the time reaches its allocated time slot. Conflicts between nodes can be avoided within the same frequency. In the CT algorithm, the smaller the serial number of the time slot is, the higher the time priority of the time slot is. Nodes with smaller serial number of the time slot should send data before those with larger serial number.
## 4 Node Insertion Algorithm for Determining Viewpoints
### Node Propagation Stoppage Criteria
#### 4.2.2 Direct Method  
The idea behind this method is to check for convergence and divergence of the current node being inserted with respect to that of its neighboring nodes (if they already exist). In particular, if the node is located at (i,j) coordinates with respect to the start point (See Section 4, Table 1), we check the distance to its neighboring nodes at (i + 1,j), (i - 1,j), (i,j + 1), (i,j - 1) as seen in Figure 16. We compare the distance between current node (i,j) and nodes at (i + 1,j), (i - 1,j) with the horizontal projection on panel \(p_{H}\) (Section 4, Figure 14) and the distance between current node (i,j) and nodes at (i,j + 1), (i,j - 1) to the vertical projection on panel \(p_{V}\). If either of the two distances is less than or more than its prespecified limit, the propagation will be stopped.  
Figure 15: Gauss maps of surfaces. (**a**) Gauss map for developable surface; (**b**) Gauss map for non-developable surface.
## 4 Stator-Side External Retrofit Techniques
### Series Dynamic Braking Resistor  
The SDBR comprises a resistor and a paralleled IGBT circuit. Generally, the SDBR is applied in series with line to restrain current during disturbances [40]. In a DFIG, the SDBR is installed between the stator and the power grid, as shown in Figure 13. By dynamically inserting a resistor in the stator, the SDBR can increase the terminal voltage during grid voltage dips. In addition, the destabilizing depression of electrical torque and power can be restricted [41]. However, when a traditional SDBR is activated, the back-to-back converter has to be disabled due to the hardship in synchronizing stator voltage, and the variable frequency switching scheme may result in undesirable voltage quality [42]. To solve these problems, a modulated series dynamic braking resistor (MSDRR) was proposed, which comprises a high-power braking resistor and two parallel IGBTs for each phase [43]. The MSDBR is also connected in series with the stator and grid, and this device is controlled by the fixed frequency  
Figure 12: Control effect comparison of rotor-side external retrofit techniques in (**a**) rotor current, (**b**) DC-link voltage, (**c**) reactive power support, and (**d**) terminal voltage.  
Pulse-Width Modulation (PWM) signals to dissipate excessive power and maintain the terminal voltage magnitude in a desirable range.  
Furthermore, a configuration which combines MSDBR with a parallel integrated capacitor was proposed in Reference [44] to improve the performance of DFIG-WTs during the transient period. Due to the application of the proposed hybrid control scheme, the induced overvoltage in the stator will not cause the converter system to lose control. As a result, rotor overvoltage can be limited. At the same time, the DC-link capacitor charging current can be controlled to restrain DC-link overvoltage.
# Research on Space Maglev Vibration Isolation Control System Modeling and Simulation
## 2. Maglev Vibration Isolation System Design
### Magnetic Circuit Structure Design  
The magnetic circuit of the actuator includes two high-performance permanent magnets and one annular guide magnet. The magnetic circuit adopts a double-loop closed form. Two permanent magnets arranged opposite each other can provide a strong, uniform magnetic field for the large air gap within the range of the coil's motion space [11; 12].  
To ensure high linearity of the actuator force output, the magnetic field part of the magnetic circuit structure must have strong uniform strength characteristics. The strong magnetic structure charging, demagnetization, and integrated assembly testing platform is mainly used for the strong uniformity charging and demagnetization processes of high-performance magnetic steel (such as neodymium iron boron N52, etc.), high-precision integrated assembly of strong magnetic structures, and magnetic field performance testing and calibration.  
The software Ansys Maxwell v12.0 is used to design and simulate magnetic fields. The magnetic field distribution of the magnetic circuit structure is shown in Figure 7. The vector represents the distribution of the electromagnetic field.
## 3 Current Techniques for OP Study
### Dual-Energy X-ray Absorptiometry
#### 3.1.1 DXA-Derived Osteoporosis Diagnosis and Fracture Risk Assessment  
In 1994, the World Health Organization (WHO) chose DXA as the reference technology to measure BMD and to formulae a diagnosis of OP. OP is diagnosed if the BMD value is at least 2.5 standard deviations below the mean BMD of young adult women [40]. Since the BMD values progressively decrease with age, the proportion of potential osteoporotic patients increases exponentially over the lifetime [41], as visible in Figure 7.  
Figure 7: Bone mineral density (BMD) distribution in women at different ages. In purple is reported the area of osteoporosis prevalence according to WHO criteria. Legend: arbitrary unit (a.u.). Image adapted from [PERSON] et al. (reproduced with permission from [41], Elsevier, 2017).  
In 1994, the WHO also recognised the value of the areal BMD measured by DXA as a reliable parameter for the assessment of bone fracture risk [42]. However, even if low BMD is commonly associated with lower bone strength, several studies demonstrated that features also often occur in people reporting only a slight decrease in BMD [41]. In fact, several other factors contribute to bone fracture risk, e.g., age, gender, body mass index, family history of fractures, use of glucocorticoids, rheumatoid arthritis, smoking or high consumption of alcohol. For this reason, different algorithms (e.g., FRAX(r), GARVAN) have been developed to combine these risk factors with the BMD assessed by DXA to more accurately calculate the individual bone fracture risk [41; 43]. Even though these tools can provide a more complete prediction of fracture risk, they are still not used for OP screening due to their low sensitivity [41].
## 4 Discussion
### Contribution of the Post-Lava Emplacement Processes to the Subsidence in the EFA  
The portion of subsidence (per meter of thickness) due to post-lava emplacement processes in lava fields inside the EFA is expected to drop on the fitted curve (as shown by the transparent blue squares with approximated subsidence-thickness slopes in brackets in Figure 9). The discrepancies between the observed (blue squares) and the approximated (transparent blue squares) values reveal that the effects produced by other potential processes on lava fields inside the EFA vary spatially from north to south. The significant discrepancies of the June 2000, Oct 2000, June 2001, and Nov 2002 lava fields suggest that the potential processes might have accounted for a larger portion of the observed signals rather than post-lava emplacement processes. The two lava fields of 2000 located in the south, especially, display inversed subsidence-thickness slopes (subsidence decreasing with an increase of thickness) with respect to others. This suggests the potential processes could have played a dominant role in the pattern of the observed displacement affecting the 2000 lava fields. In particular, the areas covered by relatively thinner lava show a higher sensitivity in the accumulation of displacement in response to such potential processes. The Aug 2004 lava field located in the north, however, presents less discrepancy (close to the fitted curve; Figure 9) and reveals a relatively slighter effect from the potential processes. Based on this exponential law, we estimated that the contribution of post-emplacement lava flow subsidence to the subsidence observed inside the EFA could range from ~22% (in the June 2000 lava field) to ~79% (in the Aug 2004 lava field). In other words, the other processes could contribute up to ~78% of the subsidence. In comparison with the rough estimate of  
Figure 9: Cumulative subsidence in lava fields normalized by lava thickness (i.e., subsidence-thickness slope) in relation with the age of lava. The red curve indicates the fitted exponential relationship, estimated by using data derived from the lava fields outside the Eastern Flank Area (EFA) (the green circles).  
\(\sim\)34% contributed by other processes derived by [PERSON] et al. (2018), the estimate of \(\sim\)78% from this study should draw more attention, since this large portion of flank motion triggered by unknown processes could be hazardous.
## 2 Linear Response Theory  
In the linear response approximation, the change of an observable \(\hat{A}\) with respect to an external field \(\hat{F}=\hat{B}F\) can be evaluated via a linear response as [39; 40]  
\[\delta A_{i}=\chi_{ij}^{A}\hat{r}_{j} \tag{1}\]  
with the linear response tensor  
\[\chi_{ij}^{A}=\lim_{\epsilon\to 0}\sum_{m,n}\frac{f(E_{m})-f(E_{n})}{E_{m}-E_{n}-i \epsilon}\langle n|\hat{A}_{i}|m\rangle\langle m|\hat{B}_{j}|n\rangle, \tag{2}\]  
where \(n\) and \(m\) are the band indices. In a periodic system, the eigenstates and eigenvalues are labeled by band index and momentum \(k\) as \(|n,k\rangle\) and \(E_{n,k}\), respectively.  
In electrical transport, we are interested in the linear response with respect to an external electric field, which means \(\hat{F}=e\hat{F}\). By using the commutation relation \(\hat{v}=\frac{i}{\hbar}[\hat{H},\hat{r}]\), one can transform the position operator to the velocity operator and the position operator matrix can be replaced by  
\[\langle n,k|\hat{r}|m,k\rangle=-i\hbar\frac{\langle n,k|\hat{v}|m,k\rangle}{E _{n,k}-E_{m,k}}. \tag{3}\]  
With this replacement, one arrives at the commonly used form of the linear response tensor  
\[\chi_{ij}^{A}=\textit{i}\textit{the}\lim_{\epsilon\to 0}\sum_{k}\sum_{m,n}\frac{f(E_{n,k} )-f(E_{m,k})}{(E_{n,k}-E_{m,k}-ie)(E_{n,k}-E_{m,k})}\langle n,k|\hat{A}_{i}|m, k\rangle\langle m,k|\hat{v}_{j}|n,k\rangle. \tag{4}\]  
The linear response tensor can be further separated into inter-band and intra-band contributions. This separation is essential for the study of topological materials.  
For the inter-band part, because \(\epsilon\to 0\), one can easily get  
\[\chi_{ij}^{A-I}=\textit{i}\textit{the}\sum_{k}\sum_{m\
eq n}\frac{f(E_{n,k})- f(E_{m,k})}{(E_{n,k}-E_{m,k})^{2}}\langle n,k|\hat{A}_{i}|m,k\rangle\langle m,k| \hat{v}_{j}|n,k\rangle. \tag{5}\]  
For the intra-band contribution, it becomes the Boltzmann formula with the constant relaxation time approximation (\(\epsilon=\hbar/\tau\)) [41]  
\[\chi_{ij}^{A-II}=-\epsilon\tau\sum_{k}\sum_{n}\delta(E_{F}-E_{n,k})\langle n, k|\hat{A}_{i}|n,k\rangle\langle n,k|\hat{v}_{j}|n,k\rangle. \tag{6}\]  
The relaxation time for the intra-band contribution is hard to estimate correctly, therefore it is not easy to give an accurate theoretical evaluation. Nevertheless, one can use it to estimate the response qualitatively, which is especially important for the symmetry analysis. In contrast, the inter-band part is easier to deal with in theory and numerical calculations. It can be accurately predicted in real materials as long as the electronic band structure is accurate enough. In particular, when we are interested in electrical current, the intrinsic part is just the Berry phase of the electronic band structure. Hence, the inter-band part plays a crucial role in topological materials.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 4 Hardware Implementation
### On-Chip Data Exchange Engine Architecture
#### 4.1.3 Structure of Address Generation  
The structure of the address generation module is shown in the figure below (Figure 13). This mainly includes the address initialization part and address update part. The address initialization part is mainly used to initialize the initial coordinates of the two-dimensional  
Figure 12: Hardware architecture of data storage and exchange engine based on superscalar pipeline.  
matrix, and convert them into the DDR3 physical address. Subsequently, the DDR3 physical address will be calculated in the address update part.  
The address initialization part mainly includes the coordinate calculation in the sub-matrix corresponding to the starting point, the calculation of the Bank, Row and Column of the DDR3 physical address, and the calculation of the intermediate parameters required for the update of the range and azimuth addresses. In addition, any coordinate in the two-dimensional data matrix can be used as the starting coordinate of the access.  
Since the addresses for continuous data access in the range and azimuth directions are different, the address update part includes an independent range and azimuth address generation module, which can update the DDR3 physical address in the continuous access process according to the access mode.
# Influence of Cutting Parameters and MQL on Surface Finish and Work Hardening of Inconel 617
## 2 Related Works
However, manufacturers are looking into reducing the amount of coolant used in machining due to the large waste and cost of coolants, as well as the large environmental impact involved in disposing of the used coolant. Near-dry machining using Minimum Quantity Lubrication (MQL) has become a proposed method to replace flood coolants due to being more environmentally friendly in comparison [9; 22]. This method of cooling is performed by depositing an aerosolized oil mist directly onto the tool-workpiece contact. MQL notably is reported to better reduce temperatures at the cutting zone due to the lack of a vapour blanket caused by the evaporation of the cutting fluid, which is typical of flood coolants and drastically reduces the rate of heat transfer [29]. MQL's primary benefit is observed on its ability to reduce work hardening effects and improve tool life while using less coolant [30]. However, this has the disadvantage of being less effective at chip flushing compared to flood coolants. Other factors that influence the surface finish and tool life include the cutting parameters. Typically, the feed rate of a cutting process is attributed as the largest factor in influencing a workpiece's final surface finish. However, in intense machining processes such as those performed in machining Inconels, it is important to observe the impact and influence of other cutting parameters on the surface finish as well. Parameters such as the cutting speed, feed rate, and depth of cut are commonly studied for their influence on the generated surface and subsurface [8; 15; 28]. This paper provides an analysis of the influence of these cutting parameters and MQL coolants on the surface integrity and work hardening of the machining of Inconel 617.
## 3 Materials and Methods
### Size Measurement Method
#### 3.2.4 Radius Acquisition  
Figure 6 demonstrates the precise determination of the sub-pixel edge of the lock cylinder's bead hole, as outlined in Section 3.1 to 3.3. However, directly obtaining the diameter of the lock core's bead hole remains a challenge. To address this, the paper employs the least squares method to fit a circle to the edge of the bead hole. The least squares circle fitting method is a statistically based detection approach. Even in scenarios where the circular target in the image is affected by factors such as uneven light intensity, the least squares method ensures the accurate positioning of the circle's center and detection  
Figure 8: Devernay interpolation principle.  
of its radius. This method relies on the statistical properties of the detected edge points to determine an accurate fit. It is worth noting that a circle can be defined mathematically by an equation (Equation (12)), which allows for precise fitting at the sub-pixel level, given accurate edge positioning and visible contours.  
\[x^{2}+y^{2}+ax+by+c=0 \tag{12}\]  
In the formula, \(x\) and \(y\) are two values of the coordinate point, and \(a\), \(b\), and \(c\) are three parameters. Therefore, Equations (13)-(15) can be obtained.  
\[d_{i}^{2}=(X_{i}-A)^{2}+(Y_{i}-B)^{2} \tag{13}\]  
\[\delta_{i}=d_{i}^{2}-R^{2}=(X_{i}-A)^{2}+(Y_{i}-B)^{2}-R^{2}=X_{i}^{2}+Y_{i}^{2 }+aX_{i}+bY_{i}+c \tag{14}\]  
\[Q(a,b,c)=\sum\delta_{i}^{2}=\sum\bigl{[}X_{i}^{2}+Y_{i}^{2}+aX_{i}+bY_{i}+c \bigr{]}^{2} \tag{15}\]  
where \(\delta_{i}\) is the difference between the square of the distance from the point \((X_{i},Y_{i})\) to the center of the circle and the square of the radius, and \(Q\left(a,\ b,\ c\right)\) is the sum of the squares of \(\delta_{i}\). When \(Q\left(a,\ b,\ c\right)\) takes the optimal solution, the equation of the fitted circle can be solved, and the center coordinates and radius of the fitted circle can be obtained. The fitted circle effect is shown in Figure 9.
## 3 Results and Discussion
### Displacement Results
Figure 13: Graph for displacement vs. thread engagement: (a) pitch 0.25 mm; and (b) pitch 0.30 mm. Figure 15-17 visually depict the relationship between displacement and pitch across various thread engagements. According to the findings outlined in Table 8, Stainless Steel Austenitic AISI 316L (annealed), boasting the highest elastic modulus of 205,000 MPa, showcases the least displacement. Conversely, Titanium Commercial Purity Grade 4 (annealed), with the lowest elastic modulus, exhibits the highest displacement. This emphasizes the significant impact of material properties, particularly elastic modulus, on the observed displacement trends concerning pitch and thread engagement. Ultimately, it has been demonstrated that a torque of 1.1 N\(\cdot\)m is sufficient for rotating the motorized intramedullary lengthening nail across all pitch sizes. Figure 14: Graph for displacement vs. thread engagement: (**a**) pitch 0.35 mm; and (**b**) pitch 0.40 mm. Figure 15: The 10-thread engagement, displacement vs. pitch. Figure 16: The 15-thread engagement, displacement vs. pitch. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Materials** & **Elastic Modulus (MPa)** \\ \hline Stainless steel austenitic AISI 316L(annealed) & 205,000 \\ Titanium commercial purity Grade 4 (annealed) & 112,000 \\ Titanium alpha beta alloy Ti-6 Al-4V (annealed) & 119,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Material properties of elastic modulus. Figure 17: The 20-thread engagement, displacement vs. pitch.
## 3 Modeling Framework
### Ground Structure Method
#### 3.1.2 Homogenization Strategy  
In this strategy, topological information obtained from the discrete strategy will be homogenized. As depicted in Figure 7, The GS computed a layout configuration that also reflects the material's distribution over the design domain. For example, in the pre-defined points of the design domain, a greater number of bars at the same point means more materials in this area, which also indicates a high stress level. Therefore, this homogenization method is a size parameter homogenized process for arranging the bar members' materials to the corresponding point.  
As one location may belong to two or more bars' ends, and the locations can also be influenced by the surrounding bars, three steps are executed to homogenize the truss connections to the mass data. Figure 8 shows a separate process for homogenizing the bar member's materials to the corresponding points. The corresponding points are selected by the following steps:  
1. The searching domain is established among the two ends of the bar.
2. Calculating the distance from the corresponding point to the bar member.
3. Allocating the size parameter associated with the respective distance.
4. Data summation and normalization processing for every point, \(w^{\ell}\): \[w^{\ell}=\left(\sum_{i=1}^{m}W_{i}\right)/w^{\ell}_{max},\] (4)  
where \(w^{\ell}_{max}\) is the maximum computed weight data, and \(W_{i}\) is the weight from the ith corresponding bar. After the above four steps, the topology data will change from the bar size data to the stress-weight data of the points (seeds) in the domain.  
Figure 6: Map the location from wing surface (u, v) to the corresponding plane (x, y).  
Figure 7: Panel structure sample obtained by GS method under a uniaxial compression load.