esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials
### Study Sites
#### 2.1.2 Tomakomai (TMK)  
The TMK study area is in the Tomakomai National Forest, which is approximately 15 km northwest of Tomakomai, Hokkaido, in northern Japan. The area of larch forest is approximately 100 ha. From 2001-2003, the average temperature was 6.5 \({}^{\circ}\)C, and the site was warm in summer and snowy in winter. The mean annual rainfall was 1055 mm. The growing season extends from April to October, while there is little vegetation cover in other months.  
The flux tower was located in the center of the larch forest (42\({}^{\circ}\)44\({}^{\prime}\)13.1\" N, 141\({}^{\circ}\)31\({}^{\prime}\)7.1\" E, 115 masl), and the surrounding trees were approximately 45 years old at the time of this study. The height of the vegetation canopy was 18 m in 2003. The dominant species near the tower were classified as either overstory or understory species. The overstory species included Japanese larch, birch, spruce, and Japanese elm, and the understory species were ferns and other herbs. The LAI values near the flux tower in the overstory and understory layers were 5.4 m\({}^{2}\)/m\({}^{2}\) and 3 m\({}^{2}\)/m\({}^{2}\), respectively, in August 2005 [72].  
The flux tower contained eight levels of meteorology instrumentation for measuring different variables such as temperature and humidity above and within the canopy, which were measured with a platinum resistance thermometer and a capacitive hygrometer (HMP-45D, Inc., Logan, UT, USA) at 2 m, 5 m, 8 m, 14 m, 18 m, 22 m, 27 m, and 40 m. The PPFD was measured at 2 m, 5 m, 14 m, 18 m, and 40 m by a quantum sensor (LI-190S, LI-COR Inc., Lincoln, NE, USA) in the tower and on the forest floor. At 27 m, the CO\({}_{2}\) flux between this ecosystem and the atmosphere was measured by open and closed-path EC instruments (LI-7500 and LI-6262, LI-COR Inc., Lincoln, NE, USA). The tower was destroyed by a typhoon in 2004, and monitoring therefore ceased. All of these data were recorded at 30-min intervals. LAI was measured every month from July to December 2003.  
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## 2 Background
### Circuit Breakers  
Software-based circuit breakers [16] work in a similar fashion to the electrical ones that toggle a magnetic switch to protect appliances from excess current on the power lines. In such failure scenarios, the circuit breaker trips from the closed into the open state to break the flow of the current to the appliance. This way, it protects the appliance from breaking down completely.  
We use a software variant of circuit breakers (1) to protect distributed data processing workflows from unauthorized access by other parties or (2) to disable the execution of the data procession functionality of the microservice. We group sets of authorization rules per feature toggle into an authorization policy and encapsulate the evaluation of the policy in a circuit breaker. The state transitions of a circuit breaker are depicted in Figure 3. Under normal operations, the circuit breaker is closed. If authorization is denied, the circuit breaker trips and switches from the closed into the open state. As a result, it disables external access to the data processing functionality of the microservice, as well as the database it encapsulates and the corresponding feature toggle from being switched.  
Figure 3: State transitions of a basic circuit breaker.  
Figure 2: Feature toggles for returning cached results of a data-intensive process to improve performance.  
The software circuit breaker can also cache authorization decisions for a period of time (i.e., a back-off period) during which the previous decision is automatically returned without re-evaluating the authorization policies. To do so, after the back-off period has passed, the circuit breaker automatically shifts from the open into the half-open state, and if the next evaluation of the authorization policy succeeds, the circuit breaker resets to the closed state. If access is denied again, then the circuit breaker reverts back from the half-open to the open state until the next timeout of the back-off period occurs. Figure 4 provides a high-level Java implementation for dynamic access control evaluation at runtime. It provides the two functionalities a typical circuit breaker has to offer, i.e., the trip() and reset() methods, with a back-off period of 10,000 ms to attempt a reset whenever the circuit breaker changes to the State.OPEN state.  
The caching feature of circuit breakers is useful when the evaluation of authorization policies is computationally expensive. This is, for example, the case when a risk-based access control decision relies on processing large amounts of data to quantify the risk and where the continuous evaluation of such authorization policies would jeopardize the overall performance of the data processing workflow.  
Figure 4: Java-based implementation of a circuit breaker.
## 2 System Model
### Energy Transfer and Information Transmission
The average received SINR of the transmission from sensor \(l\) to AP can be expressed (since the proposed scheme does not rely on the instantaneous CSI, we consider average received SINR as the performance metric):  
\[ \begin{split}\mathbb{E}\left[\text{SINR}_{l}\right]& =\mathbb{E}\left[\frac{P_{l}\mathbf{w}_{D,l}^{H}\mathbf{h}_{l} \mathbf{h}_{l}^{H}\mathbf{w}_{D,l}}{\sum\limits_{k=1,k\
eq l}^{L}P_{k} \mathbf{w}_{D,l}^{H}\mathbf{h}_{k}\mathbf{h}_{k}^{H}\mathbf{w}_{D,l}+\sigma ^{2}}\right]\\ &\approx\frac{\mathbb{E}\left[P_{l}\mathbf{w}_{D,l}^{H}\mathbf{h }_{l}\mathbf{h}_{l}^{H}\mathbf{w}_{D,l}\right]}{\mathbb{E}\left[\sum\limits_ {k=1,k\
eq l}^{L}P_{k}\mathbf{w}_{D,l}^{H}\mathbf{h}_{k}\mathbf{h}_{k}^{H} \mathbf{w}_{D,l}+\sigma^{2}\right]}.\end{split}  \tag{13}\]The approximation in the second line is based on the Mullens inequality [33], which is widely employed in the literature of massive MIMO. The approximation has been proven tight when the number of antennas (i.e., \(N_{x}\) and \(N_{y}\)) is large [10].
# A Hybrid Brain Storm Optimization Algorithm to Solve the Emergency Relief Routing Model
## 2 Related Work
### Route Optimization for Emergency Relief  
Academics have studied current emergency relief routing issues to identify precise solutions. In the context of the traveling salesman problem (TSP) and vehicle routing problem (VRP), two alternative objective functions are available: one for minimizing the maximum arrival time (minmax) and one for minimizing the average arrival time (minavg). [PERSON] et al. investigated the impact of using these alternative objectives and found that routing costs could increase [40]. Computational experiments were conducted to identify instances where TSP and VRP solutions significantly differed from optimal minmax and minavg solutions. Coordinating vehicle routes in post-disaster mass distribution and evacuation activities can increase the effectiveness of material relief. This can be accomplished by taking into account the three objective functions in the model: maximizing the total agency utility, maximizing the worst agency utility, and minimizing the deviation between the best and worst utility [41], or by establishing hierarchical clustering and routing procedures [42]. Setting up a supply system of intermediate warehouses [43] and setting demand parameters as fuzzy variables [44] can improve fault tolerance when providing relief operations. While focusing on rescue efficiency, it should also reduce additional traffic congestion [45]. When assigning relief operations, relief vehicles with different functions should be coordinated reasonably [46]. In computing relief routes, greedy heuristics [47], extended insertion algorithms, and the tabu search [48] are often used for large-scale emergency relief problems. For relief distribution problems that consider travel time, total cost, and the reliability of bulk delivery [49], genetic and differential evolutionary algorithms can be used to solve them. Scatter search and variable neighborhood search could be used to speed up the solving process [50]. Finally, the fast and fair distribution of supplies is the key to successful relief operations [51]. Multi-attribute decision-making techniques [52] and rolling optimization techniques [53] can help improve the reliability of the selected routes.  
The aforementioned studies have made significant contributions to path selection. However, in the context of ERRP, factors beyond minimizing cost and time must be considered. Prioritizing patients in critical conditions is crucial during ERRP operations. Therefore, we propose a model that incorporates deprivation cost to simulate relief needs more accurately. The proposed ERRP model considers time windows, vehicle load capacity, and deprivation cost to create a more precise simulation of the actual post-disaster material distribution scenario.
# Acoustic Backscatter Communication and Power Transfer for Batterless Wireless Sensors
## 3 Materials and Methods
### Backscatter Modulation
Reader and tag are not synchronized, leading to an arbitrary face offset \(\phi_{0}\) between the carrier and the intermediate frequency. Furthermore, the amplitude of the incoming carrier \(a_{0}\) depends on the channel between the reader and tag. Using the trigonometric identity  
\[\sin(a)\cdot\sin(b)=\frac{1}{2}[\cos(a-b)-\cos(a+b)], \tag{4}\]  
we can rewrite Equation (3) as  
\[s(t)=\frac{4a_{0}}{2\pi}\sum_{k=1}^{\infty}\frac{\cos\left((\omega_{\mathrm{c }}-(2k-1)\omega_{\mathrm{i}})t+\phi_{0}-\phi_{\mathrm{t}}\right)-\cos\left(( \omega_{\mathrm{c}}+(2k-1)\omega_{\mathrm{i}})t+\phi_{0}+\phi_{\mathrm{t}} \right)}{2k-1}, \tag{5}\]  
showing that the backscattered signal does not contain a frequency component at \(f_{\mathrm{c}}\). The shift of the transmission frequency enables the reader to remove the self-interference at \(f_{\mathrm{c}}\) before amplification and sampling, leaving only the reflected signal in an infinite amount of sidebands around \(f_{\mathrm{c}}+(2k-1)\cdot f_{\mathrm{i}}\) and \(f_{\mathrm{c}}-(2k-1)\cdot f_{\mathrm{i}}\), where \(k\in\mathbb{N}^{+}\). The whole process is sketched in Figure 5.  
Since the backscatter modulation--the switching of the transistors--does not require much power, the MCU is the dominant contributor to total power consumption. It requires running hardware timers and sufficiently fast clocks to generate the square wave. Thus, power-aware implementation of the tag node can greatly reduce the required energy buffer and charging times. For CMOS technology, the dynamic power \(P_{\text{dyn}}\) that an MCU consumes scales quadratically with the supply voltage \(V\) and linearly with the MCU's clock frequency \(f_{\text{mcu}}\), i.e.,  
\[P_{\text{dyn}}\propto f_{\text{mcu}}\cdot V^{2}. \tag{6}\]  
Therefore, we select a dedicated low-power MCU (STM32L073 RZ) and leverage all its power-saving features. This includes a reduced supply voltage of only 1.8 V, saving nearly 70% of power consumption compared to 3.3 V. Furthermore, choosing the internal low-power oscillator and scaling the clock frequency to the minimum requirement is advantageous. As a downside, the on-chip low-power oscillators are unstable and inaccurate, introducing an SFE that must be detected and compensated at the receiver. We will explain this in Section 3.4.
# Upscaling Customer Access Network Using Spectrum Conversion-Slicing-Duplication Technique
## 3 Results and Discussion
### Calculation for Number of Users  
The main objective of this project is to increase the scalability by using the spectrum slicing and duplication technique. The maximum separation ratio that we can obtain is 6 \(\times\) 32 \(\times\) 32 \(\times\) 32, which will reach approximately 196,608 users. The separation ratio represents the components or devices, which are the AWG, power splitter, WDM Demux and power splitter. This arrangement in the network has surpassed the maximum number of users for the existing network, which is 256 users.
# Research on Interference Mechanism of 25 Hz Phase Sensitive Track Equipment from Unbalanced Current
###### Abstract  
In a 25-Hz phase-sensitive track circuit, traction backflow is unevenly distributed in the two rails, resulting in interference caused by the 50 Hz unbalanced current, which leads to misoperation of relays and other equipment in the circuit. Focusing on the mechanism of unbalanced current generation, this paper probes the causes of track circuit equipment interference and innovatively analyzes the mechanism of the cable transformer and relay affected, in order to find a method to suppress the interference of the 25 Hz phase-sensitive track equipment. Firstly, the mechanism of unbalanced current generation is explained, and the influence of the unbalanced impulse current on the choke transformer and binary two-bit relay is analyzed. Secondly, the DC magnetic bias, the second side voltage of the choke transformer and the excitation current, flux density, core loss of choke transformer and relay under a different unbalance impulse current are simulated. Then, the unbalanced current simulation test, unbalanced current test during driving and grounding wire test are carried out. Finally, it is concluded that the unbalanced impulse current causes magnetic saturation of the choke transformer, then affects voltage sag of the relay coil, resulting in misoperation of equipment. The conclusions of this paper can play an important guiding role in studying the influence of unbalanced current and restraining the interference of the 25 Hz phase-sensitive track circuit.  
25 Hz Phase-Sensitive track circuit; red ribbon; track reflux; traction power supply; unbalanced current
## 2 The Equivalent Model of MMC Considering AC Side Power and MMC Control  
### The Impact of AC Active Power and MMC Controllers  
The basic structure of the MMC is illustrated in Figure 1. The converter consists of six arms, and the upper and lower arm in the same phase form a phase unit. Each arm consists of two parts, i.e., \(N\) series-connected identical sub-modules (SMs) and an arm inductor \(L_{arm}\)The equivalent arm resistor and the SM capacitor are denoted as \(R_{arm}\) and \(C_{0}\), respectively. The smoothing reactor is denoted as \(L_{\rm dc}\).  
Here in Figure 1, \(u_{ij}\) and \(i_{ij}\) are the arm voltage and the arm current, where \(j\) (\(j=a\), \(b\), \(c\)) denotes phase and \(r\) (\(r=p\), \(n\)) denotes the upper or lower arm. \(i_{ij}\) is the MMC AC output current in phase \(j\). \(u_{ij}\) is the AC voltage at the point of common coupling (PCC) in phase \(j\). \(u_{dc}\) is the DC voltage, and \(i_{dc}\) is the DC current.  
At any time instant, the instantaneous power \(p_{\rm dc}\) at the DC terminal is the sum of the instantaneous AC active power at the AC terminal \(p_{\rm ac}\), the instantaneous powers resulting from the derivative of energy of the phase reactors \(p_{\rm L}\) and the module capacitors \(p_{\rm CONV}\). Here, the phase reactor losses and converter losses are neglected. The relationship could then be described in Equation (1):  
\[p_{dc}=p_{ac}+p+L_{CONV} \tag{1}\]  
From the above equation, accurately estimating the branch currents and node voltages requires the equivalent MMC model well to reflect the power balance in the transient excursion. \(p_{\rm CONV}\) results from the variation in time of the controlled voltage, which is also the voltage across the module capacitor. In steady-state, this voltage is equal to the DC voltage at the terminal of the MMC. In transient excursion, the controlled DC voltage might be different from the terminal DC voltage due to the action of the controllers. Therefore, \(p_{\rm CONV}\) has to be estimated differently based on DC control mode and the terminal DC voltage during the transient.
# Joint Object Detection and Multi-Object Tracking Based on Hypergraph Matching
## 6 Conclusions  
We designed an MOT framework based on hypergraph association. Through the feature extraction and detection module, we achieved initial localization and detection of objects. By integrating the features from object detection with those from feature extraction, we enhanced the consistency between the current frame features and the tracklet features, avoiding issues such as identity swapping and tracklet fragmentation due to the loss or distortion of object detection. We modelled the data association process using a hypergraph neural network, effectively capturing the relational information between objects. Hypergraph matching was used to associate both occluded and non-occluded objects, maintaining the visual consistency of the tracked objects. The experimental results showed that this method achieves good qualitative and quantitative analysis on three MOT datasets, thereby proving its effectiveness in improving the robustness and accuracy of MOT tasks.  
Methodology, Z.C.; Validation, Z.C.; Writing--original draft, Z.C.; Writing--review and editing, Y.D. ([PERSON]); Project administration, X.T.; Funding acquisition, Y.D. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (Nos. NSFC 61925105, 62227801, 62322109, 62171257, and U22B2001), the State Key Laboratory of Space Network and Communications, the New Cornerstone Science Foundation through the YPLORER PRIZE, and the Tsinghua University (Department of Electronic Engineering)-Nantong Research Institute for Advanced Communication Technologies Joint Research Center for Space, Air, Ground, and Sea Cooperative Communication Network Technology.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Biomechanical Characterization of Abdominal Aortic Aneurysm: The Rupture Mechanism
## 4 Discussion
However, this formula is not sufficient for the complicated rupture prediction. Some studies have shown that wall characterization through factors such as the metabolism, calcification, intraluminal thrombus, and compliance has the potential to predict the AAA growth and further rupture [37]. It has been widely accepted that the mechanical properties of AAA are essential for rupture prediction. Uniaxial tensile tests have shown that different regions of AAA have different yield stress/strain, and the lower values may contribute to AAA rupture [23]. However, another comparative study has shown that ruptured AAAs were not found to be weaker on average than unruptured ones [24]. Microstructure characterization using AFM herein provides more additional mechanical details of AAA. We observed fragmented texture and heterogeneous stiffness in the AAA samples. Higher stiffness was attributed to the high density of collagen, while weaker regions with lower stiffness were attributed to the formation of microvessels and regions between fragments. This weak region was speculated to be associated with a higher risk of AAA rupture initiation and propagation. Higher stiffness with a fragmented texture does not guarantee a higher toughness or larger fracture strain. This study emphasizes microstructure changes in the prediction of small AAA rupture, a scenario in which AAA diameter does not play a significant role. Microstructure changes may also help explain the significantly different risks of AAA rupture between genders [38]. According to this research, the criterion for elective AAA repair could be further enhanced by considering microstructural changes and histological changes such as the formation of microvessels and fragmented texture. Several limitations in this study need to be mentioned. First is the sample size; the five AAA mice and four control mice led to large variability in the testing datasets. It is expected that a larger sample size may mitigate unstable quantifications. Additionally, our samples were harvested exactly after 4 weeks. Considering dynamic AAA progression within the microstructure, and the morphological changes, more time points are needed to capture AAA progression. Furthermore, co-registration between the AFM scanning and the histological images could be enhanced and thus lead to more precise quantification of each component.
## 3 Analysis and Results  
In order to study the transmission spectrum and field distribution of the LC-infiltrated device, the finite element method based commercial software, COMSOL Multiphysics, is adopted in our simulations throughout the paper. It is capable of handling dispersive media and is thus suitable for the full-wave simulation of the LC-based devices. The dielectric properties of LC, which is fitted by Equations (1), (2) and (5) under different bias electric fields, can be described by using COMSOL, and then the performance of LC-based devices can be analyzed in the terahertz range.  
To start the numerical simulation of the demultiplexer, the geometry of the main channel is chosen to be \(L\) = 240 \(\upmu\)m, \(W\) = 5 \(\upmu\)m, as shown in Figure 2. Copper is chosen as the metal surrounding the main channel where the LC material of E7 is infiltrated.
## 4 Dynamic Analysis of the PIM System
### Sensitivity Analysis of the Boundary Conditions of System Parameters
#### 4.2.2 Mass of Large Floating Structures  
The mass of the deployed structure ranges from 550 to 900 tons, while other parameters are held constant. Figure 14 illustrates the time history of tension at the cable-structure connection point and the time history curves of the \(x\)- and \(y\)-axis coordinates of the structure's center of gravity under varying structural mass conditions. Figure 14a shows a significant increase in the axial force at the cable-structure connection point as the structure mass increases. Despite the increase in axial force, the trend of its variation remains consistent with previous research findings. Specifically, when the structure mass increases from 550 to 750 tons, the increase in axial force within the \"resonance zone\" is modest, whereas in the subsequent rising stage, there is a stable growth. This suggests a linear relationship between the mass of the deployed structure and the tension at the connection point.  
Figure 14a illustrates the horizontal displacement of the structure's center of gravity, demonstrating that changes in structure mass exert minimal impact on horizontal displacement. Figure 14c and the zoomed-in version in Figure 14d depict the changes in the horizontal velocity of the structure's center of gravity. With the increase in structure mass, the maximum horizontal velocity slightly increases, yet this increment does not significantly affect the \"oscillation zone\". Figure 14b,e display the changes in the vertical displacement and vertical velocity of the structure's center of gravity over time, indicating that these metrics remain unaffected by variations in structure mass.  
Therefore, the variation in structure mass exerts a minor impact on the entire system, primarily evident in the linear trend in cable tension. This finding offers a crucial perspective for understanding the effects of varying structure masses on the dynamic response of the deployment system.  
Figure 13: (**a**) Time history diagram of axial force at the cable–structure connection under different flow velocities. (**b**) Horizontal displacement at the center of mass. (**c**) Vertical displacement at the center of mass. (**d**) Vertical velocity at the center of mass. (**e**) Horizontal velocity at the center of mass. (**f**) Enlarged time history diagram of horizontal velocity at the center of mass.