esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Analysis
### Verification of Analytical Models and Their Throughput and Fairness Analysis
In comparison with the scenario without RTS/CTS exchange, throughputs of single-link devices for all multi-link DCF schemes were more robust, according to either an increase in the number of stations or variousness of the multi-link DCF schemes. As a result, MLD's aggressive channel access could increase throughput to the MLD as long as the RTS/CTS exchange was turned on, while the throughput of a single-link device did not degrade. In other words, when the number of stations was small, then the wireless medium was more likely to be idle, and aggressive channel access, i.e., MLA-S, could fill these idle gaps. Figure 10: Sum of saturated throughput for all links, (**a**) when RTS/CTS exchange is off, and (**b**) when RTS/CTS exchange is on. Figure 12 shows expected throughput ratios of an MLD compared to a single-link device in the same environment throughout this subsection. The throughput ratio stands for the proportion of an MLD's throughput to a single-link device's throughput. Since it is assumed that two stations are affiliated with one MLD, it is evident that the expected throughput of MLD compared to a single-link device was exactly double in the case of asynchronous transmission. In addition, this ratio was not that different whether RTS/CTS exchange was applied or not due to the RTS/CTS exchange effect on a portion of data within TXOP. Based on this figure, we can see that the higher the priority of the MLD, the higher the throughput of the MLD. Especially in MLD-S, when there were few stations, MLD captured the channel more frequently than single-link devices, leading to a significant throughput difference between MLDs and single-link devices. As the number of stations increased, the shortened CWs caused channel congestion and sharply decreased throughput ratio. Furthermore, we can see that MLD's throughput generally ranged under 2 except MLD-S. This is because channel access on the auxiliary link could fail due to a collision caused by single-link devices. Figure 11: Separated throughput on each MLD and single-link device per a link, (**a**) when RTS/CTS exchange is off, and (**b**) when RTS/CTS exchange is on. Figure 12: Throughput ratio between MLDs and single-link devices. Furthermore, in SLA, an MLD was allowed to access the wireless medium only through the designated link. Consequently, compared to synchronous multi-link DCFs, such as MLA-S, MLA-L, and MLA-A, an MLD using SLA had less opportunity to access the channel. Recalling that the throughput ratio between the MLD and the single-link device was exactly 2, we can see that the difference between the Async value and the SLA value was due to the disallowing channel access of the auxiliary link.
## 2 Attacks on Sensor Network Routing  
Attacks on sensor network routing have been discussed in several papers [9][10][11][16]. Most of the attacks fall into one of the following categories: spoofed, altered, or replayed routing information [9][16], selective forwarding [16], sinkhole [16], _Sybil_[10], wormhole [11], and HELLO flood (unidirectional link) attacks [16]. We briefly describe those attacks on sensor networks:  
* _Spoofing, altering, or replaying routing information_[9, 16]: By spoofing, altering, or replaying routing information, the adversaries can create routing loops, attract or repel network traffic, extend or shorten source routes, generate false error messages, partition the network, increase end-to-end latency, etc.
* _Selective forwarding attacks_[16]: Malicious nodes may refuse to forward certain messages and simply drop them, ensuring that they are not propagated any further. Another form of this attack is that an adversary selectively forwards packets, i.e. she is interested in suppressing or modifying packets originating from a few selected nodes, reliably forwards the remaining traffic and limits suspicion of her wrongdoing.
* _Sinkhole and wormhole attacks_[16]: In sinkhole attacks, an adversary attracts nearly all the traffic from a particular area through a compromised node, creating a metaphorical sinkhole with the adversary at the center. In wormhole attack, an adversary tunnels messages received in one part of the network over a low latency link and replays them in a different part. Wormhole attacks more commonly involve two distant malicious nodes colluding to understate their distance from each other by relaying packet along and out-of-bound channels available only to the attacker.
* _Sybil attacks_[10]: In _Sybil_ attacks, a single node presents multiple identities to other nodes in the network. In particular, a Sybil attack causes a significant threat to geographic routing protocols. Using a Sybil attack, an adversary can cheat as many nodes at the different locations.
* _HELLO flood (unidirectional link) attacks_[16]: A laptop-class attacker may broadcast routing or other information with large enough transmission power and convinces every node in the network that the adversary is its neighbor. As a consequence, these nodes only relay packets to the attacker's laptop.
# The Significance of Calcium in Photosynthesis
## 6 Ca\({}^{2+}\) Is Involved in Chloroplast Movement  
In photosynthetic cells, chloroplast moves to different positions depending on light conditions. In weak light, chloroplasts are arranged evenly to follow light to absorb energy. In strong light, they are parallel to light to avoid light damage. Both blue and red light are important for photosynthesis, and chloroplast movement induced by blue light mediated and the pathway can cause a rise in intracellular Ca\({}^{2+}\) levels in _A. thaliana_[93]. When _phot1_ and _phot2_ phototropin-deficient mutants are treated with PLC inhibitor neomycin and u-73122, the intracellular Ca\({}^{2+}\) peak in _phot1_ mutants suggests an increase by blue light induction, whereas _phot2_ mutants are not significantly affected; these results indicate that PLC may mediate the phosphoinositol pathway to participate in the intracellular Ca\({}^{2+}\) rise induced by phototropin2 [93]. Further studies showed that only phototropin2 is involved in the intracellular Ca\({}^{2+}\) rise under strong blue light; by contrast, phototropin1 and phototropin2 are both involved in the intracellular Ca\({}^{2+}\) rise caused by phosphatidylinositol 3-phosphate (PI3P) under weak blue light, thereby causing chloroplast aggregation or the avoidance reaction [94]. Chloroplast motion involves filamentous actin, which is composed of aggregates of globular actin monomers, and this process is mediated by the Ca\({}^{2+}\)-CaM-dependent pathway [95].  
Red light stimulates photoreceptor protein phytochrome to regulate chloroplast movement [96], but this regulation might be Ca\({}^{2+}\)-independent [97]. Further study discovered that chloroplast movement has two kinds of motion systems: one is microtubule-based and the other is microfilament-based; blue light may regulate both the microtubule movement system and the microfilament movement system, whereas red light refers to the microtubule movement system [96]. Thus, Ca\({}^{2+}\) may eventually regulate the recombination of microfilaments to mediate chloroplast recombination [98].
# Mechanical Incipient Fault Detection and Performance Analysis Using Adaptive Teager-VMD Method
## 2 Proposed Methods
### Theory of VMD
#### 2.2.2 Performance Analysis of VMD  
From the above description, it can be seen that the sub-mode number \(K\) and the quadratic term \(\alpha\) are closely related to the effectiveness of VMD. Presetting too small \(K\) will lead to signal under-decomposition and result in mode aliasing. However, excessive \(K\) value can easily lead to modal copying. The parameter \(\alpha\) determines the bandwidth of the modes. If this parameter is set too low, the limited bandwidth of each mode is too large, which leads to mode aliasing and introduces noise. Conversely, excessive parameter \(\alpha\) will result in the absence of some valid information.  
To illustrate the effect of inside parameters on VMD performance, the signal in Figure 1b is decomposed using different [_K_, \(\alpha\)] combinations as shown in Figure 2. Therefore, the experiments show that the [_K_, \(\alpha\)] has a great influence on the performance of VMD. The identification characteristic of the VMD is critically affected by the combination [_K_, \(\alpha\)].
# Model-Free-Based Single-Dimension Fuzzy SMC Design for Underactuated Quadrotor UAV
## 6 Hardware Configuration and Experiment
### Global Positioning System (GPS)  
Our RC eye 650 is mainly embedded with GPS-based tracking system, which will help us in real time to track our underactuated quadrotor; in addition to this, it supports the faster satellite communication. This also has guidance system that works using optical and ultrasonic sensors embedded within the inertial measurement unit (IMU) as highlighted in  
Figure 23: Block diagram demonstrating the experimental setup.  
the Figure 19. The RC-eye 650 quadrotor is then operated under the influence of variable wind disturbance and variable payload conditions with the model-free single-dimension-based fuzzy sliding mode control. The acquired position errors are shown in the Figure 24. These errors are same as shown in the Figure 21. This validates the proposed algorithm.
# Truss Structure Optimization with Subset Simulation and Augmented Lagrangian Multiplier Method
## 4 Test Problems and Optimization Results
### Spatial 25-Bar Truss Structure
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**Design Variables** & **HS [21]** & **HFSD [21]** & **SSO [48]** & **FLOB [1]** & **ARC-AP [Dii]** & **SAIS [Dii]** & **TLBD [D4]** & **MSPSO [27]** & **CA [17]** & **HPSSD [41]** & **TLBD [38]** & **FPA [8]** & **WDO [39]** & **ALSSO** \\ \hline
1 & \(A_{4}\) & 0.007 & 0.000 & 0.000 & 0.000 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\
2 & \(A_{4}\) & 2.002 & 1.570 & 2.057 & 1.951 & 1.979 & 2.074 & 2.0712 & 1.988 & 2.0204 & 1.997 & 1.988 & 1.8303 & 1.9835 \\
3 & \(A_{5}\) & 2.990 & 3.198 & 2.892 & 3.005 & 3.003 & 2.061 & 2.957 & 2.996 & 3.0173 & 2.9881 & 2.994 & 3.1834 & 3.0025 & 2.9676 \\
4 & \(A_{50}\) & 0.000 & 0.000 & 0.000 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 & 0.0000 \\
5 & \(A_{50}\) & 0.014 & 0.010 & 0.014 & 0.010 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.01 & 0.002 & 0.01 & 0.01 & 0.0000 \\
6 & \(A_{50}\) & 0.088 & 0.094 & 0.097 & 0.092 & 0.099 & 0.091 & 0.091 & 0.0872 & 0.0993 & 0.0842 & 0.0885 & 0.0177 & 0.0836 \\
7 & \(A_{50}\) & 1.687 & 1.681 & 1.666 & 1.706 & 1.679 & 1.817 & 1.8299 & 1.5783 & 1.8342 & 1.8574 & 1.7575 & 1.7766 & 1.87794 \\
8 & \(A_{50}\) & 2.663 & 2.663 & 2.667 & 2.789 & 2.652 & 2.674 & 2.6788 & 2.6599 & 2.66377 & 2.6496 & 1.664 & 2.5713 & 2.6412 & 2.64677 \\ Weight (B) & 544.38 & 545.19 & 545.37 & 546.80 & 545.130 & 545.12 & 545.09 & 545.772 & 545.05 & 545.164 & 545.1375 & 545.139 & 545.146 & 545.1395 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of optimized designs for the 25-bar truss problem. Statistical data listed in Table 6 prove that the present algorithm was also very robust for this test problem. Figure 5 shows the convergence curve obtained for the last optimization run with \(N=500\). For both load cases, ALSSO detected the \(y\)-displacement constraints of top nodes as active.
## 3 Monitoring Methods
### Sub-Pixel Algorithm for Calculating Coordinates of Center of Ring Mark
#### 3.2.3 Least-Square Fitting Center Coordinates  
The least-square method is a mathematical optimization technique that finds the best function matching a set of data by minimizing the sum of squares of errors [25]. In the two-dimensional plane coordinate system, the circular equation is expressed as follows.  
\[(x-A)^{2}+(y-B)^{2}=R^{2} \tag{10}\]  
where \((A,B)\) are the center coordinates and \(R\) is the radius.  
For circle fitting with the least-square method, the optimized objective function of the error square is given as follows.  
\[S=\sum_{i=1}^{N}{[\sqrt{\left(x_{i}-A\right)^{2}+\left(y_{i}-B\right)^{2}}-R ]}^{2} \tag{11}\]  
where \((x_{i},y_{i})\) is the coordinate of characteristic points on the circular arc and \(N\) is the number of feature points involved in fitting.  
To avoid the square root, the following improvements are applied to define the error square, and a direct solution to the minimization problem is obtained. The definition is given as follows:  
\[E=\sum_{i=1}^{N}{[(x_{i}-A)^{2}+\left(y_{i}-B\right)^{2}-R^{2}]}^{2} \tag{12}\]  
Supposing that \(a=-2A,b=-2B,\) and \(c=A^{2}+B^{2}-R^{2}\), then Equation (12) may be simplified as follows:  
\[E=\sum_{i=1}^{N}{({x_{i}}^{2}+{y_{i}}^{2}+a{x_{i}}+by_{i}+c)}^{2} \tag{13}\]  
Figure 5: Results of sub-pixel algorithm detection.  
According to the principle of the least-square method, parameters \(a\), \(b\), and \(c\) must yield the minimum value of \(E\). According to the minimum value method, \(a\), \(b\), and \(c\) must meet the following conditions:  
\[\begin{array}{l}\frac{\partial E}{\partial a}=2\sum\limits_{i=0}^{n}{({x_{i}} ^{2}+{y_{i}}^{2}+ax_{i}+by_{i}+c)x_{i}}=0\\ \frac{\partial E}{\partial b}=2\sum\limits_{i=0}^{n}{({x_{i}}^{2}+{y_{i}}^{2}+ ax_{i}+by_{i}+c)y_{i}}=0\\ \frac{\partial E}{\partial c}=2\sum\limits_{i=0}^{n}{({x_{i}}^{2}+{y_{i}}^{2}+ ax_{i}+by_{i}+c)}=0\\ \end{array} \tag{14}\]  
Thus, the optimal fitting center coordinate \((A,B)\) and radius \(R\) are obtained. Therefore, after calculating the sub-pixel corner points of the circular contour, the least-square method is used to fit the sub-pixel center coordinates of the ring mark.
## 4 Architecture of Software Receiver  
The developed CRPA real-time software receiver runs on a PC platform and uses IF datasets as input where the detailed description of the data collection hardware is presented in [7]. It currently operates on GPS and WAAS L1 C/A code signals and supports four-element antenna array.  
There are a total of 60 channels, of which 48 channels are used to process data from four antennas (12 channels for each antenna element) and are dedicated to obtain the ICP differences only. The other 12 channels are used to process the beamformed composite signal using the pre-correlation beamforming approach shown in Figure 4. The STAP algorithm, which is capable of rejecting more than _N_-1 interferers is implemented in the pre-correlation approach. Each beamformed channel uses one set of weights to steer a beam toward a satellite. The weights are updated by adaptive MVDR algorithm. The steering vectors, which are inputs to the weight update, are obtained from ICP differences between tracking channels as described in the previous section. Hence, the software receiver can form beams before positioning in the pre-correlation sense.  
The weight update rate of MVDR algorithm is 1 kHz for adapting highly dynamic interferers. The current platform can achieve real-time performance with up to five time taps for STAP processing. There exists a tradeoff between the number of antenna elements, tracking channels, time taps, and weight update rates. Given the four-element antenna array, our software receiver can reject at most three broadband interferers. It can reject more than three interferers if some of them are narrowband nature. Positioning is dedicated to the beamformed composite signals. In order to illustrate the interference rejection performance, the cabling/RF chain biases need to be calculated after positioning. Then, the biases combined with weights are used to calculate the accurate angle-frequency response which provides a coarse visualization of the outputs of STAP given the electrical layout of the individual elements.  
Figure 3: Mechanism to obtain the steering vectors in the software receiver.
# High-Precision Chip Detection Using YOLO-Based Methods
## 4 Discussion
The precision, recall, and [EMAIL_ADDRESS] values for the proposed YOLOv11n+DNMS are 97.05%, 96.81%, and 96.48%, respectively. The proposed lightweight model GM-YOLOv11+DNMS achieved precision, recall, and [EMAIL_ADDRESS] values of 97.04%,96.38%, and 95.56%, respectively. Compared to the methods in the literature [28; 29], this approach has advantages. The introduction of DNMS improves the accuracy of different versions of the YOLO algorithm in chip detection. Although the idea of soft NMS has been proposed before [35], it differs from our DNMS algorithm, which is specifically designed considering the unique characteristics of our application. The DNMS algorithm presented in this paper first identifies larger objects in the image--the workpiece--and then, based on the actual machining scenario, where chips may or may not be near the workpiece, it sets different NMS thresholds, thereby enhancing the detection accuracy. Building on the model that accurately identified chips in images, we also designed the VPPA, which enables the statistical counting of chips in videos. In turning operations, as chips are cut from the workpiece and ejected into the air, their shape continuously changes. This paper introduces a specialized triggering mechanism to count the number of ejected chip particles. From Table 5, it can be seen that GM-YOLOv11+DNMS+VPPA and YOLOv11n+DNMS+VPPA accurately identified 393 and 394 out of 436 ejected chip particles, achieving an accuracy rate of 90.14% and 90.37%, respectively. Compared to other models, these two models achieved the highest accuracy, demonstrating the value of introducing GM and DNMS into YOLOv11. In general, the actual processing time in real-world scenarios is much shorter than in the video tested in this paper. This accuracy rate indicates that the chip count is very close to the true value. Additionally, GM-YOLOv11+DNMS+VPPA applies post-processing based on the GM-YOLOv11 model. The hardware environment in this study was as follows: an Intel i5-13500 HX CPU, an NVIDIA GTX 4050 GPU, and 16 G of memory. This configuration represents a relatively common computer setup. There are many models of industrial control computers and industrial edge devices that are equivalent to this configuration. This study evaluated different models based on the number of model parameters, FLOPs, and FPS, as shown in Table 5. FPS directly reflects the inference speed, and FLOPs indicates the computational load required for a single inference. When processing images, GM-YOLOv11 achieved FLOPs and FPS values of 5.72 G and 173.41, respectively. Although the FLOPs value remained relatively high, suggesting potential gaps in its application to mobile devices, the GM-YOLOv11 model has only 2 MB of parameters and a FLOPs value close to 5 G, with an FPS value significantly exceeding 30. This makes it a promising candidate for deployment on embedded devices or industrial edge devices.
## 3 Results and Discussion  
Refractive index, as well as absorption coefficient, were obtained using the THz time domain spectrometer. The range of the spectral characteristics--refractive index and absorption coefficient--was 0.25 to 2.5 THz, with a resolution of approximately 7 GHz or 615 frequencies.
## 3 Scenario for Providing a VWSN  
In this paper, we adopt the concept of the virtualization of WSNs as shown in [2] and assume that the virtualization of WSNs is realized by two types of providers. One type is an infrastructure provider that manages physical sensor nodes and physical WSNs; the other is a VWSN provider that constructs a VWSN by aggregating physical substrates from multiple infrastructure providers. We assume that a VWSN provider is responsible for constructing a VWSN topology for an application.  
In this paper, a VWSN is provided to handle user requests as shown in Figure 1. A user who wants to develop a new application selects a set of source nodes and destination nodes. This set of nodes can be abstracted through an interface provided by the VWSN provider. Then, the user sends a request to construct a new VWSN, and this message is received by a VWSN provider. When the user requests abstract resources, such as the periodic report of humidity and temperature in the northeast area of Tokyo in Japan, the VWSN provider selects a set of source and destination nodes corresponding to the user's request. The request message contains the traffic pattern between the source nodes and destination nodes, as well as the required degree of reliability. The traffic patterns describe the way that data packets are generated, such as periodic or event-driven scheduling. When the VWSN provider receives the request, it checks whether there are enough resources to construct a VWSN that satisfies the request. When there are not enough resources, the VWSN provider rejects the request. Otherwise, the VWSN provider constructs a topology and assigns at least one physical path to a virtual link. When high reliability is demanded, redundant physical paths can be assigned to a virtual link. When a low number of redundant physical paths is assigned to a virtual link, the resources of sensor nodes are more fully utilized, but low redundancy may lower reliability. At this time, the VWSN provider determines which nodes the user will be allowed to use. The allowed ones include source nodes, destination nodes and relay nodes for communication between source and destination nodes. Once the set is decided, the VWSN provider calculates routing tables for each node in the constructed VWSN and sends the information, including the traffic pattern, to the gateway that connects the Internet and WSNs and may translate protocols to communicate with nodes in WSNs. The gateway sends the information to each node in the VWSN. Over time, each node in the VWSN acts as a resource for the VWSN and sends data according to the received traffic pattern.  
Figure 1: Providing a virtual wireless sensor network (VWSN) to a user.  
As described in Section 2, it is true that it is hard to manage resources completely because of unstable channel quality and the time-varying traffic pattern. One solution for determining whether the user's request can be met or not is to limit the number of applications running on each module or node.