esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Example Analysis
### The Critical Component Identification Results  
In order to facilitate comparisons of the critical components, which are mostly responsible for the load-shedding risks, the components are designated in the order of branches, conventional units, and PVs. The correspondence between components and numbers is shown in Table 3. The proportional allocation method is used to calculate the reliability responsibility of each component, and the reliability responsibility index is computed by dividing each component's reliability responsibility by the total reliability levels.  
Based on the analytical-based method, Figure 6 depicts the outcomes of component reliability liability allocation for the forecasting days' PV output. The 10 components with the largest share of responsibility are shown in Figure 6.  
Figure 5: The dynamic reliability levels comparison result.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No.** & **Component** & **No.** & **Componet** & **No.** & **Component** & **No.** & **Component** & **No.** & **Component** \\ \hline
1 & branch1-2 & 11 & branch6-9 & 21 & branch16-17 & 31 & branch22-24 & 41 & branch6-28 \\
2 & branch1-3 & 12 & branch6-10 & 22 & branch15-18 & 32 & branch23-24 & 42 & unit of bus 1 \\
3 & branch2-4 & 13 & branch9-11 & 23 & branch18-19 & 33 & branch24-25 & 43 & unit of bus 2 \\
4 & branch3-4 & 14 & branch9-10 & 24 & branch19-20 & 34 & branches25-26 & 44 & unit of bus 5 \\
5 & branch2-5 & 15 & branch4-12 & 25 & branch10-20 & 35 & branch25-27 & 45 & unit of bus 8 \\
6 & branch2-6 & 16 & branch2-13 & 26 & branch10-17 & 36 & branch28-27 & 46 & unit of bus 11 \\
7 & branch4-6 & 17 & branch12-14 & 27 & branch10-21 & 37 & branch27-29 & 47 & unit of bus 13 \\
8 & branch5-7 & 18 & branch12-15 & 28 & branch10–22 & 38 & branch27–30 & 48 & PV of bus 12 \\
9 & branch6-7 & 19 & branch12-16 & 29 & branch21–22 & 39 & branch29-30 & 49 & PVof bus 22 \\
10 & branch6-8 & 20 & branch14-15 & 30 & branch15-23 & 40 & branch8–28 & \\ \hline \end{tabular}
\end{table}
Table 3: Numbering of the power system components.  
Figure 6 demonstrates that the reliability responsibility index of the branch between buses 2 and 5 is the highest and that reducing the branch's failure rate can increase the power system's reliability. For components with a greater impact on reliability, operators can implement an emergency reinforcement maintenance strategy for the component of the power system. In addition, operators can also formulate reliability-oriented power system dispatch methods based on the dynamic reliability evaluation methods and the critical component identification methods.
# Time-Dependent Performance of a Multi-Hop Software Defined Network
## 1 Introduction
On the other hand, discrete event simulations of transients require many hundreds of independent repetitions of simulation runs to achieve a sufficient statistical accuracy, making the computation times of such simulations prohibitive [48]. Thus, in this paper we extend the approach we developed in [49] to the time-dependent analysis of multiple SDN switches using diffusion approximations [50; 51], which are very convenient to analyze in a time-dependent regime. The accuracy of diffusion approximations has been validated in industry-based research over many decades [52; 53; 54; 55], including for patented techniques [26], and also validated in many academic papers [56; 57; 58]. Their advantage includes a more accurate representation of interarrival and service processes, the ease of obtaining delay predictions from traffic measurements, and much faster numerics for transients than discrete queueing models [59] or simulations. Thus, we compute the transient behaviour of each SDN switch after changes occur in its input traffic rate. Packet loss probabilities can also be computed even when they are \"tiny\" and impossible to estimate by conventional means. The analysis we undertake considers both single SDN switch and multiple interconnected SDN switches controlled by an SDN controller. The rest of the article is organized as follows. Section 2 presents the queueing model of an SDN switch, justifies its simplifications, and introduces the diffusion approach's principles. In Section 2.1, we give a detailed steady-state diffusion model of a single queueing station representing the SDN switch, and in Section 2.2 we extend this to the transient-state model. Section 3 presents a numerical analysis of the model from Section 2.2. We choose various parameters of the traffic and determine a scenario for the traffic changes. The obtained results include queue distributions, mean queues, and loss probabilities at the switch as a function of time. Section 4 presents the network model; the network may have any number of switches and any topology. Section 5 studies the transient behaviour of a chosen network of several nodes. It also gives a simple example of how the model may predict queue evolution and minimize the total backlog at nodes during a time interval. Conclusions are given in Section 6.
## 5 Use Case: Dynamic Radio/MEC Resource Allocation and Slicing Management in High Mobility Scenarios
### System Evaluation
#### 5.2.4 Scalability Analysis  
The system has been thoroughly evaluated in tests involving two, three, and four clients. Its scalability is determined not by the number of clients but by the capacity of each client to process multiple simultaneous slice requests efficiently. This feature is essential for ensuring the system's adaptability to dynamic and high-demand environments, as outlined in the use case traffic profiles.  
Each client in the system is designed to manage multiple slice requests, thereby ensuring effective scaling in the presence of higher traffic loads. The scalability of the system is governed by the number of slice requests received per second per client. This number is determined by the characteristics of the traffic profile, which in turn affect the arrival rates and fluctuations in resource demand.  
To assess this, we have analyzed the total number of requests processed over a one-hour period for each traffic profile based on the request arrival rates defined in Table 6. Table 9 presents the estimated minimum and maximum number of requests the system can process in one hour (3600 s) for each scenario.  
In order to make a more precise assessment of the system's capacity, an estimate has been made of the number of active slice requests at any given moment within the system. This estimate considered both the arrival rate of requests and the average life cycle duration before resources were released. The estimated active requests are presented in Table 10.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Traffic Profile** & **Estimated Active Requests** \\ \hline Dense urban mobility & 54–135 \\ Moderate suburban & 72–180 \\ Mobility on highways & 90–225 \\ Event-based mobility & 240–480 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Estimated number of active requests in the system.  
Figure 16: Comparison of training between centralized and federated systems.  
These values provide a more detailed perspective on the system's operational scalability:  
* In _dense urban mobility_ scenarios, the system actively manages between 54 and 135 slice requests at any given time.
* In _moderate suburban_ areas, the system handles between 72 and 180 active requests, adapting to a more stable but resource-intensive workload.
* In _highway mobility_ environments, the system processes between 90 and 225 simultaneous active requests, ensuring sufficient resources for vehicular applications.
* For _event-based mobility_, where extreme demand surges occur, the system must scale to manage up to 480 simultaneous active slice requests at peak load.  
The findings corroborate the system's capacity for adaptability and scalability, as evidenced by its effective management of thousands of slice requests per hour under diverse high-mobility conditions. The efficacy of the system in handling substantial request volumes, as evidenced by its ability to support real-world O-RAN deployments with varying traffic demands, underscores its superiority over approaches that merely increase the number of clients.
# Integrating Prior Knowledge into Attention for Ship Detection in SAR Images
## 2 Related Work
### Attention Mechanisms  
Attention mechanisms make the model focus on more critical features. Squeeze-and-Excitation Networks (SENet) [66] and Convolutional Block Attention Module (CBAM) [67] are two of the most popular attention mechanisms. SENet computes channel weights by compressing the information of all points in the space and uses the fully connected layer to exploit the correlation between channels, effectively improving the accuracy of the network. In [68; 69], the researchers added SENet to the feature extraction network to enhance the representation of vehicle features. Compared to SENet, which simply uses the channel attention mechanism, CBAM additionally uses the spatial attention mechanism. One of the advantages of CBAM is that it is plug-and-play and can be easily incorporated into various algorithms. [PERSON] et al. [70] integrated CBAM into YOLO to help YOLO better find regions of interest. [PERSON] et al. [71] made their VGG-style network more focused on effective features by embedding CBAM. [PERSON] et al. [72] combined CBAM with SSD to effectively suppress useless information and improved the pedestrian detection accuracy of the algorithm.  
Attention mechanism has been extensively used for object detection in SAR images. [PERSON] et al. [73] designed a lightweight attention mechanism which has significant performance and efficiency for the detection task of multi-class SAR targets in both moving and stationary situations. Combined with feature pyramids, SENet achieved competitive performance in the SAR bridge detection [74], because the SENet enables detectors to suppress irrelevant information while obtaining additional valid information. [PERSON] et al. [75] used a mixture of feature pyramid networks and CBAM to further process the features enhanced by scattering information, effectively adapting to the discrete and variable nature of aircraft in SAR images.  
Attention mechanism is useful for ship detection as well. The ship attention module proposed by [PERSON] et al. [21] distinguishes the strong scattering points of artificial facilities on land and ships in water by generating a feature map containing rich texture features, which effectively reduces false alarms in the land area and improves the accuracy of the algorithm. [PERSON] et al. [7] designed a Spatial Group-wise Enhance (SGE) attention module to reduce the problem of adjacent ships being ignored. SGE reduces the computational effort in the form of channel grouping, while enhancing the spatial features of each group of channels, providing more semantic features. Their experimental results showed that their method has better detection capability for densely docked ships and makes a contribution to ship detection in complex scenarios. [PERSON] et al. [76] designed a dynamic shrinkage attention mechanism to address the impact of speckle noise on SAR images, which can automatically learn the soft threshold required by the denoising algorithm to achieve feature-level denoising.
## 5 Adaptive Decision Threshold Design Method Based on Auxiliary Channel
### Symbolic SNR \(\mu\) and Noise Power \(P_{n}\) Estimation  
According to Section 5.1, \(\lambda\) is related to \(\mu\). The frequency error and code synchronization error will lead to the deterioration of \(\mu\), which will affect the selection of \(V_{th}\). Therefore, the influence of frequency error and code synchronization error on SNR is calculated through formula derivation. See Appendix B for the derivation process.  
SNR after despreading is  
\[SNR=\left[\frac{\sin(\pi fT)}{\pi fT}\right]^{2}\frac{NA^{2}h^{2}(-\tau T_{c}) }{\sigma_{n}^{2}}=\left[\frac{\sin(\pi fT)}{\pi fT}h(-\tau T_{c})\right]^{2} \left(\frac{E_{s}}{N_{0}}\right) \tag{18}\]  
According to (18), \(\mu\) is affected by the synchronization error of the spread spectrum code and carrier frequency difference. It is assumed that the threshold SNR required by the system is \((E_{s}/N_{0})_{th}\) dB. In the signal acquisition phase, it can be assumed that \(|fT|<1/4\), \(|\tau T_{c}|<1/4\). Then, through calculation, when \(0<\alpha\leq 1\), SNR deteriorates 1.82 dB \(\leq d\leq\) 2.34 dB. The signal-to-noise ratio of the front receiving channel of the  
Figure 11: The relationship between \(\lambda\) and \(P_{acq}\).  
Figure 12: Acquisition threshold estimation error \(\Delta V\) diagram.  
receiver deteriorates to \(d^{\prime}\) and is considered at a maximum of 0.5 dB. Then, the actual working SNR based on logarithmic representation is  
\[\mu\geq\left(\frac{E_{s}}{N_{0}}\right)_{th}-d-d^{\prime}\ \text{dB} \tag{19}\]  
where \(\frac{E_{s}}{N_{0}}=\frac{E_{h}}{N_{0}}+10\text{lg}m+10\text{lg}N(\text{dB})\), \(m=\begin{cases}1,&\text{when}\ BPSK\\ 2,&\text{when}\ QPSK\end{cases}\).
## 3 In-Situ Monitoring
### Monitoring Results  
#### 3.2.1 Horizontal Displacement of the Enclosure Wall along Its Depth  
The horizontal displacement of the enclosure wall along its depth direction was evaluated at the monitoring points W1, W2, W3, W4, and W5. These points were located at depths ranging from 1 m to 22 m, as depicted in Figure 4. The construction of the pit followed the procedure outlined in Table 3, and the first monitoring data were recorded as C0 curve when the excavation depth reached 0.7 m in Step 3. The second data monitoring was performed and recorded as C1 curve at the end of Step 3. The third data monitoring was performed and recorded as C2 curve at the end of Step 4, and so on until the seventh data monitoring was performed and recorded as C6 curve at the end of Step 8. Ten days after the completion of Step 8, the eighth data monitoring was conducted for monitoring points W1, W2, and W4, recorded as C7 curve. The maximum horizontal displacements of the monitoring points W1, W1, W2, W3, W4, and W5 were 27.19 mm, 31.64 mm, 34.16 mm, 34.5 mm, and 19.76 mm, respectively.  
Figure 4: \(Cont.\)  
Figure 3: Layout of monitoring points (unit: m).  
Given the shallow depth of the pit, the maximum horizontal displacements of the monitoring points were all relatively close to the ground surface. Monitoring points W2, W3, and W4 were located in the middle part of the pit edge and had less cross bracing, resulting in larger horizontal displacements. Monitoring point W1 benefited from more cross bracing, leading to a slightly smaller horizontal displacement. Monitoring point W5, situated at the corner of the pit, had a surplus of cross bracing, explaining the smallest horizontal displacement.
# Soybean Weed Detection Based on RT-DETR with Enhanced Multiscale Channel Features
## 2 Materials and Methods
### EMCF-RTDETR Model
#### 2.2.2 EA-AIFI  
The AIFI mechanism, as an integrated feature input attention mechanism, plays a crucial role in RT-DETR, driving the model to efficiently and accurately detect objects. However, the multi-head self-attention mechanism in AIFI, although capable of capturing rich feature relationships in sequences, suffers from high computational complexity, large memory consumption, and limited scalability, particularly when handling long sequence inputs. These drawbacks significantly impact efficiency and performance. To address these issues, this paper introduces Efficient Additive (EA) Attention [41], which optimizes computations using an additive approach, reduces memory requirements, and enhances scalability. As a result, EA significantly boosts the efficiency of processing long sequences while maintaining its expressive capability, making it more suitable for large-scale applications, like weed detection. The EA Attention structure is shown in Figure 4.  
Figure 4: EA Attention structure.  
The EA Attention mechanism primarily simplifies the computational process by eliminating the expensive matrix multiplication operations in traditional self-attention mechanisms, adopting linear transformations and element-wise multiplications instead. This significantly reduces the computational complexity. Moreover, this mechanism relies solely on the interaction between queries and keys, eliminating the explicit interaction between keys and values, which allows it to be applied at all stages of the network while maintaining a high context-capturing capability. Consequently, it provides a consistent context of information captured across different resolutions.  
The specific process involves embedding the input into a matrix \(X\) and applying a linear transformation to obtain the query matrix \(Q\) and the key matrix \(K\). Then, by applying a learnable weight vector \(\omega_{\alpha}\) to the query matrix \(Q\), the global query vector \(\alpha\) is calculated. After that, a weighted average is used to generate the global query \(q\). Finally, the global query \(q\) is multiplied element-wise with the key matrix \(K\) to form the context representation \(C\). The calculation process is shown in the equation below:  
\[\alpha=\frac{Q\omega_{\alpha}}{\sqrt{d}} \tag{3}\]  
\[q=\sum_{i=1}^{n}\alpha_{i}Q_{i} \tag{4}\]  
\[C=K\odot q \tag{5}\]  
where \(d\) is the dimension of the query, which is used for normalization. \(\odot\) is element-level multiplication.
## 1 Introduction
In contrast, solution treatment, age-hardening and SPIF have the largest residual stresses, which cause the largest geometric accuracy errors. This also shows that higher residual stresses lead to higher geometric accuracy errors. The results of [PERSON]'s study showed that the strength and properties of the material were difficult to improve after annealing treatment of AA2024 aluminum alloy sheet without any subsequent heat treatment [22]. [PERSON] [23] reported that the forming performance increases with the decrease of forming angle, and the combination of reducing step size and forming angle can significantly improve the forming performance. The forming angle of SPIF is more significant for the formability of Al-Cu alloy sheet than parameters such as tool shape, tool diameter, sheet thickness, and tool speed, but this has not yet been tested in SPIF of Al-Li alloy made component, which is more complex system for its rich secondary phase particles. As is commonly understood, there are four methods to improve strength: fine crystallization strengthening, deformation strengthening, solution strengthening and precipitation strengthening. Al-Li alloy belongs to precipitation strengthening aluminum alloy, and good precipitation strengthening effect can be obtained through appropriate heat treatment process. In order to understand the precipitation behavior of Al-Li alloy, many scholars have conducted extensive basic research on the relationship between mechanical properties and microstructure [24; 25; 26]. Much of the literature explains that the strength improvement of aluminum lithium alloy mainly relies on the precipitation phase such as \(\theta^{\prime}\) phase and T\({}_{1}\) phases, which can obtain better strengthening effect. The reason is that T\({}_{1}\) phase has a particularly high aspect ratio, and better shear resistance than other precipitated phases relative dislocations, which can better improve the work hardenability [27; 28]. Based on the aforementioned research, we understand that there is a significant lack of studies focused on improving the geometric accuracy of Al-Li alloy sheets through SPIF without sacrificing high-performance components. Therefore, further research on SPIF of Al-Li alloys is necessary. This work investigates the effect of forming angle on the geometry accuracy and performance of the SPIFed Al-Li alloy sheet via numerical simulation and experimental validations.
## 2 Robust Optimization
\begin{array}{l}\
u_{a}=\frac{h_{a}+n_{a}}{2}\ \,\ \ \ \ \ \ n_{a}=\left\{ \begin{array}{ll}\left|f_{i}^{s^{*}}\right|+1-\text{sgn}\,h_{a}&\text{if} \,\left\lceil h_{a}\right\rceil+\left|f_{i}^{s^{*}}\right|\text{ is even}\\ \left|f_{i}^{s^{*}}\right|-\text{sgn}\,h_{a}&\text{if}\,\left\lceil h_{a}\right \rceil+\left|f_{i}^{s^{*}}\right|\text{ is odd}\end{array}\right.\\ h_{a}=\frac{\Gamma_{i}}{\max\limits_{j\in\left\lceil f_{i}^{s^{*}}\right\rceil }\left\lvert s_{ij}^{s^{*}}\right\rvert}\ \,\ \ \ \ \ \,\ \,f_{i}^{s^{*}}=\left\{j:\left\lvert\delta_{ij}^{s^{*}}\right\rvert>0 \right\}\ \,\ \ \ \delta_{ij}^{s}=\min\left\{1,\frac{\left\lvert\delta_{ij}\right\rvert x_{j}^{s }}{2\left\lvert x_{j}\right\rvert x_{j}^{s^{*}}}\right\}\end{array} \]  
In Equation (8), \(\delta_{ij}^{s}\) is a parameter used for the proof of the upper bounds and its definition depends on the uncertainty set adopted: the definition reported in Equation (8) is related to a polyhedral + box set and makes \(\delta_{ij}^{s}\in\left[0,\ 1\right]\). The subscript \(r\) corresponds to the index of the \(\max(1,\ \left\lceil\Gamma_{i}\right\rceil)^{th}\) largest \(\left\lvert\delta_{ij}\right\rvert x_{j}^{s}\). Essentially, it is \(\delta_{ij}x_{j}^{s}\) scaled by a factor related to the specific uncertainty set. The vector \(x^{*}\) represents the solution of the robust optimization. However, in [24] it is observed that Equation (8) does not provide the expected reduction in the a posteriori risk when \(\max\limits_{j\in\left\lceil f_{i}^{s}\right\rceil}\left\lvert\delta_{ij}^{s }\right\rvert=1\) (that is the case of polyhedral and polyhedral + box sets) or when the \(i\)th constraint is inactive. An alternative formulation of the a posteriori bound is:  
\[\varepsilon_{i}^{post}=\frac{1}{2^{n_{b}}}\sum_{k=\left\lceil v_{b}\right\rceil }^{n_{b}}\left( \begin{array}{c}n_{b}\\ k\end{array} \right) \tag{9}\]  
where  
\[ \begin{array}{l}\
u_{b}=\frac{h_{b}+n_{b}}{2}\ \,\ \ \ \ \ n_{b}=\left\{ \begin{array}{ll}\left|f_{i}^{s}\right|+1-\text{sgn}\,h_{b}&\text{if}\,\left \lceil h_{b}\right\rceil+\left|f_{i}^{s}\right|\text{ is even}\\ \left|f_{i}^{s}\right|-\text{sgn}\,h_{b}&\text{if}\,\left\lceil h_{b}\right \rceil+\left|f_{i}^{s}\right|\text{ is odd}\end{array}\right.\\ h_{b}=\frac{h_{i}\left(x^{*}\right)}{\max\limits_{j\in\left\lceil f_{i}^{s} \right\rvert}\left\lvert x_{j}^{s}\right\rvert}\ \,\ \ \ \ J_{i}^{s}=\left\{j:\left\lvert\delta_{ij}\right\rvert x_{j}^{s }\right\rvert>0\right\}\ \,\ \ \ h_{i}(x^{*})=b_{i}-\sum\limits_{j}a_{ij}x_{j}^{s},\end{array} \]  
But, even if it generally provides lower probabilities than Equations (8) and (7), it is not exactly the analogue of Equation (7), and thus it can yield higher a posteriori probabilities. Therefore, it is always preferred to apply both the Equations (8) and (9) and take the minimum probability generated.