esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### The Result of Ablation Study  
As shown in Table 4, all introduced module modifications significantly improved the model's detection capabilities, thereby validating the effectiveness of our enhancement strategies.  
The incorporation of the AKConv module brought about a substantial increase in Precision (P), reaching 0.688. AKConv's strength lies in its adaptive, variable convolutional kernel size and position, which allow it to dynamically adjust its receptive field based on the unique geometric structures of steel defects, such as the linear characteristics of cracks or the punctiform distribution of patches. This precise feature capture capability effectively enhances the model's ability to recognize various defect morphologies. The introduction of the BiFPN module demonstrated outstanding performance in Recall (R), improving to 0.712. BiFPN leverages an innovative bidirectional weighted feature fusion mechanism to efficiently integrate feature information from multiple scales. This approach enables the model to comprehensively utilize both global contextual information and local fine-grained details, significantly boosting its recall rate for diverse defect types. The Detect_DyHead module contributed most comprehensively to the overall model performance, achieving the best results for both mAP50 and mAP50-95 metrics, reaching 0.745 and 0.410, in that order. Detect_DyHead enhances the detection head's adaptability by dynamically optimizing across three orthogonal dimensions: scale, space, and channel. This adaptive adjustment capability ensures that the model can achieve more precise and robust predictions when handling complex and varied defects.  
The synergistic action of these modules significantly contributes to the overall performance gains of the model. AKConv focuses on refined local feature extraction, BiFPN excels at efficient multi-scale information integration, and Detect_DyHead optimizes the adaptability of the detection head at a macroscopic level. They form a beneficial complementary relationship, collectively enhancing the comprehensive performance of the YOLO-MAD model.
## 5 HUVEC Model Applications
### Angiogenesis  
Angiogenesis is a hallmark of repair, expansion, and remodeling in physiological and pathological conditions. This process includes EC activation, migration, and proliferation that ends with the formation and organization of tubular structures to form new blood vessels [113; 146; 147]. This mechanism plays an essential role in vascular remodeling during ischemic events [148]. Because the growth of collateral vessels offers an alternative route for irrigation when a coronary artery is blocked, angiogenesis may decrease myocardial damage and improve prognosis [149]. HUVEC models have been used to study the molecular and signaling pathways involved in this process [150; 151; 152; 153; 154]. Pro-angiogenic factors are known as potential therapeutic agents to treat ischemic heart disease and protect EC in the arteries [108; 150; 151; 152; 153; 154].  
[PERSON] et al. identified 400 microRNAs differentially expressed in HUVEC associated with angiogenesis under hypoxic conditions [157]. MicroRNA-939 is negatively regulated in patients with coronary artery disease, and this reduced expression is associated with increased coronary collateral circulation [149]. The authors suggest these microRNAs are potential biomarkers and therapeutic targets in ischemic heart disease.  
Finally, HUVEC models have been used to assess the pro-angiogenic effect of proteins, such as irisin [108], and drugs, such as Evolocumab [148], metformin [155], and cilostazol [156] which may offer additional therapeutic benefits in CVD.
## 4 Implementation
### Proposed Modulation Scheme
#### 4.3.3 Communication Distance Calculation  
Among the factors for evaluating the performance of an OCC system, communication distance is also a very important factor, in addition to BER and SNR. This is one of the factors that makes the difference between OCC systems and Li-Fi or VLC. By applying a deep-learning method on the receiver side for object tracking and detection, the proposed scheme can communicate and retrieve data from many different transmitters simultaneously. However, the distance calculation needs to be carefully considered to avoid light interference on the camera or excessive noise, causing the communication of the entire system to be affected when communicating with many transmitters simultaneously. To calculate the maximum distance needed for communication from the camera to the LED array, the following factors need to be considered: focal length of the camera, communication distance between the receiver and transmitter sides, and consecutive distance between two LEDs. The relationship between them is shown in Figure 6:  
Figure 6: Relationship between the focal length and communication distance.  
Equation (6) gives the relationship between the communication distance and focal length of the camera:  
\[\frac{d[m]}{D\_LEDs[m]}=\frac{f[mm]}{d\_LEDs[mm]} \tag{6}\]  
whereas f and d stand for the working distance and the focal length of the camera, respectively, D_LEDs represents the actual physical distance between two LEDs in a real-world experiment, and d_LEDs stands for the distance between these two LEDs as captured in the image. With Equation (6), one can determine the shortest path between two LEDs. This minimum distance between two LEDs within two pixels is determined by using RoIs based on the deep learning method and the Nyquist theorem. Equation (7) gives the formula for calculating the minimum distance between two LEDs:  
\[D\_LEDs[m]=2\times\frac{d[mm]}{f[mm]}\times\frac{h\_sensor\_image[m]}{N\_pixel\_image\_row} \tag{7}\]  
where the N_pixel_image indicates how many pixel rows there are in the image and h_sensor_image indicates the height of the sensor image in millimeters. With the working distance dependent on the transmitted light power, the theoretical distance is expressed as d [m], which simplifies the computation. When ambient noise lights have a higher power than the transmitter, interference occurs. Consequently, it becomes feasible to establish the maximum number of users by considering the real-world environment.
## 7 Conclusions  
This work researched the effects of different types of cyber attacks on the control system of a grid-connected DFIG-based wind farm. A multi-agent DDPG AI controller was designed and leveraged to mitigate the adverse effects of cyber attacks on the energy management system of a wind turbine generator. The performance of the proposed DDPG controller was compared with that of a VPI control-based mitigation method. The proposed technique was simulated and validated utilizing the MATLAB/Simulink software. This paper can be concluded as follows:  
* The proposed DDPG method can effectively mitigate the adverse effects of cyber attacks, regardless of which set point is manipulated or whether the PWM signal to the rotor-side converter is blocked.
* The performance of the proposed DDPG method is better than that of the VPI method, as shown in the plots and the RMSE table presented in the results section.
* This research offers a novel approach to mitigating cyber attacks on DERs by offering a low-cost approach to maintaining wind farm stability during a malicious cyber-attack on the converter management system.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Attack Type**} & \multicolumn{2}{c}{**Active Power RMSE**} & \multicolumn{2}{c}{**Reactive Power RMSE**} & \multicolumn{2}{c}{**Rotor Speed RMSE**} & \multicolumn{2}{c}{**DC-Link Voltage RMSE**} \\ \cline{2-13}  & **DoS** & **FDIA** & **PI** & **VPI** & **DDPG** & **PI** & **VPI** & **DDPG** & **PI** & **VPI** & **DDPG** & **PI** & **VPI** & **DDPG** \\ \hline Case Study & 1 & ✓ & - & 6.97 & 7.07 & 0.88 & 1.78 & 1.87 & 1.10 & 0.119 & 0.120 & 0.006 & 2.07 & 1.89 & 2.32 \\ CaseStudy & 2 & - & ✓ & 8.08 & 0.94 & 0.08 & 212 & 1.08 & 1.81 & 0.20 & **0.004** & 0.003 & **14.459** & 26.462 & 57.14 \\ Case Study & 3 & - & ✓ & 18.3 & 0.75 & 0.79 & 29.65 & 0.80 & 0.77 & 0.423 & 0.005 & 0.001 & 582.43 & 2.23 & 2.22 \\ \hline \hline \end{tabular}
\end{table}
Table 2: RMSE comparison table.  
**Author Contributions:** Conceptualization, N.F. and M.H.A.; methodology, N.F.; software, N.F.; validation, N.F. and M.H.A.; formal analysis, N.F.; investigation, N.F.; resources, M.H.A.; data curation, N.F.; writing--original draft preparation, N.F.; writing--review and editing, M.H.A.; visualization, N.F.; supervision, M.H.A.; project administration, M.H.A.; funding acquisition, M.H.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** Data are contained within the article.  
**Acknowledgments:** The authors are pleased to acknowledge the funding support from the Electrical and Computer Engineering department at the University of Memphis, USA to complete this work.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results and Discussion
### Calculation of the Linear-Elastic Strain Energy Density of Bulk Defects
At first, the stress field induced by the defect contour was calculated featuring a relatively coarse mesh in the neighbourhood of the imperfection. Subsequently, a sub-model was defined, which represents the control volume \(\Omega_{i}\) utilized for calculating the linear-elastic SED. In analogy to the definition of the control volume at sharp notches [28], the utilized control volume was centred in a node \(P_{i}\) of the respective defect contour, where the size of the control volume was defined by \(R_{c}\), evaluated in Section 3.1. A minimum of three elements were utilized within the control volume, as suggested in [24; 62]. Compared to stress-based concepts, obtained SED results were not significantly affected by the size of the finite elements. Hence, rather coarse element sizes could be utilized without sacrificing accuracy. After defining the control volume, the sub-model was analysed and the averaged SED \(\Delta\overline{W}_{i}^{(e)}\) was calculated from the numerical results of the elements defining \(\Omega_{i}\). The procedure was then repeated for the next node \(P_{i+1}\) along the defect contour, until the whole perimeter of the planar projection was assessed. The representative fatigue  
Figure 10: Simulation strategy to assess defect-afflicted cast steel specimens. parameter \(\Delta\overline{W}_{97.5\%}^{(e)}\) was defined as the SED value, exceeding 97.5% of all values along the defect contour. Figure 11 presents the determination of \(\Delta\overline{W}_{97.5\%}^{(e)}\) through the example of the defect contour found on the Y-view of specimen #26. The left-hand side depicts the contour plot of \(\Delta\overline{W}_{i}^{(e)}\) along the perimeter of the imperfection, while the diagram on the right-hand side shows the evaluation of \(\Delta\overline{W}_{97.5\%}^{(e)}\) based on the discrete numerical results. It should be emphasized, that \(\Delta\overline{W}_{97.5\%}^{(e)}\) was evaluated utilizing a gross stress of \(\Delta\sigma_{g}=1\,\)MPa. Due to the linear-elastic finite element analyses involved in the determination of \(\Delta\overline{W}_{97.5\%}^{(e)}\), the fatigue strength \(\Delta\sigma_{LLF,Defect}\) of the respective defect-afflicted specimen can be calculated following Equation (7), where \(\Delta\sigma_{0}\) represents the fatigue strength of the plain material at the stress ratio \(R\) to be assessed. \[\Delta\sigma_{LLF,Defect}=\sqrt{\frac{\left(\Delta\sigma_{0}\cdot\Delta\sigma _{g}\right)^{2}}{2E\cdot\Delta\overline{W}_{97.5\%}^{(e)}}} \tag{7}\]
# Matching Consignees/Shippers Recommendation System in Courier Service Using Data Analytics
## 2 Literature Review
### Clustering Technique  
A cluster is a collection of data objects that are similar to each other and dissimilar to objects of other clusters [58]. There are several clustering algorithms among which K-means and Hierarchical Clustering method are the two most prominent clustering algorithms. [PERSON] and [PERSON] [59] compared the strengths and weaknesses of K-means and Hierarchical Clustering Techniques. Their study indicated that K-means delivers better performance and is suitable for large datasets. K-means is very sensitive to the choice of a starting point for partitioning items into K initial clusters and is used to assign each record in the dataset to only one initial cluster [60]. Although the major limitation is the requirement of the number of clusters k as an input, there exists a technique to find the suitable number of groups of datasets called \"k-optimal\" that can yield effective and accurate solutions [61]. Validation studies are conducted using the elbow method, which runs K-means clustering on the dataset for each value of k-optimal. A line chart of the average centroid distance is then plotted for each value of k. If the line chart resembles a knee, then the \"bend\" represents the optimal value of k [62]. The performance of different clustering methods has been compared in several reports [25; 63]. Clustering techniques are widely integrated with RFM concepts for discovering patterns and relationships hidden in datasets. This is not only to develop a product/service to satisfy customers but also to track customer purchasing behaviors and to present distinct products/services for each segment. There are many applications of RFM and clustering techniques in different sectors, including the retail industry [31; 64; 65], banking [28], patient analytics in dental clinics [38], and e-commerce [23]. The literature also shows that several studies have developed modified K-means approaches that can improve efficiency and run time. However, these may lead to complications, and the procedures are complex when applied to large amounts of data or real data sets. Therefore, this study applies the traditional K-means algorithm because of its efficiency in classifying a large number of continuous numerical values of high dimensions.
## 3 System Model and Problem Formulation
### NR-V2X-NOMA Communication Model
If we denote the signal \(i\) with the current maximum received power as follows:  
\[C_{i\to j}^{t}=\frac{h_{s}^{t}h_{i\to j}^{t}p_{i}^{t}}{L_{d}(d_{i\to j})}, \tag{5}\]  
then, other signals with received power lower than signal \(i\) are denoted as follows:  
\[C_{k\to j}^{t}=\frac{h_{s}^{t}h_{k\to j}^{t}p_{k}^{t}}{L_{d}(d_{k\to j})}. \tag{6}\]  
The set of other vehicles whose received signal power is lower than vehicle \(i\) is denoted as \(\mathcal{I}_{i}=\{k\in V_{i}\backslash i\mid C_{k}^{t}<C_{i}^{t}\}\). Then, the expression for the SINR obtained by vehicle \(j\) using NOMA is as follows:  
\[\eta_{i\to j}^{t}=\frac{C_{i\to j}^{t}}{\sum\limits_{k\in\mathcal{I}_{i}}C_{ k\to j}^{t}\sigma_{k,j}^{t}+p_{n}}. \tag{7}\]  
When vehicle j decodes the message with the highest power from vehicle i, the message power of the vehicle in \(\mathcal{I}_{i}\) is used as the interference power, and the magnitude of the interference is affected by the degree of channel overlap \(\sigma\). Therefore, by adjusting the power allocation, the SINR of each message can be increased, thereby increasing the likelihood of successful communication.
# Dynamic Shortest Paths Methods for the Time-Dependent TSP
## 3 A Branch-and-Price Algorithm
### Valid Inequalities
#### 3.4.6 Unitary AFCs  
Unitary admissible flow constraints (AFCs) were introduced in [15] for the SDTSP. In terms of the TDTSP, they can be derived as follows:  
Summing up the flow conservation constraints of (TDTSP-A) over a set \(S\subseteq V^{\mathcal{T}}\) not containing any vertices \(s_{\theta}\) yields the equation \(x(\delta^{-}(S))=x(\delta^{+}(S))\). Based on an incoming arc \((u,v,\theta)\in\delta^{-}(S)\), it can be relaxed to \(x_{uv,\theta}\leq x(\delta^{+}(S))\). This constraint by itself is obviously redundant. However, any tour \(T\) that enters \(S\) via \((u,v,\theta)\) has to leave \(S\) using some arc \((u^{\prime},v^{\prime},\theta^{\prime})\in\delta^{+}(S)\) such that \(v^{\prime}\
eq u,v\), yielding the unitary AFC inequality  
\[x_{uv,\theta}\leq\sum_{\begin{subarray}{c}(u^{\prime},v^{\prime},\theta^{ \prime})\in\delta^{+}(S)\\ v^{\prime}\
eq u,v\end{subarray}}x_{u^{\prime}v^{\prime},\theta^{\prime}}.\]To separate these inequalities, we first observe that stronger unitary AFC inequalities correspond to smaller cuts, that is, a set \(S^{\prime}\) with \((u,v,\theta)\in\delta^{-}(S^{\prime})\) and \(\delta^{+}(S^{\prime})\subseteq\delta^{+}(S)\) produces a stronger inequality than \(S\). As a result, any vertex in \(S\) except for \(v_{\theta+c_{uv}(\theta)}\) can be removed, provided that it does not contain any arc entering from another vertex in \(S\). We can therefore assume that \(S\) only contains vertices reachable from \(v_{\theta+c_{uv}(\theta)}\). Similarly, we can also assume that \(S\) contains no copies of \(u\) and \(v\), other than \(v_{\theta+c_{uv}(\theta)}\) itself. Hence, the separation of unitary AFC inequalities can be conducted by solving a min-cut problem for each arc \((u,v,\theta)\) with \(x^{*}_{uv,\theta}>0\), with capacities based on the values of \(x^{*}\).
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 2 Circuit Structure and Operation Analysis
### Description of Operating Range
#### 2.3.3 Stage III [\(t_{3}<t\leq t_{4}\)]  
As shown in Figure 6c, \(Q_{1}\) and \(Q_{4}\) turn on at \(t=t_{3}\) and achieve ZVS while Q\({}_{2}\) and Q\({}_{3}\) remain at the cut-off state. In this stage, \(V_{DC\_Grid}\) passes the energy through \(C_{\prime 1}\), \(L_{\prime 1}\), \(C_{\prime 2}\), and \(L_{\prime 2}\), and it delivers the energy to the battery side via the isolated transformer. After being rectified by intrinsic diodes \(D_{B5}\) and \(D_{B8}\), the energy charges the battery, whereas \(L_{m}\) is absent from resonating due to the magnetizing inductor \(L_{m}\) being clamped by the output voltage \(V_{Bat}\) and the secondary resonant capacitor voltage  
Figure 6: The conduction path of each stage in Region II: (**a**) Stage I conduction path; (**b**) Stage II conduction path; (**c**) Stage III conduction path; (**d**) Stage IV conduction path; (**e**) Stage V conduction path.  
\(V_{C72}\), and thus \(i_{Lm}\) keeps on rising linearly. At \(t=t_{4}\), \(i_{L71}\) equals \(i_{Lm}\), and this stage terminates. In the meantime, current \(i_{DBS,8}\) falls to 0, and thus ZVS is achieved.
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 4 Results
### Comparative Analysis of SHSnet  
In this section, the performances of various deep learning models are compared. For this comparison, the network was selected based on different architecture or encoder characteristics to cover a wide range of cases. For the VGG-based model, Typical-RC-AI was selected, as this has been shown to work for a wide range of cases. For the Unet-based model, CrackUnet (7...15), which was specifically designed for crack segmentation, was selected. From DeepLabV3+ architecture, Sarmiento's Model and Song's Model were selected. From the SegNet model, CrackSegnet was selected. The standard models FCN and SHSnet were compared on a crack dataset collected from laboratory tests. Note that the number with CrackUnet denotes the depth of the encoder. Their performances are reported in Table 3.  
It was observed that fully convolutional neural networks such as FCN produce one of the lowest scores. Models based on DeeplabV3+ use ASPP units to improve segmentation, leading to higher performance. As compared to them, CrackSegnet and CrackUnet have better performance; this can be attributed to attention modules and other specific changes in the network design. In comparison to these models, SHSnet has superior performance. This could be due to the large RF + BN, which allows a superior understanding of the context and leads to higher accuracy, especially when the input size is small.  
It was observed among these deep learning models that the proposed network has at least 10x fewer parameters than the second-best-performing models; thus, it is more efficient. The results also highlight that, probably due to the appropriate design of SHSnet to deal with the additional complexity of the cracks along with the high imbalance in the  
Figure 6: Effect of loss function on the segmentation results: (**a**) original photograph; (**b**) SHSnet; (**c**) SHSnet with cross-entropy (CE) loss. Note: cracks are overlaid on the original picture for better visualization. Note: the window in white is zoomed for better visualization.  
dataset, SHSnet has shown to perform superior in segmenting SHCC cracks. A comparative example is also shown in Figure 7. This result also reinforces this behavior.
###### Abstract  
Drought, an intricate natural phenomenon globally, significantly influences the gross primary productivity (GPP) and carbon sink potential of tropical forests. Present research on the drought response primarily focuses on natural forests, such as the Amazon rainforest, with relatively limited studies on tropical plantations. Therefore, for a comprehensive understanding of global climate change, accurately evaluating and analyzing the sensitivity and vulnerability of rubber plantation GPP to various drought characteristics is crucial. The Standardized Precipitation Evapor-transpiration Index (SPEI) was used in this research to quantify drought intensity. The Spatially Explicit Individual Based Dynamic Global Vegetation Model (SEIB-DGVM) was localized based on observation data from the Hainan Danzhou Tropical Agro-ecosystem National Observation and Research Station. Subsequently, the calibrated model was utilized to simulate the dynamic process of rubber plantation GPP under multi-gradient drought scenarios (2 extreme boundaries \(\times\) 3 drought initiation seasons \(\times\) 4 drought intensities \(\times\) 12 drought durations \(\times\) 12 SPEI time scales). The results show that the sensitivity and vulnerability of rubber plantation GPP exhibit significant differences under drought scenarios in different initiation seasons; GPP exhibits higher sensitivity to extreme, long-duration flash droughs in the early rainy season. Regarding vulnerability, the impact of extreme, long-duration flash droughs on GPP is most pronounced. This research lays the foundation for estimating the impact of droughs on the GPP of rubber plantations under future climate change scenarios, providing a scientific basis for enhancing regional ecological restoration and protection.  
SPEI; SEIB-DGVM; multi-gradient drought scenarios; Hainan Island