esa-sceva/satcom-chunk-collection huggingface dataset:
# New Possibilities for Constructing Heuristic Solutions to Problems of Electromagnetic Diffraction
## 1 Introduction
### Known Heuristic Approaches  
The following heuristic approaches are known: the method of geometrical optics (GO) [5], the method of physical optics (PO) [5], the geometrical theory of diffraction (GTD) [6; 7; 8] and the method of edge waves (MEW) [9; 10; 11]. The term physical theory of diffraction (PTD) can be used for the MEW and, in an extended interpretation, for all heuristic methods.  
In heuristic approaches, a balance should be obtained between complexity and accuracy. Rigorous analytical decisions are accurate, but often very complex. The PO approximation can be obtained for any object, but the accuracy may not be enough. However, sometimes, the accuracy of the PO approximation is quite sufficient, as in the case of calculating the amplitude of the main beam and the first side lobe of a reflector antenna.  
For relatively large objects, strict calculation is impossible due to limited computer resources. While calculating the scattering of signals on conventional radar targets, it used to be enough to take into account only the contribution of specular points; now, in problems of diffraction on targets with a reduced radar signature, they mainly use the MEW. The same applies to the calculation of propagation in an urban environment. The success of this or that approximation depends on the accuracy required in a particular practical problem.  
Heuristic solutions are based on hypotheses, or \"postulates\" [8]. For example, the GTD postulate says that when scattered by an object of finite dimensions, the field of a finite size edge at the observation point is considered to be the same as if the incident ray field was reflected from an infinite edge. On the infinite edge, you can always find the point of the stationary phase at which the reflection occurs. At the edge of finite dimensions, the stationary phase point does not always exist; therefore, using the GTD, no solution at all can be obtained. This circumstance was the reason for the choice of the MEW (instead of the GTD) for calculating of low radar signature objects made using the \"Stealth\" technology [10].  
The MEW postulate is that for a scatterer of finite dimensions, the field at the finite size edge is considered the same as at the edge of a semi-infinite scatterer with an edge of the same profile. In contrast to the GTD, a solution for the MEW (as well as for the PO method) can always be obtained (although sometimes with an inaccurate amplitude). The amplitude inaccuracies are caused by the fact that the field at the edge of finite dimensions is considered constant and the additional disturbance at the ends of the edge is neglected. In order to take this perturbation into account, it is necessary to investigate the field on a plane angular sector, for example, by numerical methods [12].
###### Abstract  
A new analytical model to analyze and optimize the electrical characteristics of 4H-SiC trench-gate metal-oxide-semiconductor field-effect transistors (TMOSFETs) with a grounded bottom protection p-well (BPW) was proposed. The optimal BPW doping concentration (N\({}_{\text{BPW}}\)) was extracted by analytical modeling and a numerical technology computer-aided design (TCAD) simulation, in order to analyze the breakdown mechanisms for SiC TMOSFETs using BPW, while considering the electric field distribution at the edge of the trench gate. Our results showed that the optimal NBPW obtained by analytical modeling was almost identical to the simulation results. In addition, the reverse transfer capacitance (C\({}_{\text{gd}}\)) values obtained from the analytical model correspond with the results of the TCAD simulation by approximately 86%; therefore, this model can predict the switching characteristics of the effect BPW regions.  
power semiconductor; silicon carbide; trench MOSFETs; bottom protection p-well; device simulation; analytical modeling +
Footnote †: journal: Applied Physics Letters  
1
# Star Type Wireless Sensor Network for Future Distributed Structural Health Monitoring Applications
## 2 Materials and Methods
### Networking Protocol and Hardware Selection
#### 2.1.1 Wireless Networking Protocol  
Wi-Fi, Zigbee--an IEEE 802.15.4 based specification, Bluetooth, and Bluetooth low energy (BLE) were considered as potential networking protocols for the system. The comparison can be seen in Table 1. Durham Cathedral has ten bells, and since one sensor is required for each bell, a networking protocol that can support at least ten nodes is required. The requirement on the data rate is based on a network with one computer to collect the data and 254 sensor nodes where each node sends five 32 bit floats at a rate of 100 Hz. The distance requirement is based on the size of the bell frame. The distances in Table 1 are specified for an open environment, since the devices would be operating in an enclosed environment their range would be reduced significantly [15]. From Table 1, it is clear that Wi-Fi is the only protocol that meets the requirements for this application.
## 3 Results
### Confusion Matrices  
Some of the obtained confusion matrices are shown in Figure 9A-H, where high, medium, and low are the intensities of the body imbalance. The confusion matrix evaluates the performance of a classifier and represents it in the form of a summary by comparing predicted and true labels. In case of body imbalance intensity classification into high, medium, and low, confusion matrices obtained were of multi-class classification. Therefore, all the diagonal values are true positives for the corresponding class. The false positives in the confusion matrices are those that are predicted positive but belong to any of the remaining two classes, whereas the false negatives are those that are predicted negative incorrectly and should have been predicted positive, as shown in Figure 9A-H.
## 6 Conclusions  
The dynamic model of an ISP servo system including a linear model and a nonlinear model is established. In the modeling of nonlinear factors, based on the strong nonlinear fitting ability of an artificial neural network, the data model of friction and backlash is established. Based on the order spectrum analysis of transmission error and the principle of main order invariance, the harmonic superposition model of transmission error is established. With the addition of a nonlinear model, the accuracy of the dynamic model of an ISP servo system is greatly improved.  
On the basis of the established ISP servo system dynamic model and performance indicators, the quantitative relationship between the three internal disturbance factors (such as friction torque, transmission error, and backlash), the three external disturbance factors (such as road disturbance torque, equivalent inertia, and mass unbalanced torque), and the servo performance of the system is obtained by simulation. It is concluded that friction torque and road disturbance torque are the most influential internal and external factors on servo performance, respectively. The above conclusions are verified by experiments. For every 25% reduction in friction torque, the servo performance is improved by 17%. For every 25% increase in road disturbance torque, the servo performance of the system is reduced by 12%. The mapping relationship between the internal and external disturbance factors and the servo performance of the system provides us with clear measures to improve the servo performance, and provides a reference for the ISP precision transmission component selection, servo performance indicator distribution, and servo performance prediction.  
Conceptualization, B.L. and D.F; Formal analysis, Y.L.; Funding acquisition, X.X. and D.F.; Investigation, B.L. and X.X.; Methodology, X.X. and D.F.; Project administration, D.F.; Resources, X.X.; Software, B.L. and Y.L.; Validation, B.L. and Y.L.; Visualization, B.L.; Writing--original draft, B.L.; Writing--review and editing, X.X. and D.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 52305079 and Grant No. U19A2072.
# An Inverter Topology for Wireless Power Transfer System with Multiple Transmitter Coils
## 2 System Description and Operation
### Mode of Operation  
The proposed system is operated in two modes, namely normal mode and detection mode. Both modes are dependent on the operation (on and off) of detection switches. The timing diagram and theoretical waveforms are given to describe the switching operations of both modes. The upper and lower switches (\(\mathrm{S_{U}}\) and \(\mathrm{S_{L}}\)) work in the complementary manner. The angle \(\alpha\) is defined as the adjusting angle of an inverter voltage, which can be varied from 0 to 180 degrees by adjusting the duty cycle of the upper switch's gate signal (\(\mathrm{V_{GS,SU}}\)). For simplicity, all the power switches are assumed to be ideal, and the dead time between switch \(\mathrm{S_{U}}\) and \(\mathrm{S_{L}}\) is neglected.
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## 4 Simulations and Results
### Performance with Different Network Sizes  
The performance of UWALOHA, UW-CSMA/CA, SFAMA, and ROPA, and that of the proposed method under different network sizes are shown in Figure 4. The arrival rates of the Poisson distribution were set to 100 in all simulations.  
Figure 4a shows that UWALOHA achieves the highest throughput when the number of nodes is less than 8 but drops quickly as the network size increases. UW-CSMA/CA, SFAMA, and ROPA reach their maximum throughput of 0.30, 0.40, and 0.53, respectively, at the network size of 20. The throughputs of DS and DS without SO become saturated after the network size reaches 26. They achieved the maximum throughputs of 0.70 and 0.65, respectively, which were the highest in the  
Figure 4: Performance of UWALOHA, UW-CSMA/CA, SFAMA, and ROPA protocols and that of the proposed method under different network sizes. (**a**) Normalized throughput; (**b**) Packet delivery rate; (**c**) Average end-to-end delay; (**d**) Average energy consumption.  
simulation, because the proposed method provides more concurrent transmission opportunity so that more nodes can transmit their data in one transmission cycle. DS also outperformed DS without SO in the simulation results.  
In Figure 4b, the delivery rates of all methods decrease as the network size increases because the network load increases with the network size. UWALOHA had the lowest delivery rate and tended to be zero when the network size was larger than 20. The delivery rates of UW-CSMA/CA, SFAMA and ROPA were higher than that of UWALOHA. DS results in the highest delivery rate in the simulation.  
Figure 4c shows the average end-to-end delays of the four conventional protocols and that of the proposed method. The average delays of all methods drop as the network size increases because larger networks introduce more collisions. UWALOHA works better than the others when the network size is below four and better than UW-CSMA/CA and SFAMA when the network size is below eight. This occurs because no overhead is needed in UWALOHA. However, when the network size becomes large, its average delay increases significantly due to more collisions and retransmissions. UW-CSMA/CA and SFAMA are worse than ROPA and the proposed method because of their serial transmission modes. The proposed method outperforms ROPA in different network sizes.  
Figure 4d shows the average energy consumption of the four conventional protocols and that of the proposed method. UWALOHA has the highest average energy consumption, especially when the network size becomes large. The handshaking can effectively reduce collision so that average energy consumptions of the handshaking based protocols are much smaller than UWALOHA in large-scale networks. Among the four handshaking-based protocols, DS resulted in the lowest energy consumption in almost all the network sizes.
# Enriching Knowledge Base by Parse Tree Pattern and Semantic Filter
## 4 Pattern Generation
### Semantic Similarity as a Semantic Filter
#### 4.2.1 WordNet-Based Similarity  
Assume that a relation \(r\) is expressed with \(m\) words of \(\mathbf{r}=\{rw_{1},\,\ldots,\,rw_{m}\}\) and the words in the pattern \(p\) except entity words are \(\mathbf{p}=\{pw_{1},\,\ldots,\,pw_{n}\}\). Then, the WordNet-based similarity \(sim_{WN}(p,r)\) can be computed as an average similarity of all possible combination word pairs of \(\mathbf{p}\) and \(\mathbf{r}\). That is,  
\[sim_{WN}(p,r)=\frac{1}{n\cdot m}\sum_{rw\in\mathbf{r}}\sum_{pw\in\mathbf{p}} sim_{wn}(pw,rw),\]  
where \(sim_{wn}(pw,rw)\) is a word similarity between two words \(pw\) and \(rw\) by WordNet.  
Among various WordNet-based word similarities, Jiang-Conrath similarity [35] is reported to be best which is defined as  
\[sim_{jk}(pw,rw)= \frac{1}{2\cdot\log P(lcs(pw,rw))-(\log P(pw)+\log P(rw))},\]  
Figure 3: A parse tree of a sentence _“[PERSON] works for [PERSON]’s company, SCINet.”_ and the derived pattern candidate from the parse tree.  
where \(lcs(pw,rw)\) is the lowest-common-subsumer of \(pw\) and \(rw\) on WordNet, and \(P(w)\) is the information content of \(w\) computed with an independent corpus. Since \(sim_{jc}\) is not bounded, we use a bounded Jiang-Conrath similarity as WordNet-based similarity \(sim_{tun}\), where the bounded Jiang-Conrath similarity is given as  
\[sim_{tun}(pw,rw)=\frac{sim_{jc}(pw,rw)}{T}.\]  
Here, \(T\) is the maximum value of Jiang-Conrath similarity.
## 3 Anti-Interference Power Control Based on Reinforcement Learning
By transferring knowledge from similar environments, the algorithm reduces the randomness of early exploration and accelerates learning convergence. In particular, \(\xi\) experiments of anti-jamming UACN transmission in similar scenarios are considered before the learning process of transmitter strategy optimization begins. Each experiment lasts for time \(k\), during which the transmitter observes the current state, including the bit error rate, the signal-to-dry-noise ratio, and other anti-jamming transmission performances of the underwater acoustic communication network, and relays information. Then, the transmitter mix strategy is selected according to the greedy strategy. The preparation phase of the HPTR algorithm is shown in Algorithm 1. ```
Initialize \(\alpha\), \(\beta\), \(\delta\), \(\xi\), \(s^{(0)}\), \(A\), \(Q=0\), \(V=0\), \(\pi=\dfrac{1}{A}\) For\(n=1,2,\cdots,\xi\)do for\(k=1,2,\cdots,K\)do  By choice \(a^{(k)}=\left[x^{(k)},p_{s}^{(k)}\right]\in A\) if\(x^{(k)}=1\)then  The transmitter triggers the relay with power \(p_{s}^{(k)}\) to forward the information else  The transmitter forwards the information directly to the receiver at power \(p_{s}^{(k)}\) endif  Measure the signal-to-noise ratio of current information \(SINR_{S,T}^{(k)}\), \(SINR_{S,R}^{(k)}\), \(SINR_{R,T}^{(k)}\),  Acquired utility function \(U_{S}^{(k)}\)  According to calculation \(Q^{*}\left(s^{(k)},a^{(k)}\right)\)  According to calculation \(\pi^{*}\left(s^{(k)},a^{(k)}\right)\) endfor endfor  Output \(Q^{*}\) and \(\pi^{*}\)
```  
**Algorithm 1**HPTR preparation phase  
By utilizing the initial Q-values and the mixed policy table generated by Algorithm 1, Algorithm 2 is initialized with these values to facilitate learning. In Algorithm 2, the transmitter observes the current state, which includes the _SINR_ of the transmitter-receiver link and the bit error rate (BER) received at the receiver from the previous time slot. Based on the observed state, the transmitter selects an action according to predefined decision rules and evaluates the utility of the chosen action. During each time slot, the Q-function and the mixed strategy table are updated using a formula for updating. Through this iterative and interactive process, the transmitter gradually learns an optimal anti-jamming strategy within the dynamic game framework of anti-jamming transmission. As a result, the proposed approach effectively enhances the anti-jamming performance of the transmission system. ```
Connect algorithm 1  Initialize \(\delta\alpha\), \(\beta\), \(s^{(0)}\), \(A\) \(Q=Q^{*}\), \(V=0\), \(\pi=\pi^{*}\) for\(k=1,2,\cdots,K\)do  By choice \(a^{(k)}=\left[x^{(k)},p_{s}^{(k)}\right]\in A\) if\(x^{(k)}=1\) then  The transmitter triggers the relay with power \(p_{s}^{(k)}\) to forward the information
```  
**Algorithm 2**HPT algorithm
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 5 Ablation Study  
To validate the effectiveness of the improvements to Mask R-CNN, a series of ablation experiments were conducted using the same dataset. The experiments aimed to isolate and assess the impact of each major architectural component. Specifically, we began with the original Mask R-CNN using ResNet50 as the backbone. Next, we replaced the backbone's Bottleneck component with the EPCB to evaluate its impact on edge feature extraction. We then substituted the backbone with EP-Net to assess overall performance gains. Finally, we merged the multi-branch structure of EP-Net into a single-branch structure to test inference speedup. The results are summarized in Table 4.  
Table 4 presents the results of ablation experiments evaluating the effectiveness of the improvements made to the Mask R-CNN model. The results show the impact of EPCB, EP-Net, and the single-branch structure on detection, segmentation, and FPS metrics. Replacing the convolutional module in the ResNet50 backbone with EPCB enhances edge feature extraction, increasing mAP-bbox from 0.592 to 0.774 and mAP-segm from 0.313 to 0.787. Subsequently, the addition of the EP-Net backbone further improves performance, particularly in hole defect detection, with AP-bbox-50-hole rising from 0.397 to 0.98 and AP-segm-50-hole from 0.397 to 0.913. These results demonstrate EP-Net's ability to extract more discriminative features. Finally, to improve inference speed, the multi-branch structure of EP-Net was consolidated into an equivalent single-branch design. This optimization led to a significant increase in inference speed, with FPS rising from 50.96 to 78.85 while maintaining high detection accuracy (mAP-bbox = 0.986) and segmentation performance (mAP-segm = 0.979). These findings demonstrate that the proposed architecture not only enhances performance but also maintains efficiency, making it suitable for real-time industrial CT defect detection.  
Figure 14 compares the test results of the final improved Defect R-CNN and the baseline Mask R-CNN model on three defect types. Due to the small size of the defects, zoomed-in views are provided to better visualize both the detection accuracy and confidence scores. While both models can locate defects, Mask R-CNN shows significantly lower confidence levels than Defect R-CNN. For hole defects, Mask R-CNN outputs confidence scores of only 34.6% and 55.9%, whereas Defect R-CNN achieves 100% and 96.7%. Similarly, for loose and side defects, Mask R-CNN yields confidence scores of 32.4% and 36.8%, much lower than the 100% and 99% confidence of Defect R-CNN. These results not only reinforce the superior feature extraction and robustness of the proposed Defect R-CNN but also visually validate the improvements observed in the quantitative metrics. The significant boost in detection confidence highlights the effectiveness of incorporating EPCB and the EP-Net backbone in enhancing both accuracy and model reliability.
# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 2 Fundamentals
### Goal-Oriented Requirements Analysis Modeling Language iStar 2.0  
Goal-oriented requirements engineering (GORE) is a systematic and formalized approach to software requirements engineering that centers around the identification, analysis, specification, and management of stakeholder goals and objectives. GORE methods have been prominent in software engineering, finding applications in diverse domains such as requirements engineering, organizational modeling, adaptive system modeling, and software architecture representation,.  
In GORE, the main focus is on understanding the desired outcomes, needs, and intentions of stakeholders and translating these high-level goals into well-defined concrete requirements for a software system [20]. This process involves specialized modeling languages and techniques to represent and analyze dependencies, conflicts, and trade-offs among different goals, as well as the means and constraints to achieve them. GORE ensures that software systems align effectively with stakeholders' objectives while providing a structured and traceable foundation for the entire software development lifecycle.  
The i* framework is primarily a requirements modeling framework that is used for both REs and RAs in GORE. In the analysis phase of IoT requirements, the i* framework serves as an RA framework, specifically within GORE. It helps model and analyze stakeholder goals, dependencies, and constraints, making it highly relevant for structuring IoT requirements analysis. The i* framework provides a means to represent and analyze relationships (represented as (->), dependencies (represented as ->), and constraints between various stakeholders, their goals, tasks, and the resources needed to achieve those goals.  
A refined version of i*, known as i* 2.0 (iStar 2.0), enhances the foundational principles of goal-oriented methodologies. The core language iStar 2.0 refines the fundamental concepts of i*, establishing a cohesive and transparent set of core principles. This foundation serves as a platform for advancing future developments and creating educational materials focused on goal-oriented methodologies.  
The formal delineation of iStar 2.0 encompasses the following key components:  
* Intentional Elements: Goals (ellipse ), soft goals (eight-shaped ), tasks (hexagon ), and resources (rectangle ) that represent the intentions of stakeholders.
* Actors (circle ): Entities (e.g., individuals, organizations, or systems) that pursue goals and perform tasks.
* Actor Links: Relationships between actors, such as dependencies or collaborations.
* Intentional Elements Links: Connections that capture goal refinement, task decomposition, or means-end relationships.
* Model Views: Different perspectives facilitate stakeholder analysis and decision making, such as the Strategic Dependency (SD) view (->) and the Strategic Rationale (SR) view (depicted as a dashed circle ).