esa-sceva/satcom-chunk-collection huggingface dataset:
# Constructing More Complete Control Flow Graphs Utilizing Directed Gray-Box Fuzzing
## 6 Evaluation
### Application: Security Analysis (RQ4)  
In this paper, we focus on how to construct more complete CFGs, which is a prerequisite for many program analyses. There are many applications that may benefit from our work, such as program verification, vulnerability detection, and code similarity analysis. At present, except vulnerability detection, we have not sufficiently evaluated our approach in specific applications, as it will take a lot of effort and knowledge to evaluate various  
Figure 11: The reachable code scale of CFGs constructed by static-based, CFG-based, and DGF-based approaches.  
applications. Therefore, in this section, we take the vulnerability detection as an example to illustrate the practical significance of our work.  
Figure 12 shows a real vulnerability (CVE-2015-5221) [38] hidden in the image processing library JasPer. The vulnerability is located deep in the program and may be triggered only when the specific call sequence is executed, i.e., \(main\to jas\_image\_decode\to mif\_decode\to mif\_hdr\_get\to mif\_process\_cmpt\).  
Unfortunately, in the call sequence \(jas\_image\_decode\to mif\_decode\) is an indirect jumps. If analysts conduct the security analysis based on the CFGs constructed by the traditional static tool, they may not be able to resolve these indirect jumps and miss the vulnerability. On the contrary, our approach can construct more complete CFGs containing this indirect jump relation and help analysts discover this vulnerability.  
Although DGF-CFGConstructor is still a prototype, it has been able to analyze the real-world programs and been applied to vulnerability detection. In the future, we will improve the prototype and make it suitable for more usage scenarios.
# Self-Optimizing Traffic Steering for 5G mmWave Heterogeneous Networks
## 3 Self-Optimizing Traffic Steering
### Use Cases
#### 3.3.3 Energy Efficiency Improvement  
Since mmWave APs are densely deployed in the HetNet, a large amount of energy is consumed. Therefore, flexible and agile mmWave APs' sleeping management by the self-optimizing traffic steering strategy should be designed for energy saving. For example, lightly loaded mmWave APs can be switched off and the associated users can be steered to nearby APs according to their specific needs. For practical implementation, incentive schemes need to be designed to compensate the performance degradation of the steeredusers, whereby a certain balance can be achieved between mmWave APs and users. Such problems can be modeled as multi-player games and thus game theory tools can be applied to seek the optimized and equilibrated solutions among players. Meanwhile, machine learning, such as deep learning and statistical learning techniques, can also be utilized to solve the problem.
# A Power Based Analysis for a Transonic Transport Aircraft Configuration through 3D RANS Simulations
## 4 Conclusions and Recommendations  
A power-based analysis was performed to study the flow field over a transonic transport aircraft through 3D compressible Reynolds-averaged Navier-Stokes simulations. The error of mechanical power imbalance in the segregated computational domain was examined. The power conversion process of the wing body configuration and the boundary layer ingestion configuration were studied and presented.  
Power-based analysis was capable of illustrating the process of power conversion in the 3D flow field, clearly visualizing the change in power associated with the aircraft components. For the wing body configuration, the input power, \(\text{DV}_{\infty}\), was converted into viscous dissipation and wake power at the outlet plane. Viscous dissipation mainly occurred in the boundary layer over the aircraft surface, taking up 31% of the total power input. The mechanical power of the aircraft wake took about 35% of the total power input at the outlet. For the BLI configuration, the input power, \(\text{P}_{\text{s}}\), provided by the actuator disc model was converted into viscous dissipation and wake power at the outlet plane as well as net force power. The viscous dissipation of the boundary layer over the aircraft surface was about 28% of the total power input. The wake power of the downstream flow took about 22% of the total power input at the outlet plane. About 10% of the total power input was in the form of net force power that accelerated the aircraft.  
Figure 17: Subdomain error study of BLI configuration.  
The simulation results showed that the power imbalance error of the entire computational domain was 34.1% for the DLR-F6 wing body configuration, even if the difference in the drag coefficient was less than 3% compared to the experimental results. This study shows that the convergence in aircraft drag does not necessarily lead to a small power imbalance error. The error in 3D simulation is much higher than that in 2D simulations. The high power imbalance error is mainly associated with the wing.  
Efforts were made in this work to reduce the power imbalance error. High-order discretization methods and an increasing mesh density in the outer region of the wing boundary layer and in the vicinity of the trailing edge were beneficial for reducing the error. However, the high value of error limits the precision of power-based analysis applied to 3D RANS simulations. Attempts to increase the mesh size to a higher order of magnitude were kept for future work due to limited computational resources in this study. Moreover, alternative simulation methods could be considered for power-based analysis in future work.  
Conceptualization, writing--original draft preparation, P.L.; Resources, writing--review and D.L.; writing--review and editing, L.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# A Convolutional Neural Network as a Potential Tool for Camouflage Assessment
## 1 Introduction
### Computational Camouflage Metrics  
Image-based camouflage effectiveness assessment algorithms compute object saliency using digital object-background contrast metrics [1; 17; 18]. These metrics frequently have little or no relation to human visual perception. Furthermore, to compute overall object saliency, an object's position or area should be known in advance. In addition, there is no unique definition of object saliency (e.g., one can arbitrarily adopt the average or maximum saliency over the object support area [1]).  
Another approach to the image-based assessment of camouflage effectiveness is visual saliency models [1]. These models typically use human vision models to compute local contrast maps (local saliency) from multiple multiresolution feature maps. Similar to simple contrast metrics, the overall object saliency metric is ill-defined, while the object position and outlines are required for its computation [1].  
A promising avenue is presented by artificial neural networks. Even though neural networks have been designed with engineering goals and not to model brain computations, they show a large resemblance to the primate visual hierarchy [19]. The fact that these models can perform many visual tasks up to a level similar to that of humans shows that the models do resemble human visual processing to some extent. However, it is also clear that such models can be deceived in ways humans are unaffected by [20]. Also, models can pick up coincidental correlations with no general validity [20; 21]. The question, therefore, remains to what extent these models resemble human visual processing (and in what way they deviate from it), and whether they can be used to model human performance. [PERSON] et al. [22] have used a generative adversarial network framework to evolve camouflage pattern design for tree bark textures with patterns that show improved performance with an evolution cycle, as validated by humans and the model evaluator, indicating that their evaluator resembles the human perception of camouflage. [PERSON] et al. [23] used deep learning techniques together with genetic algorithms to design and evaluate camouflage patterns of (virtual) spheres against a more complex (less texture-like) background (plants, occlusion, etc.) and showed that their algorithm was capable of generating patterns well suited for the environment and predicting human search performance of the best and worst pattern types.  
Here, we investigate the resemblance of the performance of a pre-trained algorithm (YOLO: You Only Look Once [24]) with the human perception of camouflaged soldiers/persons. This type of algorithm is widely used (e.g., in video surveillance) and shown to perform well for detecting persons in urban scenes. It remains to be tested as to whether it can cope with a more military scenario, i.e., camouflaged persons in both urban and woodland settings.
## 4 Numerical Calculation Results and Analysis
### Discussion of Seismic Time-History Response Results
#### 4.3.1 Liquid Sloshing  
Under the excitation of seismic waves, the liquid surface near the tank wall has a long period of large vertical sloshing. According to Table 3, under the excitation of three selected seismic waves, the liquid sloshing shows great difference. Under the El Centro ground motion, the maximum vertical sloshing height on the liquid surface is 1.078 m when the horizontal X + Y excitation is considered, which is 2.09 times higher than the X excitation; however, it does not reach the contact edge between the tank dome and tank wall. The results obtained by the El Centro ground motion illustrate that the vertical vibration response of the liquid is mainly affected by the horizontal seismic wave excitation, whereas the vertical seismic wave excitation has only a slight effect on the surface liquid sloshing. This is due to the significant difference between the predominant frequency of the vertical seismic wave and liquid sloshing frequency of 8.43 Hz and 0.228 Hz, respectively. Under the other two ground motions, the influence of the direction of the seismic wave on the sloshing of the liquid is not obvious.
# Lem-Detector: An Efficient Detector for Photovoltaic Panel Defect Detection
## 2 Related Work
### Feature Fusion Methods  
Deep learning approaches for photovoltaic panel defect detection face challenges stemming from the wide variation in defect scales. Single-layer features often lack sufficient multi-scale information, hindering accurate detection of defects across different sizes. Consequently, enhancing model robustness to scale variations and accurately detecting multi-scale defects is crucial.  
Feature Pyramid Networks (FPNs) [21] have gained widespread adoption in object detection networks due to their multi-layer feature fusion mechanism, which improves multi-scale representation capabilities [22; 23]. However, the reduction in feature channels and layer-by-layer transmission methods within FPNs can lead to information loss, particularly affecting the detection of small targets and limiting accuracy for multi-scale object detection [24].  
Various approaches have been proposed to address these challenges. [PERSON] et al. [25] combined CNNs with a feature pyramid structure in a Faster R-CNN-based solar cell defect detection method, enhancing robustness to defect scales by capturing and fusing multi-scale feature information. [PERSON] et al. [26] integrated attention mechanisms with bidirectional feature pyramids to improve detection accuracy and model robustness. [PERSON] et al. [27] focused on local and edge information during feature fusion, improving accuracy in medical image segmentation. [PERSON] et al. [28] proposed a dual pyramid network that further integrates multi-scale features, allowing different feature layers to share similar semantic characteristics, enabling precise localization and classification. [PERSON] et al. [10] focused on fusing higher-level features with richer semantic concepts.  
RT-DETR, specifically, utilizes an Attention-based Intra-scale Feature Interaction (AIFI) module for intra-scale interactions in high-level features, reducing computational redundancy. Feature fusion is achieved through a Cross-scale Feature Fusion (CCFF). While this approach reduces computational complexity and memory usage, it can result in some information loss. This is particularly detrimental for photovoltaic panel defect detection, as these images often contain many small-scale defects that occupy few pixels and carry limited information. This fusion method leads to poor feature representation for small-scale defects, negatively impacting model accuracy.  
To address this limitation, we propose a multi-scale multi-feature fusion approach during the feature fusion process. This approach maximizes the retention of small-scale information, effectively enhancing the multi-scale detection performance of photovoltaic panels.
# Enhanced Efficient EMT-Type Model of the MMCs Based on Arm Equivalence
## 2 General Structure, Control Scheme, and Previous Models of the MMC  
### General Structure  
As illustrated in Figure 1a, the three-phase MMC consists of six arms, and each arm is composed of numerous half-bridge submodules (HBSMs) and an inductor (\(L_{0}\)) in series [20]. \(N\) is the number of HBSMs per arm. The arm voltage and the arm current are \(v_{\eta j}\) and \(i_{\eta j}\), respectively. Here, the subscripts \(r\) (\(r=p\), \(n\)) denote the upper and lower arms; \(j\) (\(j=a\), \(b\), \(c\)) denote one of the three phases. \(v_{\rm{sj}}\) (\(v_{\rm{sa}}\), \(v_{\rm{sb}}\), and \(v_{\rm{sc}}\)) and \(i_{\rm{vj}}\) (\(i_{\rm{va}}\), \(i_{\rm{vb}}\), and \(i_{\rm{vc}}\)) are the AC voltage and current in phase \(j\) at the AC side of the MMC, respectively; \(L_{\rm{ac}}\) is the equivalent reactance of the AC system.  
Each HBSM contains two IGBTs, two anti-parallel diodes and a capacitor (\(C_{0}\)). The firing signals of the two IGBTs are \(T_{1}\) and \(T_{2}\), respectively. \(v_{\rm{C},\  
angle detected by DDSRF-PLL; \(v^{+}_{\text{sdq}}\left(v^{+}_{\text{sdq}}=\left[\begin{array}{cc}v_{\text{sd}}&v_{ \text{vq}}\end{array}\right]^{\text{T}}\right)\) and \(i^{+}_{\text{vdq}}\left(i^{+}_{\text{vdq}}=\left[\begin{array}{cc}i_{\text{vd} }&i_{\text{vq}}\end{array}\right]^{\text{T}}\right)\) are the \(dq\) axis positive-sequence voltage and current component vectors, respectively, and similarly for \(v^{-}_{\text{sdq}}\)\(i^{-}_{\text{vdq}}\), \(i^{+*}_{\text{vdq}}\), \(i^{-*}_{\text{vdq}}\), \(v^{+*}_{\text{dq}}\) and \(v^{-*}_{\text{dq}}\). Here, the superscripts \(+\) and \(-\) denote positive-sequence and negative-sequence components; the superscript \(*\) denotes the command references of the variables. \(p_{\text{s}}\) and \(q_{\text{s}}\) are the instantaneous active and reactive power [22], and \(P_{\text{s}}^{*}\) and \(Q_{\text{s}}^{*}\) are the command references for the output active and reactive power of the MMC, respectively. \(V^{*}_{\text{dc}}\) is the DC voltage command reference, and \(V^{*}_{\text{s}}\) is the command reference for the amplitude of AC voltage \(v_{\text{s}j}\).  
The control scheme is decomposed to an outer-loop power controller and an inner-loop current controller [23]. According to the active- and reactive-power control objectives, the outer-loop power controller generates the \(dq\) axis positive-sequence current command references \(i^{+*}_{\text{vd}}\) and \(i^{+*}_{\text{vq}}\) for the inner loop current control, respectively. The inner-loop current controller regulates the \(dq\) axis positive-sequence current components (\(i^{+}_{\text{vdq}}\)) at their references (\(i^{+*}_{\text{vdq}}\)), and eliminate the negative-sequence current components (\(i^{-}_{\text{vdq}}\)) with their references (\(i^{-*}_{\text{vdq}}\)) set as zero, respectively [24]. Due to the conceptual and implementational simplicity, nearest level modulation (NLM) is adopted for a MMC with a high number of SMs [11].
## 3 Proposed Coordinated Control Method  
### Assumptions and Control Strategies  
The proposed method involves five assumptions to simplify the control strategies. First, all controlled vehicles are connected and strictly adhere to the vehicle speed guidance strategy. Second, when vehicles enter the transition section's speed guidance zone, they will only pass through based on the green phase time of the downstream intersection in  
Figure 5: Variations in the maximum queue length model across different cycles.  
the current or the next cycle. The scenario of speed guidance extending beyond two cycles and vehicles stopping and queuing for a second time is not considered. Third, the flow of vehicles merging from the non-coordinated right-turn phase is signal-controlled, and the phases for all three merging directions should be continuous and consecutive. Fourth, considering the speed limits of urban roads and primary highways in China, which range from 30 to 70 km per hour, and the fact that vehicles on certain roads can reach speeds of 70 km/h, the speed limit for this model was set at 70 km per hour to better explore the model's control effects. However, due to safety considerations, speeds exceeding 60 km per hour should be used with caution. Finally, to ensure that the simulation objects can meet the proposed coordinated control optimization method, the phase sequences need to be re-optimized based on the original number of phases. The optimized phase sequences are shown in Figure 6.  
To address this problem, the coordination involves three strategies: (1) green phase extension strategy at the downstream intersection, (2) dynamic adjustment of phase-difference strategy at the upstream and downstream intersections, and (3) queue-speed guidance strategy at the transition section. Strategy 1 timely extends the green phase time for the coordinated direction at the downstream intersection as it approaches its end to address the issue of queued vehicles. This strategy aims to reduce unnecessary delays for the queued vehicles. Simultaneously, Strategy 3 guides target vehicles to accelerate or decelerate as appropriate, based on the coordinated phase signal state and traffic flow conditions, to minimize the instances of vehicles coming to a stop and waiting.
# On the Use of LoRa Technology for Logic Selectivity in MV Distribution Networks
## 7 Experimental Tests
### Tolerance to Connection Problems  
In the event that, for any reason, the Blind or Trip messages do not reach their destination, for example, due to interference in the channel or fault in the communication infrastructure, the logic selectivity will not work. The scenario that must be avoided, however, is one in which a fault is supplied by the network for a time exceeding the limits reported in Table 9, as this can lead to damages to the distribution network.  
The designed logic selectivity system is, in this regard, tolerant. The firmware of each PR, in fact, was developed in such a way as that if a fault is detected and neither a Blind nor a Trip message is received within the Maximum Blind Waiting time, the PR commands the tripping of the circuit breaker anyway. Therefore, the fault is correctly powered off even if connection problems of the PRs are experienced.  
To emulate this situation, a test with the fault 67N.S1 was set up, wherein the fault was located between PR2 and PR3 and with a configuration of SF = 12 and BW = 250 kHz. The SF and BW pair corresponds to a \(T_{packet}\) of 413.7 ms, which is much greater than the Maximum Blind Waiting time for this type of fault (330 ms).  
In this case, the relays SR, PR1, PR2, when not receiving the Blind signal in time, will command the opening of the circuit breaker together, as shown in the scheme in Figure 22.  
Since the SR and PRs commanded their breakers at the same time, all the breakers upstream of the fault are tripped. The selectivity, in this instance, is not fulfilled, because the entire radial network, from SR to TR, suffers from the power outage, but the fault is not further supplied, preventing damages to the network.
## 2 Materials and Methods
### Characterization of the Imaging Performance  
We assessed the image quality in terms of resolution, contrast, and spatial homogeneity using the phantoms presented in Section 2.3.3. The full-width at half-maximum (FWHM) criterion was used to assess the 3-D resolution of individual point-like objects. For the contrast, two criteria were evaluated and compared: a variant of the cystic resolution (CyR) and the simple contrast between an anechoic region and a speckle region. We studied the influence of both the angular sampling parameter \(\Delta\alpha\) and the transmit mode (straight or electronically steered).
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Protocol Process
#### 5.2.2 Cardinality Calculation  
In the cardinality calculation phase, the small health app developer and the large medical institution perform a series of carefully designed encryption and decryption operations to blind the small health app developer's elements securely and compute the intersection cardinality of the two sets. The specific operations are as follows:  
1. Element Blinding and Interactive Encryption Operations: The small health app developer and the large medical institution interact through a series of asymmetric encryption and decryption operations to blind the small health app developer's elements. Specifically, the small health app developer calculates \(C_{j}=h_{j}^{\beta_{j}}\) and sends \(C\) to the large medical institution. The large medical institution uses its private key \(\alpha\) to compute \(C_{j}^{\prime}=C_{j}^{\alpha}\) and sends \(C^{\prime}\) back to the small health app developer.
2. Cardinality Computation: After receiving \(C^{\prime}\), the small health app developer checks whether they belong to the filter \(CF\) through the check operation \(CF.check\), thereby calculating the cardinality of the intersection of the sets. Specifically, after receiving \(H(X)^{\alpha}\) sent by the large medical institution, the small health app developer computes \(item_{j}=C_{j}^{1/\beta_{j}}\) and uses the result to query the filter \(CF\) to obtain the intersection's cardinality \(sum\).