esa-sceva/satcom-chunk-collection huggingface dataset:
# Toward Vulnerability Detection for Ethereum Smart Contracts Using Graph-Matching Network
## 5 Evaluation
### Performance Evaluation
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Vulnerability** & **Method** & **Acc (\%)** & **Rec (\%)** & **Pre (\%)** & **F1 (\%)** \\ \hline \multirow{4}{*}{Reentrancy} & SmartCheck & 55.70 & 74.95 & 62.08 & 67.91 \\  & Slitter & 70.51 & 85.93 & 75.83 & 80.57 \\  & DR-GCN & 66.21 & 81.78 & 71.08 & 76.06 \\  & ASGVulDetector & **84.96** & **95.37** & **84.17** & **89.42** \\ \hline \multirow{4}{*}{Timestamp dependency} & SmartCheck & 40.41 & 77.44 & 37.45 & 50.49 \\  & Slitter & 74.19 & 89.47 & 77.27 & 82.93 \\  & DR-GCN & 50.59 & 84.22 & 47.55 & 60.78 \\  & ASGVulDetector & **87.02** & **94.59** & **89.09** & **91.76** \\ \hline \multirow{4}{*}{Block info dependency} & SmartCheck & - & - & - & - \\  & Slitter & 67.77 & 84.46 & 70.43 & 76.81 \\  & DR-GCN & - & - & - & - \\  & ASGVulDetector & **88.99** & **94.01** & **91.30** & **92.63** \\ \hline \multirow{4}{*}{Tx.Origin} & SmartCheck & 41.12 & 69.34 & 43.69 & 53.60 \\  & Slitter & 60.96 & 82.09 & 62.97 & 71.27 \\ \cline{1-1}  & DR-GCN & - & - & - & - \\ \cline{1-1}  & ASGVulDetector & **81.18** & **87.58** & **88.35** & **87.97** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison for _ASGVulDetector_ (‘-’ denotes not applicable). four methods, _ASGVulDetector_ performs the best in all cases. Specifically, _ASGVulDetector_ improves the most of the three competitors in the four types of vulnerabilities on F1 score by 8.9, 8.8, 15.8 and 16.7 percentages, respectively. These improvements reveal the effectiveness of the enriched graph-based representation as well as the classification power of the graph-matching network. As shown in Table 4, _ContractFuzzer_ almost has the lowest detection performance in all cases. In the case of Tx.Origin, it even cannot find vulnerabilities in reasonable time. This is due to the fact that the fuzzing technique takes random inputs to trigger bugs, and thus its performance heavily relies on code coverage and testing time. _Oyente_, as a symbolic execution tool, is superior to _ContractFuzzer_ since it can resolve code branches by constraint solving. However, it is still limited in complex branches, such as resolving a concrete value for a condition. In contrast to the above two methods, _Securify_ is more stable and has better performance in all cases. This benefits from the formal methods used in _Securify_, but formal specifications covering all Solidity functions still remain challenging. _BASGVulDetector_ wins the three competitors in all cases, improving _Security_ in the four types of vulnerabilities on F1 score by 22.0, 23.6, 28.1, and 28.7 percentages, respectively. These improvements are contributed by our abstract representation of the bytecode, as well as the power of the GMN model.
# A Parallel Open-World Object Detection Framework with Uncertainty Mitigation for Campus Monitoring
## 4 Results
### Experiment Settings  
**Datasets.** According to the typical setup of OWOD, all classes from the training set are grouped in \(T\) incremental tasks. Following [14], we set \(T\) as 4, and adopt the Pascal-VOC [55] and MS-COCO [56] dataset. When learning the task 1, we treat classes and data from Pascal-VOC as the training set, and the remaining 60 classes of MS-COCO are considered unknown. For subsequent tasks, the class division strategy is exactly the same as that in ORE. For evaluation, we use the Pascal-VOC test set and MS-COCO validation set.  
**Implementation details.** We implement our method based on closed world detection model D-DETR [16]. During the training of the known object detector and the objectness-driven discovery module, the SGD optimizer is used and the batch size is set to 8. The IOU threshold \(P_{t}\) in the collaborative open-world classifier is set to 0.4. During both the initial training and the incremental learning stage, the initial learning rate of the known object detector and the objectness-driven discovery module is set to \(2\times 10^{-4}\). We train the known object detector for 50 epochs in the initial training stage of the task 1, and train 50 epochs to learn new known classes during the incremental learning. Additionally, we trained for 50 epochs in each stage for data replay to address catastrophic forgetting, following a strategy similar to OW-DETR [17]. About the objectness-driven discovery module, we train it for 80 epochs only in the initial training stage.  
**Evaluation metrics.** To conduct a fair and comprehensive evaluation of the proposed method, we first employ widely recognized metrics in object detection, such as mean Average Precision (mAP) and Recall, as evaluation benchmarks for known and unknown classes. Additionally, we utilize other common-used open-set evaluation criteria, such as Wilderness Impact (WI) [40], Unknown Detection Recall (UDR) and Unknown Detection Precision (UDP) [15].
## 3 Results
### Spatial Decomposition Results Based on Different Regression Models
#### 3.2.2 Spatial Decomposition Results of Demographic Data with Different Regression Models  
The twelve trained models were applied to the test set to predict the population density in the grid scale. The spatial decomposition results of the demographic data generated by the twelve regression models are shown in Figure 3.  
Figure 3: \(Cont.\)  
from the Elastic Net model. It is worth noting that, except for the Elastic Net model, there were no zero-valued grid cells in the decomposition results of the remain eleven algorithms. Among the results generated by the three nonlinear regression algorithms, the distribution of cells with high predicted populations were wider than those of the linear algorithms, especially the SVR model. According to the demographic data in the subdistrict scale, it seems that the nonlinear regression algorithms revealed the spatial heterogeneity of population distributions more clearly.
## 2 Materials and Methods
### Environmental Capacity Assessment System (ECAS)
#### 2.2.3 Comprehensive Assessment  
In the formulation and implementation of a TMDL plan, the U.S. Environmental Protection Agency has established corresponding provisions: when water quality exceeds 10% of the water quality index value stipulated by the Clean Water Act, the water body will be threatened by pollutants and the water quality will be considered as unsafe [39]. In addition, during the calculation of water EC, in order to cope with the uncertain risks existing in the future utilization of water resources and ensure the attainment of environmental water quality objectives, a certain proportion, namely, the safety margin of water EC, should be reserved in advance in the allocation of EC [40]. In the setting of the safety margin, the range of safety margin is related to the degradation speed of the harmful or toxic degradable pollutants; the higher the degradation speed, the safer it is. For general degradable pollutants, the degradation rate can be reduced by 10% or 20% (the stricter rate is selected from existing data experience), or the standard value of water quality target can be reduced by 10%, or the allocation of key pollution sources can be reduced by 10% [41]. In this study, the evaluation indexes of EC were all non-toxic or non-harmful pollution factors, and for Haizhou Bay, there were no empirical data as the reference standard. Therefore, the variation of EC of these four pollutants was set at 10% of their EC. Ultimately, 10% was set as the upper limit of the change in EC, that is, reclamation would be strictly prohibited if its impact on EC exceeds 10%.  
In view of the specific pollutant discharge method and environmental quality of Haizhou Bay, we introduced an environmental capacity feasibility assessment index, and established an environmental capacity assessment system (ECAS) to quantitatively evaluate the environmental impact of all possible reclamation schemes. The evaluation system divided the feasibility of the schemes into three grades: feasible, basically feasible and infeasible (Table 3). \(R\) is the comprehensive evaluation index, which was determined by the average value of all indicators. When \(R\geq 8\), the reclamation scheme was feasible. When \(8>R\geq 4\), the reclamation scheme was basically feasible. When \(R<4\), reclamation was not feasible. Finally, feasible and basically feasible schemes were the acceptable reclamation schemes in this study, which were included in the calculation of the appropriate scale reclamation schemes.
# Computational Vibro-Acoustic Time Reversal for Source and Novelty Localization
## 3 Governing Equations of Structure and Acoustic Mediums and Their Coupling
Using finite elements for the vibro-acoustic system [18], the semi-discrete equation of motion [19] for the coupled system in matrix form, is \[ \begin{bmatrix}M_{s}&0\\ q_{f}c_{f}^{2}H^{T}&M_{f}\end{bmatrix}\begin{bmatrix}\ddot{d}\\ \dot{p}\end{bmatrix} + \begin{bmatrix}K_{s}&-H\\ 0&K_{f}\end{bmatrix}\begin{bmatrix}d\\ p\end{bmatrix} = \begin{bmatrix}f_{s}\\ f_{f}\end{bmatrix}  \tag{6}\]  
where \(M_{s}\) and \(K_{s}\) stands for the mass and stiffness matrices of the structural part and \(M_{f}\) and \(K_{f}\) for the fluid, respectively. The degrees of freedom indicated by \(d\) refer to kinematic variables (i.e., displacements and rotations) while \(p\) stands for the acoustic pressure and \(f_{s}\), \(f_{f}\) are excitation forces on the structural and acoustic part, respectively. Matrix \(H\) actually contains the coupling coefficients. Finally, some damping could be considered in the form of Rayleigh damping, among other possible models.
# Low-Cost Area-Efficient FPGA-Based Multi-Functional ECDSA/EdDSA
## 4 Results
Despite the throughput and the number of Slices2/Ops being impressive, their design requires too many resources for only key generation, and its practical applications are limited. In this experiment, we estimate a DSP as 100 slices [9] to calculate the slices equivalently. The comparison is performed based on the number of slices, number of DSPs, and the working frequency (Freq) in MHz. Other technical details, including Slices2/MHz, Slices2/Ops, throughput in kilo-bits-per-second (Kbps), and Slices2/Kbps, are added to evaluate the efficiency of the design. In this study, we target a low-resource, multi-functional design. Therefore, the number of slices is emphasized rather than the other aspects. The ECPM is the essential part of an elliptic curve cryptography design. We also compare our processing core, with four PEs, with other proposals in Table 2. Table 2 reveals that the processing core occupies nearly 70% of the total resource. In comparison with other ECPM proposals, the resource consumption of our ECPM design is significantly lower, even though the core supports multiple curves. Table 2 also reveals that the DSP-based architectures [13; 22] provides high throughput but consumes too many resources for a single curve. Therefore, they cannot be deployed on resource-constrained applications. We also deploy our system on the Xilinx VC707 Evaluation Kit. Figure 8 illustrates the experimental system, including a RISC-V softcore processor, a Secure Hash Algorithm 2 (SHA2) core, and the EdDSA/ECDSA core with Curve25519 and NIST P-256/384/521. The RISC-V processor communicates with the SHA2 core and EdDSA/ECDSA core through a communication bus. The implemented SHA2 core could perform three SHA2 variants, which are SHA2-256, SHA2-384, and SHA2-512 in a single design. The RISC-V processor configures the operating mode for the SHA2 core and EdDSA/ECDSA core, controls the SHA2 core to perform the hash on the input message, and then transfers the hash value to the EdDSA/ECDSA core. When the EdDSA/ECDSA process terminates, the processor reads the results and then compares them with the correct results in [2; 6]. A computer connects with the RISC-V processor through UART to monitor the experimental process. Such a design system allows the processor to perform the SHA2 hash process
# Detection of Denial of Service Attack in Cloud Based Kubernetes Using eBPF
## 5 Results and Discussion
### CPU Overhead  
In our proposed solution, we have measured the CPU utilization using eBPF programs and without them. To determine the CPU overhead, we measured it by running it on the master node and the worker nodes.  
1. CPU overhead on the Master Node  
To determine the overhead of CPU utilization, we have used the Linux tool for the recording of the CPU utilization statistics, i.e., system activity report (SAR). In an average of every 10 s, the record was examined. When stats are gathered, either the eBPF application is used or not, as illustrated in Figure 4, to record the information. When using the eBPF programs versus not using them, there is rarely any change in CPU usage. Thus, it was established that the master node's CPU overhead is not present.  
2. CPU overhead on the Worker Node  
The worker node of the Kubernetes cluster also has a VM launched on it. Our proposed solution also recorded the statistics of the CPU utilization of the usage with the help of the SAR tool on an average of every 10 s. Although we have done the same experiment on the worker node as on the master node, we have recorded the stats as shown in Figure 5, which is with and without eBPF. Similarly, it is revealed that there is no such overhead on the utilization of the CPU.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{3}{c}{**eBPF Hooks**} & \multicolumn{3}{c}{**Action**} & \multicolumn{3}{c}{**Efficiency of Solution**} \\ \hline
**kprobe\_tcp\_v4** & **bpf\_skb\_load\_** & **kcpstates-** & **No. of** & **Arbitrary** & **DDOS** & **Robest for** & **Solution** \\
**connect** & **bytes (Load,** & **tcpstates-** & **Connects-** & **Whreshold** & **Value (\(\geq\)10)** & **Attack** & **Genuine** & **Total No. of** & **with Respect** \\
**tcp\_probe\_ipv4\_connect** & **BFF\_CUR-** & **ENT\_CPU** & **BFF\_CPU** & **BFF\_CPU** & **BFF\_CPU** & **BFF\_CPU** & **BFF\_CPU** \\ \hline
10.0.0.11 & 50 & Yes & 40 & 10 & 80\% & 100\% \\ \hline
10.0.0.12 & 30 & Yes & 25 & 5 & 84\% & 100\% \\ \hline
10.0.0.13 & 9 & No & N/A & N/A & N/A & N/A \\ \hline
10.0.0.14 & 40 & Yes & 20 & 20 & 50\% & 100\% \\ \hline
10.0.0.15 & 6 & No & N/A & N/A & N/A & N/A \\ \hline
10.0.0.16 & 15 & Yes & 13 & 2 & 87\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental Results.  
Figure 4: Master Node CPU Overhead Analysis.
# SDR Proof-of-Concept of Full-Duplex Jamming for Enhanced Physical Layer Security
## 1 Introduction
Although there are already works on FD jamming for secrecy purposes [18; 19], as far as we are aware there is no practical proof-of-concept with a hardware/software implementation (the authors would like to disclose that this work has been presented, only in part, at the international conference CSNDSP2020 [20]). Rather, existing works with implementations of physical-layer security schemes tend to focus on secret-key agreement strategies [21; 22], deriving security from signaling efforts such as beamforming, channel estimation and spread spectrum [23; 24; 25], or using the physical layer to authenticate users through radio signal analysis [26; 27; 28]. In this work we combine coding for secrecy mechanisms with FD interference generation by the legitimate receiver and the cancellation of the effect of SI, and we make a proof-of-concept of this scheme showing it to be possible to secure communication using only noise generated by [PERSON], even when [PERSON] has a degraded channel with respect to [PERSON]. We consider the use of interleaved/scrambled coding for secrecy with a hidden key (ICS-HK/SCS-HK) [29; 30]. This state-of-the-art coding mechanism is a  
Figure 1: System setup for implementing self-interference cancellation for secrecy. three stage coding scheme [4] whereby a random key that is used to scramble/interleave data [31; 32] is punctured before transmission to produce a so called cliff-effect of forward error-correcting codes [33]. Secrecy can be achieved as long as [PERSON] experiences a worse signal-to-interference-plus-noise ratio (SINR) upon reception as compared to [PERSON]. This is assured by the use of FD jamming and SI cancellation by [PERSON]. Regarding the SI cancellation, four state-of-the-art algorithms are considered: least mean square (LMS), normalized least mean square (NLMS), recursive least squares (RLS) and QR decomposition recursive least squares (QRDRLS) [34; 35]. In summary, this paper contributes to the literature by providing a software-defined radio (SDR) implementation of the aforementioned scheme using Ettus Universal Software Radio Peripheral (USRP) B210 devices, and GNU radio [36]. The paper further provides an evaluation in a real-world scenario, and a critical analysis on the performance of SCS-HK added by [PERSON]'s FD jamming, and the effectiveness of several SI cancellation algorithms that can be employed by [PERSON]. The remainder of the paper is organized as follows. In Section 2, concepts of PLS are introduced along with the mechanisms used on the prototype implementation, which is described in Section 3. In Section 4 the setups and metrics are explained and in Section 5 the results are presented. Finally, Section 6 concludes the paper.
## 4 Discussion
### Ablation Studies
#### 4.1.1 Progressive Spatiotemporal Feature Fusion and Restoration  
In the proposed BCSR-STF, the multi-layer PSTFR module serves as the core component, designed to perform iterative processing across multiple scales. To evaluate the effectiveness of this multi-layer structure, a single-layer variant named BCSR-STF-S is introduced for an ablation study. In BCSR-STF-S, feature extraction is applied only once to the input images from both the reference and prediction times, while all other settings remain unchanged. The results, presented in Figure 11, demonstrate that BCSR-STF consistently outperforms BCSR-STF-S. This improvement is attributed to the multi-layer design, which provides greater adaptability and better addresses challenges arising from varying image resolutions and temporal differences. At coarser scales, the model captures global information using lower-resolution features, whereas at finer scales, it restores high-resolution details and enhances spatiotemporal features, thereby mitigating the risk of information loss.
## 2 Methodology
### Performance Evaluation  
We evaluated the performance of the proposed model in two steps. First, we quantitatively assessed the quality of the artificial images using the Frechet inception distance (FID) and the inception score (IS) metrics. Second, we performed the data augmentation and classified the images using the Transformer models ViT, PVT, DeiT, and CoAtNet for each dataset, evaluating the accuracy of each case. The idea was to compare how the XAI models can improve the quality of the generated images compared to the original architectures and how this impacts the classification of the Transformer models.
# A Compact Broadband Planar Inverted-F Antenna with Dual-Resonant Modes
## 5 Conclusions  
This paper proposes a compact and wideband PIFA with dual-resonant modes of TM\({}_{1/2,2}\) and TM\({}_{3/2,0}\). Two types of slots are introduced to improve the antenna performance. First, by etching a pair of horizontal slots for perturbating the current distribution of TM\({}_{1/2,2}\) and TM\({}_{3/2,0}\), the dual-mode operation is achieved, and the radiation pattern can be improved as well. Then, another pair of vertical slots is introduced to improve the impedance matching. The antenna was analyzed with parametric studies conducted to verify the operating principle. It was also fabricated and measured for demonstration. The antenna prototype achieves an impedance bandwidth of 14.5% (3.17-3.67 GHz) and a peak gain of 6.23 dBi, with good broadside radiation performance over the whole band. The antenna has a very compact size of 0.45 \(\times\) 0.26 \(\times\) 0.03 \(\lambda_{01}\)[3]. It is therefore considered as a suitable candidate for future mobile terminal applications. It is also worth pointing out that stimulating and utilizing more modes, such as three to four modes in a PIFA to cover both n78 and n79 bands in 5G, could be a meaningful future study.  
Conceptualization, X.D. and W.Y.; methodology, Z.Q. and X.D.; software, Z.Q.; validation, W.Y.; formal analysis, Z.Q.; investigation, J.C.; resources, J.C.; data curation, Z.Q.; writing--original draft preparation, Z.Q.; writing--review and editing, Z.Q.; visualization, W.Y.; supervision, W.Y.; project administration, W.Y.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62071256, the National Natural Science Foundation of Jiangsu under Grant BK20201438 and Qing Lan Project of Jiangsu Province, and is also sponsored by the State Key Laboratory of Millimeter Waves (Nanjing).  
Not applicable.  
The authors declare no conflict of interest.
# Multi-Agent Modeling and Jamming-Aware Routing Protocols for Movable-Jammer-Affected WSNs
## 2 Background and Related Work
### Jamming Attacks in WSNs
They also discussed some possible security mechanisms, such as authentication, encryption, privacy protection, intrusion detection, etc. There is also a significant category of jamming research that employs game theory to optimize the action strategy of one or more parties. [PERSON] et al. [31] studied an active defense mechanism against jamming attacks in multi-hop relay networks, utilizing one or more network sources to introduce a spoofed network flow on disjoint routing paths, confusing the interferer and making it impossible to distinguish between real and fake communication flows. They analyzed the impact of deceptive routing on network performance and security and proposed a game theory-based approach to optimize deceptive routing strategies. [PERSON] et al. [32] have proposed a game theory-based approach to analyze and design solutions for eavesdropping and jamming problems in next-generation wireless networks. The authors have considered three types of participants in the game--legitimate users, eavesdroppers, and interferers, and established game models in various scenarios, such as static games, dynamic games, and repeated games. The study has focused on the strategic interactions and equilibrium outcomes among the players and has proposed algorithms and mechanisms to enhance network security and efficiency. The impact of jamming on WSNs and its mitigation strategies have been extensively studied, and numerous review articles have been published [16; 17; 33; 34]. These articles not only categorize the different types of jamming attacks but also explore anti-jamming techniques at various levels of WSN. However, surprisingly, there has been no research on the effects of movable jammers. Therefore, one of the primary objectives of this study is to address this gap in the literature.