esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Current US Regulations  
The new FAA reauthorization bill that was signed into law the week of 14 February 2012 by President [PERSON] creates a fast track for the integration of civil unmanned aircraft systems into the national airspace. The legislation states that the FAA and the UAS industry must work together to develop a \"comprehensive plan\" by mid-November (2012) that will safely achieve the full integration of UAS by 30 September 2015. Deadlines for certain smaller systems are set even sooner. For example, first responders will be allowed to fly small UAS weighing 2 kg or less within 90 days. Within six months, the FAA must designate six test ranges where the UAS can fly to develop their sense-and-avoid capabilities.  
Recent FAA draft proposed UAS operations guidelines identify an upper limit of 25 kg to define small UAS (sUAS) platforms. The FAA is currently developing strategies for implementation of this platform category into operational use, which could lead to final rules on civilian and commercial sUAS operations in the US by mid-2013. The FAA's sUAS Aviation Rulemaking Committee (ARC) has recommended dividing unmanned aircraft into operating groups ranging in maximum takeoff weight from 2 kg to 25 kg. The committee recommended that the largest aircraft be restricted to \"extreme remote\" class G airspace [18,19].  
Within one year, small UAS under 55 pounds will be allowed to fly in the Arctic regions of the US, 24 h a day, at an altitude of at least 2,000 feet. By mid-2014, small UAS that weigh less than 55 pounds will be allowed to fly in the NAS. The legislation says that all UAS must have a \"sense and avoid capability\", and standards will be set for the licensing of operators. The FAA also will be required to study the causes of accidents involving UAS [20].  
Potential users should be aware of additional regulations that may affect the acquisition or transportation of UAS components. In the United States, the Department of State has responsibility for the control of the permanent and temporary export and temporary import of defense articles and services pursuant to the Arms Export Control Act (\"AECA\") and Presidential Executive Order 11958. Among other functions the AECA provides for enforcement of International Traffic in Arms Regulations (\"ITAR\"). ITAR regulations do preclude export of certain UAS components that can be deemed as under ITAR control, such as some GPS units, and inertial navigation systems for UAS. A UAS investigator should ensure that ITAR requirements are met when exporting or using UAS equipment in foreign locations [21].
# A Dual-Channel End-to-End Speech Enhancement Method Using Complex Operations in the Time Domain
## 4 Evaluation Results  
### Comparisons with the NABFCN Model  
In the experiments, the proposed CNABCFCN model and the baseline NABFCN model were objectively evaluated using short-time objective intelligibility (STOI) [25] and perceptual evaluation of speech quality (PESQ) [26]. The evaluation was conducted on the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Name** & **Input Size** & **Hyperparameters** \\ \hline input & (B, 16,000), (B, 16,000) & \\ \hline shared-LSTM & (B, 100, 160) & (160, 512) \\ \hline split-LSTM & (B, 512) & (512, 256) \\ \hline split-LSTM & (B, 512) & (512, 256) \\ \hline linear & (B, 256) & (256, 26) \\ \hline \hline \end{tabular}
\end{table}
Table 4: NAB model, where B represents the batch size and the format of hyperparameters is the size of the input and output channels of LSTM.  
Figure 7: NABFCN structure.  
dataset produced using the configurations in Figures 4 and 5. The results of the sound source at 0 degrees are shown in Table 5, while the results of the sound source in the range of \(-45\) to \(45^{\circ}\) are shown in Table 6.  
Table 5 reveals that, with a stationary sound source at \(0^{\circ}\), the CANBCFCN network exhibited significant enhancement over NABFCN at all SNRs. This finding illustrates the potential benefits of complex operations. Table 6 shows a similar conclusion, that is, CANBCFCN outperformed NABFCN. Nonetheless, the ability of the models to suppress noise was somewhat reduced in complex acoustic scenes, where there were multiple target directions to consider. This result was expected given the complex task.  
Figure 8 presents enhanced spectrograms to highlight the differences between the NABFCN and CANBCFCN models. A visual comparison reveals that the output of NABFCN retained residual noise at middle and high frequencies, whereas CANBCFCN significantly suppressed noise, resulting in minimal speech distortion.
# An Energy-Efficient Game-Theory-Based Spectrum Decision Scheme for Cognitive Radio Sensor Networks
## 5 Conclusions  
In this paper, we have proposed a spectrum decision scheme called EGSD for CRSNs, which supports the two operation modes of coordination mode and data collection mode. The core of EGSD is the two spectrum selection algorithms of random selection called EGSD-R and game-theory-based selection called EGSD-G. The simulation results show that EGSD has longer lifetime and lower overhead compared to the conventional MVSD scheme. In the best scenario, EGSD outperforms MVSD by 46.6% longer lifetime and 31.7% lower overhead. The improvements are mainly because EGSD is a distributed method that requires fewer control packet exchanges to the sink. The other sources of the performance improvement are the simple yet energy-aware clustering method, the predictions by a Markov chain (for EGSD-G), and a data collection mode that consumes less energy than the coordination mode. We have also observed that EGSD-G performs slightly better than EGSD-R. In future works, we intend to: (1) combine the game theory with other machine-learning techniques to exploit the spectrum usage pattern of PUs; (2) incorporate real measurements of PU spectrum usage; and (3) consider application-specific sensor placements. In addition, the situation that PUs have a favorite channel will be taken into consideration.  
The authors wish to thank the editor and anonymous referees for their helpful comments to improve the quality of this paper. This research was supported in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2013R1A1A2011744). Correspondence should be addressed to [PERSON] ([EMAIL_ADDRESS]).  
The individual contributions of authors are as follows. The first author developed and simulated the spectrum decision scheme. The second author directed the research and contributed to the refinement of the scheme and the interpretation of simulation results. The paper was drafted by the first author and subsequently revised and approved by the coauthor.  
The authors declare no conflict of interest.
## 6 Performance Evaluation
### Simulation Results Analysis  
In this section, we analyze the performance of the TAFLRLR protocol under different simulation parameter settings. In the simulation experiments, the MNs employ mainly two attack methods: the HELLO packet flooding attack and the selective forwarding attack.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Simulation Parameter** & **Value** & **Unit** \\ \hline Simulation scene range & 500 \(\times\) 500 \(\times\) 500 & m \\ Simulation time & 800 & s \\ Proportion of MNs & 10–30\% & \\ DATA packet size & 154 & Bytes \\ The moving speed of the nodes & 1–3 & m/s \\ Transmitting power & 2.0 & w \\ Receiving power & 0.1 & w \\ Idle power & 0.01 & w \\ Topology & Fixed/Random & \\ Initial energy & 1000 & J \\ Number of experiments & 30 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameter setting.  
Assuming that an MN can only use one attack mode. The MN with a selective forwarding attack directly discards the received packet. The MN with the first HELLO packet flooding attack forwards HELLO packets with a larger communication range than a normal node. By contrast, the MN with the second HELLO packet flooding attack forwards the received HELLO packets repeatedly.
## 2 Suggested Topology and Its Operation
### Operating States  
Various outputs can be produced from any power-converting topology if its output voltage has inverting and non-inverting characteristics for both polarities of the input voltage. Therefore, investigation of the proposed topology is explored with respect to its non-inverting, inverting, and zero-state with respect to the positive and negative input voltage.
# A Customized Semantic Segmentation Network for the Fingerprint Singular Point Detection
## 4 Experimental Results
### Testing Dataset and the Evaluation Criteria  
The proposed algorithm is tested on the testing set of SPD2010, which has 290 fingerprint images. According to SPD2010 competition's instructions, the evaluation criteria are as follows: (1) the evaluation will consider the quantity and type of the detected singular points as well as their distance to the ground truth. (2) For a ground truth singular point \((x_{0},y_{0},t_{0})\), if a detected singular point \((x,y,t)\) satisfies \(t=t_{0}\) and \(sqrt((x-x_{0})^{2}+(y-y_{0})^{2})<10\) at the same time, the point is considered to be truly detected. Otherwise, it is called a miss. (3) The detection rate is defined as the ratio of the truly detected singular points to all ground truth singular points. (4) The miss rate is defined as the ratio of the missed singular points to all ground truth singular points. (5) The false alarm rate is defined as the ratio of the falsely detected singular points to all ground truth singular points. (6) If all singular points are truly detected and there are no spurious singular points in a fingerprint, the fingerprint is considered to be 'correctly' detected. In our experiment, we evaluated the detection methods strictly according to the evaluation criteria of SPD2010, which is good for evaluating our algorithm and comparing it with the existing ones objectively and conveniently.
# A Quantum-Resistant FHE Framework for Privacy-Preserving Image Processing in the Cloud
## 3 Proposed Framework
### Enhanced Counter Synchronization and Session Management
#### 3.5.3 Session Management  
The framework implements comprehensive session lifecycle management:  
* Active Session Timeout: 2 h of inactivity.
* Key Rotation: Automatic every 10,000 messages or 24 h.
* Graceful Shutdown: Explicit session termination messages.  
The protocol's message transmission logic, detailed in Algorithm 6, implements a robust and secure communication mechanism. As shown in Algorithm 6, each transmission operates on a session_state object S containing critical security parameters: the session key \(K_{AES}\), IV base \(IV_{base}\), sequence number, and message counter. This algorithm's exponential backoff strategy and comprehensive error handling, which is further elaborated in Algorithm 7, ensure reliable message delivery even under adverse network conditions.  
```
0: Payload \(P\), session state \(\mathcal{S}=(K,IV_{base},seq,ctr)\)
0: Transmission status {SUCCESS, FAILURE}
1: Message Construction:
2:\(seq^{\prime}\leftarrow\mathcal{S}.seq+1\)
3:\(\tau\leftarrow\textsc{CurrentTimestamp}()\) {Anti-replay timestamp}
4:\(IV\leftarrow\textsc{HKDF}(IV_{base},seq^{\prime}||\mathcal{S}.ctr)\) [Secure IV derivation]
5:\(H\leftarrow(seq^{\prime},\mathcal{S}.ctr,\tau)\) [Associated data]
6:\((C,T)\leftarrow\textsc{AES-GCM-Enc}(P,\mathcal{S}.K,IV,H)\)
7:\(M\leftarrow(H,C,T)\)
8: Reliable Transmission with Exponential Backoff:
9:for\(i=1\) to \(n_{max}\) where \(n_{max}=3\)do
10:\(\textsc{Transmit}(M)\)
11:\(timeout\gets 500\times 2^{i-1}\) ms [Exponential backoff]
12:if\(\textsc{ReceiveAck}(timeout)=\textsc{True}\)then
13:\(\mathcal{S}.seq\gets seq^{\prime}\)
14:\(\mathcal{S}.ctr\leftarrow\mathcal{S}.ctr+1\)
15:return SUCCESS
16:endif
17:endfor
18:return FAILURE
```  
**Algorithm 6** Authenticated Message Transmission Protocol
# HGTMFS: A Hypergraph Transformer Framework for Multimodal Summarization
## 5 Experiments
### Ablation Study
Specifically, HGTMFS records a ROUGE-1 score of 45.00, surpassing the scores of mean aggregation (40.55), full self-attention (43.12), and homogeneous attention (42.88) by 4.45, 1.88, and 2.12 percentage points, respectively. The method also exhibits substantial improvements in ROUGE-2, ROUGE-L, and BLEU-1 to BLEU-4, outperforming the best-performing baseline method by 0.56, 0.65, 0.36, 0.58, 0.77, and 0.53 percentage points across these metrics. Similarly, on the CNN dataset, HGTMFS demonstrates its superiority with a ROUGE-1 score of 31.00, which exceeds that of mean aggregation (28.00), full self-attention (30.00), and homogeneous attention (29.50) by 1.00, 3.00, and 1.50 percentage points. The model \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Daily Mail Dataset**} \\ \hline Method & ROUGE-1 & ROUGE-2 & ROUGE-L & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 \\ \hline Mean aggregation & 40.55 & 22.47 & 38.23 & 37.25 & 27.58 & 19.80 & 14.76 \\ Full self-attention & 43.12 & 27.54 & 40.50 & 39.87 & 30.24 & 23.18 & 18.12 \\ Homogeneous attention & 42.88 & 26.23 & 39.76 & 38.72 & 29.43 & 21.97 & 16.84 \\
**HGTMFS (ours)** & **45.00** & **28.10** & **41.15** & **40.23** & **30.82** & **23.95** & **18.65** \\ \hline \hline \multicolumn{8}{c}{**CNN Dataset**} \\ \hline Method & ROUGE-1 & ROUGE-2 & ROUGE-L & BLEU-1 & BLEU-2 & BLEU-3 & BLEU-4 \\ \hline Mean aggregation & 28.00 & 10.00 & 20.00 & 20.00 & 14.00 & 9.50 & 7.00 \\ Full self-attention & 30.00 & 11.00 & 21.50 & 21.50 & 15.00 & 10.50 & 7.50 \\ Homogeneous attention & 29.50 & 10.50 & 21.00 & 21.00 & 15.50 & 10.00 & 7.75 \\
**HGTMFS (ours)** & **31.00** & **11.50** & **22.00** & **22.15** & **15.98** & **11.45** & **8.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation Study: Comparison of Different Methods on ROUGE and BLEU Metrics for Daily Mail and CNN Datasets. consistently achieves the highest scores in ROUGE-2, ROUGE-L, and BLEU-1 to BLEU-4, showing notable performance gains of 0.50, 0.50, 0.65, 0.48, 0.95, and 0.35 percentage points over the strongest baseline across these metrics. These results validate the effectiveness of integrating the group transformer mechanism into the hypergraph neural network within the HGTMFS framework, highlighting its superior capability in capturing complex dependencies and interactions within the data. The consistent outperformance across both datasets reinforces the potential of our method for broader applications in similar contexts.
# Comparative Study to Evaluate Mixing Efficiency of Very Fine Particles
## 5 Numerical Analysis Results
### Summary of Results  
To enhance the clarity of comparative analysis, a summary of all simulated cases and their corresponding results is provided in Table 9. This table includes the total particle counts, simulation durations, average Lacey Mixing Index (LMI) values, and LMI-based errors for each mixer type.  
The data clearly demonstrates that CGM significantly reduced the number of particles and computational time, often by over 90%, while maintaining acceptable LMI accuracy.  
Figure 22: Particle distribution snapshots at 6, 18, 24, and 30 s under various CGM factors in the Conical-Screw Mixer.  
In most cases, the average error in LMI remained within 5%, confirming the practical applicability of CGM for large-scale DEM simulations.  
Notably, the ribbon blade mixer achieved the highest final LMI (0.972) and the lowest error (2.54%), whereas the tumbling V mixer showed the lowest final LMI (0.300) and the highest error (12.39%). These trends suggest that mixing systems with inherently strong macroscopic flow features are generally less sensitive to CGM-induced simplification.
## 2 Materials and Methods
### Wavelet Transforms of the Surface Profiles  
Wavelet-based multi-resolution analysis was further applied to investigate the characteristic length scales of untreated and heat-treated Ti64 specimens. Previously, wavelets have been applied for surface analysis to find discontinuities, edge, and isolate and analyze surface components [29; 30; 31]. In this study, a continuous 1D complex-valued Morlet wavelet was applied to transform these height signals obtained from 3D surface measurements, so that surface features at different length scales can be extracted and quantified. According to [PERSON] [32], the wavelet transform coefficient \(W(a,b)\) of a continuous real-valued signal, such as the height signal \(H(x)\), can be defined as the inner product between \(H(x)\) and an analyzing wavelet \(W(a,b)=\int_{-\infty}^{\infty}H(x)\varnothing_{a,b}^{*}(x)dx\), where \(\varnothing_{a,b}^{*}(x)=\left(1/\sqrt{a}\right)\varnothing(\left(x-b\right)/a)\), \(a\) and \(b\) are respectively the space dilatation and translation parameters, and the asterisk of \(\mathcal{O}\) represents the complex conjugate. The corresponding complex-valued 'Morlet' wavelet is \(\varnothing(x)=\left(e^{i\omega_{0}x}\right)\left(e^{-x^{2}/2}\right)\) and in this study, we set \(\omega_{0}=2\pi\)[33]. Note that the analyzing wavelet \(\varnothing_{a,b}(x)\) is a dilatated and translated version of a mother wavelet \(\varnothing(x)\) with resolution \(a^{-1}\) and position \(b\)[32]. The spatial wavelet coefficient \(W(a,b)\) provides the strength information of the height signal, and gives the phase information in space domains. Since the Morlet wavelet can identify the local energy density at each given scale, the characteristic scales of surface features may be analyzed. As \(W(a,b)\) was obtained, the effective wavelet energy spectrum in the space domain can be defined as \(E_{w}(a)=\frac{1}{L}\int_{0}^{L}\lvert W(a,b)\rvert^{2}db\), where \(L\) is the total sampling length of the height signal \(H(x)\) for untreated and heat-treated specimens.
## 2 Related Work
### XAI and Adversarial Attacks  
XAI was originally developed to express the reasoning behind ML model outputs, thereby enhancing their reliability and trustworthiness. However, it can also play a role in detecting adversarial evasion attacks [26; 27; 28]. This can involve training new detectors using XAI explanations or providing these explanations to expert analysts for identifying adversarial samples. Additionally, XAI has been employed to enhance evasion attacks and develop more sophisticated attack strategies [37; 38].  
[PERSON] et al. [26] presented a novel detection approach for evasion attacks in binary deep neural network (DNN) classifiers. They utilized SHAP values computed for the internal layers of the binary classifier to distinguish between normal and adversarial inputs across the popular CIFAR-10 and MNIST image classification benchmark datasets. The proposed adversarially trained detector demonstrates high detection accuracy and strong generalization ability in detecting various evasion attacks. By varying the hyperparameters and applying different levels of perturbations, their detector can capture diverse patterns of adversarial samples. Similarly, in [28], [PERSON] et al. proposed an XAI adversarial training defense by fine-tuning their technique using the SHAP explanation of FGSM adversarial samples. They used two benchmark cybersecurity datasets, including Android malware and network traffic security. [PERSON] et al. [27] introduced an accurate and model-agnostic explainable detection method for adversarial samples using SHAP values. The proposed detector achieved a detection accuracy of 77% and 88% on Electronic Health Records and Chest X-ray datasets, respectively. They introduced both fully and semi-supervised methods, capable of generalizing to different attack methods without requiring retraining. Moreover, their work led to an improvement of over 10% in the existing adversarial detection for both datasets.  
Unlike the use of XAI as a defense against adversarial attacks in [26; 27; 28], [PERSON] et al. [37] employed the feature-based explanations of model predictions to guide the crafting of adversarial samples. Their attack involved adding consequential perturbations, which are likely to induce model evasion while avoiding non-consequential perturbations that are unlikely to contribute to evasion. The proposed attack is model-agnostic and applicable across various threat models, model architectures, and distance metrics. It enhances the evasion rate of state-of-the-art attacks while requiring fewer perturbations across both white-box and black-box threat models on the CIFAR-10 and MNIST datasets. [PERSON] et al. [38] proposed a white-box and non-targeted attack that generates adversarial inputs by misleading target DNNs and deceiving their coupled interpretation models, namely, ADV2. They demonstrated that, with ADV2, the adversary can arbitrarily designate an input's prediction and interpretation in skin cancer diagnosis.
# Fragility Curves for RC Structure under Blast Load Considering the Influence of Seismic Demand
## 7 Conclusions  
This paper presented a simplified procedure to evaluate the safety of a framed RC structure under blast load highlighting the seismic design influence. The considered framed building has been designed considering four different seismic demand corresponding to specific locations in Italy characterized by different PGA. Then, a simplified SDOF system has been obtained from a pushover analysis in order to perform several Monte Carlo analyses aimed at highlighting what is the performance of the considered building under blast load in both a deterministic and probabilistic framework.  
The structure designed to withstand the highest seismic load (L'Aquila) has been proven to have better performance also in the case of blast load. Thus, the influence of the seismic demand in the building design is evident. Indeed, the structure designed with the lowest seismic demand (Cagliari) presents the worst structural behavior under the blast load. This can be clearly seen from the fragility curves Figures 10-12, probability Tables 7-10, and also from the deterministic maximum drift presented in Figure 9.  
The obtained results can be useful for safety evaluation in the case of a terrorist attack. Indeed, the fragility curves and the probability of threshold exceedance can help the designer in evaluating what should be a \"safe\" distance for the given structure that can be obtained with fence system or bollards.  
Further development of this work is expected, applying this method to existing structures, like those described in [50; 51]. It is also interesting to merge the proposed method with other assessment approaches and retrofitting techniques considering other kind of structures [52; 53; 54].  
[PERSON] conceived the illustrated strategy and the theoretical formulation contributing also to the numerical analysis; [PERSON] developed the numerical and analytical analysis; [PERSON] analyzed the numerical results and contributed to the theoretical formulation. All the authors wrote the paper. All authors have read and agreed to the published version of the manuscript.  
The financial support of the Autonomous Region of Sardinia under grant PO-FSE 2014-2020, CCI: 2014-IT05 SFOP021, through the project \"Retrofitting, rehabilitation and requalification of the historical cultural architectural heritage (R3-PAS)\" is acknowledged by [PERSON].  
The authors declare no conflict of interest.