esa-sceva/satcom-chunk-collection huggingface dataset:
# A Comparative Study on the Laser Welding of Ti6 Al4V Alloy Sheets in Flat and Horizontal Positions
###### Abstract  
Laser welding has been increasingly utilized to manufacture a variety of components thanks to its high quality and speed. For components with complex shapes, the welding position needs be continuously adjusted during laser welding, which makes it necessary to know the effects of the welding position on the quality of the laser welds. In this paper, the weld quality under two (flat and horizontal) welding positions were studied comparatively in the laser welding of Ti6 Al4V titanium alloy, in terms of weld profiles, process porosity, and static tensile strengths. Results show that the flat welding position led to better weld profiles, less process porosity than that of the horizontal welding position, which resulted from the different actions of gravity on the molten weld metals and the different escape routes for pores under different welding positions. Although undercuts showed no association with the fracture positions and tensile strengths of the welds, too much porosity in horizontal laser welds led to significant decreases in the strengths and specific elongations of welds. Higher laser powers and travel speeds were recommended, for both flat and horizontal welding positions, to reduce weld porosity and improve mechanical properties.  
titanium alloy; laser welding; welding position; porosity; weld profile +
Footnote †: journal: Applied Sciences
## 5 Conclusions and Future Work
The data can also be used in the activation of time-sensitive Internet of things (IoT) software or devices as well as the establishment of the algorithm on fixed-rail transportation, such that the SBSs selected by the toss-and-catch algorithm for a handover can ensure the normal operation of these time-sensitive application programs or equipment while a train is moving. Lastly, we hope that the toss-and-catch algorithm can be applied to the Internet of vehicles (IoV) of general vehicles for pre-handover SBS selection. Even though general vehicles have a lower maximum speed compared to fixed-rail public transportation systems, the uncertainty of the operating tracks would increase the difficulty of analysis. Therefore, if the track path (including the turns and unfixed tracks) of a vehicle can be predetermined, then the toss-and-catch algorithm can serve as a basis for the development of another algorithm or the modification of the existing algorithm, which also takes into account non-fixed-rail-based IoT devices. Going forward, the toss-and-catch algorithm (or a modified version of it) and artificial intelligence (AI) application could be further integrated and applied to emerging IoV technologies (e.g., the application of AI-based object detection to detect highway accidents [29]). This would enable us to take the integration of the two emerging fields of 5G IoV and AI to the next level. **Author Contributions:** Conceptualization, Y.-L.C. and S.-H.W. ; methodology, Y.-L.C. and S.-H.W. ; software, S.-H.W. ; validation, Y.-L.C. ; formal analysis, Y.-L.C. ; investigation, Y.-L.C. and S.-H.W. ; data curation, Y.-L.C. and S.-H.W. ; writing--original draft preparation, Y.-L.C. ; writing--review and editing, Y.-L.C. ; visualization, S.-H.W. ; supervision, Y.-L.C. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Acknowledgments:** The authors wish to express their appreciation for the financial support provided by the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., under Contract MOST 109-2221-E-019 -051-MY2. **Conflicts of Interest:** The authors declare no conflict of interest.
# Aggregatable Subvector Commitment with Efficient Updates
## 4 Security Analysis  
### Assumptions  
In this section, the soundness for our scheme is validated based on the following assumptions.  
Figure 1: Our scheme’s model. The committer selects a vector to commit, usually a vector \(\textbf{v}=\{v_{0},v_{1},v_{2},v_{3},\ldots,v_{n-1}\}\). The vector is transformed into a commitment \(C\) and the \(C\) is sent to the verifier. The commitment is an encrypted or hidden form that contains information about the vector. The verifier receives the commitment \(C\), the vector value \(v_{i}\) at a location \(i\) and its location proof \(\pi_{i}\), and verifies that \(v_{i}\) is indeed stored at the \(i\)-th location. Additionally, our model consists of two critical phases: the Update Algorithm and the Addition Algorithm. During the Update Algorithm phase, by specifying the difference \(\delta\) and the corresponding update index \(i\), the committer can update the commitment and proofs. In the Addition Algorithm phase, after inserting a new value \(v_{n}\), the committer has to update the commitment and proofs accordingly.  
**Assumption 1** (\(t\)-Strong Diffie-Hellman (\(t\)-SDH) [29]).: _Selecting \(\tau\in_{R}\mathbb{Z}_{p}^{*}\) uniformly at random, provided the input \((t+1)\)-tuple \((g,g^{\tau},\ldots,g^{\tau^{t}})\in\mathbb{G}^{t+1}\), against any adversary \(\mathcal{A}_{t-\mathbf{SDH}}\), we define the condition for any \(c\in\mathbb{G}_{p}\)\(\{-\tau\}\) as follows:_  
\[P_{r}\Big{[}\mathcal{A}_{t-\mathbf{SDH}}(g,g^{\tau},\ldots,g^{\tau^{t}})=(c,g ^{\frac{1}{t+c}})\Big{]}\leq\text{negl}(\lambda). \tag{31}\]  
**Assumption 2** (\(t\)-Strong Bilinear Diffie-Hellman (\(t\)-Sbdh) [30; 31]).: _Selecting \(\tau\in_{R}\mathbb{Z}_{p}^{*}\) uniformly at random, provided the input \((t+1)\)-tuple \((g,g^{\tau},\ldots,g^{\tau^{t}})\in\mathbb{G}^{t+1}\), against any adversary \(\mathcal{A}_{t-\mathbf{SDH}}\), we define the condition for any \(c\in\mathbb{G}_{p}\)\(\{-\tau\}\) as follows:_  
\[P_{r}\Big{[}\mathcal{A}_{t-\mathbf{SDH}}(g,g^{\tau},\ldots,g^{\tau^{t}})=(c,e (g,g)^{\frac{1}{t+c}})\Big{]}\leq\text{negl}(\lambda) \tag{32}\]  
_For the above assumptions, they will be used to prove the soundness for our scheme._
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 3 Robust ENF Estimation Techniques
### Robust ENF Enhancement Techniques
#### 3.3.1 Weighted Harmonics Combination  
The ENF signal is embedded in multiple harmonics depending on the nominal frequency. For audio recordings, the ENF is present in either even or odd harmonics depending on the type of microphone used. Similarly, in video recordings, the harmonics are the multiple of illumination frequency, which is twice the nominal frequency. Other harmonics can be leveraged to obtain accurate fluctuations for noise interference in targeted frequencies. Therefore, a weighted combination of the harmonics' spectral bins can result in a noise-tolerant spectrum for reliable frequency estimation [54]. The SNR values are computed as a ratio of the PSD (\(s(f)\)) in the ENF fluctuation range (\(f_{c}\)) to the PSD in the spectral band of interest (\(f_{v}\)). The optimal ENF fluctuation in the U.S. is \(\pm 0.02\).  
\[w_{k}=\frac{\sum_{k=1}^{L}s(f_{o}-f_{c},f_{o}+f_{c})}{\sum_{k=1}^{L}s(f_{o}+f_{c },f_{o}+f_{v})+s(f_{o}-f_{c},f_{o}-f_{v})}\]  
where \(L\) is the maximum number of harmonics carrying ENF fluctuations and can be combined. Using the weights computed for windowed spectral bins, the final spectrum \(S(f)\) is evaluated, and quadratic interpolation can be used to estimate the spectral peaks and frequency fluctuations.  
\[S(f)=\sum_{k=1}^{L}w_{k}s(f)\]  
In our approach, we used weighted estimation from multiple harmonic bins to identify the frequency with the highest SNR compared to other harmonics. For audio recordings, due to the nature of the microphone used, the ENF is either embedded in the even harmonics or odd harmonics. Similarly, for video recordings, the ENF is embedded on the Frames Per Second (FPS), the illumination frequency, and the type of artificial light used. With the weight matrix, the ENF with the highest SNR is identified, and then, the following filtering algorithm is used to enhance the ENF in that frequency range.
# Sustainable Fire Protection: Reducing Carbon Footprint with Advanced Coating Technologies
## 2 Materials and Methods  
### Materials  
MFR's principal component is based on cork, a natural insulator obtained from the bark of the cork oak, one of the few materials that have a negative CO2 footprint because the raw material in nature fixes the CO2 existing in the air and does not require cutting down the tree that generates it, repeating the process cyclically.  
A study has proven that a cork stopper of a wine bottle retains 234 g of CO2 [9] MFR coating characteristics include CO2 absorption (over 9-12 kg/year of CO2 per square meter/mm [9]. of applied thickness, contributing to an impressive circular economy result), anti-condensation effect; resistance to marine environments, impact, and stretching; thermal insulation; fire resistance; and anti-icing effect.  
The production of MFR involves a micromization technique for natural-cork particles to a size in the range of 10-20 microns. This process enhances the material's distribution within the resin and facilitates the application to the desired final thicknesses. Cork-loaded resin contains more than 85% natural cork by volume, which, once cured, ensures that the resulting film imparts all the inherent properties of cork.  
Study hypothesis: It is hypothesized that the MFR coating will outperform traditional fire protection systems, for example, intumescent coatings, due to its higher proportion of natural materials, such as cork. This is expected to result in superior corrosion resistance, thereby reducing the required thickness to achieve equivalent fire protection levels and minimizing degradation in corrosive environments. Additionally, natural cork's ability to absorb CO2 and the acrylic water-based, solvent-free formulation of the product further enhances its environmental benefits.
## 6 Open Challenges, Future Prospects and Research Directions
### Advanced Wavefront Sensors and Deformable Mirrors  
The development of more advanced wavefront sensors is critical for improving the ability of AO systems to detect and correct for atmospheric distortions in real-time.  
Enhanced Wavefront Sensing: Future research will likely explore new sensor technologies that offer higher sensitivity, faster response times, and greater accuracy. Innovations in wavefront sensing could include the use of novel materials and designs that improve performance while reducing power consumption and cost. These sensors will be essential for detecting subtle changes in the optical wavefront caused by turbulence, allowing for more precise corrections.  
High-Precision Deformable Mirrors: DMs are central to the operation of AO systems, as they physically adjust to correct distortions in the optical wavefront. Future advancements will focus on developing high-precision DMs that can respond more quickly and accurately to detected distortions. This includes the exploration of new actuator technologies and mirror materials that offer improved performance characteristics. Additionally, research will aim to increase the number of actuators on DMs, providing finer control over the mirror surface and enabling better correction of complex wavefront distortions.  
Integration of Machine Learning Algorithms  
ML algorithms are anticipated to be pivotal in developing predictive and dynamic AO systems with real-time optimization.  
Predictive and Dynamic AO Systems: ML algorithms are anticipated to play a significant role in optimizing AO systems by predicting and compensating for dynamic environmental changes. ML can be used to analyze historical and real-time data to predict atmospheric conditions and preemptively adjust the AO system. For example, neural networks can be trained to recognize patterns in AT and optimize the response of wavefront sensors and DMs accordingly. This predictive capability will enhance the robustness and efficiency of FSO communication by reducing latency and improving correction accuracy [389].  
Real-Time Optimization: ML algorithms can also be employed for the real-time optimization of AO systems. By continuously learning from the incoming data and adjusting system parameters on-the-fly, these algorithms can ensure optimal performance under varying conditions. This dynamic adaptation will be particularly valuable for mobile FSO platforms, such as UAVs, where environmental conditions can change rapidly.
## 4 Simulation Experiment of IABCBAS Algorithm Path Planning Application
### Comparison Experiment of Two-Dimensional Environment Simulation  
In the initial stage, the improved ABC algorithm performs global search to generate multiple feasible paths, thereby enhancing the global search capability and verifying the effectiveness of path planning. The fitness value is calculated to determine the highest-quality global path, and the path points corresponding to the pheromone distribution are passed to the BAS algorithm. This reduces the blindness of the BAS algorithm's search, strengthens its local optimization capability, and accelerates convergence.  
The SSA, ACO3, and BAS3 algorithms are selected for comparative simulation experiments on path planning. Each algorithm runs 50 experiments in the fence map in Figure 5, with the following parameters: maximum iteration number = 500; search space dimension = 30; and population size = 100. All experiments are conducted under identical parameters. The path planning results in simple and complex environments are compared in Figures 7 and 8, while the convergence comparison of different algorithms in these two environments is presented in Figure 9.  
with a shorter planned path that is not affected by obstacles in space, thus achieving the best overall planning performance.  
The comparison of the average values of the four algorithms over 500 iterations is presented in Table 5. For path planning in the complex environment map, the convergence times of the SSA, BAS3, and ACO3 algorithms increase with the number of obstacles, converging to the optimal solution at the 49 th, 42 nd, and 39 th iterations, respectively. Additionally, the number of turns increases, and the path length becomes longer. The proposed IABCBAS algorithm yields an overall smoother path. Compared with SSA, BAS3, and ACO3, the number of turns is reduced by 46.32%, 28.74%, and 25.31%, respectively. Moreover, the planning time for the shortest path is 10.28 s faster than the average of the three algorithms. This indicates that the algorithm maintains superior planning performance in complex environments, meeting the current requirements of engineering applications.
# An Energy Demodulation Based Fiber Optic Sensing System for Landslide Early-Warning
## 1 Introduction  
Disasters associated with severe landslides are common world-wide. Landslides contribute thousands of deaths per year and economic losses from property damage in the billions of dollars [1; 2; 3]. Although numerous attempts at monitoring the landslides [4; 5] have been undertaken since the early 1900s to evaluate the stability of the landslides. Conventional methods for landslide early-warning include inclinometer, displacement meter, strain gauges, water level meter, photogrammetry, and others [6]. These warning tools are point-monitoring or small-scale, sometimes, which cannot acquire the sufficient key signals for early-warning of a landslide [7].  
It is difficult for current technologies to forecast the occurrence location, time, and intensity of displacement accurately. For the mitigation and prevention of landslide disasters, it is important to develop an effective early warning system for landslides. Slope stability is mainly affected by three basic factors, namely geo-structural, environmental, and human-induced (artificial) factors. Geo-structural factors that can trigger landslide events include the proximity to active faults, slide-prone geological formations, pre-existing fracture zones, degree of slope, degree of rock weathering, and the physical and mechanical parameters of the soil. Environmental factors may include the intensity and duration of a rainfall, and the movements of groundwater [8]. Man-made or artificial factors that add to these natural land-slide inducing elements may include activities such as blast vibrations, excavation, and transportation. No matter which element affects the stability of a slope, the outcome is the deformation of the slope. Thus, an effective early-warning of a landslide should focus on the deformation monitoring of the slope.  
In this paper, an innovative landslide early-warning technology is introduced. This technology is called energy demodulation based fiber optic sensing system for landslide warning (FOS-LW for short), and has built-in unique features such as graded alarm, real-time response, remote monitoring, and a passive optical networks [9; 10; 11]. It is able to monitor the early deformation of both soil and rock mass of slopes and thus serves as an early-warning technology for landslides.
# Cybersecurity for AI Systems: A Survey
## 3 AI Attacks and Defense Mechanisms
### Types of Failures
#### 3.1.1 Categories of Unintentional Failures  
Unintentional failures happen when AI/ML systems produce an unwanted or unforeseen outcome from a determined action. It happens mainly due to system failures. In this research we further categorize different types of unintentional failures.  
* **Reward Hacking:** Reward hacking is a failure mode that an AI/ML system experiences when the underlying framework is a reinforcement learning algorithm. Reward hacking appears when an agent has more return as reward in an unexpected manner in a game environment [61]. This unexpected behavior unsettles the safety of the system. [PERSON] et al. [62] proposed a new multi-step reinforcement learning framework, where the reward function generates a discounted future reward and, thus, reduces the influence of immediate reward on the current state action pair. The proposed algorithm creates the defense mechanism to mitigate the effect of reward hacking in AI/ML systems.
* **Distributed Shift:** This type of mode appears when an AI/ML model that once performed well in an environment generates dismal performance when deployed to perform in a different environment. One such example is when the training and test data come from two different probability distributions [63]. The distribution shift is further subdivided into three types [64]: 1. Covariate Shift: The shifting problem arises due to the change in input features (covariates) over time, while the distribution of the conditional labeling function remains the same. 2. Label Shift: This mode of failure is complementary to covariate shift, such that the distribution of class conditional probability does not change but the label marginal probability distribution changes. 3. Concept Shift: Concept shift is a failure related to the label shift problem where the definitions of the label (i.e., the posteriori probability) experience spatial or temporal changes.  
[PERSON] and [PERSON] proposed an operator-based hierarchy of solutions that are stable to the distributed shift [65]. There are three operators (i.e., conditioning, intervening and computing counterfactuals) that work on a graph specific to healthcare AI. These operators effectively remove the unstable component of the graph and retain the stable behavior as much as possible. There are also other algorithms to maintain robustness against the distributed shift. [PERSON] et al. [66] proposed a data-driven approach, where the learning of models occurs using data from diverse environments, while [PERSON] et al. [67] devised bounded magnitude-based robustness, where the shift is assumed to have a known magnitude.  
* **Natural Adversarial Examples:** The natural adversarial examples are real-world examples that are not intentionally modified. Rather, they occur naturally, and result in considerable loss of performance of the machine learning algorithms [68]. The instances are semantically similar to the input, legible and facilitate interpretation (e.g., image data) of the outcome [69]. Deep neural networks are susceptible to natural adversarial examples.
# Dynamic Behavior of Twin-Spool Rotor-Bearing System with Pedestal Looseness and Rub Impact
###### Abstract  
The twin-spool rotor-bearing system plays a crucial role in the aero-engine. The potential manufacturing defect, assembly error, and abnormal working loads in the rotor-bearing system can induce multiple rotor failures, such as bolt looseness and rub impact. However, the prediction of the fault rotor dynamic behavior for the aero-engine remains a difficult frontier in numerical modeling. We present a dynamic model of the twin-spool rotor-bearing system, the failure model of bearing seat loosening, and the failure model of rub impact by using second-type Lagrangian equations, finite element theory, and the Timoshenko beam theory. In particular, to improve the accuracy of the numerical model, the rotating speed control equation and the actual aero-engine parameter are taken into account. An analysis is conducted on the impact of critical failure parameters, such as looseness stiffness and rub impact initial gap, on the vibration behaviors of the essential components of the twin-spool rotor system and on the entire engine. Additionally, this paper examines the twin-spool rotor-bearing system affected by looseness-rub coupled failures. The obtained conclusions can serve as a theoretical foundation for optimizing the structure and diagnosing faults in the aero-engine rotor system.  
turbofan; twin-spool rotor; vibration; aero-engine failure; dynamic behavior
# Chinese Event Detection without Triggers Based on Dual Attention
## 4 Experiment
### Experiment Results Analysis
#### 4.2.3 Ablation Analysis  
EDWTDA is made up of four major components: the word embedding layer, the feature construction layer, the dual attention mechanism layer, and the focal loss function. To figure out how much each of these four components influences the model, one of them is removed or replaced at a time, and the performance is compared with the original model.  
Table 7 shows that the dual attention mechanism layer has the greatest impact. The bold part represents the optimal experimental results. Removing this layer decreases the F1-score by 6.15% and 6.25%, respectively, on the two datasets, which indicates that this layer is the core component of event detection without trigger words. The dual attention mechanism layer raises the attention weight of the event trigger word within the sentence as well as that of the sentence within the document. It allows the model to pay more attention to the location of the event trigger word without special markers and improves event detection precision and recall. The F1-score decreases by 2.99% and 3.19%, respectively, on the two datasets after changing the word embedding layer from ALBERT to skip-gram. It can be seen that the word embedding layer is second only to the dual attention mechanism layer. This demonstrates that ALBERT can dynamically learn context and break the limitation that skip-gram cannot represent polysemous words. Meanwhile, the word embedding vector represented by ALBERT contains richer meanings, contributing to the determination of event type. With focal loss replaced by cross-entropy, the F1-score decreases by 2.33% and 2.39% on the two datasets, respectively, showing that focal loss can reduce the sample imbalance and improve the model's generalization ability. The feature construction layer has the least impact. After removing it, the F1-score only decreases by  
Figure 5: The F1-score of EDWTDA varies with the value of \(\lambda\).  
0.48% and 0.43%. It means that lexical annotation and entity labeling have a subtle influence on model performance but do help identify trigger words. Trigger words are mainly verbs or nouns; entity labels are mostly O. The feature construction layer can filter parts of the words in the sentence and improve the model's prediction ability.