esa-sceva/satcom-chunk-collection huggingface dataset:
# User Comment-Guided Cross-Modal Attention for Interpretable Multimodal Fake News Detection
## 3 Methodology
### Fusion of User Comments and News Content
#### 3.5.2 User Comment and Text Fusion (UCT)
```
0: Comment features \(F_{C}\), image features \(F_{V}\), text features \(H_{T}\), importance weights \(S_{importance}\)
0: Comment-image fusion features \(F_{CI}\), comment-text fusion features \(F_{TC}\)
1:// UCI: Comment-Image Fusion
2:\(Q_{CI}\leftarrow\text{Linear}_{Q}(F_{C})\)\(\triangleright\) Comment queries
3:\(K_{CI}\leftarrow\text{Linear}_{K}(F_{V})\), \(V_{CI}\leftarrow\text{Linear}_{V}(F_{V})\)\(\triangleright\) Image keys and values
4:\(A_{CI}\leftarrow\text{softmax}\bigg{(}\frac{Q_{CI}K_{CI}^{T}}{\sqrt{d_{k}}} \bigg{)}\)\(\triangleright\) Attention weights
5:\(F_{CI}\gets A_{CI}V_{CI}\)\(\triangleright\) Weighted fusion
6:// UCT: Comment-Text Fusion
7:\(Q_{CT}\leftarrow\text{Linear}_{Q}(F_{C})\)\(\triangleright\) Comment queries
8:\(K_{CT}\leftarrow\text{Linear}_{K}(H_{T})\), \(V_{CT}\leftarrow\text{Linear}_{V}(H_{T})\)\(\triangleright\) Text keys and values
9:\(A_{CT}\leftarrow\text{softmax}\bigg{(}\frac{Q_{CT}K_{CT}^{T}}{\sqrt{d_{k}}} \bigg{)}\)\(\triangleright\) Attention weights
10:\(F_{CT}\gets A_{CT}V_{CT}\)\(\triangleright\) Weighted fusion
11:// Aggregated Comment Representation
12:\(C_{agg}\leftarrow\sum_{i=1}^{n}S_{importance}(c_{i})\cdot c_{i}\)\(\triangleright\) Importance weighting
13:\(F_{TC}\leftarrow\text{Concat}(H_{T},C_{agg})\)\(\triangleright\) Feature concatenation
14:return\(F_{CI}\), \(F_{TC}\)
```  
**Algorithm 1** User comment fusion algorithm. Algorithm 1 establishes a comprehensive framework for integrating user comments with multimodal news content. The UCI branch enables the model to identify visual elements that users discuss or question, while the UCT branch captures supplementary or contradictory information from comments regarding textual content. The importance weighting mechanism ensures that high-quality, credible comments play a more significant role in the detection process. This dual-branch design fully utilizes the diversity of user comments, considering both the verification of visual content by comments and the supplementation and correction of textual content.
# Solving the Parallel Drone Scheduling Traveling Salesman Problem via Constraint Programming
## 4 Computational Experiments
### Main Experimental Results
#### 4.2.2 Instances Proposed in [PERSON] et al. in [7]  
A second program of experiments to test the new CP model was carried out on the instances originated from TSPLIB [21] in [7]. For each original TSPLIB instance considered, several PDSTSP instances were created; we refer the reader to [7; 14] for the details. The results cover instances with 48 to 229 customers (first number in the name of each instance); the results are presented for several methods (we show the best results disclosed in a single publication where alternative methods are discussed) together with those of the new CP model. It should be noted that the latter is the first effective exact solving method used for these instances. The results are grouped by original TSPLIB instance and are summarized in Tables 2-7. For each heuristic method, the best upper bound is reported, while, for the CP method, the optimal solution cost, the time required to retrieve the optimal solution (_Sec found_), and the time to prove its optimality (_Sec opt_) are annotated. In the tables, we report in italics any suboptimal result, and, in bold, newly improved upper bounds. In this case, the results are taken from the original papers and, therefore, are likely to have been obtained on different hardware configurations.  
Examining the results reported in Tables 2-7, several observations can be made. The first is that the heuristic methods are extremely effective. The last methods particularly retrieved almost all the optimal solutions (without proving their optimality). The CP solver was able to marginally improve one solution over the previously best known results. It should be noted that this result indirectly validates the quality of the heuristic methods previously proposed. The times required to solve instances to optimality varied from a few seconds for the smaller instances to more than 17,000 s for the largest and most difficult instance.
# Large Thermal Expansion LTCC System for Cofiring with Integrated Functional Ceramics Layers
## 2 Materials and Methods
### Sample Characterization  
The particle size of the powders was measured using laser diffraction (Mastersizer 2000, Malvern Panalytical, Malvern, UK). The specific surface S of the powders was determined from nitrogen adsorption/desorption isotherms (BET, Nova 2000, Quantachrome Corporation, Boynton Beach, FL, USA). The shrinkage of the laminates was monitored using a push-rod DIL402 SE dilatometer (NETZSCH-Geratebau GmbH, Selb, Germany) on heating to 900 \({}^{\circ}\)C with heating rates of 2 K/min, 4 K/min, or 8 K/min, respectively, followed by 4 h dwell time at 900 \({}^{\circ}\)C. The contact force of the push rod was set to a minimum value of 0.01 N. The coefficient of thermal expansion (CTE) was measured on sintered samples using a heating rate of 4 K/min. In addition, averaged shrinkage values in _xy_- and _z_-directions were obtained from measurements of dimensions of test samples before and after sintering using a vernier caliper. The green and sintered densities were obtained from the weight and dimensions of 40 \(\times\) 60 mm\({}^{2}\) samples measured at 15 different points using the micrometer screw. The porosity of the multilayer samples was measured with Hg porosimetry (Pascal 140/440, Thermo Fisher Scientific Inc., Waltham, MA, USA).  
Thermal analysis of the CT708 initial powder and of the green tapes was performed using a TG/DTA 92-16.18 system (SETARAM SA, Caluire, France) and a DSC unit (STA PT1600, Linseis Messgerate GmbH, Selb, Germany).  
Phase formation was evaluated using X-ray diffraction (XRD) with a D8 (Bruker AXS GmbH, Karlsruhe, Germany). Qualitative phase analysis was performed using the EVA software package with the ICDD PDF-2 (2002) database [18], whereas the content of individual phases in the sintered samples was obtained through Rietveld refinements using Topas 6 software (Bruker AXS GmbH, Karlsruhe, Germany). The microstructure ofthe multilayer samples was studied using a scanning electron microscope (SEM, ULTRA 55, Zeiss Microscopy GmbH, Oberkochen, Germany). Elemental analysis was performed with energy dispersive X-ray spectroscopy (EDX) using a XFlash 5030 (Bruker Nano GmbH, Berlin, Germany). The permittivity and loss tangent of the sintered samples were measured at room temperature at 1 kHz using a LCR meter (Wayne Kerr Europe GmbH, Iserlohn, Germany), samples metallized with AgPd paste, fired at 650 \({}^{\circ}\)C) and in the frequency range from 10 MHz to 1 GHz using an E4991A impedance/materials analyzer equipped with a 16453A dielectric material test fixture (Agilent Technologies, Inc., Santa Clara, CA, USA).
## 4 Performance Evaluation  
REL is evaluated through a testbed (small-scale) and OMNET++ simulator (large-scale). The testbed results are used to calibrate the simulation experiments, and also to analyze the performance of REL in real experiments. The objective of the simulation experiments is to evaluate the REL and compare it with both AODV and LABILE in terms of energy-efficiency, latency and data delivery in a large-scale scenario.
## 1 Introduction
#### Key Contributions  
* **Proposal of a novel hybrid method:** We introduce JADEDO, a new optimization algorithm that fuses the dispersal stages of the dandelion optimizer with the adaptive mutation and crossover mechanisms of JADE, aiming to leverage both global exploration and local exploitation in a unified framework.
* **Benchmark validation:** Comprehensive experiments on the IEEE CEC2022 benchmark functions, encompassing unimodal, multimodal, and hybrid landscapes, demonstrate JADEDO's superior or highly competitive performance in terms of solution accuracy, convergence speed, and robustness.
* **Real-world applicability:** We validate JADEDO on three engineering design problems (pressure vessel, spring, and speed reducer) and an attack-response optimization scenario. In each case, JADEDO identifies effective solutions under strict constraints, highlighting its capacity to handle high-stakes, complex optimization tasks.
## 3 Methodology
### Optimizer  
The optimization problem is solved using the MATLAB function _fmincon_[51], which is suitable for finding the minimum of an objective function subject to constraints. _fmincon_ uses the \"interior-point\" algorithm by default, which converts a constrained problem into a series of unconstrained problems by adding a barrier to the objective function. This barrier prevents the solutions from approaching too close to the constraint boundaries, keeping them within the feasible region. As the algorithm progresses, the influence of the barrier is reduced, allowing the solution to approach the optimum within the permitted limits.  
The interior-point algorithm follows a path known as the \"central path\", guiding the solution toward the optimum while remaining within the feasible region. During this process, the algorithm ensures that the Karush-Kuhn-Tucker (KKT) conditions [52], which are fundamental to guaranteeing optimality in constrained optimization problems, are met. These conditions include primal feasibility, dual feasibility, stationarity, and complementary slackness.  
This methodological approach is implemented systematically, evaluating all possible radial configurations and adjusting the flexible buses' variables to find the most efficient configuration with minimized operational costs.
## 3 The Proposed PHM System
### The Framework
This information includes labels on the operating condition, component-level features, system-level features, Health Indicators, and the RUL of each component. These data are integrated with historical data to train diagnostic and prognostic models, which will be applied at the edge during machinery functioning for real-time inference. At this level, which is only accessible by the machine producer, it is possible to aggregate the information depending on the analysis objective (component-level, system-level, client-level). In the last case, the producer can use this information for building an optimal maintenance plan in terms of time and human resources. In other words, this layer allows addressing the second producer requirement. Finally, in the knowledge integration and decision-making layer, the PHM system is integrated with existing offline databases, e.g., CMMS. At this level, information related to the available resources, their cost, and scheduled maintenance interventions on less critical components (e.g., preventive maintenance) is integrated with data collected through the PHM system to have a global vision of maintenance activities and their costs. This results in optimized scheduling of the maintenance interventions and production, as long as the spare parts production can be triggered when necessary. Finally, the most critical components, or activities, can be identified, giving the possibility to better design both the machine and the maintenance service. The definition of the physical architecture, i.e., the set technologies for the communication between layers, is not the main topic of the present paper. However, one can consider the architecture model presented in [80] and [81] as a possible guideline for the physical implementation of the proposed framework.
## 3 Test Data Analysis
### Z Vibration Level Analysis
#### 3.5.1 Train Speed Calculation  
According to \"Technical Guidelines for Environmental Impact Assessment--Urban Rail Transit. Beijing, China: Ministry of Ecology and Environment\" HJ453-2008 [34], for the environmental vibration caused by train operations, the train speed correction formula at different speeds is:  
\[C_{V}=20\lg\frac{\
u}{v_{0}} \tag{6}\]  
where:  
\(v_{0}\)--the reference speed of the source intensity, in km/h;  
\(v\)--the running speed of the train, in km/h.  
Therefore, the speed of the train needs to be calculated. In the analysis, the displacement-waveform bandpass filtering [35; 36] is mainly used to obtain the displacement time- domain curve of the rail measuring point, as shown in Figure 23.  
The running speed of the train is calculated based on the time between the wheels and the distance between the wheels. Since the measured vehicle is a subway B-type vehicle [37], the design speed of the vehicle is 80 km/h, and the basic parameters of the vehicle are shown in Table 2. Therefore, it can be calculated that the data for the four operating conditions of straight line segment--ordinary monolithic track, straight line segment--steel-spring-floating slab track, curved segment--ordinary monolithic track, and curved segment--steel-spring-floating slab track are: 71.36 km/h, 73.00 km/h, 66.87 km/h, 63.16 km/h. It can be seen that the train speed in the straight line segment is higher than that in the curved segment.
## 6 Discussion
### Computational Performance of Different Prediction Methods  
To compare the computational performance of the proposed model, the BPNN and Extreme Learning Machine (ELM) [37] were selected for comparative analysis. The two data prediction methods also use QPSO to get the optimal calculation parameters, and the calculation results of 100 calculations were shown in Table 10.  
It could be seen from Table 10 that the early warning error rate of QPSO-LSSVM was only 4.41%, and the early warning accuracy was significantly higher than that of QPSO-BP and QPSO-ELM. The prediction model constructed by BPNN had the largest misjudgment rate, which was 4.37 times that based on the LSSVM model. Under the same optimization algorithm, the average calculation time of LSSVM was the shortest, and the optimal solution could be found the fastest. It was considered that this method had the advantages of being a simple model and having a fast calculation speed.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Early Warning Model** & **QPSO-BP** & **QPSO-ELM** & **QPSO-LSSVM** \\ \hline Average number of misjudgments & 3.28 & 1.74 & 0.75 \\ Average prediction error & 19.29\% & 10.24\% & 4.41\% \\ Average calculation time & 687.52 & 374.24 & 203.38 \\ Average iteration times at convergence & 439.34 & 233.01 & 134.71 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Calculation results of different early warning models.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Optimization** & \begin{tabular}{c} **Average** \\ **Number of** \\ **Misjudgments** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Prediction** \\ **Error** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Calculation** \\ **Time** \\ **Convergence** \\ \end{tabular} &
\begin{tabular}{c} **Average Iteration** \\ **Times at** \\ **Convergence** \\ \end{tabular} \\ \hline TE & 4.14 & 24.35\% & - & - \\ GA & 1.01 & 5.94\% & 1574.24 & 574.14 \\ PSO & 1.27 & 7.47\% & 654.04 & 377.08 \\ GSA & 1.05 & 6.18\% & 317.17 & 229.07 \\ WOA & 1.24 & 7.29\% & 357.81 & 197.43 \\ QPSO & 0.75 & 4.41\% & 203.38 & 134.71 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Calculation results of different optimization algorithms.  
According to the calculation principles of the three algorithms, the LSSVM determined the segmentation hyperplane only with fewer support vectors, while BPNN or ELM followed the law of large numbers in nonlinear modeling [38]. When the training data samples of BPNN or ELM were more, their prediction results were closer to the truth. The number of sample sets of BPNN or ELM was often required to be more than ten times that of input variables. Therefore, under the condition of sufficient historical data and large research samples, the BPNN or ELM method had more advantages than LSSVM in nonlinear modeling ability and computational performance. However, when the historical data was insufficient or the research samples were small, the nonlinear modeling ability and computational performance of LSSVM were significantly better than those of the BPNN or ELM methods.
## 2 Related Works
### Lightweight Machine-to-Machine Protocol  
OMA Lightweight M2M (LWM2M) [38] is a protocol from the Open Mobile Alliance for M2M or IoT device management. It is a communication protocol used between client software on a M2M device and server software on a M2M management and service enablement platform. In order to use the LWM2M protocol for remote management of M2M facilities it has four characteristics: (1) it features an architectural design based on REST, which is usually appealing to software developers, (2) defines a resource and data model that is extensible, (3) has been designed with performance and the constraints of M2M devices in mind, and (4) it reuses and builds over an efficient secure data transfer standard, since CoAP has been standardized by the Internet Engineering Taskforce (IETF) as a variation of the Internet's HTTP protocol (appropriate for data transfer to and from low-cost connected IoT devices).  
As it can be inferred, LWM2M provides several interfaces built on top of Constrained Application Protocol (CoAP) to perform management of a wide range of remote embedded devices and connected appliances in the Internet of Things application domain, with the objective of performing remote service enablement and remote application management. LWM2M is targeted in particular at constrained devices, e.g., devices with low-power microcontrollers (40 MHz), small amounts of Flash (100 KB) and RAM (10 KB) or batteries expected to last for years over network variable availability [39].
## 2 Materials and Methods
### Classification of Referable Diabetic Retinopathy  
Diabetic retinopathy (DR) is the leading cause of avoidable blindness across the world, mainly due to the lack of accessibility to the screening exams. The International Clinical Diabetic Retinopathy Severity Scale (ICDRDSS) provides guidelines for assessing the severity of diabetic retinopathy in the scale of 0-4 [30]. A scale equal to or above 2 is called referable DR, indicating that the patient is encouraged to visit an ophthalmologist for closer monitoring of the progress (Figure 1). Therefore, it is important to triage patients in screening centers and refer them to ophthalmologists for efficient and timely treatments. In this work, we focused on binary classification to differentiate between referable and non-referable DR in color fundus photographs.
## 3 Parametric Study
### Evolution of the Portable Wideband Antenna Scheme  
This section discusses the evolutionary stages of the proposed portable wideband directional antenna scheme (model III), consisting of three antenna generations: first, second, and third generations. As shown in Figure 7, the first generation was the antenna scheme without a reflector and director, and the second generation was the antenna scheme with the corrugated reflector but without the director. The third generation was the proposed portable wideband directional antenna scheme with the corrugated reflector and the V-shaped director.  
Figure 5: The simulated antenna gains of models I, II, and III.  
Figure 6: Simulated xz- and yz-plane radiation patterns of the antenna models I, II, and III.  
Figure 8 compares the simulated impedance bandwidth (\(1\,S_{11}\,|\,\leq-10\) dB) of the first-, second-, and third-generation antenna schemes. The first-generation antenna scheme achieved a narrow impedance bandwidth, covering 830-925 MHz, rendering the scheme operationally unsuitable for digital terrestrial television reception. The impedance bandwidth of the second-generation antenna scheme closely resembled that of the third-generation scheme, covering a 468-907 MHz frequency band. Both the second- and third-generation antenna schemes covered the entire target operating frequency band of 470-890 MHz. However, the overall gain of the third-generation antenna scheme was higher than that of the second-generation scheme, as shown in Figure 9.  
Figure 10 compares the simulated xz- and yz-plane radiation patterns of the first-, second-, and third-generation antenna schemes. The radiation pattern of the first-generation antenna scheme was of omnidirectionality due to the absence of the corrugated reflector (Figure 7a). The first-generation scheme failed to cover the entire target operating frequency band (Figure 8). The radiation patterns of the second- and third-generation antenna schemes were closely similar. However, the gain of the third-generation scheme was higher than that of the second-generation scheme.  
Figure 8: Simulated impedance bandwidth (\(1\,S_{11}\,|\,\leq-10\) dB) of the first-, second-, and third-generation antenna schemes.  
Figure 7: Evolutionary stages of the portable antenna scheme: (**a**) first generation, (**b**) second generation, and (**c**) third generation.
# Fracture Assessment of Weld Joints of High-Strength Steel in Pre-Strained Condition
## 2 Manufacturing of the HSB 600 High-Performance Steel Welds
### Double-Edge Through-Thickness Crack Panel within a Crack in the HAZ  
The fracture resistance of the butt-welded joints subjected to tension in the pre-strained condition was estimated by considering two effects: (1) toughness deterioration due to pre-strain (ductile-to-brittle transition temperature shifts due to pre-strain), and (2) toughness correction for constraint loss in the target specimen.  
The target specimen for fracture assessment was a double-edged, through-thickness crack panel (ETCP) with a crack in the HAZ, as shown in Figure 2. The crack length \(2a\) ranged from 10 mm to 100 mm. It was assumed that 3.0% tensile pre-strain was applied to the HAZ region. The pre-strain amount was measured with line interval lengths near the HAZ area. Lines were drawn at intervals of 3 mm in the loading direction of the specimen, before tensile loading, to measure the pre-strain amount in the HAZ. As the width of the HAZ was 3 mm, an interval of 3 mm was used to measure the pre-strain amount of HAZ. The tensile load applied until the 3 mm interval became 3.09 mm, especially near the HAZ.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen Symbol** & **Young’s Modulus E, (MPa)** & **Yield Strength YS, (MPa) * & **Tensile Strength TSL, (MPa)** & **Yield-to-Tensile Ratio Y/T * \\ \hline BM*-1 & 204,600 & 620 & 679 & 0.91 \\ BM-2 & 207,800 & 613 & 693 & 0.88 \\ BM-3 & 205,000 & 632 & 691 & 0.91 \\ BM-4 & 206,700 & 576 & 684 & 0.84 \\ BM-5 & 205,100 & 593 & 680 & 0.87 \\ BM-6 & 208,300 & 589 & 688 & 0.86 \\ Average & 206,250 & 604 & 686 & 0.88 \\ \hline \hline \end{tabular}  
* BM: base metal, YS: 0.2% proof stress, Y/T= YS/TS.  
\end{table}
Table 1: Mechanical properties of the HSB600 steel plate used.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Welding Process** & **Current (A)** & **Voltage (V)** & **Speed (cm/min)** & **Heat Input (kJ/cm)** & **Preheat Temp. (\({}^{\circ}\)C)** & **Interpass** \\ \hline SAW & 700 & 34 & 29 & 50 & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of the welding consumable (wt. %).  
Figure 1: Groove geometry and macro section of the weld joint.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Steels** & **C** & **Si** & **Mn** & **P** & **S** & **Fe** \\ \hline HSB600 (25 mm) & 0.15\(\leq\) & 0.75\(\leq\) & 2.00\(\leq\) & 0.30\(\leq\) & 0.007\(\leq\) & bal. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Chemical composition of the HSB600 steel plate used (wt. %).