esa-sceva/satcom-chunk-collection huggingface dataset:
# Leverage Boosting and Transformer on Text-Image Matching for Cheap Fakes Detection +
## 5 Experimental & Results
### Discussions  
First, we made use of Spotfake [23] as a training baseline approach based on its simplicity--fine-tuning and concatenating visual and textual embedding to classify the veracity of the news. We leveraged Spotfake architecture on the given training and testing data of COSMOS. In particular, when training, we created out-of-context content by selecting captions and images from different sources' captioned images and not-out-of-context content from the same source captioned images. When evaluating, we classified both (caption\({}_{1}\), image) and (caption\({}_{2}\), image). If both the captions were not-out-of-context,the triplet (caption\({}_{1}\), caption\({}_{2}\), image) was not-of-context, and the other was out-of-context. The method gave poor results based on the different attributes between training and testing data, and the method could not overcome and generalize the issue.  
Next, downstream from another dataset approach, we chose EANN. We used the same method from Spotfake to evaluate the performance--classify both (caption\({}_{1}\), image) and (caption\({}_{2}\), image). On the MediaEval2015 dataset [45], EANN could achieve a 71.5% accuracy point. However, when downstream of COSMOS, the method produced unqualified results, even though MediaEval2015 consists of a large corpus of textual news and various cases of misused images, similar to the COSMOS dataset. The current training and downstream approach to a given news dataset is limited in categories, domains, and types of news and may not perform well in reality.  
Compared to the baseline, our methods improved the 6.5% accuracy score. Furthermore, in relation to [PERSON] et al.'s [44] research, our method has equal accuracy and has a higher recall and F1-score. [PERSON] et al. [44] took advantage of handcraft features by matching the most relevant fake news keywords (fake, hoax, fabrication, supposedly, falsification, propaganda, deflection, deception, contradiction, defamation, lie, misleading, deceive, fraud, concocted, bluffing, made up, double meaning, alternative facts, tricks, half-truths, untruth, falsehoods, inaccurate, disinformation, misconception) and alternated captions in testing datasets with fake words (\"was true\" and \"was not true\") to compare semantic features. Our methods used various semantic understandings in computer vision and natural language processing on large-scale datasets to assess the correlation between the original image and caption. The impact of each image-text matching method is also present in our paper.  
In Figures 8 and 9 we show a few examples of our false negative (FP) and our false positive (FN) predictions. As we can see in the false negative cases, the content of news and the abstract relation with the corresponding image are hard to distinguish, even by humans, and much news needs an expert or time with search tools to determine. For false positive cases, our
# Reliability and Risk Centered Maintenance: A Novel Method for Supporting Maintenance Management
## 1 Introduction
Accordingly, discussing the integration of RCM and RBM into a single method is extremely relevant to this field of research. Accordingly, this paper proposes a novel method that integrates RCM and RBM strategies to support maintenance management. The proposed Reliability and Risk Centered Maintenance (RRCM) method adapts the traditional RCM method to incorporate risk management into maintenance planning decision-making. Thus, it allows the organization to determine cost-effective maintenance plans that ensure the reliability of its physical assets while considering and prioritizing the risks associated with their potential functional failures. The proposed method application is demonstrated through a case study considering the operational context of a hydroelectric power plant. The remaining of this paper is organized as follows: Section 2 provides an overview of the RCM and RBM methods within maintenance management. Section 3 presents the proposed RRCM method. Section 4 presents a case study with the application of the proposed method considering the context of a hydroelectric power plant. Finally, Section 5 presents the authors' conclusions and recommendations for future work.
## 4 Experiments and Results
### Results
#### 4.4.4 Comparative Testing of Different Models  
In this paper, Fundus-DANet was compared with the EfficientNetB3 model of [PERSON] et al. [29], the MCGL-Net model of [PERSON] et al. [30], the MCGS-Net model of [PERSON] et al. [31], and the DCNet of [PERSON] et al. [32]. The results of the experiments are shown in Table 6. The experiment yielded an ACC score of 94.46% for the MCGL-Net, which was 1.46% higher than the model in this paper. However, the model performed worse regarding AUC and \(\kappa\) metrics, coming in at 1.62% and 3.22% lower, respectively, than the experimental model. These results indicate that the model in this paper performed better regarding dataset balance. Both DCNet and MCGL-Net performed better in terms of the F1_score, striking a better balance between comprehensiveness and accuracy while demonstrating significant generalization capabilities.
# Are Strong Baselines Enough? False News Detection with Machine Learning
## 3 Results
### Experiment 4: Novel ChatGPT-Generated Dataset  
For the fourth and final set of experiments, we used the novel ChatGPT-generated dataset and the LIAR dataset [4]. The novel ChatGPT-generated dataset consists of 200 pieces of false news, generated by ChatGPT-3.5 based on imaginary prompts that we gave to the AI, as well as 100 true news pieces generated by picking actual news articles from various sources, summarizing them, and making ChatGPT-3.5 rewrite the article based on the given summary. As various AI tools have become more widespread in today's world, and seeing how easily and quickly we were able to create false news articles using ChatGPT, it is important to think of ways we could efficiently develop ways to identify AI-generated content. In this experiment, the first classifying experiment was conducted by only using the ChatGPT-generated dataset, the second classifying experiment was conducted using the ChatGPT-generated dataset as the train data and the LIAR dataset as test data, and the third classifying experiment was conducted vice versa.  
Like in the previous experiments, we first built a bag-of-words model using CountVectorizer and TfidfTransformer. The methods used for these experiments were the same as in Sections 3.2 and 3.3.  
When conducting experiments on the ChatGPT-generated dataset, the split ratio in all of the experiments was 80:20.  
The first experiment was conducted only using the ChatGPT-generated dataset. This was implemented to build a baseline of results that we could use to compare the results achieved from the more complex experiments. The results for the first experiment can be seen in Table 22, and the ROC curve for this experiment can be observed in Figure 17. From this table, we can see that all the classifiers were able to achieve good results, but the PA classifier was able to achieve more consistent precision, recall, and F1 scores, as well as low FPR and FNR scores in comparison to the other two classifiers.  
Figure 17: ROC curve for the classifying results conducted using only the ChatGPT-generated dataset.
# A 40 Mb/s VLC System Reusing an Existing Large LED Panel in an Indoor Office Environment
###### Abstract  
With advances in solid-state lighting, visible light communication (VLC) has emerged as a promising technology to enhance existing light-emitting diode (LED)-based lighting infrastructure by adding data communication capabilities to the illumination functionality. The last decade has witnessed the evolution of the VLC concept through global standardisation and product launches. Deploying VLC systems typically requires replacing existing light sources with new luminaires that are equipped with data communication functionality. To save the investment, it is clearly desirable to make the most of the existing illumination systems. This paper investigates the feasibility of adding data communication functionality to the existing lighting infrastructure. We do this by designing an experimental system in an indoor environment based on an off-the-shelf LED panel typically used in office environments, with the dimensions of 60 x 60 cm\({}^{2}\). With minor modifications, the VLC function is implemented, and all of the modules of the LED panel are fully reused. A data rate of 40 Mb/s is supported at a distance of up to 2 m while using the multi-band carrierless amplitude and phase (CAP) modulation. Two main limiting factors for achieving higher data rates are observed. The first factor is the limited bandwidth of the LED string inside the panel. The second is the flicker due to the residual ripple of the bias current that is generated by the panel's driver. Flicker is introduced by the low-cost driver, which provides bias currents that fluctuate in the low frequency range (less than several kilohertz). This significantly reduces the transmitter's modulation depth. Concurrently, the driver can also introduce an effect that is similar to baseline wander at the receiver if the flicker is not completely filtered out. We also proposed a solution based on digital signal processing (DSP) to mitigate the flicker issue at the receiver side and its effectiveness has been confirmed.  
visible light communication; LED; carrierless amplitude and phase modulation (CAP); LED driver; bias-tee; lighting infrastructure +
Footnote †: journal: Sensors  
2021
## 2 Materials and Methods
### Industrial Use Case  
Last but not least, the NIRSpec use case represented an application of the method on a real and complex structure with a reduced number of sensors while predicting the element stress. Consequently, in the case where the models are able to make accurate predictions for those three scenarios, the method can be concluded as useful.  
Figure 4: The academic use case with six highlighted sensors and element 100.  
The considered use case scenario concerns an actual structure corresponding to the NIRSpec instrument's optical bench. The optical bench is equipped with ten sensors to predict the stress in one element (see Figure 5). This case makes it possible to evaluate the potential of the method for a real and complex structure with more complex eigenmodes and a limited number of sensors. In this use case, the stress is to be predicted because it represents a good indicator for the structure's physical state and enables the evaluation of the model's performance to predict other metrics than the force, as in [2].  
In order to determine the stresses at the highlighted elements in Figure 5, a FEA was conducted with MSC NASTRAN version 2018.1.0. The structure was discretized by 96,073 nodes and 104,182 elements spanning from one-dimensional elements (i.e., rods and beams) over shell (triangular and quadrangular) to solid elements (tetrahedral, hexahedral, and pentangular). As boundary conditions, the FEA was subjected to forces and moments for each kinematic mount derived from the CLA, where phase information was provided as well. This approach is referred to as the multi-excitation method (MEM). All dynamic analyses were based on modal decomposition, and they are therefore modal frequency response analyses ranging from 5 Hz to 200 Hz. For each of these frequency steps, the von Mises stress was evaluated at the ten selected elements and used to train the ANN. It should be noted that this equivalent stress was used for this paper only. AIRBUS has developed a dedicated equivalent stress suited to predicting ceramic failure.
## 3 Underlying Terminology and Concepts
### Multi-Sensor Data Harmonisation  
The compliancy to ARD requirements is the first fundamental step for addressing the concept of interoperability at the satellite level. Yet, two interoperable datasets cannot be merged seamlessly until the sensor-specific features are appropriately characterised and adjusted by applying the relevant correction factors. For optical sensors, these features typically include: radiometric calibration, geometric co-registration, spectral bands definition, spatial resolution and sun-viewing conditions. Harmonisation is the process of solving such sensor-specific features in order to blend different mission datasets in a consistent manner. The need for multi-mission harmonisation is a generic concept, which is relevant to any satellite product level (L1 and L2), and it is notably being addressed in the context of climate studies and regional applications, as further discussed below.  
* Harmonisation in a climate context--Harmonisation has a strong relevance for the generation of CDR, when multi-sensors datasets are merged to derive a consistent series of observations, spanning a time frame relevant for climate studies, typically more than 20 years. In this context, special attention is given to rigorous estimation of uncertainties to reliably disentangled sensor-related artefacts from climate-relevant trends [55]. A generic framework for harmonising the series of AVHRR (Advanced Very-High-Resolution Radiometer) radiance observations was developed in the frame of the Fiducoe project [67; 68]. This project has further developed its own terminology, distinguishing between \"harmonisation\" and \"homogenisation\" of satellite data series. Following Fiducoe's terminology, a harmonised satellite series is one where all the calibrations of the sensors have been made consistent with a reference dataset, which can be traced back to known reference sources, in an ideal case back to SI. Unlike harmonisation, homogenisation consists in adding the constraint that all satellites would give the exact same signal when looking at the same location at the same time. According to Fiducoe's recommendations, homogenisation should be avoided for CDR generation, since the application of adjustment factors can induce scene-dependent biases that are difficult to characterise a-posteriori.
* Harmonisation in an application context--In addition to climate studies, harmonisation is increasingly relevant for regional applications, where observations from a virtual constellation of similar sensors are blended with the primary goal to enhance revisit time. The most obvious examples of such a strategy are the NASA Harmonised Landsat and Sentinel-2 (HLS) product [69] and the ESA Sen2 Like project [10], aiming at similar objectives of harmonising and fusing the Sentinel-2 and Landsat-8 data streams. Harmonisation within these two projects means providing a seamless stream of sensor-agnostic data from the two missions to the users, gridded to a common map projection, atmospherically corrected and cloud screened using a single algorithm, normalised to a fixed sun-viewing geometry and spectrally adjusted.
# TSA-GRU: A Novel Hybrid Deep Learning Module for Learner Behavior Analytics in MOOCs
## 5 Discussion
### Performance Across the Training Epochs  
The TSA-GRU model was tested over a wide range of epochs (5, 10, 20, 30, 40, and 100 epochs) to determine the optimal training duration needed to better capture sequential patterns from students' interaction data. The results indicated that the model converges very fast, with the best overall performance at 5 epochs. It was at this time that the training loss was about 0.0302 and the training accuracy was about 95.69%; the test loss was about 0.0211, while test accuracy was almost 95.96% (or, as reported in the comparative summary, 95.60%). Interestingly, while there were further small reductions in loss between various epochs--for instance, at 100 epochs, the training loss was 0.0277--there was only marginal improvement in accuracy; in some cases, longer training resulted in a minor decrease or some cycling of accuracy. This implies that TSA-GRU well captures the important dependencies that are sequenced in student interactions within a few training epochs. So, after that point, it enters a phase in which added epochs give no further advantage. This rapid convergence is also indicative of the strength of the model as well as its computational efficiency, a crucial consideration for educational use cases in real-world scenarios. In order to alleviate concerns about possible overfitting, we explicitly assessed the model's learning behavior over the training process. The lack of divergence between training and testing curves, its rapid convergence, and its stability provide evidence that TSA-GRU is very robust. In the results section, we added a statement signaling this to be more transparent and demonstrating how effectively the model generalizes beyond the training data.
# A Weighted and Distributed Algorithm for Range-Based Multi-Hop Localization Using a Newton Method
## 2 Reducing the Impact of Outliers in the Estimation Process  
Commonly when data \(\mathbf{X}=\{x_{1},x_{2},\cdots,x_{n}\}\) are analyzed and processed, they are assumed as iid (independent and identically distributed) with normal distribution, where the estimation of a statistical parameter \(\mu\) can be carried out under the Maximum Likelihood Estimator (MLE) technique as  
\[\hat{\mu}=\operatorname*{arg\,min}_{\mu}f(\mathbf{X},\mu), \tag{1}\]  
where  
\[f(\mathbf{X},\mu)=\prod_{i=1}^{n}\exp^{-\frac{(x_{i}-\mu)^{2}}{2\sigma^{2}}}, \tag{2}\]  
represents the joint density function for the data set \(\mathbf{X}\), \(\mu\) denotes the mean, and \(\sigma^{2}\) the variance. For the estimation of \(\hat{\mu}\), the \(-\ln f(\mathbf{X},\mu)\) is applied, which results in the minimization of the sum of square errors as the data have the same dispersion \(\sigma^{2}\):  
\[\hat{\mu}=\operatorname*{arg\,min}_{\mu}-\ln f(\mathbf{X},\mu)=\operatorname* {arg\,min}_{\mu}\sum_{i=1}^{n}\epsilon_{i}^{2}, \tag{3}\]  
where \(e_{i}=x_{i}-\mu\). However, by using the quadratic loss function in (3), a noticeable amplification of atypical errors (i.e., outliers) is obtained, leading to a bad estimation of \(\mu\). The outliers effect can be exemplified with the next case: Assume that \(\{x_{a},x_{b}\}\subset\mathbf{X}\) are atypical measurements; thus, (3) can be rewritten as  
\[\hat{\mu}=\operatorname*{arg\,min}_{\mu}f(\mathbf{X},\mu)=\operatorname*{arg\, min}_{\mu}\left(\sum_{i\
otin\{a,b\}}^{n}\epsilon_{i}^{2}+e_{a}^{2}+e_{b}^{2} \right). \tag{4}\]  
Then, the higher the error values on \(x_{a}\) and \(x_{b}\), the larger the bias in the estimation of \(\mu\). To mitigate this effect, a technique commonly used is to modify the minus log Gaussian likelihood with variances \(c_{i}\) depending on each particular regression point as follows:  
\[f(\mathbf{X},\mu)=\prod_{i=1}^{n}\exp^{-\frac{(x_{i}-\mu)^{2}}{2c_{i}z^{2}}}, \tag{5}\]  
which implies the following quadratic loss:  
\[\hat{\mu}=\operatorname*{arg\,min}_{\mu}-\ln f(\mathbf{X},\mu)=\operatorname* {arg\,min}_{\mu}\left(\sum_{i\
otin\{a,b\}}^{n}\frac{\epsilon_{i}^{2}}{2c_{i} ^{2}}+\frac{e_{a}^{2}}{2c_{a}z^{2}}+\frac{\epsilon_{b}^{2}}{2c_{b}z^{2}} \right), \tag{6}\]  
this means that taking \(c_{a}\) y \(c_{b}\) large enough, then \(\frac{\epsilon_{a}^{2}}{2c_{a}z^{2}}+\frac{\epsilon_{b}^{2}}{2c_{b}z^{2}}<<e_{ a}^{2}+e_{b}^{2}\); consequently, the outliers at the point \(a\) and \(b\) will have less effect in (6) than in (4) in the estimation of \(\mu\).
## 6 Conclusions  
1. Based on the survey line of shallow seismic exploration in Xietan Village, the composite drilling section in Xietan Village revealed that the exact location of the Hetan-Guotan buried fault is approximately 35 m to the south of the position identified by the shallow seismic exploration of Xietan Village's survey line, with an apparent dip of 78\({}^{\circ}\). Moreover, the burial depth of the uppermost point is 35 m. According to the dating test results of the samples, we found that its latest activity period was the mid-late Middle Pleistocene and it was inactive at the end of Middle Pleistocene and since the Late Pleistocene; thus, it is not an active fault.
2. According to the survey line of shallow seismic exploration in Lizui Village, the composite drilling section in Lizui Village revealed that the exact location of the Zhenjing-Zhenbei buried fault is about 50 m to the north of the position identified by the shallow seismic exploration of Lizui Village's survey line, with an apparent dip of 67\({}^{\circ}\). Moreover, the burial depth of the uppermost point is 71 m. According to the dating test results of the samples, we found that its latest activity period was the Early and Middle Pleistocene, and it was inactive at the end Middle Pleistocene and since the Late Pleistocene; thus, it too is not an active fault.
3. Influenced by the activity of Tianjingshan Fault, the Hetan-Guotan buried fault and the Zhenjing-Zhenbei buried fault were weakly active in the Early and Middle Pleistocene until they ceased to be active in the Late Pleistocene. Due to the large scale and strong activity of Tianjingshan Fault, the regional strike-slip deformation is mainly concentrated in the south of Zhongwei Basin. The two buried faults are small in scale, measuring 12.5 km and 8.8 km, respectively. It is concluded that the two buried faults do not present the conditions for destructive earthquakes in the future. Presumably, the two buried faults will be dominated by small earthquake activity in the future. It is highly unlikely that they would have threatened the urban security of Zhongwei City.  
Data curation, C.L.; Formal analysis, C.L. and Q.L.; Methodology, C.L. and Q.L.; Project administration, Q.L.; Supervision, P.D.; Validation, Z.W., Z.L., S.Y. ([PERSON]), S.Y. ([PERSON]) and Y.W.; Formal analysis, C.L., Q.L.; Investigation, C.L., Z.W. and Z.L.; Resources, P.D., Z.W., Z.L., S.Y. ([PERSON]), S.Y. ([PERSON]) and Y.W.; Writing--original draft, C.L.; Writing--review & editing, Q.L. and P.D.; Visualization, S.Y. ([PERSON]), S.Y. ([PERSON]) and Y.W. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Urban active fault detection project of China Earthquake Administration (Seismic active fault detection and seismic hazard assessment in Shapotou District, Zhongwei City); the Second Tibetan Plateau Scientific Expedition and Research Program (STEP)(2019 QZKK0901); and National Key R&D Program of China (2017 YFC1500101).  
Not applicable.
###### Abstract  
The aim of this study was to determine the effects of pasture (cocksfoot cv. porto (CFP) and lucerne) and supplementation of grazing lambs with pellets with or without plant oil infusion on performance and carcass characteristics. Forty-eight White Suffolk x Corridedate first-cross wearers were randomly assigned to one of four treatments in a split-plot experimental design: (1) CFP or lucerne pastures only (control); CFP or lucerne pastures supplemented with pellets infused with oil from (2) canola (CO); (3) rice bran (RBO) and (4) no oil pellets (NOP). Lucerne and CFP pastures were considered as the main plot effect, and played the role of basal pastures. Lambs grazing lucerne or CFP pastures with pellet supplementation achieved carcass weights of >22 kg at 9 weeks, which met the specific requirements of Asian and United States of America export markets. Pellet supplementation did not affect final liveweight, average daily gain, body length, withers height and chest girth of grazing lambs. Dressing percentage of lambs grazing CFP pasture with pellet supplementation and lambs grazing lucerne pasture with RBO supplementation increased compared with lambs on pasture grazing only. Although supplementing lambs on CFP pasture with CO had relatively negligible impact on feed conversion efficiency, it significantly increased over the hook trade value compared with lambs grazing CFP pasture only. In conclusion, lucerne or CFP pasture plus pellet supplementation produced lamb carcasses >22 kg suitable for the export market. CO had relatively low feed cost per unit daily gain (0.9 $AU/kg on CFP pasture and 0.6 $AU/kg on lucerne pasture) and could also be used as a tactical supplementation tool for increasing the carcass weight of lambs grazing CFP pasture.  
carcass; cocksfoot; lucerne; prime lambs; canola oil; rice bran oil