esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Fine-Tuning  
* Embeddings: No custom embeddings were utilized. For the mBART model, mBART embeddings were used, which are similar to BART embeddings, while for the XLM-Roberta model, XLM-Roberta embeddings were utilized, which are similar to BERT embeddings. These embeddings convert the tokenized text into numerical vectors that capture semantic meanings.
* Training: The concatenated inputs (hypothesis + premise) along with their respective labels, are used to fine-tune the models. The goal is for the model to predict the correct label for each input pair.  
We fine-tuned the XLM-Roberta and mBART models on the XNLI dataset using standard procedures and evaluated their performance using metrics such as accuracy and F1 score. The following subsections present and discuss the findings from our experiments, highlighting the strengths and limitations of each model in handling cross-lingual data.  
Figure 1 represents steps taken to fine-tune our models on the XNLI dataset. First, we broke the dataset into hypotheses and premises. Then, we concatenated them and tokenized them to train the model.  
Figure 2 represents the steps taken while testing the model on our data. We first concatenate the hypothesis and premise, just like in the training data. Then, we create two sets of data by translating the sentences before testing. After testing, we compared the metrics of the two subsets of data.  
The code for our study can be accessed on this Google Colab notebook: [[https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) pJullFfnPfGfdyugGhfLLAY08Y07 XlwmB?usp=sharing]([https://colab.research.google.com/drive/1](https://colab.research.google.com/drive/1) pJullFfnPfGfdyugGhfLLAY08Y07 XlwmB?usp=sharing), accessed on 16 May 2024. This includes various sections of the experiment: data processing, training the model, translating datasets, and evaluating. This code was run multiple times on different language pairs to obtain the data that we presented in the study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Hypothesis** & **Premise** & **Label** \\ \hline (Read for [PERSON]’s take on [PERSON]’s findings.) & [PERSON] had an opinion on [PERSON]’s findings. & 0 entailment \\ Gays and lesbians. & Heterosexuals. & 2 contradiction \\ But a few Christian mosaics survive above the apse: the [PERSON] with the infant [PERSON], with the Archangel [PERSON] to the right (his companion [PERSON], to the left, has vanished save for a few feathers from his wings). & Most of the Christian mosaics were destroyed by Muslims. & 1 neutral \\ Issues in data synthesis. & Problems in data synthesis. & 0 entailment \\ \hline \hline \end{tabular}
\end{table}
Table 1: Samples from the XNLI dataset.
## 5 Conclusions  
In this study, the setting time of SFRM was assessed using the EMI sensing technique. To justify the validity of the EMI sensing technique, a standardized test method, the penetration resistance test, was performed and the results were compared with each other. In the penetration resistance test, it was found that the setting time of the SFRM was accelerated, which is an artifact due to the fibers mixed in SFRMs. However, the setting time of the SFRMs assessed by the EMI sensing technique was consistent regardless of the amount of mixed fibers. In the EMI sensing technique, the setting time assessment is directly affected by the stiffness gain of the mortar phase in direct contact with the PZT sensor; thus, the effect of fibers on the setting time assessment is minimized. This means that the EMI sensing technique is a more effective tool than the penetration resistance test to evaluate the setting time of fiber-reinforced mortar.  
J.-C.L. and C.-J.L., conceptualization, validation, data curation, writing--original draft and review editing. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Initial Setting Time** & **Final Setting Time** & **Time Gap between the Initial** \\  & **(min)** & **(min)** & **Set to the Final Set (min)** \\ \hline Plain & 370 & 540 & 170 \\ F-0.5 & 390 & 560 & 170 \\ F-1.0 & 370 & 530 & 160 \\ F-2.0 & 390 & 560 & 170 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Setting times of fiber-reinforced mortar determined by EMI sensing technique.  
Funding: This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Enhancement of Conventional Beat Tracking System Using Teager-Kaiser Energy Operator
## 2 Dataset and Methods
### TKEO Influence  
We applied the proposed method with the TKEO included on more recordings and observed, that in cases, where the tones are fast (e.g., violin playing thirty-second notes), or the energy difference is very low, this method does not detect every onset properly. Adding the TKEO increased the detection tolerance of fast changes in the signal. This means that the operator added additional \"latency\" to the signal values. It also decreased the ability of this system to capture low-energy spectral components. In general, fewer onsets were detected--only strong and more rhythmically important onsets remained. This is the advantage of the TKEO in the system. It suppresses less dominant spectral components and very fast tones even though onset detectors are usually set to do the opposite.  
Figures 6 and 7 show another analysed track--a violin solo in a very fast tempo. There is a clear difference in spectrograms for the described detector and the same detection with the TKEO included. Most of the tones are quite visible in the spectrogram of the first figure. However, the system with the TKEO has its changes in the spectrum vaguer and blurry which means that onset function detected a lower number of onsets (especially between the 1 st and the 4 th second of this track). In this case, the conventional system detected more onsets correctly but that still does not indicate that estimation of GT would be also more accurate.
## 3 Challenges
### Security  
Wireless sensors are normally deployed in an unattended environment, and are prone to security and privacy issues. CR wireless sensors can be attacked physically, and the data can be stolen. CR-WSNs are more vulnerable to security threats than the conventional WSNs, because there is no strict cooperation between PUs and SUs communication. The data collected by CR wireless sensors can be sniffed, destroyed or altered by unauthorized entities. In addition, attackers can interfere with PUs transmission or prevent the use of the channel by SUs through spectrum sensing data falsification (SSDF). CR-WSNs should have some acceptable level of security robustness against these potential threats and attacks. In addition to the security issues in conventional WSNs [53], additional security challenges are there for CR-WSNs. Some of the security issues in CR-WSNs can be as follows:(a) unacceptable interference to licensed users; (b) prohibiting the use of idle channels for SUs; (c) prohibiting the use of common control channels by virtually creating a bottleneck problem; (d) access to private data; (e) modification of data; and (f) injection of false data. Some possible attacks in CR-WSNs are discussed below.
# Phishing Node Detection in Ethereum Transaction Network Using Graph Convolutional Networks
## 5 Evaluation
### Baseline Methods  
We empirically compared the performance of our proposed approach with the performance of the Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF).  
1. SVM represents the examples as vectors in space, and it chooses a hyperplane that represents the largest separation between examples in order to classify them.
2. As a statistical classification method, LR models a binary dependent variable using a logistic function and obtains the corresponding probability of the class of examples.
3. RF is an ensemble learning method that constructs a large number of decision trees at training time and outputs the modes of the classes as the classification result.  
Since the above classification approaches require vectors as input, we utilized Deepwalk [20], trans2 vec [8], ProNE [23], and NETSMF [22] to obtain node embeddings for the baseline methods.  
DeepWalk is the first Word2 vec-based node vectorization model. It uses the random walking paths of nodes on the network to imitate the process of generating text, and then treats the paths of the nodes as the equivalents of sentences and applies the language model to vectorize each node. Trans2 vec introduces biased random walks to determine whether each walk is affected by transaction time bias or amount bias, and then it concatenates these two biases to balance their effects.  
Different from DeepWalk and Trans2 vec, ProNE and NETS-MF use matrix factorization directly to embed graphs. We introduce them into the baseline system to evaluate our scheme from another perspective. The embedding vector generated by ProNE contains both localized smoothing information and global clustering information, making it able to utilize the graph information more effectively. NETSMF is proposed to provide an efficient way to obtain embeddings from large graphs.  
The baseline models were DeepWalk-SVM, DeepWalk-LR, DeepWalk-RF, Trans2 vec-SVM, Trans2 vec-LR, Trans2 vec-RF, ProNE-SVM, ProNE-LR, ProNE-RF, NETSMF-SVM, NETS-MF-LR, and NETSMF-RF. We ran these baseline models on the entire transaction network and obtained the corresponding embeddings for all nodes. To ensure that the comparison was relatively fair, we used the publicly released source codes in the DeepWalk and ProNE papers and their default parameters. For Trans2 vec, we added random walking weights in the source code of DeepWalk, following the parameters proposed in [8] to build this baseline model. Moreover, for NETSMF, we also used their source code. However, we reduced the number of training rounds to 80 due to the high usage of memory during training. (After only 80 rounds of training on the Ethereum transaction data, NETSMF had used more than 200 GB of memory.)
## 4 CNN-Based Signal Classification Model  
Machine learning is commonly used to instead artificial visual interpretation in image classification which can be roughly divided into: shallow learning and deep learning [32]. Shallow learning includes: SVM, Boosting, Logistic Regression, etc. Deep learning includes: convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), etc. A large number of experiments and practices have verified that the shallow learning performs poorly in processing high-dimensional data, but deep learning makes up for this shortcoming. By using multi-level non-linear processing units, it has advantages in extracting deep structural features, and is more suitable for tasks such as visual recognition [33; 34], audio recognition [35; 36], and natural language processing [37; 38].  
As a deep feedforward network, CNN's core is to simulate the learning behavior of the human brain by constructing a neural network model, and optimize the parameters of the CNN model through training iterations [39; 40]. The classic CNN models contains LeNet-5, AlexNet, ZF-Net, VGGNet, GoogLeNet, ResNet, and DenseNet.  
AlexNet [41] is a CNN framework proposed by [PERSON] and [PERSON] when they participated in the 2012 ImageNet competition. They introduce the Relu activation function and Dropout to improve training speed and prevent overfitting. The advantages of AlexNet are simple calculation and fast convergence speed.  
Figure 11: Comparison of four types time-frequency images: (**a**) STFT; (**b**) CWT; (**c**) WVD; and (**d**) Cohen.
# Frequency Response of Optically Pumped Magnetometer with Nonlinear Zeeman Effect
## 4 Results and Discussions
It proves that the model proposed in this work is more precise to characterize the frequency response of the magnetometer in a large magnetic field, within which the non-linear Zeeman effect is non-negligible. Figure 2: Frequency responses of the Bell–Bloom cesium OPM with different detunings between the Larmor frequencies and the modulation frequencies of the pump beam at a biasing magnetic field of 1.15 \(\upmu\)T. The figures in the left column represent the amplitude-frequency responses, while the figures in the right column represent the phase-frequency responses. The blue dots are the experimental results and the red lines are the simulated results. Figure 3: Magnetic resonances and frequency responses of the Bell–Bloom cesium OPM at a biasing magnetic field of 50 \(\upmu\)T and 70 \(\upmu\)T. The figures in the left column represent the results with 50 \(\upmu\)T biasing magnetic field while the figures in the right column represent the results with 70 \(\upmu\)T biasing magnetic field. The figures in the top, the middle, and the bottom panels correspond to the magnetic resonances, the amplitude-frequency responses, and phase-frequency responses, respectively. The blue dots are the experimental results and the red solid lines are the simulated results according to the model in this work. The eight fitted individual Lorentz curves are presented in the top panel. The yellow dashed lines in the middle and bottom panels are the simulated results according to the first-order Butterworth low-pass filter. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Resonance No.**} & \multicolumn{3}{c}{**50 \(\upmu\)T**} & \multicolumn{2}{c}{**70 \(\upmu\)T**} \\ \cline{2-5}  & **Linewidth (Hz)** & **Amplitude (Arb. Unit)** & **Linewidth (Hz)** & **Amplitude (Arb. Unit)** \\ \hline
1 & 6.5 & 0.058 & 11.1 & 0.050 \\
2 & 7.2 & 0.38 & 11.2 & 0.46 \\
3 & 7.2 & 0.62 & 12.4 & 0.66 \\
4 & 8.7 & 1.0 & 14.9 & 1.0 \\
5 & 8.7 & 0.93 & 14.9 & 0.90 \\
6 & 7.3 & 0.57 & 12.4 & 0.54 \\
7 & 7.5 & 0.34 & 11.7 & 0.45 \\
8 & 6.5 & 0.053 & 11.1 & 0.048 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Fitted linewidth and amplitude of each Lorentz curve in the magnetic resonances of the Bell–Bloom cesium OPM at a biasing magnetic field of 50 \(\upmu\)T and 70 \(\upmu\)T.
# NuCap: A Numerically Aware Captioning Framework for Improved Numerical Reasoning
## 4 Experiments
### Counting Performance  
#### 4.5.1 Metrics  
To evaluate the model's ability to generate numerically accurate captions, we employ accuracy and F1 score as the primary evaluation metrics. Given our focus on explicit numerical reasoning, we refine standard metrics to more appropriately reflect counting performance.  
We define counting accuracy to reflect both successful number predictions and failure cases where no number is generated. Specifically, we assign a score of \(-1\) to captions lacking any numeric content, unlike conventional metrics that ignore such failures. This adjustment yields a more faithful evaluation of the model's capacity for numeric understanding.  
To assess the precision-recall trade-off, we use an adjusted F1 score. The traditional F1 computation evaluates only correctly predicted numbers and excludes captions without numbers, leading to overly optimistic results. To address this, we treat failure cases as false negatives during recall computation. This adjusted F1 score penalizes models that frequently omit numeric outputs, ensuring a more rigorous and fair assessment of numerical reasoning ability.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **BELU-1** & **BELU-4** & **METEOR** & **ROUGE** & **CIDEr** \\ \hline BLIP (base) [20] & 71.2 & 34.4 & 26.2 & 53.8 & 108.1 \\ BLIP (large) [20] & 67.0 & 28.9 & 30.2 & 53.3 & 104.0 \\ GIT (base) [37] & 78.4 & 36.7 & 28.5 & 57.6 & 122.8 \\ GIT (large) [37] & 80.6 & 38.6 & 29.5 & 59.1 & 131.1 \\ MeaCap\({}_{TF}\)[12] & 38.0 & 4.5 & 14.0 & 26.1 & 48.3 \\ ExpansionNet v2 [11] & 82.8 & 41.5 & 30.3 & 60.5 & 140.4 \\ GRIT [26] & 84.2 & 42.4 & 30.6 & 60.7 & 144.2 \\ NuCap & 83.5 & 42.0 & 30.5 & 60.7 & 143.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Captioning performance of different models.  
All evaluations are performed on CountBench [24], a benchmark designed to test object-count grounding in VLMs. For quantitative evaluation of the counting performance, we also include confusion matrix analysis.
# Theory of Electrical Breakdown in a Nanocomposite Capacitor
## 3 Results  
### Breakdown Parameters in a Nanocomposite Capacitor  
Equations (1)-(3), which describe the electrical breakdown, can be written in terms of the applied voltage, \(V\), and the applied field strength, \(E_{0}=V/d\), as  
\[j_{e}(d)=j_{e}(0)\exp\left(\frac{\gamma V}{1-3f\beta}\right), \tag{12}\]  
\[j_{e}(0)=AT^{2}\exp\left(-\frac{W}{kT}\right)\exp\left(\frac{B^{\prime}E_{0}^{ 1/2}}{kT}\right) \tag{13}\]  
and  
\[j_{e}(0)=C^{\prime}E_{0}^{2}\exp\left(-\frac{D^{\prime}}{E_{0}}\right), \tag{14}\]  
respectively. Here, the new parameters  
\[B^{\prime}=\frac{B}{(1-3f\beta)^{1/2}}, \tag{15}\]  
\[C^{\prime}=\frac{C}{(1-3f\beta)^{2}} \tag{16}\]  
and  
\[D^{\prime}=D(1-3f\beta) \tag{17}\]  
determine the field and temperature dependencies of the injection current in a nanocomposite capacitor.  
The breakdown voltage for a nanocomposite capacitor, \(V_{B}^{\prime}\), is related with the one for an NPs free capacitor, \(V_{B}\), as follows  
\[V_{B}^{\prime}=(1-3f\beta)V_{B}. \tag{18}\]  
For NPs with \(\epsilon_{i}>\epsilon_{h}\), the quantity \(\beta\) is positive and therefore Equation (18) describes a lowering of the breakdown voltage with an addition of NPs.
## 2 Materials and Methods
### Proposed Method
#### 2.3.1 Dataset  
The creation of a GAN model requires a dataset that consists of many samples, which are very influential for the generator results because the GAN model has a very high bias towards the dataset. Therefore, collecting image samples for a dataset is an essential stage. In this work, we used several datasets for compilation, namely, face images from generated photos [17], Fair Face Dataset [18], IMED [19], personal collection (volunteers), and CUHK Face Sketch database (CUFS) [20]. The dataset composition was 1243 from generated photos, 88 from CUFS, 20 from Fair Face Dataset, and 14 from IMED and private collections (from volunteers). The total of the entire dataset was 1365 faces. The whole dataset had no sketches other than CUFS. We generated sketches of these faces to complete the dataset representing Indonesian faces.  
Datasets compiled from multiple sources were not the same size. Therefore, we changed the dimensions of each image in the dataset to the desired size of 256 \(\times\) 256 pixels. The images and sketches were combined into the same image to simplify the project structure. The image was placed on the left, and the sketch was placed on the right. This image had dimensions of 256 \(\times\) 512. By combining images and sketches, we did not need separate folders for the two. The dataset was stored in one folder in JPEG format.  
After unifying all the datasets in the same folder, we separated the dataset used for testing purposes from the dataset used for validation purposes. [PERSON] et al. [21], in their paper, used a ratio of 20:1 which resulted in 16,860 samples for training and 842 for validation. However, because our dataset was only 1365, while GAN requires a lot of data for training, we only used 10 samples for validation. The selection of data for validation was done randomly. The final dataset was placed in two different folders, namely, training and validation, where each image in the folder consisted of two images, namely, the original photo image and the sketch.  
After preparing the dataset, we created a pipeline that contained several preprocessing steps before the data could be input into the GAN model, namely:  
1. Splitting. In the splitting step, the image was separated into two different images, i.e., the original image and the sketch.
2. Converting to tensors. Because the GAN model can only accept tensor input, both images were converted into a tensor with the float data type.
3. Resize and augmentation. The resize step was carried out to ensure that the input tensor was in the appropriate size. In addition, we added noise to the training data, such as mirroring and cropping them arbitrarily in this step. This was to increase the variety of the training data.
4. Normalization. At this stage, each pixel in the image was normalized and converted to a corresponding value in a range of 0-1. This was to ensure that each pixel had the same distribution, and to speed up the GAN's convergence during training.
## 4 Discussions
### Key Problems Associated with Coupled Hydrologic and Hydraulic Modelling  
The advantages of a physical distributed model go along with requiring extensive hydrological input data, high computing capabilities, and long computational times and thus increases the overall effort to successfully setup and calibrate the model. Input grid discretization is a priori in MIKE SHE and has to be selected wisely regarding available data, required accuracy, and computational effort. Like other hydrological models, such as SWAT, MIKE SHE can also simulate catchments size up to thousands of km\({}^{2}\), but for physical distributed models this increases the spatially distributed input data even more than models based on hydrological response units (HRU) do. Like semi distributed models, MIKE SHE cannot represent sub-grid heterogeneity. In case of limited data availability, satellite data can be very helpful for topography and land use estimation, but additional terrestrial data acquisition (e.g., soil, climatic, aquifer data, etc.) needs to be gathered. Lack of detailed river cross sections, variation of seasonal Manning's n, drainage maps, and leakage coefficient rates impact the simulated river flows. Limited availability of GW boreholes and monitored GW levels to produce GW contour maps makes it harder to delineate the GW divide, and it is sometimes possible to have cross-boundary GW flows. Calibration and validation efforts increase enormously in physically-distributed models with limited observed data, risking the compensation of structural model errors with incorrect model parametrization. Sufficient amount of observed monitoring data will help to provide ease in model setup and during the calibration process.