esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Eastern Mediterranean  
The interconnection of ENBOSAP with the NTA of seismicity has been further developed in a later study [62] focusing in the area N\({}_{23^{o}}^{52o}\)E\({}_{5^{o}}^{50^{o}}\) around Eastern Mediterranean which was based on the EQ catalog of the United States National Earthquake Information Center. In a fashion similar to that in Japan, the simultaneous application of criteria to two different areas selected on the basis of the network properties of ENBOSAP has led to characteristic minima \(\beta_{min}\) that precede the strongest EQs of M \(\geq\) 7.1 in the area N\({}_{28^{o}}^{52o}\)E\({}_{7^{o}}^{44^{o}}\), see the yellow dashed rectangle in Figure 1. Table 2 compiles the target EQs and their preceding \(\beta_{min}\). The results obtained by ECA for the last two columns of this table are shown in Figure 3. We observe that for \(\tau=19\)d \(\Delta T=164\)d a very small \(p\)-value is found, which turns out to be p\({}_{EM}=5.80\times 10^{-7}\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \(M_{JMA}\) & **Latitude (\({}^{\circ}\))** & **Longitude (\({}^{\circ}\))** & **EQ Date** & \(\beta_{min}\) **Date** \\ \hline
7.8 & 42.78 & 139.18 & 1993-07-12 & 1993-06-07 \\
8.0 & 41.78 & 144.08 & 2003-09-26 & 2003-07-14 \\
7.8 & 27.05 & 143.93 & 2010-12-22 & 2010-11-30 \\
9.0 & 38.10 & 142.86 & 2011-03-11 & 2011-01-05 \\ \hline \end{tabular}
\end{table}
Table 1: The epicenters and dates of occurrence of all EQs with \(M_{JMA}\geq\) 7.8 during the study period 1 January 1984 to 11 March 2011 within the area N\({}_{25^{o}}^{46o}\)E\({}_{125^{o}}^{146o}\) as reported by the Japan Meteorological Agency together with the dates of their precursory variability minima (see Table 4 and Section 5 of [59]).  
Figure 2: Results of ECA for the study made [59] in the area N\({}_{25^{o}}^{46o}\)E\({}_{125^{o}}^{146o}\) of Japan: The colour scales in each panel correspond to (**A**) the precursor coincidence rate and (**B**) the precursor \(p\)-value which were obtained by applying CoinCalc[3] for various values of \(\tau\) and \(\Delta T\). The rectangles in each case indicate the region of \(\tau\) and \(\Delta T\) at which our study is focused in (**C**). The latter panel is the same as (**B**) in expanded (time) scale.  
Figure 3: Results of ECA for the study made [62] in the area N\({}_{28^{\prime}}^{50^{\prime}}\)E\({}_{7^{\prime}}^{44^{\prime\prime}}\) of eastern Mediterranean: The colour scales in each panel correspond to (**A**) the precursor coincidence rate and (**B**) the precursor \(p\)-value which were obtained by applying CoinCalc[3] for various values of \(\tau\) and \(\Delta T\). The rectangles in each case indicate the region of \(\tau\) and \(\Delta T\) at which our study is focused in (**C**). The latter panel is the same as (**B**) in expanded (time) scale.
## 4 Discussion  
In this paper, a more accurate time-domain finite-difference algorithm model under the spherical coordinate system based on UPML boundary is established, which effectively solves the reflection problem existing in the PEC boundary. This algorithm is used to numerically calculate the field-intensity attenuation and polarization angle of VLF waves in the ionosphere, and compared and analyzed with the traditional algorithm to verify the accuracy of the algorithm.  
On this basis, single-frequency signals are used to study the relationship between the phase, amplitude, and ionospheric parameters' propagation of VLF electromagnetic waves in the magnetized plasma ionosphere. From the simulation results, it can be seen that in the vertical direction, the phase of electromagnetic waves at lower heights basically changes linearly, while the influence of the ionosphere on electromagnetic waves at a higher height causes the phase-detached linear change. The lower the frequency of electromagnetic-wave radiation, the lower the probability of phase anomalies occurring at the altitude in its propagation. The electron density and collision frequency of the ionosphere will also have different effects on the phase of electromagnetic waves when they change. When the electron density increases or the collision frequency decreases, it will lead to an increase in the phase impact of the ionosphere, causing the phase abnormality of electromagnetic waves at a lower altitude. The research results of this paper provide theoretical support for the engineering implementation of on-board very-low-frequency communication, but the actual ionospheric propagation mechanism is more complicated. VLF downlink analysis, receiver design, and ionospheric noise interference are the focus of the next study.  
The research results of this article provide theoretical support for the engineering implementation of spaceborne very-low-frequency communication, but the actual ionospheric propagation mechanism is more complex. The analysis of VLF downlink, receiver design, and ionospheric noise interference are the focus of the next research step.  
The authors confirm the following contributions to the paper: study conception and design: Conceptualization, J.C. and M.D.; Funding acquisition, J.C. and M.D.; Investigation, Q.X.; Methodology, K.H.; Project administration, K.H.; Resources, Q.X.; Supervision, K.H.; Validation, M.D.; Visualization, K.H.; Writing--original draft, M.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Xi'an Science and Technology Bureau, grant number 23 GXFW0017.  
The data that support the findings of this study are available from the author, [PERSON], via [EMAIL_ADDRESS] upon reasonable request.  
Special thanks are given to the reviewers of this paper for their valuable feedback and constructive suggestions, which greatly contributed to the refinement of this research.  
The authors declare no conflicts of interest.
# Analyzing LLAMA3 Performance on Classification Task Using LoRA and QLoRA Techniques
## 5 Conclusions  
This article demonstrates the results of using a decoder-based Llama-3 model in a classification task. The experiments in this study also showed the benefits of using PEFT techniques, such as LoRA and QLoRA, in a sentiment analysis task. The results obtained using the LoRA technique ensure that the low-rank adaptation matrix successfully emphasizes the crucial features for the downstream task that were learned but not highlighted in the pretraining phase. Furthermore, a LoRA paper [14] used encoder models, such as RoBERTa, DeBERTa, GPT-2, and GPT-3, to demonstrate the efficacy of the LoRA technique. This paper complements the original work by demonstrating the effectiveness of the LoRA technique when incorporated into a decoder model (Llama-3). Additionally, we observed the differences in performances of both the LoRA and QLoRA techniques when all the linear layers were adapted versus when only the attention layers were considered.  
This experiment also demonstrated how adjusting the hyperparameter values can give QLoRA, which requires less GPU memory, a similar performance gain as that of LoRA. This study also noted the effects of the hyperparameters on the metrics and recommended theoptimal values of \(\alpha\) and \(r\) for LoRA and QLoRA in classification tasks at different adaption (all-linear and attention-layer) settings. A higher value of \(\alpha\) resulted in underfitting for all-linear-layer adaptations but was good when only attention layers were adapted.  
Additionally, according to the analyses, a higher value of 'r' is not recommended for all-linear and attention-layer adaption settings, as it results in poor adaptation. In other words, an increase in the value of \(r\) did not cover a more meaningful subspace, so a low-rank adaptation matrix was adequate. To conclude, according to these analyses, we recommend QLoRA with a value of \(r>8\) for attention-layer adaptation and QLoRA or LoRA for all-linear-layer adaptation with \(r\leq 8\).  
Conceptualization, R.P.; Methodology, R.P.; Validation, P.K.; Formal analysis, P.K.; Resources, V.G.; Data curation, P.K.; Writing--original draft, R.P.; Writing--review & editing, V.G.; Supervision, V.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in huggingface library at [[https://huggingface.co/datasets/Yelp/yelp_review_full/tree/main/yelp_review_full](https://huggingface.co/datasets/Yelp/yelp_review_full/tree/main/yelp_review_full)]([https://huggingface.co/datasets/Yelp/yelp_review_full/tree/main/yelp_review_full](https://huggingface.co/datasets/Yelp/yelp_review_full/tree/main/yelp_review_full)).  
The authors declare no conflicts of interest.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Lightning
#### 2.4.2 GLM  
Observations from the Geostationary Lightning Mapper (GLM) on the Geostationary Operational Environmental Satellite (GOES) 16 were also used. GLM-16 continuously monitors a hemispheric field of view (FOV) centered on 75.2\({}^{\circ}\) W, which encompasses North and South America (landmasses where CYGNSS does not provide wind observations), the eastern Pacific Ocean, the western Atlantic Ocean, as well as the Gulf of Mexico and the Caribbean Sea. GLM detects total lightning with a detection efficiency of approximately 70%, and its instantaneous field of view (IFOV) varies between 8-14 km depending on viewing angle [31; 32]. Six months from each of 2018 and 2019 (1 July through 31 December), for a total of 12 months of observations, were included in this study.  
As noted by [31; 32], the GLM-16 FOV varies as a function of latitude. Thus, to ensure continuous monitoring, only CYGNSS tracks occurring (or overlapping) within the box marked by \(\pm\)38\({}^{\circ}\) latitude, and 128 to 22.5 \({}^{\circ}\)W longitude, were considered when comparing to GLM-16 (Figure 1). This ensured  
Figure 1: Analysis regions for this study. The observed map is the ISS LIS field of view (FOV), and WWLLN observes the full domain as well. The blue box shows the CYGNSS FOV. The dashed red box is the domain used for GLM analysis in this study. Land regions have no valid CYGNSS winds.  
that these tracks were always within the GLM-16 FOV regardless of latitude. Because of the continuous FOV, GLM-16 lightning was matched to CYGNSS tracks in a similar manner to WWLLN. However, coordinates for flash centroids were used as the basis of comparison. GLM-16 group and event data were ignored in this study.  
The advantage of using GLM-16 is that it provides continuous coverage of total lightning, with high detection efficiency. The disadvantage is that the dataset is not global, and thus a longer analysis period was required to obtain a comparable number of samples to the WWLLN dataset, despite the higher lightning detection efficiency. The reason GLM-16 was used in this study was to confirm whether trends observed by the CG-focused WWLLN analysis were also observed when using a total lightning dataset.
## 3 Proposed Method
### Data Acquisition  
To begin with the proposed algorithm, we initially acquire data. First we deal with different conditions on highways. For example, we come across the multi-class objects, such as different types of vehicles, motor bikes, and pedestrians on the roads. Similarly, we also faced severe and crucial challenges, such as massive traffic jams and overlapped vehicles. Therefore, to systematically acquire the data as shown in line (6) of Algorithm 1, we collected the dataset under two different situations, which are (i) High Density Traffic (HDT) scenes that contains multiple objects in an image and (ii) the Low-Density Traffic (LDT) scene that contains only one class per image, with zero overlaps. For improved training, the images of the LDT and the HDT dataset are placed separately.  
**The LDT Scenes:** This dataset was gathered from daily real-time traffic places, for example open parking lots, less crowded roads, and places with fewer crowds. The objective of assembling this dataset is to separately train the model on each class. We collected a total of 600 images from three classes, which are cars, motor cycles, and pedestrians. Example images of the few of the LDT images are shown in Figure 3a.  
Figure 3: Sample images of our collected dataset: (**a**) Low density traffic scenes and (**b**) High density traffic scenes.  
**The HDT Scenes:** This dataset was collected in congested places, for example public parking lots, big shopping malls, main highways, and places near main traffic sign boards. We gathered a total of 1800 images of the aforementioned classes. We also collected this dataset by thinking about crucial factors, for instance varying illumination, partial/full/long term occlusions, along with collections of objects regardless of size, scale, shape, or appearance. A few such sample images are shown in Figure 2(b). The statistics of both the low- and high-density dataset along with each class annotations are described in Table 2.  
**Video Dataset:** Along with the images, we also gathered a video dataset from the different locations of main highways, such as crossway bridges. A few of the sample images of our collected video dataset are shown in Figure 3(a). It can be seen that our collected dataset has different types of vehicles that appear in the image. Moreover, the vehicles' resolution also varies. Collecting such a diverse dataset helps us to develop a robust, reliable, and accurate vehicle detection method, which we believe can be used in any real-time application.
# Imbalanced Diagnosis Scheme for Incipient Rotor Faults in Inverter-Fed Induction Motors
## 5 Experimental Results
### Feature Selection  
After applying the ReliefF algorithm, the feature selection results for both power supplies are shown in Figure 5. In this figure, the red dashed line is the threshold for the features selected for the classification stage. Although a threshold choice is arbitrary, this threshold is set to 0.06, representing the percentile of best-scoring features to select. The analysis of this figure provides some interesting results. The algorithm highlights more relevant features for the line supply case than the inverter-fed case. It is also noticeable what happens with the \(m_{3}\) and \(c_{3}\) statistics: they were the worst features in the analysis with line-fed IM data but are selected for the inverter-fed case. The loss of relevance for _Skew_ in the inverter-fed IM case is also appreciable. However, as was expected, \(LSH\) and \(RSH\) are the most significant features for all power supply cases. Furthermore, the \(\Gamma\) features proposed in [17] do not show outstanding relevance for the diagnosis.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Supply** & **Severities** & **Imbalance Ratio (IR)** &
\begin{tabular}{c} **\#Samples** \\ **(Healthy-Faulty)** \\ \end{tabular} & **\% (Healthy-Faulty)** & **Case Number** \\ \hline \multirow{3}{*}{Line-fed} & R1–R3 & 12 & 195–15 & 92.85–07.15 & 1 \\  & R1–R3 & 7 & 105–15 & 87.50–15.50 & 2 \\  & R1–R3 & 3 & 45–15 & 75.00–25.00 & 3 \\ \hline \multirow{3}{*}{Line-fed} & R1–R5 & 12 & 195–15 & 92.85–07.15 & 4 \\  & R1–R5 & 7 & 105–15 & 87.50–15.50 & 5 \\  & R1–R5 & 3 & 45–15 & 75.00–25.00 & 6 \\ \hline \multirow{3}{*}{Inverter-fed} & R1–R3 & 12 & 195–15 & 92.85–07.15 & 7 \\  & R1–R3 & 7 & 105–15 & 87.50–15.50 & 8 \\  & R1–R3 & 3 & 45–15 & 75.00–25.00 & 9 \\ \hline \multirow{3}{*}{Inverter-fed} & R1–R5 & 12 & 195–15 & 92.85–07.15 & 10 \\  & R1–R5 & 7 & 105–15 & 87.50–15.50 & 11 \\ \cline{1-1}  & R1–R5 & 3 & 45–15 & 75.00–25.00 & 12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Description of the experimental datasets  
Figure 5: Results of the feature selection with the ReliefF algorithm: a line-fed case at the (**top**) and an inverter-fed case at the (**bottom**) of the figure.
## 4 Numerical Study: A 10-Story Shear Building
### Training and Validating of MLP Model
Similarly, the calculated control force of the MLP model was also scaled before it was imposed on the shear building. Table 2 shows the training results considering various number of acceleration steps in the input layer. It can be found that the RMSE between the desired and predicted control force is smaller than 2.0% for all cases, indicating that the MLP model is able to learn the control force generated from an LQR with optimized weighting matrices through the corresponding controlled acceleration responses. Thus, it was expected that the implementation of the MLP model as depicted in Figure 8 should be able to generate similar control force to the LQR control force. The ode5 solver using the Dormand-Prince formula was adopted for the control simulation in Simulink. Table 2 also shows the RMSE between the desired and predicted control force in the control simulation model. All the simulations were computed using a sampling rate of 200 Hz. However, the control simulation model was divergent and became inexecutable for the cases of 10, 50, and 100 acceleration steps due to error propagation in the control loop. On the other hand, the cases of 120, 150, and 200 acceleration steps all led to stable and satisfactory simulation results. Among the three successful cases, the computational effort of the 120 steps was the least. Therefore, it was suggested that a minimum number of 120 steps was needed as the input of the MLP model. Accordingly, a minimum number of 1210 neurons was required in the input layer for the MLP model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Number of Acceleration Steps**} \\ \hline  & **10** & **50** & **100** & **120** & **150** & **200** \\ \hline Training RMSE (\%) & 1.95 & 0.40 & 0.62 & 0.68 & 0.89 & 0.87 \\ Simulation RMSE (\%) & N.A. & N.A. & N.A. & 1.77 & 1.82 & 1.70 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Training results considering various acceleration steps for the multilayer perceptron (MLP).
# Micromachining in Powder-Mixed Micro Electrical Discharge Machining
## 3 Influence of Powder Characteristics
### Powder Size  
[PERSON] et al. [52] observed the introduction of molybdenum disulfide (MoS2) powder at sizes 2 \(\upmu\)m, 50 nm and 10 nm in the dielectric fluid, respectively. They found that at a powder concentration of 5 g/L, MoS2 with 50 nm size produced a better surface quality of microholes in Inconel 718, without leaving any black traces or cones in the center of the hole. Furthermore, compared to the other powder sizes (10 nm and 2 \(\upmu\)m), 50 nm MoS2 powder was the best size to achieve the highest material-removal rate. An insignificant impact in material-removal rate was provided by the 10 nm MoS2 powder, whilst the 2 \(\upmu\)m MoS2 powder was not suitable for \(\upmu\)-EDM due to the discharge gap distance in the conventional \(\upmu\)-EDM being only in the range of a few microns [53]. Figures 6 and 7 summarize the influence of powder properties on enhancing material-removal rate and the surface quality of the machined workpiece.
## 2 Materials and Methods  
### PBO Project  
The National Science Foundation's (NSF) Plate Boundary Observatory (PBO), which began construction in 2004, was completed in 2008 [40]. The PBO is a core network of 1100 continuously operating GNSS stations. The network also contains 1 Hz and 5 Hz high sampling rate stations capable of observing millimeter changes in GNSS station locations over days to years. The essence of this is GNSS satellites transmitting signals, which are L-band microwave signals (-1.2 and -1.5 GHz). The ground receiver antenna receives both direct and reflected signals, while the reflected signals vary with soil moisture, snow depth, and vegetation conditions. The changes in surface reflections are recorded in the signal-to-noise ratio (SNR) data, which is then solved to quantify soil moisture, snow depth, and vegetation growth rate. The network is the only one operating on the principle of GNSS-IR technology. Soil moisture data from the PBO project can be downloaded from the International Soil Moisture Network (ISMN) website and available on the PBO data portal. The basic parameters of all stations of the PBO network in the study area are shown in Table 1.
# Human Action Recognition Based on Foreground Trajectory and Motion Difference Descriptors
## 5 Experimental Results and Discussion
Finally, in order to evaluate the overall performance of the algorithm, the recognition accuracy was used as the evaluation index, and the results were compared with those of references [2; 3; 10; 14] on UCF sports and YouTube's datasets, respectively. The comparison results are shown in Table 2.  
As can be seen from Table 2, the recognition accuracy of this algorithm was 92.8% on the UCF sports data set, which was 2.8%, 0.8%, 1.4%, and 2.9% higher than that of references [2; 3; 10; 14], respectively. On the YouTube data set, the recognition accuracy of this algorithm was 89.6%, which was 4.3%, 0.7%, 2.0%, and 3.5% higher than that of references [2; 3; 10; 14], respectively. Recognition accuracy was improved to a certain extent. This was because the proposed algorithm more accurately extracted trajectories related to behavior and ensured the accuracy of behavior representation. Meanwhile, the motion time was modeled effectively by MDD, which improved the recognition accuracy of the whole algorithm. To evaluate the computational complexity of the proposed foreground trajectory, we computed the average time consumption per video for both trajectory extraction and descriptor representation on UCF sports. The experiment compared the proposed trajectory with reference [12] with Intel I7 (3.6 GHz CPU), only a single CPU core was used, and both of the codes were not optimized. Time consumptions for the above two trajectories were compared in Table 3. Extraction of the foreground trajectory was somewhat more computationally expensive than [12]. This was because foreground trajectory used a motion-based sampling strategy, and it needed to traverse every moving point in the next frame, which was time consuming. But the number of the foreground trajectory was much smaller than [12], so the descriptor computation and encoding process for the former saved much more time. In addition, we did not count the time consumption of optical flow for PCA and GMM training because the processes between these two types of trajectories were similar.
## 2 Materials and Methods
### Design of Experiment  
The features (c.f. Section 2.4) were obtained form all the basmati rice sample images (in total 40 images) as discussed in aforementioned Sections. Among them, features obtained from 30 images were used to train the ANFIS and features derived from the remaining 10 images were used for testing. Subsequently, the classification accuracy was estimated with actual class of test data and ANFIS output results. Furthermore, the performance of ANFIS was compared with standard classification techniques of support vector machines (SVM) and K-nearest neighbours (KNN). The optimal margin and kernel parameters (radial basis function) for the Soft-margin SVM classifiers were determined using grid search and 10-fold cross-validation. Each image contains \(\sim\)15 rice kernels (objects). However, for representation we have kept only 1 image and have shown all image processing steps involved in result section (c.f. Section 3).  
Furthermore, the effectiveness of morphological features was analyzed for a segmented test image (for each object). The histograms of individual feature can provide a rationale by relating the features to the physical properties derived from an image based technique associated with the grain class. In addition to the classification accuracy assessment, the milling efficiency (the ratio of broken grain and whole grain) was assessed using the broken grain and whole grain objects derived from a test image.