esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Visual Waterline Detection for Inland Marine Unmanned Surface Vehicles
## 4 Experiments and Evaluation
### Results and Analysis on Our Deep Waterline Detection Approach
#### 4.3.1 Investigating the Scale of WLpeephole  
Specifically, the scale of WLpeephole includes the size of observing field (denoted as _r_) and the size of recognizing field (denoted as _s_). Thereby, we carry out our algorithm repeatedly on the same optical image (presented in Figure 6) in the case of 10 different (_r_, _s_) pairs. Then, ten relevant _F1-scores_ are calculated, as shown in Table 3. Among them, Figure 6a,b illustrates the visual result in the case of (48, 24) and (60, 30) for the (_r_, _s_) pair, respectively.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Scales**} & \multicolumn{3}{c}{_r = H/36_} & \multicolumn{3}{c}{_r = H/22.5_} & \multicolumn{3}{c}{_r = H/18_} \\ \cline{2-10}  & \(r\) & \(s\) & _F1_ & \(r\) & \(s\) & _F1_ & \(r\) & \(s\) & _F1_ \\ \hline \(s=r/3\) & 30 & 10 & **0.685** & 48 & 16 & 0.841 & 60 & 20 & 0.915 \\ \hline \(s=r/2\) & 30 & 15 & 0.712 & 48 & 24 & 0.865 & 60 & 30 & **0.943** \\ \hline \(s=2r/3\) & 30 & 20 & 0.706 & 48 & 32 & 0.858 & 60 & 40 & 0.929 \\ \hline \end{tabular}
\end{table}
Table 3: Quantitative comparisons on _F1-score_ by setting ten different scales of WLpeephole, respectively (here, \(H\) denotes the height of an image, \(\lambda=10\) pixels, the bold refers the best result).  
From Table 3, we observe that our waterline detection algorithm is effective in a practical inland scenario, even though the scale of WLpeephole impacts on its resulting accuracy more or less. Among of the ten displayed \(F1\)-_scores_, the one (i.e., 0.943) is highest when the \((r,s)\) pair is set to (60, 30), which actually represents the best detection effect that has been attained in this group of experiments, just as shown in Figure 6b. The reason is that, in this case, more contextual information relevant to the waterline and more sufficient information about the waterline itself have been fed into our waterline discriminator WLdetectNet, which benefits from having chosen a bigger and more appropriate receptive field as far as possible by the current \((r,s)\) pair. Instead, Figure 6a shows the worst visual result that corresponds to the case of (30, 10) in Table 3, in which \(F1\)-_score_ presents the lowest (i.e., 0.685). However, a too big receptive field also decays the detection effect. For example, when the \((r,s)\) pair is set to (90, 45), its \(F1\)-_score_ gets just 0.835. It is because our marking strategy to approximate potential waterlines in Algorithm 1 is simplistic, so that a fine-grained marking effect in an estimated image-map is difficult to achieve in the case of setting such a big scale of WLpeephole. Subsequently, the accuracy of detection suffers more frustration. As a result, we suggest that the \((r,s)\) pair in Algorithm 1 can be empirically set to (60, 30), which is usually a good choice for practical applications based on our waterline detection algorithm, especially for detecting a 1080 \(\times\) 1440 image captured by our inland USV.
## 3 Results and Discussion
### Surface Renewal  
To evaluate the impact of drops merging into film regions on surface coverage, surface renewal, and drop size distribution, the following points are considered. Surface renewal by sweeping is dependent on the locations of departing drops. Departing at a higher elevation will result in a higher swept area and a lower \(f\) value and vice versa. This is shown in Figure 7, where a larger decline signals sweeping at higher elevations. In addition, sometimes successive departing of nearly vertically aligned drops causes sweeping of common area. This will leave drops within the unswept region to grow undisrupted, forming a more populated region and maintaining higher \(f\) values. Based on previous points, the elevation of all departing drops and corresponding swept areas is evaluated at half surface height, \(b/2\). This approach disregards the effect of successive sweeping of common regions and averages the location of departing drops.  
For each simulation run, the number of departing drops, their corresponding swept areas, and the sum of merging drops' base areas are all recorded. The total surface renewal rates of sweeping and merging drops per unit area, s\({}^{-1}\) are presented in Figure 9. As \(R_{D}\) increases, surface renewal rates for DWC within the hybrid surface decline then rise for all \(\Delta T\). In comparison with full DWC, surface renewal within the hybrid surface is improved for \(R_{D}\geq 0.5\). The shape of one surface renewal profile or curvature is due to the effect of merging drops. The contribution of merging is indicated by the fraction number at each condition in Figure 9. The value 0 implies that merging has no effect and sweeping is the only mode of surface renewal. When \(R_{D}\) reaches unity, surface renewal rates are improved by 85% at \(\Delta T=2\)\({}^{\circ}\)C and 64% at \(\Delta T=8\)\({}^{\circ}\)C.  
The FWC region width is another important parameter, and as a result, surface renewal rates per unit area are degraded. In the present work, FWC region width was ignored and left for future analysis in evaluating the thermal performance of hybrid surfaces.
## 4 Results
### Water Chemistry
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sample** & **Mn (\(\mu\)g/L)** & **Zn (\(\mu\)g/L)** & **Ba (\(\mu\)g/L)** & **T1 (\(\mu\)g/L)** & **Fe (\(\mu\)g/L)** & **As (\(\mu\)g/L)** \\ \hline
**MCL** & **50** & **3000** & & **2** & **200** & **10** \\ W-1 & 4.0 & 574 & 50 & 0.05 & 23 & 1.0 \\ W-2 & 1.0 & 87 & 235 & \textless{}0.05 & \textless{}15 & 1.7 \\ W-3 & 6.3 & 25 & 106 & 0.05 & 20 & 1.8 \\ W-4 & 1.0 & 25 & 82 & 0.05 & \textless{}15 & 1.1 \\ W-5 & 242 & 49 & 24 & 0.05 & \textless{}66 & 4.2 \\ W-6 & 1.4 & 16.0 & 93 & 0.05 & \textless{}15 & 1.3 \\ W-7 & 9.9 & 826 & 93 & 0.61 & \textless{}15 & 1.0 \\ W-8 & 1.0 & 241 & 201 & 0.05 & \textless{}15 & 2.1 \\ W-9 & 1.0 & 22 & 267 & 0.05 & \textless{}15 & 1.0 \\ W-10 & 1.0 & 87 & 235 & 0.05 & \textless{}15 & 1.7 \\ W-11 & 2.1 & 17.0 & 326 & 0.05 & \textless{}15 & 2.4 \\ W-12 & 1.1 & 23 & 349 & 0.05 & \textless{}15 & 2.7 \\ W-13 & **303** & 159 & 20 & 0.05 & **402** & 1.0 \\ W-14 & **230** & 45 & 203 & 0.05 & **1428** & **59** \\ W-15 & 0.46 & 202 & 0.55 & 0.04 & **366** & 2.6 \\ W-16 & **258** & 148 & 31 & 0.05 & 62 & 1.0 \\ W-17 & **231** & 49 & 2.9 & 0.05 & 45 & 2.1 \\ W-18 & **137** & 15.0 & 2.2 & 0.05 & \textless{}15 & 1.0 \\ W-19 & **335** & 11.0 & 8.8 & 0.05 & 31 & 1.4 \\ W-20 & **200** & 22 & 128 & 0.05 & 150 & 1.0 \\ W-21 & **253** & 14.0 & 18.0 & 0.05 & 173 & 3.6 \\ W-22 & 5.3 & 209 & 69 & 0.05 & 33 & 1.0 \\ W-23 & **127** & 14.0 & 290 & 0.05 & **708** & 2.5 \\ W-24 & **99** & 4.3 & 148 & 0.05 & **677** & 1.0 \\ W-25 & 1.0 & 462 & 195 & 0.05 & \textless{}15 & 2.6 \\ W-26 & **527** & 223 & 5.3 & 0.05 & **6465** & **102** \\ W-27 & **590** & 189 & 6.6 & 0.05 & **1880** & **24** \\ W-28 & **54** & 88 & 5.0 & 0.05 & **487** & 1.0 \\ W-29 & 1.0 & 5.0 & 244 & 0.05 & \textless{}15 & 1.8 \\ W-30 & **898** & 43 & 18.0 & 0.05 & **2585** & 5.7 \\ W-31 & 1.0 & 8.2 & 131 & 0.05 & \textless{}15 & 1.2 \\ W-32 & **454** & 539 & 16 & \textless{}0.05 & \textless{}15 & 1.4 \\ W-33 & 43 & 16.0 & 118 & 0.05 & 48 & 1.5 \\ W-34 & **193** & 38 & 0.61 & 0.06 & **1313** & **1103** \\ W-35 & 7.8 & 62 & 233 & 0.05 & **260** & 2.7 \\ W-36 & 76 & 15.0 & 4.0 & 0.05 & **213** & 2.8 \\ W-37 & 8.9 & 66 & 237 & \textless{}0.05 & **255** & 2.6 \\ W-38 & **762** & 123 & 2.6 & \textless{}0.05 & 9750** & **27** \\ W-39 & 5.9 & 12.0 & 199 & 0.05 & \textless{}15 & 1.0 \\ W-40 & 23 & 238 & 71 & 0.05 & **459** & **247** \\ W-41 & **56** & 36 & 91 & 0.10 & 59 & 1.4 \\ W-42 & **55** & 30 & 80 & 0.10 & 29 & 1.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Considered trace elements’ concentration in most of the groundwater from W1 to W172 wells together with the maximum concentration level (MCL, \(\mu\)g/kg) imposed by Italian regulations for groundwater. Values in bold exceed the MCL.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 2 Materials and Methods
### KAN-Mixer  
The KAN-Mixer [23] architecture initiates with an input image \(X\) of shape \([B,C,H,W]\), where \(B\) represents the batch size, \(C\) signifies the number of channels, \(H\) denotes the height, and \(W\) specifies the width of the image. Subsequently, the image is fragmented into nonoverlapping patches using the image-to-patches module, each of size \(P\times P\), leading to the creation of \(\frac{H}{P}\times\frac{W}{P}\) patches. Ultimately, the image is restructured into a sequence of patches \(X_{\text{patches}}\) with shape \([B,N,P^{2}\cdot C]\), where \(N\) signifies the total number of patches.  
After processing each patch independently with the PerPatchKAN module, a KAN-Linear transformation is applied to project the patch into a higher-dimensional space, enhancing the representation of the patch data. The KAN token mixing layer facilitates inter-location communication by independently transforming each token through KAN-Linear layers, amalgamating spatial information while conserving channel information. Similarly, the Channel Mixing KAN layer enables inter-channel communication within each token by independently transforming each channel through KANLinear layers, amalgamating channel information while retaining spatial information.  
The final output of the KAN module is derived by aggregating information from the transformed patches through the application of layer normalization, followed by computation of the mean across the token dimension to obtain a fixed-size representation. Subsequently, a KANLinear layer is employed to project the aggregated representation to the desired output dimension, ultimately yielding the final output \(Y\) with a shape of \([B,n_{\text{output}}]\). Here, \(n_{\text{output}}\) denotes the number of output classes or the desired dimensionality of the output.
## 5 Experimental Results and Discussion
### Discussion  
The evaluation results show that the proposed approach, based on CNN, performed better than other types of models in identifying and classifying Arabic hate speech. The improved performance was largely due to the use of pre-trained embeddings, reaching 92% accuracy in detecting hate speech and 93% accuracy in classifying it into the three classes of hate speech. This further highlight that CNN is better suited for extracting local features from text and that it benefits from the context of pre-trained embeddings. However, an examination of the findings reveals some errors in specific areas, as well as several limitations that need to be discussed.  
Even though CNN showed promising results, it was compared to transformers such as AraBERT. Specifically, transformers outperform CNNs in modelling long-range dependencies, and the agreement ratio is 89%, which is slightly lower than CNN's 92%. Notably, fine-tuned Mambo was fairly equal in multilingual hate speech detection to the others, although Mambo demanded significant fine-tuning for the specificity of the Arabic language. These comparisons show that the proposed CNN achieves a good trade-off between performance and required computation, especially in the case of short texts such as tweets.  
In terms of efficiency, this study showed that the framework had reliable performance, with its computational complexity ideal for practical use. The string matching takes only a few seconds when working with the dataset of 10,000 tweets, which suggests that the method can be useful for monitoring in real time. It achieves lower computational costs than transformers, making the model suitable for applications with limited computational resources.  
This makes the identification of false positives a moot point; nevertheless, it is ethically questionable. Misclassifications can negatively impact the interactions on the platform and the trust that users put in it by silencing genuine speech. This is a problem that has to be solved by designing AI systems that provide a way to inspect content that AI has deemed problematic, and by implementing bias-detection features that would lower the likelihood of missteps made by AI. The conclusions of the present study align with the challenges and solutions suggested by [PERSON] et al. (2018) about the difficulty of dealing with abusive language in Arabic social media.
# Impact of the Elevation Angle on CYGNSS GNSS-R
## 6 Results on the Surface Roughness and the Soil Moisture Effect  
This section is structured in two different parts. First, the evolution of \(\Gamma_{\mathrm{LHCP,CyGNSS}}\) with \(\theta_{e}\) is studied as a function of h for different levels of SMC over the land-surface coverage enabled by CyGNSS. As such, this analysis allows for the evaluation of the impact of \(\theta_{e}\) at a global scale. It provides useful information for the selection of the preferred operational angular range depending on the specific application from, e.g., a small satellite. In the second part, the correlation of \(\Gamma_{\mathrm{LHCP,CyGNSS}}\) and SMC is assessed as a function of \(\theta_{e}\) over sparsely vegetated areas with differentiated h levels, so as to minimize vegetation effects. This sensitivity study focuses on the impact of h. As such, the conclusions derived from this part could help to define SMC estimation methods using multi-angular information from GNSS-R. This is an interesting approach that previously has been applied from, e.g., the Soil Moisture Ocean Salinity (SMOS) mission [22].
## 2 Proposed FFT-DCNN Architecture
### Fast Fourier Transform Layer  
Fast Fourier transform (FFT), as a discrete Fourier transform algorithm, was first proposed by [PERSON] and [PERSON] in the year 1965. It can reduce the number of computations needed for N points from \(O\big{(}N^{2}\big{)}\) to \(O(N\log N)\). With the increasing of sampling points, this method can save more computational resources.  
Features extraction of FFT is shown in Figure 2. First of all, utilizing a sliding window acquires n consecutive time-domain data from the original signal. Then, every window data are transformed frequency information via FFT method, which is described as:  
\[X_{k}=\sum_{n=0}^{N-1}x_{n}e^{-i2\pi kn/N}\ \ \ \ k=0,\ldots,N-1 \tag{1}\]  
where \(x_{0},\ldots,x_{N-1}\) is a complex number and \(X_{k}\) is time-domain data. \(N\) represents the number of sampling points.
# Effect of the Inter-Ring Delay Time on Rock Fragmentation: Field Tests at the Underground Mine
###### Abstract
It is theoretically feasible to improve rock fragmentation by generating better stress wave interaction through short delay times; however, many researchers have not been able to improve rock fragmentation based on this approach. [PERSON] simulated step blasting with five delay times (0, 100, 500, 1000, and 2000 microseconds), and the results showed that simple stress interactions did not affect rock fragmentation [9]. [PERSON] and [PERSON], based on numerical simulation results, do not support the idea that short delay times can significantly improve rock fragmentation through stress wave interaction [10; 11; 12]. [PERSON] and [PERSON] conducted a series of detailed small-scale tests, and the results showed no significant differences or improvements in fragmentation compared to the absence of shock wave interaction when the delay was within the range of interaction time [13]. Regarding free faces, [PERSON] proposed that long-delay blasting could produce new free faces for the next detonation hole, increasing rock fragmentation [14]. [PERSON] pointed out that larger delay times are generally used in blast engineering to provide good free faces for the next row [3] (p. 327). In terms of collisions, it is commonly believed that the optimal delay time should allow for the fragment velocity of the latter blasting hole to be greater than that of the former blasting hole, to strengthen the collision and supplement fragmentation. The above research is mainly limited to open-pit blasting, analyzing the impact of short delay times between holes on rock fragmentation from the perspective of stress superposition. There is not much literature analyzing the impact of delay time on fragmentation from the perspectives of free surfaces and collisions. Additionally, most research has focused on short delay times between holes, and there is limited research on delay times between rings (rows). Especially in underground mine tunnel excavation blasting, due to the presence of only one free surface in the tunnel and the confinement effect of rocks, longer IRDTs are usually used. However, there has not been a deeper study of the IRDT, so it is necessary to conduct research on the effect of IRDTs on rock fragmentation. In this paper, digital electronic detonators were used to study the inter-ring delay blasting in the Beizhan iron mine, and the effect of IRDTs on rock fragmentation was summarized.
## 2 Materials and Methods  
### Satellite Data  
Sentinel-2 (S2) satellite images, acquired from September 2017 to December 2019 with cloud cover lower than 90%, were collected for granules T32 TPP', whose spatial extent corresponds to the test site (Figure 1). The Multi-Spectral Instrument (MSI) sensor onboard S2 satellites is characterized by high spatial resolution (10 m, 20 m, and 60 m), high revisit capacity (5 days with a two-satellite constellation), and 13 spectral bands from the visible-to-shortwave infrared. Sentinel-2 MSI L2A data were collected from the Theia archive, representing the bottom of the atmosphere (BOA) reflectance, orthorectified, terrain-flattened, and atmospherically corrected with the MACCS-ATCOR Joint Algorithm (MAJA) [8].  
After resampling all spectral bands to a 10 m resolution, pixels corresponding to clouds, cloud shadows, snow, and water in the product quality flag masks or corresponding to topographic shadows (identified by product quality flags representing the topographic mask and low-sun mask, with the latter considering the pixel aspect, slope, and sun zenith angle) were masked out from further analysis.  
Landsat-8 (L8) satellites images, acquired from September 2017 to December 2019 with cloud cover lower than 90%, were collected for tiles T192030' and T193029', which overlay the S2 granule selected as a test site. The Operational Land Imager (OLI) sensor onboard L8 satellite is characterized by high spatial resolution (30 m), medium revisit capacity (16 days), and 9 spectral bands from the visible-to-shortwave infrared. Landsat  
Figure 1: Main vegetation cover in the study area (Italy).  
8 OLI Collection 2 data were collected from the USGS archive, representing the top of the atmosphere (L1 TP) and bottom of the atmosphere (L2 SR) reflectance, orthorectified, terrain-flattened, and atmospherically corrected. Pixels corresponding to clouds, cloud shadows, snow, shadows, and water in the product quality flag masks were masked out from further analysis. The bottom of atmosphere reflectances (L2 SR), after being stacked with sun and view angles L1 TP (using the SNAP processing graph available at [[https://github.com/ffilipponi/SNAP_LS_C2_BiophysicalLandsat8](https://github.com/ffilipponi/SNAP_LS_C2_BiophysicalLandsat8) Op]([https://github.com/ffilipponi/SNAP_LS_C2_BiophysicalLandsat8](https://github.com/ffilipponi/SNAP_LS_C2_BiophysicalLandsat8) Op), accessed on 8 December 2021), were correct for changes in illumination due to terrain elevation [9].
## 3 The Problem of Selecting the Next Network
### Network Selection Process according to the Proactive Cross-Layer Protocol  
The network selection process is based on the past, present, and future data modeled by the following block diagram of Figure 3.  
Figure 2: Test scenario with eight WiFi access points with a coverage area of 100 m.  
In block A, the historical data is used to know where the mobile device has been physically connected. That is, it uses the GPS data set and GPS and \(R_{i}^{l}\).  
In block B, the current data is sampled in order to analyze the current state of the system and make a compensation if it is necessary. That is, it uses the Kalman filter that was proposed by [PERSON] in 1960, where a set of mathematical equations efficiently estimates the state of a linear system that minimizes the estimated error to achieve the optimization. The state of vector \(x\) is defined by the set of data [p_x_v_x_a_x_p_y_v_y_a_y_], which describes the movements of the mobile in time \(t\) where p_x_v_x_a_x corresponds to the latitude point, velocity, and acceleration, andp_y_v_ya_y corresponds to the parameter length of a mobile. For the measure of the change in the state of the mobile without \(\Delta t\), we use the kinetic equation of movement ([PERSON], 1960). This block is supported by the technique using the AV data set. Next, it uses Equation (1), to draw a possible route and anticipate a possible network change.  
\[x=x_{0}+v\Delta t+0.5a^{2}\Delta t \tag{1}\]  
\[v=v_{0}+a\Delta t \tag{2}\]  
where:  
\(x_{0}\) is the initial state of the vehicle, \(v\) is the velocity, and \(a\) is the acceleration of the vehicle (assuming constant acceleration at time \(t\)).  
In block C, a forecast of a possible future location is calculated and, using classification artificial intelligence techniques, it is predicted which the possible network is that will be reached. In this block, a bayesian classifier is used.
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 2 Experiments and Data Preprocessing
### Data Preprocessing
This study enhanced the infrared data to meet the data requirements for network model training and to enhance the model's generalization ability for different processing conditions. Considering that the scanning directions in the experiment were along the X- and Y-axes, data diversity was increased through image rotation and flipping operations. Specific operations include flipping along the X-axis, the Y-axis, and in both directions, generating 7 new images from each image. After data augmentation, the number of infrared images increased to 2776, and the number of visible-light images increased to 5552. It should be noted that the optimal cladding quality is achieved within the laser cladding process window determined in the experiment, which can obtain a steady-state melt pool morphology. This enables the proposed multi-source data fusion method to have sufficient generalizability in the application of steady-state melt pool morphology in various laser processing processes. However, it must be acknowledged that the practicality under extreme conditions, such as large melt pool fluctuations, ultra-high-speed processing, and a high power density, still needs to be assessed. Figure 2: Data processing flow of infrared temperature distribution images and visible-light images. This study constructed an image fusion dataset, a data fusion dataset, and two video frame interpolation datasets, all allocated in an 8:1:1 ratio for training, validation, and testing sets, respectively. Specific configurations are shown in Table 1. Dataset 1 comprises 2776 images of the melt pool temperature distribution and 2776 images of the visible-light melt pool morphology. This dataset was used to test the performance of the image fusion module. Dataset 2 contains 2776 images of the melt pool temperature distribution and 5552 images of the visible-light melt pool morphology. This dataset was used to test the performance of the proposed data fusion method and to simulate the application of the proposed method in a real processing scenario. The video frame interpolation module uses two types of datasets: Dataset 3, based on a pre-trained infrared video frame interpolation module, contains 2776 infrared images and 5552 visible-light images, where infrared images generate a set of data every 1 frame, totaling 2729 sets; visible-light melt pool morphologies generate a set every 1 frames, totaling 5505 sets, with each set containing 1 consecutive image. Dataset 4 is a pre-training dataset for the video frame interpolation model, containing 1388 infrared images, generating a set of data every 1 frame, totaling 1341 sets.