esa-sceva/satcom-chunk-collection huggingface dataset:
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 2 Methodology
### Bat Algorithm  
The bat algorithm (see Figure 2) is a swarm intelligence algorithm inspired by the echolocation produced by bats when interacting with their surroundings [23]. The echolocation starts with the emission of short and loud sound waves released by bats to identify their prey, obstacles or resting cracks in the dark. The time-lapse for the emitted sound to bounce back reveals the prey's distance, direction and speed. All bats use echolocation to measure distance and distinguish between targets and obstacles [39]. The algorithm keeps a record of the bat's velocity, position, frequency, varying wavelength, loudness and pulse emission. The loudness is measured in the range between A\({}_{\text{min}}\) and A\({}_{0}\), while the pulse emission is logged between 0 and 1, where 0 represents no pulse, and 1 refers to the highest rate of the bat's emission. The bat algorithm is suitable to handle both continuous and discrete optimization matters. One of the advantages of this algorithm is the ability to reach quick convergence at the initial stage and shift from exploration to exploitation when optimality is near [40].  
The mathematical equations that relate to the velocity and location can be defined as:  
\[f_{i}=fmin+(fmax-fmin)\beta \tag{1}\]  
\[v_{i}^{t}=v_{i}^{t-1}+\Big{(}x_{i}^{t-1}-x_{*}\Big{)}f_{i} \tag{2}\]  
\[x_{i}^{t}=x_{i}^{t-1}+v_{i}^{t} \tag{3}\]  
where:  
Figure 1: Architecture of LSTM blocks.  
\(\beta\in[0,1]\) is the random vector from a uniform distribution;  
\(f_{i}\) is the initial frequency;  
\(v_{i}^{t}\) is the velocity at \(t\) iteration;  
\(x_{i}^{t}\) is location at \(t\) iteration in a d-dimensional search or solution space.  
The loudness and pulse emission rates are represented below:  
Figure 2: Bat algorithm flowchart.  
\[A_{i}^{t+1}=\alpha A_{i}^{t} \tag{4}\] \[r_{i}^{t+1}=r_{i}^{0}[1-\exp(-\gamma t)] \tag{5}\]  
where:  
\(0<\alpha<1\) and \(\gamma>0\) are constants;  
\(\alpha\) is the constant reducing loudness, and \(\gamma\) is the constant increasing pulse rate.
# Application of ANN in Predicting the Cantilever Wall Deflection in Undrained Clay
## 2 Materials and Methods
### Introduction of ANN  
The ANN, which works similarly to human cognition, is one of the best machine learning models to handle complex nonlinear problems, and it has been successfully applied to many geotechnical problems. Typically, an ANN model consists of an input layer, one or more hidden layers, and an output layer. The input layer receives the information from the input data and transforms it into the hidden layer. The hidden layer and the output layer perform the complex computations of the ANN model. In this work, the ANN model was constructed in the spirit of multilayer perceptron, typically trained  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Data** & \(B\) **(m)** & \(H_{c}\) **(m)** & \(t\) **(m)** & \(K_{0}\) & \(s_{u}/\sigma_{v}\)\({}^{t}\) & \(E_{ur}\)/\(s_{u}\) & \(\delta\) **(m)** \\ \hline Minimum & 10.00 & 0.50 & 0.60 & 0.50 & 0.30 & 199.38 & 4.48 \(\times\) 10\({}^{-4}\) \\ Mean & 68.59 & 2.74 & 1.04 & 0.79 & 0.46 & 500.51 & 0.055 \\ Maximum & 150.0 & 6.00 & 1.60 & 1.10 & 0.70 & 900.00 & 0.753 \\ Standard Deviation (StD) & 44.44 & 1.82 & 0.27 & 0.12 & 0.12 & 201.31 & 0.109 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistical results of these datasets.  
Figure 2: Cantilever wall deflections by Plaxis 2D versus measured values.  
using the feed-forward back-propagation algorithm [31]. For simplicity, only one hidden layer was used, as previous research [32; 33] has reported that the ANN model with one hidden layer can perform well in handling similar problems. Hence, the final ANN model consisted of a single input, hidden, and output layer.  
Figure 3: Relationship between input and output parameters.  
The so-called weights, biases, and activation functions are used to connect to the neurons of different layers, and based on them, the whole ANN model can be represented in a mathematical form, as shown below:  
\[Y=f\left(X\right) \tag{1}\]  
\[f\left(X\right)=f_{2}(b_{2}+W_{2}\times(f_{1}(b_{1}+W_{1}\times X))) \tag{2}\]  
where \(Y\) is the final output for the ANN model; \(X\) is the matrix of input parameters; \(f_{1}\), \(B_{1}\), \(W_{1}\) are the activation function, the weight matrix, and the biases vector of the hidden layer, respectively; and \(f_{2}\), \(B_{2}\), \(W_{2}\) are the activation function, the weight matrix, and the biases vector of the output layer, respectively. For the activation functions, this study adopted the commonly used LOGSIC function and PURELIN function in the hidden and output layers, respectively. Their functions are presented in Figure 5.  
Figure 4: Correlation matrix of the database.  
In the training process of the ANN model, the connecting weights and biases are adjusted to minimize the error of the ANN. A term named the mean square error (MSE) is widely utilized to terminate the training of the model. The MSE can be calculated by Equation (3) below:  
\[\text{MSE}=\frac{1}{N}\sum_{i=1}^{N}(e_{i})^{2} \tag{3}\]  
where \(N\) represents the number of input datasets; \(ei\) is the difference between the predicted and actual results.
## 5 Experiments and Results
### Discussion  
The algorithm proposed in this paper offers a solution for the recognition of aircraft activity at airports. By combining the DCNN-based object detection and an improved template matching method, aircraft can be accurately detected and stably tracked, and its activity can be identified based on its speed. The algorithm can process aircraft targets of different sizes, and is equally  
Figure 13: Speed measurements for each aircraft in test Video 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Data**} & \multicolumn{4}{c}{**Time Consumption**} \\ \cline{2-5}  & \multicolumn{2}{c}{**RTX2080 Ti**} & \multicolumn{2}{c}{**Jetson AGX Xavier**} \\ \cline{2-5}  & **Detection** & \begin{tabular}{c} **Tracking and Activity** \\ **Recognition** \\ \end{tabular} & \
\begin{tabular}{c} **Detection** \\ **Recognition** \\ \end{tabular} &
\begin{tabular}{c} **Tracking and Activity** \\ **Recognition** \\ \end{tabular} \\ \hline Video 1 & 3.47 s & 4.84 ms & 19.33 s & 18.27 ms \\ \hline Video 2 & 3.76 s & 51.9 ms & 19.78 s & 172.76 ms \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time consumption comparison of the algorithm on different computing platforms.  
effective under different complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning, entering, and exiting.  
The algorithm's time consumption has been evaluated on an embedded computing platform, where it exhibited a delay of tens of seconds in the two testing videos. However, compared with tracking-by-detection methods, the algorithm in this paper achieves a relatively efficient performance under the premise of ensuring high accuracy. Nonetheless, the algorithm can still effectively support applications in which short delays are acceptable, such as monitoring whether an airport is operating or not. Based on on-board dynamic event recognition, selective download of segment data of interest can be performed. Compared with traditional ground processing methods, the proposed method significantly improves the efficiency of event acquisition and reduces the amount of satellite-to-ground data transmission.  
With appropriate improvements, the algorithm is expected to be used for time-critical tasks, such as real-time detection and tracking of take-off aircraft. These high-value dynamic events usually occur at the end of the runway. Therefore, geographic information can be incorporated to further narrow the range of images processed by the algorithm. By reducing the detection range and the number of aircraft in the field of view, the time consumption of the algorithm can be significantly reduced. Additionally, using satellites with on-board, real-time processing capabilities to form a constellation would enable the continuous real-time tracking of areas of interest and the relay tracking of moving targets.
## 3 Results
### Spatial Distribution Characteristics of GPP  
#### 3.2.1 Overall Spatial Distribution  
To illustrate the distribution of the annual mean GPP in the study area, yearly GPP data from 2001 to 2020 and the 20-year mean were visualized using ArcGIS (Version 10.8), as shown in Figure 3 (displaying only 2001, 2010, 2020, and the 20-year mean). Overall, the GPP distribution exhibits a west-high, east-low pattern, associated with high forest cover in the west and dominant urban development in the east. By comparing the GPP  
Figure 2: Fitted trends between the GOSIF GPP, MODIS GPP, and MODIS-LANDSAT GPP estimated by the CASA model. **(a)** Fitted trend for GOSIF GPP. **(b)** Fitted trend for MODIS GPP.  
changes in northwestern Hubei over time, the areas along the Han River and its tributaries show a slightly lower GPP than the surrounding areas, which can be linked to increased development and human activities. Meanwhile, southern northwestern Hubei displays a significant upward GPP trend over time. The Shennongjia Forest District consistently maintains high GPP levels, which can be closely tied to the establishment of forest reserves and the regional ecological restoration policies. In some river areas, GPP values were detected despite being classified as water bodies. This uncertainty may result from mixed pixels or sub-pixel heterogeneity, where water pixels may contain vegetated features such as riparian vegetation, small islands, or shallow water with aquatic plants. Additionally, errors in land cover classification and spatial resolution mismatches may contribute to these values. Therefore, such GPP values are not necessarily erroneous but may reflect actual photosynthetic activity from vegetated elements within nominal water pixels. The reason for low GPP at low elevation may be mostly due to the widespread distribution of cropland and non-vegetated land cover types (Figure 1b), which show much lower GPP than the forests in the high elevation area.  
Figure 3: Spatial distribution of GPP in the study area for different years and the mean over 2001–2020. (**a**) GPP distribution in the study area for 2001. (**b**) GPP distribution in the study area for 2010. (**c**) GPP distribution in the study area for 2020. (**d**) Mean GPP distribution in the study area (2001–2020).
# Assessment of _Bacillus pumilus_ Isolated from Fresh Water Milieu for Bioflocculant Production
## 3 Results and Discussion
### Optimization of Culture Conditions for Biofoculant Production
#### 3.2.2 Effect of Nitrogen Source on Biofloculant Production  
Nitrogen sources play a crucial role in the production of biofloculants [42]. Different microorganisms utilize either organic or inorganic nitrogen sources, or both, to produce biofLoculants [43]. The effect of organic, inorganic and multiple nitrogen sources on biofoculant production by the test bacteria were evaluated. As shown in Figure 2, it was evident that the complex nitrogen source composed of yeast extract; urea and ammonium sulfate was the most preferred for biofoculant production by  
Figure 1: Effect of carbon sources on biofoculant production.  
_Bacillus pumilus_ strain as opposed to the individual nitrogen sources. Urea as a sole nitrogen source also supported biofleculant production (54%) when compared with other single nitrogen sources such as peptone, yeast extract, tryptone and ammonium sulfate.  
Similarly, [PERSON] et al. [44] also observed that _Cobenia_ sp. poorly utilized all tested inorganic and organic nitrogen sources when used individually as sole sources of nitrogen, but the nitrogen source composed of urea, yeast extract and ammonium sulfate resulted in high flocculating activity of 92.2%. In another study by [PERSON] et al. [45], the most favorable medium for production of biofleculant by _Klebsiella_ sp. contained three nitrogen sources (urea, ammonium sulfate, yeast extract), which supported an optimum flocculating activity of 94.7%. [PERSON] et al. [46] observed that for _Serratia ficaria_, a multiple nitrogen source (beef extract and urea) supported a higher flocculating activity of 94.1% when compared to sole organic or inorganic nitrogen sources. These results suggest that it is necessary to test not only for the effect of individual nitrogen sources on biofleculant production but for the effect of combined nitrogen sources as well. All inorganic nitrogen sources (ammonium sulfate, ammonium nitrate, and sodium nitrate) tested for biofleculant production by _Chryseobacterium daeguense_ W6 led to poor production of both the biofleculant and cell growth, whereas the organic nitrogen sources proved to be better sources for biofleculant production, for which tryptone was the most favorable exhibiting a flocculating activity of more than 90% [47]. Generally, it has been reported in the literature that organic nitrogen sources are more suitable for biofleculant production and are more easily absorbed by the cells when compared to inorganic nitrogen sources [48].
## 4 Discussion
### Smartphone and Gait  
#### 4.2.1 Effect of Smartphone Use on Walking Speed  
Six studies have reported decreased walking speeds associated with smartphone use [24; 26; 29; 34; 35; 39]. Compared to single-task walking without a smartphone, dual-task walking--such as reading text messages--resulted in an approximate reduction of 0.16 m/s in walking speed [26]. Additionally, a study involving 308 participants aged 20 to 60 y indicated an average reduction of 0.31 m/s while sending text messages [24]. These findings underscore smartphone use as a significant factor contributing to reduced walking speed, highlighting substantial risks associated with texting while walking.  
Notably, older adults (aged 60 and older) experienced a more pronounced decrease in walking speed, averaging 0.4 m/s slower while texting. Specifically, among older individuals, walking speed dropped by approximately 1.3 m/s when using smartphones [35]. Such outcomes illustrate that smartphone use leads to cognitive distraction and diminished situational awareness. Activities like sending or reading messages significantly impair walking speed due to diverted visual attention and cognitive overload. Therefore, it is recommended to perform these tasks while stationary. Research has also shown that smartphone use during obstacle negotiation alters the center of mass, leading to gait imbalance, increased visual distraction, and heightened pelvic movement, all of which contribute to gait instability [29]. Increased smartphone addiction further diminishes gait attention and significantly raises the risk of accidents [40]. Reduced walking speed poses safety concerns, particularly at crosswalks where focused attention is critical [34].
## 5 Discussion
### Limitations on Scalability to Higher Magnification  
While the DRFAN achieves strong performance under the \(\times\)4 super-resolution setting, its current architecture is not explicitly optimized for higher scaling factors such as \(\times\)6 or \(\times\)8. Preliminary experiments indicate noticeable quality degradation at such magnification levels due to the increasing semantic gap between low-resolution inputs and their high-resolution counterparts. This is a known challenge in SR models, especially under lightweight constraints.  
Figure 12: Box plot comparison of PSNR results for different lightweight SR models on the rice dataset. Red lines show the median PSNR of each model.  
To address this, future extensions of DRFAN may incorporate progressive upscaling strategies, such as cascaded SR stages, or use degradation-aware modules to modulate reconstruction paths adaptively. These methods can help bridge the semantic gap and preserve consistency across extreme scaling conditions.
# Salient Object Detection via Fusion of Multi-Visual Perception
## 5 Experiments
### Evaluation Metrics  
To comprehensively evaluate the performance of the algorithm, four quantitative evaluation indicators are used: the precision-recall (\(PR\)) curve, ROC curve, F-measure, and mean absolute error (\(MAE\)).  
Figure 6: An example of the saliency map acquisition process. (**a**) Raw input. (**b**) Final fusion saliency map. (**c**) Superpixel segmentation map. (**d**) Saliency map. (**e**) Binary map. (**f**) Ground truth.  
The binary maps are obtained from the saliency maps by applying a threshold from 0 to 255. Then, the precision (\(P\)) and recall (\(R\)) value pairs are obtained from these binary images and the ground truth, and a \(PR\) curve is plotted. \(P\) and \(R\) can be calculated as:  
\[P=\frac{|B\cap GT|}{|B|},R=\frac{|B\cap GT|}{|GT|}, \tag{10}\]  
where \(B\) is the binary map, and \(GT\) is the ground truth.  
The ROC curve is the curve obtained by the false positive rate (FPR) and true positive rate (TPR). FPR represents the probability of mispredicting a positive sample among all negative samples, while TPR means the probability of being correctly predicted as a positive sample among all positive samples.  
The F-measure is also obtained through the \(P\)-\(R\) value pair, which is calculated as:  
\[F_{\beta}=\frac{(1+\beta^{2})P\times R}{\beta^{2}P\times R}, \tag{11}\]  
where \(\beta=0.3\), indicating that improving precision is more important than improving recall.  
Mean absolute error (\(MAE\)) is a simple and reliable binary map evaluation metric. It is computed as the mean of the pixel-level absolute error between the binary map (\(M\)) and the corresponding \(GT\), defined as:  
\[MAE=\frac{1}{M\times N}\sum_{i=1}^{M}\sum_{j=1}^{N}|B_{ij}-GT_{ij}|, \tag{12}\]  
where \(M\) and \(N\) represent the height and width of the image, respectively.
## 3 Results and Discussion
### Light Microscopy of Asphaltenes  
We used light microscopy to determine the melting points [62; 63; 64; 65] of all samples analyzed in this study.  
From the microscopy analysis, the results obtained from the DSC measurements are confirmed: microphotographs are reported in Figure 2, and the initial and final temperature values of the solid-liquid transition are reported in Table 3. In fact, while the non-aged bitumen shows a well-defined solid-liquid transition taking place in a short temperature interval, the oxidized bitumen (PAV) shows a gradual melting of the sample taking place in a wider temperature range that begins around 160 \({}^{\circ}\)C and finishes at 175 \({}^{\circ}\)C. This justifies the presence of more peaks for the calorimetric curve obtained for the aged sample in addition to the inflection point (TG), while in the case of the non-aged bitumen sample just one common inflection point can be observed. In the case of the asphaltenes coming from the PAV bitumen treated with Agents 3 and 4, self-consistent results are obtained: Agent 3 brings the inflection points back to values similar to those for the non-aged bitumen, whereas Agent 4 brings back the inflection points to values similar to those of PAV-aged bitumen. We intend to highlight the fact that the sample treated with Agent 3, having a higher transition temperature range compared to the non-aged sample, shows a calorimetric curve with a more complex pattern and presenting more peaks, keeping anyway the same temperature range as for the non-aged bitumen. The different behavior of the melting points of the various asphaltene samples is due both to the different aggregation between them and to their different chemical composition. In fact, when a bitumen is exposed to an aging process, it tends to incorporate oxygen, forming epoxy and sulfoxide bonds and also significantly reducing the aromatic component [41]. This leads to the formation of larger clusters that consequently will tend to melt at higher temperatures. When an additive with a fluxing effect (such as Soy Oil or Agent 4) is added to the aged bitumen, since the latter only has a physical interaction with the bitumen, there is no variation either in the chemical composition or in the size of the clusters, and therefore the melting points remain very similar to aged bitumen. On the other hand, when an additive with a regenerating effect (such as TPI or Agent 3) is added, it chemically interacts with the asphaltenic part of the bitumen, reducing the size of the clusters and above all giving the asphaltene sample a chemical composition very similar to that of non-aged bitumen, restoring the aromatic component and reducing the oxidized parts [41]. Consequently, the melting point values of asphaltenes are very similar to those recorded for non-agedbitumen. The melting points for the RTFOT samples of the bitumen Type 1 are also in line with the scenarios reported above [41].  
Figure 2: (**a**) Melting points of asphaltenes of non-aged bitumen; (**b**) Melting points of PAV; (**c**) Melting points of PAV + Agent 3; (**d**) Melting points of PAV + Agent 4.
## 3 Mitigation Scheme for SAR
### Adaptive Filtering
#### 3.3.1 Recursive Filter  
The most popular adaptive filtering technique is the least-mean-square (LMS) algorithm, which can achieve good compromise for the convergence speed, stability, complexity, and adaptability that are usually required at the same time. The filter coefficients are determined by minimizing the mean square prediction error. In contrast to RFI modeling and estimation, the RFI environment as a whole is modeled as an autoregressive process, narrowband with respect to the radar bandwidth. Hence, most of the RFI signals, narrow and wideband, are estimated in one pass of the adaptive filter. [PERSON] et al. constructed a time domain least-mean-square (TDLMS) adaptive filter to address the narrow-band RFI with multiple stationary and non-stationary emitters in wideband NASA AirSAR signals [18; 19]. It has also been verified for the E-SAR [49; 50; 51] and PALSAR system [52]. Its convergence rates are limited by the ratio of the largest and smallest eigenvalues of the autocorrelation matrix of the input signal. To speed up the convergence speed and the computational complexity, a variant of the frequency domain least-mean-square (FDLMS) method is proposed [53; 54]. The LMS based methods may be sensitive to nonstationary RFI environment. To improve the capability of fast-tracking time varying signal and noise statistics, [PERSON] et al. proposed to use an adaptive line enhancer (ALE) controlled by the normalized LMS (NLMS) algorithm to eliminate RFI [55]. [PERSON] et al. proposed a normalized LMS filter with clipping of strong scatterers to reduce the asymmetric range side-lobes level [56]. To improve the computation efficiency, the Wiener filter is also applied on the SAR data [57; 58]. The LMS-based methods suffer from dealing with the wide band interferences and time-varying sources.