esa-sceva/satcom-chunk-collection huggingface dataset:
# Search Space Reduction for Localization and Tracking of an Acoustic Source
## 2 The Experiment  
The SACLANTCEN 1993 Mediterranean Experiment is described in detail in the literature [16]; thus, a compact description is presented in this section. Transmissions took place in a shallow water area north of the island of Elba in an environment with a mild bathymetry and over a two day period on 26 and 27 October 1993. A vertical array (VLA) with 48 hydrophones spanning the water column from 18.7 to 112.7 m was deployed on the morning of the 26 th, and on the same day, a source was deployed at a stationary position 5.8 km north of the VLA; on the 27 th starting at 14:00 h, a support ship moving northward towed a source with similar characteristics to the one deployed at the stationary position. The stationary source was attached to a surface buoy, which was itself tethered to a ballast lying on the bottom; the length of the tether cable was such that the position of the stationary source was only known to be within a circle of radius 200 m with its center at the ballast. Depending on wind conditions, the stationary source could be closer to or further from the VLA within that circle. GPS coordinates for the VLA and the source ballast are indicated in Table 1; the corresponding accuracy for each position was around 100 m. Two signals, called RM2 and RM5, were transmitted at different moments by the stationary source, while the moving source transmitted only the RM5 signal; both RM2 and RM5 were transmissions of pseudorandom noise with bit shift registers for which there is no available information. According to the literature, center frequencies for RM2 and RM5 corresponded to 335 and 170 Hz, respectively [16]; however, frequency-based optimization indicated that a better fit between replicas and data could be achieved using slightly different values of frequency (see Section 2.2).
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Discussion
In contrast, the proposed method benefits from the integrated _DCP_ pre-dehazing module, which eliminates most haze in the initial step 1, despite the image appearing darker due to the _DCP_'s influence. By step 100, our model rectifies the dark color issues through the RefineNet model, and the model converges with minimal enhancements to the dehazed image's quality. As a result, our zero-shot dehazing technique outperforms others in terms of convergence efficiency. **Network size comparison. ** Since zero-shot dehazing methods train the model for each degraded image, the training time complexity is important when evaluating the performance of the algorithm. A complex network architecture is not suitable for zero-shot image dehazing because it will significantly increase the training time complexity. Therefore, we compared the network size of different zero-shot dehazing methods to show the superiority of our method. As shown in Table 6, since the ZID and YOLY have similar network architecture, they have comparable network sizes and are substantially larger than those of the other methods. The DDIP, ZIR, and our method all possess a relatively lightweight network, but our method is the lightest. **Training time complexity analysis. ** In order to further evaluate the training efficiency of a zero-shot dehazing method, we compared the execution time of various methods for processing a 512 \(\times\) 512 pixel RS hazy image. According to Table 7, the ZIR has the lowest training efficacy, requiring more than five minutes to infer a haze-free image. Due to their similar network structures, the ZID and YOLY have a comparable training time complexity. Figure 7: An ablation study for the loss function. (**a**) Hazy image; (**b**) w/o reconstruction loss; (**c**) w/o TV loss; (**d**) w/o _DCP_ loss; (**e**) w/o minimum pixel intensity loss; (**f**) Ours. As described in Section 3.5, thanks to the pre-dehazing _DCP_ module and a lightweight network design, our proposed method has the highest training efficiency and outperforms all other methods by a significant margin.
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 3 Methodology
### Time-Aware Attention
#### 3.2.2 Time Attention Weight by Combining with Self-Attention  
After getting the time importance \(\alpha_{t}\), we obtain time attention weight by combining with self-attention. Time attention weight is used to make time-aware representation. We adopt Transformer's self-attention to combine with our attention. \(Q\in\mathbb{R}^{n\times d}\), \(K\in\mathbb{R}^{n\times d}\), \(V\in\mathbb{R}^{n\times d}\) of self-attention are represented by each weight, \(W_{q,k,v}\in\mathbb{R}^{d\times d}\), and the hidden states of BiLSTM, \(h\in\mathbb{R}^{n\times d}\). \(d\) is the hidden dimension size, and it is maintained during the attention process.  
We transform \(Q,K\) into \(Q_{t}=\alpha_{t}\cdot Q\in\mathbb{R}^{n\times d},K_{t}=\alpha_{t}\cdot K\in \mathbb{R}^{n\times d}\). They are new representations considering time importance. As shown in Figure 4, each query-key pair, \(Q_{t}-K_{t}\), \(Q-K\), is used for scaled-dot product, where \(d_{k}\) is the scaling factor of dimension size. The summation of each result is represented by hyperbolic tangent to get attention weight \(\alpha_{TA}\). We use hyperbolic tangent to expand the range of attention representation, considering the summation process.  
\[\alpha_{TA}=tanh(\frac{QK^{\top}}{\sqrt{d_{k}}}+\frac{Q_{t}K_{t}^{\top}}{\sqrt{d _{k}}}) \tag{6}\]  
\[Z_{t}=dropout(layernorm(FFN(V_{t})))+h \tag{7}\]  
The value of TA, \(V_{t}\in\mathbb{R}^{n\times d}\), is produced by time attention weight, \(V_{t}=\alpha_{TA}\cdot V\). After that, we get time-aware representation \(Z_{t}\in\mathbb{R}^{n\times d}\) from Equation (7), which consists of layer normalization, feed-forward network, and dropout as the Transformer did [22]. The layer normalization is used to normalize the inputs and stabilize the learning process. As the hidden dimension size is maintained, we can apply the skip-connection and make the model to be optimized easily. Finally, \(Z_{t}\) gets through the prediction layer, which consists of three fully connected layers.
###### Abstract  
The presence of dyes in waterbodies poses severe problems in human and aquatic creatures, and the development of treatment methods for the removal of these pollutants is of utmost importance. This research study investigates the elimination of methylene blue (MB) from an aqueous solution using zero-valent iron nanoparticles synthesized from sweet line pulp waste (nZVISLP). The purity, chemical composition, and crystalline size of nZVISLP were investigated using microscopic and spectroscopic studies. A maximum MB removal efficiency of 98.9% was obtained at the following optimal conditions: C\({}_{0}\): 10 mg/L, dosage: 1.2 g/L, and temperature: 25 \({}^{\circ}\)C. To understand the adsorptive removal characteristics of nZVISLP, the investigational adsorption data were tested with conventional kinetic and isotherm models. Furthermore, a differential evolution optimization (DEO) technique was used to estimate the optimal intrinsic parameters in the isotherm and kinetic models. For the various evaluated isotherms, the correlation coefficient (\(R^{2}\)) values for the Freundlich and Sips isotherm models were -0.98, thus confirming the aptness of these isotherms to represent MB adsorption onto nZVISLP. The robustness of non-linear models was verified by statistical metrics, thus validating the performance of the optimization technique. The results derived from this study affirm the potential of an ecofriendly biogenic nanomaterial, nZVISLP, for MB adsorptive removal.  
adsorption; methylene blue; zero-valent iron nanoparticles; differential evolution optimization; process modeling; sweet line pulp waste +
Footnote †: journal: Applied Sci. 2019, 9, 5112; doi:10.3390/app9235112
## 2 Materials and Methods
### Design of the Models
#### 2.4.1 Parameters of the Models  
In this section, the CNN method was established to predict the local site amplification based on earthquake observation records. The sampling data used in the proposed CNN models involved 44 seismic events. The input data of the samples were extracted from 40 seismic incidents recorded at 7 stations (Table 3). The geological exploration data of the 7 stations were provided by New Zealand's Institute of Geological and Nuclear Sciences (GNS) and the observation records were downloaded from the New Zealand's geohazard information network (GeoNet) ([[https://www.geonet.org.nz](https://www.geonet.org.nz)]([https://www.geonet.org.nz](https://www.geonet.org.nz)), accessed on: 27 June 2021). The output data of the samples were directly computed using Equation (2) from the downloaded observation records. A series of input variables of the proposed CNN model were specified: the station latitude, longitude, magnitude, focal depth, epicentral distance, soil layer thickness, V\({}_{30}\) (equivalent shear wave velocity at a calculated depth of 30 m in the overlying soil layer) and the observation records of the bedrock PGAr (peak ground acceleration of rock). Based on a number of the input variables and output variables of the samples, an effective and reasonable CNN model was trained, and a CNN-based prediction method was obtained. With the trained CNN model, the ground motion amplification factors of the Lower Hutt Valley could be predicted, and the amplification characteristics of this engineering site could be investigated.  
A 1-D CNN structure was established using the 'Deep Learning Toolbox' of MATLAB (MathWorks Inc., Natick, MA, USA), including convolution layers, activation layers (leaky ReLU activation function) and one fully connected layer (FC). The convolution kernel size and layer number were adjusted according to different situations. The CNN structure was designed according to the ground motion situation considered in this study. The weights of the CNN and the internal parameters were adjusted to achieve the best local site amplification prediction effect. The data were preprocessed and then input into the CNN for network training. Part of the dataset was used for network training, and the other part was used for testing. Furthermore, padding was employed to preserve the information on the edges.
## 3 Results and Discussion
### Infrared Image Analysis and Calculations
#### 3.2.2 Calculation of the Zone Length Variation over the Bar Length  
Once the values of pixels were converted to millimeter unity of distance, the variation of the molten zone length can be plotted according to its position along the bar. That is another form of the analysis the data collected by an IR camera, as seen in Figure 13. This graph describes the zone length changes along the bar for the measuring example of Figure 9, where each letter (A-L) corresponds to the respective picture frames. It can be observed that as the zone moves towards the middle of the crucible, the length of the molten zone decreases considerably, followed by an increase when it approaches the end of the crucible. This happens probably because the middle of the bar is where the heat loss reaches its maximum value, being dissipated equally to both directions of the bar; while at both extremes of the bar, the heat can only flow to one side of the bar. Additionally, the edges of the graphite crucible also act as a susceptor for the induction heating, collaborating to result in an increase in the heat transferred to the bar. Such variation of zone length degrades the refining efficiency; therefore, it is preferred to keep a big, but constant zone length during initial zone passes, as well as a smaller and constant one during the following. Another possibility would be to keep a smaller zone length in the both ends, but bigger ones in the middle of the bar in one single pass. Via the digital detection of the zone length in this work by applying an infrared camera, a promising solution to automatically adjust the zone length can be conducted in the zone refining process. It can work in such a way that, e.g., the infrared camera detects the zone length online, then inputs the signal to a program model (such as applying fuzzy logic control). Finally, the program model gives an output signal to the zone refining equipment to fulfill the control of the zone length by regulating the power, heater movement velocity or cooling water flux, etc. That would be a great modification of the way of increasing the efficiency, as well as productivity of the zone refining technique.  
Figure 12: Macrograph of the longitudinal section, in the position at the middle of the bar (done using Barker’s reagent for 30 s at room temperature).  
Figure 13: Graph of zone length variation across the crucible, the positions on the graph referring to the time lapse images from Figure 9.
## 3 Results and Discussion
### Verification of the Corrected Shape Coefficient  
Corrected shape coefficient of type I SIF with a V-shaped notch has been obtained, and a comparison of the results between this paper and the literature needs to be made in order to verify its effectiveness. In Section 2.6, the verification method has been introduced, thus the corrected shape coefficients not containing a V-shaped notch \(F_{\mathrm{N}}\) can be calculated by Equations (6)-(10). The comparison of the corrected shape coefficients obtained in this paper and those in reference [31] is shown in Figure 10. It shows that the fitting curves of the two are approximate, suggesting that the corrected shape coefficient containing a V-shaped notch obtained by the FEM is acceptable and reliable.  
In practical engineering applications, when calculating the crack-related parameters of components with a V-shaped notch, such as in ampoule bottles, we can directly query the required stress intensity factor and shape coefficient according to the graph or fitting  
Figure 10: Comparison of corrected shape coefficients without a V-shaped notch \(F_{\mathrm{N}}\) obtained by the FEM in this paper and in the literature. (**a**) \(R/r=1.2\). (**b**) \(R/r=1.4\). (**c**) \(R/r=1.6\). (**d**) \(R/r=1.8\).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline _a/lc_ & \multicolumn{4}{c}{_R/r_} \\ \cline{2-5}  & **1.2** & **1.4** & **1.6** & **1.8** \\ \hline \multirow{4}{*}{0.2} & \(1.90357(a/t)^{2}+\) & \(2.52857(a/t)^{2}-\) & \(2.47716(a/t)^{2}-\) & \(2.73214(a/t)^{2}-\) \\  & \(0.02693(a/t)+\) & \(0.63657(a/t)+\) & \(0.35966(a/t)+\) & \(0.49364(a/t)+\) \\  & \(3.65907\) & \(3.84057\) & \(3.66329\) & \(3.69364\) \\  & \(1.6(a/t)^{2}-\) & \(1.03571(a/t)^{2}+\) & \(1.92857(a/t)^{2}-\) & \(2.20714(a/t)^{2}-\) \\  & \(0.291(a/t)+\) & \(0.28424(a/t)+\) & \(0.49857(a/t)+\) & \(0.392124(a/t)+\) \\  & \(3.3385\) & \(3.26621\) & \(3.38957\) & \(3.36914\) \\  & \(1.44286(a/t)^{2}-\) & \(0.175(a/t)^{2}+\) & \(0.2(a/t)^{2}+\) & \(1.15357(a/t)^{2}+\) \\  & \(0.430(a/t)+\) & \(0.5125(a/t)+\) & \(0.46(a/t)+\) & \(0.09493(a/t)+\) \\  & \(2.95586\) & \(2.926\) & \(2.915\) & \(2.97157\) \\  & \(-0.125(a/t)^{2}+\) & \(0.52143(a/t)^{2}\) & \(0.35(a/t)^{2}+\) & \(0.55714(a/t)^{2}+\) \\  & \(0.5925(a/t)+\) & \(0.09243(a/t)+\) & \(0.189(a/t)+\) & \(0.27886(a/t)+\) \\  & \(2.475\) & \(2.68243\) & \(2.618\) & \(2.63214\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The binomial fitting results of the corrected shape coefficient \(F_{\mathrm{I}}\) and the crack relative depth.  
binomial expression in this paper. It can also be utilized to calculate the crack-related parameters of the component without a V-shaped notch based on the FEM.
# Power Up: Comparing PAPE Responses in Free-Weight vs. Flywheel Resistance Training Systems
## 1 Introduction
Given the difficulty in defining maximal resistance, other parameters such as MPV [17; 19] and concentric peak power with a given load [1; 6; 12] have been proposed. However, similar peak power values can be attained at various flywheel moments of inertia (force typically increases at greater resistances, velocity typically decreases at greater resistances, and power is force multiplied by velocity) [21]. Furthermore, peak power measures refer to only a single point of the load-velocity relationship. On the contrary, MPV considers the entire concentric phase and is the most used parameter for monitoring free-weight intensity in real time [21]. Any of the aforementioned factors may also differ between repetitions. Aspects such as fatigue or the specific device's inter-repetition variability could affect any comparisons. It is necessary to understand how whole-body kinetic and kinematic variables compare between devices throughout the entire movement and across multiple repetitions when matched for MPV [17]. When attempting to match the exercise intensity of the squat through MPV, the kinetic and kinematic responses tend to differ between systems depending on the phase of the movement being analyzed [17]. While in the eccentric phase of the movement, the flywheel shows higher velocities than free weights; free weights exhibit higher velocity values in the concentric phase of the movement, as well as higher force application in both the concentric and eccentric phases of the exercise. Despite this, it remains uncertain whether resistance training with a similar load using free weights or flywheel devices produce similar PAPE effects on physical performance, or whether they vary, as seen in their kinetic and kinematic responses. Therefore, the aim of this study was to analyze the PAPE effect of a resistance training bout using free-weight and flywheel devices, where the intensity was matched through MPV, on vertical jump performance and linear sprinting. We hypothesize that, since the exercise intensity is similar for both devices, the PAPE effect on vertical jump and linear sprint performance will be similar for both groups.
## 3 Results
### Modeling Adsorption from Uranium in Solution
#### 3.2.1 Batcth Experiment
On the other hand, at neutral pH and high ionic strength, forming internal spheres at the edges and amorphous surfaces of clays with uranium causes the deposition of surface complexes to be the predominant sorption process. Isotherm models do not consider the precipitation phenomenon since the nucleation kinetics of solid phases are considered to be very slow; hence, it is preferred to establish semi-empirical models of surface complex formation as a function of the chemical conditions of the water [63]. The sediments were analyzed by SEM-EDS after the corresponding batch adsorption experiments. The micrographs are shown in Figure 10 for pH = 5 and in Figure 11 for the removal of U(VI) at pH = 7 (Appendix.2). It can be observed that U(VI) is homogeneously distributed throughout the sediment, although there is a predominant accumulation of U(VI) in certain particles. Figure 12 shows a micrograph obtained by backscattered electrons in SEM for sediments after U(VI) adsorption at pH = 5, while Figure 12 shows the SEM micrograph by backscattered electrons at pH = 7. The brightest areas corresponded to a high uranium content, and it is plausible that in the adsorption at pH = 7, there was uranium precipitation, which explains why the Langmuir and Freundlich adsorption models did not have a good fit with the uranium removal data in the prepared solutions. The results of the EXAFS analysis corresponding to the C\({}_{2}\) clay type from the experiment at \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**pH**} & \multicolumn{3}{c}{**Langmuir Constants**} & \multicolumn{3}{c}{**Freundlich Constants**} \\ \cline{2-7}  & **Qm/mg\(\cdot\)kg\({}^{-1}\)** & **K\({}_{\rm L}\)/L\(\cdot\)mg\({}^{-1}\)** & **R\({}^{2}\)** & **KF/(mg\(\cdot\)g\({}^{-1}\))/n** & **1/n** & **R\({}^{2}\)** \\  & & & & & **(L\(\cdot\)mg\({}^{-1}\))/n** & **1/n** & **R\({}^{2}\)** \\ \hline
5,0 & \(44.8\times 10^{3}\) & 0.20 & 0.94 & 13.4 & 0.30 & 0.59 \\
7.0 & \(9.7\times 10^{3}\) & 0.04 & _0.22_ & 20.5 & \(-\)0.26 & _0.17_ \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The Langmuir and Freundlich isotherm parameters for U(VI) adsorbed on sediments at two different pH values. pH = 7 are presented in Appendix B.2, Table 3. These analyses suggest that the precipitated compound is similar to uranyl nitrate.
# Analysis of Urban Residents' Travelling Characteristics and Hotspots Based on Taxi Trajectory Data
## 2 Materials and Methods  
The study outlines a four-stage process for analyzing residents' travel patterns and hotspots using taxi trajectory data, as depicted in Figure 1. The first stage focuses on data preprocessing, including the removal of default values, duplicates, redundant values, noise, and outliers. The second stage conducts a time feature analysis, segmenting data into larger and smaller time frames to extract origin-destination (OD) points and identify residents' travel time patterns. The third stage involves density-based cluster analysis (using the HDBSCAN algorithm) and kernel density analysis (employing a Gaussian-function-based method) on selected taxi OD points to determine areas of high travel activity. The final stage delves deeper into these hotspots, performing tasks like road network matching, hotspot detection, and visualization to identify hotspot paths.
## 4 Case Study
### Project Profile  
Engineering cases from [PERSON] (2019), [PERSON] (2019), and [PERSON] (2019) were used to validate the reliability of the proposed model, as shown in Figure 16.  
Figure 15: Comparison of the accuracy values before and after preprocessing.  
The Zhongnanshan Highway Tunnel is 18.02 km long, with a longitudinal ventilation scheme of three shafts. Among them, the No. 2 shaft is located 30 m upstream from the middle of the Shuidongzi ditch on the north slope of Qinling. The upper 30 m of the passage stratum is the Quaternary Holocene slope flood deposit, in which the surface layer is avalanche block soil with a lithology of mixed gneiss, specifically type II enclosing rock. The lower part consists of mixed gneiss, and some sections are interspersed with residue of oblique long homblende gneiss containing black mica. The rock is an intact type VI enclosure with little tectonic influence. The maximum horizontal principal stress is 21.04 MPa in the section with a large shaft burial depth and NW28\({}^{\circ}\) direction, which is a high in situ stress that may lead to a rockburst.  
The Daxiangling Tunnel is a key Beijing-Kunming expressway project in the Ya'an to Luguhu section of Sichuan Province. It is located in a mountainous area on the southwestern edge of the Sichuan Basin. The tunnel reaches a maximum depth of 1701 m and is classified as a deep and long mountain crossing highway tunnel.  
The Cangling Tunnel is located in the Cangshan Mountain area of Zhejiang Province, with a maximum buried depth of 768 m. The main lithology is weathered tufaceous shale and K-feldspar granite. The Cangling Tunnel crosses both the local fault zone and lithological contact zone. In the section away from the fault zones, the rock mass is relatively intact with a uniaxial compressive strength of 160 MPa. The results of in situ stress tests and inversion show that the maximum principal stress in the tunnel area ranges from 17.9 to 27.3 MPa, indicating the potential risk of rockburst. The segments of K97 + 702 to K98 + 080, K98 + 225 to K98 + 425, K98 + 637 to K99 + 638, and K99 + 638 to K100 + 127 in the Cangling Tunnel were selected for rockburst prediction.