esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results  
### Score Map Test  
As mentioned in Section 2.2, a score map based on a Gaussian-like distribution is introduced to represent the onset time of micro-fracture waveforms. Different values of \(\sigma\) yield the different ranges of the score map. Subsequently, the values of 0.5, 1.0, and 1.5 are used to generate an equal number of semisynthetic noisy waveform samples with different opening ranges of the function map, respectively. To achieve a better model performance, the sample dataset is randomly divided into three parts: training (80%), validation (10%), and test (10%) datasets, respectively.  
Table 1 presents the accuracy results of the onset time picking and detection on the validation and test datasets under different values of \(\sigma\). For onset time picking, the validation and test accuracy achieve an optimal performance under a \(\sigma\) value of 1.0, with a significant difference in the performance under \(\sigma\) values of \(\sigma\) 0.5 and 1.5. For detection, the highest validation and test accuracy is attained when \(\sigma\) is 1.5. In addition, the validation and test accuracy of the value of \(\sigma\) 1.0 in the detection is basically closer to the onset time picking. Therefore, a PDNetwork with a better overall performance can be achieved with a \(\sigma\) value of 1.0.
## 3 Results
### Simple Correlations between the Measures  
We also wanted to determine whether there is a correlation between subjective glare perception and any of the psychophysiological measures. As the de Boer scale was not a significant predictor in any of the linear mixed-effect models, only a very weak positive correlation (r = 0.168; \(p\) = 0.002) was found between the de Boer ratings and the average skin conductance response after each block of glare, and for the blinking frequency, the correlation was near to zero (r = 0.036; \(p\) = 0.496). However, the de Boer ratings were found to be weakly negatively correlated with age (r = -0.241; \(p\) < 0.001), suggesting that older drivers tend to subjectively perceive glare as more disturbing than younger drivers. On the other hand, age was negatively correlated with average SCR (r = -0.325; \(p\) < 0.001) after each block of glare, and positively correlated with average blinking frequency (r = 0.213; \(p\) < 0.001), indicating that older drivers' psychophysiological responses to glare are weaker than in younger drivers. Finally, blinking frequency was weakly negatively correlated  
Figure 6: Relationship between the adapted de Boer scale ratings and the combination of the driver’s and the oncoming traffic’s headlights.  
with SCR (r = \(-\)0.160; \(p\) < 0.001), suggesting that blinking actually decreases with a higher stress response.
# GraphDBSCAN: Optimized DBSCAN for Noise-Resistant Community Detection in Graph Clustering
## 3 Proposed Method
### Improved Newman-Girvan Algorithm
Compared with existing methods, this method has higher accuracy in community detection, reduces the need for manual parameter tuning, and is more robust to noise. Input: G (Graph Structure)  
Output: Detected Communities  
1. // Feature Extraction  
2. SFM \(\leftarrow\) ExtractProximityFeatures(G) // Algorithm 1  
3. // Dimensionality Reduction  
4. SFM_reduced \(\leftarrow\) AutoEncoder(SFM, encoding_dim = 5)  
5. // Optimal DBSCAN Parameter Selection  
6. bestEps, minPts \(\leftarrow\) OptimizeDBSCANParams(SFM_reduced) // Algorithm 2  
7. // Outlier Detection and Removal  
8. G_filtered \(\leftarrow\) DBSCAN(G, bestEps, minPts)  
9. // Community Detection  
10. Communities \(\leftarrow\) ImprovedNewmanGirvan(G_filtered, targetCommunities) // Algorithm 3  
11. Return Communities  
**Algorithm 4**. Proposed method.
###### Abstract  
Sea ice motion and deformation have generally been measured using low-resolution passive microwave or mid-resolution radar remote sensing datasets of daily (or few days) intervals to monitor long-term trends over a wide polar area. This feasibility study presents an application of high-resolution optical images from operational satellites, which have become more available in polar regions, for sea ice motion and deformation measurements. The sea ice motion, i.e., Lagrangian vector, is measured by using a maximum cross-correlation (MCC) technique and multi-temporal high-resolution images acquired on 14-15 August 2014 from multiple spaceborne sensors on board Korea Multi-Purpose Satellites (KOMPSATs) with short acquisition time intervals. The sea ice motion extracted from the six image pairs of the spatial resolutions were resampled to 4 m and 15 m yields with vector length measurements of 57.7 m root mean square error (RMSE) and \(-11.4\) m bias and 60.7 m RMSE and \(-13.5\) m bias, respectively, compared with buoy location records. The errors from both resolutions indicate more accurate measurements than from conventional sea ice motion datasets from passive microwave and radar data in ice and water mixed surface conditions. In the results of sea ice deformation caused by interaction of individual ice floes, while free drift patterns of ice floes were delineated from the 4 m spatial resolution images, the deformation was less revealing in the 15 m spatial resolution image pairs due to emphasized discretization uncertainty from coarser pixel sizes. The results demonstrate that using multi-temporal high-resolution optical satellite images enabled precise image block matching in the melting season, thus this approach could be used for expanding sea ice motion and deformation dataset, with an advantage of frequent image acquisition capability in multiple areas by means of many operational satellites.  
sea ice motion; sea ice deformation; maximum cross-correlation; high-resolution; multi-sensor; KOMPSAT 1
## 3 Examples of QS-Based Activities of _Pseudomonas_ spp. Affecting Spoilage of Fish
### QS and Biofilm Formation  
The formation of biofilms is a stepwise process involving the initial attachment of bacteria to surfaces, microcolonies growth and maturation into expanding structures, and further detachment of aged microorganisms [46]. It has been proposed that AHL-mediated QS is involved in all stages of biofilm formation. The influence of the _las_ AI, 3-oxo-C12-HSL on biofilm maturation in _P. aeruginosa_ has been described by [PERSON] et al. [47]. Strain deficient in the production of 3-oxo-C12-HSL formed very thin biofilms that lacked the three-dimensional architecture observed with the parent. In addition, while the wild-type biofilm was resistant to sodium dodecyl sulfate (SDS), the biofilm formed by the _las1_ mutant was easily dispersed upon exposure to SDS [47].  
The relationship between QS and biofilm formation was also described indirectly by evaluating the effects of AIs on twitching and swarming motilities, rhamnipids, and expolysaccharides (EPS) production [33]. Swarming motility, which is an organized form of structure translocation, is useful in the early stages of biofilm development and is regulated by the _rhl_ system similarly to twitching motility--a flagella-independent way of translocation necessary for microcolony development [48]. EPS holds all cells of biofilm in the near vicinity to enable QS interactions [49]. Rhamolipid production is involved in several aspects of biofilm formation, such as the formation of microcolonies, maintaining the open channel structure, facilitating mushroom-shaped structures, and aiding cell dispersion [50].  
In general, the addition of AHLs to the culture medium was shown to affect EPS (aliphate)/rhammolipid production, twitching and swarming motilities, and biofilm maturity by _Pseudomonas_ spp. [30; 51]. Inhibiting OS would be, therefore, an alternative to combat the biofilm problems [51; 52]. Adhered _Pseudomonas_ spp. are found in many locations on seafood processing lines, despite the fact that thorough cleaning and disinfection are carried out regularly. Moreover, biofilms of _Pseudomonas_ spp. enhanced the colonization of _Listeria monocytogenes_ on food contact materials which promoted food contamination [53].
## 4 Experiments and Analysis
### Data Pre-Processing  
#### 4.3.1 Data Cleaning  
This work uses the home-made 1.26-million-line Mongolian-Chinese parallel corpus by the Artificial Intelligence and Pattern Recognition Laboratory of Inner Mongolia University of Technology in China, which covers a wide range of fields, including news records, medical reports, proper nouns or phrases, two-part allegorical sayings, daily conversations or online chats, excerpts of literary works, terms and nouns related to the computer field, etc. Many of these bilingual parallel sentence pairs are irregular sentence pairs that need to be modified or deleted. After cleaning and processing, including data denoising, text standardization, and text deduplication, a total of about 20,000 sentence pairs that are irregular and difficult to meet the requirements of the specification are deleted, leaving 1,244,139 lines.  
Figure 10: Experiment Flow Chart.
## 4 Prediction of Water Inrush Risk Based on PMGIM
### Agglomeration Evolution of PMGIM
#### 4.1.1 PMGIM Agglomeration Mechanism  
The variation trend of PMGIM is primarily influenced by structural polarization effects and the diffusion effect of grouting. The structural polarization effect, such as faults at the 182602 working face and the Zhonglicun synclinal axis, indicates that the injected slurry increases pore pressure in the formation. Consequently, the corresponding Mohr circle of the rock mass shifts leftward, moving the Mohr-Coulomb circle below the envelope and bringing the Mohr circle closer to the strength line. When the shear stress applied to section MN exceeds the shear strength of the rock mass (\(\tau_{s}\)), shear failure occurs, leading to fracture expansion and triggering microseismic events (see Figure 9).  
The more developed the natural structures are, the more easily microseismic events are stimulated during the grouting process, forming the HH region characterized by high microseismic activity and concentration. The diffusion effect of grout implies that as the grouting process continues, cracks in numerous areas are effectively sealed and rock layers are strengthened. This makes it increasingly difficult to excite microseismic events, resulting in a significant reduction in their occurrence.
# Multi-Input Attention Network for Dehazing of Remote Sensing Images
## 2 Related Work
### Neural Networks  
In recent years, increasing efforts have been dedicated to data-driven methods using deep learning. The end-to-end learning of deep neural networks can potentially solve many problems in traditional algorithms. Researchers first estimate the transmittance in the atmospheric scattering model of Equation (1) by building a neural network and then restore the hazy image according to the model.  
[PERSON] et al. [14] used a network based on multi-scale feature extraction to restore images according to the degradation model. It takes the hazy image as input and outputs the transmittance map. [PERSON] et al. [15] used a coarse-scale network that took the hazy image as an input to estimate the rough transmittance map, then fed into the fine-scale network to obtain an optimized transmittance map, and finally obtained a more refined dehazing image. [PERSON] et al. [16] took the transmittance and atmospheric light in Equation (1) as one variable and used a neural network to estimate it. Different from the previous estimation of atmospheric light by experience, this method uses the learning ability of the networkto perform the estimation. Neural networks demonstrate powerful feature extraction capability, which greatly advocates the research on end-to-end direct dehazing networks.  
[PERSON] et al. [18] proposed an end-to-end gated context aggregation network to improve the finesse of dehazing results. It combines smooth hole convolution and multi-level feature fusion techniques. [PERSON] et al. [19] proposed an attention-based grid dehazing network (GridDehazeNet), adding a densely connected grid network to effectively alleviate the bottleneck problem of traditional multi-scale networks. The attention module enables the network to better estimate model parameters. In [20], a Domain Adaptation framework was proposed. It employs a bidirectional transformer network to bridge the gap between synthetic and real domains by transforming images from one domain to another. This method effectively reduces the gap between the synthetic hazy image and the real hazy image. In [23], a spatial attention-based adversarial generative network was proposed to dehaze remote sensing images. The model is separately trained based on haze and small-scale cloud, and, finally, effectively removes both interferences. In [15], SkyGAN was proposed for haze removal in aerial images. The network reconstructs multispectral data from RGB bands in aerial images and then uses a conditional generative network to train these reconstructed data. This method can effectively expand multispectral datasets.  
Overall, there are many issues in applying the natural image dehazing methods to remote sensing images. Most research on the dehazing of remote sensing images focuses on the visible light band. The recovery from hazy images is also limited. In this paper, we propose a multi-input multi-spectral remote sensing image dehazing network. It can effectively remove haze in multi-spectral remote sensing images.
# Remote Sensing in Urban Forestry:
## 4 Concluding Remarks  
In tandem with the ever-growing availability of high-resolution remotely sensed imagery from diverse airborne and spaceborne platforms, research continues to broaden the application and utilisation of remote sensing techniques to support and advise urban forest studies, such as the accurate modelling and quantification of urban ecosystem services [27; 35]. To effectively and adequately realise the potential of remote sensing, a strong connection and collaboration between urban foresters and remote sensing experts is highly advised. This conjoint synergy would allow to recognise and address the aforementioned challenges as well as ensure the optimisation of urban forests and their ecosystem services for long-term human wellbeing. \begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline  & & \multicolumn{6}{c}{**Themes**} \\ \cline{2-10}
**Applications** & \multicolumn{2}{c}{**Multi-Source**} & \multicolumn{2}{c}{**Multi-Scale**} & \multicolumn{2}{c}{**Multi-Temporal**} & \multicolumn{2}{c}{**Multi-Scale**} \\ \cline{2-10}  & \multicolumn{1}{l}{**Single Remote**} & \multicolumn{1}{l}{**Multiple Sources of**} & \multicolumn{1}{l}{**Satellite Imagery**} & \multicolumn{1}{l}{**Satellite Imagery**} & \multicolumn{1}{l}{**Satellite Imagery**} & \multicolumn{1}{l}{**Airborne LIDAR**} & \multicolumn{1}{l}{**Shor Series**} & \multicolumn{1}{l}{**Long Series**} & \multicolumn{1}{l}{**Local/District- City-Regional Level**} \\  & \multicolumn{1}{l}{**Sourse**} & \multicolumn{1}{l}{**Satellite Imagery**} & \multicolumn{1}{l}{**LiDAR**} & \multicolumn{1}{l}{**Imergy**} & \multicolumn{1}{l}{**and Aerial Imagery**} & \multicolumn{1}{l}{**Short Series**} & \multicolumn{1}{l}{**Long Series**} & \multicolumn{1}{l}{**Local/District- City Level**} & \multicolumn{1}{l}{**City-Regional Level**} \\ \hline Tree biomass & & & [58,58] & & [47] & & & & \\ Forest inventory & & & [60] & & [71] & & & & \\ Canopy cover & [81] & [87] & [62,68] & [99] & [90,91] & [87,89,91] & [42,92] & [81] & \\ Urban heat island & [93] & [51,83,77,47,89,94,97] & & [66,72,73] & [98] & [73,74,99] & [57,72,78] & [72,95] & \\ Carbon storage & [82] & [99] & [85,59] & & [83,44,100] & & & [99,92] & \\ Water retention & [81] & & & & [81] & & & \\ Air quality & [101] & [102] & & & [103] & & [101] & \\ Forest fire & & & & [104] & [105] & & & [104] & \\ Tree species & [48,101,106,107,108] & [102,109,110] & [6,411,112] & [113] & [43,69,103,114,116] & [108,109] & [88,105,107] & [101] & \\ Greenspace & & & & & & [75,74] & [77,117,120] & [5,75] & [77] \\ configuration & [117] & [75,77,118,120] & [5] & & & & [75,74] & [77,117,120] & [5,75] & [77] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classified studies in terms of applications and themes.
# Add new tracks that are only present in the new frames of GraphB
## 4 Conclusions  
According to the reconstruction results in Section 3, the proposed method can generate more meaningful point clouds compared to the original depth map. For example, in Image Dataset 2, the proposed method generated 2,588,931 point clouds, whereas the original depth map method generated only 814,300. In Image Dataset 3, the proposed method generated 2,759,780 point clouds, whereas the original depth map method generated only 943,360. In terms of depth information accuracy, an accuracy of 72.33% was achieved, whereas the original depth map method achieved only 24.93%. One major disadvantage of the proposed method is the running time, since monocular depth estimation and Poisson distribution techniques are required to generate the mesh. The experiment involving photographs being taken of a tangible cultural heritage object highlighted the importance of lighting conditions. As described earlier, all images in this work were taken at The National Art Museum of China, Beijing; therefore, it was not possible to control the lighting and other environmental factors to create the most suitable surroundings for our reconstruction method.  
The experimental results highlight key takeaways. Compared to the depth map method, the proposed method offers significantly improved accuracy in depth information and point cloud generation, though at the cost of longer running time. Additionally, the proposed method appears particularly effective for image datasets featuring high color contrast between objects and backgrounds. It is important to note that this conclusion is currently based on visual observation; further analysis of specific color features (hue, saturation, etc.) is required. Future work should explore the method's performance with diverse image datasets (varying sizes and types) and prioritize algorithm speed optimization.  
Conceptualization, P.V. and X.L.; methodology, P.V.; software, F.P.; validation, P.V., C.C. and F.P.; formal analysis, P.V. and X.L.; investigation, X.L.; resources, P.V.; data curation, P.V.; writing--original draft preparation, P.V. and F.P.; writing--review and editing, P.V.; visualization, F.P.;supervision, P.V. and X.L.; project administration, P.V.; funding acquisition, P.V. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science, Research, and Innovation Fund (NSRF) and [PERSON]'s University of Technology North Bangkok under Contract no. KMUTNB-FF-66-18.  
The image dataset and code used in this study are available from the corresponding author upon reasonable request.  
This research partially utilized computational resources provided by the China Scholarship Council (CSC) for the Senior Visiting Scholarship Program of the School of Computer Science and Technology, Beijing Institute Technology (BIT).  
The authors declare no conflicts of interest.
## 2 Fundamental Concepts
### Wiretap Channel Models
#### 2.3.3 Multiple-Access Wiretap Channels  
Multiple-access channels are another important form of multi-user network in which more than one legitimate source sends information to a common destination user in the presence of eavesdroppers that try to intercept and obtain the transmitted signals, as illustrated in Figure 4.  
In this model, the transmitters are required to cooperate in order to guarantee secure transmission over the multi-access channel. However, due to the geographic isolation, the transmitters need to cooperate in a distributive manner. It is assumed that \(k\) transmitters are equipped with \(\mathbf{N}_{Ak}\) antennae and that one intended user and one eavesdropper have \(\mathbf{N}_{B}\) and \(\mathbf{N}_{E}\) antennae, respectively. Thus, the signals that are received by the intended user and an eavesdropper, respectively, are described as [51]:  
\[y_{B}=\sum_{k=1}^{k}\mathbf{H}_{k}x_{sk}+\mathbf{n}_{B} \tag{5}\]  
Figure 4: A system model for a multiple-access channel.  
Figure 3: A system model for a broadcast wiretap channel.  
\[y_{E}=\sum_{k=1}^{k}G_{k}x_{sk}+\mathbf{n}_{E} \tag{6}\]  
where \(x_{sk}\) is the \(n_{Ak}\times 1\) transmitted signal with an average power constraint or a covariance matrix constraint. \(\mathbf{H}_{k}\in\mathbb{C}^{N_{B}\times N_{Ak}}\) and \(\mathbf{G}_{k}\in\mathbb{C}^{N_{E}\times N_{Ak}}\) are the channel matrices from [PERSON] to the intended user and unintended user, respectively. \(\mathbf{n}_{E}\) and \(\mathbf{n}_{B}\) are the AWGN vectors at the eavesdropper and intended receiver, respectively. In this survey, the secure multiple-access scenario is investigated from the fundamental viewpoints of information-theoretic security.
## 5 Noise Source Localization and Identification Experiment  
To verify the effectiveness and advantages of the proposed method in noise source localization and identification this section describes the noise source localization and identification experiments conducted for both single and double sound sources. In an open hall, an open SMA with 64 elements, randomly and uniformly distributed, was used to obtain the radiation signal of small speakers located at known positions. The experimental results were used to investigate the accuracy and effectiveness of the SMA extrapolation method in practical testing and the application effect of the proposed method in low-frequency noise source localization. The signal generator in the test transmission system generates a signal to drive the air sound source (small speaker) to radiate sound waves into the air. In the receiving system, the open SMA receives the signal, and then the B&K PULSE multi-channel data collector collects the data. Finally, the computer stores the data and displays the received signals of each element of the SMA in real time. The photos of the test site are shown in Figure 9. During the test, the two sound sources are located at \((1\,\mathrm{m},91^{\circ},78^{\circ})\) and \((1\,\mathrm{m},89^{\circ},160^{\circ})\) in the spherical coordinate system established with the center of the open SMA as the coordinate origin. In the single-source test, the transducer speaker located at \((1\,\mathrm{m},91^{\circ},78^{\circ})\) was turned on. In the case of two sound sources test, the two speakers were turned on simultaneously.
# A Review of Medication Adherence Monitoring Technologies +
## 4 A Review of Medication Adherence Monitoring Systems
### Fusion-Based Systems
#### 4.4.1 Proximity-Sensor Systems  
In [86], [PERSON] et al. have designed a medication adherence system that was built with a cylindrically shaped 7-compartment pillbox, a wristband device, and a computer that all communicate with each other wirelessly. The pillbox is comprised of an Arduino microcontroller, a motor, a ZigBee transceiver, and an RFID reader. In addition, each compartment is embedded with a diode and a photo diode for detecting pill removal. The function of the MCU is to control the motor such that it rotates the compartment towards the user when it is time to take medication and when the RFID-based wristband is detected in the proximity of the pillbox. The wristband embeds an IMU, an RFID tag, and an LED. Hence, it is used for collecting motion data associated with pill picking and taking. Furthermore, the RFID tag and the LED are used for RFID proximity detection within the pillbox and reminding purposes, respectively. Once the wristband is detected in the proximity of the pillbox, the IMU is enabled to sample the motion data for possible hand gestures detection that is related pill taking. Finally, in order to verify the pill removal, the photo diode outputs a voltage to be measured by the MCU, at a given level depending on the light received from the LED and the blockage that may or may not result from the pill in that compartment.