esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### [Nii]/[DOC] Ratio  
The metal and organic matter binding affinity, [Me]/[DOC] ratio, has been used to understand the binding ability of metals to organic matter in the environment [12; 15; 16; 17; 18; 20; 21; 22; 23]. Figure 2 shows the [Ni]/[DOC] ratios in the fractioned AEOM and DOM solutions, in our study. The ([Ni]/[DOC])\({}_{\text{DOM}}\) ratio was significantly higher than ([Ni]/[DOC])\({}_{\text{AEOM}}\) (\(p<0.001\)). The ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios were higher than natural water DOM samples, which ranged from \(1.6\pm 0.0\) to \(4.1\pm 1.5\) umol/g-C [12]. The ratios were comparable to those impacted by anthropogenically-influenced DOM samples, which ranged from \(5.8\pm 1.3\) to \(14.7\pm 0.2\) umol/g-C [12]. The ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios were lower than the alkaline extracted soil solution \(5.81\pm 2.38\) umol/g-C [22] and water extracted soil solution, which ranged from \(11.3\) to \(75.4\) umol/g-C [19]. The low ([Ni]/[DOC])\({}_{\text{AEOM}}\) could be due to high organic carbon concentration extracted by alkaline solution and the chemical properties of AEOM, which had a low binding affinity with Ni.  
The DOM fractioned \(<\)10 kDa had ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios \(13.16\pm 7.18\) umol/g-C, which was significantly higher than ratios of fractionated DOM \(>\)10 kDa (\(3.26\pm 2.46\) umol/g-C, \(p=0.003\)) (Figure 2). In the AEOM, the ratios had a different trend; the ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios of molecular weight \(<\)1 kDa (\(0.94\pm 0.34\) umol/g-C) were significantly lower than ratios of fraction \(>\)1 kDa (\(2.04\pm 0.33\) umol/g-C, \(p<0.001\)).  
[PERSON] et al. [21] reported that the different molecular weight DOM had different ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios. The ([Ni]/[DOC])\({}_{\text{DOM}}\) ratios were \(4.6\) and \(8.3\) umol/g-C for the LMW (\(<\)1 kDa)  
Figure 1: (**a**) Ni percentages for size-fractionated DOM/AEOM. (**b**) Organic carbon percentages for size-fractionated DOM/AEOM.  
Figure 2: [Ni]/[DOC] ratios with size-fractionated DOM and AEOM.  
and HMW (1 kDa-0.45 \(\upmu\)m) DOM, respectively in a WWTP final effluent. In 24 soil AEOM solutions, the HMW ([Ni]/[DOC])\({}_{\text{AEOM}}\) ratios 7.29 \(\pm\) 2.27 \(\upmu\)mol/g-C were significantly higher than the LMW ([Ni]/[DOC])\({}_{\text{JEOM}}\) ratios 4.22 \(\pm\) 2.38 \(\upmu\)mol/g-C [22]. In the present study, the ([Ni]/[DOC])\({}_{\text{DOM}}\) of molecular weight <10 kDa was significantly higher than molecular weight >10 kDa but LMW ([Ni]/[DOC])\({}_{\text{AEOM}}\) (<1 kDa) was significantly lower than HMW ([Ni]/[DOC])\({}_{\text{AEOM}}\) (>1 kDa). The ratio difference followed the different molecular weights in the present study, which may be caused by the exchange of POM and DOM in sediment.
# MuIm: Analyzing Music-Image Correlations from an
## 4 MuIm Dataset
### Data Acquisition and Labeling
#### 4.1.1 Artistic Segment  
The artistic portion of the MuIm dataset focuses on emotional music excerpts and artistic images, capturing complex emotions and nuanced meanings inherent in such works. We identified limitations in using standard valence-arousal (VA) models for categorizing emotional states in artistic content, necessitating a more comprehensive approach.  
For data collection, emotional-semantic music data were sourced from [40], resulting in 1841 five-second music samples gathered from YouTube and annotated through crowdsourcing by approximately 1900 participants. Each sample was labeled for 28 distinct emotions using a 9-point Likert scale, with an average of 31 observers per sample, providing nuanced emotional context.  
Art data were curated to align with the emotional analysis performed on the music dataset. Art professionals collected 2319 art pieces from the WikiArt website [52], covering a diverse range of genres and styles. Figure 3a shows the web interface for collecting emotion-evoking art. Crowdsourced evaluations were conducted, with participants divided into the general public, music experts, and art specialists. Using a 9-point Likert scale, each art piece was annotated with 28 predefined emotions, mirroring the methodology applied to the music data, with an average of six observers per art sample to reduce bias. Figure 3b shows images of the labeling website.  
Figure 3: (**a**) Illustration of expert-guided art image collection interface and (**b**) images of 28-category emotion labeling in art using a 9-point Likert scale.  
To enhance the semantic information, art images were accompanied by metadata such as genre, style, title, and artist. Music data were labeled with titles, and additional semantic insights were generated using models such as [53] for image captions and [13] for music captions. Furthermore, a pretrained LLM [54] was utilized to generate links between music and imagery by extracting common semantics between music and images, similarly to [55], as specified in Table 3.
# A Computer-Aided Detection System for the Detection of Lung Nodules Based on 3D-ResNet
## 2 Data  
The dataset in this paper was from the largest available open dataset (i.e., the Lung Image Database Consortium (LIDC-IDRI)) [16] and the Tianchi racing data set [17]. The LIDC-IDRI database was initiated by the American National Cancer Institute, aiming to study early cancer detection in high-risk groups. The LIDC-IDRI database contains a total of 1018 CT images that come from seven institutions and were labeled by four experienced radiologists in two stages. In the first stage, each radiologist independently diagnosed and labeled the nodule position. The following are the three kinds of labeled objects: (1) R (diameter) \(\geq\) 3 mm nodules, (2) R \(<\) 3 mm nodules, and (3) R \(\geq\) 3 mm non-nodules. In the second stage, annotations from all four radiologists were reviewed in an unblended fashion and each radiologist decided to either accept or reject each annotation. The database is very uneven, and the thickness of CT images varies from 0.6 to 5.0 mm. According to the recommendations of the American Radiology Association [18], we discarded the scans with a thickness greater than 3 mm, yielding 888 scans. According to the current screening rules for lung cancer, we treated only nodules that were classified as R \(\geq\) 3 mm as significant, while nodules that were R \(\geq\) 3 mm and non-junctions were classified as meaningless. Since the nodule labels were labeled by different radiologists, the nodule labels whose distances were closer than the sum of their radii were merged. The merger regulation is to average their diameter and coordinates separately. We selected the nodules labeled by 3 or 4 of the 4 radiologists as standard positive nodules. These damage tissues were the detection target of our algorithm. Other nodules labeled by less than 3 radiologists or nodules with R \(\geq\) 3 mm were considered as meaningless. We believe that they are neither false positive nodules nor true positive nodules.
## 5 Discussion
### Limitations  
Owing to the particularity of SAR data imaging methods, the geometric deformation of microwave images is very different from that of optical remote sensing images. Although the terrain correction of the ALOS-2 data is based on the RD geo-location model, the quantitative-accuracy analysis of the terrain-corrected image was not performed, and the contribution of the terrain correction to the improvement of image distortion has not been analyzed in depth. Furthermore, owing to the limitation of the data source, the imaging times of the Landsat OLI and ALOS-2 data used in this study differ by approximately one month. Because this time period is the stage of vigorous plant growth, the time difference may impact the classification results. In this study, fusion based on image feature level is attempted; however, other fusion methods are not explored in depth. Although it is said that with the development of science and technology and the improvement of mathematical level, the number of image-fusion methods aimed at spatial enhancement have increased, the fusion results should pay more attention to the maintenance of spatial details and spectral information.
# The Influence of Radial Stress on Mechanical Properties of Anchorage Structure
## 3 Theoretical Methods
### Analysis Using the Mohr-Coulomb Criterion  
Mohr-Coulomb strength theory can also be used to analyze the improvement of the mechanical properties of the rock around the borehole caused by confining pressure. Equation (5) is the Mohr-Coulomb formula, as shown in Figure 9.  
\[\tau=\sigma\tan\varphi+c, \tag{5}\]  
where, \(\tau\) is shear stress, MPa; \(\sigma\) is normal stress, MPa; \(\varphi\) is the internal friction angle, degree; and \(c\) is the cohesion, MPa.  
1. When \(P_{2}=0\), \(P_{1}\
eq 0\), the radial stress \(\sigma_{r}\) and tangential stress \(\sigma_{\theta}\) of the rock are shown in Figure 9 at the inner wall of the borehole. Since there is no shear stress, \(\sigma_{r}\) is the first principal stress, i.e., \(\sigma_{1}=\sigma_{r}=P_{1}\), and \(\sigma_{\theta}\) is the third principal stress, i.e., \(\sigma_{3}=\sigma_{\theta}=-P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{ 2}}\). And then the Mohr's circle \(M_{1}\) corresponding to \(\sigma_{1}\) and \(\sigma_{3}\) can be drawn in the \(\sigma-\tau\) coordinate system. The relative relationship between the Mohr's circle \(M_{1}\) and the Mohr-Coulomb strength line is shown in Figure 9. Under the conditions of \(P_{2}=0\) and \(P_{1}\
eq 0\), if the rock strength is good, Mohr's circle is neither tangent to nor intersecting with the strength line, indicating that the rock has not been damaged. If the rock is of low strength, the Mohr's circle may be tangent to or intersect with the strength line under this condition, and the rock will break down.
2. If a certain confining pressure is applied to the anchorage section, that is, \(P_{2}\
eq 0\) and \(P_{1}\
eq 0\). According to Equations (2) and (4), \(\sigma_{1}=\sigma_{r2}=P_{1}\), \(\sigma_{3}=\sigma_{\theta 2}=-P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}} +\frac{2P_{2}R_{2}^{3}}{R_{2}^{2}-R_{1}^{2}}\), and the [PERSON]'s circle under this condition is \(M_{2}\), as shown in Figure 9. Compared with \(M_{1}\), the position of \(\sigma_{1}\) is fixed, the value of \(\sigma_{3}\) moves to the positive direction of \(\sigma\) axis, and the diameter of \(M_{2}\) decreases obviously. Therefore, the Mohr's circle \(M_{2}\) is gradually away from the strength line of the rock. In this case, compared with the stress state of Mohr's circle \(M_{1}\), the rock is less likely to be damaged.
3. Continue to increase the confining pressure \(P_{2}\) of anchorage section, when \(P_{2}\) increases to a value that makes \(P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}}<\frac{2P_{2}R_{2}^{2}}{R_ {2}^{2}-R_{1}^{2}}\). According to Equation (4), \(\sigma_{3}=\sigma_{\theta 3}=-P_{1}\frac{R_{2}^{2}+R_{1}^{2}}{R_{2}^{2}-R_{1}^{2}} +\frac{2P_{2}R_{2}^{3}}{R_{2}^{2}-R_{
# A Background Subtraction Algorithm in Complex Environments Based on Category Entropy Analysis
## 1 Introduction
* Camera shake: Cameras are often installed in places like roadside poles, bridges, and the outside of buildings, where they are less likely to affect the field being monitored. However, these places are usually subjected to strong winds and the force of passing vehicles, which may cause cameras to shake. This leads to the shaking of an otherwise fixed background image, causing the background pixels with large changes in the surrounding colors to have more than two kinds of color changes, which eventually increases the difficulty of establishing an initial background. * Prolonged background masking: When the system initially creates a background, the ideal situation is that there are no moving objects to enable the system to directly grasp the color distribution of the background. However, many moving objects are usually present in the monitoring field, such as moving objects passing by or slow-moving objects--all of which obscure the background for a long period time. This can cause the system to converge the moving objects into a background, leading to erroneous detection results. A thorough literature search has indicated that none of the recently proposed methods can address the aforementioned issues with respect to capturing initial backgrounds [5; 6; 7]. Therefore, this paper proposes an initial background extraction algorithm based on color category entropy. The algorithm dynamically creates color categories through a color classifier to distinguish moving objects similar to the background during the initial background extraction process. This paper also proposes the concept of joint categories in which each color is re-clustered based on the relevance of each color category in the color space. The joint categories are allowed to dynamically change with color distribution, resulting in a more efficient establishment of color categories that match the real background distribution. In addition, the number of background categories required by color category entropy was automatically determined in this study, making it possible to establish background categories to detect moving objects even in the presence of dynamic backgrounds or camera shake. When the backgrounds were masked by moving objects for a long time during the initial background extraction process, the background convergence time was dynamically determined via color category entropy to obtain reliable background information.
## 3 HMC-MAC Protocol
### Channel Allocation Scheme and Node Activity
\(16\) channels are not sufficient in order to allow all nodes to communicate at the same time without interference when the network is dense. Hence, the channel allocation process should exploit spatial reuse or allow interfering nodes to use same channels using CSMA/CA. In our channel allocation process, we will try to decrease the interference as much as possible. A node first tries to find a free channel in its 3-hop neighborhood of its group, it it does not find one, it tries to find a free channel in its 2-hop neighborhood of its group. In case there are no available channels in its 2-hop neighborhood, the node tries to find a free channel in its 1-hop neighborhood of its group. Finally, if it does not find a free channel, it randomly takes a channel among the least used channels in its 1-hop neighborhood that are part of its group. Indeed, because each node signals its channel in the beacon it broadcasts, each node can manage the number of neighboring nodes using the same channel. Note that whenever multiple channels are available, nodes randomly choose one of the available channels. Figure 5 depicts the steps executed by each node before choosing a channel. These operations run only during the setup phase or if changes in the topology occur. The setup phase ends when all nodes have chosen frequencies. Then, data exchange phase can start. \([T_{1};T_{2}]\) is divided into intervals, each interval is divided into two time slots. During the first time slot, \(Group\)\(1\) are senders and \(Group\)\(2\) are receivers. During the following time slot nodes exchange roles, receivers become senders and senders become receivers. Each node knows its own depth. Thus, it is able to alternate between sending and receiving states. Nodes receive data frames from lower depth neighbors and transmit frames to one of their higher depth neighbors using slotted CSMA/CA algorithm. Figure 5: Algorithm for choosing the a channel.
## 5 Routing Layer in WMSN
### Routing Methodologies in WMSN
MMSPEED protocol [43], which is also an extension for SPEED protocol [44] that was designed for WSN, can differentiate between flows with different delay and reliability requirements and has significant potential in video transmission applications. However, experimental results in [42] show that MMSPEED is not compatible with some special features of Multimedia traffic such as high video frame rate and packet's information dependency. QoS-based energy-efficient routing protocol is proposed in [45] called QuESt that is capable of carrying multimedia content over WMSNs. The proposed routing protocol aims to build set of non-dominated paths that satisfy application-specific QoS parameters based on using multi-objective genetic algorithm (MOGA). It is shown that determining optimal routes satisfying multiple QoS parameters (end-to-end delay and bandwidth) simultaneously in energy-constraint sensor network is a NP-complete problem, hence the authors prefer to use MOGA algorithm as a tool to solve this NP-problem by treating the multiple QoS parameters independently without combining them into a single objective function. The end-to-end delay is modeled by using Weibullian distribution to capture the inherent long-range dependency of data traffic, and the bandwidth is modeled by taking the product distribution of the individual links. It is assumed to have one single sink and multiple sources in the network, then the possible routing paths between the sources and the sink are found using depth first search (DFS). These initial paths then are fed to the MOGA algorithm to give a status (fitness value) for the QoS parameters for each path. Finally, the proposed routing protocol will select the path that is suitable of each type of data traffic based on the QoS parameters status.
## 5 Overlapped Decoding Scheme for the Proposed QC-LDPC Code  
### Shift-Register-Based Memory Strategy  
All intrinsic messages, c2v, and v2c messages are stored in shift-register-based memory units instead of RAMs. The memory schematic we designed for overlap is depicted in Figure 9. Each row produces one c2v message per clock, and each column produces one v2c message per clock. Therefore, the data are divided into vectors and saved as an array  
Figure 8: Schematic of variable node unit.  
Figure 7: Schematic of check node unit. (**a**) XOR tree of \(B_{s}\); (**b**) operation of \(B_{m}\).  
according to the size of the submatrix in the order of rows (columns) without address conflicts.  
Since the degree of parallelism is 57, we divide elements in one submatrix, dividing each of the nine columns into a group, and save in an array. Specifically, the size of registers storing the intrinsic message is \([2(\text{width})\times 9(\text{length})]\times 57(\text{parallelism})\times 16( \text{number of submatrix in a row})\). The size of the v2c register is \(3\times 9\times 57\times 2\), the size of the c2v register is \(2\times 9\times 57\times 16\), since each c2v message is represented by 3 bits, and each v2c message is represented by 2 bits. The shift-register-based memory unit cyclic shifts 2 or 3 bits and updates the first few bits with the latest calculation results.  
In QC submatrices, every row (or column) is shifted by the previous row (or column). This means that the bits on the left of the bits processed in this clock are the bits needed for the next clock. In this way, once we decide the read port of the vector, with the cyclic shift of the register, the bits of this position are always the pending ones. This reduces the routing complexity.  
Furthermore, the storage method with a fixed read/write position can eliminate the read/write latency and further accelerate the decoding process.  
Figure 9: Schematic of check node unit. (**a**) Shift-register-based memory for intrinsic message and v2c message; (**b**) shift-register-based memory for c2v message.
# Periocular Recognition in the Wild: Implementation of RGB-OCLBCP Dual-Stream CNN
## 1 Introduction  
Biometric systems have been widely deployed since the late 1990s worldwide for identity management, banking, homeland security, etc. [1]. Among different biometric systems, face recognition enjoys flexibility, availability, and user-friendly [2]. However, biometrics experts and the police departments of the United States have agreed that the face recognition technology remains challenging after the \"Boston Marathon bombings\" in 2013 [3]. For instance, the appearances of subjects such as cosmetic products, plastic surgery or wearing masks may cause the failure of identifying the suspects. To hinder the complexity of the facial region, periocular recognition is gaining attention these days attributed to its promising recognition performance [4].  
What does periocular refer to? According to the definition in [5], periocular defines the region around the eyes, which includes the eyelids, eyelashes, and eyebrows (see Figure 1). The periocularregion demonstrates more tolerance of variability in expression and occlusion, such as crime scene where perpetrators intentionally mask part of their faces. This creates more capability of matching partial faces [6; 7]. In addition, due to the rapid growth of camera use in social networks, surveillance, and smartphones, this arguably increases the interest of periocular recognition [8; 9]. For all these reasons, periocular recognition has become an area of intense study in the biometrics and computer vision communities.  
In this paper, we address the challenges of periocular recognition in the unconstrained or \"in-the-wild\" environments that remain not well-addressed by the current works [12; 13]. This challenge is associated with the issue of dissimilarities in periocular images due to the placement of sensors, pose alignments, illumination levels, occlusions, etc. Thus, we study this problem by means of a fusion approach with dual-stream Convolutional Neural Network (CNN), which accepts RGB ocular image and a novel colour-based texture descriptor, known as Orthogonal Combination-Local Binary Coded Pattern (OCLBCP). We have also developed and shared a new database, namely Ethnic-Ocular database, by collecting the periocular region images in the wild to validate the proposed network.
## 5 Deployment of PLC for Smart Metering and Smart Grid Systems
### Grid Aspects with Influence in PLC Systems Design
#### 5.2.4 PLC Signal Injection at SSs  
In those cases where the architecture is based on the injection of PLC signals at SSs, PLC signals are mostly concentrated in the segments where the injection takes place. A differentiation can be made between systems injecting PLC signals in MV and systems injecting signals in the LV grid.  
If PLC signals are injected in MV, a further classification can be established concerning the configuration of the grid itself, as in overhead MV cables SSs are all of them connected in a bus, whilst in underground topologies, SSs are connected in a point to point topology. In bus topologies, PLC signals spread to different SSs and systems must be naturally prepared to capture the signals intended for them; in underground systems, undesired PLC signals (_i.e._, signals reaching non-planned SSs) can be considered as plain interference.  
If the injection is in LV grid, PLC signals are expected to propagate in the LV segment; typically, one substation feeds just one LV segment in a bus topology (there are exceptions in some networks, e.g., in UK, where a PoS may be simultaneously connected to several SSs). The difficulties regarding PLC signal injection are related to the reception of PLC signals coming from other SSs and LV segments, and the PLC signal connectivity of multi-transformers sitting in the same SS.