esa-sceva/satcom-chunk-collection huggingface dataset:
# Improved Recommendations Based on Trust Relationships in Social Networks
## Factors in Trust Relationships
### Definition of Trust  
In this paper, we define it as a triple, which can be described as Equation (1).  
\[T=(U,P,D) \tag{1}\]  
where \(T\) denotes the trust relationship, U is the set of users, \(P\) is the set of trust paths, and \(D\) expresses trust degrees. In addition, the value of \(D\) is determined by the form of \(P\), and \(D>0\). Specifically, we define the length of \(P\) as the number of users in the trust path and mark it as \(L(P)\). \(L(P)\geq 2\), and if \(L(P)=2\), we call \(T\) a direct trust relationship; if \(L(P)>2\), then \(T\) is an indirect trust relationship.  
Figure 1 shows an example of trust relations between users in a social network. In this figure, \(T_{ab}\), \(T_{bc}\), \(T_{ad}\), and \(T_{dc}\) are direct trust relationships; \(T_{ac}\) is an indirect trust relationship, and there are two indirect trust paths from user \(u_{a}\) to user \(u_{c}\), namely, \(P_{1}\): \(u_{a}{\rightarrow}u_{b}{\rightarrow}u_{c}\) and \(P_{2}\): \(u_{a}{\rightarrow}u_{d}{\rightarrow}u_{c}\). For an indirect trust relationship, we choose the most credible indirect path that has the highest trust degree as its trust path according to Equation (2).  
\[D=\left\{\begin{array}{l}D_{mn},\quad D_{mn}\in P_{l},\min(L(P_{l}))=2\\ \max(\prod\limits_{D_{mn}\in P_{l}}D_{mn}),\quad\min(L(P_{l}))>2\\ 0,\quad\quad\quad\quad else\\ \end{array}\right. \tag{2}\]  
where \(P_{l}\) denotes a possible trust path between two users, and \(D_{mn}\) is the trust degree of a direct trust in \(P_{l}\). The equation shows that, if there are several candidate trust paths between two users, the direct path is chosen beforehand. Otherwise, the most credible indirect path will be considered.
## 2 Materials and Methods  
### Landslide in Zhejiang  
The study area is in the southeastern coastal region of China, which features a subtropical monsoon climate characterized by hot, rainy summers and cold, dry winters, with precipitation concentrated in the summer months. The terrain is complex, consisting predominantly of mountains and hills, with mountainous regions covering 70.4% of the province's total area. The elevation gradually decreases from the southwest to the northeast. The geological structure of Zhejiang Province is dominated by the Jiangshan-Shaoxing fault zone, which is marked by extensive faults and folds. This complex geological setting, combined with the province's concentrated monsoonal precipitation, makes Zhejiang a hotspot for landslide disasters.  
For this study, we collected a dataset of 2064 landslide points from the study area and daily precipitation data from 69 meteorological stations (Figure 1a). The temporal span of the landslide dataset is from 1974 to 2015, during which the annual number of landslide occurrences was recorded (Figure 1b). The daily precipitation data cover the period from 1961 to 2024. Most shallow landslides in the study area are triggered by precipitation, with approximately 80% occurring during the peak precipitation period from May to September, which is consistent with the monsoonal distribution in Zhejiang. Landslide occurrence is positively correlated with the precipitation level (Figure 1c). However, the relationship between precipitation and landslides is not strictly linear. For example, let us consider a specific landslide event, which occurred on 26 July 2010. For this landslide, we have compared the daily precipitation data from local meteorological stations and the EAP for the 10 days leading up to the event (Figure 1d). We found that an extreme precipitation event of 106.2 mm was recorded on 24 July 2010, but this event did not immediately trigger a landslide. Instead, the subsequent increase in precipitation over the following 2 days ultimately caused the landslide, as the gradual accumulation of EAP increased the soil moisture, significantly increasing the likelihood of landslide occurrence.  
Figure 1: Overview of the study area. (**a**) Location of the study area. Green triangles represent the 2064 landslide points used in this work, while the red dots represent the weather stations. (**b**) Temporal distribution of landslide data. (**c**) Monthly precipitation and landslides in Zhejiang Province. (**d**) Daily precipitation and EAP (10 days) before a landslide that occurred on 26 July 2010.
# Aiding ICD-10 Encoding of Clinical Health Records Using Improved Text Cosine Similarity and PLM-ICD
## 4 Methodology
### PLM-ICD-C
#### 4.7.2 Architecture  
Figure 2 contains a diagram showing the flow of data and components of the PLM-ICD-C. The EHR is fed to the PLM-ICD, whose output is then fed to the improved Cosine similarity module, where it is further processed and an improved prediction is output.  
The ICS module applies the rules described in Section 4.7.1. Therefore, if needed it will calculate the Cosine similarity between the input EHR and all ICD codes, performing multiple runs and bucket category selection if applicable, in order to optimise the prediction that is output.
## 4 Timing and Carrier Frequency Synchronization through Preamble Detection  
Both timing and frequency synchronization along with the algorithm of signaling detection are described in detail in this section to showcase the advantages of the proposed preamble design and synchronization method with respect to the conventional methods.
# A Study of Regular and Irregular Neutrosophic Graphs with Real Life Applications
## 5 Complete Bipartite Neutrosophic Graphs
### Assignment Problem  
The assignment problem is a well-known and fundamental topic of study in the field of operation research. This problem can be modeled as a bipartite neutrosophic graph. Let a neutrosophic graph \(G=(V,E)\) where \(V\) denotes the nodes and \(E\) represents the arcs be represented as a neutrosophic bipartite graph, if it satisfies the condition such that \(V=X\cup Y\) and \(X\cap Y=\omega\); where \(\omega\) is a null matrix, and \(E\in X\times Y\). A neutrosophic bipartite graph \(G\) is described as a weighted bipartite graph if every edge (i.e., \(E\)) of it consists of a neutrosophic weight (i.e., \(\omega\left(i,j\right)\)). The neutrosophic weight of one-to-one matching (i.e., \(M\)) is described by the following.  
\[\omega\left(M\right)=\sum_{E\in M}\omega\left(E\right)\]  
A matching (i.e., \(M\)) is called a perfect matching if each node of \(X\) is assigned to one of the \(Y\) nodes. Therefore, a linear assignment problem is defined as determining a perfect matching in \(G\) that is maximally weighted.
## 4 Experimental Results: Short Beams
### HNSW-Transducer Measurements
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Cast Day**} & \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Top**} & \multicolumn{2}{c}{**Bottom**} \\ \cline{3-6}  & & **TOF (ms)** & \(E\) (GPa) & **TOF (ms)** & \(E\) (GPa) \\ \hline \multirow{4}{*}{Day 1} & \multirow{4}{*}{1} & 0.57672 & 37 & 0.57420 & 39 \\  & & 0.58312 & 31 & 0.57420 & 39 \\  & & 0.57660 & 37 & 0.57888 & 35 \\ \cline{2-6}  & \multirow{4}{*}{2} & 0.57164 & 42 & 0.56576 & 48 \\  & & 0.59256 & 25 & 0.57420 & 39 \\  & & 0.57924 & 35 & 0.57292 & 39 \\ \hline \multirow{4}{*}{Day 2} & \multirow{4}{*}{3} & 0.59048 & 27 & 0.57600 & 37 \\  & & 0.57988 & 35 & 0.57200 & 42 \\ \cline{1-1}  & & 0.58828 & 28 & 0.57400 & 39 \\ \cline{1-1} \cline{2-6}  & \multirow{4}{*}{4} & 0.58448 & 31 & 0.57872 & 35 \\ \cline{1-1}  & & 0.59096 & 27 & 0.57000 & 42 \\ \cline{1-1}  & & 0.59000 & 27 & 0.57600 & 37 \\ \hline Average \(E\) (GPa) Short beam \(w/c\) = 0.42 & 31.83 \(\pm\) 5.07 (15.96\%) & 39.25 \(\pm\) 3.394 (8.650\%) \\ \hline Cylinder \(w/c\) = 0.42 & \multicolumn{4}{c}{41.1 \(\pm\) 1.389 (3.38\%)} \\ \hline Cylinder \(w/c\) = 0.50 & \multicolumn{4}{c}{37.6 \(\pm\) 1.815 (4.82\%)} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Time of flight and modulus of elasticity of the beams in which Condition 3 was imposed. Finally, the TOF and the modulus of elasticity of the beams subjected to Condition 4 are summarized in Table 8. Under this condition, more water was added onto the fresh concrete. Consistently with the larger volume of liquid, the predicted modulus at the top was lower than what estimated under scenario 3. The table demonstrates that the HNSW-based measurement estimated that the \(w/c\) of the unconditioned surface of the concrete beam was close to 0.42, as it is expected, and above 0.50 for the surface of the beam that was sprinkled with 3.8 mm (0.15 in) of water. It is noted here that sampling period used in this study and the design of the metamaterial are such that the potential error in the estimate of the concrete modulus is on the order of 5%. The accuracy can be improved by simply increasing the sapling frequency of the digitizer. Nonetheless, the outcomes of the results are in line with the prediction of water in excess in the concrete beams.
## 5 Conclusions  
In this work, we propose a two-branch dehazing network CCD-Net. In the image-dehazing branch, multi-scale feature extraction and fusion are used to enhance the processing capability of the image, and the image details are gradually enhanced by different levels of feature representation. In addition, up- and downsampling operations are utilized to further enhance the multi-scale information of the image. This branch combines the channel attention (CA) and pixel attention (PA) mechanisms to dynamically adjust the important parts of the feature maps so as to optimize the image-processing effect. In the color-correction branch, the color information and details of the image are mainly enhanced by introducing Lab color space, the attention mechanism, and the multilayer convolutional neural network structure. Numerous experimental results demonstrate that CCD-Net outperforms five other methods in dehazing and effectively mitigates the impact of haze on image brightness and color. Most notably, CIEDE is reduced by 40.81% on RESIDE-indoor and 45.57% on RESIDE-6K compared to the second-best-performing model, highlighting CCD-Net's superior color-restoration capabilities. Additionally, the ablation study confirms the effectiveness of our network components.  
Conceptualization, D.C. and H.Z.; methodology, D.C. and H.Z.; software, D.C.; validation, D.C.; formal analysis, D.C. and H.Z.; investigation, D.C. and H.Z.; resources, D.C. and H.Z.; data curation, D.C.; writing--original draft preparation, D.C.; writing--review and editing, D.C. and H.Z.; visualization, D.C.; supervision, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (NSFC) under Grant 62173143.  
Not applicable.  
A publicly available dataset is analyzed in this study. These data can be found here: [[https://sites.google.com/view/reside-dehaze-datasets/reside-standard](https://sites.google.com/view/reside-dehaze-datasets/reside-standard)]([https://sites.google.com/view/reside-dehaze-datasets/reside-standard](https://sites.google.com/view/reside-dehaze-datasets/reside-standard)) (accessed on 15 November 2024).  
The authors declare no conflicts of interest.
# Channel-Hopping Using Reinforcement Learning for Rendezvous in Asymmetric Cognitive Radio Networks
###### Abstract  
This paper addresses the rendezvous problem in asymmetric cognitive radio networks (CRNs) by proposing a novel reinforcement learning (RL)-based channel-hopping algorithm. Traditional methods like the jump-stay (JS) algorithm, while effective, often struggle with high time-to-rendezvous (TTR) in asymmetric scenarios where secondary users (SUs) have varying channel availability. Our proposed RL-based algorithm leverages the actor-critic policy gradient method to learn optimal channel selection strategies by dynamically adapting to the environment and minimizing TTR. Extensive simulations demonstrate that the RL-based algorithm significantly reduces the expected TTR (ETTR) compared to the JS algorithm, particularly in asymmetric scenarios where M-sequence-based approaches are less effective. This suggests that RL-based approaches not only offer robustness in asymmetric environments but also provide a promising alternative in more predictable settings.  
cognitive radio networks; expected time-to-rendezvous; channel-hopping; reinforcement learning; actor-critic policy gradient +
Footnote †: journal: Applied sciences  
1
Footnote 1: [[https://doi.org/10.3390/appl42311369](https://doi.org/10.3390/appl42311369)]([https://doi.org/10.3390/appl42311369](https://doi.org/10.3390/appl42311369))  
Footnote †: journal: Applied sciences
# A Low-Cost Laser-Prototyped Microfluidic Device for Separating Cells and Bacteria
## 2 Materials and Methods
### Working Mechanism of the Device  
The theory of inertial particle separation, also known as the Dean effect or Dean flow, explains how particles of different sizes behave when flowing through a curved channel [51; 52]. Dean forces increase with the increasing curvature, flow rate, and channel size [53]. Particles can be moved towards the inner or outer wall of a microchannel depending on their size and the drag force they experience, which is caused by the Dean vortices. Pressure and inertial lift also contribute to the forces experienced by particles in a curvilinear channel. Particles tend to occupy equilibrium positions where the opposite lift forces are balanced, resulting in a net lift force acting on them. These equilibrium positions also form narrow bands. Particles can be focused in serpentine channels by utilizing the inertial forces that act on the particles as they flow through the curved sections of the channel [34; 54]. In particular, when a fluid containing particles flows through a curved section of the channel, inertial forces act on the particles causing them to move towards the outer wall based on their sizes. As the fluid flows into a straight section of the channel, the particles do not move laterally due to the balancing hydrodynamic forces. This results  
Figure 1: The microfluidic device is shown with: (**a**) schematic depiction; (**b**) top-view of actual picture of the device; (**c**) top-view dimensions of the device. (**d**) side-view schematic of the device is shown to illustrate the layers and tube insertion. The radius of each loop of the serpentine is 0.75 mm, radii of the spirals 1, 2, and 3 are 8.7-, 12.7-, and 16.7-mm, respectively. The actual picture of the device is injected with food dye for better visualization. (**e**) A schematic showing the working principle of the particle separation in spiral channel, \(\mathrm{F_{L}}\) and \(\mathrm{F_{D}}\) are inertial lift force and Dean drag forces, respectively.  
in the particles being focused towards the center of the channel. By arranging multiple curved and straight sections in a serpentine pattern, particles can be continuously focused based on their size. In the spiral section of the channel, dean flow, inertia forces, and drag forces are balanced at specific locations in the channel for particles based on their sizes. It is known from the literature and the inertial focusing in spiral channels that larger particles move close to the inner wall and smaller particles are pushed to the outer wall of the microfluidic channel as shown in Figure 1e[14; 32; 33]. In this work, the serpentine section of the microfluidic channel is used to focus the particles at equilibrium positions before they enter the spiral microfluidic channels. This way, different diameter particles can be separated in the spiral channel with only a single inlet for sample injection without using a sheath flow.
## 4 Conclusions  
In this investigation, we performed measurements and analysed radio frequency radiation emitted by the transmitters aboard a marine vessel, focusing on areas normally occupied by crew members and passengers. In total, 528 electric field measurements were taken. Additionally, we developed a new data collection protocol and performed various scenarios to accurately measure the radiation from all transmitters. Under the normal operating conditions, there were a few marine ship transmitters and antennas transmitting continuously, and other radios operate intermittently. By considering this, for the first time, we report measuring the electric field from each transmitter condition, which is insignificant, and this must be carefully taken into account for future studies. Our results show that the electric field levels were highest on the bridge roof and the lowest in the upper deck, and the measured values were within a range of 0.001-39.46 V/m. The limits for the general public were exceeded on the bridge roof; nonetheless, the occupational limits were respected everywhere. Hence, this complies with the occupational and general public reference levels of the ICNIRP guidelines and the ARPANSA standards. Some further conclusions that can be drawn from this paper are: (i) electric field levels were high with the VHF fixed (Sailor 6006) transmitter; and (ii) high frequency electric field levels that are radiated from the vessels' transmitters on the bridge roof will not have much impact for crew members and passengers. Nevertheless, this study should be useful as a reference for many researchers and industry professionals without direct access to the necessary equipment. Further research is desired to determine the electric field levels for a larger amount of ships using the proposed protocol in this paper. Such research would provide a basis for establishing safety distances and support the development of guidelines by suitable authorities.
## 3 Results and Discussion
### SEM Morphological and Pore Size Analysis  
One of the most critical parameters of scaffolds produced for neural tissue engineering is pore size and structure. The open-pore and interconnected meshes provide cell penetration, adhesion, and proliferation. In addition, mechanical support, removal of toxic substances, gas permeability, and cell nutrition can be achieved by a 3D porous structure. A well-developed porous structure exhibits more efficient results in drug transport systems, due to its high surface area [30]. In addition, its pore structure provides the efficient release of drugs and various biofactors loaded on the scaffold and the formation of new tissue [31]. In the present study, the surface morphology pores uniformity and average pore sizes of scaffolds were shown, using SEM imaging and pore size histograms in Figure 3. The results of SEM image indicated that average pore size distributions of pure 17 PLA scaffold resulted in 134.86 \(\upmu\)m. After the addition of the CS to the PLA solution, the average size of the pores decreased to 119.70 \(\upmu\)m. Pure PLA is a material that has an entirely smooth surface morphology, while pure CS has a pore structure that is regularly interconnected and layered [32; 33]. The smooth surface of pure PLA can be seen easily as mentioned in the literature. At the same time, after the addition of CS, the structure lost its smoothness and became a rough surface with microporosity. It is important to mention that this morphology can assure better cell adhesion, which is beneficial for faster integration. The average pore sizes of levodopa-loaded scaffolds at different concentrations (0.01, 0.03, and 0.05 mg/mL) were evaluated and were 195.10, 123.10, and 157.07 \(\upmu\)m, respectively. After loading levodopa, there was no significant difference in the surface morphology compared to the pure 17 PLA/3 CS scaffold. The required optimum pore size is different for each tissue. A study of neural tissue has shown that an average of 100-micron laminin-coated cryogel scaffolds was successfully used in the regeneration of brain neurons [34]. In neural tissue research by [PERSON] et al., axon regeneration on the poly (dimethylsiloxane) scaffold was performed by increasing pore sizes from 50 \(\upmu\)m to 200 \(\upmu\)m [35]. It was observed that adequate permeability was achieved by pore sizes smaller than 300 \(\upmu\)m, according to the study of [PERSON] et al. Consequently, it is clearly seen that the average pore sizes of  
Figure 2: FTIR spectra of the pure PLA (**a**), pure CS (**b**), pure levodopa (**c**), 17 PLA/3 CS (**d**), 17 PLA/3 CS/0.01 LEV (**e**), 17 PLA/3 CS/0.03 LEV (**f**), 17 PLA/3 CS/0.05 LEV (**g**) scaffolds.  
each of the five scaffolds are similar to those reported in the literature as being optimal and ensure the premises to be used in neural tissue engineering applications.
# Image Watermarking between Conventional and Learning-Based Techniques: A Literature Review
## 6 Discussion, Issues and Opportunities
In general, learning-based watermarking schemes also aim to find a balance between robustness and imperceptibility. However, these techniques focus only on differentiable transformations, making them vulnerable to removal attacks. Retraining the model with clean datasets, for example, can remove unstable watermarks [137]. The only solution for this kind of issue is to increase the performance of the model as much as possible. Furthermore, attackers can use fake images to destroy the watermark. In fact, modern computer vision techniques can transform the entirety of the image content. Therefore, training the deepfake generation networks with host images to produce fake ones may remove the watermark. Several efforts have been made to prevent or disturb the reuse of the watermarked image [138; 139]. Image watermarking techniques often used RGB and YCbCr color spaces. The RGB model consists of highly correlated R, G, and B channels, which offer higher capacity and correlation for the watermarking scheme. Likewise, The YCbCr color space was introduced to be more appropriate for hiding secret information, specifically in the Y channel. Despite the useful correlation between RGB channels, it is still not sufficiently used by neural networks, resulting in feature information loss. Moreover, the neural network models that are trained to learn features in one color space exhibit artifacts in the other color space [140]. Therefore, additional research should be conducted on the color space issue, since it might be used by a malicious user to discover the existence of the watermark. To summarize, the field of image watermarking has seen amazing growth as a result of various contributions produced to protect image content, particularly since the emergence of deep learning, which has substantially accelerated the field's progress and openness to modern technology. However, the traditional watermarking field is very mature and, in our opinion (shared by the community), it has reached its limits. Using deep learning for watermarking is relatively novel. Therefore, there are opportunities to find more relevant schemes to provide the best \"defense\" against \"attacks\".