esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Scalability
### Adoption of Different Model Architectures  
The evaluated solution is not limited to a specific model architecture like autoencoders; it can also be extended to other types of models. For example, Keras models such as those presented in the MLPerf Tiny benchmark [42] can be used. The custom ResNet model for image classification has a size of approximately 19 kB when included in an ML-EAT token, while the MobileNetV1 model for the visual wake word use case is about 55 kB. It is evident that the latter, with its encoded model architecture, is significantly larger than the evaluated autoencoder network. This can potentially pose a limitation when dealing with more complex model architectures.
# Therapeutic Applications of Halloysite
###### Abstract  
In recent years, nanomaterials have attracted significant research interest for applications in biomedicine. Many kinds of engineered nanomaterials, such as lipid nanoparticles, polymeric nanoparticles, porous nanomaterials, silica, and clay nanoparticles, have been investigated for use in drug delivery systems, regenerative medicine, and scaffolds for tissue engineering. Some of the most attractive nanoparticles for biomedical applications are nanoclays. According to their mineralogical composition, approximately 30 different nanoclays exist, and the more commonly used clays are bentonite, halloysite, kaolinite, laponite, and montmorillonite. For millennia, clay minerals have been extensively investigated for use in antidiarrhea solutions, anti-inflammatory agents, blood purification, reducing infections, and healing of stomach ulcers. This widespread use is due to their high porosity, surface properties, large surface area, excellent biocompatibility, the potential for sustained drug release, thermal and chemical stability. We begin this review by discussing the major nanoclay types and their application in biomedicine, focusing on current research areas for halloysite in biomedicine. Finally, recent trends and future directions in HNT research for biomedical application are explored.  
drug delivery; halloysite; nanoclay; regenerative medicine; tissue engineering; wound healing +
Footnote †: journal: Medical Imaging  
1
Footnote 1: email: m.m.  
widely used in producing ceramics and porcelain. As with many nanoclays, it is also used as a bulk filler for paint, rubber, and plastics.  
Due to their wide availability, relatively low cost, and relatively low and environmental impact, nanoclays have been widely used in preparing polymer matrix-nanoclay biomedical composites. These include bone repair, cancer therapy, drug delivery, tissue engineering, wound healing, and 3D printing [3; 6; 8; 10]. Laponite, halloysite, and montmorillonite are among the most widely used clays. Halloysite nanotubes (HNTs) and montmorillonite have been used for drug delivery, gene delivery, cancer therapeutics, tissue engineering, and wound healing applications. Halloysite has been intensively studied because of such properties as high mechanical strength, high porosity, thermal resistance, and sustained drug release capability. Because of these properties, increased research is being focused on using HNTs as drug delivery systems and in bioactive bandages, tissue engineering scaffolds, and regenerative medicine. After a brief overview of the major nanoclays, this review will focus on the therapeutic applications of HNTs.
# Bayesian Feature Fusion Using Factor Graph in Reduced Normal Form
## 2 Materials and Methods
### Model Evaluation
#### 2.3.2 Conditional Entropy  
The capability of the system to provide sharp responses on class variable, given all observed variables, can be obtained considering the conditional entropy of \(C\) given all the others [31], which quantifies the uncertainty we have on \(C\) given the evidence:  
\[\begin{array}{ll}\mathcal{H}(C|X_{1},&\ldots,X_{N})\\ &=-\underset{x_{1},\ldots,x_{N,C}}{\sum}p_{X_{1},\ldots,X_{N},C}(x_{1},\ldots,x_{N},c)\log p_{C|X_{1},\ldots,X_{N}}(c|x_{1},\ldots,x_{N})\\ &:=-\sum_{\boldsymbol{x},c}p_{\boldsymbol{X},C}(\boldsymbol{x},c)\log p_{C| \boldsymbol{X}}(c|\boldsymbol{x})\end{array} \tag{4}\]  
Considering the \(n\)-th example, we can therefore compute the conditional entropy of \(C\) using messages as follows:  
\[\begin{array}{ll}\mathcal{H}(C|X_{1}&=\overline{x}_{1},\ldots,X_{N}= \overline{x}_{N})\\ &=-\underset{c}{\sum}p_{X_{1},\ldots,X_{N,C}}(\overline{x}_{1},\ldots, \overline{x}_{N},c)\log p_{C|X_{1},\ldots,X_{N}}(c|\overline{x}_{1},\ldots, \overline{x}_{N})\\ &:=-\sum_{c}p_{\boldsymbol{X},C}(\overline{\boldsymbol{x}},c)\log p_{C| \boldsymbol{X}}(c|\overline{\boldsymbol{x}})\\ &=-\sum_{c}p_{\boldsymbol{X},C}(\overline{\boldsymbol{x}},c)\log\frac{p_{ \boldsymbol{X},C}(\overline{\boldsymbol{x}},c)}{p_{\boldsymbol{X}}(\overline{ \boldsymbol{x}})}\\ &=-\sum_{c}p_{\boldsymbol{X},C}(\overline{\boldsymbol{x}},c)\log p_{ \boldsymbol{X},C}(\overline{\boldsymbol{x}},c)+\sum_{c}p_{\boldsymbol{X},C}( \overline{\boldsymbol{x}},c)\log p_{\boldsymbol{X}}(\overline{\boldsymbol{x}} )\\ &=-\sum_{c}b_{C}(c)f_{C}(c)\log b_{C}(c)f_{C}(c)+\log p_{\boldsymbol{X}}( \overline{\boldsymbol{x}})\sum_{c}b_{C}(c)f_{C}(c)\\ \end{array} \tag{5}\]  
if \(f_{C}(c)\) is uniform  
\(=-\frac{1}{|C|}\sum_{c}b_{C}(c)\log b_{C}(c)+\frac{1}{|C|}(\log p_{ \boldsymbol{X}}(\overline{\boldsymbol{x}})+\log|C|)\sum_{c}b_{C}(c)\)  
since \(b_{C}(c)\) is normalized  
\(=-\frac{1}{|C|}\sum_{c}b_{C}(c)\log b_{C}(c)+\frac{1}{|C|}\log p_{\boldsymbol{ X}}(\overline{\boldsymbol{x}})+\frac{1}{|C|}\log|C|\)  
\(\propto-\sum_{c}b_{C}(c)\log b_{C}(c)+\log p_{\boldsymbol{X}}(\overline{ \boldsymbol{x}})+\log|C|\)  
Since \(\log p_{\boldsymbol{X}}(\overline{\boldsymbol{x}})\) and \(\log|C|\) are constant respect to \(c\), we focus only on the first term. As Likelihood, we can average Conditional Entropy over the Training and the Test Set.
## 4 Experimental Setup
### Training Detail  
For a traditional equalizer, the weights are reset after each retraining, so there is no memory effect. However, neural networks have a memory effect on the training  
Figure 11: Block diagram and scene of the experimental setup. CMOS-based camera in HUAWEI P20 PRO is the receiver and single RGB-LED drived by I/O ports of FPGA is the transmitter.  
Figure 10: The signal construction with repetitive packet transmission in a color channel. For all packets, the headers comprised the same three sequences.  
Figure 9: Offline processing of OCC decoding scheme. (**a–c**) are respectively R, G, and B signals of the raw column selected; (**d–f**) are respectively the R, G, and B signals of the selected column after enhancement; (**g**) Block diagram of the proposed offline OCC decoding scheme.  
samples, which is harmful to communication systems, because obviously, the sequence to be transmitted should be unpredictable. Thus, in this work, we used two videos capturing random messages generated separately for the training process and test process in order to avoid the performance over estimation. The number of training epochs is set to 25 and the Adaptive Momentum Estimation (Adam) optimizer is used after calculating the Softmax loss. Note that some hyperparameters such as regularization items, the number of basic blocks and kernels, and the number of hidden neurons in LSTM will be changed under different symbol rate conditions, for the purpose of searching global optimal solution effectively.
# Efficient Edge Detection Method for Focused Images
## 2 Related Work  
Edge detection methods have a long history. The most commonly known class of edge detection methods is based on the pointwise approach. The main known icons are the Roberts algorithm [3], Prewitt operator [4; 5], Sobel method [6; 7], and the most commonly used Canny algorithm [8]. These methods are mainly defined as filters based usually on the first or the second derivative of the function representing an image. These derivatives are often combined with the pre- or postprocessing methods or are improved in different ways [9; 10; 11; 12; 13]. These algorithms are fast but are not noise resistant.  
Another class of edge detection methods is the one represented by the geometrical approach. The most known is the method based on the Hough transform [14]. Another approach is based on moment computation [15; 16]. More recently, a new method was developed based on the multiresolution geometrical edge detection [17; 18; 19]. All the geometrical methods treat edges as line or curve segments. From this follows that they are rather slow in comparison to the pointwise methods, but they treat shapes in a geometrical way and are noise resistant. Thanks to that, these methods can be used as feature extractors in object recognition.  
A quite different class of methods is based on the Machine Learning approach. The most known algorithms are the random forests [20; 21; 22] and the k-Means algorithm [23; 24; 25]. There are also different variations of such methods [26; 27]. These methods are efficient and are relatively not time-consuming. They are more sophisticated than pure filtering methods since they analyze the image content.  
Finally, these days, many algorithms are based on Deep Learning of convolutional neural networks (CNN). The first attempt to change the classical approach from bottom-up to top-down multiscale edge detection was proposed with the use of DeepEdge [28]. Another known algorithm is HED [29], which is also multiscale. Then a number of different approaches were proposed based on CNN [30; 31; 32; 33; 34]. Recently, a simple, lightweight, and efficient algorithm was proposed based on the Pixel Difference Network [35]. All CNN methods are based on feature detection. Thus, they can detect edges in a more intelligent way than simple pointwise algorithms. However, these methods require a priori a huge amount of learning data to be further trained and used. Additionally, the training is time-consuming.
## 4 NFV and SFC Frameworks Taxonomy
### Resource Allocation and Service Orchestration Frameworks
#### 4.1.8 Slick Framework  
The Slick framework was proposed by [PERSON] et al. [15], which allows for the programming of network functions with a control program that describes how packets can be processed in a specific traffic set. Traffic flow is specified by applications that provide information on the elements that need to be traversed by the packets in the network. The controller deploys the selected processing elements and deploys them on the machine. The task of resource management in the framework is handled by the slick-run-time, which ensures that the links and processing elements are not overloaded with packets beyond capacity.  
Network functionalities are placed as software elements, which can be installed dynamically or at the initialisation time. Events are sent to the Slick controller by the modular processing elements; thus, Slick takes care of the placement of network functions and the steering of traffic between packet processing elements. In terms of evaluation, the proposed framework was evaluated using a Mininet SDN network emulator with various network topologies. The controller was run on a separate VM, while the emulator was run on another VM with 60 SDN switches. Both VMs had eight CPU cores. One of the goals of Slick, which was achieved, is to maximise bandwidth utilisation between various network functions.  
Slick allows the network programmer to use a high-level language to describe a module that handles the steering of traffic, as well as the placement of lightweight functions in arbitrary locations along the service chain. Slick does not consider other environments, such as the edge of the network; thus, their implementation is restricted to data-center environments.
# Numerical Study of the Flow and Blockage Ratio of Cylindrical Pier Local Scour
## 3 Numerical Method
### Configurations of the Computational Domain  
As a result of the implementation of the \(\sigma\)-transformation, the research code can only model partially submerged prismatic obstacles. However, as stated in [PERSON], 2018 [18], the influence of the height of the obstacle over the scouring process is negligible for \(H/D\) larger than 3 (\(H\) being the height of the pier), which is the case studied herein. Therefore, a test of height \(H=0.09\) m is equivalent to one with \(H=0.16\) m due to its diameter being \(D=0.01\) m.  
Figure 1 shows a global view of the computational domain for the narrow channel case in the present study. The configuration and dimensions are the same as those used in the laboratory experiments performed by [PERSON], 2018 [18]. The numerical domain is a \(30D\) long, \(10D\) wide, and \(16D\) deep cuboid with a cylinder situated at the axes' origin, such that the domain is symmetrical in the \(y\)-direction with \(10D\) upstream and \(20D\) downstream of the cylinder.  
Figure 2 shows a two-dimensional view of the mesh used in the simulations. The mesh consists of layers of stacked prismatic elements, which are extended from an unstructured triangular 2D mesh in the horizontal direction. To obtain the turbulent flow around the cylinder, the minimum size of the mesh (triangle length \(l\)) is set about \(l=0.015D\) and \(l=0.02D\) in the horizontal and vertical directions, respectively. The largest triangles are close to the walls with an average length of \(l=0.25D\) (zone 1). In the middle region (zone 2), the mesh reaches a maximum of \(l=0.1D\). Note that higher mesh density is used close to the cylinder (zone 3) to capture the vortex structure. The total number of vertex and cell-centered points in the horizontal are 20,123 and 39,721, respectively. For this mesh, we have 205 vertex points around the cylinder. In the vertical direction, the mesh is comprises 96 layers of elements and the height of the cells increases geometrically as they are further from the bed. The first cell center near the bed is set to be situated in the viscous sub-layer (\(z_{+}<5\)). In the clear-water case, the first cell center close to the bed is at \(z_{+}=2.82\), \(u_{+}=23.68\) at the cell center closest to the surface.
## 4 Spatial Distribution of Urban Vitality  
### Spatial Distribution Pattern of Urban Vitality  
Urban vitality in central Qingdao exhibits a clustered distribution pattern of \"weak east-west, strong central, multi-enter, cluster-structured\", with significant spatial heterogeneity (Figure 3). From a comprehensive spatial perspective, areas with elevated vitality typically develop along major roads and near rail transit, forming a corridor along the southern coastline of Qingdao. Functionally, high-value areas of urban vitality are predominantly concentrated around municipal and district-level commercial and employment centers. At the subdistrict level, urban vitality exhibits distinct spatial patterns, with a marked decline from the central core towards the outskirts. Significant spatial differentiation is evident, with high-value areas primarily located on subdistricts such as Zhongshan Road, Taipingdang, Dunhua Road, Zhan Shan and Hong Kong Middle Road in the Shinan and Shibei Districts. In contrast, subdistricts located in the northeastern sector of the central area, especially within Chengyang District and the eastern part of Huangdao District, demonstrate comparatively lower levels of urban vitality. At the block level, a general trend of gradual decline in vitality is observed moving outward from the city's central business district, with areas exhibiting strong comprehensive vitality predominantly forming a belt-like pattern along the central zones of major urban areas in Qingdao, while the eastern and western sections of Chengyang District display comparatively lower levels of comprehensive vitality.  
The Getis-Ord Gi* analysis identified cold and hotspots within the study region, highlighting spatial patterns that align closely with the low-value and high-value zones identified at both subdistrict and block levels (Figure 4). Qingdao's prominent vitality pole is centered on the central urban districts of Shinan and Shibei, which function as the economic core and cultural tourism hub, characterized by comprehensive urban infrastructure, convenient public transportation and high-density human activity zones. Additionally, smaller vitality poles have emerged in central business districts such as Chengyang and Huangdao. However, limited transportation accessibility in western Chengyang and northern Huangdao results in low functional densities, while underdeveloped supporting facilities constrain their long-term capacity to sustain population retention, consumption and residential stability.  
Figure 4: Distribution of urban vitality hotspots and coldspots in central Qingdao.  
Figure 3: Distribution of urban vitality in central Qingdao.
# Using Artificial Neural Network Models to Assess Hurricane Damage through Transfer Learning
## 3 Transfer Learning
### Artificial Neural Network Models
#### 3.2.2 MobileNet  
The MobileNet architecture focuses on streamlining the convolution layers through depthwise separable convolutions to build an attenuated deep neural network [25]. The separation of the convolution into two layers, one for filtering the inputs and one for combining the outputs with the depthwise convolution, significantly decreases the magnitude of computation and model scale. This in turn generally leads to low latency for incorporating the MobileNet model into classification and object detection.  
Additionally, the MobileNet architecture makes use of two global hyper-parameters: a width multiplier and a resolution multiplier. The width multiplier aims to shrink each layer of the network in a uniform fashion, while the resolution multiplier is applied to the input image which results in reducing each subsequent layer by the same parameter [25]. We incorporated MobileNet V1 to match the target input resolution and maintain consistency with the choice of primitive model architectures.
## 2 Theoretical Discussion
### Dynamic Factor Calculation Methods Outlined  
#### 2.3.1 Eisenmann Calculation Method (DAF\({}_{\text{Eisenmann}}\))  
The dynamic factor method (as in accordance with the UIC method adopted in Korea) is currently used in both intercity and highspeed railway track design. Details of the [PERSON]'s equation, variables, and parameters are shown in Table 2 (regarding exceeding probability application) and Table 3 (regarding track quality) explained below [20].  
\[DAF=1+\phi \tag{6}\] \[DAF=1+\phi\big{(}1.0+0.5\frac{\text{V}-60}{80}\big{)},\text{ in case of freight train}\] \[DAF=1+\phi\big{(}1.0+0.5\frac{\text{V}-60}{190}\big{)},\text{ in case of passenger train}\]  
[PERSON]'s DAF (also represented as _i_) is calculated through the formula presented in the above Equation (6), and based on this, it is used by modifying it according to the characteristics of trains in Korea. In the case of the vehicle considering the wheel load caused by the track misalignment and cant shortage or cant excess, it is as follows.  
\[Q_{eff}=Q\times 1.2 \tag{7}\]  
Subsequently, the following formula is used for the dynamic load due to the exceptional impact caused by the vertical vibration of the vehicle's elastic part caused by irregularity at the wheel/rail contact point [9]  
\[DAF_{Eisenmann}=Q_{eff}\times\text{DAF} \tag{8}\]  
where,  
\(Q\): Static wheel load (kN),  
\(Q_{eff}\): Effective wheel load (kN),  
\(DAF_{Eisenmann}\): dynamic factor using the Eisenmann method  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Exceeding Probability (\%)** & **t** & **Application** & **Note** \\ \hline
68.3 & 1 & Contact Stress, Track bed & t: Increase rate of standard deviation that depends on the confidence interval, value 3 is recommended for safety and reliability \\ \hline
95.4 & 2 & Lateral load, Ballast & t: Increase rate of standard deviation that depends on the confidence interval, value 3 is recommended for safety and reliability \\ \hline \hline \end{tabular}
\end{table}
Table 2: Track exceeding probability application condition [20; 21; 22; 23].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Track Quality** & \(\mathbf{\Phi}\) & **Note** \\ \hline Very good & 0.1 & \\ Good & 0.2 & \(\Phi\): Coefficient dependent on track quality \\ Poor & 0.3 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Track quality and application condition.
# Analyzing Port State Control Data to Explore Future Improvements to GMDSS Training
## Materials and Methods
### The Treatment of the Deficiencies Linked to Poor Training of OOW on Board  
The catalog of deficiencies that PSCO can include in the radio inspection report has a unique code, as shown in Table 5. One of the most important parts of the radio inspection is to ensure the ability of the GMDSS operators to use all the equipment and follow the correct communication procedures. The officer of the watch should be capable of transmitting and receiving distress alerts and distress traffic operating all GMDSS installation possibilities. The deficiencies related to codes 5101, 5115, 5116, and 5118 are selected in this study as more direct indicators of possible deficiencies in the continuing training of shipboard officers in charge of maritime communications (Table 5).  
The classification of ships according to the proposed HRSC3 (Table 4), would yield a total of 9116 inspections (38.4%), which involves 6566 ships (49.8%) and 10,003 defective codes (32.4%). Trying to prevent isolated cases, the subset considered for the analysis will be made for those ships in which this deficiency has been found repeatedly (at least two inspections in the period, as appears in condition 2 in Table 4). The problems related to Certificates for radio personnel (codes 01203) are excluded from this paper because the interest lies in the evidence of the fluid operation of the radio communications installation and its management rather than whether a certificate is expired.
## 5 Wax Chemical Inhibitors
### Wax Dispersant  
Wax dispersants are a group of surfactants that adsorb onto pipe wall surfaces and decrease the wax adhesion either by altering the wettability of the pipe wall or by forming a thin layer where wax crystals shear off easily. A wax dispersant also adsorbs onto wax crystals and forms a wax crystal lattice structure in the crude oil. The wax crystal structure later reduces the growing crystals' morphology and delays the formation of a three-dimensional crystal. This altered spherical-like crystal from a large plate-like crystal is expected to increase the ability of the crude oil to flow [81; 82]. Note that the wax dispersant has limited effectiveness when not used with a polymeric flow improver. It is reported that a wax dispersant works extremely well with a polymeric flow improver due to its characteristic of hindering wax from settling and depositing at the pipe wall surface.  
In the presence of water, surfactants such as alkyl sulfonates and fatty amine ethoxylates can also function as wax dispersants. Interaction between the wax particles and hydrophilic groups of the surfactant disperses the wax particles to smaller sizes. This can lead to the prevention of wax agglomeration and deposition [83; 84]. Attributed to the molecular structural factor, these surfactants were reported to perform the best with longer chain esters in reducing the pour point and surface tension, altering the morphology as well as reducing co-crystallization [85; 86; 87].