esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Formulation and Solution of the Optimization Problem  
In this section, a multi-objective optimization problem is formulated to optimize RACH success probability and minimize the power consumption of NB-IoT terminals in LEO satellite-based IoT networks. To obtain the appropriate CE configuration parameters for the NB-IoT terminal, we employ NSGA-II to solve this optimization problem. Moreover, we find the optimal parameters in the Pareto-front solution set to meet the RACH success probability requirement. According to different application requirements, we also design a random access parameter configuration method to minimize the power consumption under the constraint of the RACH success probability requirement.
## 1 Introduction  
Over the past 50 years, electricity networks evolved from the \"local grid\" networks in the beginning of the century to interconnected electric grids, based on generating stations of notable scale (1000-3000 MW) distributing power to major load centres that divided energy to a large number of individual consumers. The generating stations, or power plants, were built in order to provide massive amounts of energy, due to the nature of power generation technologies in use (hydroelectric, coal, oil, and gas). By the end of the 20 th century, however, this model proved to be unreliable and inadequate. First of all, the demand forecast techniques and the data processing technologies could not efficiently provide the desired energy at the desired time, thus power distribution was based upon rough average classifications. Moreover, the emerging environmental issues and the geopolitical interdependence of power sources limited the development of economies of scale. The main challenges that a modern electricity network has to face are [1]:  
* Privacy issues between energy suppliers and customers;
* Security threats from cyber attack;
* National goals to employ alternative power generation sources;
* Significantly more complexity in maintaining stable power with intermittent supply;
* Conservation goals that seek to lessen peak demand surges during the day so that less energy is wasted in order to ensure adequate reserves;
* High demand for an electricity supply that is uninterrupted;
* Digitally controlled devices that can alter the nature of the electrical load and result in electricity demand that is incompatible with a power system that was built to serve an \"analog economy\".  
These challenges require the development of an intelligent, self-balancing, integrated electric network that makes use of the modern ICT techniques to manipulate and share data. The smart grid technology tries to answer these needs. In this survey, we propose an overview of the main aspects of smart grids development and implementation. In Section 2 we give two different definitions of the smart grid concept. In Section 3 we will analyse how the smart grid paradigm modifies the energy market. In Section 4, we will review its technical aspects. In Section 5 we will see how a smart grid can be optimized. In Section 6 we will review the existing open source smart grids solutions. In Section 7 we will review the regulatory aspects related to the smart grids. In Section 8 some conclusions are given.
## 4 Experimental Results
### Compression of ResNet-18 and ResNet-152 Using Tensor Decomposition Methods
#### 4.2.2 Compression of ResNet-152  
For ResNet-152, we conducted experiments on a single compressed variant, ResNet-152 (Tucker-CP). Tucker decomposition was applied to all layers in the third block, while CP decomposition was applied to all layers in the fourth block.  
This compression reduced the model size from 233.5 MB to 93.3 MB, achieving a compression ratio of 2.5\(\times\). The number of parameters was reduced from 58 million to 23 million, with only a minor accuracy drop from 96.2% to 95.1% on CIFAR-10 and from 69.2% to 62.4% on Tiny ImageNet.  
We observed a slight increase in inference time. For example, for the ResNet-18 model compressed by a factor of \(\times\)12.2, the inference time increased from 0.6 s to 0.8 s and from 5.1 s to 5.3 s for \(\times\)2.5 compressed ResNet-152. While, theoretically, inference time should decrease due to the reduced number of parameters, in practice, the opposite trend was observed.  
This can be attributed to the limitations of modern GPU architectures, which are heavily optimized for performing a few large matrix multiplications efficiently. When dealing with compressed models, transferring multiple smaller matrices to the GPU cache incurs overhead that is comparable to the time required for performing the matrix multiplications themselves. As a result, this data transfer overhead diminishes the expected speed-up from the reduced parameter count.  
Addressing this issue may involve leveraging hardware accelerators designed specifically for the efficient handling of smaller matrix operations or implementing low-level cache optimization strategies to reduce the cache transfer overhead of small matrices within accelerator memory. These approaches offer promising pathways to enhance the inference efficiency of compressed neural networks.  
Overall, our experiments demonstrate that the proposed tensor decomposition-based compression approach is highly effective at reducing the model size and number of parameters while maintaining competitive accuracy. Furthermore, the method has minimal impact on inference performance, making it suitable for resource-constrained applications.
## 4 Design of Multi-Constraint Optimized Genetic Algorithm
### Multi-Dimension Constraints in Cyber-Physical Domain
#### 4.2.2 Conditions for Optimizing the Efficiency  
With the fundamental constraints above, several optimization constraints are further proposed for finding a nice solution \(\pi\). In fact, for the genetic algorithm, the composition of these constraints constitutes the fitness function.  
**Condition 1 (Amount of Platform Services \(A_{PS}\))**: \(A_{PS}\) is the amount of carrier services for a special solution, which is equal to the number of robots that perform the mission \(M\) cooperatively. When there exist different solutions, the solution with fewer carrier services should be given priority.  
**Condition 2 (Time Cost \(A_{TC}\))**: When a solution is found, its \(A_{TC}\) can be calculated according to a group of arguments, covering every service and its target. Generally, the smaller \(A_{TC}\) means a mission is performed more efficiently.  
**Condition 3 (Global Distance \(A_{GD}\))**: \(A_{GD}\) is the whole distance all robots will cruise. It is obvious that this value represents the efficiency and cruising cost for a mission.  
**Condition 4 (Damage Cost \(A_{DC}\))**: \(A_{DC}\) indicates the possible cost of damaged resources during the execution of a planned solution. This value reflects the evaluated overhead of a special solution, depending on the status of environment.  
**Condition 5 (Mission Earning \(A_{ME}\))**: \(A_{ME}\) represents the achievement degree of one mission, in other words, the average success rate of vital services.  
With these conditions, we can further design a weighted function \(F()\) to evaluate the performance of one solution \(\pi\). This function is defined as Formula (4), where each \(\beta_{xx}\) is a float weight factor between 0 and 1, and \(f_{n}()\) is a function for normalizing the value of each variable into the same magnitude as defined in Formula (5). According to different execution policies, the value of each \(\beta_{xx}\) can be adjusted adaptively. As described, \(F()\) is in fact the fitness function of GA.  
\[F(\pi)=\sum_{xx=PS}^{ME}\beta_{xx}\times f_{n}(A_{xx})\quad xx=\{PS,TC,GD, DC,ME\} \tag{4}\]\[f_{n}(x)=\frac{x-x_{min}}{x_{max}-x_{min}}\times 100 \tag{5}\]
## 3 Results and Discussion
### Evaluation of Similarities Among Classification Methods
#### 3.3.2 Similarity Evaluation Based on Qu
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Classification**} & \multirow{2}{*}{**Grade**} & \multicolumn{5}{c}{**NB**} & \multirow{2}{*}{**Overall**} & \multirow{2}{*}{**Kappa**} \\ \cline{3-3} \cline{5-8}  & & **SS \({}^{\gamma}\)** & **PSS** & **PUS** & **US** & **Total** & **Accuracy (\%)** & **Coefficient** \\ \hline \multirow{4}{*}{Qu \({}^{2}\)} & SS & 166,718 & 286,376 & 0 & 0 & 453,094 & \multirow{4}{*}{0.31} \\  & PSS & 0 & 127,084 & 286,818 & 0 & 413,902 & \multirow{4}{*}{0.31} \\  & PUS & 0 & 0 & 356,212 & 0 & 356,212 & \\  & US & 0 & 0 & 213,096 & 75,110 & 288,206 & \multirow{4}{*}{0.31} \\  & Total & 166,718 & 413,460 & 856,126 & 75,110 & 1,511,414 & \multirow{4}{*}{0.31} \\  & (100.0) x & (30.7) & (41.6) & (100.0) & (68.1) w & & \\ \hline \multirow{4}{*}{EI} & SS & 5872 & 0 & 0 & 0 & 5872 & \multirow{4}{*}{0.21} \\  & PSS & 160,846 & 286,376 & 0 & 0 & 447,222 & \multirow{4}{*}{0.21} \\  & PUS & 0 & 127,084 & 856,126 & 53,595 & 1,036,805 & \multirow{4}{*}{0.21} \\  & US & 0 & 0 & 0 & 21,515 & 21,515 & \\  & US & 0 & 0 & 0 & 21,515 & 21,515 & \\  & Total & 166,718 & 413,460 & 856,126 & 75,110 & 1,511,414 & \multirow{4}{*}{0.31} \\  & (3.5) & (69.3) & (100.0) & (28.6) & (50.4) & & \\ \hline \multirow{4}{*}{GI} & SS & 107,169 & 0 & 0 & 0 & 107,169 & \multirow{4}{*}{0.21} \\  & PSS & 59,549 & 286,376 & 0 & 0 & 345,925 & \multirow{4}{*}{0.21} \\  & PUS & 0 & 127,084 & 489,885 & 0 & 616,969 & \multirow{4}{*}{0.21} \\  & US & 0 & 0 & 366,241 & 75,110 & 441,351 & \multirow{4}{*}{0.31} \\  & Total & 166,718 & 413,460 & 856,126 & 75,110 & 1,511,414 & \multirow{4}{*}{0.31} \\  & Total & (3.5) & (69.3) & (57.2) & (100.0) & (72.7) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall accuracy and kappa coefficient for the reclassification methods based on NB using pixel counts.
# CLensRimVision: A Novel Computer Vision Algorithm for Detecting Rim Defects in Contact Lenses
###### Abstract  
Automated optical inspection (AOI) plays a pivotal role in the quality control of contact lenses, safeguarding the safety and integrity of lenses intended for both medical and cosmetic applications. As the role of computer vision in defect detection expands, our study probes its effectiveness relative to traditional methods, particularly concerning subtle and irregular defects on the lens rim. In this research study, we propose a novel algorithm designed for the precise and automated detection of rim defects in contact lenses called \"CLensRimVision\". This algorithm integrates a series of procedures, including image preprocessing, circle detection for identifying lens rims, polar coordinate transformation, setting defect criteria and their subsequent detection, and, finally, visualization. The method based on these criteria can be adapted either to thickness-based or area-based approaches, suiting various characteristics of the contact lens. This approach achieves an exemplary performance with a 0.937 AP score. Our results offer a richer understanding of defect detection strategies, guiding manufacturers and researchers towards optimal techniques for ensuring quality in the contact lens domain.  
contact lens; computer vision; automatic optical inspection; defect detection; image processing 2023239610  
1  
[PERSON]  
This paper introduces a novel algorithm tailored for the robust and accurate detection of defects in the rim area of contact lenses. Leveraging advanced image processing techniques combined with mathematics and statistics, this system aims to offer superior detection capabilities, ensuring higher quality and safety standards for contact lens users worldwide. We focus on this area because it directly interacts with the rim of the human eye, making it crucial for the wearer's comfort. Defects, such as burrs, can cause irritation to wearers. Figure 1a,b illustrate examples of a clear lens and a colored and patterned contact lens edge with some defects and a lack of smoothness from our real dataset. This kind of defect is called a burr, which refers to a small, unwanted, and often sharp-edged or -ridged material that can form on the surface of a workpiece as a result of various machining or manufacturing processes. In the yellow box, display a larger view of samples showing defects in the rim area of both transparent and patterned contact lenses.  
Our paper's contributions are as follows.  
* We proposed a novel image-processing-based algorithm to detect burr defects on the rim of contact lenses.
* We applied this comprehensively using mathematics and statistics to a computer vision algorithm.
* We presented information relevant for manufacturers and researchers in the contact lens sector, providing them with insights and methodologies that can be directly applied to improve quality control automation processes.
## 4 Optimization Algorithm
### The PSO-MC Algorithm  
The PSO algorithm has many potential advantages compared with other intelligent optimization algorithms; however, it suffers difficulty in dealing with stochastic variables. Therefore, the particle swarm optimization algorithm cannot be used to deal with the wind turbine, PV, and electrical demand uncertainty problems in an efficient way. To improve this situation, the stochastic variables--for example, the output of wind turbines, PVs, and network demands--should be converted to the determined variables. MC is an efficient simulation tool that is widely used to model power system operation parameters by predicting the probability distribution of demands of renewable generators in advance. In this paper, a combined PSO-MC method is proposed to optimize the chance constrained programming, which relates to the distributed system optimization.
## 8 Results
### Distribution of Effects on Susceptible Zones
#### 8.3.3 Anomalous Sea Waves/Tsunami  
Despite the fact that the southern Ionian Islands have been mainly affected by strike-slip earthquakes, considered insufficient for triggering tsunami, [PERSON] identified 26 historical and recent earthquakes with subsequent anomalous sea waves/tsunami with mild-to-moderate impacts on the coastal natural and built environment of the southern Ionian Islands [55]. These effects could be attributed either to seabed displacements and ruptures accompanying strike-slip earthquakes or submarine and coastal slope failures triggered by the earthquakes' ground motion [58; 59; 60; 61]. Moreover, [PERSON] concluded that the most susceptible areas in the southern Ionian Islands to tsunami effects are the coastal areas of funnel-shaped days and of narrow straits [55]. Such susceptible areas comprise: (a) in Cephalonia, the eastern coastal Erissos peninsula, the eastern coastal Paliki peninsula, the western and northeastern coastal parts of the Aenos Mt fault block and the Argostoli Bay, (b) in Ithaki, the Vathi Bay and the western coastal part of the island and (c) in Zakynthos, the coastal area of the homonymous city and the coastal part of Laganas Gulf. Among these areas, the anomalous waves/tsunami reported after the generation of the August 1953 earthquakes affected the coastal area of Argostoli Bay in Cephalonia, of Vathi Bay in Ithaki and of Zakynthos city in the homonymous island.
# The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends
## 4 Taxonomy of IoD
### Security
#### 4.4.2 Privacy  
The privacy issues of the IoD arise from three major areas: Sensors, communications, and multi-UAV [41]. Eaves-dropping and keylogging are the popular methods in data interception which is aimed to affect the privacy of the flowing information between the drone and communication center [42]. Furthermore, the IoD utilizes the open-access communication environment which is vulnerable to many security and privacy issues such as message integrity, and disclosure of the UAV identity [2]. The recent advancement in drone usage by civilian and business entities further increases the possible harm to the right of the individual or organization to privacy [43].
# System Reliability Assessment with Imprecise Probabilities
###### Abstract  
The exact statistical characteristics of some components may be unavailable because of the limited sample information in practical engineering. One challenge that system reliability analysis faces is dealing with limited sample sizes, which introduces the potential for a high level of uncertainty in the analysis results. In this paper, we propose a procedure for the reliability analysis of complex systems with a limited number of samples. Bayesian inference is used to estimate the parameter intervals of the life distributions of the components with a limited number of samples. Then, probability boxes (p-box) are constructed from the parameter intervals to represent the life distributions of the components with a limited number of samples. In addition, the theory of survival signature is applied to calculate the reliability of the system with a mixture of precise and imprecise knowledge of the life distributions of the components. Finally, two numerical examples are given to illustrate the validity of the methods.  
system reliability; limited sample; uncertainty; p-box; survival signature +
Footnote †: journal: Applied Sciences
## 5 Innovative Approaches for Enzyme Immobilization
### Nanomaterial-Based Enzyme immobilization
#### 5.1.6 Organic-Inorganic Hybrid Nanomaterials-Based Enzyme Immobilization  
Organic-inorganic hybrid nanoflowers (hNFs) have attracted significant attention for their simple synthesis, mild reaction conditions, high stability, and biocompatibility [148]. They are produced through biomimetic mineralization, where metal ions interact with protein solutions to form functional nanostructures without hazardous chemicals, even under challenging conditions [149].  
Enzyme immobilization on hNFs significantly enhances catalytic activity and stability compared to the results using free enzyme or conventional methods. For instance, thermophilic lipase QLM from Alcaligenes sp. immobilized on Cu\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)-based hNFs exhibited excellent reusability and stability, retaining activity after eight cycles or 4 weeks at ambient temperature. After 10 min at 80 \({}^{\circ}\)C, the immobilized enzyme retained 51.6% of its activity, while the free enzyme kept only 19.8%. It also maintained 78.6% of its activity at pH 5.0 compared to 32.9% for the free form, and showed a lower Km value (15.54 mM vs. 24.42 mM), indicating stronger substrate affinity [150].  
Collagenase immobilized on Col-Zn-hNFsand magnetic cross-linked hybrid nanoflowers (mcCNFs) also showed superior catalytic performance, with greater stability across wide pH and temperature ranges [149; 151]. These immobilized enzymes also have promising medical and industrial applications, while their larger surface area increases substrate concentration at the enzyme's active site, boosting efficiency [152; 153].
## 3 Results and Discussion
A mean value of the refractive index for the microsphere was an average over all the points inside the microsphere that corresponded to a plane. Before the deconvolution (presence of diffraction), the mean value of the refractive index seemed to be a function of the z-position. We mainly observed a decrease of refractive index values while we moved away from the focal plane. This was due to the diffraction dependence along the optical axis, which becomes more significant as we moved away from the focal plane. From \(z=-0.1\)\(\upmu\)m to \(z=-0.2\)\(\upmu\)m, an increase occurred. This could explain an effect of the vibration which occurred during the measurement. This induced the sample to be shifted from a plane to another during the measurement. This was a drawback for the TIE method, which required the precise motion of the sample. After the deconvolution (taking into account 3D-PSF), the mean value of the refractive index was quasi-constant. The value in the focal plane where diffraction effects are greatly reduced was also in agreement with the refractive index of 1.586, which is the reference value [9] at 640 nm. In Figure 5, we can see the size's map \(t(x,y)\) of the specimen. It was obtained by using Equation (12), taking \(n_{f}=1.587\) as the mean value of microsphere's refractive index in the focal plane and \(n_{m}=1.33\) as the refractive index of the distilled water used in the sample preparation. The microsphere's diameter as deduced from the map was \(d=0.943\)\(\upmu\)m. This value represents the intensity of the pixel in the middle of the microsphere (Figure 5). The graph for the mean values of the refractive index as a function of z-position also led to the recovery of the diameter of the microsphere. The diameter was defined by the full width at half maximum (FWHM) indicated in the graph by the double arrow (Figure 6). The diameter, which was deduced from the graph, is 1.1 \(\upmu\)m. These two diameter values were close to 1 \(\upmu\)m (the reference diameter of the microsphere used for the microscopic examination in this work), and they were in relative variation, between 6% and 10%. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{Z=-0.4}\) & \(\mathbf{Z=-0.3}\) & \(\mathbf{Z=-0.2}\) & \(\mathbf{Z=-0.1}\) & \(\mathbf{Z=0}\) & \(\mathbf{Z=0.1}\) & \(\mathbf{Z=0.2}\) \\ \hline Refractive index & & & & & & & \\ before deconvolution & & & & & & & \\ Refractive index after deconvolution & & & & & & & \\ \end{tabular}
\end{table} 
Table 1: Mean values of the refractive index of the microsphere at the wavelength 640 nm for each plane \(Z\) (\(\upmu\)m). Figure 4: 3D refractive index reconstruction at 640 nm for a polystyrene microsphere with a diameter of 1 \(\upmu\)m, without PSF consideration (**a-g**), and with PSF consideration (**h-n**).
## 4 Discussion/Conclusions  
### Microplastic Analysis (FTIR Spectroscopy)  
When assessing the results generated, several parameters must be associated with each other and, if possible, correlated. One is the type and hardness of the plastics as well as the influence of friction-enhancing granular or crystalline substances and the resulting effect on the number of microplastics. The resulting number of microplastics influences the potential amount of degradation products generated by thermal stress in the oven. Meaning for real application that abraded mass of plastic particles from the production of for example cake or bread in plastic kitchenware are approximately proportional to their thermal decomposition products in the oven. The amount of the produced microplastic particles is shown for each polymer (Figure 1). This makes it possible to determine which material was the most resistant to mechanical force. The plastics melamine and PS show the highest abrasion without salt treatment, which can be explained by considering the Rockwell hardness, which, compared to the other plastics, is in the high range for melamine (898 particles) between 67.00 and 86.00 and PS (827 particles) at 60.00-75.00 (Table 1). The plastics ABS and SAN show a relatively similar range of Rockwell hardness of 20.00-70.00 (ABS) and 10.00-80.00 for SAN, which is reflected in the only 4% difference in microplastic count. LDPE has the lowest microplastic count with only 331 particles, which can be explained by a very low Rockwell hardness value of 1.00-20.00. Furthermore, the influence of friction-increasing substances such as salt or other components, which could occur in the added ingredients when mixing in a plastic bowl, was tested. It was found that adding salt to the plastic ABS when stirring in water can cause a tripling of the microplastic count from 591 particles to 1890 particles. This means that the type of ingredients and the type of processing in a plastic bowl can also have an influence on the microplastic content.