esa-sceva/satcom-chunk-collection huggingface dataset:
# Reliability-Enhanced Camera Lens Module Classification Using Semi-Supervised Regression Method
###### Abstract  
Artificial intelligence has become the primary issue in the era of Industry 4.0, accelerating the realization of a self-driven smart factory. It is transforming various manufacturing sectors including the assembly line for a camera lens module. The recent development of bezel-less smartphones necessitates a large-scale production of the camera lens module. However, assembling the necessary parts of a module needs much room to be improved since the procedure followed by its inspection is costly and time-consuming. Consequently, the collection of labeled data is often limited. In this study, a reliable means to predict the state of an unseen camera lens module using simple semi-supervised regression is proposed. Here, an experimental study to investigate the effect of different numbers of training samples is demonstrated. The increased amount of data using simple pseudo-labeling means is shown to improve the general performance of deep neural network for the prediction of Modulation Transfer Function (MTF) by as much as 18%, 15% and 25% in terms of RMSE, MAE and R squared. The cross-validation technique is used to ensure a generalized predictive performance. Furthermore, binary classification is conducted based on a threshold value for MTF to finally demonstrate the better prediction outcome in a real-world scenario. As a result, the overall accuracy, recall, specificity and f1-score are increased by 11.3%, 9%, 1.6% and 7.6% showing that the classification of camera lens module has been improved through the suggested semi-supervised regression method.  
semi-supervised regression; camera lens module; pseudo-label; deep neural network; modular transfer function +
Footnote †: journal: Applied Sciences
# A Novel Discriminating and Relative Global Spatial Image Representation with Applications in CBIR
## 4 Datasets and Performance Evaluation
### Evaluation Measures
#### 4.2.1 Precision  
The performance of the proposed method is measured in terms of precision \(P\) and recall \(R\), which are the standard measures used to evaluate CBIR. Precision measures the specificity of the image retrieval, and it gives the number of relevant instances retrieved in response to a query image. The Precision (\(P\)) is defined as  
\[P=\frac{Number\ of\ relevant\ images\ retrieved}{Total\ number\ of\ images\ retrieved} \tag{9}\]
# Stimuli-Responsive Photonic Crystals
## 4 Active Tuning of the Photonic Band Gap
### Biologic Stimuli  
Photonic crystals can be easily functionalized with appropriate recognition groups that allow for the detection of specific biomolecules. Due to the change in color, colorimetric detection is quick and easy [16; 111]. These sensors change their optical properties not only when in contact with classical biomolecules like sugars, creatinine or glucose [112; 40; 113], but also larger ones such DNA [42; 43; 70; 71] and proteins [72; 73; 74; 114].  
\begin{table}
\begin{tabular}{c c c} \hline
**Stimuli** & **Mechanism** & **References** \\ \hline Chemical & Infiltration of vapours, solvents or ions & [20; 30; 55; 56; 57; 58; 59; 60; 61] \\ Thermal & Application of \(\Delta\)T to thermoresponsive materials & [48; 62; 63; 64] \\ \hline Magnetic & Interaction between an external magnetic field & \\  & and magnetic nanoparticles & [65; 66; 67; 68; 69] \\ \hline Biological & Detection of different species by & \\  & functionalization with recognition groups & [27; 40; 42; 43; 70; 71; 72; 73; 74; 75] \\ \hline Mechanic & Application of a mechanical force to an & \\  & elastomeric matrix & [17; 31; 39; 44; 47] \\ \hline Light & Stimulation of photosensitive materials, dyes & \\  & and LC by light & [51; 75; 76; 77] \\ \hline  & Reorientation of infiltrated LC & [78; 79; 80; 81; 82; 83] \\ \cline{2-3} Electric & Electrochemical process & [33; 46; 84; 85; 86; 87; 88; 89; 90] \\ \cline{2-3}  & Electrophoretic forces & [91; 92; 93; 94; 95] \\ \hline \end{tabular}
\end{table}
Table 2: Summary of main stimuli used for tunable photonic crystals.  
Recently, silver has been integrated inside 1D PhCs in order to exploit its antibacterial properties and detect the presence of bacteria [27; 75; 115; 116; 117]. [PERSON] et al. fabricated a hybrid plasmonic-photonic device applying a silver layer on top of a TiO\({}_{2}\)/SiO\({}_{2}\) PhC. At the Ag/bacteria interface, there is a generation of polarization charges due to a \"biodoping\" mechanism. This triggers a change in the PBG of the sensor when exposed to _Escherichia coli_[27]. % start a new page without indent 4.6 cm
## 4 Discussion
### Surface Displacement
#### 4.2.2 Relationship between Support Pressure and Maximum Displacement  
As previously mentioned, a normal probability curve to describe the transverse ground surface settlement trough over a tunnel was proposed by [PERSON], the maximum settlement is equal to:  
\[S_{\mathrm{cmax}}=\frac{V}{\sqrt{2\pi i}} \tag{5}\]  
where \(V\) is ground loss volume induced by tunneling construction.  
Based on the above analysis, it was found that the surface deformation has a strong correlation with supporting pressure and buried depth. To carry out a quantitative analysis of the relationship between the support pressure and maximum displacement, the ground loss volume \(V\) in this paper is described as:  
\[V=m\frac{\Delta P}{P_{0}}\frac{\pi}{4}D^{2} \tag{6}\]  
where \(m\) is the supporting pressure control coefficient related to the buried depth of the tunnel, \(D\) is the diameter of the tunnel, \(P_{0}\) is the initial pressure on the excavation which equals the sum of static water and lateral earth pressure, \(\Delta P\) is the support pressure difference, for the excessive condition, it's \(P_{s}-P_{0}\), for the insufficient condition it's \(P_{0}-P_{s}\).  
In this study, only the ground deformation before the excavation face reaches the limit state was investigated since it is not useful for practical engineering to clarify the relationship between displacement and support pressure beyond the limit pressure. Therefore, based on the centrifuge test, it was found that the relationships between the maximum surface displacement with support pressure are almost linear as Figure 19 illustrated. The proposed supporting pressure control coefficient m can be derived as a function of buried depth.  
Additionally, the critical displacement where the deformation no longer changes linearly is different under various conditions. For excess support pressure, the critical heave is approximately equaled to 9 mm (only buried depth of 0.5 D and 1.0 D can be obtained) but at different support pressure ratios which are approximately 4.8 and 6 respectively. It can be inferred that the greater the buried depth, the greater the support pressure ratio required. For insufficient support pressure, the maximum critical settlement varies from approximately 8.5 mm to 22 mm and the related support pressure ratio varies from approximately 0.8 to 0.67 as C/D changes from 0.5 to 1.5, which indicates that the  
Figure 19: The relationship between support pressure ratio and maximum surface displacement: (**a**) excess support pressure; (**b**) insufficient support pressure.  
greater the buried depth, the greater the maximum critical settlement and the smaller the related support pressure ratio.
## 4 Discussion
### The Influence of Soil Moisture  
Despite the different local settings in the three study sites (cliffs, dunes, vegetation, etc.), SAR-SLs are always detected in the upper part of the beach and far from the inter-tidal zone, which suggests that SAR most likely detects the wet-dry sand boundary. This may be due to the polarization method used in the analysis (VH). When using VH polarization, SAR is likely to detect the interface between wet and dry sand, as the signal shows distinct backscatter signatures according to soil moisture. In this context, the dielectric constant of the material being imaged plays an important role in SAR-SL detection. A dielectric constant is a measure of how a material responds to an electric field, also known as relative permittivity. According to the dielectric constant, the backscatter from radar signals is different, and it allows for the distinguishing of sand from other surfaces, such as vegetation and water bodies. Furthermore, SAR is sensitive to sand moisture, resulting in distinct backscatter signatures between dried and wet sand. Because of the presence of water, dry sand has relatively low moisture content and a low dielectric constant, while wet sand has a high dielectric constant. Wet sand is more efficient in absorbing the SAR signal, resulting in a different backscattered signal response in the SAR image. As a result, wet sand backscatters a weaker signal than dried sand. Although these properties can help to identify the shoreline as the wet-dry interface, it can be an issue in sites where night dew humidifies the sand, affecting SAR-SL detection, as the contrast between the SAR signals from wet and dry sand will not be clear.  
Other studies suggest the use of images obtained from other polarization methods to detect shorelines from SAR sensors [51]. Thus, the results obtained here appear to be interesting findings for coastal erosion analysis. Using this configuration (i.e., VH), it is possible to provide a detailed description of the beach, enabling a better understanding of the erosion and deposition processes along the coast. However, other causes of sand wetting, such as rainfall or morning dew, should be further investigated at the three study sites.
## 4 Corrosion Protection Measures
### Cathodic Protection
#### 4.2.2. Impressed Current Method of Cathodic Protection
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Protection Type** & **Classification** & **Material** & **Advantages** & **Disadvantages** & **Applicable Place** \\ \hline \multirow{8}{*}{} & \multirow{4}{*}{sacrificial anode} & Mg and magnesium alloy, & The self-corrosion is small, and the long-term discharge process rarely polarizes; large discharge per unit weight, uniform output current, good mechanical properties, low price, wide source & High requirements for anticrowse coating after metal consumption to replace regularly & Not suitable for high-resistance environments \\ \cline{2-6}  & & & & High grounding resistance. & \\ \cline{2-6}  & & & & It has great influence on the surrounding buildings, & Shallow surface soils with low resistivity and where the protected object is relatively simple \\ \cline{2-6}  & & & & current of & relatively simple \\ \cline{2-6}  & & & & complex structures & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \multirow{8}{*}{} & \multirow{8}{*}{Deep-well anode} & \multirow{8}{*}{Deep-well anode} & The anode grounding resistance is small, the current distribution is even, and the interference with other buildings is small & Construction is more complex, maintenance and replacement difficulties, high cost & Areas with low resistivity or high soil resistivity \\ \cline{2-6}  & & & & & complex, maintenance and underground, Landmark model \\ \cline{2-6}  & & & & replacement difficulties, high cost & metal complex with regional \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \multirow{8}{*}{} & Flexible anode & Mostly doped conductive & Close to the anode; can protect objects with complex shapes; high current utilization; low current loss; when used for pipeline protection, the potential distribution is relatively uniform. The cost is low & Current reliability and life & It is not suitable for cases are lower than MMO & with abnormally large \\ \cline{2-6}  & & & & & under high currents & discharge densities \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \cline{2-6}  & & & & & \\ \hline \multirow{8}{*}{} & \multirow{8}{*}{MMO Flexible anode} & \multirow{8}{*}{Noble metal oxide} & Inherits the advantages of high discharge & The cost is higher than the flexible anode of conductive polymer & The cost is higher than the flexible anode of any situation \\ \cline{1-1} \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & \\ \cline{1-1} \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Types of cathodic protection.
# Energy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
## 1 Introduction
### Radio Optimisation Techniques
To determine users' optimal pair of uplink transmission rate and power, the maximum value of one resource is assumed and the other one is determined, so the ratio is equal to the optimal one. Although users update their uplink transmission rate and power in the same step, the obtained solution remains inferior compared with the corresponding solution of the actual joint two-variable optimisation problem discussed in [53]. [PERSON] et al. [53] proposed a novel utility-based game theoretic framework to address the problem of joint transmission power and rate allocation in the uplink of a cellular wireless network. Initially, each user is associated with a generic utility function that can properly express and represent mobile users' degree of satisfaction in relation to the allocated system's resources for heterogeneous services with various transmission rates. Then, a joint utility-based uplink power and rate allocation (JUPRA) game is formulated, where each user selfishly aims to maximise his/her utility-based performance under the imposed physical limitations, and its unique Nash equilibrium is determined with respect to both variables (i.e., uplink transmission power and rate). The JUPRA game's convergence to its unique Nash equilibrium is proven, and a distributed, iterative and low-complex algorithm for computing JUPRA game's equilibrium is introduced. The results showed the superiority of the proposed framework over other various state-of-the-art approaches.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### Overview of Representative Volume Elements
#### 2.2.4 Unit Cells Obtained through Topology Optimization or Other Numerical Methods  
According to [PERSON] et al. (2017), TPMS-based RVEs are site-dependent, and it might be difficult for TPMS-based unit cells to balance scaffold elasticity and permeability. In contrast, Topologically Optimized Microstructures (TOMs) are microstructures that have been topologically optimized and smoothed; such microstructures can be deployed as the RVEs for CSs. [PERSON] et al. (2017) suggest that such TOMs are advantageous over TPMS as they allow for anisotropic optimal design (Figure 5). Another way to obtain a TOM is described by [PERSON] (2017). The article shows one way to create libraries of unit cells to allow hierarchical design. There are two ways to construct those boundaries, either by image-based design approaches, or using CAD software. In the study, they rely heavily on imaging techniques to create the scaffold architecture with 3D anatomic defects. Globally, their process goes as follows: the microstructure is optimized for maximum permeability and the effective modulus must match the human bone tissue. Subsequently, they use  
Figure 4: Examples of TPMS; from left to right—[PERSON]’s Gyroid, [PERSON]’s Primitive, [PERSON]’s Diamond and [PERSON]’s first surface. In each case the bottom picture shows the results when the cells are duplicated.  
Figure 3: Gyroid structure for varying threshold, t = 0, t = 0.5 and t = 1.  
imaging techniques to design the scaffold exterior. Following this, the global anatomic and architecture design are integrated. They then use AM to fabricate the design [89]. Similarly to [PERSON] et al. [73], [PERSON] et al. [74] have used topology optimization to optimize the microstructures of parts.
## 7 IoMT Healthcare Services and Applications
### Remote Patient Monitoring  
IoMT devices are primarily utilized for remote patient monitoring in healthcare. This technology enables real-time monitoring of patients, even when they are outside of a healthcare facility. Consequently, healthcare professionals can assess a patient's health quickly and accurately, providing timely and appropriate care [98]. Health metrics such as heart rate, blood pressure, temperature, and more are automatically collected by IoT devices from patients who are not physically present in healthcare facilities. As a result, patients are relieved from the need to travel to their providers or manually collect this information. The collected data is then transmitted to a software application that can be viewed by healthcare professionals and/or patients. Algorithms are utilized to analyze the data, generate alerts, and recommend treatments. While remote patient monitoring holds the potential to improve patient outcomes, it is crucial to address privacy and security concerns. Additionally, there is concern that insurance companies could misuse patient data to discriminate against individuals with pre-existing conditions. One major challenge is the risk of hackers gaining unauthorized access to patient data transmitted wirelessly.
## 3 Results and Discussion  
### Analysis of the AHT Cycles Demanded in the RF and Appropriate Working Pairs  
Seven settlements from different climatic zones of the RF are chosen for further detailed analysis (Figure 2), namely, Astrakhan (I zone), Moscow (II zone, western part of the RF), Vladivostok (II zone, eastern part of the RF), Omsk (III zone), Arkhangelsk (IV zone, northern part of the RF), Yakutsk (IV zone, eastern part of the RF), and Oymyakon (special zone). Using the METEONORM database [42], the following climatic indices are collected for each month of the year and for each settlement selected, namely, the average day and night temperatures as well as the average monthly temperature (Figure 3). These data are used for further analysis of the relevant AHT applications for each climatic zone. The climate in the RF is quite cold, therefore, the most demanded AHT applications are heating, seasonal heat storage, and amplification of the ambient heat [38; 43]. The heating applications can be realized during winter time only if a free heat source with a temperature of 2-20 \({}^{\circ}\)C is available to drive the evaporation process, which can be underground water, waste heat, non-freezing natural water basins, etc. Another opportunity is the use of highly effective solar collectors, which can provide heat at 10-20 \({}^{\circ}\)C even under cold climatic conditions.  
Figure 3: Average day (**a**) and night (**b**) temperatures for the climatic RF regions selected.  
Figure 2: Climatic zones of the Russian Federation (RF) [44] selected for the present analysis.
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 4 Proposed Solution Approach  
The shortest distances and the shortest paths between all pairs of nodes on the graph are first calculated using the Floyd-Warshall algorithm. After this phase, the simulated annealing algorithm is used to solve the problems. The simulated annealing algorithm was independently developed and introduced to the literature by [PERSON] et al. [23] and [PERSON] [24]. The SA is a stochastic neighborhood search method that imitates the physical annealing process where a solid is slowly cooled until the minimum energy state is achieved. Therefore, not only improving solutions are always accepted, but also non-improving (inferior) solutions can be accepted by probability acceptance of the Metropolis criterion. This probability acceptance function is the main mechanism of the SA algorithm, and this function prevents getting stuck into local optima. Because of its simplicity and efficiency, the SA algorithm has been widely applied to solve most combinatorial optimization problems.  
In this study, the proposed solution approach and its components are designed to be highly flexible, making them easily applicable to solving different ARPs. The solution representation was constructed in a dynamic manner, allowing for its size to be longer and shorter during the search process. This enables the effective use of various neighbor search operators. Furthermore, the algorithm uses \(8\) different neighbor search operators. Among these, the \(k\)-route shorten, smart edge direction change, and smart reverse operators are novel. Furthermore, the edge postpone operator is used in combination with the merge and split procedures, which is another aspect of the innovation of the proposed algorithm.  
Under this heading, detailed explanations of the algorithm's components, including the solution representation, neighbor search operators and constraint handling strategy used in the structure of the algorithm are explained. Additionally, the working mechanism of the algorithm and its designed components are presented.
# The Influence of Wood Moisture Content on the Processes of Freezing and Heating
## 2 Materials and Methods  
Pine wood (_Pinus sylvestris_ L.) from the University Forest Enterprise of the Technical University in Zvolen was used for the experiment. The diameter of the log was 37 cm on the narrow end. The total length was 4 m. Mean annual growth was 6 \(\pm\) 1 mm and the proportion of late wood was 52%.  
Two groups of samples with a thickness of 30 mm, width of 120 mm, and length of 500 mm were cut from sapwood. Their average oven-dry density was 395.5 kg\(\cdot\)m\({}^{-3}\) with a variance from mean value of \(\pm\) 3.4 kg\(\cdot\)m\({}^{-3}\). The moisture content of the first group was above the fiber saturation point (_FSP_). The second group was air-conditioned to an approximately 35% moisture content.  
The average moisture content according to [16], moisture gradient, and the oven-dry density according to [17] for each sample were determined. The temperature on the cross-section of the samples was conducted using T-type thermocouples (Cu-Ni), which were connected to the measuring unit Comet MS6R (COMET SYSTEM, Ltd., Roznov pod Radhostem, Czech Republic). The position of thermocouples on the cross-section is shown in Figure 1.  
Thermocouples were placed in the middle of the sample length. The measuring depth was 60 mm. The temperature was measured in 15 samples.  
For all the samples, the moisture gradient was determined before and after the drying process. The samples were separated into layers, in which the moisture content of each layer was determined by the oven-dry method. The moisture gradient was calculated using:  
\[\Delta\mathit{MC}\ =\ \mathit{MC}_{c}-\left(\frac{\sum\ \mathit{MC}_{s}}{n} \right)\ (\%) \tag{1}\]  
where \(\Delta\mathit{MC}\) is the moisture gradient (%), \(\mathit{MC}_{c}\) is the moisture content of the middle layer (%), \(\mathit{MC}_{s}\) is the moisture content of the surface (%), and \(n\) is the number of layers.