esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Validation Methodology
#### 2.2.2 Validation of Path Planning  
As the path is planned after setting the GSD and overlap as the targets, the image obtained during real flight using the designed waypoints must satisfy them. To verify this, the GSD and overlap are investigated.  
The GSD of the image captured in real flight was compared with that set as the target. Because of the characteristics of the object, depth exaggeration may occur, causing the GSD to change. To remove this exaggeration, an area of interest (AOI) was defined in the object and the GSD for the area was calculated. GSD was computed in both the vertical and horizontal directions because the GSD of the horizontal axis and that of the vertical axis are different.  
Figure 6 illustrates line segment \(l_{k}\) of length \(L_{k}\) captured. If \(l_{k}\) has a length of \(p_{sk}\) in the horizontal direction and \(p_{yk}\) in the vertical direction in the image, as shown in Figure 6,  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Function** & & **Indicator** \\ \hline \multirow{2}{*}{Path planning} & \(\bullet\) & GSD accuracy \\  & \(\bullet\) & Overlap accuracy \\ \hline \multirow{2}{*}{Flight simulation} & \(\bullet\) & Image coincidence rate \\ \hline \multirow{2}{*}{Shaded area detection accuracy} & \(\bullet\) & Shaded area detection accuracy \\ \hline \end{tabular}
\end{table}
Table 2: Functions and indicators for validation of the simulator.  
Figure 5: Validation process of three functions and their indicators in the overall simulation process.  
and the GSDs in the horizontal and vertical directions are \(G_{x}\) and \(G_{y}\), respectively, then the relationship between them is  
\[Gx^{2}.p_{xx}{}^{2}+G_{y}{}^{2}.p_{yk}{}^{2}=L_{k}{}^{2} \tag{6}\]  
If the width and height of the sensor are \(s_{w}\) and \(s_{h}\), respectively, and the pixel width and height of the image are \(p_{w}\) and \(p_{h}\), respectively, then the relationship between \(G_{x}\) and \(G_{y}\) is given by Equation (7). From Equations (6) and (7), the GSD can be calculated in each direction.  
\[G_{y}=\frac{s_{h}}{p_{h}}\frac{p_{w}}{s_{w}}G_{x} \tag{7}\]  
The overlap in the captured image during real flight is compared with the overlap set as the target. As in the GSD examination, owing to the characteristics of the object, depth exaggeration may occur and change the overlap. To remove the exaggeration, an AOI was placed in the object and the overlap for the area was analyzed. In this study, the term \"overlap\" refers to both forward and side overlap. As a result, the examination was conducted for two types of overlap.
## 3 Field Studies and Measurements
### Rockfall Potential of the Study Area
Seismicity of a region defines the geographical distribution, occurrence times, magnitudes, mechanisms, and damage of earthquakes that occurred in that region. Seismicity studies are mostly carried out to evaluate the seismic risk of a region. In order to compare a potential earthquake effect and the blast effect to be applied in the scope of this study, the necessary data was obtained from the Turkey Earthquake Hazard Map, taking into account the Turkey Building Earthquake Regulation 2018 [36] (Figure 5). In this case, considering the 2018 TBER; \"earthquake ground motion level\" is taken as earthquake level (EL-2), exceedance in 50 years is 10% (recurrence period is 475 years), and \"local soil class\" is ZC (weak rocks with weathered cracks). The following values are obtained. Ss (Short period map spectral acceleration coefficient) = 0.646  
S1 (Map spectral acceleration coefficient for 1 s period) = 0.163 \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Risk Class**} & **Total Weighted Score 1–100** & \multirow{2}{*}{**Risk**} & **Indicative Protection Measures (the Choice is Site Specific)** \\ \hline \hline III & 41–60 & Medium & Light measures (such as bolts, nets, removal of unstable blocks, simple light fences) \\ \hline \hline IV & 61–80 & High & Combination of active (such as bolts, anchors) and passive (such as nets, wire rope cables, buttress walls, fences removal of unstable blocks) measures \\ \hline V & 81–100 & Very High & Critical state of stability, combination of generalized or/and strong active and passive measures. Residual risk to be accepted. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Trenches dug to reduce the risk of rockfall; their width reaches 2 m and depths up to 1 m.  
Figure 3: Large blocks reaching several meters in size (5–10 tons) breaking off from the source rock and reaching areas very close to the village. Research pits and their locations vary depending on the type, size, importance of the structure to be built, and the characteristics of the soil. A research pit was dug to a depth of 1.5 m and in five different locations. It was designed to reveal the soil profile, to determine the soil model to be used in the 3D rockfall analysis, and to determine the gabion wall foundation soil properties (Figures 6 and 7). After the lithological identification made in all research pits, talus of 50-70 cm was cut first and then tuffs with acidic character were cut. For this reason, it is recommended that approximately 50 cm of cover layer should be excavated and stripped before the gabion wall is built. Also, it is recommended that a foundation excavation be made so that the gabion base is at least 50 cm inside the tubes. Figure 5: The maximum ground acceleration value according to the location of the study site in the AFAD Turkey Earthquake Hazard Map and the earthquake ground motion level with a probability of exceeding 10% in 50 years (recurrence period 475 years). Figure 6: Location of the research pits opened within the scope of this study. Figure 7: Appearance of the research pits.
## 2 Materials and Methods
### Fractal and Multifractal Analysis Methods  
#### 2.2.1 Fractal Analysis by DFA Method  
DFA is a method used for analyzing time series with long-term correlations, which may be non-stationary, meaning they contain changing mean values or trends [44; 45; 46]. DFA is particularly effective for detecting hidden patterns and correlations in chaotic data or data containing trends. This method is often applied in the study of ECG signals to reveal fractal structures and long-term dependencies that standard time series analysis methods cannot detect. The main idea of DFA is to remove local trends in the time series and then examine the scalability of fluctuations around these trends. The main parameters that are calculated by DFA are:  
* \(\alpha_{1}\) and \(\alpha_{2}\), which measure the short-term and long-term correlations in the time series, respectively;
* \(\alpha_{\text{all}}\) summarizes the behaviour of the time series for all scales considered and is related to the Hurst parameter. The relationship between these two parameters is \(\text{H}=\alpha_{\text{all}}\).  
DFA provides important information about correlation structures in the data, allowing the analysis of long-term dependencies often associated with the fractal nature of ECG signals.
# Investigation of Crack Propagation and Healing of Asphalt Concrete Using Digital Image Correlation
## 2 Materials and Methods
### Healing  
During the tests, the resilient modulus and resilient horizontal strain of the specimens were recorded by the UTM, according to EN 12697-24:2012 Annex E [36]. By plotting this information for both fatigue tests before and after the healing procedure, it is possible to define the extension index (EI) and recovery index (RI) based on Figure 3. The EI is defined in Equation (1) [37] and the RI is calculated according to Equation (2) [2], where 100% means the complete recovery and 0% means no recovery at all.  
EI \[=\frac{\text{C2}}{\text{C1}}\] (1) RI \[=\frac{\text{E2}}{\text{E1}}\] (2)  
Figure 2: Experimental setup; (**Left**) schematic view of the setup, (**Right**) specimen, camera, and LED spots placed in front of the UTM, only the top camera is active and the whole system is covered with a black plastic cover.  
C1 and C2 are numbers of loading cycles, and E1 and E2 are the averages of the resilient modulus for the first 100 cycles of each step of the test. As explained in the introduction, these recoveries are not only due to the healing phenomenon and include recoveries based on other mechanisms as well.  
In the next step, the acquired images during the experiments were processed using the Istra 4D software. The facet size and grid spacing were set to 23 and 8 pixels, respectively. Using this software, it is possible to calculate the horizontal strain on the surface of the specimen during the experiments. To show the healing around each microcrack, a region of interest was defined around the areas with high strain and the mean horizontal strains in those regions were calculated and compared through the experiments. Furthermore, to define a general healing factor for the asphalt mixture, the number of facets with horizontal strain higher than 10 \(\mu\)E (P) over the total number of facets (P\({}_{\text{total}}\)) was calculated for all images, see Figure 4. Three values were derived from this figure:  
* Healing time (HT), which represents the time it takes for the surface strain to reach its minimum value,
* Percentage of the strain recovery (H\(\%\)) defined as the percentage of strain recovery on the surface of the sample,
* Strain recovery speed (SR) which represents the speed of strain recovery on the surface of the specimens.  
Figure 4: Number of the facets with horizontal strain higher than 10 \(\mu\)E (P) over the total number of facets (P\({}_{\text{total}}\)) including the definition of healing time (HT), percentage of the strain recovery (H\(\%\)), and strain recovery speed (SR).  
Figure 3: Resilient modulus and resilient horizontal strain of the specimen recorded by the universal testing machine (UTM) during the tests.
# Two-Channel OTDM System for Data-Center Interconnects: A Review
## 5 Conclusions  
With a multilevel PAM modulation format, it is anticipated that 800 gigabit and 1.6 terabit Ethernet will be deployed soon. Considering the SINAD, the modulation level of PAM would not exceed eight. To further increase the line speed despite the limitation,  
Figure 14: Photograph of an OTDM transmitter fabricated on a SiP chip.  
a two-channel OTDM system was proposed, which is reviewed in this paper. In the system, the two-channel multiplexed signals are generated using a sinusoidal pulse and detected using a single PD. Compared to the four-channel OTDM system, the optical pulse can be simply generated using a modulator. We believe that this system is commercially deployable by integrating the transmitter into a single chip. Research has demonstrated that a transmitter consisting of three 17.2 GHz MZMs could generate the 150 Gb/s OTDM-PAM-8 signal when the FEC limit was set to \(3.8\times 10^{-3}\). The generated signal was transmitted over a 1.9 km SSMF in the C-band wavelength region. The BER performance could be further improved by using an FFE-based demultiplexer (which is the first layer of a multilayer equalizer) and a nonlinear MIMO equalizer (which is the second layer equalizer). In addition, the use of a phase-alternating pulse allows for increasing the transmission distance. By replacing a constant-phase pulse with a phase-alternating pulse, the transmission distance of the 200 Gb/s OTDM-PAM-4 signal (the FEC limit was set to a BER of \(3.8\times 10^{-3}\)) increased from 1.0 km to 1.9 km (in the C-band region). Thus, we believe that the two-channel OTDM system can be utilized to generate a 400 Gb/s/\(\lambda\) (when using modulators with >30 GHz bandwidth) with sufficient transmission distance.  
Investigation, S.B.; supervision, H.-J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute for Information & Communications Technology Promotion(IITP) grant funded by the Korean government(MSIT) (2021-0-00809, Development of Tbps Optical Communication Technology).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine
## 3 Test Verification
### Simulation Test 1
As shown in Table 7, when both soft and hard faults were considered, our method achieved a relatively high classification accuracy, thanks to the circle model-based feature engineering that ensures the even distribution of faults across the circuit, in the light of the circuit features. On the contrary, [PERSON]'s method only considers the degree of distinction between features through feature selection, and thus achieved poorer accuracy than our method. The lowest accuracy and efficiency were observed in [PERSON]'s method, which resorts to traditional measures for feature engineering and classifier design. The high efficiency of our method in fault diagnosis is attributable to the ELM classifier, which enjoys a fast classification without involving any optimization algorithm.
## 3 Experimental Setup
Although active servo systems were utilized in our system, the passive stability of the system was a prerequisite for the precise stabilization of the cavity length and the relative phase between the LO and the signal beam. Several efforts were implemented in this direction. We placed the NOPA device on an invar plate, and enclosed it in an acrylic plastic glazing cover, in which several small holes were drilled for the pump and down-converted beams. The nonlinear crystal was placed in an oven, which was precisely temperature-controlled in order to maintain the crystal at a constant phase-matching temperature. In all, the observed fluctuation of the relative phase locking was around 8.7 mrad. _The balanced homodyne detection (HD) system_: The quadrature entanglement between the signal and the idler modes were measured with two sets of HD systems. First, the down-converted fields from both the NOPA and NOPO were separated by two dichroic beam splitters (DBS). Then the entangled light fields and the corresponding LO interfered on a 50:50 beam splitter with an interference visibility of 98%, and were subsequently detected by the HDs, which were built from two InGaAs photodiodes (Beijing Lightsensing Technologies Ltd., Beijing, China) and two Si photodiodes (Beijing Hamamatsu Photon Techniques INC, Beijing, China). The measured quantum efficiencies were around 92% at the wavelengths of both 0.8 \(\upmu\)m and 1.5 \(\upmu\)m. The quantum correlation (anti-correlation) noise spectrum of the amplitude quadrature difference and phase quadrature sum were recorded by a spectrum analyzer.
## 5 Conclusions  
The results of the proposed damage assessment method indicate a strong correlation between the deflection response ratio and stiffness reduction rate in RC slab bridges. This correlation allows for the identification of the current state of a bridge based on measurement data, without the need for precise performance evaluations typically obtained through existing bridge inspection systems. As mentioned in the Introduction, this method is expected to address the issues associated with existing displacement estimation methods using acceleration. Furthermore, if data are continuously accumulated using this method, it is anticipated that reliability can be achieved without the need for separate structural analysis.  
While the proposed method does not quantify the bridge condition, it effectively assesses the risk of damage relative to current stiffness levels. Bridge management authorities can utilize these foundational data to prioritize repairs and reinforcements effectively. By carrying this out, the proposed method has the potential to reduce the budget and personnel traditionally required for comprehensive inspections. Moreover, the applicability of this damage assessment method extends beyond bridges in South Korea to RC slab bridges in service globally. In conclusion, the proposed method is expected to be feasible and beneficial, offering a practical approach to bridge maintenance management that enhances efficiency and cost-effectiveness while ensuring structural safety.  
Conceptualization, H.-M.C.; Data curation, H.-M.C.; Formal analysis, H.-M.C.; Funding acquisition, H.-M.C., K.-S.J. and K.-T.P.; Investigation, H.-M.C. and K.-S.J.; Methodology, H.-M.C.; Project administration, K.-T.P.; Resources, H.-M.C. and K.-S.J.; Software, H.-M.C. and K.-S.J.; Supervision, K.-T.P.; Validation, H.-M.C. and K.-T.P.; Visualization, H.-M.C. and K.-S.J.; Roles/Writing--original draft, H.-M.C.; and Writing--review and editing, H.-M.C. and K.-S.J. All authors have read and agreed to the published version of the manuscript.  
This research was partially supported as part of a project funded by the Ministry of Science and ICT and the Korea Institute of Construction Technology (Project No. 20240142-001, Development of a DNA-based smart maintenance platform and application technologies for aging bridges).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 IoMT-Related Technologies
### Wireless Sensor Networks (WSN)  
A WSN consists of a set of self-powered sensor nodes with wireless computing and communication capabilities. The autonomous sensors often have limited energy resources and are capable of collecting, processing, analyzing, and disseminating information via radio waves (e.g., via ZigBee technology). A sensor node is usually composed of information-capture interfaces, a microprocessor, a memory unit, a communication interface, and a battery as a power source (Figure 2). These sensors can be attached to objects/persons or deployed in the environment according to the needs. There are several types of sensors depending on the phenomena monitored or the type of data such as temperature, humidity, position, or light sensors [12, 23, 32].  
In a medical environment, sensors have become indispensable today, including in smart homes because they can be used to measure physical parameters of the monitored person (i.e., a patient) such as heartbeat, blood pressure, body temperature, and brain signals. The information collected and sent back via the network to the cloud is then processed and analyzed by the system before being transferred by the cloud via the Internet to the various actors (doctor, emergency services, etc.). This type of network, therefore, allows health professionals to remotely monitor the patient on the basis of the information received.
# Water Supply Systems for Settlements with Arsenic-Contaminated Groundwater--Making the Right Choice
## 3 Results and Discussion
### Drinking Water Treatment Options Based on Source Water Quality
#### 3.2.3 Treatment Options Using a Central Supply from Better Quality Sources  
The complex treatment schemes presented in Figures 5 and 6 indicate that it is advisable to, at least, consider the possibility of pumping water of better quality from more distant sources. This is the fundamental idea behind the second centralised approach for solving the water supply issues of these municipalities. From the available data on the groundwater quantity and quality in the vicinity [8], it was concluded that the nearest location with better quality water from Kanjiza is the settlement Velebit, while for Senta it is in Tresnjevac, a settlement also in the municipality of Kanjiza but located just 14 km from the town Senta. In terms of water quantity, the available hydrogeological data suggest both of these sources have adequate groundwater reserves to supply larger centralised systems. Since the major towns, Kanjiza and Senta, both require more water than is currently abstracted, additional wells will be required whichever approach is taken (note that well costs have not been included in this strategic analysis, they are for the next development phase of the decision-making process). The groundwater from both the sites at Velebit and Tresnjevac requires the much simpler treatment Scheme B (Table 1, Figure 7), which includes aeration/degasification, retention, sand filtration, and disinfection. These two options will be further evaluated taking into account techno-economical aspects.  
Figure 6: Processes required for adequate water quality in Senta, where the source contains CH\({}_{4}\), CO\({}_{2}\), NH\({}_{3}\), Fe, Mn, organic matter \(<\) 20 mgKMnO\({}_{4}\)/L, and As \(>\) 50 mg/L (Scheme F, Table 1).
# Alginate Modification and Lectin-Conjugation Approach to Synthesize the Mucoadhesive Matrix
## 2 Alginate Structure and Properties
### Biocompatibility and Degradability of Alginates  
Alginate is favored for cell encapsulation due to its low toxicity and its gelation ability under cell suitable conditions, because the distribution of M and G fragments along its chain varies with the alginate source and processing methods. The various characteristic affects its biocompatibility, purification, molecular weight, and intrinsic viscosity [13; 14]. Further purification is suggested to reduce the risk of immune response to developed antibodies against the encapsulation material [15]. Several impurities have been found in sodium alginate, such as heavy metals, endotoxins, proteins, and polyphenols [2].  
The four main degradation processes of alginate are physical, thermal, chemical, and biodegradation. Physical degradation is mostly due to the rupture of glycosidic bonds, by ultrasound, for example [16; 17]. The radiation of ultraviolet light can promote photochemical degradation to alginate [18]. This degradation is performed under photochemical UV/TiO\({}_{2}\) reaction at pH 7. The degradation leads to the formation of alginate oligomers without changing its chemical structure. The third mechanism is chemical degradation, the most often used method to low molecular weight alginate. This type of degradation can be achieved by oxidation reaction. The oxidation of alginate is discussed in the following chapters. The biodegradability of alginate hydrogel can be affected by the crosslinking agent utilized in the gelation process [19]. Lower molecular weight alginate, obtained by oxidation, is more biodegradable than the original sodium alginate.