esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 5 Simulated Environment and Demonstration
### Demonstration  
Figure 9 provides the corresponding polygons of City 1, City 2, and City 3 with 2054, 6080, and 7946 vertices. By applying the CoordRecongruit algorithm, the corresponding numbers of inner points could be calculated, which were 89,209, 156,928, and 146,626. Figures 7 and 10a,b, respectively, provide the distributions of the 10,000 random targets in City 1, City 2, and City 3. In addition, some information, such as the area and population of the cities, can be merged for various applications. These cities cover about 272, 2215, and 2595 km\({}^{2}\) and contain populations of about 2.65 million, 2.82 million, and 2.77 million people, respectively. In addition to the numbers of targets in specific areas, the average number per square kilometer or the average number of targets per million people in each area may be more helpful information. Table 2 provides the densities with geographical information. For the area density per square kilometer and the population density per million people, City 1 has 328 and 33,644, respectively, City 2 has 71 and 55,648, respectively, and City 3 has 56 and 52,804, respectively. Regarding the area density, City 1 is the first, City 2 is the second, and City 3 is the third. In terms of population density, City 2 is the first, City 3 is the second, and City 1 is the third. If the targets are people who need to be managed during an epidemic, these data may be used to manage the epidemic in real time.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \# & **Target** & **Area** & **Population** & **Area Density** & **Population Density** \\ \hline City 1 & 89,209 & 272 & 2.65 & 328 & 33,664 \\ City 2 & 156,928 & 2215 & 2.82 & 71 & 55,648 \\ City 3 & 146,266 & 2592 & 2.77 & 56 & 52,804 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Targets in cities with their geographical information. For a targeted city, the “target” represents the number of targets, “area” provides the area in square kilometers, “population” refers to the population in millions of people, “area density” refers to the number of targets per square kilometer, and “population density” refers to the number of targets per million people.  
Figure 8: The simulated environment. (**a**) The distribution of residents per square kilometer by village; (**b**) the distribution of the targeted points for the simulation.  
In these three examples, the time consumed for each example was only tens of seconds. The results show that our method is capable of handling a large number of targets in a wide range.
## 4 Discussion
### TMDs Prevalence and Etiology
#### 4.1.1 Age and Growth-Related Changes  
TMDs symptoms frequently emerge or fluctuate during adolescence, largely due to rapid craniofacial growth that impacts TMJ structure and function [47; 48]. Structural changes in the TMJ and surrounding musculature during puberty, such as condylar growth and remodeling, are associated with symptoms including joint sounds, muscle tenderness, and restricted mandibular movement [47; 48]. For instance, [PERSON] et al. (2019) highlighted that pubertal development influences TMJ hypermobility and clicking sounds, which are commonly observed during this growth period [47]. These growth-related changes are dynamic, meaning that the TMJ adapts to rapid skeletal and dental development, potentially exacerbating or resolving TMDs symptoms over time [47; 49].  
Adolescence represents a critical window for TMDs development, with pubertal staging often correlating more closely with TMDs symptoms than chronological age [48; 50]. For example, studies have shown that the prevalence of temporomandibular pain increases with advancing pubertal development stages [48; 50]. Recognizing the impact of craniofacial growth during this period is essential for early diagnosis and the development of treatment strategies that accommodate the ongoing anatomical and functional changes in the TMJ [47; 49].
## 3 Devices under Test and Setup
### Test Principle and Process  
This test is mainly used to obtain the relationship between the transient voltage and the change of the laser focal plane position. The specific test process is as follows:  
(1) Determine the zero position of the test:  
In experiments with a multiple energy pulse laser test, we must determine for each device being tested an absolute reference position, using the laser position of the device interface of different materials (generally choosing the wafer surface of the device) with a small speckle method in focus, the specific process is a vertically incident laser focused on the surface of the device with the minimum size and maximum intensity of light. We define this position as \"zero.\" Zero is the absolute position reference of the device under test during each test.  
Since the laser spot is visually focused, some errors between the so-called \"zero\" and the actual interface position are also possible. The principle for determining the laser spot is that the spot has the smallest diameter and the brightest intensity, which corresponds to the most clear reflection caused by the change in the refraction coefficient when the laser beam passes through the covering layer and enters the sensitive area in between. However, since the spot is visibly focused, it is difficult to determine the change in the spot size at the micron level, which means that the error between the zero point and the actual SiO\({}_{2}\)/Si interface should be at the micron level.  
(2) Determine the incident range of the laser scanning area:  
For a particular device, once the zero point is determined, the laser incidence range can be defined according to it. Typically, the laser incident range is within a few hundred micrommeters either side of zero.  
(3) Irradiate the measured area by moving the laser focal plane in small steps within the range (Z-scanning technology):  
After determining the zero point and laser incident range of the device under test, the focal plane of the laser is positioned on one side of the range boundary, and then the focal plane is moved in small steps. The step size varies depending on the device being measured and the diameter of the beam. Typically, the move step is in the range of 0.5-1.0 \(\upmu\)m.  
(4) Test and record the voltage change in the DUT:  
Test the collected voltage in real time after each movement. Specifically, 300 pulses should be recorded after each movement and then averaged at each position point in order to obtain the average at each point.  
(5) Obtain the functional relationship of the voltage change with the focal plane:  
Based on the above measured data, the voltage varies with the laser pulse energy and the focal plane position, as a function. The Y-axis represents the average quantity at each position. Since the laser beam is incident vertically to the surface of the device, the X-axis position is defined as the Z-position, corresponding to the distance traveled along the Z-axis of the DUT. In the data graph, the right side of \(z=0\) indicates that the focal plane of the laser beam moves in the internal direction of the DUT.
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 2 System Model
### AoI Analysis Model  
This subsection presents the AoI analysis model for fused information corresponding to the scenario described in Section 2.1 and describes the evolution of AoI. As depicted in Figure 2, GU \(k\) reserves two queues, \(queue_{1}\) and \(queue_{2}\), for storing the received \(m_{1}\) and \(m_{2}\), respectively. GU \(k\) generates the fused status information \(m_{0}\) using the \(m_{1}\) and \(m_{2}\) stored at the first position of \(queue_{1}\) and \(queue_{2}\) and then discards them immediately. Moreover, define the AoI of the \(m_{i}\) being stored at the \(j\)th position of \(queue_{i}\) at time \(t\) as \(\Delta_{m_{i}}^{j}(t)=t-u_{m_{i}}^{j}(t)\), where \(u_{m_{i}}^{j}(t)\) is the timestamp. Once GU \(k\) generates a new \(m_{0}\), the \(m_{i}\) stored at the head position of \(queue_{i}\) is discarded.  
The AoI of the latest fused status information \(m_{0}\), abbreviated as AoI\({}_{0}\), evolves as follows.  
\[\text{AoI}_{0}(t)=t-u_{m_{0}}(t)+\max\Bigl{\{}\Delta_{m_{i}}^{1}(u_{m_{0}}(t)) \ |\ i\in\{1,2\}\Bigr{\}}, \tag{9}\]  
where \(u_{m_{0}}(t)\) is the timestamp of the latest \(m_{0}\) before time \(t\). Equation (9) indicates that AoI\({}_{0}(t)\) is reset to the larger value between \(\Delta_{m_{i}}^{1}(u_{m_{0}}(t))\) and \(\Delta_{m_{2}}^{1}(u_{m_{0}}(t))\) at the completion instant of an \(m_{0}\) generation. Define \(M_{i}\) as the number of transmitted bits for each \(m_{i}\). Typically, \(M_{i}\sim\exp(1/\overline{M_{i}})\) with mean \(\overline{M_{i}}\)[42], leading to the transmission time \(\Delta t_{i}\) for each \(m_{i}\), where \(\Delta t_{i}\sim\exp(1/\overline{\Delta t_{i}})\) with mean \(\overline{\Delta t_{i}}=\overline{M_{i}}/R_{i}\). Therefore, the updating of \(m_{1}\) and \(m_{2}\) can be modeled as two different Poisson processes with rate \(\lambda_{1}=R_{1}/\overline{M_{1}}\) and \(\lambda_{2}=R_{2}/\overline{M_{2}}\). Additionally, \(m_{0}\) is generated by GU \(k\) as the Poisson process with rate \(\mu\).
## 3 Results and Discussion
### Biochemical Oxygen Demand (BOD\({}_{5}\)), Nitrogen, and Phosphorus
#### 3.2.1 Biochemical Oxygen Demand  
Over the reported four-week period, the vermifiltration unit showed the ability to remove between 66 and 83% of the influent BOD\({}_{5}\), which is consistent with removal values reported in the literature for various wastes [19; 20; 21; 22]. Although fluctuations were observed, the unit showed the capacity to remove close to 80% of BOD\({}_{5}\) at 28, 42, and 49 days of operation. According to the Portuguese law, the maximum allowed BOD\({}_{5}\) value for discharge from a wastewater treatment plant is 25 mgO\({}_{2}\)/L, and the removal efficiency should be at least 70% [50]. The observed removal of BOD\({}_{5}\) by vermifiltration generally met the national legal requirements. A further significant decrease of organic matter measured as BOD\({}_{5}\) was observed in the hydroponic unit after the initial removal by vermifiltration, showing a positive contribution of this additional treatment stage to the decrease of wastewater organic contamination.
## 4 Results and Discussion
### Flexural Performance of Fiber-Reinforced Geopolymer Concrete
#### 4.3.3 Energy Absorption Capacity (Toughness)  
The fibers used in the structure of concrete enhanced the mechanical properties of concrete like the control of crack initiation under loading and the fracture toughness of concrete. Toughness showed the capacity of fiber in flexural test by energy absorption, control crack initiation and propagation. Toughness was obtained by calculating the total area under the load-deflection curves as described in ASTM C1609 [23]. The effect of two fiber types with three volume fractions on toughness is illustrated in Figure 20.  
With all specimens generated with fiber, the toughness increases with the increase in the volume fraction. For the specimens using polypropylene fiber, the value of toughness was from 7.174 to 13.461 N.m. The variation of toughness depended on the type of polypropylene fiber and volume fractions. With series P2 and 1.5% fiber, the toughness had the maximum value of 13.461 N.m. Similar to P series, the H series geopolymer concrete generated with steel fiber also had the same effect. The highest toughness of HFRGC was 24.191 N.m when added with 1.5% of fiber into the concrete mixture. Generally, HFRGC had always more flexural behavior than PFRGC, with a great difference of toughness, about 44.36%, in accordance with 24.191 and 13.461 N.m.
## 4 Experiments and Results
### Data
#### 4.1.2 Variables Selection  
We can obtain information on the factors that affect construction costs at the sites mentioned above. These factors contain information on independent variables--gross floor area (m\({}^{2}\)), site area (m\({}^{2}\)), building area (m\({}^{2}\)), building height (m), typical floor height (m), the number of floors, the number of base floors, the number of parking spaces, the use of the building, etc. The number of independent variables is important, as opposed to learning parameters, and it directly affects the estimation model's accuracy and computational efficiency [2]. Therefore, after calculating the Pearson correlation coefficient, independent variables with low correlation coefficient values were excluded. Finally, six independent variables--gross floor area, building area, building height, the number of floors, the number of base floors and the number of parking spaces--were used in the experiment. Construction cost is expressed in the unit of KRW 1 million because the numerical scale is large when expressed in won. The descriptive statistics of the independent and dependent variables are given in Table 1.  
Figure 2: Distribution of collected construction cost data.
## 3 Modelling Framework
### Traction-Separation Law  
The failure mechanism for damage modelling consists of two steps in Abaqus: damage initiation criterion, and damage evolution. The traction-separation relationship has two zones: the linear elastic traction zone, and the damage evolution zone (softening), see Figure 4. The elastic behavior of a cracked element is expressed in terms of an elastic constitutive matrix that connects normal and shear stresses to normal and shear separations. In the case of 3D, the nominal traction stress vector (\(t\)) is composed of three components: \(t_{n}\), \(t_{s}\), and \(t_{t}\), which represent the normal and shear tractions in two directions, respectively. The corresponding separations are denoted by \(\delta_{n}\), \(\delta_{s}\), and \(\delta_{t}\). The elastic behavior can then be written as:  
\[\mathbf{t}=\left\{\begin{array}{c}\mathbf{t_{n}}\\ \mathbf{t_{s}}\\ \mathbf{t_{t}}\end{array}\right\}=\left[\begin{array}{ccc}\mathbf{K_{mn}}&0&0\\ 0&\mathbf{K_{ss}}&0\\ 0&0&\mathbf{K_{tt}}\end{array}\right]\left\{\begin{array}{c}\mathbf{\delta_{n}}\\ \mathbf{\delta_{s}}\\ \mathbf{\delta_{t}}\end{array}\right\}=\mathbf{K}\mathbf{\delta} \tag{2}\]  
The elastic response is governed by the stiffness \(K_{mn}\), \(K_{ss}\), and \(K_{tt}\), the so-called penalty stiffness, whose value is calculated as a function of the two adjacent material stiffnesses [22]. It has been proved that its value has no influence on the overall sample stiffness [23] and their values have been taken at 1 \(\times\) 10\({}^{6}\) MPa/mm in this study. Furthermore, the normal and shear penalty stiffnesses are assumed to be decoupled, meaning that a pure normal opening of the interface does not produce shear forces, and vice versa [3]. It is assumed that the initial response is linear [17]. However, damage can be represented by various damage evolution laws, such as a linear and nonlinear traction-separation response, once a  
Figure 3: The modelling techniques.  
damage initiation criterion has been met. In this study, the bilinear traction-separation law will be used, see Figure 4.
## 5 Applications of Wireless EM Energy Transfer and Propagation in SCs
### IPT Batteries Charging in EVs in SCs
#### 5.2.3 RA and OH Approaches in SC Context  
The wireless IPT charging of EVs in an SC-friendly manner is appropriate for shared and autonomous EVs, buses, trans, ships, medium-duty delivery trucks, drones, etc. IPT charging modes may be full static (in public stations or at home), full dynamic (electric roads), mixed dynamic-static mode, discontinuous electric roads, or split static charging points. A full static mode corresponds to a limited range and requires high battery storage. A mixed dynamic-static mode is mostly suitable for highways and necessitates moderate battery storage. An intermittent dynamic mode matches a partially fixed trajectory, entails low battery storage, and is appropriate for public mobility with road portions having challenging charging infrastructures. The mode using distant static charging points relates to electric buses charging at their stops, for which the battery storage depends on the number of stops, the distance, and the static duration. All these charging modes entail security protections against EMF exposure.  
RA and OH approaches intend, for the different charging modes, an appropriate eco-design, clean energy use, reduction of harmful effects, risk evaluation, and safeguarding of biodiversity. Furthermore, the management of energy between the EV and the grid might help the RA. Therefore, we can use control algorithms for grid-to-vehicle (G2V) and vehicle-to-grid (V2G) operating modes [90; 91]. Additionally, it is required to guarantee the interoperability of different IPTs (ground and vehicle sides) [82; 92] and deliver a fitting charging profile allowing for better RA [93]. In addition, connectivity and autonomous driving capability will make EVs much safer [94].
## 3 Results and Discussion
### Adsorption Method
The treated AMD sample had the pH value of 7.2, and finally contained about 35 mg L\({}^{-1}\) of Mn, 427 mg L\({}^{-1}\) of Ca and 1650 mg L\({}^{-1}\) of sulfur. Then, the Mn removal test with 5 g of chitosan as an adsorbent was conducted. Figure 7 shows change in the Mn concentration before and after the adsorption removal test using 5 g of chitosan. The results of Mn concentration in the simulated solutions are also presented in Figure 7. Due to the adsorption into chitosan, the Mn concentration in the simulated solutions reduced to around 2 mg L\({}^{-1}\). This result was equivalent to approx. 90 mass % of Mn removal. Investigations are continuing by incubating the chitosan in the treated AMD water for 24 h. As a result, the Mn concentration decreased to about 100%. Figure 5: Effect of input amounts of chitosan on Mn removal from the simulated solutions at pH 6.7 and 7. Figure 6: Change in Mn concentration and the amount of Mn removal at pH 6.7 and 7 as a function of input amount of chitosan. 10 mg L\({}^{-1}\), higher than that in the simulated solutions (Figure 7). It was considered that this result might be caused by the co-existing elements such as Ca, S, etc. Actually, 44 mass % of Ca and 50 mass % of S were also removed from the treated AMD. Sulfur content is mainly derived from sulfate ion (SO\({}_{4}\)\({}^{2-}\)). It was confirmed that chitosan contributed to the removal of not only Mn but SO4\({}^{2-}\) from the treated AMD at below pH 8. Chitosan derives from external skin of Crustacea such as crab shell which corresponds to an inedible part. It is suggested that wastes from food processing industries for marine products, etc. can be used for chitosan. However, the removal efficiency leaves much to be desired. In this study, the adsorption removal capacity was mainly investigated. Some approaches will be required to advance the efficiency and apply to the actual operation. For that, it is considered that a deacetylation degree of chitosan will be improved, and that chitosan hydrogels will be prepared with some other materials which have the higher adsorption capacity, and so on.
## 5 Methodology
### Data Analysis  
We analyzed experimental data in quantitative and qualitative ways. First, we quantitatively compared the following.  
* Task completion time: The seconds to point out all playback positions corresponding to three given contexts.
* Accuracy: The percentage of the correct playback positions against the ground truth. We regard the position marked by the participant when it is within five sentences from the ground truth.
* Usability: _System Usability Scale_ (SUS) score on 10 5-point Likert scale items [43].  
We conducted one-way repeated measures ANOVA (RM ANOVA) to compare the three methods on the task completion time, accuracy, and usability. By [PERSON]'s test, we found all the variables do not violate the assumption on the homogeneity of variance. When the ANOVA result is significant, we conducted a series of paired \(t\)-test (two-tailed) with Bonferroni correction as a posthoc test. All the hypothesis testing was performed with a significance level of 5%.  
On the other hand, the survey responses to the open-ended questions were qualitatively analyzed. In the survey, the participants were asked to describe limitations in auditory information browsing without visual supports and compare their browsing experience with the browsing methods.
# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 1 Introduction
Here, AI/ML-based networks can facilitate: (i) resource allocation and network reconfiguration [10], (ii) system response and resilience and anomaly detection [6], and (iii) vulnerability prediction, leveraging digital twins [11]. Likewise, ML, being a category of AI, offers strong predictive capabilities to wireless networks and leads to autonomous network operation. In this sense, ML algorithms are categorized into the following: (i) supervised learning, being appropriate for labeled datasets, as training relies on predefined samples (i.e., input/output pairs); (ii) unsupervised learning, suitable for cases with unlabeled datasets and capable of extracting patterns and performing classification, based on to the relative positions of the associated data points [12]; (iii) reinforcement learning (RL), where intelligent agents interact with the environment and the transition to a new state is based on the action that maximizes a predefined reward [13]. In the case of RL, a high number of state-action pairs might exist, and in practice, neural networks are trained to define the best possible action per state, giving rise to deep reinforcement learning (DRL) algorithms. Nonetheless, conventional centralized ML cannot always be applied in heterogeneous 6G settings, mainly due to the following: (i) excessive data communication overhead and bandwidth limitations since centralized ML is based on the centralization of massive data through transmissions from diverse sources, (ii) increased energy consumption, related to data transfers, (iii) a high number of transmission errors due to path-loss and fading, and (iv) data privacy concerns, as data may carry sensitive user information. As a result, decentralized ML paradigms have been introduced to tackle these issues and enable the smooth integration of ML in communication networks. In recent years, the concept of federated learning (FL) has emerged as well, where ML models are trained in a decentralized and distributed fashion. To this end, each participating node in the FL process trains a local model based on the available dataset. Afterwards, the ML model parameters (e.g., weights in a neural network) are periodically sent to a master FL server for aggregation and inference in all the participating nodes. Hence, a dual goal can be achieved since, on the one hand, transmission of privacy-sensitive data does not take place, and on the other hand, faster convergence times can be achieved due to the parallel training of individual models [14; 15].
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
## 1 Introduction
### Solar Photovoltaic System  
The energy output of a solar photovoltaic (PV) system can be obtained by the following steps [11]:  
1. Estimate the area of the photovoltaic panel.
2. Evaluate the tilt angle of the photovoltaic panel.
3. Use data from the U.S. National Aeronautics and Space Administration (NASA) [12] to evaluate solar radiation at latitudes and longitudes.
4. Collect the conversion efficiency of solar photovoltaic panels and calculate the output of the panels in kilowatts.
5. Take the average of the temperature at that location, and then calculate its impact on the photovoltaic panel.
6. Use Equation (1) to calculate the solar panel's energy output: \[\textit{Solar Panel Output}=\textit{Solar Array Area}\times\textit{ Conversion Efficiency}\times\textit{Solar Radiation}\] (1)  
The costs of different types solar photovoltaic tables were gathered in an online store [13] and are summarized in Table 2.