esa-sceva/satcom-chunk-collection huggingface dataset:
# DK-SLAM: Monocular Visual SLAM with Deep Keypoint Learning, Tracking, and Loop Closing
## 4 Experiments  
This section discusses the implementation details and evaluates the pose estimation performance of our proposed DK-SLAM on two widely adopted public datasets: the KITTI dataset, representing the car-driving scenario, and the EuRoC dataset, representing drone navigation. Moreover, we conducted an extensive ablation study to validate the effectiveness of our proposed key modules, including learned feature extraction, coarse-to-fine matching strategy, and online learning-based Bag-of-Words (BoW).
## 5 Results and Analysis
### Urban (Scenario I)
#### 5.1.4 Carrier Aggregation  
Implementing carrier aggregation between 3.5 GHz and 28 GHz can further reduce the number of sites. For carrier aggregation, the macro-RRUs are randomly moved to appropriate micro-RRUs iteratively without compromising coverage, capacity constraints, or QOS. For Scenario I and Scenario II, two extra macro-RRUs sites are needed to have at least a coverage of 98%. A total of 10 macro-RRUs are needed, but the total number of RRUs sites is reduced by eight.
## 3 Results
### Muscle Activities
#### 3.2.1 Grade Effects  
The mean EMG signals of the hip, knee, and ankle extensor muscles increased generally during most of the slope walking, especially during the 10\({}^{\circ}\) slope walking (GM, \(p=1.32\times 10^{-8}\); HA, \(p=2.33\times 10^{-16}\); RF, \(p=0.2\times 10^{-5}\); and GA, \(p=6.74\times 10^{-9}\)). Compared to the level walking, these increases were statistically significant for GM, RF, and AT at the 10\({}^{\circ}\) grade and for HA and GA at the 5\({}^{\circ}\) and 10\({}^{\circ}\) grades (Figure 2). Compared to the level walking with the same loads, the mean EMG of GM increased greatly during walking at the 10\({}^{\circ}\) grade by 46% to 207%, HA increased by 110% to 226%, RF increased by 44% to 203%, AT increased by 48% to 68%, and GA increased by 30% to 100% (shown in Table S2).  
As the grade increased, the muscle activity of the hip extensor muscles (GM and HA, especially GM) increased both in the activation value and duration (Figure 3a). The maximum activation value of the GM changed from 0.52 to 1.24, and the activation duration increased from about 28% to 48% of one gait as the grade increased. The maximum activation value of the HA changed from 0.52 to 0.81, and the activation duration changed from 20% to about 45% of one gait as the grade increased.  
The muscle activity of the knee extensor muscle (RF) increased most in the activation value, shown in Figure 3b. The maximum activation value of the RF increased from 0.82 to 1.60 during the early stance stage and from 0.4 to about 0.9 during the early swing stage.  
As expected, the muscle activity of the ankle extensor (GA) increased considerably in the muscle activation value (Figure 3c). The maximum activation of the GA during the median and the late stances increased highly, from 0.52 to 1.07. Compared to the ankle extensor, the muscle activity of the ankle dorsiflexion (AT) increased slightly, from 0.48 to 0.57 during the early stance stage and swing stage.
## 2 Many-Objective Optimal Power Flow (MaOPF) Problem
### Objective Functions
#### 2.1.4 Voltage Stability Index  
To enhance the system reliability, the voltage stability index (VSI) is improved to verify system stability and prevent the system from voltage collapse. The L-index is one of the voltage stability indices used to observe the voltage collapse proximity. The value of the L-index is between 0, indicating no-load condition, and 1, expressing voltage collapse. So, if the L-index value is minimized, the voltage stability of the system is enhanced. The L-index can be computed as the following equation:  
\[L_{j}=\left|1-\sum_{i=1}^{N_{g}}F_{ji}\frac{V_{i}}{V_{j}}\right|\,,\,j\in \alpha_{L} \tag{7}\]  
where \(\alpha_{L}\) is the set of load buses; and the values of \(F_{ji}\) can be received from the matrix \(F_{LG}\) as follows:  
\[F_{LG}=-[Y_{LL}]^{-1}[Y_{LG}] \tag{8}\]  
To find the admittance matrix, consider a system consisting of \(N\) buses and \(N_{g}\) generators. The network equation between voltage and current is found as presented below:  
\[\begin{bmatrix}I_{L}\\ I_{G}\end{bmatrix}=\begin{bmatrix}Y_{LL}&Y_{LG}\\ Y_{GL}&Y_{GG}\end{bmatrix}\begin{bmatrix}V_{L}\\ V_{G}\end{bmatrix} \tag{9}\]  
where \(I_{L}\), \(I_{G}\), and \(V_{L}\), \(V_{G}\) are the currents and voltages at the load buses and generator buses.  
Thus, the maximum L-index value from all load buses is selected as the objective function to be minimized as the following equation:  
\[f_{4}(x,u)=\max\big{(}L_{j}\big{)} \tag{10}\]  
where \(f_{4}\) is the maximum L-index value of all load buses; and \(L_{j}\) can be computed by (7).
# A Review of Research on Diagnosability of Control Systems Based on Structural Analysis
## 2 Diagnosability Architecture for Control Systems Based on Structural Analysis
### Fault Diagnosis Based on Structural Analysis
#### 2.2.3 Fault Diagnosis Process Based on Structural Analysis  
As shown in Figure 2, the steps of fault diagnosis based on structural analysis are as follows:  
Step 1: Establish the system behavior model. Based on the structure and function of the system, and based on FMECA, establish the behavior model.  
Step 2: Establish the structure model. Based on the behavioral model, analyze the constraint relationship between the constraint equations and variables. Then, describe the structure model as a bipartite graph or incidence matrix.  
Step 3: Analyze the structural characteristics. Based on the structural model, use the matching of the bipartite graph or DM decomposition to obtain the structurally overdetermined subsystem. Based on the matching, the matching ties of the unknown variables are found, and the unmatched constraints are identified to construct the ARR.  
Step 4: Find the set of MSOs. Through DM decomposition, find the structurally overdetermined subsystem and design the MSO optimization algorithm to solve the MSO set.  
Step 5: Analyze the fault diagnosability. Analyze the structural properties of MSOs containing the ARR. Establish a sensitive relationship between the faults and MSOs. Then, qualitatively analyze structural detectability and isolability.  
Step 6: MSO Optimization. Based on the sensitive relationship between MSOs and the faults, the appropriate set of MSOs is optimized, and the residuals are constructed.  
Step 7: Diagnosis strategy design. The faults are detected and isolated based on the sensitive relationship between the residuals and the faults.
## 2 UAM Vehicle and Airspace Environment Analysis
### Establishment of Assumptions for UAM Flight Operations  
Since UAM operating environments have not yet been fully developed or established to operate in low-altitude environments, operational assumptions are needed for this study. The assumptions for UAM vehicles to avoid collisions with other aircraft (current airplanes and unmanned aerial vehicles) and to operate safely are as follows [4]:  
* Operations are carried out within the scope of established laws and policies.
* For safe operations, stakeholders also do their best to maintain safety and security.
* The UAM vehicle is designed and developed to be able to operate within certain airspaces and to satisfy required performances.
* It satisfies the communication navigation surveillance and information (CNSi) performances that are built for UAM operations by CNSi infrastructure agency.
* UAM can operate both controlled and uncontrolled zones within the range that allows strategic and tactical collision avoidance and separation. However, real-time information sharing should be available within ATM, UTM, and UATM.
* Various systems, infrastructure, and policies for UAM are flexibly applied according to the maturity level of development.
* During UAM operations, a situation must be considered in which all or part of the operating system (e.g., UATM systems, etc.) can suddenly shut down, so a backup system and an alternative system need to be installed.  
In addition, for this study, specific assumptions are required for the conceptual design of holding areas for UAM vehicles. The current holding area is established at a high altitude not affected by obstacles, but holding areas for UAM vehicles are located in places where there are many obstacles and terrain constraints, so more conditions should be considered. Therefore, assumptions for UAM vehicles to enter the holding area and perform the procedure have been established as follows [6]:  
Figure 3: Airspace interaction concept [4].  
* Holding areas are established in sections where the turning radius is permitted.
* UAM vehicles that satisfy the required performance can enter the holding area.
* When using the UAM corridor while cruising, autonomous flight is given priority, but the pilot can control the vehicle manually in the case of an abnormal situation.
* The UAM corridor size is based on CORUS' RUNP-50m.  
The assumptions about the concepts of UAM operations and holding areas established through this study can be changed as the concepts are expanded on in the future.
# Influence of Applied Loads on Free Vibrations of Functionally Graded Material Plate-Shell Panels
## 2 Formulation of the P-FGM Model  
An FGM is made by mixing two distinct isotropic material phases, for example, a ceramic and a metal. In this work, the material properties of an FGM plate/shell structure are assumed to change continuously throughout the thickness (Figure 1), according to the volume fraction of the constituent materials, given by the power law function (Bao and Wang [14]). In addition, the continuous variation of the material mixture is approximated using a certain number of virtual layers, k, throughout the thickness direction-layer approach. The volume fraction of the ceramic and metal phases for each virtual layer is defined according to the power law:  
\[V_{c}^{k}=\left(0.5+\frac{\overline{z}}{h}\right)^{p};\ V_{m}^{k}=1.0-V_{c}^{k} \tag{1}\]  
where \(\overline{z}\) is the thickness coordinate of the mid-surface of each layer and p is the gradient index of the power law.  
Once the volume fractions of the ceramic and the metal, \(V_{c}^{k}\) and \(V_{m}^{k}\), are defined, [PERSON]'s modulus \(E\), the shear modulus G, the Poisson ratio \(\
u\), or the mass density \(\rho\) of each virtual layer \(k\) of the FGM plate/shell can be determined:  
\[\begin{array}{l}E_{k}=V_{c}^{k}E_{c}+V_{m}^{k}E_{m};\ G_{k}=V_{c}^{k}G_{c}+V_ {m}^{k}G_{m}\\ v_{k}=V_{c}^{k}\,v_{c}+V_{m}^{k}\,v_{m};\,\rho_{k}=V_{c}^{k}\,\rho_{c}+V_{m}^{k }\,\rho_{m}\end{array} \tag{2}\]
# RDSP: Rapidly Deployable Wireless Ad Hoc System for Post-Disaster Management
## 3 System Architecture of the RDSP Scheme
Therefore, relay \(a_{1}\) sends the request message to relay \(a_{2}\). Afterwards, relay \(a_{2}\) sends it to relay \(a_{3}\) having ID 1. Finally, relay \(a_{3}\) sends the request message to server S having ID 0. Figure 7c shows how the Ack message is sent back to the client device from server S. According to Algorithm 1, server S sends back the acknowledgment message to relay \(a_{3}\). As described in Algorithm 4, if the acknowledgment message is received, the relay device sends the received acknowledgment message to a neighbor having \(Max_{ID}\). Therefore, relay \(a_{3}\) sends the acknowledgment message to relay \(a_{2}\) having ID 2. Afterwards, relay \(a_{2}\) sends it to relay \(a_{1}\) having ID 3. Finally, relay \(a_{1}\) sends it to client C.  
Figure 7: \(Cont.\)
## 5 Data Analysis and Result
### Result  
#### 5.3.1 Hypothesis Test  
Table 5 illustrates the results of the multiple linear regression analyses and provides insight into the relationship between the stage factors and the overall co-creation experience. The VIF values of the factors in each stage are less than 5, indicating that there is no multicollinearity in the regression model. The factors in each stage have a causal relationship with the co-creation experience of the Location-based AR games, which affects the overall co-creation experience to varying degrees. Before the co-creation experience, we propose that there are positive and meaningful relationships between AR Participation Psychology (\(\beta\) = 0.166, \(p\) < 0.003), Awareness and Knowledge (\(\beta\) = 0.230, \(p\) < 0.001), and Social Relevance (\(\beta\) = 0.150, \(p\) < 0.005) and the overall co-creation experience, with Awareness and Knowledge showing a strong causal relationship.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Desired Values** & \\ \hline CMIN/DF & \textless{}3.0 & 1.536 \\ CFI & \textgreater{}0.90 & 0.950 \\ TLI & \textgreater{}0.90 & 0.944 \\ IFI & \textgreater{}0.90 & 0.950 \\ RMSEA & \textless{}0.050 & 0.046 \\ NFI & \textgreater{}0.80 & 0.870 \\ RFI & \textgreater{}0.80 & 0.854 \\ R2 & \textgreater{}0.30 & 0.379 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model fitness.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean (SD)** & **KMO** & **AVE** & **CR** \\ \hline AR psychological engagement & 3.53 (0.96) & 0.721 & 0.704 & 0.877 \\ Awareness and knowledge & 3.34 (0.98) & 0.824 & 0.701 & 0.902 \\ Social Relevance & 3.58 (1.21) & 0.844 & 0.757 & 0.925 \\ Real-time & 3.49 (1.05) & 0.845 & 0.751 & 0.923 \\ Storytelling & 3.59 (0.86) & 0.815 & 0.712 & 0.907 \\ Interaction & 3.83 (0.93) & 0.804 & 0.715 & 0.908 \\ Immersive & 3.64 (0.96) & 0.828 & 0.698 & 0.901 \\ Generate memories & 3.55 (1.16) & 0.730 & 0.611 & 0.825 \\ Emotional resonance & 3.52 (1.16) & 0.843 & 0.636 & 0.875 \\ Overall co-creation experience & 3.86 (0.70) & 0.710 & 0.725 & 0.887 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Assessment of the construct measurement.  
On-site visitation of Real-Time (\(\beta\) = 0.254, \(p\) < 0.001), Storytelling (\(\beta\) = 0.175, \(p\) < 0.002), and Interaction (\(\beta\) = 0.126, \(p\) < 0.018) had a positive impact on the overall co-creative experience and were accepted based on quantitative results. However, we also found that Immersion (\(\beta\) = \(-\)0.074, \(p\) < 0.172) in this dimension did not have any significant effect, resulting in this hypothesis not being valid.  
The hypothesis for the post-visitation dimension was partially confirmed, where Emotional Resonance (\(\beta\) = 0.160, \(p\) < 0.009) had a significant effect on the overall co-creation experience. However, we found that Generate Memories (\(\beta\) = \(-\)0.089, \(p\) < 0.145) did not hold concerning it.
## 3 Results and Discussion
### Shape Configuration for Analysis of Various VRFB Flow Frames  
The conventional VRFB flow frame, the maximum velocity deviation (\(D_{\mathrm{p}}\)) of the electrolyte entering the electrode reached 11.89%. It means very low non-uniformity. Owing to the S-shaped flow channel of the existing VRFB, the flow velocity of the electrolyte entering the electrode from the inlet channel was the highest at the outlet on the right, and the difference between the velocities decreased toward the left. Because each channel and the electrode were directly attached to 90 outlets and guides, the pressure drop increased, owing to the decrease in the area of the flow channel. As stated earlier, the maximum velocity deviation (\(D_{\mathrm{p}}\)) in the existing VRFB flow frame indicated low uniformity. To address this problem, a flow analysis was conducted for various flow frames and the changes in flow uniformity and the pressure drop were analyzed.  
Two cases were considered to improve the shape of the flow frame, as shown in Figure 13. In CASE 1, the shape of outlets was the same as that in the existing flow frame, as shown in Figure 13a. However, the number of outlets was reduced to 45 and the area per outlet was increased to decrease the pressure drop. In addition, to eliminate the stagnation points close to the curved parts of the flow channel, a 1/4 elliptical shape was applied to enable the electrolyte to flow smoothly. The lengths of the outlets were gradually decreased from the right to left to change the flow channel resistance depending on the locations of the outlets.  
Figure 12: Velocity profile according to electrode position.  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Pressure Drop (Pa)** \\ \hline Inlet channel & 44,320 \\ Electrode & 96,150 \\ Outlet channel & 45,970 \\ Entire region & 186,440 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Pressure drop in inlet and outlet channels, electrode, and entire flow frame.  
In CASE 2, the initial inlet was divided into five sections, as shown in Figure 13b. The flow rate was the same for each section. A mixing zone with the characteristics of a porous medium and high resistance was installed to combine the flow of the electrolyte from each section. In particular, an elliptical structure was installed in the first section to prevent the flow from being discharged vertically and spread it to the left and right. The size of the ellipse is 4 mm in width and 2 mm in length.
## 2 Material and Methods
### Repeatability Analysis  
Autocorrelation analysis was used to study the further part of the signal, in particular the periodic peaks. In the course of analysis, it was noticed that in the case of the discussed anomaly, there are significant peaks in the multiples of the 50 th lag in the autocorrelation. Additionally, as the distance to the damaged element decreases (and thus its loudness increases), the discussed peaks become larger and more distinct. These behaviors are the same for most of the studied frequency bands. Similar results were obtained by the authors of [24], who also related the presence of a peak in the 50 th lag to damage to the rotating element. Figure 7 shows an example of the autocorrelation from the signal for 120 s of the measurement and the frequency band: 8550-8721 Hz.  
Therefore, at this stage of the algorithm, autocorrelation is performed from the signal of each frequency band (\(x(m)\) extracted from spectrogram(\(m,\omega\)) by slicing). Autocorrelation \(z[k]\) is calculated for each of them (Equation (3)) and later smoothed with a moving average.  
\[z[k]=(x(m)*x(m))(k-N+1),\quad\text{for }k\ =\ 0,\ 1,\ \ldots,\ (||x(m)||\cdot 2)-2 \tag{3}\]  
where \(N=|\ |\ x(m)|\ |\ \text{and}\ |\ |\ x(m)|\ |\ \text{is the length of x(m). To find the peaks, a function from the SciPy library was used (find_peaks). In addition, some restrictions were applied: the minimum distance between peaks was used to exclude the peaks, which are too close to each other, and peaks prominence was used to exclude insignificant peaks. In the case of  
Figure 6: Procedure of slicing spectrogram to multiple signals, where each represents signal power in time for one specified frequency.  
Figure 7: Autocorrelation from frequency signal with marked peaks and tested interval.  
peak prominence, the minimum value was equal to the maximum value of autocorrelation for the current signal, divided by 5. Then, the detected peaks were checked for whether any of them were present in the previously defined range of autocorrelation lags (between 45 and 55).
## 1 Introduction
This procedure releases heavy metals into different fractions, including \"exchangeable\", \"bound to carbonates\", \"occluded in Fe-Mn oxides\", \"organic fractions\", and the \"residual fraction\" [58; 59]. In general, heavy metals derived from anthropogenic sources (i.e., Cd, Cu) are present in the more easily extractable fractions, contrary to those derived from lithogenic sources and which are mainly associated with the residual fraction [56; 60]. Thus, sequential extraction approaches are valuable to complement source identification studies. Among aquatic ecosystems, saline lakes are highly vulnerable to heavy metal pollution. These ecosystems usually function as sinks for agricultural, industrial, and urban wastes, acting to some extent as barriers against pollution [61]. Moreover, they are mainly located in closed hydrological systems in arid and semiarid regions. These basins are often closed drainage areas with no outlet other than evaporation. This type of environment, combined with the low precipitation and high evaporation rates typical of arid climates, accumulates and biomagnifies pollutants compared to freshwater systems [62]. Moreover, salinity plays an important role in the bioavailability of labile organic matter and, thus, in the release of chemical species [63]. Importantly, saline lakes are characterized by high primary productivity mainly related to microorganisms adapted to extreme conditions. This yields a wide range of unique biogeochemical features [64; 65; 66; 67]. These environments not only host a rich microbial diversity, but also a high diversity of larger organisms (e.g., birds), which can play an important role in nutrient dynamics [68; 69]. In this study, we compare heavy metal patterns in sediments, soils, and greater flamingo (_Phoenicopterus roseus_) tissues from the Petrola Lake protected area (CentralSpain). The lake is a heavily modified endorheic waterbody that can reach hypersaline conditions during the driest periods. Moreover, it is home to a significant colony of the greater flamingo, which has nested there in recent years [70]. The inputs of pollutants from agricultural sources and urban wastewater to the waterbody affect the water quality of the ecosystem [71; 72]. We hypothesized that heavy metal concentrations in sediments and soils of the study area may exceed the threshold levels for lacustrine environments, which in turn may lead to bioaccumulation of these metals in the food web (i.e., flamingos). Despite its importance, few studies have addressed heavy metal pollution in wetlands from these two angles simultaneously. This study was designed to determine the concentration and identify the potential sources of heavy metals in the Petrola Lake protected area, including both soil/sediments and greater flamingo tissues.
# Cellular and Virtualization Technologies for UAVs:
## 4 Testbed Experimentation
### Indoor Flight with Connectivity Loss  
Once the feasibility of the testbed was demonstrated in the previous Section 4.3, the experiment is repeated with the introduction of a connectivity loss between the UAV and the UAV-controller. UAVs must have some degree of autonomy when communications are lost with the UTM in order to avoid accidents or collisions with other aircrafts, since losing connectivity with this element could be disastrous for the UAV if no alternative method to calculate its flying trajectory is available, and it is important to have in mind that communications depend on an external network that can introduce errors and temporary disconnections to the UTM. In order to reproduce this situation, the interface that provides connectivity to the RPi is switched off. Figure 7c,d shows that the connectivity is lost around second 110.  
In the meantime, the RPi is continuously monitoring the connectivity with the UAV controller. After detecting a connectivity loss, it waits for a predetermined time (set through  
Figure 6: Measurements in indoor flight.  
a programmable parameter) to check if the communications have been recovered. As it is not the case, the UAV performs an emergency landing routine, as shown in Figure 7b. This instruction can be modified by any other procedure, such as a return to home (RTH) instruction, or remain static (hovering) in a certain position waiting for new communications from the UTM. However, it is essential to point out that the UAV must have some autonomy in this decision-making process, ensuring that all these instructions can be safely performed and, in addition, using detect and avoid mechanisms to evade obstacles while executing these instructions.