esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The simultaneous localization and mapping (SLAM) problem for mobile robots has always been a hotspot in the field of robotics. Simultaneous localization and mapping for robots using visual sensors and laser radar is easily affected by the field of view and ground conditions. According to the problems of traditional sensors applied in SLAM, this paper presents a novel method to perform SLAM using acoustic signals. This method enables robots equipped with sound sources, moving within a working environment and interacting with microphones of interest, to locate itself and map the objects simultaneously. In our case, a method of microphone localization based on a sound source array is proposed, and it was applied as a pre-processing step to the SLAM procedure. A microphone capable of receiving sound signals can be directly used as a feature landmark of a robot observation model without feature extraction. Meanwhile, to eliminate the random error caused by hardware equipment, a sound settled in the middle of two microphones was applied as a calibration sound source to determine the value of the random error. Simulations and realistic experimental results demonstrate the feasibility and effectiveness of the proposed method.  
microphone localization; acoustics; SLAM; sound source array; time delay estimation +
Footnote †: journal: Applied Sciences
## 2 Configuration Design of Truss-Link Mechanism  
### Concept Design  
For full deployment of deployable structures with truss-links, the total degrees of freedom (_DOF_) has to be checked to determine whether the structure is over-constrained. If the _DOF_ of the deployable structure becomes negative, the deployment will fail due to over-constraint problems. First, we analyzed the _DOF_ by referring to RADARSAT-1 and RADARSAT-2 satellites using truss-links [13; 14]. Two-dimensional (2D) designs of one payload wing of the satellite were considered, Type 1 (RADARSAT-1) and Type 2 (RADARSAT-2), to analyze the _DOF_, as seen in Figure 1. We employed [PERSON]'s equation, as shown in Equation (1) to evaluate the total _DOF_[22].  
\[SysDOF=3(L-1)-2J_{1}-J_{2} \tag{1}\]  
Here, _SysDOF_ represents the total _DOF_ of the system, \(L\) represents the number of bodies, \(J_{1}\) represents the number of joints with 1 _DOF_ such as a revolute joint, and \(J_{2}\) represents the number of joints with 2 _DOF_ such as a universal joint. As a result, Type 1 has nine _DOF_, and Type 2 has seven _DOF_. The greater number of _DOF_ in Type 1 compared with Type 2 is advantageous for preventing over-constraint but has the disadvantage of making the structure heavier owing to a greater number of links than Type 2. Thus, the deployment simulation model is designed based on Type 2 because it is lightweight due to the small number of truss-links and short total length. The total _DOF_ calculation results are summarized in Table 1.
## 3 Results and Discussion
### Detection of DA  
Next, we investigate the CV response of electrodes in DA solutions. Chitosan/rGO/SPCE, and tyrosinase/chitosan/rGO/SPCE showed distinct oxidation peaks at 0.17 V in 10 \(\upmu\)M DA solution; the response current levels were also much higher than those of SPCE and rGO/SPCE (Figure 2B). Tyrosine/chitosan/rGO/SPCE showed similar with a slight decrease in current compared with chitosan/rGO/SPCE, which was attributed to the fact that immobilized enzyme hinders charge transport along the electrode [44]. The influence of pH on the electrochemical response of tyrosinase/chitosan/rGO/SPCEs toward DA was examined. As shown in Figure S4, the peak current level of DA increased with pH, in the pH range of 4.0-7.0, and decreased with a further increase in pH. Therefore, pH of 7.0 was used in the remaining experiments.  
As shown in Figure 3A, the peak current intensity increased steadily with DA concentrations; this is attributed to the oxidation of DA to o-quinone by tyrosinase. In addition, the peak current level of DA increased linearly with the logarithmic value of DA concentrations in the range of 0.4-8 \(\upmu\)M and 40-500 \(\upmu\)M. Figure 3B shows the linear regression equation and correlation coefficient (R\({}^{2}\)). The detection limit for DA was calculated at 22 nM based on the signal-to-noise ratio (S/N) = 3. Table S1 compares the linear ranges and limit of detection (LOD) of various carbon-based electrochemical biosensors. The electrode developed in this study shows sensitivity equivalent to, or exceeding, those of existing electrochemical sensors for DA detection. Overall, the sensing electrodes fabricated in this study show a broad linear range and a good detection limit.  
Figure 3: (**A**) CV curves of tyrosinase/chitosan/rGO/SPCE in the presence of various concentrations of DA (0.1-500 \(\upmu\)M). (**B**) Calibration curve between logarithmic value of DA concentration and oxidation peak currents of the tyrosinase/chitosan/rGO/SPCE. Error bars represent the standard deviation from triplicate measurements. (**C**) CV of tyrosinase /chitosan/rGO/SPCE at 500 \(\upmu\)M of DA with various scan rates (10-500 \(\upmu\)Ns\({}^{-1}\)). (**D**) Calibration curve between square root of scanning rate and oxidation/reduction peak currents.  
Figure 3C shows the CV results obtained under various scanning rates (in 500 uM DA, in 0.01 M of PBS at pH 7.0). Increase in both reduction and oxidation peak current levels were observed as the scanning rate increased from 10 to 500 mVs\({}^{-1}\). Furthermore, a positive shift in the anodic peak potential and a negative shift in the catholic peak potential were observed with an increase in the scanning rates. Figure 3D shows a linear relationship between the peak currents and the square root of the scan rate, indicating diffusion-controlled electrochemical kinetics. The diffusion coefficient was calculated to be 4.93 \(\times\) 10\({}^{-6}\) cm\({}^{2}\)/s (Table S2).
## 2 Data and Methods
### Data Collection and Processing
#### 2.2.2 MODIS Time-Series Data  
We selected MODIS products from 2007 to 2015 as the other main source of data, which were obtained from the level 1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland ([[https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov)]([https://ladsweb.modaps.eosdis.nasa.gov](https://ladsweb.modaps.eosdis.nasa.gov))). The MODIS land surface temperature (LST) product (MOD11A2) provides daytime temperature with a spatial resolution of 1 km and temporal resolution of eight days [29]. We used the red and near-infrared reflectance bands of MOD09A1 product with a spatial resolution of 500 m to obtain eight-day NDVI (Normalized Difference Vegetation Index) time series [30]. NDVI data with a time resolution of 16 days were obtained from MOD13A2 with a spatial resolution of 1 km [31]. Datasets from MODIS eight-day GPP (MOD17A2H) at 500-m spatial resolution were collected [32]. The bilinear interpolation method was used to resample the MODIS data to a 0.25\({}^{\circ}\) resolution, which was consistent with that of SM data.
###### Abstract  
Oxidative stress is linked with inflammation, diabetic complications, and advanced glycation end products formation. Intake of flavonoid-rich foods has been reported to have a beneficial effect on human health. The aim of this study was to verify the therapeutic potential of _Phyllanthus emblica_ and _Azadirachta indica_ against glycation and other oxidative stress-induced complications such as inflammation using in vitro study. Ethanol extracts of _Phyllanthus emblica_ fruit pulp and dried leaf of _Azadirachta indica_ were prepared to investigate in vitro anti-inflammatory and anti-glycating potentials. In a DPPH assay, the EC\({}_{80}\) value of extract of _P. emblica_ and _A. indica_ was found to be 1532.36 \(\pm\) 0.17 and 1380.61 \(\pm\) 0.27 \(\upmu\)g/mL, respectively. The FRAP value of _P. emblica_ and _A. indica_ extract was 86.6 and 32.12 \(\upmu\)g ascorbic acid/100 mg dry weight of the extract. The maximum percentage of H\({}_{2}\)O\({}_{2}\) scavenging activity was 71.30% and 67.38%, respectively. Extracts of _P. emblica_ and _A. indica_ showed maximum inhibition of heat-induced BSA denaturation by 62.42% and 53.00%, heat-induced denaturation of egg albumin, by 50.84%%/% and 44.31%, and heat-induced hemolysis by 54.44% and 50.21%. Both extracts (600 \(\upmu\)g/mL) significantly reduced the browning, structural changes, aggregation, and AGEs formation. Our biophysical studies confirmed the AGEs formation was inhibiting the potential of extracts. Thus, our findings confirm that these extracts are a rich source of antioxidants and may be utilized to prevent the oxidative stress-induced destruction of biomolecules, glycation, and in the therapy of a variety of health problems, including inflammation. Further, a combination of extracts of _P. emblica_ and _A. indica_ may be extremely useful in preventing and treating health problems.  
reactive oxygen species (ROS); oxidative stress; anti-inflammatory activity; anti-glycating property; anti-diabetic potential +
Footnote †: journal: Applied Sci.
## 5 Challenges and Open Directions
### Balancing Imperceptibility and Detectability  
Achieving a balance between imperceptibility and detectability is a fundamental challenge for watermarking techniques. Ideally, watermarks should remain invisible to human observers while being easily detectable by automated systems. Techniques like Gaussian Shading [22] provide a promising solution by embedding watermarks in the latent space using standard Gaussian distributions, thereby avoiding direct pixel perturbations that might degrade image quality. Additionally, the WaterDiff approach [30] integrates perceptual metrics to ensure minimal perceptual difference between watermarked and non-watermarked content. Furthermore, AEROLADE [52] uses autoencoder reconstruction errors to enhance the detectability of latent watermarks without relying on supervised training, thus improving traceability while preserving imperceptibility. Future work should aim to improve these approaches by integrating perceptual metrics directly into the training process, allowing watermarks to adapt dynamically to different content types while maintaining a consistent level of imperceptibility.
## 2 Methodologies
### Performance of the GAN-SDAE System  
Deep learning can adaptively mine vibration-fault features from the signal spectra. To examine the feature-mining capability of the GAN-SDAE, we used t-SNE to extract the features from the output of the last layer of the GAN-SDAE. The details of the t-SNE algorithm have been documented [46]. Visual analysis of the categories for Label 1 to Label 10 based on the t-SNE is shown in Figure 16.  
Figure 14: The membership function of the generated samples.  
Figure 15: Fault diagnosis results with varying amounts of training samples.  
It is evident that the t-SNE was able to group the faults according to their types and that this method accurately distinguished ten types.  
The performance of the GAN-SDAE was tested using four datasets in different load domains. The diagnostic results from the proposed GAN-SDAE were compared with those from SDAE. They were also compared with the results from classic machine learning algorithms, namely support vector machine (SVM), back propagation networks (BPN), and DAE. The comparisons are shown in Figure 17.  
It is evident that the accuracy of GAN-SDAE for all four loads was above 90% and was higher than the accuracy of the other four methods. The accuracy from SDAE was above 80%, which was higher than other traditional machine learning methods. The SVM performed worst in the comparison, with an average accuracy below 75%. In summary, with the support of GAN, the capability of the SDAE was markedly increased.
# Landsat Surface Product Validation Instrumentation:
## 3 ASD and UAS Technology Assessment
### Accuracy
#### 3.2.6 5% Permaflect Panel Accuracy  
Figure 15 shows the accuracy error results for the 5% Permaflect panel. These results are unexpected in that they are all biased low, whereas the Permaflect 55% panel accuracy errors were all biased high. However, they are consistent in the sense that they shifted consistently on an absolute scale--the EROS results remain lower in value than the SDSU results. In this case, the EROS measurements have larger error than the SDSU measurements and range from approximately 0.003 to 0.007 while SDSU measurement errors are generally 0.003 or less. Surprisingly, the UAS results are the most accurate. However, these results are explained by the notion of accuracy and the traceability paths mentioned previously. In both  
Figure 13: Reflectance measurement accuracy using the Mystery Black panel.  
cases, the Permalflect panels were calibrated using the same traceability path, and, relative to one another, the measurements have the same tendency--that is, SDSU measurements are larger than EROS measurements. For this particular panel, due to unknown causes, there is an overall bias present that has shifted the reference level.
## 3 System Overview
### Basic Notations
#### 3.2.2 Camera Projection  
The grayscale image grabbed at time step \(i\) is denoted by \(\mathbf{I}_{i}\). The measurement of a point \(\mathbf{P}_{C}=[X_{C},Y_{C},Z_{C}]^{\top}\) in the camera oriented coordinate system can be represented by \(\mathbf{p}=[u,v,1]^{\top}\).  
The pinhole camera model is a very common and effective model:  
\[\mathbf{p}=\frac{1}{Z}\begin{bmatrix}f_{x}&0&c_{x}\\ 0&f_{y}&c_{y}\\ 0&0&1\end{bmatrix}\begin{bmatrix}X\\ Y\\ Z\end{bmatrix}=\frac{1}{Z}\mathbf{K}\mathbf{P}=\pi_{c}(\mathbf{P}_{C}) \tag{7}\]  
where \(\mathbf{K}\) represents the camera intrinsics and is generally considered to be a constant that is fixed after the camera is produced. \(\mathbf{K}\) can be obtained by calibration. It describes the 3D point-to-image projection relationship, with the projection function denoted by \(\pi_{c}(\cdot)\).  
It is possible that the depth of the 3D point is lost after projection; on the contrary, if the depth \(Z\) corresponding to measurement \(\mathbf{p}\) is given, then the 3D point \(\mathbf{P}_{C}\) in the camera coordinate system can be computed by an inverse projection function \(\pi_{c}^{-1}(\cdot)\):  
\[\mathbf{P}_{C}=\pi_{c}^{-1}(\mathbf{p},Z)=Z\mathbf{K}^{-1}\mathbf{p} \tag{8}\]
# A Simple Multi-Parameter Method for Efficient Charging Scheduling of Electric Vehicles
## 2 Materials and Methods
### EVs' Charging Scheduling Method
#### 2.2.5 Electrical Network Loading Consideration  
In this study, a tariff can be applied when the distribution network load exceeds 80% of the daily maximum value in order to limit the contribution of the PL to the overloading of the distribution network. The applied tariff is estimated in Equation (21) and the augmented electricity price including the tariff is provided in Equation (22). The electricity price was considered to be the same for EVs' charging.  
\[\text{tariff}(\text{t})=\left\{\begin{array}{c}1+(\text{L}(\text{t})-\ 0.8 \cdot\text{L}_{\text{max}}^{\text{grid}})/  
The proposed charging scheduling method is not applied in scenarios 1 and 2 (SC1, SC2). In scenario 1 (SC1), the EVs are charged with the maximum power of their charging power converter, while in scenario 2 (SC2), they absorb the average power required to achieve their state-of-charge target. Discharging of the EVs and the power limit of the transformer of the PL were not considered as an option in SC1 and SC2.  
In scenarios 3 (SC3), 4 (SC4), 5 (SC5) and 6 (SC6), the proposed charging scheduling method was applied. In SC4 and SC6, the proposed tariff was applied to the electricity price when the load of the local electric distribution network exceeds 80% of its maximum value. In SC5 and SC6, the power limit of the transformer of the PL was considered, and none of the aforementioned constraints were applied in SC3.  
The nominal power of the PL transformer used in this study has been considered equal to 400 kVA. The PL operation scheduling time period was divided into time intervals of duration, \(\Delta t\) = 15 min. In total, 432 EVs participated in the proposed charging schedule method during the examined 24 h period. The total energy required to satisfy all EVs' charging target (in all scenarios) was 2835 kWh. The major results obtained for the examined operation scenarios are depicted in Figures 8-13.  
Figure 11: PL’s active power in scenario 4 and electricity price.  
Figure 12: PL’s active power in scenario 5 and electricity price.  
Figure 10: PL’s active power in scenario 3 and electricity price.
## 4 Simulations and Experiments
### Simulations  
In order to verify the effectiveness of the proposed method, two parts of simulations are carried out in this section. In the first part, the simulated original-gyro signal, the signal processed by DFBGA and the signal processed by proposed method are compared with the reference signal to verify the ability to suppress gyro drift and maneuvering interference. The signal with phase lag and the signal processed by proposed method are compared with the reference signal to verify the ability to suppress phase lag. In the second part, the simulated original-gyro signal, the signal processed by DFBGA and the signal processed by proposed method is applied as the feedback signal of the control system and the position response is compared with the position command. The model parameters of the LOS control system of the optoelectronic platform in [25] are used as a reference.
# A Novel Capsule Network Based on Wide Convolution and Multi-Scale Convolution for Fault Diagnosis
## 4 Experimental Analysis
### Visualization of Results  
This section visualizes the experimental results of B\(\rightarrow\)A in the generalization experiments. Specifically, for all samples of the dataset A, the feature expressions of all the convolutional layers and capsule layers are reduced to a two-dimensional distribution by means of t-distributed stochastic neighbor embedding (t-SNE), as shown in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Algorithms** & **A\(\rightarrow\)B** & **A\(\rightarrow\)C** & **B\(\rightarrow\)A** & **B\(\rightarrow\)C** & **C\(\rightarrow\)A** & **C\(\rightarrow\)B** & **Average** \\ \hline WMSCNN-I & 94.48\% & 83.79\% & 95.86\% & 93.10\% & 88.10\% & 89.83\% & 90.86\% \\ \hline WMSCNN-II & 94.31\% & 83.97\% & 91.72\% & 88.45\% & 82.93\% & 86.38\% & 87.96\% \\ \hline WMSCCN & 95.17\% & 97.24\% & 98.79\% & 96.55\% & 93.10\% & 97.76\% & 96.44\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results of the generalization of the capsule network.  
Figure 6: Visualization of all the samples of dataset A in each layer of WMSCCN after t-distributed stochastic neighbor embedding (t-SNE) dimensionality reduction. (**a**) Visualization of all the samples of dataset A in conv1 layer after t-SNE. (**b**) Visualization of all the samples of dataset A in conv2 layer after t-SNE. (**c**) Visualization of all the samples of dataset A in the multi-scale convolutional layer after t-SNE. (**d**) Visualization of all the samples of dataset A in the primary capsule layer after t-SNE. (**e**) Visualization of all the samples of dataset A in the digit capsule layer after t-SNE.  
Experimental results show that as the network structure deepens, test samples of the same fault gradually gather and eventually become independent clusters. In the expression of the multi-scale convolutional layer, the expressions of OuterRace-0.021 fault, InnerRace-0.014 fault, and InnerRace-0.007 fault are linearly inseparable at this layer; after the primary capsule layer, the characteristic expressions of the three types of fault are linearly separable. This shows that the model's non-linear expression ability is constantly increasing. In summary, the WMSCCN model maps inseparable features to a non-linear separable space, effectively identifying different fault types.
## 4 Simulation Results and Discussion
### Experimental Results & Discussion
Furthermore, the response time for processing the computation tasks with respect to different data sizes is shown in Figure 5. This figure shows that the time gradually increases as the data size increases. In addition, there is a significant gap between the two scenarios. Figure 3: Response time in relation to the different number of users. Figure 2: Energy consumption in relation to the different number of users. our model and the other policies, which increases as the data size increases. In addition, the UAV's server executions policy nearly reaches the cloud execution at a data size of 45 MB, and will then exceed this for larger data sizes. This variation is explained as follows. Increasing data sizes cause an increase in communication time, which then results in increased energy consumption. Nevertheless, our model can be adapted to process the tasks at best locations, UAVs, cloud, or locally, thereby optimizing the energy and time. Figure 4: Energy consumption in relation to different values of input data size. Figure 5: Response time in relation to different values of input data size. Finally, the consumption of energy and response time for processing the tasks in relation to different numbers of UAVs is presented in Figures 6 and 7, respectively. It is shown from these two figures that the local and cloud execution scenarios are not affected by the number of UAVs, whereas the energy consumption and response time of the other three scenarios (i.e., UAVs server, model in [39], and proposed model) gradually decrease as the number of UAVs increases. In addition, our model can achieve a lower energy consumption and response relative to the other two scenarios. This is because the costs in terms of energy consumption and response time are decreasing as the mobile users are allocated more resources (i.e., by increasing the number of UAVs), whereas the local execution and cloud execution scenarios do not depend on UAV resources. Figure 6: Energy consumption in relation to different number of UAVs. Figure 7: Response time in relation to different number of UAVs.
# A Decision Framework for Choosing Telecommunication Technologies in Limited-Resource Settings
## 3 A Decision Framework
### Adopting a Business Approach
In circumstances of higher population density cellular infrastructure will develop organically and eventually have a lower cost than any other option. Reasons for no presence of a local carrier could be either low population density or inhospitable terrain, for example, rugged mountains located in low-resource settings, or both. In both situations the most common choices are the following technologies:  
1. TV white spaces (TVWS)
2. Long Distance Wi-Fi (WiLD)
3. Satellite  
The choice between above technologies will depend mostly on the funding capabilities and the physical terrain. Physical environment, such as geographical layout is particularly important when deciding between using WiLD or TVWS, since WiLD networks are comprised of point-to-point wireless links that use high-gain directional antennas with a line of sight (LOS) over long distances (10-100 km) [28]. The cost differences are also a significant factor, mainly because WiLD does not require a license, while TVWS might. Figure 2: Key partners.