esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 2 Materials and Methods
### Data Processing  
Raw marker data were processed in Qualisys Track Manager [14] with trials manually screened, markers labelled and exported to Visual3D (V3D) [15]. A model template based on the PiG model was built for each trial based on V3D guidance [16], which ignored any redundant markers that were placed on the body as required by the CGM2 model. A model template based on the CGM2.3 model was also built for each trial based on pycgm2 [3] and Vicon ProCalc guidance [17]. Unique model templates for each trial were constructed to ensure that differences in marker placement were also measured in static calibration files.  
Raw marker positions were filtered using a low-pass, 4 th order Butterworth filter at 6 Hz. A 3 rd order polynomial gap filling was performed on all gaps in the data <10 frames. No gaps greater than 10 frames were present. Heel strike (HS) and toe-off (TO) were defined as described by [PERSON] et al. (2008) [18]. The start and end of each gait cycle was defined by the heel strike of the foot with each cycle normalised to 100%. Segments and joint centres were constructed and defined in V3D based on static standing trials. Ankle, knee and hip joint angles were defined as the orientation of the distal segment relative to the proximal segment. The cardan sequence xyz was used in line with ISB recommendations [19]. Discrete measures of left and right ankle, knee and hip joint flexion/extension angles were calculated at maximum (Max), minimum (Min), TO and HS of each gait cycle completed within the 120 s trial.  
Two methods were used to define the ankle angle in the PiG model. Firstly, the standard PiG method was employed, as described by [PERSON] (2022) [16] (Figure 1a). Secondly, an alternative method defined the foot segment with the proximal joint centre as the ankle joint centre (AJC), the distal joint centre as the toe and the lateral ankle marker as the lateral marker to define the segment orientation (Figure 1b). Reliability of these two approaches were compared.
## 6 Example and Validation  
Consider a bidirectional 500 W single-phase grid-connected converter shown in Figure 9 (the downstream DC-DC converter is represented by the power load element \(p_{L}\)[55]) operating at a switching frequency of 30 kHz with \(L=3.5\) mH. The value of the DC link capacitance was set to \(C_{DC}=385\) uF [22] and the DC link voltage set point was selected as \(V_{DC}^{*}=400\) V [51]. The desired performance merit pair was selected as \(THD^{*}=0.05\), \(PM_{V}^{*}=40^{\circ}\). The grid frequency uncertainty was assumed to be 1%, i.e., \(\alpha\in[\alpha_{\min}=0.99\), \(\alpha_{\max}=1.01]\) were considered for both 50 Hz and 60 Hz mains and the corresponding magnitude was set to \(V_{M}=325\) V. According to the process of controller coefficient tuning proposed in the previous section:  
1. \(\beta=7.5^{\circ}\) and \(PM_{V}^{*}=40^{\circ}+7.5^{\circ}\).
2. \(\theta_{n}=1.2\), \(\xi_{n}=0.45\) and \(\lambda=0.066\).
3. \(THD^{*}=0.05\) and \(\alpha_{min}=0.99\).
4. \(\xi_{f}=0.047\) and \(\omega_{n}=2\pi\)-\(45\) rad/s.
5. \(V_{M}=325\) V, \(C_{DC}=385\) uF and \(V_{DC}^{*}=400\) V.
6. \(K=76\) and \(\tau=0.0032\).  
The resulting Bode diagram of the DC link voltage loop gain (cf. (33)) is depicted in Figure 10, indicating a crossover frequency of \(\omega_{CV}=2\pi\)-\(52\) rad/s and a phase margin of \(39.2^{\circ}\) in accurate agreement with \(PM_{V}^{*}=40^{\circ}\).  
It is interesting to compare the expected performance to the one demonstrated in [51]. Here, voltage deviation of \(\sim\)10 V is expected (cf. (47)) under a 40\({}^{\circ}\) phase margin. On the other hand, a DC link deviation of 20 V was attained in [51] under a 58\({}^{\circ}\) phase margin. It may be then concluded that imposing a minimal tolerable phase margin yields an optimized DC link voltage deviation, as stated at the end of Section 4.
# Quality Analysis of 3D Point Cloud Using Low-Cost Spherical Camera for Underpass Mapping
###### Abstract  
Three-dimensional point cloud evaluation is used in photogrammetry to validate and assess the accuracy of data acquisition in order to generate various three-dimensional products. This paper determines the optimal accuracy and correctness of a 3D point cloud produced by a low-cost spherical camera in comparison to the 3D point cloud produced by laser scanner. The fisheye images were captured from a chessboard using a spherical camera, which was calibrated using the commercial Agisoft Metashape software (version 2.1). For this purpose, the results of different calibration methods are compared. In order to achieve data acquisition, multiple images were captured from the inside area of our case study structure (an underpass in Wiesbaden, Germany) in different configurations with the aim of optimal network design for camera location and orientation. The relative orientation was generated from multiple images obtained by removing the point cloud noise. For assessment purposes, the same scene was captured with a laser scanner to generate a metric comparison between the correspondence point cloud and the spherical one. The geometric features of both point clouds were analyzed for a complete geometric quality assessment. In conclusion, this study highlights the promising capabilities of low-cost spherical cameras for capturing and generating high-quality 3D point clouds by conducting a thorough analysis of the geometric features and accuracy assessments of the absolute and relative orientations of the generated clouds. This research demonstrated the applicability of spherical camera-based photogrammetry to challenging structures, such as underpasses with limited space for data acquisition, and achieved a 0.34 RMS re-projection error in the relative orientation step and a ground control point accuracy of nearly 1 mm. Compared to the laser scanner point cloud, the spherical point cloud reached an average distance of 0.05 m and acceptable geometric consistency.  
spherical camera; 3D point cloud; assessment; geometric features; relative orientation; data acquisition +
Footnote †: journal: _Sensors_**2024**, 24, 3534. [[https://doi.org/10.3390/s24113534](https://doi.org/10.3390/s24113534)]([https://doi.org/10.3390/s24113534](https://doi.org/10.3390/s24113534))
## 2 Materials and Methods
### Data Processing  
Ventilatory thresholds 1 and 2, as well as \(\dot{\mathrm{VO}}_{\mathrm{2}\mathrm{peak}}\), were determined by two independent experts using the Cortex MetaSoft software suite 5.5.1 before exporting the dataset for further analysis. All further data processing steps and analyses were conducted using a custom script written in MATLAB R2023a (The MathWorks Inc., Natick, MA, USA). All cardiopulmonary data were first cleaned by removing outliers that exceeded the mean of a 7-breath window by more than 2 standard deviations and then smoothed by applying a 7-breath moving average [28]. Cardiopulmonary datasets from visit 2 were split into 3 min intervals, so that for each trial 5 \(\times\) 3 = 15 intervals of gas exchange data were used for further analysis. From these 15 3 min intervals, the final 60 s of data were averaged and used to determine mean oxygen consumption as the first out of two quantification approaches to RE (\(\mathrm{VO}_{\mathrm{2}}\), mL-kg-1-min-1). Those data were also used to calculate energy consumption by use of [PERSON]'s non-protein respiratory quotient equations [4; 40]. Mean energetic cost of transport (ECT, kcal-kg-1-km-1) was then calculated as the second quantification approach to RE, accounting for differences in energy yield per volume of oxygen and normalized with respect to body mass and running speed. Relative RE changes (\(\%RE\)) during each condition were then calculated by  
\[\%RE=\frac{RE_{\mathrm{OWN}}-RE_{\mathrm{CPRS}}}{RE_{\mathrm{OWN}}}.100\%\]  
with \(RE\) being either \(\dot{\mathrm{VO}}_{\mathrm{2}}\) (mL-kg-1-min-1) or ECT (kcal-kg-1-km-1). Positive \(\%RE\) values indicate a RE improvement while negative \(\%RE\) values indicate RE deterioration. Metabolic steady state was confirmed visually, and limited anaerobic contribution was assured by a respiratory exchange ratio (RER) of RER \(\leq\) 1.0 during all trials [4; 41]. Above that threshold, a substantial anaerobic lactic contribution to energy metabolism by anaerobic glycolysis is evident, corresponding to a physiological state beyond the first ventilatory threshold. In such instances, the calculation of energy expenditure based on gas exchange rates is no longer sufficient, and lactate accumulation rates should be considered as well [4]. To circumvent the necessity for additional, invasive measurements of lactate accumulation, this metabolic condition was excluded by defining the RER exclusion criterion.
# Antioxidant Properties of Selenium Nanoparticles Synthesized Using Tea and Herb Water Extracts
## 3 Results and Discussion
### Synthesis and Characterization of SeNPs
Figure 3 shows the SEM and TEM images obtained for the most homogeneous SeNPs (synthesis in the infusion/selenium ratio 2:1). The obtained results allowed us to assess the size and shape of the synthesized SeNPs. In each case, spherical nanoparticles were obtained. In the case of those obtained by synthesis using teas, they had larger dimensions (108 nm for green and 102 nm for black tea, respectively). The smallest nanoparticles were obtained from the synthesis using mint; they had a size of 54.8 nm. Replacing mint with chamomile resulted in SeNPs with dimensions of 95.4 nm. From the point of view of potential biomedical applications, only those obtained with the use of herbal extracts have dimensions that allow them to cross the cell membrane barrier (i.e., less than 100 nm). \begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{3}{c}{**Tea Infusions**} \\ \hline
**Selenium/Infusion Ratio** & **Black Tea** & **Green Tea** \\ \hline
1:1 & 0.317 & 0.253 \\
1:2 & 0.943 & 0.920 \\
1:3 & 0.644 & 0.909 \\
1:4 & 0.260 & 0.911 \\
2:1 & 0.105 & 0.129 \\
3:1 & 0.541 & 0.815 \\
4:1 & 0.881 & 0.818 \\ \hline \multicolumn{3}{c}{Herbal infusions} \\ \hline Selenium/infusion ratio & Chamomile & Mint \\ \hline
1:1 & 0.481 & 0.944 \\
1:2 & 0.890 & 0.130 \\
1:3 & 0.751 & 0.146 \\
1:4 & 0.829 & 0.154 \\
2:1 & 0.109 & 0.082 \\
3:1 & 0.322 & 0.109 \\
4:1 & 0.920 & 0.115 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The polydispersity index (PDI) of synthesized SeNPs. Figure 3: SEM and TEM images of the SeNPs obtained using chamomile, mint, black, and green tea extract. The synthesis was carried out in a reagent ratio of 2 to 1 (infusion/selenium). The stability of the obtained SeNPs was investigated within a period of 48 h. The observed changes in the average diameter obtained by DLS analysis were not significant, indicating good stability of the investigated nanoparticles. It should be mentioned that SeNPs obtained by synthesis using tea infusions were so strongly stabilized by the extract components that it was impossible to separate them from the solution by centrifugation. Surface complexation is reported to affect the intrinsic stability of nanoparticles by regulating its colloidal stability. [PERSON] [45] postulated that the nature and stability of NPs can be theoretically predicted through a mechanistic understanding of the surface complexation processes. Moreover, the colloidal stability (or rate of dissolution) of nanoparticles can be regulated by controlling the particle size and surface capping or through functionalization techniques. In the case of our studies, further investigation involving the measurement of zeta potential is planned. The zeta potential of the nanoparticles, indicating their surface charge density, affects the stability of the nanoparticles, but also determines the way in which they interact with biological systems.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 4 Proof of Stability
### Proof of Stability of Altitude Subsystem  
The Lyapunov function is defined as follows for the altitude subsystem:  
\[V_{h}=\frac{1}{2}e_{h}^{2}+\frac{1}{2}e_{\gamma}^{2}+\frac{1}{2}e_{\theta}^{2}+ \frac{1}{2}e_{Q}^{2} \tag{50}\]  
By deriving the above formula, we can obtain the following:  
\[\dot{V}_{h}=e_{h}\dot{e}_{h}+e_{\gamma}\dot{e}_{\gamma}+e_{\theta}\dot{e}_{ \theta}+e_{Q}\dot{e}_{Q} \tag{51}\]  
Bringing Equations (28), (29), (31), (35), (37), (38), (40) and (44) into Equation (51),  
\[\begin{array}{l}\dot{V}_{h}=e_{h}(V\gamma-\dot{h}_{d})+e_{\gamma}(S_{\gamma} \theta+f_{\gamma}-\dot{\gamma}_{d})+e_{\theta}(Q-\dot{\theta}_{d})+e_{Q}(g_{Q} \dot{e}_{e}+f_{Q}-\dot{Q}_{d})\\ =-c_{2}e_{h}^{2}-c_{3}e_{\gamma}^{2}-c_{4}e_{\theta}^{2}-cge_{Q}^{2}\leq 0 \end{array} \tag{52}\]  
According to Lyapunov stability, the altitude subsystem is uniformly stable. Simultaneously, it can also be proved that everything in the altitude subsystem is bounded.  
According to Function (52), Function (50) is a bounded and decreasing function, which can be derived as follows:  
\[\int_{0}^{\infty}{[e_{h}^{2}(t)+e_{\gamma}^{2}(t)+e_{\theta}^{2}(t)+e_{Q}^{2}( t)]dt}={[V_{h}(0)-V_{h}(\infty)]}/{l}<\infty \tag{53}\]  
According to Formula (53), \(e_{h}\), \(e_{\gamma}\), \(e_{\theta}\) and \(e_{Q}\) are square integrable. Therefore, the altitude tracking error converges to 0, and the altitude subsystem is asymptotically stable.
## 2 Materials and Methods
### Differential Expression Analysis and Functional Enrichment Analysis  
The R package \"limma\" [12] was used to normalize, log transform, and analyze the differential expression of the original array data. The \(p\)-value and log2 fold change (log2 FC) were calculated. Subsequently, \(\mathrm{1\,log2 FC}\)\(\mathrm{1\,>3}\) and \(p\)\(\mathrm{<0.05}\) were considered to be differentially expressed. R packages \"clusterProfiler\" [13], \"ggplot2\" [14], and \"DOSE\" [15] were used to perform the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analysis on the DEGs. Only GO terms and KEGG pathways with \(p\)\(\mathrm{<0.05}\) were considered statistically significant and selected for visualization.
## 4 Model Validation and Analysis
### Comparative Study  
In this Section, we compare the experimental results of the PVM model with those from the MTAF-TST model proposed by [PERSON] et al. [31] and the 2 DU-Net+CLSTM model studied by [PERSON] et al. [37], using the non-food crop dataset. The reason for selecting these two models is that they are representative studies in the field of time-series remote-sensing image semantic segmentation and provide comprehensive benchmarks for evaluating the effectiveness and advantages of the proposed method. Additionally, we replaced the decoder in the PVM model with a U-Net decoder to verify the performance of the decoder in our model.  
The proposed PVM method was compared with the two aforementioned methods and the widely used semantic segmentation decoder as follows:  
1. MTAF-TST model: This is a spatiotemporal transformer (MTAF-TST) semantic segmentation framework based on multi-source temporal attention fusion, utilizing optical and SAR time-series data for land cover semantic segmentation. The model uses a Transformer-based spatiotemporal feature extraction module to mine long-range temporal dependencies and high-level spatial semantic information, making it highly suitable for multi-class semantic segmentation of large-area time-series data.
2. 2 DU-Net+CLSTM: This model combines 2D U-Net for image segmentation and CLSTM for time-series processing, used for pixel-level crop type classification from remote-sensing images. By capturing spatial features and temporal dependencies, it provides instantaneous crop state information.
3. U-Net Decoder: The U-Net decoder adopts an upsampled (deconvolution) structure with skip connections. It recovers spatial resolution by concatenating feature maps from the encoder with upsampled feature maps from the decoder. It is suitable for multi-class segmentation tasks, making predictions for each pixel for multi-class classification.  
Following these three methods, testing was conducted on both the self-made dataset and the Canadian Cropland Dataset. The models were trained and validated independently for each scenario using the same evaluation metrics.
# Antioxidant Properties of _Jatropha curcas_ L. Seed Shell and Kernel Extracts
## 3 Results and Discussion
### ABTS Radical Scavenging Assay  
Figure 3 displays the ABTS radical scavenging results for the seed shell and kernel extracts obtained from different ethanol solutions. Similar to the trend displayed in Figure 2, a trend of increasing ABTS radical scavenging activity with increasing extract concentration was observed, regardless of the type of extraction solution used. The seed shell extracts obtained from concentrated ethanol solutions had high ABTS radical scavenging activity. The shell extract obtained using 95% ethanol had the best IC\({}_{50}\) value in the ABTS free radical scavenging assay. The result that an increase of ethanol concentration had in a higher ABTS radical scavenging activity agrees with the report about _Salvia juriscii_[31] and _Withania somifera_[32]. The IC\({}_{50}\) values in the ABTS assay were \(161.00\pm 5.40\), \(50.19\pm 2.96\), \(21.47\pm 0.91\),  
Figure 2: DPPH radical scavenging activities of the seed (**a**) shell and (**b**) kernel extracts from different ethanol solutions.  
\(12.17\pm 0.67\), and \(6.75\pm 0.51\) ug/mL for shell extracts obtained using 0%, 25%, 50%, 75%, and 95% ethanol solutions, respectively (Figure 3a). The corresponding IC\({}_{50}\) values for the extracted kernels were \(62.61\pm 2.71\), \(134.80\pm 9.04\), \(142.11\pm 16.83\), \(211.72\pm 23.64\), and \(329.71\pm 48.14\) ug/mL, respectively (Figure 3b). Therefore, the shell extracts had in a higher ABTS radical scavenging activity than kernel extracts, corresponding to the report about _Corylus jacquemontii_[33]. The IC\({}_{50}\) of ascorbic acid in water solution was \(4.08\pm 0.25\) ug/mL. The results of the ABTS and DPPH free radical scavenging assays indicated that the extracted _J. curcas_ shells had strong free radical scavenging activity and could be used as effective free radical scavengers.  
Figure 3: ABTS radical scavenging activities of the seed (**a**) shell and (**b**) kernel extracts from different ethanol solutions.
## 2 Modern Physical Layer
### Modulation  
By modulating the signal, one or more properties (amplitude, phase or frequency) of a periodic waveform (carrier signals) are changed according to another signal (modulating signal) that contains the information to be transferred. Using this technique, the modulating signal spectrum is matched to the current communication channel characteristics.  
As previously described, maximum spectral efficiency is mandatory to exploit the limited bandwidth available in the underwater acoustic channel, and, in addition, energy constraints must be taken into account. Hence, several research works were focused on finding the optimal modulation scheme.
## 2 Methods
### Merit Function  
The real chromatic aberration is corrected for optimization. The damped least-squares method [20,21] is applied for optimization of the design for correction of the chromatic aberration. A merit function is defined as the summation of the squared values of the weighting differences between the aberrations and their target values. The formula can be written as  
\[\phi=\sum_{i=1}^{m}w_{i}^{2}(e_{i}-t_{i})^{2}, \tag{15}\]  
where \(m\) is the total summation number; \(w_{i}\) is the weighting factor; \(e_{i}\) is the aberration; and \(t_{i}\) is the target value. We define the function \(f(x_{1},x_{2},\ldots,x_{n})\) as  
\[f_{i}=w_{i}(e_{i}-t_{i}). \tag{16}\]  
Before optimization, the \(n\) variables are denoted as \(x_{10}\), \(x_{20}\), \(\ldots\), \(x_{n0}\); the m aberrations before the optimization are \(f_{10}\), \(f_{20}\), \(\ldots\), \(.\prime\),\(f_{m0}\). After the optimization process, the variables are denoted as \(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}\), and the aberrations as \(f_{1}\), \(f_{2}\), \(\ldots\),\(f_{m}\). Here, we define a matrix \(A\), in which the elements are  
\[A_{ij}=\frac{\partial f_{i}}{\partial x_{j}}, \tag{17}\]  
We get the equation  
\[X=-\left(A^{T}A+pI\right)^{-1}A^{T}f_{0}, \tag{18}\]  
where \(A^{T}\) is the transposed matrix of \(A\); \(I\) is a unit matrix, \(p\) is a damping factor; and \(f_{0}\) is the matrix containing the elements \(f_{10}\), \(f_{20}\), \(\ldots\), \(f_{m0}\). If \(x\) and \(x_{0}\) are the matrices containing the elements \(x_{1}\), \(x_{2}\), \(\ldots\), \(x_{n}\), and \(x_{10}\), \(x_{20}\), \(\ldots\), \(x_{n0}\), respectively, we can obtain  
\[x=x_{0}+X. \tag{19}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**No** & **Glass Type** & **No** & **Glass Type** & **No** & **Glass Type** & **No** & **Glass Type** \\ \hline  
F47 & & \\ \hline \end{tabular}
\end{table}
Table 1: Schott glass type and numbers.
## 10 Simulation Results and Discussion
### Packet Delivery Ratio  
It is assumed that the delivery ratio is directly proportional to the number of nodes increased in the network [57]. Therefore, the density plays a part and parcel role in the underwater environment. It is clear that the BIN-TANVEER has the highest PDR results due to the functional nature of the proposed mechanism. As BIN is working for the rescue nodes to avoid the data loss, hence the PDR is achieved at the desired amount even in the density of the network. When the trap of the empty region is going to start the avoidance of data loss, alternative nodes in the form of BIN are considered the best substitute to deliver the data to nearby sonobuoys. Meanwhile, the TANVEER describes the average success ratio of packet transmissions, i.e., it is neither high nor low, because it operates is single parameter total packet delivered to the sonobuoys. The dynamic topology of the network is also affecting the efficiency of data transmission due to nodes movements.  
As compared to the baseline approach of L2-ABF with the proposed technique is shown in Figure 13. The energy consumption of both techniques is slightly high due to the means of data transmission. The data forwarding in L2-ABF is based on angle cone calculation, whereas TANVEER computed the angle thrice. It also requires more energy for some recovery mechanism. The DPD-TANVEER has achieved the desired results because it considers the only paths that ensure bypassing the empty regions causes high PDR.
## 3 Research Methodology
### Federated Learning Algorithm  
The research methodology used in this work is described in Algorithm 1. Local model training is performed at the Fog layer and the CPS layer, two separate levels of the system. Key parameters like the number of communication rounds, local models, learning rate, and aggregation technique are set during the initialization phase. Each CPS node defines its local datasets, and a global model is initialized. The Fog layer functions throughout multiple runs. The local models obtained from CPS nodes are combined to update the global model. It is subsequently broadcast to all nodes and transmitted separately to each CPS node. The local model training CPS layer specifically targets individual CPS nodes. Every node starts by initializing a local model and then updates it during communication rounds using the global model and its local dataset. The optimized local model is, after that, returned to the Fog layer. This method facilitates collaborative learning while ensuring privacy, as the global model is updated by aggregating information from all CPS nodes. The organized exchange of information between the Fog and CPS layers enables the implementation of federated learning inside the CPS framework.