esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Path Following Control Design
### Fractional Order PID Controller  
The heading controller of AUV is designed by the FOPID control method. Compared with conventional PID, the FOPID controller gives better performance owing to having extra two parameters. The transfer function can be written as Equation (33):  
\[C(s)=J_{P}+J_{I}s^{-\lambda}+J_{D}s^{\mu}, \tag{33}\]  
where \(0\leq\lambda,\mu\leq 1\). \(\lambda\) and \(\mu\) are the order of integral and differential controller, respectively. If \(\lambda=1\) and \(\mu=1\), it is a PID controller. If \(\lambda=1\) and \(\mu=0\), it is a PI controller. If \(\lambda=0\) and \(\mu=1\), it is a PD controller [36], as shown in Figure 11.  
Therefore, the heading error between a desired heading obtained by guidance law and the current heading obtained by AHRS (_Attitude and Heading Reference System_) is taken as the input of an FOPID heading controller. Then, by the linear combination calculation and adjustment of the proportion, integral and differential coefficients, the error of the heading is reduced. Finally, the system reaches a stable state to realize the heading control of AUV.
## 3 System Overview
### Security Features  
Our solution is based on a selection of the service set identifier (SSID), Wi-Fi security, and Grafana authentication. First, the SSID is the name of wireless service to be joined and has to be used with Wi-Fi protected access II (WPA2) and pre-shared key (PSK) configurations to authenticate and encrypt data. Next, Grafana login authentication is used to prevent anyone who is not the device owner from logging in and viewing. A demilitarized zone (DMZ) network architecture is used to secure the nodes. Port 3000 on the Raspberry Pi is forwarded to the Internet through the router firewall.
## 5 Experimental Results
### Test Set Comparison Results  
Our experimental results on the KITTI test dataset are shown in Table 2. D4 LCN [18] uses the depth-guided convolution generated by the depth map to extract the image's features for directly estimating the 3D object box; operations such as convolution and pooling in the process of the features' extraction will result in losing some detailed information. In comparison, we extract and learn the features in three different modal forms on the original image, which extracts the information of the original image more comprehensively and accurately and also makes up for the lost information in the keypoint branch to a certain extent. At the same time, it is worth noting that compared with the validation set's experimental performance, the test set's experimental performance has declined because the data used in the training of the depth estimator overlap with the data used in our algorithm training, which has a data overfitting problem. It is a common problem for a class of algorithms that utilize a depth estimator to provide extra depth information, for instance, ROI-10D [32] also suffers from the same problem.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(AP_{3d}\)} & \multicolumn{3}{c}{\(AP_{bev}\)} \\ \cline{2-7}  & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline MultiFusion [36] & 10.53/7.85 & 5.69/5.39 & 5.39/4.73 & 22.03/19.20 & 13.63/12.17 & 11.60/10.89 \\ \hline ROI-10D [32] & -/9.61 & -/6.63 & -/6.29 & -/14.50 & -/9.91 & -/8.73 \\ \hline MonoPair [44] & -/16.28 & -/12.30 & -/10.42 & -/24.12 & -/18.17 & -/15.76 \\ \hline SMOKE [34] & -/14.76 & -/12.85 & -/11.50 & -/19.99 & -/15.61 & -/15.28 \\ \hline RTM-3D [8] & 19.47/20.77 & 16.29/16.86 & 15.27/16.63 & 24.74/25.56 & 22.03/22.12 & 18.05/20.91 \\ \hline Pseudo-Lidar [19] & -/19.50 & -/17.20 & -/16.20 & - & - & - \\ \hline UR3D [45] & 26.30/28.05 & 16.75/18.76 & 13.60/16.55 & 36.15/37.35 & 25.25/26.01 & 20.12/20.84 \\ \hline Lite-FPN [31] & -/18.01 & -/15.29 & -/14.28 & -/24.83 & -/20.50 & -/17.65 \\ \hline D4 LCN [18] & 24.29/26.97 & 19.54/21.71 & 16.38/18.22 & -/34.82 & -/25.83 & -/23.53 \\ \hline AM3D [17] & -/32.23 & -/21.09 & -/17.26 & -/43.75 & -/28.39 & -/23.87 \\ \hline PatchNet [12] & -/35.1 & -/22.0 & -/19.6 & -/44.4 & -/29.1 & -/24.1 \\ \hline Ours & **31.61/34.85** & **21.67/22.59** & **18.76/19.81** & **43.84/44.71** & **29.66/30.67** & **24.78/25.05** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of and on KITTI val1/ val2 sets.
## 3 Results  
### Quantization Setting Dependence Results  
Quantization setting dependence was evaluated by means of ICC, as indicated in Section 2.4.1. In order to consider only radiomic features with excellent robustness, the cutoff value was set to 0.9. Figures 2-6 show all the features--divided by category (i.e., GLCM, GLRLM, GLSZM, GLDM, NGTDM)--that obtained \(ICC\geq 0.9\) in at least one quantization level.  
Figure 2: Dependence of the quantization level of GLCM radiomic features, considering different values (i.e., 8, 16, 32, 64, 128, and 256) of the ‘bincount’ PyRadiomics parameter. The features _ClusterProminence_, _ClusterShade_, _ClusterTendency_, _Correlation_, _JointAverage_, and _SumAverage_ were discarded, as the ICC did not overcome the cutoff for any ‘binCount’ setting.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Score (Robustness)** & **ICC Condition** & **SD Condition** \\ \hline
5 (very high) & \(\geq\)90\% & \(\leq\)10\% \\
4 (high) & \(\geq\)85\% & \(\leq\)20\% \\
3 (medium) & \(\geq\)80\% & \(\leq\)30\% \\
2 (limited) & \(\geq\)75\% & \(\leq\)40\% \\
1 (low) & \(\geq\)70\% & \(\leq\)100\% \\
0 (very low) & \(<\)70\% & \(>\)100\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Conditions to evaluate the robustness of each radiomic feature category.  
Figure 4: Dependence of the quantization level of GLSZM radiomic features, considering different values (i.e., 8, 16, 32, 64, 128, and 256) of the ’binCount’ PyRadiomics parameter. The features _ConvLev  
Table 3 summarizes the total of the robust features obtained for each quantization level.
## 2 Materials and Methods
### OCT Image Acquisition and Calculation of OAC  
The OCT images were collected from the surface of the samples in order to monitor the alterations that are induced by erosion, as well as the protection effectiveness promoted by each treatment modality. Tomographic images were obtained before the erosion cycle (day 0), immediately after receiving treatment (day 10), and at the end of demineralization challenge (day 15). The images were collected using an OCT system (OCT--OCP930 SR, Thorlabs Inc., Newton, NJ, USA) operating with a super-luminescent light-emitting diode as light source (SLED) with 2 mW of optical power, central wavelength of 930 nm, spectral bandwidth of 100 nm, axial resolution of 4.0 \(\upmu\)m, and lateral resolution of 6.0 \(\upmu\)m. OAC was calculated from OCT images (B-scan) using an algorithm that was developed by our team on LabView plataform (National Instruments, Austin, TX, USA). For this, the attenuation of the OCT signal penetrating the dentine matrix was estimated by fitting its curve with a Beer-Lambert-type function, in which the intensity of the backscattered light is described as a function of the penetration depth in the sample:  
\[\mathrm{I(z)}=\mathrm{I_{0}\cdot e^{-2 az}}\ +\ C \tag{1}\]  
where \(\mathrm{I(z)}\) represents the detected intensity as a function of depth z, \(\mathrm{I_{0}}\) is the incident intensity, a is the OAC, and C is a constant.  
The method is described in more detail in [PERSON] et al. (2014) [21]. The images were acquired in the same position at all time by a mark made with a dental drill on the sides of the samples, allowing for its assessment over time.
## 2 Materials and Methods  
To calculate downscaled SMAP/CYGNSS TB, we first calculated CYGNSS reflectivity. Next, we collocated SMAP and CYGNSS data, calculated the spatially-varying slopes of the linear regressions between SMAP emissivity and CYGNSS reflectivity, and used a variation of the SMAP active-passive TB downscaling algorithm to calculate 3 km SMAP/CYGNSS TB.
## 1 Introduction
It has the characteristics of high efficiency, wide spectrum and low toxicity, and effective control of _Pseu-doperonospora cubensis, Blumeria graminis, Erysiphe cichoracearum, Plasmopara viticola_, and _Magnaporthe grisea_ etc., and is suitable for cucumber, rice, tomato, rape, and other crops and fruits and vegetables [33, 34, 35, 36]. However, Pyr has a low solubility in water and is hardly translocated in plants [37], which inhibits its application. The chemical structure of Pyr is shown in Figure 1. In this work, Pyr is selected as the model pesticide in the MSN delivery system. A comparative study among different particle sizes of Pyr-loaded carbon quantum dots-MSN was conducted to figure out the size effect of MSN on Pyr loading, release, and delivery in cucumber plants. To improve the transportation performance of pesticide in plants, carbon quantum dots-MSN within 15, 100, and 200 nm were synthesized to load Pyr, respectively. The physical properties of the three kinds of Pyr@M were characterized,  
Figure 1: Chemical structure of pyraoxystrobin. including their composition, structure, control release performance, and loading content. After root treatment, the uptake and transportation behaviors of Pyr@M in cucumber plants were studied. This research showed that the particle size of the nano-carriers has a great effect on pesticide loading, release, and delivery in plants.
# A Bio-Inspired Control Strategy for Locomotion of a Quadruped Robot
## 2 The Foot WT Planning
### Foot WT Planning  
Foot WT mainly involves swing phase and stance phase. As shown in Figure 1, the arc \(\widehat{ACB}\) is the swing phase trajectory and \(\widehat{BA}\) is the stance phase trajectory. In [10], a trajectory planning method based on compound cycloid is used to design the swing phase trajectory with the advantage of smooth motion and small mechanical shock. In addition, the method is modified and applied to a quadruped robot foot WT planning in [11]. The modified trajectory is defined as  
\[x =S\Big{[}\frac{t}{T_{m}}-\frac{1}{2\pi}\sin\left(2\pi\frac{t}{T_{m }}\right)\Big{]} \tag{2}\] \[y =\left\{\begin{array}{ll}2H\Big{[}\frac{t}{T_{m}}-\frac{1}{4\pi }\sin\left(4\pi\frac{t}{T_{m}}\right)\Big{]}&0\leq t<\frac{T_{m}}{2}\\ 2H\Big{[}1-\frac{t}{T_{m}}+\frac{1}{4\pi}\sin\left(4\pi\frac{t}{T_{m}}\right) \Big{]}&\frac{T_{m}}{2}\leq t<T_{m}\end{array}\right.\]  
where \(S\) is the stride length, \(H\) is the maximum height of leg raise, and \(T_{m}\) is the swing phase period. By analyzing the first and second derivatives of the equations, we can know that the velocities and accelerations on \(x\) and \(y\) of the foot are 0 at the time \(t=0\) and \(t=T_{m}\), which reduces the mechanical shock, energy loss and makes the robot's movement more smooth.  
Generally, constant motion is used to plan the stance phase trajectory [11; 24]. However, huge acceleration jumps will occur at the start and end moment of the stance phase while using the constant motion to plan it directly, which is harmful to the robot and its locomotion. While taking the example of the planning method of swing phase trajectory to plan the stance phase trajectory, the robot will continue to accelerate and decelerate in its stance phase, which affects the stability and locomotion performance of the robot terribly. In this paper, a compromise approach using the constant motion guiding with a short period of sinusoidal velocity at its start and end moment is adopted to plan the stance phase trajectory. Thus, the smooth transition from zero to constant velocity and constant velocity to zero is realized, which greatly reduce the acceleration jumps and speed fluctuations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Link No. \(i\)** & **Joint Variable \(\theta_{i}\) (Rad)** & **Offset \(d_{i}\) (mm)** & **Link Length \(a_{i-1}\) (mm)** & **Link Angle \(a_{i-1}\) (Rad)** \\ \hline
1 & \(\theta_{1}+\theta_{h}\) & 0 & \(l_{1}\) & 0 \\
2 & \(\theta_{1}+\theta_{k}\) & 0 & \(l_{2}\) & 0 \\ \hline \end{tabular}
\end{table}
Table 1: The D-H notation parameters.
## 5 Reconstruction Methods
### VAE-Based Models  
Variational Autoencoders (VAEs) [69] use an encoder to map input data into a space of latent random variables, and then reconstruct the data from this space with a decoder.  
LSTM-VAE [41], the former network is replaced by LSTM in VAE. However, LSTM-VAE ignores the temporal dependence between stochastic variates. Hence, OmniAnomaly [42] was proposed, the model is the first MTSAD algorithm capable of handling explicit temporal dependencies among random variables. It provides a novel method for entity-level anomaly explanation through stochastic recurrent neural networks, the objective is to learn the normal patterns of data and utilize reconstruction probability for anomaly detection. Additionally, InterFusion [43] is an unsupervised method that models both the inter-dependency and temporal dependency of MTS simultaneously. Its core idea involves modeling the normal patterns within data using a hierarchical VAE with two stochastic latent variables, each learning low-dimensional embeddings of either inter-metric or temporal aspects. Unsupervised VAE is vulnerable to the influence of anomalous inputs. SLA-VAE [44] addresses the challenge of obtaining labeled data in InterFusion throughsemi-supervised learning, utilizing active learning to leverage a small number of highly uncertain samples for learning and updating online system anomaly detection models. LARA [16] solves the need for online systems to quickly adapt to new distributions with a lightweight retraining approach. LARA designs the retraining process as a convex problem to prevent overfitting and ensure rapid convergence. It also has the capability to utilize historical data without the need for storage.
## 4 Results and Discussion
### TEG's Maximum Electrical Efficiency in Relation to Thermoelectric Materials  
The value of the figure of merit (\(Z\overline{T}\)) of the semiconductor behavior of a TEG module depends on the type of materials used. Bismuth telluride Bi\({}_{2}\)Te\({}_{3}\) was used in the current study. From Equation (9), the maximum efficiency \(\eta_{max}\) of the system can be calculated.  
Figure 10: Voltage for 15 \(\times\) 10 TEG panel systems.  
Figure 12 depicts that the average maximum efficiency achieved by the TEG panels was 1.77%. The lowest and highest values obtained during the experiments were 1.601% and 2.1%, respectively. The increase in this proposed mechanism is significant. This efficiency is considered important because the system operates under a low temperature difference compared to other systems, such as car exhaust and chimneys, among others. These conditions have twice the temperature difference used in the current work.  
In Figure 12, the relation between \(\Delta T\) and \(\eta_{max}\) seems linear. The estimated regression equation for the data of this figure is:  
\[\eta_{max}\ =-0.089+0.049\ \Delta T \tag{14}\]  
where the value of the correlation between \(\Delta T\) and \(\eta_{max}\) is 0.9998 i.e., positively high correlated.  
A significant improvement in efficiency and output power is observed. This occurs since this proposed system uses heat sources that depend on available water rather than relying on other variables and unstable heat sources. As such, this system is independent, cost-effective, and does not use complex mechanisms.  
Table 2 represents a comparison of the research discussed above in this paper as well as others with the results of the current work. The results are either better than or competitive with systems presented in earlier studies. Most often, the proposed system gives better  
Figure 11: Power for 15 \(\times\) 10 TEG panel at load of (50 W/5 \(\Omega\)).  
Figure 12: Efficiency of a 15 \(\times\) 10 TEG panel system.  
results, especially if utilization time is extended for many hours. The duration of each experimental run was only a few minutes, but if it lasted for many hours then the energy production would have been 1.435 kWh.
## 3 Methods  
### Data Preparation  
The measurement data should be as free from errors as possible. However, this is rarely the case. Data used in this work contain wrong values, gaps, outliers, spikes, drops, and other types of noise. Data also contain wrong or different formats of timestamps. These common problems affect the readiness of data for analysis, the quality of the time series, and, further, the quality of the analysis.  
To ensure the systematic patterns of the time series data more salient, we applied a series of data preprocessing techniques to the raw data for each PV system. (1) Data were filtered according to standard IEC61724-1:2017 [48]. Namely, the daytime data were selected by eliminating irradiance values below 200 W/m\({}^{2}\) and above 1500 W/m\({}^{2}\). For T\({}_{\rm amb}\), only values between \(-\)40 and 60 \({}^{\circ}\)C were used. For the module temperature, T\({}_{\rm m}\), values between T\({}_{\rm amb}\) and T\({}_{\rm amb}\) plus 30 \({}^{\circ}\)C were included [49]. Wind speed values, \(\
u\), between 0 to 30 m/s were included. (2) The data were thoroughly checked to ensure consistency and to detect gaps. Duplicates in the data were detected and removed. (3) Further general outlier removal was applied based on predefined criteria depending on the characteristic of the data. If a variable value, X, minus the population mean, \(\mu\), is greater than two times the standard deviation, \(\sigma\), then that specific X value is an outlier, and it will be excluded.  
\[|\mathrm{X}-\mu|\mathrm{>2\sigma} \tag{8}\]  
(4) Any abnormal value, such as largely negative and dead values that were stuck for one hour or longer, were removed. (5) Due to many incomplete records based on the previous treatments, we selected data with high monitoring fractions (MFs), either intrahours, intradays, or intramonth. A minimum MF of 85% was found to be optimal to keep the data quality high without losing too much data.
# The Potential of LEO Satellite-Based Opportunistic Navigation for High Dynamic Applications
## 4 Performance Characterization
### Effect of Error Sources
#### 4.2.1 Measurement Noise
Figure 11: Mean and standard deviation (std) of bursts time of arrival errors. \begin{table}
\begin{tabular}{c c c c} \hline  & **X** & **Y** & **Z** \\ \hline \(\Delta\tau\) (m) & 128.84 & 95.90 & 166.60 \\ \hline  & Roll & Pitch & Yaw \\ \hline \(\Delta\epsilon\) (deg) & 1.49 & 1.88 & 4.18 \\ \hline \(\Delta(\delta t)\) (ms) & 34.68 & & \\ \hline \(\Delta(\dot{\delta t})\) (ppb) & 3.51 & & \\ \hline \end{tabular}
\end{table} 
Table 3: Root mean square error for position, attitude and time. The performances presented in this paragraph, assuming perfect satellite orbits and clocks, can be considered the best we could expect from this navigation filter in the conditions of the studied scenario. They will be considered as a reference in the subsequent paragraphs to assess the effect of the other error sources.
## 5 In Vitro Biological Performance of HEA
CCK-8 assay showed the presence of a higher number of metabolically active viable cells in contact with the Ti\({}_{40}\)Zr\({}_{20}\)Hf\({}_{10}\)Nb\({}_{20}\)Ta\({}_{10}\) than on the TiZrHf and CP-Ti surfaces, suggesting the enhanced capacity of the analyzed BioHEA to sustain increased viability and proliferation of the HGFs. This finding was also supported by the results of the CCK-8 assay performed on the leaching solutions of these biomaterials, indicating that the newly designed BioHEA is more compatible with HGF cells than TiZrHf and especially CP-Ti, holding great potential for dental applications. The above in vitro studies have not yet approached the expression of cell-type specific markers, but the HEA's capacity to support cell differentiation was predicted by assessing the changes induced in cells' sizes and shapes following direct contact with the materials' surfaces. However, a recent paper by [PERSON] et al. reports on the potential of the Ti\({}_{50}\)Zr\({}_{25}\)Nb\({}_{20}\)Cu\({}_{2.5}\)Ag\({}_{2.5}\) HEA to induce osteogenic differentiation of MC3T3-E1 preosteoblasts grown in contact with its surface by studying the gene expression and activity of alkaline phosphatase (ALP) known as an early marker of osteoblast differentiation, as well as mRNA levels of bone morphogenetic protein (BMP)-2 as an osteoinductive factor. This study was comparatively performed on the Ti6 Al4V alloy, and the results obtained revealed lower intracellular ALP activity and mRNA expression levels of ALP and BMP-2 than those exhibited by Ti\({}_{50}\)Zr\({}_{25}\)Nb\({}_{20}\)Cu\({}_{2.5}\)Ag\({}_{2.5}\) on the 7 th day of culture, suggesting its superior bioactivity [77]. In summary, despite the recent progress in designing new metallic materials with promising potential in the biomedical field, like HEAs, there is still room for improvement in the assessment of their biological performance since mainly cell culture-based studies have been developed so far. To the best of our knowledge, [PERSON] et al. investigated the in vivo biocompatibility of HEAs from the Mo-Ta-Nb-Ti-Zr system, specifically the (MoTa)\({}_{0.2}\)NbTiZr that was implanted in the thigh muscles of ten-week-old male C57 BL/6 mice in the shape of cylindrical rods. The histological evaluation of the respective muscle at four weeks indicated that the implant did not induce any toxicity or muscle damage, suggesting the biosafety of this HEA [81]. Likewise, [PERSON] et al. 's model showed very recently that \(\text{Ti}_{50}\text{Zr}_{25}\text{Nb}_{20}\text{Cu}_{2.5}\text{Ag}_{2.5}\) HEA exhibits enhanced biocompatibility and higher antibacterial properties than the conventional Ti6 Al4V alloy, indicating its suitability for the peri-implant tissue regeneration [77].