esa-sceva/satcom-chunk-collection huggingface dataset:
# An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers
###### Abstract  
GNSS attitude determination has been widely used in various navigation and positioning applications, due to its advantages of low cost and high efficiency. The navigation positioning and attitude determination modules in the consumer market mostly use low-cost receivers and face many problems such as large multipath effects, frequent cycle slips and even loss of locks. Ambiguity fixing is the key to GNSS attitude determination and will face more challenges in the complex urban environment. Based on the CLAMBDA algorithm, this paper proposes a CLAMBDA-search algorithm based on the multi-baseline GNSS model. This algorithm improves the existing CLAMBDA method through a fixed geometry constraint among baselines in the vehicle coordinate system. A fixed single-baseline solution reduces two degrees of freedom of vehicle rigid body, and a global minimization search for the ambiguity objective function in the other degree of freedom is conducted to calculate the baseline vector and its Euler angles. In addition, in order to make up for the shortcomings of short baseline ambiguity in complex environments, this paper proposes different validation strategies. Using three low-cost receivers (ublox M8T) and patch antennas, static and dynamic on-board experiments with different baseline length set-ups were carried out in different environments. Both the experiments prove that the method proposed in this paper has greatly improved the ambiguity fixing performance and also the Euler angle calculation accuracy, with an acceptable calculation burden. It is a practical vehicle-mounted attitude determination algorithm.  
GNSS; low-cost; attitude determination; single-frequency single-epoch Article  
An Improved Single-Epoch Attitude Determination Method for Low-Cost Single-Frequency GNSS Receivers  
[1][PERSON]  
1 School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China; [EMAIL_ADDRESS] (X.W.); cyuzwbhu.edu.cn (C.X.); [EMAIL_ADDRESS] (Y.Z.); [EMAIL_ADDRESS] (H.Z.)  
Beijing Key Laboratory of Urban Spatial Information Engineering, Beijing 100038, China  
[3]Guangxi Key Laboratory of Spatial Information and Geomatics, Guilin University of Technology,  
Guilin 541004, China  
[4]Correspondence: [EMAIL_ADDRESS]; Tel.: +86-139-0713-5933
## 2 Data Collection and Curation Process
### Data Curation Process  
A literature review was conducted to determine which compounds have previously exhibited compatibility or incompatibility for encapsulation to establish the relationship between ACs and WMs. This review was conducted to establish a standard for comparing the values of the parameters for each compound.  
[PERSON] et al. [30] discovered that certain peach polyphenols are stable after being encapsulated in vitro and in vivo in sodium alginate matrices using Pickering high internal phase emulsions. Thus, it was assumed that the ACs listed in Table 1 were compatible with sodium alginate as a wall material. Consequently, a comparison of the case-specific parameters led to the establishment of the following conclusions:  
* In two out of three instances, the molecular weight of the WM was greater than the AC.
* The logP of the WM was always less than the logP of the AC.
* The numbers of HBAs and HBDs of the WM were consistently more significant than those of the AC.
* In two out of three instances, the PSA of the WM was greater than that of the AC.
* The HAC of the WM was consistently more significant than that of the AC.
* In two out of three instances, the CGCs and CnGCs of the AC were zero and one, respectively; both parameters were greater or equal for the WM.
* The HGCs and AGCs of the AC were always smaller than the AGCs and AGCs of the WM.
* There is a direct proportional relationship between the value of the WM and the PSA such that as the WM increases, so does the PSA.  
In this model, the compatibility between AC and WM is evaluated solely based on their chemical and physical interactions. The influence of the encapsulation type, operational conditions, and other chemicals was not studied.  
Other WMs did not exhibit the same response, and the active component's encapsulation was lost [30] due to temperature and other factors. Under particular conditions, encapsulating a specific AC with maltodextrin was inefficient. This result led to the conclusion that, under normal conditions, maltodextrin cannot be compatible with certain compounds [31]. Curdlan oligosaccharide, which requires a binding agent and electrostatic charges to encapsulate compounds, encountered a similar situation; however, it was determined that despite the curdlan's high encapsulation efficiency, it could not achieve the same results on its own [32].  
Table 1 shows combinations of compounds that, according to the literature, were deemed incompatible for encapsulation [31; 32]. Thus, the following conclusions were reached:  
In half of the instances, the AC had a greater MW than the WM.
* In every instance, the logP of the WM was less than that of the AC.
* The HBA of the WM was consistently more significant than that of the AC. The WM had greater values than the AC for the HBD four out of five times.
* In half of the instances, the PSA of the AC was higher than that of the WM.
* In all cases, the CGCs and CnGCs of the AC were more significant than or equal to the values of the WM.
* The HGCs and AGCs of the WM were always more significant than those of the AC.
## 3 Results
### \(R_{1}\) and \(R_{2}\) Values and \(R_{1}\) and \(R_{2}\) Map Images  
As shown in Figure 8a, r\({}_{1}\) and r\({}_{2}\) values were estimated to be 19.1 and 53.7 s\({}^{-1}\)mM\({}^{-1}\) (r\({}_{2}\)/r\({}_{1}\) = 2.80) (Table 1), respectively, from the slopes in the plots 1/T\({}_{1}\) and 1/T\({}_{2}\) versus Gd-concentration using an equation 1/T\({}_{1}\) = r\({}_{1}\)C + I (i = 1, 2) where C is the Gd-concentration and I is the intercept. The obtained r\({}_{1}\) and r\({}_{2}\) values were approximately 5 and 10 times higher than those of commercial Gd-chelates [5; 6], respectively. Dose-dependent contrast enhancements were observed in both R\({}_{1}\) and R\({}_{2}\) map images (Figure 8b), demonstrating the ability of the nanoparticles to enhance both T\({}_{1}\) and T\({}_{2}\) MR contrasts in vitro.  
Figure 8: (a) Plots of 1/T\({}_{1}\) and 1/T\({}_{2}\) versus Gd-concentration (slopes are r\({}_{1}\) and r\({}_{2}\) values, respectively). (b) Dose-dependent R\({}_{1}\) and R\({}_{2}\) map images showing contrast changes with Gd-concentration.  
Figure 7: M\(-\)H curve at 300 K. Net M value (i.e., only Gd\({}_{2}\)O\({}_{3}\) nanoparticles without PASA) was estimated using the net mass of Gd\({}_{2}\)O\({}_{3}\) nanoparticles obtained from TGA and used in the plot.
# Energy Efficient Routing in Wireless Sensor Networks: A Comprehensive Survey
## 5 Reliable Routing
### Multipath Based
_Energy Aware Routing_ (ERA) [165] is an improved version of the BDCP protocol that combines the clustering operation of BDCP and a multi-hop routing called Directed Virtual Backbone (DVB) to transmit data to the base station. When the protocol starts the clustering process, the base station constructs DVB and divides the cluster heads in levels, according to their distance from the base station. The base station is set in the level 0 as the parent node of level 1 cluster heads, the cluster heads of level 1 are the parent nodes of cluster heads in level 2 and so on. In addition, it is possible for a cluster head to have multiple parent nodes resulting in multiple paths to the base station. The use of ERA extends network lifetime. Yet, DVB does not provide optimum routing performance. A synopsis of the characteristics of the aforementioned multipath based energy efficient routing protocols is presented in Table 14. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Protocol** & **Advantages** & **Disadvantages** & **Scalability** & **Mobility** & **Routing** & **Robustness** \\ \hline \multirow{4}{*}{ROAM [155]} & It informs about & & & & & \\  & unreachable & & & & & \\  & destinations and & The periodic updates & Limited & Limited & Any route can be & Limited \\  & provides unnecessary & & need to be sent. & & & \\  & packet prevention. & & & & & \\  & It reduces overhead of & & & & & \\  & routing and delay to & It has overhead due to & & & & \\  & setup a backup route & various types of & Good & Good & Any route can be & Good \\  & with label & messages. & & & & used. & \\  & information. & It does not depend on & It may cause overhead & & & \\  & individual nodes to & due to redundant & Good & Good & The routes that & \\  & deliver data. & data. & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Synopsis of Reliable Routing protocols belonging to Multipath based subcategory.
# Directional Active Noise Control with a Local Minimax Error Criterion
## 2 Design of a Fixed Optimal Filter in the Frequency Domain with the Mean Squared Error Criterion  
A block diagram of a feedforward multi-channel system with a single reference signal in the frequency domain at frequency \(w\) is shown in Figure 1[21].  
Based on acoustic superposition, the residual noise at frequency \(w\) can be expressed as:  
\[\mathbf{E}(jw)=\mathbf{P}\left(jw\right)+X\left(jw\right)\mathbf{W}^{T}\left( jw\right)\mathbf{G}\left(jw\right) \tag{1}\]  
where the superscript \(T\) indicates a transpose operation. \(X\left(jw\right)\) is the complex reference signal; \(\mathbf{W}\left(jw\right)\) is a column vector of \(K\) complex control filter coefficients; \(\mathbf{G}\left(jw\right)\) is a matrix of \(K\times M\) complex transfer functions; and \(\mathbf{P}\left(jw\right)\) is a row vector of \(M\) complex primary noise. To be concise, the variable \(jw\) is omitted and:  
\[\mathbf{R}(jw)=X(jw)\mathbf{G}(jw) \tag{2}\]  
The MSE criterion can be expressed as:  
\[\begin{split} J_{1}&=\mathbf{E}^{H}\mathbf{E}\\ J_{1}&=\mathbf{P}^{H}\mathbf{P}+\mathbf{P}^{H} \mathbf{R}\big{(}\mathbf{W}^{T}\big{)}^{H}+\big{(}\mathbf{W}^{T}\big{)}^{H} \mathbf{R}^{H}\mathbf{P}+\big{(}\mathbf{W}^{T}\big{)}^{H}\mathbf{R}^{H} \mathbf{W}^{T}\end{split} \tag{3}\]  
where the superscript \(H\) is the Hermitian operator. According to Equation (3), the sum-of-the-squared of residual noise at discrete locations can be formulated as a quadratic function of the vector \(\mathbf{W}^{T}\). The optimal solution of the sum of the squared residual noise at error points can be found directly by differentiating Equation (3) with respect to \(\mathbf{W}^{T}\) and setting the gradient equal to zero. The result of the operation is:  
\[\mathbf{W}_{1}=-\Big{[}\mathbf{R}^{H}\mathbf{R}\Big{]}^{-1}\mathbf{R}^{H} \mathbf{P} \tag{4}\]  
Figure 1: Block diagram of a feedforward multi-channel system with a single reference signal in the frequency domain.
## 3 Results and Discussion
### TPC, TFC and Antioxidant/Antiradical Activities
In what concerns to the ABTS assay, the MAE presented the best result (17.74 \(\upmu\)g AAE/g dw), while the CE showed the worst (15.55 \(\upmu\)AAE/g dw). Significant differences (\(p<0.05\)) were observed between extracts. [PERSON] et al. also evaluated the ABTS scavenging capacity for the different extracts of _S. europaea_[35]. For the UAE extract, the authors reported that values varied between 4.5 and 22.8 mg TE/g dw, respectively, for the extraction with 100% and 40% of ethanol, while the highest result for the SFE extraction was obtained with 40% of ethanol (1.33 mg TE/g dw). In another study, [PERSON] et al. reported a greater capacity of _S. ramossissima_ cultivated with six different salinities to scavenge ABTS [2]. The IC\({}_{50}\) values ranged between 2.12 and 4.44 mg/mL, being higher than those presented in Table 1.  
The possible correlation between antioxidant/antiradical activities (FRAP and ABTS assays) and the TPC and TFC results was evaluated by a Pearson correlation for both extracts. Regarding the CE, a weak positive correlation was observed between the TFC and FRAP (\(r^{2}=0.0982\); \(p<0.05\)) as well as between TPC and FRAP (\(r^{2}=0.0878\); \(p<0.05\)) and TPC and ABTS (\(r^{2}=0.1330\); \(p<0.05\)). Weak negative correlations were also observed between TFC and ABTS (\(r^{2}=-0.0484\); \(p<0.05\)) and ABTS and FRAP (\(r^{2}=-0.5292\); \(p<0.05\)). Nevertheless, a strong positive correlation was observed between TPC and TFC (\(r^{2}=0.7312\); \(p<0.05\)), demonstrating that flavonoids are probably the most predominant phenolic compounds. Similar results were achieved for the MAE extract, being determined again a strong positive correlation between TPC and TFC (\(r^{2}=0.7134\); \(p<0.05\)). Once again, the correlations between TFC and ABTS (\(r^{2}=-0.3059\); \(p<0.05\)), TFC and FRAP (\(r^{2}=-0.0746\); \(p<0.05\)), ABTS and FRAP (\(r^{2}=-0.5886\); \(p<0.05\)) and TPC and FRAP (\(r^{2}=-0.1865\); \(p<0.05\)) were negative. Besides that, a strong negative correlation between TPC and ABTS (\(r^{2}=-0.8024\); \(p<0.05\)) should be highlighted. Considering the correlation of Pearson for this extract, it is possible to infer that flavonoid are probably the phenolic compounds present in highest amounts, as further demonstrated by the identification and quantification of phenolic compounds by HPLC-PDA. Nevertheless, it should be resulted that the spectrophotometric assays employed may present some drawbacks, such as quantification methods, reaction kinetics and, most importantly, interferences that could result of the presence of different compounds such as sugars, aromatic amines and ascorbic acid [24; 30].
## 3 Results
### Construction of the Nitrogen Content Model Based on the Wavelet Coefficients
The positions of the wavelet coefficients with the highest correlation between the nitrogen content and wavelet coefficients in the upper 3 leaves were mainly concentrated in 1360-1370 nm, with a correlation coefficient of 0.76 and a corresponding scale of 8. The global sensitivity index of the upper 4 leaf was highest between the 1700-2500 nm band range, and the corresponding scales were 3, 4, 5, and 9. The correlation between the nitrogen content of the upper 4 leaf and the wavelet coefficient was concentrated between the 1740-1750 nm band, with a correlation coefficient of 0.63 and a corresponding scale of 5. Based on the screened sensitive wavelet coefficients, PLRS, SVM, RF, and MLR methods were used to construct models for the estimation of leaf nitrogen content at different leaf positions and to validate the model accuracy, and the results are shown in Table 7.  
Overall, the accuracy of estimation of leaf nitrogen was improved with the increasing leaf positions. The results showed that the best model for estimating the nitrogen content of flag leaf was MLR with modeling and validation \(R^{2}\) of 0.84 and 0.89, respectively. The best model for upper 1 leaf nitrogen content estimation was SVM with modeling and validation \(R^{2}\) of 0.74 and 0.78, respectively. The best model for the upper 2 leaf nitrogen content estimation was MLR with the modeling and validation \(R^{2}\) of 0.73 and 0.74, respectively. The best model for nitrogen content estimation in the upper 3 leaf was MLR with modeling and validation \(R^{2}\) of 0.58 and 0.73, respectively; the best model for nitrogen content estimation in the upper 4 leaf was MLR with modeling and validation \(R^{2}\) of 0.51 and 0.57, respectively. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sort**} & \multicolumn{2}{c}{**Flag Leaf**} & \multicolumn{2}{c}{**Upper 1 Leaf**} & \multicolumn{2}{c}{**Upper 2 Leaf**} & \multicolumn{2}{c}{**Upper 3 Leaf**} & \multicolumn{2}{c}{**Upper 4 Leaf**} \\ \cline{2-10}  & **(\(\lambda\))** & **\(\mid\)r\(\mid\)** & **(\(\lambda\))** & **\(\mid\)r\(\mid\)** & **(\(\lambda\))** & **\(\mid\)r\(\mid\)** & **(\(\lambda\))** & **\(\mid\)r\(\mid\)** & **(\(\lambda\))** & **\(\mid\)r\(\mid\)** \\ \hline
1 & C4 (2173) & 0.87 & C3 (1701) & 0.87 & C3 (1695) & 0.79 & C8 (1367) & 0.76 & C5 (1745) & 0.63 \\
2 & C4 (2172) & 0.87 & C3 (1703) & 0.87 & C3 (1694) & 0.79 & C8 (1366) & 0.76 & C5 (1746) & 0.63 \\
3 & C4 (2174) & 0.87 & C3 (1700) & 0.87 & C3 (1696) & 0.78 & C8 (1370) & 0.76 & C5 (1744) & 0.63 \\
4 & C4 (2171) & 0.87 & C3 (1704) & 0.87 & C3 (1693) & 0.78 & C8 (1369) & 0.76 & C5 (1747) & 0.63 \\
5 & C4 (2175) & 0.87 & C3 (2168) & 0.87 & C3 (1697) & 0.78 & C8 (1368) & 0.76 & C5 (1743) & 0.63 \\ \hline \hline \end{tabular} Note: \(\mid\)r\(\mid\) represents the absolute value of the correlation coefficient and the \(\lambda\) in the bracket represents the number of bands.  
\end{table} 
Table 6: Correlations of different leaf position wavelet coefficients and leaf nitrogen content in winter wheat.
## 3 Experimental Results
According to experimental results, DCT yields the highest accuracy among other dimension reduction methods. As expected, HWT yields lower accuracy than DCT and gives higher accuracy than averaging filter. Both cross-validation with training data and performance measurement with testing data indicate that accuracy is increasing by the increment of feature vectors' size. Additionally, increment slope smoothly decreases by the increment of feature vectors' size. However, an optimum threshold in terms of compression rate is required in order to judge the system whether it is applicable in real time. Therefore, the compression rate of 90% (240 number of features are reduced to 24) is chosen as the optimum value of compression, which yields 83.9% accuracy while it is being used with DCT. Here, it is assumed that accuracy greater than 80% is applicable and reasonable in real-time applications. On the other hand, the compression rate can be lowered to get higher accuracies depending on the performance of hardware in which the HAR system is running. If the performance of hardware is high enough to process the LSTM classification in real time without dimension reduction, proposed method yields 91.7% accuracy without dimension reduction, whereas previous method can achieve 70.2% accuracy only. This also proves that proposed method is dramatically (around 30%) better than previous method in terms of keeping higher information in feature vectors, which results in yielding higher accuracies in all the cases.
# Real-Time Terrain-Following of an Autonomous Quadrotor by Multi-Sensor Fusion and Control
## 3 Quadrotor Integrated Control
### Height and Vertical Velocity Based on a PID Controller  
The multi-sensor control method is a classic control method mainly designed by the PID control principle. The differential equation of the PID controller is as follows:  
1. Continuous form \[u(t)=k_{p}e(t)+k_{i}\underset{0}{\overset{}{\int}}e(\tau)d\tau+k_{d}\frac{de(t)}{ dt},\] (31) where \(u(t)\) is the output of the control system, \(k_{p},k_{i}\) and \(k_{d}\) is the proportional, integral and the differential gain. And \(e\) is the error between the set point and the feedback value, \(t\) represents the current time, and \(\tau\) is an integral variable of which the value is from 0 to the current time.
2. Discrete form \[u(t)=k_{p}e(t)+k_{i}\underset{i=0}{\overset{}{\sum}}e(i)+k_{d}[e(k)-e(k-1)].\] (32)  
The PID control system of the quadrotor is based on a hierarchical control of inner and outer loops [49; 50] and it can be further subdivided into four levels including position control, attitude control, control distribution and power distribution as shown in Figure 4.  
As the height control of the quadrotor has nothing to do with the attitude of the body, there is an independent feedback input \(z\) in the height control system and the height correction is output after the height PID controller. Due to the balance between gravity and lift when the quadrotor is hovering, it is necessary to add gravity compensation to the height controller. When there is no error in the height, the controller can still output a stable value to control the motor rotation to maintain the hovering state. The simulation structure of the height controller is shown in Figure 5.  
Figure 4: Closed loop block diagram of the autonomous control of the quadrotor.
## 6 Health Effects and Toxicity of GLY and GBHs  
### Cellular Impact  
In a rat cardiac cell model, no substantial harm was seen at glyphosate doses that are significant to the environment. However, toxicity was found to be dose-dependent with increasing doses of TN20 surfactant at a constant glyphosate dosage [208], indicating that GBH components, including glyphosate, may be harmful. Additionally, this study discovered that this combination of glyphosate and TN20 induced the proliferation of tumour cells. Apoptosis and necrosis caused their deaths equally; however, when TN20 levels were increased, apoptosis was the predominant mode of cell death [208]. Rats exposed to a mixture of 12 chemicals, including glyphosate, for 18 months showed some cytotoxic effects in testis and kidney cells [209]. However, determining glyphosate's role in this cytotoxicity is difficult [206].  
On the one hand, investigations of glyphosate alone on human cells, both fibrosarcoma and healthy cells, found no substantial cytotoxicity at environmentally relevant amounts [210] whereby GBHs show toxicity that is dose-dependent, even when producing at the sub-agricultural level. Roundup, like another GBH product called glyphogan, killed human Sertoli cells, with glyphogan causing the most substantial harm [211]. The necessity of investigating GBHs rather than glyphosate alone is highly required.  
The glyphosate appeared to be highly genotoxic in Nile tilapia erythrocytes [209], but only affects both fibrosarcoma and healthy human cells at high doses [210]. These findings show that GLY and GBHs have the potential to be genotoxic, cytotoxic, or both and that the effects vary depending on the cell type.
# High-Speed VCSEL-Based Transceiver for 200 GbE Short-Reach Intra-Datacenter Optical Interconnects
## 3 Experimental Results and Discussion
### Transmission of 80 Gb/s up to 500 m Exploiting the High-Speed Single-Mode VCSEL Module  
Figure 6 shows the indicative optical eye diagrams at 32 and 40 Gbaud in the back-to-back configuration that were acquired with a 70 GHz equivalent time oscilloscope. As expected, the increase at the symbol rate of the signal leads to the closure of the eye pattern since the effect of intersymbol interference (ISI) is more decisive.  
Figure 7 presents the measured bathtub curves for the cases of 32 and 40 Gbaud input signals for both transmission scenarios, which were plotted against the average received optical power in the receiver. The green and red solid lines correspond to the obtained optical signal at 32 Gbaud in back-to-back configuration and after propagation over 500 m of SSMF, respectively. Following the same rationale, the dotted black and blue lines refer to the received optical signal at 40 Gbaud for the two different transmission scenarios. As observed, for the case of 32 Gbaud and within a power range of 10  dBm (between \(-\)10 dBm and 0 dBm), the acquired BER values lie well below the hard FEC (Forward Error Correction) limit for both transmission configurations. At 40 Gbaud, despite the reasonable degradation that is observed and is mainly caused by the 3 dB bandwidth of the VCSEL, the measured BER values lie below the hard FEC limit even after propagation over 500 m of SSMF. It should also be noted that the limiting operation of the commercial receiver used in the experimental setup imposed an additional penalty to the received signal. Especially for power values higher than 0 dBm, the BER values deteriorate since the device loses its linear response. The VCSEL's excellent performance makes it an ideal module for implementation in high-speed short-reach optical interconnects.  
Figure 6: PAM-4 optical eye diagrams at (**a**) 32 and (**b**) 40 Gbaud in back-to-back configuration.