esa-sceva/satcom-chunk-collection huggingface dataset:
# Quadcopters in Smart Agriculture: Applications and Modelling
## 5 Challenges and Future Directions
### Data Processing  
#### 5.2.1 Challenges  
Agricultural quadcopters are equipped with various sensors that generate a massive amount of data, such as high-resolution aerial images, data collected from ground sensors, multispectral data, and more. This data must then be analyzed and interpreted to provide feedback and enable proactive actions. However, processing large amounts of data is very challenging, especially in real-time, due to computational challenges and data instability [154; 155; 156].
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
###### Abstract  
Dilution of Precision (DOP) is routinely used in GNSS to assess the quality of the constellation geometry for the positioning algorithm. Those DOP indicators are computed from the estimation covariance of a snapshot weighted least squares (WLS) estimate under certain hypotheses. This paper proposes to define DOP indicators for GNSS solutions based on Factor Graph Optimization (FGO). FGO solutions have become popular in the GNSS domain. They allow to easily model probabilistic contraints, called factors, over a large time window, by mixing observations and motion constraints accross consecutive epochs. The solution is solved by performing a batch WLS estimation for the states at all considered epochs, using all available factors. Due to the simple nature of the estimation algorithm--a WLS solution--it is possible to derive the theoretical estimation error covariance, which will indicate the accuracy of the computed solution. In this paper, a formula is proposed to approximate the DOP for the FGO solution. Then, the formula is validated in various scenarios involving fixed or changing satellite visibility.  
GNSS; factor graph optimization; precise positioning 2025 88, 41  
Felderation ENAC SAE-SUPAERO ONERA, Universite de Toulouse, 31000 Toulouse, France; [EMAIL_ADDRESS] (H.C.); [EMAIL_ADDRESS] (J.L.)  
* Correspondence: [EMAIL_ADDRESS]
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
###### Abstract  
This communication proposes a gain design method of an \(\alpha\)-\(\beta\) filter with linear frequency-modulated (LFM) waveforms to achieve optimal range prediction (tracking) of maneuvering targets in steady-state. First, a steady-state root-mean-square (RMS) prediction error, called an RMS-index, is analytically derived for a constant-acceleration target. Next, a design method of the optimal gains that minimizes the derived RMS-index is proposed. Numerical analyses demonstrate the effectiveness of the proposed method, as well as producing a performance improvement over the conventional Kalman filter-based design method. Moreover, the theoretical relationship between range tracking performance and a coefficient for range-Doppler coupling of LFM waveforms is clarified. Numerical simulations using the proposed method demonstrate LFM radar tracking of maneuvering targets and prove the method's effectiveness.  
\(\alpha\)-\(\beta\) filter; range prediction filter; linear frequency modulated waveform; LFM radar +
Footnote †: journal: Applied  
_Accepted: 20 December 2017_; Published: 23 December 2017
## 3 Moving Virtual Sensing Algorithms  
As it is most likely that the virtual location is not spatially fixed, moving virtual sensing algorithms have been developed in recent years. These moving virtual sensing algorithms estimate the error signals at a number of virtual locations that move through the sound field. A number of moving virtual sensing algorithms have been developed including _the remote moving microphone technique_[10], _the adaptive LMS moving virtual microphone technique_[11] and _the Kalman filtering moving virtual sensing method_[12]. A discussion of these algorithms is presented as follows.
## 2 Materials and Methods  
### Design of the Study and the Experimental Data  
We analyzed the records of 63 healthy men aged 21 \(\pm\) 3 years (mean \(\pm\) standard deviation) with an average level of physical activity. The exclusion criteria from the experimental group were the presence of neurological and mental disorders in volunteers and diseases of the cardiorespiratory system. As a cognitive task, we used the Stroop Color Word Test (SCWT) [44] and the mental arithmetic test (MAT) [45], which are standard tests for inducing moderate levels of stress. The subjects performed the stress-inducing cognitive tasks for the first time and had no experience in participating in such experiments.  
The study protocol included four successive stages: 6-min rest (R1), 6-min SCWT (S1), 6-min rest (R2), and 6-min MAT (S2) (Figure 1a). At the R1 and R2 stages, volunteers were asked to relax. During the SCWT, the subject was presented with a sequence of colored words--names of colors for which the color of the letters did not match the color denoted by the word. There were 360 combinations in total, presented in random order at an interval of 1 s. The volunteer had to mentally pronounce the color of the letters (Figure 1b). During the MAT, the volunteer was presented with three-digit numbers and had to sum the digits. If the number obtained as a result of summation contained two digits, they had to sum these up again, continuing this cycle until the final resulting number was a single digit. After obtaining a single-digit number, the subject was asked to decide whether this number was even or odd and press the corresponding button (Figure 1c). In total, each volunteer was presented with a set of 72 numbers. The numbers changed every 5 s.  
During the testing, EEG signals were recorded using the 8-3 system for electrode placement. The signals were recorded using a standard certified digital electrocardiogram Encefalan_EEGP-19/26 [46] with a sampling frequency of 250 Hz and a bandpass filter of 0.016-70 Hz.
## 4 Test Results
### Experimental Results in Generator Mode  
The variable load \(R_{L}\) in the test bench described in Section 2.3 has 5 values of resistance, which gives the corresponding number of electrical load for the generator on each of the four pre-selected rotor speeds. Prior the tests with the load, the no-load losses were determined by the Formula (2) via running the machine on the same rotation speeds but without any electrical load.  
Figure 16 shows the phase loss divided by the frequency of the current (J/cycle), depending on the average effective value of the current in the phase. The dotted line also shows data on losses in the phase with the fixed rotor installed. For these lines, the rotor angles with the maximum loss values are taken.  
We can see a good coincidence of the curves at different frequencies for the generator mode; therefore, the nature of the losses is hysteresis, since it does not depend on the frequency. The level of losses with a movable rotor is much higher. The increase in losses is associated with additional hysteresis losses in the HTS layers of the winding tapes, which occur in an alternating external magnetic field.
###### Abstract  
During its long service life, an arch dam affected by a combination of factors exhibits a typical time-varying characteristic in terms of its structure and material properties, and the deformation in the dam structure can directly and reliably reflect the health and service status of dams. Therefore, an accurate deformation prediction is an important part of dam safety monitoring. However, due to multiple factors, dam deformation data often tend to be highly volatile, and most existing deformation estimation techniques employ a single algorithm, which may not effectively capture the potential change process. A hybrid model for dam deformation prediction has been proposed to overcome this problem. First, dam deformation data are decomposed into three components by seasonal and trend decomposition using loess. Second, a convolutional neural network-gated recurrent unit (GRU) hybrid model, which optimizes hyperparameters using the sparrow search algorithm, is used to capture the nonlinear relationships that exist in each component. Finally, the final prediction result of dam deformation is the comprehensive output of multiple submodules. The deformation monitoring data (period: 2009-2019) of a parabolic variable-thickness double-curved arch dam located in China are considered as the survey target. The test results indicate that the proposed model is suitable for short-term and long-term prediction and outperforms other models in terms of higher robustness to abnormal sequences than other conventional models (R\({}^{2}\) differs by 5.50% and 7.87%, respectively, in short-term and long-term predictions for different measurement points, while other models differ by 9.78% to reach 15.71%, respectively). Among the models studied, the GRU shows better robustness to abnormal series than the LSTM with good prediction accuracy, fewer parameters, and a simpler structure. Hence, the GRU can be employed for dam deformation prediction in practical engineering.  
d 2022
# Quadcopters in Smart Agriculture: Applications and Modelling
## 2 Types of Quadcopters
### Commercial Quadcopters
#### 2.1.3 DJI Matrice 100  
The DJI Matrice 100's maximum flight time depends on its payload, which can reach a maximum of 1 Kg. Many agricultural applications found in the literature were based on the Matrice 100, such as field monitoring and mapping [57; 58; 59; 60], and plant health assessment [61; 62]. As illustrated in Figure 5, the Matrice 100 was used in a field at Aarhus University, Flakkebjerg, Denmark, to evaluate the crop height and assess its volume for field surveying.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Type** & **Weight** & **Battery** & **Maximum Flight Time** & **Maximum Speed** & **Camera** & **Release Year** & **Reference** \\ \hline
3 DR Iris+ & \begin{tabular}{c} 1282 g (including \\ battery) \\ \end{tabular} & 5100 mAh LiPo 3S & 16 min to 22 min & 11 m/s & GoPro camera & 2014 & [49] \\ \hline DJI Phantom 3 Standard & \begin{tabular}{c} 1216 g (including \\ battery and \\ propellers) \\ \end{tabular} & 4480 mAh LiPo 4S & 25 min & 16 m/s & \begin{tabular}{c} 12 MP camera \\ capturing 2.7K \\ videos at \\ 24/25/30 fps \\ \end{tabular} & 2015 & [50] \\ \hline DJI Matrice 100 & \begin{tabular}{c} 2355 g (including \\ TB40T battery) \\ 243I g (including \\ TB40D battery) \\ \end{tabular} & \begin{tabular}{c} TB40T 4500 mAh \\ L9P6 6S or TB48D \\ 5700 mAh LiPo 6S \\ \end{tabular} & 13 min to 40 min & 22 m/s & Not applicable & 2015 & [51] \\ \hline DJI Inspire 1 Pro & \begin{tabular}{c} 2870 g (including \\ propellers and \\ battery) \\ \end{tabular} & 4500 mAh LiPo 6S & 15 min & 18 m/s & \begin{tabular}{c} 16 MP camera \\ capturing 4K \\ videos at \\ 24/25/30 fps \\ \end{tabular} & 2016 & [52] \\ \hline DJI Phantom 4 & \begin{tabular}{c} 1380 g (including \\ battery and \\ propellers) \\ \end{tabular} & 5350 mAh LiPo 4S & 28 min & 20 m/s & \begin{tabular}{c} 124 MP camera \\ capturing 4K video \\ at 24/25/30 fps \\ \end{tabular} & 2016 & [53] \\ \hline DJI Matrice 210 & \begin{tabular}{c} 3840 g (including \\ two \\ or 4570 g \\ (including two \\ TB55 batteries) \\ \end{tabular} & \begin{tabular}{c} 3850 mAh LiPo 4S \\ \end{tabular} & 31 min & 20 m/s & \begin{tabular}{c} Supports: \\ Zemituse X4S, \\ Zemituse X4S, \\ Zemituse X30, \\ Zemituse X4S, \\ Zemituse X4S, \\ Zemituse X4S, \\ Zemituse X4S, \\ SICANTRANGE \\ 3 PX, and Sentera \\ AG3Y10 \\ \end{tabular} & 2017 & [54] \\ \hline DJI Matrice 2 Pro & \
\begin{tabular}{c} 907 g \\ \end{tabular} & 3850 mAh LiPo 4S & 31 min & 20 m/s &
\begin{tabular}{c} 20 MP camera \\ capturing 4K \\ videos at \\ 24/25/30 fps \\ \end{tabular} & 2018 & [55] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Specifications of different commercial quadcopters used in smart agriculture.  
Figure 4: DJI Phantom 3 Standard (photo by [PERSON], sourced from Unsplash under its free license [56]).
# Bioactive Compounds from Herbal Medicine Targeting Multiple Myeloma
## 3 Flavonoids in Multiple Myeloma
### Chrysoeriol  
Chrysoeriol (Figure 3) is a methoxy flavone [140, 141, 142, 143], extracted from Rooibos tea (_Aspalathus linearis_) [144].  
This bioactive compound has been shown to have antioxidant, anti-mutagenic, anti-inflammatory, anti-obesity properties [141, 143, 145, 146, 147, 148], a cytotoxic effect in human lung carcinoma [149], and antitumor activity on breast cancer [150].  
[PERSON] et al. [151] studied the anti-myeloma activity of chrysoeriol in vitro. Chrysoeriol has been shown to be a selective inhibitor of PI3K-AKT-mTOR pathway. It inhibited the proliferation of KM3 and RPMI 8226 cells, but did not affect the proliferation of the normal peripheral blood mononuclear cells. Chrysoeriol blocked the cell cycle of MM cells in the G2/M phase and inhibited the phosphorylation of the AKT protein in MM cells. Chryseoriol significantly decreased p-AKT expression and increased Cyclin B1 and p21 protein expression.  
Figure 3: The chemical structure of chrysoeriol.
# Trigonometric Inference Providing Learning in Deep Neural Networks
## 5 Future Research and Challenges  
To the best of our knowledge, this is the first study to utilize a trigonometric approximation of all parameters in the training of deep neural networks. However, on modern GPUs and CPUs, we were unable to show the efficiency of our training methods because they are specifically optimized for multiplication. We set as our future study the design of a hardware computation engine for trigonometric inference. Here, we briefly discuss the challenges. Although a simple serial CORDIC module requires significantly fewer logic resources than a multiplier, it has certain flaws in terms of speed. To accelerate the CORDIC module for neural network inference, the following two schemes are worth applying:  
(1) CORDIC optimization for small values:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model (ResNet50) and Result** & **Accuracy** & **Training Loss** & **Validation Loss** \\ \hline CNN (\(\mu(BN)=1.0\)) & 0.7171 & 0.0582 & 1.0614 \\ TrigInf (\(\mu(BN)=0.7\)) & 0.7058 & 0.0701 & 1.2853 \\ TrigInf (\(\mu(BN)=0.5\)) & 0.7124 & 0.0644 & 1.6003 \\ TrigInf (\(\mu(BN)=0.1\)) & 0.7205 & 0.0577 & 0.9063 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation results on Cifar-100 with BN layers’ mean set to different values.  
Figure 6: Training curves and Validation loss of GoogleNet and ResNet5 on CIFAR-100 dataset. The left subplots denote the validation accuracy and loss records on GoogleNet and the right subplots denote those on ResNet50.  
Although randomly distributed, layer inputs, weights, and back-propagated errors tend to have small variances in large deep neural networks. Starting the rotation from the mean value can reduce the number of iterations for the CORDIC modules because the mean value can be selected as a pre-computed angle.  
(2) Neural network training with lower-precision CORDIC:  
As mentioned in Section 2, training with a lower precision can provide a comparable performance under many scenarios. Reducing the model precision to 16-bits, 8-bits, or even fewer bits will significantly decrease the CORDIC iteration times. Therefore, the training efficiency is higher when a lower precision is required.
## 2 Materials and Methods
### Performance Indicators
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Contingency Table**} & \multicolumn{2}{c}{**Measured**} \\ \cline{2-4}  & **Rainfall** & **No Rainfall** \\ \hline \multirow{2}{*}{Estimated} & Rainfall & \(a\) & \(b\) \\  & No Rainfall & \(c\) & \(d\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Contingency table to assess the accuracy of the daily GLDAS, MERRA, TRMM, and GPM products in the state of Mato Grosso, Brazil. The table was not applied to GPCP since it does not provide estimates of daily precipitation. The ratio (dimensionless) of occurrences of precipitation events to those that did not occur was obtained through the False Alarm Ratio (FAR) indicator, which has an ideal value of zero (Equation (4)):  
\[FAR=\frac{b}{a+b}. \tag{4}\]  
The ability of a product to correctly (or not) precipitation events was determined by the Probability of Detection (POD) indicator (dimensionless ratio). POD values close to 1 indicate better model accuracy in predicting the precipitation event (Equation (5)):  
\[POD=\frac{a}{a+c}. \tag{5}\]  
The tendency of a precipitation product to overestimate or underestimate the total number of events (thus potentially the precipitation amounts) was determined by the BIAS (dimensionless) (Equation (6)), where BIAS = 1, BIAS > 1, and BIAS < 1 represent perfect agreement, overestimation, and underestimation, respectively:  
\[BIAS=\frac{a+b}{a+c}. \tag{6}\]  
The proportion in the number of correctly predicted precipitation events to the cases where there were no precipitation events was provided by Critical Success Index (CSI) indicator (dimensionless), with ideal value of 1 (Equation (7)):  
\[CSI=\frac{a}{a+b+c}. \tag{7}\]  
In the validation of models of natural phenomena, it is necessary to determine if their behavior is similar to that of the observed. Thus, the Taylor diagram was used to concisely summarize the degree of correspondence between estimated and measured data [28]. Taylor diagram allows to visually presents descriptive statistical summary that include the standard deviation, correlation coefficient (\(r\)), and the RMSE [28].
## 5 Application of CEVTI  
In this section, we apply the CEVTI algorithm to the case of coded Code Division Multiple Access (CDMA) and in the case of uplink massive MIMO systems. As discussed above, CEVTI works as a formal optimization framework based on variational inference with tempering. It has the properties of guaranteed convergence and smooth descent, which lead to a better local optimum.
## 4 Conclusions and Discussion  
To satisfy the need to accurately monitor emotional stress based on the ECG signal, we tried to add different attention mechanisms to the CNN and BiLSTM layers of a CNN-BiLSTM network separately and to the whole network to explore the effectiveness of the attention mechanism. According to that result, we found the most effective attention model that pushed the performance by exploiting the attention mechanism. That result showed that the attention mechanism is effective for solving psychological stress recognition based on ECG signals. More importantly, the experimental results indicated that the combination of attention mechanism and RNN is more effective for this subject compared with CNN, thereby improving the combination of the attention mechanism. Thus RNN will become our future research direction. During the experiment, the impact of motion noise was not considered, so how to evaluate motion noise and eliminate its influence are also a main research focuses for the future. To the best of our knowledge, this is the first time that psychological stress has been recorded using ECG and analyzed using a deep learning framework with attention mechanism. We hope the application of an attention mechanism in psychological stress recognition using ECG can provide an important reference for other researchers.  
Conceptualization, P.Z. and F.L.; methodology, P.Z., L.D. and Z.F.; software, T.Y. and R.Z.; validation, X.C.; formal analysis, X.C. and L.D.; investigation, P.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key Research and Development Project 2018 YFC2001101, 2018 YFC2001802, 2020 YFC2003703, 2020 YFC1512304, National Natural Science Foundation of China (Grant 62071451), and CAMS Innovation Fund for Medical Sciences (2019-IZM-5-019).  
This research was reviewed and approved by the IRB of Beijing Tiantan Hospital, Capital Medical University (no. KYSQ 2019-013-01).  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The data presented in this study are available on request from the corresponding author. As the data were generated during this study, we did not find an appropriate platform to share the data.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 6 Results and Discussion
### Experiment (C)  
In some specific applications such as Spoken Dialogue Systems (SDS), a short response time might improve the user experience and the general usability of the system.  
In this experiment, we want to understand how much the response time varies among the various platforms. Since the servers that run these platforms are not in the same location, we executed the test three times in three different moments of the day. In Figure 9, each column is the average of the response times on the entire test set, while the yellow line represents the average of the three executions during the day. The results are expressed in milliseconds, and we use Rome Time Zone (CEST) to express times. Luis is the fastest platform, while Watson and Wit.ai are the slowest and Watson presents similar response times during the day. Wit.ai's performance seems to suffer from some excessive servers loading during particular times of the day; in fact, it has excellent results in the experiment performed at 11 p.m. while in the remaining two tests it shows the slowest response times. It should be noted that all platforms have average response times for each moment of the day under 400 ms, which is a reasonable response time for a messaging system.
# Recent Advances in Photodetectors Based on Two-Dimensional Material/Si Heterojunctions
## 3 Photodetectors Based on 2D Material-Si Heterojunctions  
A variety of 2D material/Si heterojunction photodetectors have been extensively studied to date. In this session, we will present the recent advancements in 2D material/Si heterojunction photodetectors. Since graphene has been the subject of intense research, we will first discuss graphene/Si heterojunction photodetectors. Subsequently, we will examine TMD/Si heterojunction photodetectors. Notably, although NMD falls under the category of TMD, we will devote a separate section to discussing NMD/Si heterojunction photodetectors due to their unique properties. Following this, we will discuss other types of 2D material/Si heterojunction photodetectors.
## 5 Conclusions  
This study introduced a method to validate the geometric positioning accuracy of SAR images using high-precision automatic trihedral CRs as ground control points. We evaluated the geometric positioning accuracy of the recently launched C-SAR/01 and C-SAR/02 through a series of experiments conducted at the Xilinhot SAR satellite calibration and validation site. The initial findings indicate that the geometric positioning errors of C-SAR images, without the use of control points, were influenced by various factors including the satellite, imaging mode, orbit directions, and others. With the exception of two images that displayed positioning errors near 150 m, most CRs in the other images achieved a range-direction positioning accuracy better than 60 m and an azimuth-direction positioning accuracy better than 80 m. Consequently, the C-SAR series of satellites can ensure the required geometric accuracy for subsequent applications.  
[PERSON] wrote the manuscript and designed the comparative experiments; [PERSON] supervised the study and revised the manuscript; [PERSON] and [PERSON] assisted [PERSON] in designing the article structure and conducting experiments. [PERSON] and [PERSON] assisted [PERSON] in obtaining experimental data. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the National Key R&D Program of China (No.2022 YFC2807000/ 2022 YFC2807003), the Common Application Support Platform for Land Observation Satellites of China's Civil Space Infrastructure (CASPLOS_CCSI), and the China High-resolution Earth Observation System (21-Y20B01-9003-19/22).  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.