esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Since AAC should essentially learn a nonlinear state-feedback controller, it is of interest to obtain such an initial (possibly suboptimal) controller, and this will be shown possible using the VRFT design and tuning framework. This would be significant since model-free AAC approaches are data-hungry in practice and any initial suboptimal solution would shorten the convergence time. Under such motivation, the combination of VRFT and AAC is used to achieve ORM tracking control. The resulting AAC design consists of two neural networks (NNs), one for the controller called the actor NN and one for the cost function approximation called the critic NN. The correction signals during the adaptive learning are backpropagated through the larger NN resulted from cascading the actor and the critic NNs, hence the AAC architecture belongs to the deep reinforcement learning approaches from the literature [32]. The mixed VRFT-AAC approach developed in this paper is applied to a real-world Multi-Input Multi-Output (MIMO) nonlinear coupled constrained laboratory vertical two-tank system for water level control. The approach proposed as follows is novel with respect to the state-of-the-art since:  
* it introduces an original nonlinear state-feedback neural network-based controller for ORM tracking, tuned with VRFT, serving as initialization for the AAC learning controller that further improves the ORM tracking and accelerates convergence to the optimal controller. This leads to the novel VRFT-AAC combination;
* the case study proves implementation of the novel mixed control learning approach for ORM tracking. The MIMO validation scenario also demonstrates good decoupling ability of learned controllers, even under constraints and nonlinearities. Comparisons with a model-free batch fitted Q-learning scheme and with a model-based batch-fitted Q-learning approach are also offered. Statistical characterization case studies in different learning settings are given. * theoretical analysis ensures that the AAC learning scheme preserves the CS stability throughout the updates and converges to the optimal control. The paper is organized as follows: the next section formulates the ORM tracking control problem in an optimal control framework and offers a way to solve it using VRFT (Section 3) and AAC design (Section 4). Validation case study, useful implementation details, comparisons with similar control learning techniques, thorough investigations and discussions of the observed results, are all presented in Section 5. The concluding remarks are highlighted in Section 6.
## 3 Brief Overview of an Artificial Neural Network (ANN)  
An artificial neural network (ANN) is a powerful instrument in regression analysis and classification of data [35]. A multilayer feed forward perceptron neural network based on an error back-propagation was used in this study. Based on Figure 2, ANN includes at least three layers. With more than three layers, they can be divided to three types of layers. These layers type include input, output, and hidden layer. In this method, the input vector is weighted (\(w_{i}\)), and a bias (\(b\)) is added to this value for each neuron, as shown in Equation (1).  
\[y_{i}=\sum_{j=1}^{m}w_{ij}x_{j}+b_{i}, \tag{1}\]  
where \(x_{1}\), \(x_{2}\),..., \(x_{m}\) are input vectors, \(w_{i1}\), \(w_{i2}\),..., \(w_{im}\) are the weight of neuron \(I\), and \(b_{i}\) is bias.  
For describing a nonlinear relationship of input and output data, a nonlinear process on \(y_{i}\) is needed. This nonlinear process is called transfer (activation) function and is shown in Equation (2).  
\[z_{i}=\varphi(y_{i}), \tag{2}\]  
The selection of an appropriate transfer function for training the network is one of the influential parameters on the training procedure [36]. In this study, different transfer functions were used to achieve a highly accurate prediction model. The used transfer functions were Elliot sigmoid, logarithmic sigmoid, and linear and symmetric sigmoid transfer function.  
Feed forward networks have an adaptive learning ability because of the adjustability of the neuron's connection weights. The adjustment of the weight is known as knowledge storing. This indicates the need for comprehensive training to obtain an appropriate connection weight [37]. It should be noted that the applied algorithm in this study was a supervised algorithm, which needs both input-output pairs to perform network training. Equations (3) and (4) summarize the training procedure of networks:  
\[g_{t}=\
abla f(t), \tag{3}\]  
\[w_{j+1}=w_{j}-ag_{t}, \tag{4}\]  
where \(w\) is the vector of weight, \(f\) is the objective function, \(g\) is the error gradient, and \(a\) is the learning rate. The iteration loop with all training datasets is referred to as an epoch, and training procedures sometimes need a couple of epochs to respect the considered criteria for stopping the iteration [38].  
Figure 2: Nonlinear model of a neuron.
# Frequency Domain Full Waveform Inversion Method of Acquiring Rock Wave Velocity in Front of Tunnels
## 2 Method
### Frequency Domain FWI
#### 2.2.3 LBFGS Algorithm  
This paper adopts an improved quasi-newton algorithm LBFGS to optimize the algorithm, which occupies less computing memory and does not need to store Hessian matrices. It has second-order convergence and low computational complexity. The algorithm is constructed in the following steps:  
1. First, give a simple positive definite matrix \(H_{k}^{(0)}\), generally using Equation (13) given by [PERSON] [36]: \[H_{k}^{(0)}=\frac{s_{k}^{T}y_{k}}{||y_{k}||^{2}}I.\] (13)
2. The construction form of the pseudo-Hessian operator with limited memory in the LBFGS algorithm is shown below: \[H_{k}^{(j+1)}=\left(I-\frac{sky_{k}^{T}}{s_{k}^{T}yk}\right)H_{k}^{j}\left(I- \frac{yks_{k}^{T}}{s_{k}^{T}yk}\right)+\frac{sk_{k}^{T}}{s_{k}^{T}yk},j=0,1, \ldots,m,\] (14) where \(s_{k}\) is the update amount of the speed model \(v\), \(y_{k}\) is the update amount of the gradient, \(H_{k}^{j}\) is the Hessian operator with limited memory obtained for the kth time, and m is the dimension of the speed update amount and the gradient update amount. The larger the m, the larger the amount of calculation and memory storage, and the higher the inversion accuracy. Generally, the value range of m is [5; 30].
3. Let \(\rho_{k}=\frac{1}{s_{k}^{T}y_{k}}\), \(V_{k}=\left(I-\rho_{k}y_{k}s_{k}^{T}\right)\); substituting into Equation (14) can get the pseudo-Hessian operator expression. After recurrence, we can know the relationship between the pseudo-Hessian operator of the (k + 1)th iteration and the positive definite Equation (12) given at the beginning.  
\[\begin{array}{l}H_{k+1}=H_{k}^{(m+1)}=\left(m_{k}^{T}\ldots m_{k-m}^{T} \right)H_{k}^{(0)}(m_{k-m}\ldots m_{k})+\\ \sum_{j=0}^{m}\rho k-m+j\left(\prod_{l=0}^{m-j-1}m_{k-l}^{T}\right)sk-m+js_{k- m+j}^{T}\left(\prod_{l=0}^{m-j-1}m_{k-l}^{T}\right).\end{array} \tag{15}\]  
According to Equation (15), it can be seen that the LBFGS algorithm only needs to store \(m+1\) times of speed update amount, \(sk\), and gradient update amount, \(yk\). In this way the pseudo-Hessian matrix \(H_{k+1}\) required for the construction of the descent direction can be calculated. It only uses the information of the first-order partial derivative in the process of constructing the Hessian matrix and reduces the complexity of the calculation. The inversion accuracy of the LBFGS method is higher than the gradient method.
## 4 Experiments and Discussion
### Evaluation Metrics of Demodulation Performance  
To evaluate the performance of the proposed demodulation system, MSE (Mean Square Error), MAE (Mean Absolute Error), \(R^{2}\), and RMSE (Root Mean Square Error) were chosen as performance evaluation metrics, which can be expressed as Equations (6)-(9).  
\[MSE=\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}, \tag{6}\]  
\[MAE=\frac{1}{m}\sum_{i=1}^{m}|(y_{i}-\hat{y}_{i})|, \tag{7}\]  
\[RMSE=\sqrt{\frac{1}{m}\sum_{i=1}^{m}(y_{i}-\hat{y}_{i})^{2}}, \tag{8}\]  
\[R^{2}=1-\frac{\sum_{i}(\hat{y}_{i}-y_{i})^{2}}{\sum_{i}(\overline{y}_{i}-y_{i} )^{2}}, \tag{9}\]  
where \(y_{i}\) is the center wavelength of the FBG sensor measured by OSA, \(\hat{y}_{i}\) denote the FBG center wavelength output by the proposed system, \(\overline{y}\) represent the average of the central wavelength acquired by OSA, and \(m\) denotes the number of samples. The smaller the RMSE, MSE, and MAE, the lower the model's wavelength interrogation error. \(R^{2}\) is an evaluation metric of the fitted regression's effect. The closer it is to one, the better the model effect.
## 3 Results and Discussion
### The Protease Activities of ML Silage  
The protease activities of ML silage are shown in Table 3. Carboxypeptidase, aminopeptidase and acid protease were the exopeptidases mainly responsible for the first stages of proteolysis [8]. Carboxypeptidase activity increased (\(p<0.01\)) when the ensiling time was prolonged. The increased activity of carboxypeptidase may be related to the decrease in pH towards 5.4, at which point carboxypeptidase was optimally active [32]. However, carboxypeptidase activity remained stable in the silage treated with 2% CA during the ensiling process. However, carboxypeptidase activity was consistently higher than 60.0 units/h/DM at 30 days of ensiling in all treatments, which could partly account for the relatively high non-protein nitrogen content of ML silage (Table 2). Aminopeptidase had low tolerance to pH and the optimal activity of aminopeptidase was observed at pH 7.0 [18]. Aminopeptidase activity gradually decreased (\(p<0.01\)) in this study, which was consistent with the change in pH value (Figure 1). Moreover, aminopeptidase activity sharply decreased (\(p<0.01\)) with the addition of CA, which might be due to the direct acidification of CA. Similarly, [PERSON] et al. [32] and [PERSON] et al. [8] observed that formic acid decreased aminopeptidase activity by rapid acidification of the silage. Furthermore, aminopeptidase can release free amino acid residues to provide an additional easily assimilated carbon source for lactic acid bacteria via deamination, accumulating extracellular ammonia-N [8]. Thus, the CA-inhibited aminopeptidase activity might partially account for the decrease in ammonia-N (Table 2). In contrast to carboxypeptidase and aminopeptidase, acid protease activity was slightly increased (\(p<0.05\)) in the silages treated with CA during the ensiling process. Although the pH in the silages treated with CA decreased, the pH still approached the optimal level for acid protease (4.50 in alfalfa). Thus, CA enhanced the acid protease activity. Although acid protease activity was lower than carboxypeptidase activity, it varied from 47.1 to 55.2 units/h/DM, which also promoted the hydrolysis of proteins. Overall, the relatively high activity of carboxypeptidase and the increased activity of acid protease in the silages treated with CA suggested that more CA should be added to ML silage to adjust the pH below 4.5, which might better reduce the degree of protein hydrolysis and improve the silage quality of ML. To further study the relationship between protease and protein fractions, [PERSON]'s correlations were conducted on the data for protein fractions and protease activity. As shown in Table 4, carboxypeptidase activity was negatively correlated (\(p<0.01\)) with true protein content but positively correlated (\(p<0.01\)) with non-protein nitrogen and ammonia-N content,
# A Distributed RF Threat Sensing Architecture
## 4 Test Environment and Experiment Setup
### Experiment Setup and Measurements
#### 4.2.2 Low-Power Constant Jamming Attack in 5.8 GHz Frequency Band  
The second scenario involved a lower-power jamming signal, emulating a much longer distance between the jammer and the sensor. For this scenario, jamming was only performed at 5.8 GHz, as shown in Table 3.  
Low-power RF jamming was also emulated by using a Vaunix signal generator emitting only 10 dBm, at the central frequency of the band. The jamming was performed in two sequential bursts of 40 and 80 s, respectively. The signal was again detected, triggering an alarm, as shown in Figure 10, demonstrating the capabilities of the proposed sensor.
# Development of Simultaneous Dual-Resolution Digital Holography System
###### Abstract  
This research paper is focused on the development of a digital holography system for simultaneous dual-resolution measurements. Digital holography has been widely used for deformation measurements and non-destructive testing (NDT) due to its advantages of high sensitivity, high accuracy, and whole-field, non-touch measurements. A traditional test only has one spatial resolution, which can cause a big deformation to be indistinguishable or minor defects to be ignored. Both large and small fields of view should be observed to reach a multi-spatial resolution measurement. Usually, multiple separate tests are used to observe the different sized fields of view, resulting in higher costs and longer required testing times. Furthermore, these tests may not be repeatable in some cases. This paper presents research on a novel digital holography system that achieves dual spatial resolution measurements simultaneously by testing different-sized fields of view with a single camera. The novel system has two optical channels with two optical layouts of holography to measure deformation. By changing the combined focus length, the two holographic setups have different fields of view, i.e., one has a large and the other has a small field of view. To realize a simultaneous test, the polarization technique is used to avoid cross-interference between the two optical layouts. Finally, spatial carrier fringes with different orientations are introduced into the two holographic setups by appropriately adjusting the reference beam of each setup. The different oriented spatial carrier fringes enable the spectrums of the two interferograms to be separated after a FT (Fourier transform) and the phase distributions of the two interferograms can be extracted and separated by windowing the spectrum to perform an IFT (inverse Fourier transform). The phase distributions can then be used to analyze and calculate the deformations. The experiment using this system is described in this paper and the practicability of this method is verified by the obtained experimental results.  
Keywords:dual spatial resolution; speckle pattern interferometry; holographic interferometry; spatial phase shift method; polarization technology +
Footnote †: journal: Medical Engineering, School of Engineering and Computer Science, Oakland University, Rochester, MI 48309, USA 1
## 4 Problem Definition and Motivation
For example, when \(o_{1}\) in Figure 3 crosses the boundary of \(q_{4}.R\) as depicted in the figure, it sends the \(UpdateResult\) message to the server. However, because \(o_{1}\) is not matched to \(q_{4}.V\) (_i.e._, \(o_{1}.a_{2}\
eq q_{4}.v_{2}\) and \(o_{1}.a_{3}\
eq q_{4}.v_{3}\)), \(o_{1}\)'s movement does not affect the result of the corresponding query \(q_{4}\); hence, \(o_{1}\) can ignore \(q_{4}.R\), and check its movement against only \(q_{1}.R\) (because \(o_{1}\) is matched to \(q_{1}.V\)) and send the \(UpdateResult\) message if necessary. In our previous paper [1], we proposed the BQR-tree, which is the extension of the QR-tree. Each node \(N\) of the BQR-tree additionally stores the summary of non-spatial values (or intervals) each query specifies in the form of bit-vector. With the bit-vector information stored in \(N\), the server can identify whether non-spatial values (or intervals) can be found in \(N\) or not. Therefore, when searching the resident domain of each moving object \(o\), if there is no non-spatial values (or intervals) to which \(o\) is matched in a given node \(N\), \(N\) can be the additional part of \(o\)'s resident domain. (Note: In the BQR-tree, each subspace of the entire space corresponds to each node.) This relieves the first drawback because the server can assign \(o\) a larger resident domain if possible. However, in the BQR-tree method (BQRT),the capability \(o.Cap\) of \(o\) is still measured by the number of spatial query ranges without considering non-spatial values (or intervals) because the BQR-tree is a naive form of the enhanced QR-tree, where tree construction (operations of insertion and deletion) is based mostly on the spatial information. In addition, the searching the additional part of \(o\)'s resident domain tends to be computation-intensive. On the other hand, BQRT overcomes the second drawback by filtering needless spatial query ranges. In the next section, in order to remedy the problems stated above, we propose the GQR-tree that supports efficient evaluation of CM range monitoring queries. Table 1 summarizes the primary notation we use throughout the paper. \begin{table}
\begin{tabular}{l l} \hline \hline
**Notation** & **Explanation** \\ \hline \(o\) & A moving object \\ \(o.loc\) & The current location of \(o\) \\ \(o.A\) & The non-spatial attribute values of \(o\) \\ \(o.bv\) & The object bit-vector of \(o\) \\ \(q\) & A CM range monitoring query \\ \(q.R\) & The spatial query range \(q\) specifies \\ \(q.V\) & A set of non-spatial query values (or intervals) \(q\) specifies \\ \(q.bv\) & The query bit-vector of \(q\) \\ \(g\) & A query group \\ \(g.bv\) & The group bit-vector of \(g\) \\ \(N\) & A GQR-tree node or its corresponding subspace of the entire workspace \\ \(g\_N\) & A set of \(g\)’s elements (queries) whose spatial query ranges are covered by or partially intersect \(N\) \\ \(|g\_N|\) & The cardinality of \(g\_N\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Frequently used notation. Figure 3: Example of content-matched (CM) range monitoring queries.
## 5 Conclusions and Future Perspectives
In conclusion, the PANTHYR system prototype was successfully tested in a basic configuration (without autonomous power supply and without data transmission over the internet) giving robust performance and high-quality hyperspectral data. The system prototype meets the requirements for future worldwide deployment in a network for hyperspectral validation of water reflectance data from satellites. Conceptualization, D.V., K.R., and A.C.; methodology, D.V., K.R., and A.C.; software, D.V. and Q.V. ; validation, Q.V. and M.B. ; formal analysis, K.R., Q.V., and M.B. ; writing--original draft preparation, K.R. and D.V. ; writing--review and editing, A.C., Q.V., M.B., and D.V. This research was funded by BELSPO (Belgian Science Policy Office) in the framework of the STEREO III program project HYPERMAQ (SR/00/335), and by the European Space Agency PRODEX/HYPERNET-OC project. The AAOT fieldwork was partially funded by the European Space Agency FRM4 SOC project. The authors would like to thank the Plymouth Marine Laboratory (PML) and Consiglio Nazionale delle Ricerche (CNR), Dr. [PERSON], Tartu Observatory, and the FRM4 SOC project for organizing the FICE at AAOT and for providing mounting frames for the M3 TRIOS system radiometers. Tartu Observatory provided the radiometric calibration of all five RAMSES instruments used here. Dr. [PERSON] is acknowledged for providing the AERONET-OC data and for kind advice. TRIOS Gmbh is acknowledged for providing details of the serial protocol communication for the RAMSES instruments, allowing us to write the necessary data acquisition software. For the latter, the RFLEX code provided by [PERSON] provided some useful ideas on how to proceed. [PERSON] provided appreciated technical feedback. We thank the three anonymous reviewers for many constructive comments on the first version of this paper. The authors declare no conflicts of interest. 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.
## 3 Implementation
### The 3D Inter-Vehicle Projection Model  
The key step to realize cooperative augmentation is to model the geometric projective relation between two view images. As shown in Figure 3, the same object will map in a different location, scale, and shape in the front vehicle (vehicle A) and host vehicle (vehicle B) images. It is obvious that the object's points in image plane A and B are according to some geometric projective constrains. We suppose that the view angle between the two cameras is limited, and thus, the shape deformation will be ignored here. Therefore, the mapping relation between two image planes satisfies some linear geometric transformation. In our model, affine transformation, a non-singular linear transformation [22], is adopted here. It has the matrix representation in block form:  
\[P_{B}=HP_{A}=\left[\begin{array}{cc}A&T\\ 0^{T}&1\end{array}\right]P_{A} \tag{1}\]  
Figure 2: Architecture of the cooperative visual augmentation algorithm.  
with A a \(2\times 2\) non-singular matrix, \(T\) a translation 2-vector, and \(0^{T}\) a null 2-vector. \(P_{A}\) and \(P_{B}\) represent points sets in image plane A and B.  
Our geometric projection model is shown in Figure 3. \(O_{a}\) and \(O_{b}\) denote the optical centers of the two cameras, and \(\Pi_{a}\) and \(\Pi_{b}\) are the correspondence image planes. Points \(v\) and \(t\) represent 3-space points of vehicle and tree, respectively, in the Euclidean world frame. Applying projective geometry, 3D point \(v\) in \(\Re^{3}\) (three-dimensional Euclidean space) is mapped to points \(v_{a}\in P_{A}\) and \(v_{b}\in P_{B}\) in \(\Re^{2}\) (two-dimensional Euclidean space) in image planes \(\Pi_{a}\) and \(\Pi_{b}\). Similarly, \(t_{a}\in P_{A}\) and \(t_{b}\in P_{B}\) are the mapping points of the 3D point \(t\) in \(\Re^{3}\). Here, the tree can be seen by both vehicles (vehicle A and B); however, the red vehicle is visible to vehicle A and is occluded to vehicle B. Illustrated in Figure 3, \(v_{a}\) is the known image point and \(v_{b}\) is the unknown point that needs to be estimated. The estimation process based on this model is as follows:  
1. Suppose we have \(n\) points \(T^{i}\in\Re^{3}\) (\(i=1,\cdots,n\)) seen by both vehicles, matching pair points \((i^{i}_{a},t^{i}_{b}),i^{i}_{a}\in P_{A},t^{i}_{b}\in P_{B}\) will be obtained correspondingly.
2. The projection matrix, geometric transformation parameters, \(H\) are estimated based on the \(n\) matching points pair \((i^{i}_{a},t^{i}_{b})\).
3. Through \(H\) and \(v_{a}\), the occluded point \(v_{b}\) can be calculated.  
There is an assumption that if the space points \(v\) and \(t\) are coplanar then there exists a precise projective transformation. However, in fact, they are usually at different depths which will cause a deviation in projection. This situation will result in an inaccurate estimation of point \(v_{b}\). In order to obtain a more accurate result, depth information is adopted here to improve the mapping results. We propose a new deep-affine transformation to solve this problem. This part is detailed in Section 3.4 of implementation.
## 4 Discussion
### Limitations  
Despite the small number of patients involved in the study (\(n\) = 5) and only TLSO designs being analysed, the sample size was large enough to show that characterisation of this process is possible. Demographics also present a major factor. This study analyses the data from Sri Lankan female patients from ages 11 to 16 years old, with right and left curves, and varying Risser grades. This range would have likely led to larger variability than had these factors been accounted for. Therefore, future work to elucidate trends in more controlled subsets of patients is warranted and may lead to improved quantification of the rectification process. A larger study on more brace designs and more curve types would be required before an algorithm can be developed for clinical use. Moreover, the characterisation results may vary depending on the orthotisl involved in the manual rectification. To support the statistical significance of the found parameters, a larger number of patients and orthotists will need to be involved in a new study.