esa-sceva/satcom-chunk-collection huggingface dataset:
# Recent Developments in Instrumentation of Functional Near-Infrared Spectroscopy Systems
## 1 Introduction
A source-detector separation of 3 cm is a reasonable compromise between depth sensitivity and SNR in the brain studies of adults population [18; 19] while a source-detector separation of 2 to 2.5 cm is reasonable for the brain of infants population [20; 21]. fNIRS instrumentations continually improve, facilitated in part by the availability of compact semiconductor optical photodiodes at the wavelength of interest. Nowadays, commercially compact and wearable systems are also accessible for imaging and spectroscopy. Even with such advancements, fNIRS systems continue to be a subject of practical developments to make them wearable and as compact as possible. The pros and cons of fNIRS have been reviewed in a number of articles [10; 22; 23]. Ref. [23] specifically discusses the main features of the commercially available fNIRS systems. fNIRS technology is experimentally flexible, silent, and can be easily integrated with positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or electroencephalography (EEG). Nevertheless, fNIRS systems have two main limitations, namely a low spatial resolution of about 1 cm and its ability to get the hemodynamic response at the outer cortex only [23]. Over the last few decades, fNIRS systems have been designed by utilizing different NIRS techniques. These techniques can be categorized into three main types: (i) continuous wave (CW) by measuring the light attenuation using a constant tissue illumination; (ii) frequency-domain (FD) by utilizing the phase delay and attenuation of detected light; and (iii) time-domain (TD) by measuring the shape of short pulses after propagation through tissues. The signal acquired by these techniques is then post-processed using signal processing algorithms. Accordingly, various systems and techniques  
Figure 1: Illustration of the light signal propagation via a biological tissue after it has been partially absorbed and scattered. have been the subject of many informative articles and reviews. Table 1 represents a brief list of those review papers that focused on the fNIRS systems and their applications. However, most of the reviews have been focusing on the differences among these three techniques and their applications rather than the instrumentation of such systems. Therefore, the main goal of this paper is to remedy this gap by examining the main differences and similarities between the CW, TD, and FD techniques in building the fNIRS system from an instrumentation point of view. The manuscript is organized as follows: First, the light-tissue interaction at NIR represented by the effects relevant to fNIRS namely the absorption and scattering, and the basics of NIR instrumentation are presented in Section 2. In Sections 3-5, we discuss CW, FD, and TD fNIRS recently developed instrumentations. Section 6 then presents a comparison across these three modalities and finally, the paper is summarized in Section 7.
## 2 Models for Voltage Distributions and Measurement Data
### Parameter Estimation  
To approximate the measured distributions, we use a parameter estimation approach presented in [37]. The distributions \(f(x\mid L_{i})\) are modeled by a piecewise-defined function  
\[f(x\mid L_{i})=\frac{1}{n_{i}}\cdot\begin{cases}c_{i}\cdot e^{\lambda_{i}(x-x_ {i})}&x<x_{i}\\ \frac{1}{\sqrt{2\pi\
u_{i}^{2}}}e^{\frac{-(x-x_{i})^{2}}{2\sigma_{i}^{2}}}&x \geq x_{i}\end{cases} \tag{1}\]  
Figure 2: Measurements of TLC flash memory in different life-cycle states.  
with  
\[c_{i}=\frac{1}{\sqrt{2\pi\sigma_{i}^{2}}}e^{\frac{-(c_{i}-\mu_{i})^{2}}{2\sigma_{ i}^{2}}}. \tag{2}\]  
With the cumulative distribution function \(\phi(x)\) of the standard normal distribution  
\[n_{i}=1+\frac{c_{i}}{\lambda_{i}}-\phi\left(\frac{x_{i}-\mu_{i}}{\sigma_{i}}\right) \tag{3}\]  
ensures the normalization  
\[\int_{-\infty}^{\infty}f(x\mid L_{i})dx=1. \tag{4}\]  
The proposed model requires four parameters, i.e., \(\sigma_{i},\mu_{i},\lambda_{i}\), and \(x_{i}\). We model the exponential tail to the left by an exponential distribution and the rest by a normal distribution. This leads to a precise estimation of the measured distributions, which is shown by the fitted curve in Figure 3. This figure shows measurements of the voltage distribution \(f(V_{th}|L_{7})\) of a fresh memory together with a fitted distribution.  
The magnitude of the skewness of the measured distributions is very small. Consequently, the parameters \(\sigma_{i}^{2}\) and \(\mu_{i}\) can be computed from the sample mean and the sample variance, as the effects of the exponential tail on the mean and variance can be neglected. For the exponential distribution, a least squares regression line is fitted to the logarithm of the measured distribution. The distributions of the lower states also have an exponential tail towards the higher voltages, which is not modeled as it is negligible compared with the tail to lower voltages.  
We use the curve fitting to determine the optimal threshold voltage \(V_{opt}(r_{i})\) for each read reference such that the bit error probability is minimized, i.e., the optimal threshold voltage is defined as  
\[V_{opt}(r_{i})=\underset{V}{\text{argmin}}\int_{V}^{\infty}f(x\mid L_{i-1})\; dx+\int_{-\infty}^{V}f(x\mid L_{i})\;dx. \tag{5}\]
## 3 Time-Frequency Point Selection
### SPTFDs Properties  
Consider an \(N\times M\) whitening matrix \(\mathbf{W}\) with unitary mixing matrix \(\mathbf{U}=\mathbf{WA}\) and  
\[(\mathbf{WA})(\mathbf{WA})^{\mathrm{H}}=\mathbf{U}\mathbf{U}^{\mathrm{H}}= \mathbf{I}, \tag{23}\]  
where \(\mathbf{I}\) denotes the identity matrix. Pre- and postmultiplying \(\mathbf{D}_{\mathbf{xx}}(t,f)\) by \(\mathbf{W}\) results in the whitened matrix, defined as  
\[\underline{\mathbf{D}}_{\mathbf{xx}}(t,f)=\mathbf{W}\mathbf{D}_{\mathbf{xx}}(t,f)\mathbf{W}^{\mathrm{H}}=\mathbf{U}\mathbf{D}_{\mathbf{ss}}(t,f)\mathbf{U}^ {\mathrm{H}}, \tag{24}\]  
where the middle equation is from (22) and (23). The whitening step results in a linear model. The whitening matrix is calculated as an inverse square root from the data covariance matrix [33] or the STFDs matrix [4]. The whitening matrix is based on independent signals. However, the SPTFDs matrix needs not the above condition. The SPTFDs matrix in (22) and (24) guarantees the effective subspace algorithms to conduct a class of issues, for instance, blind sources separation and high-resolution DOA estimation [34].
## 3 Results and Discussion
### Impedance Spectroscopic Studies
The pyroelectric measurements, widely described in our previous paper [49], reveal the presence of significant thermally stimulated depolarization currents (TSDC), which are proof of the existence of a large amount of space charge in the ceramic material. The discussed TSDC completely disappear in the case of lanthanum-modified PBZT 25/70/30 ceramics [33], causing the aforementioned increase in grain and grain boundary resistance. The presented results seem to remain consistent with the results shown for PLZT-type ceramics, where the La dopant reduces p-type conductivity [50; 51]. In order to check this possible effect, measurements of electric conductivity (\(\mathrm{\sigma}\)) as a function of temperature were carried out over a wide temperature range. The results are shown in the form of the dependence \(\mathrm{ln}\sigma\) vs. 1/T (Figure 9a). The lanthanum ion modification caused a significant reduction in electric conductivity in comparison with undoped ceramics, which is in good agreement with our assumptions. The linear character of the \(\mathrm{ln}\sigma\) vs. 1/T dependence allows for determining the activation energy of DC conductivity (\(\mathrm{E_{aD}}\)) for ceramics with various La contents. The value decreases with the increase in lanthanum ion concentration (Figure 9b). Figure 8: Dependence of the \(\mathrm{ln}\mathrm{R_{G}}\)(1/T) and \(\mathrm{ln}\mathrm{R_{GB}}\)(1/T) of PBZT 25/70/30 ceramics doped with lanthanum. The similarity in the nature of the changes in activation energy for DC conductivity and conductivity in grain boundaries, caused by modification with lanthanum ions, suggests that the main microstructural element responsible for conductivity in the discussed materials is the grain boundaries.
## 3 Experiment and Analysis
### Experiment
#### 3.1.2 Evaluation Indexes of the Experiment  
This paper mainly introduces RepVGG network structure and SK attention mechanism based on the yolov5 algorithm and replaces the original NMS with Adaptive NMS. The improved algorithm is called the yolov5-RSAN algorithm. The improved algorithm is compared with the original algorithm, and the experimental results show that the improved algorithm has a great improvement in performance. In the detection algorithm, the performance evaluation indexes mainly include Precision, Recall, mAP, AP, fps, etc.  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{2}\]  
\[\text{Recall}=\frac{TP}{TP+FN} \tag{3}\]  
In the formula, \(TP\) indicates the correct target quantity detected, \(FP\) indicates the wrong target quantity detected, and \(FN\) indicates the undetected target quantity. With the Recall value as the horizontal coordinate and the Precision value as the vertical coordinate, the P-R curve of the recall rate can be obtained, and the area enclosed by the P-R curve and the horizontal and vertical axes is the AP value. The larger the AP value is, the better the detection effect is. The calculation method of the AP value is shown in Formula (4).  
\[\text{AP}=\int_{0}^{1}p(r)dr \tag{4}\]  
mAP is obtained by averaging the AP value of each object so as to measure and evaluate the overall detection effect of the target detection algorithm model. fps is a measure of the speed of the detection algorithm. fps indicates how many pictures (frames) can be processed and detected per second in the target network, which can be simply understood as the refresh rate of the image, that is, how many frames occur per second. fps is an indicator to evaluate the speed of the object detection algorithm; the shorter the time is, the faster the speed will be. There is the following calculation formula:  
\[\text{fps}=\text{frameNum}/\text{elapsedTime} \tag{5}\]
## 2 Materials and Methods
### LED Current Control  
The correct operation of the ZAD controller makes possible to model the diode as a current source with a gain equal to \(1-d\). Based on this consideration, the equivalent circuit of the output node is reported in Figure 3. This figure also includes a cascade LED current controller, which automatically defines the reference value of the ZAD controller. Moreover, the figure considers the temperature compensator, which defines the LED current reference, thus ensuring a regulated luminous flux produced by each group of LED.  
The differential equation in the capacitor is given in Equation (27). The Laplace domain leads to the transfer function given in Equation (28) between the current of the LED \(i_{LED}\) and the current of the inductor regulated by the ZAD controller \(i_{Lref}\).  
\[i_{c}=i_{L}(1-d)-\frac{v_{c}+i_{c}r_{c}}{Z_{L}} \rightarrow \quad C(Z_{L}+r_{c})\frac{dv_{c}}{dt}+v_{c}=i_{L}(1-d)Z_{L} \tag{27}\] \[\frac{i_{LED}}{i_{Lref}}=G_{LED}=\frac{(1+sCr_{c})(1-d)}{C(Z_{L} +r_{c})s+1} \tag{28}\]  
Considering that the ESR resistance (\(r_{c}\)) of the output capacitor is small with respect to both the impedance \(Z_{L}\) of commercial LEDs and the impedance of the capacitor at the natural frequency, the previous transfer function can be simplified as in Equation (29).  
\[G_{LED}\approx\frac{1-d}{CZ_{L}s+1}\quad\text{if}\quad\left\{\frac{r_{c}}{Z_{L }}<<1\quad\text{and}\quad r_{c}\omega_{n}C<<1\right\} \tag{29}\]  
The LED current controller is designed with a PI structure as presented in Equation (30), which leads to the closed-loop transfer function between the LED current (\(i_{LED}\)) and the desired value (\(i_{LEDref}\)) given in Equation (31).  
\[G_{cv}=k_{p}+\frac{k_{i}}{s} \tag{30}\] \[\frac{i_{LED}}{i_{LEDref}}=\frac{\frac{1-d}{Z_{L}C}\left(sk_{p}+k _{i}\right)}{s^{2}+\frac{1+k_{p}(1-d)}{Z_{L}C}s+\frac{k_{i}(1-d)}{Z_{L}C}} \tag{31}\]  
Contrasting the denominator of (31) with the canonical second-order transfer function \(s^{2}+2\rho\omega_{n}s+\omega_{n}^{2}\), and taking into account that the settling time (in the 2% band) of a second-order system is approximated by \(t_{s}=\frac{3.9}{\rho\omega_{n}}\), then the natural frequency \(\omega_{n}\) of the closed-loop system is calculated as given in Equation (32). Finally, the parameter \(k_{p}\) is calculated in Equation (33) to impose the desired settling time \(t_{s}\), while the parameter \(k_{i}\) is calculated in Equation (34) to impose the desired damping ratio for the LED current.  
\[\omega_{n}=\frac{3.9}{\rho t_{s}} \tag{32}\] \[k_{p}=\frac{1}{1-d}\left[\frac{7.8Z_{L}C}{t_{s}}-1\right]\] (33) \[k_{i}=\frac{Z_{L}C}{1-d}\left(\frac{3.9}{\rho t_{s}}\right)^{2} \tag{34}\]  
This control structure and parameters design will be tested in the next section.  
Figure 3: Equivalent circuit of the closed-loop ZAD controller.
# Simulation and Optimisation of Utility-Scale PV-Wind Systems with Pumped Hydro Storage
## 2 Modelling Approach
If the renewable surplus power is lower than the pump nominal rated power (\(P_{P\_N}\)), the difference is purchased from the grid to run the pump at rated power. If the purchase electricity price is between the two setpoints \(\rightarrow\)**'no arbitrage'** (dead band for arbitrage): if there is surplus power, it is sold to the grid; if there is net load, it is covered by the hydro turbine only if the purchase electricity price is higher than the load set point limit (\(Pr_{E\_purch}(t)>Pr_{E\_purch\_load\_stp}\)) and the net load is higher than a specific percentage of the turbine nominal rated power (\(P_{Net\_load}(t)>P_{\%}/100\cdot P_{HT\_N}\)). In this way, the hydro turbine is used to supply the net load (using stored energy) only when the purchase price and power are high (avoiding turbine low efficiency at low power). **Type B** system (not allowed to purchase electricity from the grid for arbitrage). Unlike Type A, it is not allowed to purchase electricity from the grid for arbitrage in this system (purchasing electricity from the grid is allowed only to supply the net load). In this type of system, the selling price is the electricity price used for comparison with arbitrage setpoints. For each time step, the energy arbitrage operates as follows: if the sell electricity price is higher than the high set point (\(Pr_{E\_sell}(t)>Pr_{E\_sell\_High\_stp}\)) \(\rightarrow\)**'arbitrage: discharge'**, the priority is injecting (sell) electricity into the grid (net renewable generation + hydro turbine maximum power). If the sell electricity price is lower than the low set point (\(Pr_{E\_sell}(t)\leq Pr_{E\_sell\_Low\_stp}\)) \(\rightarrow\)'arbitrage: charge', the priority is storing energy (to pump water just with the renewable surplus power, not buying electricity from the grid). If the sell electricity price is between the two setpoints \(\rightarrow\)**'no arbitrage'** (dead band for arbitrage): same as in type A. For both cases, the maximum grid power (\(P_{\max\_grid}(t)\)) limits the power purchased and sold to the AC grid.
# Performance Investigation of Active, Semi-Active and Passive Suspension Using Quarter Car Model
## 3 Simulation
### Semi-Active Suspension System  
Figure 9 shows the Simulink model of the semi-active suspension that was based on the respective time domain equations and the control strategy outlined previously in Figures 5 and 6.  
In Figure 9, it can be seen that a forward gain of \(a=-1\) was used in the PID feedback loop, which was performed to ensure that the variable damping coefficient, \(b_{1}(t)\), was always positive. Furthermore, a saturation block, titled \({}^{\prime}b_{1}(t)\) limits', was used in the model to limit the variable damping coefficient, such that (10 \(\mathrm{Ns/m}\leq b_{1}(t)\leq 2500\)\(\mathrm{Ns/m}\)). The limits for \(b_{1}(t)\) were chosen based on suspension performance and safety considerations.  
Figure 9: Simulink model of the semi-active suspension.  
The PID controller of the semi-active suspension was then tuned to optimise both comfort and road holding (handling) based on the suspension performance criteria outlined previously. Sprung mass acceleration was the primary criteria for comfort; however, sprung mass displacement was also considered. Moreover, tyre deflection was used to indicate road holding performance. The PID controller gains (\(K_{p}\),\(K_{i}\), \(K_{d}\)) were manually adjusted using the trial and error method based on an understanding of the effect of each gain on the closed-loop response. The trial and error method was most suitable for this application since a number of responses, such as sprung mass displacement, sprung mass acceleration, and tyre deflection, had to be optimised. Table 2 shows the final PID gain values of the semi-active suspension system, which were obtained using the trial and error tuning method. It can be seen that only proportional gain, \(K_{p}\) was required to amplify and scale the error signal appropriately. Figure 10 highlights the variable damping coefficient response of the semi-active suspension for all three road profiles.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Neural Network Structure and Function
#### 3.1.3 Convolutional Neural Network  
The CNN [35] is a type of neural network specifically designed to handle data with grid-like structures. This network architecture excels in image processing tasks, capable of identifying two-dimensional patterns with shift, scale, and other forms of distortion invariance. The basic structure of a CNN includes convolutional layers, pooling layers, and fully connected layers.  
Convolutional Layer [36]: The convolutional layer is the core of a CNN, consisting of multiple convolutional kernels (or filters). These kernels slide over the input data and perform convolution operations to generate feature maps. Each convolutional kernel can learn specific features from the input data.  
Pooling Layer [37]: The pooling layer typically follows the convolutional layer and is used to reduce the spatial size of the data (i.e., downsampling), decrease the number of parameters in the network to prevent overfitting, and enhance the model's robustness. Common pooling operations include max pooling and average pooling.  
Fully Connected Layer [38]: The fully connected layer is usually located in the last few layers of the CNN, responsible for receiving the features extracted from the preceding layers and outputting the final prediction results. In classification tasks, a softmax layer is often appended after the fully connected layer to convert the outputs into probability distributions. Throughout the training process, the CNN continuously updates the weights of the convolutional kernels and fully connected layers using the backpropagation algorithm to minimize the error between the predicted values and the actual values.
## 4 Data Analysis and Findings  
Structural Equation Modelling (SEM) can be an effective method where the objectives indicate the investigation of associations between variables and the strength of variables affecting a construct. Furthermore, according to [PERSON] [53], SEM can be deployed to conduct multivariate regression in confirmatory and exploratory studies. Moreover, the strength of relationships can be explored in order to prioritise resources for the most important variables to better serve management purposes. Moreover, unobserved variables (i.e., constructs that are not directly measurable) can be included in such analyses; therefore, SEM provides an ideal tool for business and management research studies [49]. The objectives of the study and the nature of the data collected will determine the selection of the most appropriate SEM method. Due to the need for statistical generalisation, the relatively small sample size, novelty of the conceptual model, and capability to handle variables with non-normal distributions, Partial Least Squares Structural Equation Modelling (PLS-SEM) was considered the most appropriate SEM method, which accord with recommendations proposed by [PERSON] [49] and [PERSON] et al. [51]. PLS-SEM is a statistical application capable of providing reliable estimations from complex, predictive and hypotheses-testing models associated with almost any sample size [51]. Since the aim of PLS-SEM is to maximise variance, the metrics of PLS-SEM, including the Coefficient of Determination (R\({}^{2}\)), Significance (\(p\)-value) and (\(t\)-value), and Predictive Relevance (Q\({}^{2}\)), are capable of hypotheses testing in different combinations. The analysis was conducted using SmartPLS software application [54] according to the guidelines and instruction provided by [PERSON] et al. [51].
## CNN Model Solution Framework Foundations  
### Receiver Performance Indicators  
This study highlights the receiver's performance by modelling a decision unit strategy. The UWOC receiver unit performance is influenced by the water channel signal impairments and various noise sources on the receiver side, such as electronic thermal, optical background, dark current, and shot noise--reference [4] reviewed such noise sources. Generally, the leading performance indicators for a digital receiver are SNR and BER. The SNR is represented by the following:  
\[SNR=\frac{P_{S}}{P_{N}} \tag{13}\]  
where \(P_{S}\) and \(P_{N}\) represent the signal power and noise power, respectively.  
The BER is defined as the probability of incorrect identification of a bit by the decision circuit of the underlying receiver [81]. It is one of the most important metrics for assessing signal quality and estimating communication system performance. If the number of error bits received is \(N_{e}\) and the total bits is \(N_{t}\), then the BER is as follows:  
\[BER=\frac{N_{e}}{N_{t}} \tag{14}\]  
The relation between SNR and BER is embedded in the following formula:  
\[BER=\frac{1}{2}\cdot erfc\left(\frac{\sqrt{2\cdot SNR}}{2}\right) \tag{15}\]
# A Novel Mosaic Method for Spaceborne ScanSAR Images Based on Homography Matrix Compensation
## 3 Traditional Feature-Based Mosaic Process for SAR Images
### Image Projection  
To ensure the relative positions between images are accurate, the image to be stitched was projected to the coordinate system of the reference image by scaling, rotation and translation based on the homography matrix. \((x,y)\) are the coordinates of any pixel in the image to be stitched, and the coordinates of pixels in the projected image are represented by \((x^{\prime},y^{\prime})\):  
\[\left[\begin{array}{c}x^{\prime}\\ y^{\prime}\\ 1\end{array}\right]=\mathbf{H}\left[\begin{array}{c}x\\ y\\ 1\end{array}\right]=\left[\begin{array}{ccc}a_{x}&b_{x}&c_{x}\\ b_{y}&a_{y}&c_{y}\\ 0&0&1\end{array}\right]\left[\begin{array}{c}x\\ y\\ 1\end{array}\right] \tag{2}\]