esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Preliminary
#### Modeling of Chemical Compounds  
We represent the graph structure of a chemical compound as a graph with labels on vertices and multiplicity on edges in a hydrogen-suppressed model. Nearly 68.5% (resp., 99%) of the rank-2 chemical graphs with at most 200 non-hydrogen atoms registered in chemical database PubChem have a maximum degree at most 3 (resp., 4) for all non-core vertices in the hydrogen-suppressed model. Let \(\Lambda\) be a set of labels, each of which represents a chemical element such as \(\mathcal{C}\) (carbon), 0 (oxygen), N (nitrogen) and so on, where we assume that \(\Lambda\) does not contain \(\mathbb{H}\) (hydrogen). Let \(\operatorname{mass}(\mathtt{a})\) and \(\operatorname{val}(\mathtt{a})\) denote the mass and valence of a chemical element \(\mathtt{a}\in\Lambda\), respectively. In our model, we use integers \(\operatorname{mass}^{*}(\mathtt{a})=\lfloor 10\cdot\operatorname{mass}( \mathtt{a})\rfloor\), \(\mathtt{a}\in\Lambda\) and assume that each chemical element \(\mathtt{a}\in\Lambda\) has a unique valence \(\operatorname{val}(\mathtt{a})\in[1,4]\). We introduce a total order \(<\) over the elements in \(\Lambda\) according to their mass values; i.e., we write \(\mathtt{a}<\mathtt{b}\) for chemical elements \(\mathtt{a},\mathtt{b}\in\Lambda\) with \(\operatorname{mass}(\mathtt{a})<\operatorname{mass}(\mathtt{b})\). Choose a set \(\Gamma_{<}\) of tuples \(\gamma\,=\,(\mathtt{a},\mathtt{b},k)\in\Lambda\times\Lambda\times[1,3]\) such that \(\mathtt{a}<\mathtt{b}\). For a tuple \(\gamma=(\mathtt{a},\mathtt{b},k)\in\Lambda\times\Lambda\times[1,3]\), let \(\overline{\gamma}\) denote the tuple \((\mathtt{b},\mathtt{a},k)\). Set \(\Gamma_{>}=\{\overline{\gamma}\mid\gamma\in\Gamma_{<}\}\), \(\Gamma_{=}=\{(\mathtt{a},\mathtt{a},k)\mid\mathtt{a}\in\Lambda,k\in[1,3]\}\) and \(\Gamma=\Gamma_{<}\cup\Gamma_{=}\). A pair of two atoms \(\mathtt{a}\) and \(\mathtt{b}\) joined with a bond of multiplicity \(k\) is denoted by a tuple \(\gamma=(\mathtt{a},\mathtt{b},k)\in\Gamma\), called the _adjacency-configuration_ of the atom pair. We use a hydrogen-suppressed model because hydrogen atoms can be added at the final stage. A _chemical graph_ over \(\Lambda\) and \(\Gamma\) is defined to be a tuple \(G=(H,\alpha,\beta)\) of a graph \(H=(V,E)\), a function \(\alpha:V\to\Lambda\) and a function \(\beta:E\to[1,3]\) such that  
1. \(H\) is connected;
2. \(\sum_{uv\in E}\beta(uv)\leq\operatorname{val}(\mathtt{a}(u))\) for each vertex \(u\in V\); and
3. \((\alpha(u),\alpha(v),\beta(uv))\in\Gamma\) for each edge \(uv\in E\). Let \(\mathcal{G}(\Lambda,\Gamma)\) denote the set of chemical graphs over \(\Lambda\) and \(\Gamma\). Descriptors  
In our method, we use only graph-theoretical descriptors for defining a feature vector, which facilitates our designing an algorithm for constructing graphs.
## 2 Machine-Learning Algorithm and Factor Analysis
### Factor Analysis of Exploratory Data Analysis  
#### 2.2.1 Overview of Exploratory Data Analysis  
Exploratory data analysis (EDA) [31] is a method used to understand relationships in data. This statistical method is widely used in various fields including medicine, finance, social media, and fighting crime, to reduce the complexity of data and reveal underlying patterns [32; 33; 34; 35]. EDA is an essential tool for understanding the structure of underlying data and enhancing model interpretability. Recently, the emergence of XAI in machine-learning models enabled the automatic identification of correlations between target variables and features in existing datasets [36; 37]. This is particularly anticipated in addressing the \"black-box problem\" often faced by machine-learning models in clarifying the causal relationships between explanatory and target variables. The \"black-box problem\" refers to a situation in which the internal structure of a machine learning model is too complex to explain the model's predicted results. Therefore, it is difficult for users to understand the specific basis of the prediction results, and visualization techniques are required to increase the interpretability of machine learning models.  
The black box problem refers to a situation in which it is unclear how a machine-learning model makes predictions or classifications. This is challenging in advanced models, particularly those based on deep learning, owing to their numerous internal parameters and complex structures. However, factor analysis makes it possible to extract the main factors from a group of variables with many correlations and interpret how these factors contribute to the overall output. This helps to clarify the internal workings of the model, revealing how individual explanatory variables affect the target variable. Consequently, this aids in understanding the causal mechanisms behind the model rather than merely capturing correlations.
## 3 LOS Path Planning  
### LOS Correction Model  
Most existing area-array whisk-broom imaging systems use downward-looking cameras, with the camera pitch angle close to 0\({}^{\circ}\). During whisk-broom imaging, the LOS ground trajectory remains approximately along the same horizontal line, as shown by the shallow blue curve in Figure 3a. Under the proposed backward-squint imaging conditions, the camera pitch angle increases significantly to 45\({}^{\circ}\). Consequently, during whisk-broom imaging, the LOS deviates substantially from the vertical flight path, as illustrated by the shallow blue curve in Figure 3b.  
During aircraft flight, image motion is induced by both forward motion and scanning deviations along the wingspan direction. Considering these factors, a LOS path planning  
Figure 3: Scanning trajectory deviation in the wingspan direction: (**a**) vertical downward view whisk-broom; (**b**) backward squint whisk-broom.  
Figure 2: Aircraft body coordinate system and camera coordinate system.  
method for vertical flight path correction under backward-squint conditions is established, as shown in Figure 4.  
Considering a single whisk-broom scanning cycle, the camera performs uniform angular motion around the roll axis. Based on geometric relationships, the scanning angular velocity \(\omega\) is determined by the pitch tilt angle \(\theta\), total scanning field of view \(\phi_{\text{ITFOV}}\). field overlap ratio \(\rho\), instantaneous field of view \(\beta\), detector's vertical pixel count \(n\), and scanning efficiency \(\eta\), and can be expressed as the following equation:  
\[\omega=\frac{\phi_{\text{ITFOV}}}{\eta(1-\rho)\left[\tan\left(\theta+\frac{1} {2}n\beta\right)-\tan\left(\theta-\frac{1}{2}n\beta\right)\right]}\cdot\frac{ \upsilon}{H} \tag{3}\]  
When the aircraft's fuselage is aligned with the nadir point, the camera is backward squinted at \(45^{\circ}\) toward the ground origin, defining this moment as \(t=0\). The horizontal distance between the aircraft and the ground origin is \(L=H\). Based on the geometric relationships illustrated in Figure 4, the roll planning angle \(\phi_{N}\), the effective imaging height imaging height \(H^{\prime}\), the horizontal distance \(L\) between the aircraft and the ground point, and the pitch planning angle \(\theta_{\text{N}}\) are expressed by the following equations:  
\[\phi_{N}(t)=\omega t \tag{4}\] \[L(t)=H\tan\theta+\upsilon t\] \[H(t)^{\prime}=\frac{H}{\cos\phi(t)}\] \[\theta_{N}(t)=\arctan(\frac{H}{H^{\prime}})\]  
Substituting \(\phi_{N}\) and \(\theta_{N}\) into Equation (2) yields the normalized LOS direction vector \(\overrightarrow{v_{LOS_{N}}}\).  
\[\overrightarrow{v_{LOS_{N}}}=\left[\begin{array}{c}\sin\theta_{N}\\ -\cos\theta_{N}\sin\phi_{N}\\ \cos\theta_{N}\cos\phi_{N}\end{array}\right] \tag{5}\]  
Figure 4: Schematic diagram of vertical flight path LOS correction.
# Long-Term Nitrogen Addition Eliminates the Cooling Effect on Climate in a Temperate Peatland
## 3 Discussion
### Effects of Long-Term Nitrogen Addition on CO2 Fluxes in Peatlands
Furthermore, phenolic compounds play a crucial role in inhibiting microbial enzyme activity [50]. However, N addition can enhance PPO activity [12], potentially reducing phenolic compound concentrations. This reduction may, in turn, stimulate microbial activity and subsequently increase CO\({}_{2}\) emissions. In this study, long-term N addition significantly reduced soil C: N and increased PPO activity. As PPO activity increases, complex organic matter is broken down into simpler, more water-soluble organic compounds [51; 52], leading to a rise in DOC concentration. Since DOC is an important C source for microbes, its increased concentration can enhance microbial activity [53; 54], thereby increasing ER. However, in this study, we did not find a significant rise in ER with increasing N addition. This may be due to the substantial decline in GPP. We found that ER in this study was highly correlated with GPP. A substantial decline in GPP indicates reduced photosynthesis, which subsequently lowers the input of organic C into the soil and ecosystem [55; 56], restricting both microbial heterotrophic respiration and plant autotrophic respiration. Ultimately, this dynamic counterbalances the increase in ER driven by the enhanced peat decomposition resulting from N addition. Therefore, long-term N addition primarily regulates CO\({}_{2}\) fluxes in peatlands by affecting vegetation dynamics.
## 4 Simulation Results and Performance Analysis  
### Performance Criterion  
This section compares the proposed method with the other published methods in acquiring GNSS signals under the same simulation parameter. Three types of signal are presented for signal acquisition analysis under no consideration for mutual jamming between signals. Suppose signals are down converted to 20 MHz and the sampling frequency is 64 MHz, which fulfills [PERSON]'s sampling requirement due to the fact that the P-code has a bandwidth of 20.46 MHz. To verify the proposed method as capable of acquiring GNSS signals, the post-correlation signal-to-noise-ratio (PSNR) is adopted to evaluate the performance of different methods and is calculated as follows:  
\[\text{PSNR}=10\text{log}_{10}\frac{C_{\text{p}}}{E[C_{\text{n}}]} \tag{21}\]  
where \(C_{\text{p}}\) is the highest peak of search power during the correlation process. \(E[C_{\text{n}}]\) depicts the mean power of noise term \(C_{\text{n}}\). The proposed method was evaluated by using a developed program under the MATLAB environment.
# Data-Driven and Machine-Learning-Based Real-Time Viscosity Measurement Using a Compliant Mechanism
## 5 Results and Discussion
### Description of the Dataset  
Experimentation was performed to obtain 250 records of the piezoelectric sensor's output for each viscosity value and a total of 1500 records for different viscosity values. This dataset was subsequently split into training and testing sets, with 80% (1200 samples) used to train the ML models and 20% (300 samples) reserved for testing. The training set was employed to develop models capable of predicting viscosity based on the sensor output records. In order to determine the most effective model for viscosity prediction, the performance of Multiple Linear Regression, Decision Tree Regression, Support Vector Regression, and Random Forest Regression was rigorously evaluated and compared using several key metrics. The use of a separate testing set allowed for a reliable assessment of each model's generalization capability on unseen data.
# Dead-Beat Control Cooperating with State Observer for Single-Phase Electric Springs
###### Abstract  
Aiming at improving the performance of the existing for single-phase electric springs (ESs), such as the fastness of the voltage stabilization and the mitigation of the voltage harmonics across the critical loads (CLs), the dead-beat control cooperating with state observer is proposed in this paper. First, the \(\delta\) control is reviewed, outlining its features of regulation of the CL voltage while keeping the ES operation stable. After describing the operation of an ES in the continuous-time domain by the state-space technique, its discrete-time model is formulated using the zero-order-hold (ZOH) algorithm. Then, the control system for an ES is designed around the dead-beat control cooperating with a state observer and implementing the two typical compensation functions achievable with the \(\delta\) control, namely the pure reactive power compensation and the power factor correction. Results obtained by simulation demonstrate that the control system is able to both properly drive an ES and to implement the two functions. The results also show that the proposed control system has the advantage of eliminating harmonic components in CL voltage when grid voltage distorts.  
Keywords: electric spring; dead-beat control; renewable energy source; distributed generation; microgrids; grid connected +
Footnote †: journal: Applied Sciences
## 2 Related Work  
Target tracking is an important topic in computer vision which has been studied for decades. In fact, the existing methods can be divided in two categories: generative and discriminative [4]. For generative methods, an appearance model is used to represent the target observation and tracking is formulated to search the similar area. One representative example is the subspace model which assumes that the target observations lie in a low dimensional manifold. For example, object appearance is represented with an eigenspace [5], affine warps of learned linear subspaces [6], incrementally learned low-dimensional subspace [7], incremental tensor subspace [8], online appearance model [9], _etc_.  
Discriminative models treat tracking as a binary classification problem with the goal to separate target from background. A feature vector is constructed for each pixel of the reference image, then an adaptive ensemble of classifiers is trained to separate target pixels from background pixels [10]. The TLD tracker [11] uses P-N learning [12] to exploit the data structures and get feedback about the performance of the classifier. It is shown to be reliable in long sequence tracking.  
Sparse representation has been applied to tracking problem which shows excellent performance [3]. In the particle filter framework, each target candidate is represented sparsely as a linear combination of target templates and trivial templates. Group sparsity is integrated and very high dimensional features are used for improving tracking robustness [13]. To make full use of the sparse coefficients to discriminate between the target and the background, a robust tracking method is developed based on the structural local sparse appearance model [14]. Besides, object tracking can be formulated as a multi-task sparse learning problem, and particles are modeled as linear combinations of dictionary templates which are updated dynamically to learn the representation of each particle [15]. To exploit the relationship between particles which are represented as sparse linear combinations of dictionary templates, the tracking problem is casted as a low-rank matrix learning problem [16].  
The coefficient is obtained via solving either \(\ell_{0}\) or \(\ell_{1}\) norm minimization problem in these approaches. In [3], the \(\ell_{1}\) norm minimization is solved based on interior point method [17], which is very slow when solving large-scale \(\ell_{1}\) minimizations. A real-time tracker is proposed by adopting compressive sensing and a customized Orthogonal Matching Pursuit (OMP) algorithm to accelerate the tracking [18]. A very fast numerical solver is developed to solve the \(\ell_{1}\) norm minimization problem with guaranteed quadratic convergence based on the accelerated proximal gradient approach [19]. A tracking algorithm is developed with a static sparse dictionary and dynamic online updated basis distribution [20].
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 2 Literature Review
### Image-Based Methods
#### 2.6.3 Markerless (Amplitude/Phase) Binning
Trajectories of feature points over the sequence of CBCT projections were then formed, using a random sample consensus-image-registration method, followed by the application of PCA, to identify the trajectory related to the respiratory signal. In comparison with other image-based methods for breathing-signal extraction, such as the Fourier transform method and LIFT, the average phase shift with this technique was observed to be 3.74 and 0.48 projections lower, respectively. Peak signal-to-noise ratio for reconstructed images was 5.08 dB, 1.05 dB and 2.90 dB higher than with the Amsterdam Shroud, Fourier transform and LIFT methods, respectively. cIRPB method provide several advantages, such as being markerless, and overcomes the restrictions caused by limited-view projections. The authors highlighted the fact that their method is limited by the processing time and memory requirements. Another promising approach for extracting lung tumor motion from CBCT projections was presented by [PERSON] et al. [23]. Initially, planned CT projections were separated into two groups, with the first group of projections having only the tumor and the second group having the tumor removed. Rigid frame-wise registration was then carried out between CBCT projections and digitally reconstructed radiographs (DRRs) of the two groups. Subtraction of the registered DRR from CBCT projections resulted in intensified tumor visibility. Tumor motion was tracked through active contouring. In comparison with the ground-truth signal, the signal extracted through the proposed method resulted in an error of lower than 0.2 mm. The error for patient data was between 1.4 and 2.2 mm. In addition to the mentioned studies, other significant studies in the literature include [PERSON] et al. [24], who used PCA on fluoroscopy images to extract breathing signal. [PERSON] et al. [25] used kernel-ridge regression on planning and follow-up 4D CT data. In this work, we utilize the CBCT data only, as it provides the most recent view of the internal anatomy. A preliminary version of our research performed on phantom datasets has been previously reported and published [26]. The emerging artificial intelligence (AI) techniques have recently been adopted in different domains. AI has been applied in IGRT and has shown great potential in motion tracking in radiotherapy. In a recently published review paper [27], four main categories of motion management using AI are examined and summarized: (1) marker-based and (2) markerless tracking, where, in these two categories, the individual target is tracked throughout the treatment, (3) full- anatomy monitoring, where the intrafraction changes are monitored in the full anatomy within the field of view, and (4) motion prediction, where AI is used to account for the time latencies to localize, process and act.
# Geolocation, Calibration and Surface Resolution of CYGNSS GNSS-R Land Observations
## 2 Geolocating GNSS Reflections from Land
### Local Surface Delay Calculation
The individual Doppler terms induced by the receiver and transmitter are calculated as  
\[D_{R}=-1(\vec{R}_{v}\cdot\hat{D}_{RS})(\frac{f}{c}) \tag{7}\]  
\[D_{T}=-1(\vec{T}_{v}\cdot\hat{D}_{TS})(\frac{f}{c}) \tag{8}\]  
where \(\vec{R}_{v}\) is the receiver velocity, \(\vec{T}_{v}\) is the transmitter velocity, \(f\) is the GNSS signal transmit frequency (for GPS L1 = 1.57542 GHz), and \(c\) is the speed of light (299,792,458 m/s). Also \(\hat{D}_{RS}\) and \(\hat{D}_{TS}\) are unit vectors directed between the receiver and surface point and transmitter and surface point, respectively:\[\hat{D}_{RS} =\frac{\vec{R}-\vec{S}}{\left|\vec{R}-\vec{S}\right|} \tag{9}\] \[\hat{D}_{TS} =\frac{\vec{T}-\vec{S}}{\left|\vec{T}-\vec{S}\right|} \tag{10}\]  
where \(\vec{R}\) is the receiver position, \(\vec{T}\) is the transmitter position, and \(\vec{S}\) is a point on the local three dimensional surface grid. The predicted Doppler frequencies at every point on the local surface grid are then differenced with the Doppler frequency of the received GNSS-R observations ( \(D_{max}\) ) to obtain  
\[\delta P=P_{T2S2R}-P_{T2R}=(P_{S2R}+P_{T2S})-P_{T2R} \tag{2}\]0  
The Doppler difference contour map is then calculated over the local three dimensional surface, and the iso-Doppler contour which satisfies the condition \(\delta D=0\) represents the physical surface region in agreement with the CYGNSS-observed Doppler.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method
### Embedding and Extraction of the Watermark
#### 3.3.2 Watermark Embedding Steps  
Having described the rules of watermark embedding in detail in the previous part, we will present the watermark embedding steps in this subsection. The procedure of watermark embedding consists of the following steps.  
Step 1: Divide the input image \(I_{0}\) into non-overlapping blocks with the size of \(n\times n\), and calculate the \(MSE(p,q)\) of each block.  
Step 2: Extract histogram \(H\) from the input image \(I_{0}\) and generate the embedding range \(H_{T}\) using Equations (3) and (4).  
Step 3: In view of the histogram bin's width \(M\), determine the number of the bins from the selected embedding range firstly and then take each of three continuous bins to form a group. Suppose the height (pixel's numbers) in Bin 1, Bin 2, and Bin 3 is \(a\), \(b\), and \(c\), respectively; the embedding rules are as follows:  
Case 1: If it satisfies the condition \(|a-b|<(a+b+c)/3\quad\&\quad|a-c|<(a+b+c)/3\quad\&\quad|b-c|<(a+b+c)/3\), no pixels will be adjusted; else, the other operations are as follows:  
* If it meets \((a-b)>(a+b+c)/3\), the number of modified pixels is \(transfer_{ab}=\lfloor(a-b)/2\rfloor\) (\(transfer_{ab}\) refers to the selected pixels, which will be moved from Bin 1 to Bin 2), these selected pixels of Bin 1 will be added to \(M\);
* If it meets the condition \((a-c)>(a+b+c)/3\), firstly, choose all \(transfer_{ab}=\lfloor(a-c)/2\rfloor\) pixels which will be moved from Bin 1 to Bin 2 by adding to \(M\), then all \(transfer_{bc}=\lfloor(a-c)/2\rfloor\) pixels of Bin 2 are chosen to be modified so as to make them fall in Bin 3. To ensure the modification/adjustment degree \(M\) of each pixel, the order in the above steps cannot be changed because these pixels chosen from Bin 2 should not contain those that have been adjusted in Bin 1. The corresponding modification/adjustment degree of some pixels may be \(2\times M\) if the order is exchanged.
* The others are similar to the above.  
Case 2: If it meets the condition \(b\geq T\times a\quad\&\quad b\geq T\times c\quad\&\quad|a-c|<(a+b+c)/4\), no operation is needed. Otherwise, if it meets the condition \(a>(a+b+c)/4\), move all \(transfer_{ab}=a-(a+b+c)/4\) pixels from Bin 1 to Bin 2 by adding to \(M\); if \(c>(a+b+c)/4\), move all \(transfer_{cb}=c-(a+b+c)/4\) pixels from Bin 3 to Bin 2 by reducing \(M\).  
The other cases are similar to the two cases mentioned above.  
Step 4: According to the above histogram modification/adjustment procedure, to modify the histogram shape of each group, when adjusting the pixel value, choose blocks with larger \(MSE\).  
Step 5: These above steps are repeated until the whole PN sequence is embedded. The final modified image is the watermarked image.  
For the ease of understanding, we display the partial pseudo-code of the embedding algorithm. This is shown in Algorithm 1.