esa-sceva/satcom-chunk-collection huggingface dataset:
# The Effect of Processing Methods on Phytochemical Composition in Bergamot Juice
## 4 Discussion
### In Vitro Antioxidant Activity  
In our study the total antioxidant activity of BJ was evaluated using radical scavenging assays based on single electron transfer (SET) mechanisms (2,2-diphenyl-1-picrylhydrazyl, DPPH assays).  
The DPPH assay measured the so-called radical-scavenging activity (RSA) which is the ability of extract constituents to scavenge reactive species to stop the initiation or propagation of oxidizing chain reactions [26].  
The interaction or synergistic effect among the nutrients and/or bioactive compounds contained in citrus fruits could contribute to their antioxidant activity. Figure 3 illustrated the results of the antioxidant activity obtained for BJ tested in the present study.  
Screw press (Ep-3) BJ exhibited the lowest EC\({}_{50}\) value. Since the ascorbic acid and stachyrine content in all juice was comparable. Furthermore, the high flavanone glycosides, stachyrine, and limonoid aglycones content in screw press (Ep-3) BJ may also contribute to its potent DPPH radical activity. In fact, data reported in Figure 3, confirmed that the debittered bergamot juice (dBJ), had significant higher EC\({}_{50}\) value. The contribution of antioxidant activity, in dBJ, was due to the ascorbic acid and stachydrine content since, in batch operations, cross-linked divinyl benzene-styrene resin reduced the flavanone glycosides and limonoid aglycones up to 90% (Table 2). The debittering processing with these resins had no effect on the minerals, acid, and amino acids content of the juice, as reported by [PERSON] for navel orange juice [35].  
In the present study, the contribution of ascorbic acid to the antioxidant activities of BJ was found to be moderate (Figure 4). [PERSON] and [PERSON] [36] had underlined the significant contributory role of polyphenols in the total antioxidant activity of long-life orange juice even if ascorbic acid was present at a higher concentration (Table 1).
# Analysis of Mining Waste Dump Site Stability Based on Multiple Remote Sensing Technologies
## 3 Methodology
### Small Baseline Subset Analysis
As shown in Figure 4, line OA represents the horizontal displacement \(d_{A_{\Delta},h}\) of Point A. Line CA represents the projection of \(d_{A\Delta}\) onto the vertical plane of LOS, which can be expressed with the following equation:  
\[d_{A_{\Delta},h-los}=d_{A_{\Delta},h}\cdot\sin(\pi-\alpha_{A}+\alpha_{0}) \tag{2}\]  
where \(d_{A_{\Delta},h-los}\) is the projection of \(d_{A\Delta}\) onto the vertical plane of LOS, and \(\alpha_{A}\) is the angle of line OA in reference to the south. \(\alpha_{0}\) is the heading angle of the descending orbit with reference to south. Figure 4: The Geometry between slope direction and LOS in the Xudonggou dump site. As depicted in Figure 5, the LOS displacement of Point A (\(d_{A\_,los}\)) comprises the projections of \(d_{A\_,v}\) and \(d_{A\_,h-los}\) to the LOS direction, which can be expressed with the following equation:  
\[d_{A\_,los}=d_{A\_,h-los}\cdot\sin(\theta)+d_{A\_,v}\cdot\cos(\theta) \tag{3}\]  
where \(\theta\) is the look angle of the SAR satellite. By putting Equations (1) and (2) in Equation (3), the relationship between \(d_{A}\) and \(d_{A\_,los}\) is expressed by the following equation:  
\[d_{A\_,los}=d_{A}\cdot\cos(\beta)\cdot\sin(\pi-\alpha_{A}+\alpha_{0})\cdot \sin(\theta)+d_{A}\cdot\sin(\beta)\cdot\cos(\theta) \tag{4}\]  
Therefore, the deformation of Point A along the LOS direction is translated to deformation along the slope direction via the following equation:  
\[d_{A}=\frac{d_{A\_,los}}{\cos(\beta)\cdot\sin(\pi-\alpha_{A}+\alpha_{0})\cdot \sin(\theta)+\sin(\beta)\cdot\cos(\theta)} \tag{5}\]  
Similarly, the displacement of Point B along the slope can be retrieved with the following equation:  
\[d_{B}=\frac{d_{B\_,los}}{\cos(\beta)\cdot\sin(\pi-\alpha_{B}+\alpha_{0})\cdot \sin(\theta)+\sin(\beta)\cdot\cos(\theta)} \tag{6}\]  
where \(\alpha_{B}\) is the angle of line OB with reference to the south direction. In the study area, \(\alpha_{0}\) is approximately 11 degrees, \(\theta\) is approximately 26 degrees, and \(\beta\) is approximately 32 degrees. The parameter \(\alpha_{A}\) or \(\alpha_{B}\) varies for different points due to their various locations on the dump slope.
## 3 Genetic Algorithm with Maximum-Minimum Crossover (GA-MMC)  
### Optimization of Radiation Pattern Control of PAs  
#### 3.1.1 Optimization Problem  
Among the three steps related to controlling radiation pattern of phased array radars (PARs) for a rocket tracking system (RTS), the greatest difficulty for the designer resides in the modeling, because  
Figure 7: Scanning alternatives. (**a**) Rotational basis. (**b**) Pyramidal basis.  
it involves an optimization problem with various boundary conditions, imposed by the other steps (specification and implementation). Such optimization is a difficult task, which demands experience and time, so it needs the aid of computational tools that can be applied in different situations and requirements at the expense of small adaptations. One example occurs with PARs used in new applications. They require the adjustment of the PA radiation pattern for the new operation settings.  
Therefore, the design of PAs is a complex problem because it uses arrays with large numbers of elements and various combinations of excitation signals producing a large number of possible solutions. Additionally, beyond conventional optimization objectives, such as main lobe scanning and sidelobe reduction, it has been common to reject interfering signals due to applications in electronic warfare and eliminating interference caused by commercial activities near the PAR.  
In this context, there are several deterministic and probabilistic optimization methods to calculate solutions for that problem. In simpler cases, where only one or two optimization objectives were needed, conventional deterministic methods are very popular. Among them, the Dolph-Tschebyscheff method stands out because it is an intermediate solution between the Uniform and Binomial methods [7, 50]. However, the deterministic methods may fail when there is nonlinearity, discontinuity, multimodality, multiple optimization objectives and vast spaces of search, or noises [13, 16, 17].  
When deterministic methods do not apply, probabilistic methods are used, such as genetic algorithms (GAs), which explore large search spaces and manipulate a large number of constraints. Therefore, GAs are remarkable in design optimization of PAs, since this is a nonlinear problem of multiple objectives, in which conventional deterministic methods are vulnerable to local optimum [13, 16, 18, 19, 20, 21, 22, 23, 51, 52, 53, 54, 55, 56, 57, 58, 59]. Thus, despite the amount of calculation, at the end of the process, GAs outperform results achieved by conventional methods [54].
# A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network
## 1 Introduction
The authors of [18] analyzed the transmission characteristics of terrestrial and back-haul links to propose a greedy-based user association algorithm and a matching algorithm with user grouping for balancing the load by performing multiple iterations between users and cells. In [19], the authors noted that the current methods adopt the greedy strategy, which leads to the load imbalance problem in cells. Thus, they defined a load coefficient and added it to the reward function to make handover decisions while balancing loads. Reference [20] proposes a load balancing scheme based on a load measurement metric for both a terrestrial network (TN) and a non-terrestrial network (NTN). However, the metric ignores the impact of user changes in the future, which may result in poor performance over a long period of time. Since most existing load-balancing methods are usually performed reactively after the overload occurs [21], it would be worthwhile to design a method of active load balancing that acts before overloading occurs. Fuzzy logic is an effective pre-evaluation method [22]. Thus, in this paper, the fuzzy evaluation metric (FEM) is proposed to evaluate the impact of users on overloading for both TN and NTN. Then, we propose a joint load balancing scheme to solve the problem of overloading in STIN. The joint scheme consists of two parts. To reduce the tendency of overload before it occurs, an access algorithm is proposed: deep reinforcement learning (DRL) is a widely utilized tool to make selection decisions in a dynamic environment [23]. However, the neural network will not learn effectively with too many states and actions to be explored. Therefore, the fuzzy-logic- and reinforcement-learning-based access algorithm (FLRL-AC) is proposed. Then, to further ensure load balance, an offloading algorithm is proposed to offload users after overloading occurs: Existing studies on offloading usually only take the received signal as a single metric. To take into account the impact of dynamic changes of users, a fuzzy-logic-based offloading algorithm (FL-OL) is proposed. Finally, the fuzzy-logic-based load balancing scheme (FL-LB) is proposed, which is the combination of FLRL-AC and FL-OL. The rest is organized as follows. Section 2 describes the considered network structure and the problems and solutions discussed in this paper. Numerical and simulation results, which demonstrate and verify the analysis, are presented in Section 3. Section 4 describesadvantages and future research areas of this study. Finally, our concluding remarks are made in Section 5.
## 3 Results
### Evaluation in En Face Refocused OCTA Images  
To show the performance of the proposed method more clearly, the contrast of the original (defocused) en face OCTA image was compared with that of the en face OCTA image refocused with the proposed method, as shown in Figure 5. A set of volumetric data, 2048 (A) \(\times\) 500 \(\times\) 4 (B \(\times\) repetition) \(\times\) 500 (C), was acquired while setting the focal plane of probe lens deep inside the mouse brain. A doublet lens with a focal length of 30 mm was used as a probe lens and the input beam diameters were around  
Figure 4: A comparison of the two ways of automatic refocusing of defocused images. (**a**) Automatically recovered OCTA images by blood flow images and structural images, respectively. White arrows indicate noticeable flow signals between two approaches. Distribution of propagation length estimated by (**b**) blood flow images and (**c**) structural images corresponding to each image. The green arrow indicates a focal position. Scale bar is 500 μm.  
2 mm. The theoretical DOF of the system was calculated to be 2.4 mm in air. Figure 5a,c showed en face maximum intensity projections (MIPs) of original and recovered OCTA/OCT images acquired at certain depth ranges. We can see that small vessels and detailed structures indicated as yellow triangles are appreciated in the refocused image. The line profiles along the white dotted lines of Figure 5a were plotted in Figure 5b, showing that the OCTA image recovered by using OCTA images as guiding stars has superior contrast in comparison to the original one and the one recovered by using OCT images. Recovered and original OCT images at specific depths (180, 460, 740, and 1020 \(\upmu\)m) are shown in Figure 5c. To quantify the improvement of the image contrast, CNR values of OCT images in Figure 5c were calculated and plotted in Figure 5d.
## 3 Uncertainty Modeling Techniques
### Robust Optimization (RO)  
RO has become an indispensable tool in power systems for managing uncertainties and improving system resilience. In [51], a two-stage RO approach is introduced to assess the flexibility capacity of new power systems, focusing on balancing supply and demand while optimizing ramping capabilities and operating costs. Similarly, [52] applies a distributionally robust resilience optimization model to address uncertainties in post-disaster power system restoration, using stochastic programming combined with a Wasserstein distance-based ambiguity set to improve recovery strategies. For wind power integration, [53] proposes a data-driven distributionally RO approach that captures spatial correlations between wind farms, resulting in more accurate and cost-effective dispatching decisions for multi-resource grids. In [54], a multi-resource reserve RO model integrates wind power frequency regulation potential, improving system flexibility and frequency stability through a column-and-constraint generation algorithm. Expanding on inter-regional planning, [55] combines stochastic and RO methods to handle uncertainties in generation and transmission planning for the ASEAN region, leading to a cost reduction of 6.0% by leveraging cross-border renewable energy. Reference [56] introduces an RO model for aggregating active distribution systems into virtual power plants (VPPs), addressing uncertainties in renewable generation and dispatch orders. These studies demonstrate the versatility of RO in addressing uncertainties across different domains in power systems.  
Figure 7 compares RO with traditional optimization. Solution A, derived from traditional optimization, excels in optimality but shows considerable variation in the objective function relative to changes in the design variable, potentially extending into infeasible ranges. On the other hand, Solution B, achieved through RO, demonstrates moderate optimality and excels in robustness, maintaining a narrow dispersion in the objective function as the design variable varies.  
A typical optimization problem with uncertainty can be written as [57]  
\[\min_{x\in X}\{f(x)\ :g(x,u)\ \leq\ 0,\ \forall u\in U\} \tag{53}\]  
where \(x\in X\) is the vector of decision variables, \(f(x)\) is the objective function that should be minimized in this example, \(g(x,u)\ \leq\ 0\) represents sample constraints of the problem, where \(u\in U\) are the uncertain parameters, and \(U\) is the uncertainty set, which describes the possible realizations of the uncertain parameters.  
In RO, the goal is to ensure that the solution \(x\) is feasible for all possible values of the uncertain parameter \(u\) within the uncertainty set \(U\). The general RO formulation of a min-max problem is as follows:  
\[\underset{x\in X}{\text{minmax}}f(x,u) \tag{54}\]  
The lower problem ensures that the worst-case scenario is considered for the uncertain parameter \(u\) and the upper problem tries to find the best decision \(x\) considering this worst-case scenario.
## 3 Experiment Setup and Network Training  
### Experimental System Setup  
The detailed experimental setup of our FSO system, as illustrated in Figure 4, is delineated herein. At the transmitter side, the digital signal is subjected to the arbitrary waveform generator (AWG, KEYSIGHT M9505A). Subsequently, the transmitted signal is sent into an electronic amplifier (EA) and an attenuator (ATT). The modulated signal is then channeled to the laser via a Mach-Zehnder modulator (MZM). A collimating lens is employed to focus the emitted light. The channel encompasses a 3-m expense of free space. The frequency response diagram of the channel is delineated in Figure 4. At the receiver side, the received light is initially converged by another collimating lens before traversing an optical attenuator (OA) and a photodetector (PD, Agilent 11982A). The signal is then sampled with an oscilloscope (OSC, KEYSIGHT MXR604A). Essential received data, imperative for subsequent neural network training and testing, are seriously archived for comprehensive analysis.  
Figure 4: Experimental setup and the actual picture of the experiment.  
Figure 3: Structure of the 1D-CNN cell.  
The digital signal processing is as follows. First, a pseudo-random bit sequence (PRBS) is mapped into 32 QAM complex symbols. These symbols are then subjected to up-sampling by a factor of 4, followed by their division into in-phase (I) and quadrature (Q) components. Subsequent application of pulse-shaping filters to both components lead to their amalgamation, followed by power normalization to yield the transmitted data. Upon reception, the received digital signal, Tx, undergoes equalization of our designed equalizer. Following this, CAP demodulation is employed to demodulate the equalized signal. The resultant signal is then down-sampled and subsequently de-mapped after symbol-wise equalization, thus facilitating the recovery of the original data.
## 3 Results
No differences were found between mean SR values in preexisting hypothyroidism cases compared to euthyroid cases (1.24 \(\pm\) 0.26 vs. 1.18 \(\pm\) 0.25; \(p\) = 0.4615) (Figure 5). In the control group, no child needed treatment. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & \
\begin{tabular}{c} **Children with CAT** \\ \(n\) **= 52** \\ \end{tabular} &
\begin{tabular}{c} **Healthy Children** \\ \(n\) **= 22** \\ \end{tabular} & \(p\) **Value** \\ \hline SR mean value & 1.19 \(\pm\) 0.25 & 0.68 \(\pm\) 0.2 & \textless{}0.0001 \\ \hline SR-LTL & 1.20 \(\pm\) 0.31 & 0.66 \(\pm\) 0.19 & \textless{}0.0001 \\ \hline SR-RTL & 1.18 \(\pm\) 0.27 & 0.70 \(\pm\) 0.24 & \textless{}0.0001 \\ \hline \hline \end{tabular}  
CAT—chronic autoimmune thyroiditis; SR—Strain Ratio; SR-LTL—Left thyroid lobe SR; SR-RTL—Right thyroid lobe SR.  
\end{table} 
Table 2: Mean SR values in the two groups of children. Figure 4: The area under the receiver operating characteristic curve (ROC-curve) for SR measurements. In univariate regression analysis, the following parameters were associated with the presence of CAT: weight (\(p\) = 0.015), height (\(p\) = 0.031), BSA (\(p\) = 0.005), thyroid values (\(p\) < 0.001), ATPO antibody levels (\(p\) < 0.001), ATG antibody levels (\(p\) = 0.017), and family history of CAT (\(p\) < 0.001).Multivariate regression analysis was used to evaluate the independent factors associated with CAT. The regression model was built based on the forward stepwise method and Akaike information criteria (AIC) was used to appreciate the best model. The model including thyroid volume (\(p\) = 0.015), family history of CAT \(p\) = 0.013), and ATPO antibody levels (\(p\) = 0.003) was associated with the presence of CAT in children. A positive and significant correlation was found between SR mean values and ATPO levels (r = 0.65, \(p\) < 0.0001), thyroid volume (r = 0.49, \(p\) < 0.001), body surface area (r = 0.32, \(p\) = 0.0054), and ATG levels (r = 0.29, \(p\) = 0.0135), respectively (Table 3). No significant correlation was found between SR mean values and TSH values (\(p\) = 0.8205), FT4 values (\(p\) = 0.8232), or age (\(p\) = 0.0840).
## 3 Description of the Solution Sets for Bar Structures
### Trusses  
Let us take into the consideration a truss, which is composed of \(e\) bars and whose nodal deflections are described with \(s\) degrees of freedom. The linear system for trusses [18; 19] is given by  
\[\mathbf{B}^{\intercal}\mathbf{EBq}=\mathbf{Q}, \tag{17}\]  
where \(\mathbf{E}=\mathrm{diag}(E_{K})\in\mathbb{R}^{\epsilon\times\epsilon}\) (\(K=1,\ldots,e\)) is called elasticity matrix, \(\mathbf{B}\in\mathbb{R}^{\epsilon\times s}\) extension matrix, \(\mathbf{q}\in\mathbb{R}^{s}\) vector of unknown degrees of freedom and \(\mathbf{Q}\in\mathbb{R}^{s}\) vector of node loads. Components\(E_{K}=Y_{K}A_{K}/L_{K}\) contain the information about [PERSON]'s modulus \(Y_{K}\), cross section \(A_{K}\) and length \(L_{K}\) of \(K\)-th bar. Components of matrix **B** are defined by the truss geometry and its construction method is described in [18,19].  
Let the bars be characterized by uncertain stiffnesses \(E_{i}\) and the imprecision \(E_{i}\) be described by interval bounds, \(E_{i}\in[E_{i}]\). The solution set (4) of system (17) can be written as  
\[\Xi_{\text{uni}}=\left\{\textbf{q}\in\mathbb{R}^{s}:\left(\exists\textbf{E} \in[\textbf{E}]\right)\left(\textbf{B}^{\intercal}\textbf{E}\textbf{B} \textbf{q}=\textbf{Q}\right)\right\}, \tag{18}\]  
where \([\textbf{E}]=\text{diag}(\left[E_{1}\right],\ldots,\left[E_{e}\right])\).  
Let \(\textbf{B}^{(i)}\) be \(i\)-th row of matrix **B** (or \(i\)-th column of matrix \(\textbf{B}^{\intercal}\)). Then, due to the diagonality of matrix **E**, it is possible to present the system (17) in the form  
\[\sum_{i=1}^{e}\langle\textbf{B}^{(i)},\textbf{q}\rangle\textbf{B}^{(i)}E_{i}= \textbf{Q}. \tag{19}\]  
The set \(\mathcal{V}(\textbf{q})\) (7) is given by formula  
\[\mathcal{V}(\textbf{q})=\textbf{Q}+\sum_{i=1}^{e}\langle\textbf{B}^{(i)}, \textbf{q}\rangle\textbf{B}^{(i)}\ast\left[E_{i}\right]. \tag{20}\]  
It is worth to note that the scalar product \(\langle\textbf{B}^{(i)},\textbf{q}\rangle\) defines the extension of \(i\)-th bar, while vectors \(\textbf{B}^{(i)}\) defines directions of zonotope \(\mathcal{V}(\textbf{q})\) edges. The set \(\mathcal{U}^{+}\), necessary for finding the system of inequalities (16), is a set of vector products of all \((s-1)\)-element combinations of vectors \(\textbf{B}^{(i)}\) (\(i=1,\ldots,e\)) or in case when \(s=2\) is a set of all vectors perpendicular to \(\textbf{B}^{(i)}\). It is important that vectors obtained in this manner are independent from vector **q**. Hence, the products (14) have the form  
\[C^{(0)}(\textbf{u})=\langle\textbf{u},\textbf{Q}\rangle,\quad C^{(i)}(\textbf {u})=E_{i}\langle\textbf{B}^{(i)},\textbf{q}\rangle\langle\textbf{B}^{(i)}, \textbf{u}\rangle. \tag{21}\]  
For a more detailed illustration, the algorithm of searching for a solution set in the truss structure is shown in a flowchart in the Figure 1.
## 4 Experiment
### Experimental Results  
#### 4.3.1 Network Comparison Experiment  
In this paper, MAPC-Net is trained on two different training sets, A and B, and tested on test set A and test set B, respectively. During the experiment, the batch size is set to eight, the initial learning rate is set to 0.24, and the training decay is \(10^{4}\) per round. To speed up the learning process, momentum was introduced and set to 0.9, and stochastic gradient descent was used for a total of thirty rounds of training. At the same time, we select the SalsaNext [11] and PVCNN [19] to compare with the MAPC-Net proposed in this paper in test set A and test set B respectively. Among them, the SalsaNext algorithm is a typical point cloud semantic segmentation algorithm based on projection. PVCNN has designed a feature extraction method that combines traditional voxels and point clouds and directly performs feature extraction in three-dimensional space through the encoding and decoding structure. Its semantic segmentation comparison is shown in Figure 6.  
The results in Table 1 show that the MAPC-Net has the highest mIoU in both Test A and Test B. Compared with the projection-based SalsaNext, it has obvious advantages. The IoU of all classes is improved. The mIoU in Test A has an improvement of 12% and an 11% improvement in Test B. This is because the feature extraction directly in the three-dimensional space reduces the accuracy loss caused by the forward and reverse projections, making the semantic segmentation results more accurate. Compared with the traditional voxel and point cloud fusion algorithm, MAPC-Net also has certain advantages. The IoU of most classes exceeds the point-voxel-based PVCNN model. The mIoU in Test A is improved by 5% and 4% in Test B. This is because in outdoor scenes, the use of cylindrical voxels will make the distribution of point clouds more reasonable than cubic voxels, making the network more robust, and can have better semantic segmentation results in different off-road scenes.  
Figure 6: Semantic segmentation results of different networks in test set A and test set B. Whether in test set A or test set B, the segmentation results of MAPC-Net are closer to the ground-truth labels. Among them, magenta represents roads.  
MAPC-Net has a longer inference time than the other two models. However, since the operating frequency of the LiDAR is 5-20 HZ, and the main task during the process is to identify blocks and other obstacles, it will not travel at a fast speed. The inference time of MAPC-Net is less than 200 ms, which can be considered as real-time and can be well applied to off-road environments.