esa-sceva/satcom-chunk-collection huggingface dataset:
# The Role of Trees in Sand Dune Rehabilitation: Insights from Global Experiences
## 4 Discussion
### Tree Species Used in the Afforestation of Sand Dunes  
We inventoried 55 tree species used for the afforestation of sand dunes. Of course, the actual number is much higher, but even so, our results highlight the numerous attempts to rehabilitate these degraded lands, especially considering that, under such extreme conditions, very few tree species can survive.  
Among the identified tree genera, most species belong to _Pinus_ (nine species), _Acacia_ (six species), and _Juniperus_ (four species). Pines are used in many countries worldwide for the stabilization and rehabilitation of degraded lands [111; 112; 113]. Pines are a hardy species that tolerate a range of climates and soils. They are not demanding with respect to soil nutrients, can thrive on soils with different textures, tolerate calcium well, and can also grow on acidic soils. They are drought-resistant over time, showing adaptability to harsh site conditions, with intense sunlight or on shallow, nutrient-poor soils exposed to dryness.  
Virginia juniper (_Juniperus virginiana_) has moderate ecological requirements and is adapted to a variety of conditions. It prefers sunny areas, is cold-resistant, and tolerates drought and dry air well. As for soil, it does not have specific requirements, but it grows well in calcareous, permeable, and well-drained soils.  
However, _Acacia_ species can become invasive and may cause more harm than good on sand dunes (see Section 3.5). Besides these, other studies have identified species that can be used in difficult conditions: Norway spruce [114; 115; 116], _Ficus carica_[117; 118], _Hippphae salicidia_[119], oak species [120; 121], and _Robinia pseudoacacia_[87].
## 3 Proposed Model
### ROI Extraction Module
#### 3.3.2 Palmprint Localization  
Palmprint localization is the process of finding the location of the ROI in palmprint images by putting a bounding box or drawing a rectangle around the position of the palmprint in the images. Since CNN-based approaches have shown encouraging performance in object localization [59], we propose a CNN architecture for palmprint localization based on [PERSON]'s fast CNN (Section 3.1).  
As shown in Figure 3, we keep the first seven layers of [PERSON]'s architecture and replace the last layer with a four neuron-layer unit that outputs a bounding box. The four numbers parameterize the bounding box, determining the extracted palmprint ROI. In the architecture, the localization can be specified using four parameters \((b_{x},b_{y}),b_{w}\), and \(b_{h}\) representing the center point, width, and height of the box containing the palmprint ROI, respectively.  
Based on the fact that there are not enough palmprint images in the available databases, we employ the learning concept. To this end, the network weights of the first seven layers are pre-trained on ImageNet using the Alexnet model and the final layer is trained using our database. In other words, we freeze the parameters in all network layers, except the last one, and then we train and fine-tune pertinent parameters associated with our bounding boxes (i.e., \(b_{x},b_{y},b_{w}\), and \(b_{h}\)). The palmprint ROIs can be extracted by applying these parameters to our palmprint images.  
Considering \(\omega\) as the set of learning parameters and \(b_{x}^{\prime}(\omega),b_{y}^{\prime}(\omega),b_{w}^{\prime}(\omega),b_{h}^{ \prime}(\omega)\) as the network output values, we define \(C(\omega)\), the cost function, as follows:  
\[C(\omega)=\frac{1}{m}\ \sum_{i=1}^{m}\left(\left|b_{x_{i}}-b_{x_{i}}^{\prime}( \omega)\right|+\left|b_{y_{i}}-b_{y_{i}}^{\prime}(\omega)\right|+\left|b_{w_{ i}}-b_{w_{i}}^{\prime}(\omega)\right|+\left|b_{h_{i}}-b_{h_{i}}^{\prime}( \omega)\right|\right) \tag{1}\]  
where \(m\) is the number of samples and \(b_{x_{i}},b_{y_{i}},b_{w_{i}},b_{h_{i}}\) are the target values for the \(i\)th sample. The cost function specifies the difference between the target values and predicted ones. The objective is to determine a set of \(\omega\) minimizing \(C(\omega)\).
## 4 Results and Discussion
It is also found that the proposed rectifier is more efficient for fixed loads in which it was designed. The voltage and current in phase a of the prototype is illustrated in Figure 16. The voltage form was obtained by means of a voltage transformer for galvanically isolated measurement with a 230 V/18 V ratio, shown in Figure 10b; the current was obtained by means of a current probe on the 10 mV/A scale, shown in Figure 10a. This figure shows that the current is in phase with the phase voltage, and therefore the PF value is 98% (by  
Figure 14: Voltage and currents in phase a, of the rectifier. Figure 15: (a) THD for various load levels; (b) PF for various load levels. the digital oscilloscope), a value very close to the value given by the simulation: 99.93%, with a load of 72 \(\Omega\) (40%). This proximity to the values also shows that the prototype is working correctly. The starting process interval of the rectifier was analyzed, where it is seen in Figure 17 that there is a peak current transient interval of approximately 10 ms, with a maximum peak current of 200 to 700 A (inrush current). After approximately 30 ms, it is seen that the rectifier starts to operate with the PFC. The inrush current problem will be solved by applying an active method. This active method is based on the application of resistors at the input of each rectifier to limit the starting current, as shown in Figure 11:0 After the period of the starting current has passed, the resistors are short-circuited by means of a control and a solid-state relay. This active method to limit the inrush current has already been applied to the prototype, and it was observed that the inrush current was very low, to the point of not causing problems at the time of starting the prototype. Figure 11:1 Voltage and current in phase \(a\), of the prototype. Figure 11:2 Analysis of the starting period of the rectifier in the three phases. For the output variables, as seen in Figure 18, the transient interval is also similar. The voltage goes up, until it reaches a stable value of approximately 760 V in 50 ms. The output current also has a similar behavior, reaching a current of 26.2 A later. In the case of voltage and current ripple, they have a very low value that is practically negligible, which is good for many applications. Regarding the output voltage in the prototype, it still presents some difficulties. The measured value was about 666 V, as can be seen in Figure 8 (voltmeter with voltage divider 1/2), different from what was desired at 760 V. The problem is found in the control circuit of the output voltage together with the hall sensor (LV 25-P), and it is already being analyzed.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 4 NSGA-II for the Multi-Objective Path Planning Problem
### Position Update Operator  
The idea of this operator comes from particle swarm optimization (PSO). Based on the positions of three continuing rotation points (\(p_{i-1}\), \(p_{i}\) and \(p_{i+1}\)), the position of the middle point (\(p_{i}\)) is updated by Equation (5). This operator is shown in Figure 10.  
Figure 8: The smoothness operator is presented. In the left sub-figure, two free points are randomly generated on the two line segments where the maximum turning angle is located and are represented by blue squares. Then the intersection of the two segments is replaced with these two points. The right sub-figure shows the generated path.  
Figure 7: The safety operator is shown. In the left sub-figure, the critical point (indicated by a yellow circle) of each line segment on the path can be replaced by a point with a larger safe distance near the critical point. The sub-figure on the right shows the generated path.  
\[\begin{array}{l}v^{f}=p_{i-1}-p_{i}\\ v^{l}=p_{i+1}-p_{i}\\ v=r_{1}\times v^{f}+r_{2}\times v^{l}\\ p_{i}^{new}=p_{i}+v.\end{array} \tag{5}\]  
In Equation (5), the vector (\(v\)) should meet a restraint. The restraint is set to be \(\pm 1\%\) of the workspace coordinates. In addition, the position (\(p_{i}^{new}\)) and the line segments (\(\overline{p_{i-1}^{new}p_{i}^{new}}\) and \(\overline{p_{i}^{new}p_{i+1}}\)) should be feasible. The position (\(p_{i}^{new}\)) is adjusted continually by using Equation (6) until the above conditions are satisfied. Furthermore, \(r_{1}\), \(r_{2}\) and \(r_{3}\) are set to random numbers from zero to one.  
\[p_{i}^{new}=r_{3}\times p_{i}+(1-r_{3})\times p_{i}^{new}. \tag{6}\]
## 2 Finite-Element Model  
All 3D finite-element models were programmed using commercial finite-element package ABAQUS with small-strain analyses [35]. The geometric dimensions of tetrapod bucket foundations are illustrated in Figure 2. The diameter of the individual bucket and the width of tetrapod bucket foundations are denoted as \(D\) and \(B\), respectively. The wall thickness is adopted as 25 mm, while the diameter \(D\) of a single bucket is 10 m, which are common values for steel bucket foundations. The cross-sectional area of a single bucket foundation and tetrapod bucket foundations are denoted as \(A_{s}\) and \(A\) (\(A\) = 4 \(\times\)\(A_{s}\)), respectively. The foundation separation distance between the center of each bucket and that of the tetrapod bucket foundations, and foundation embedment depth are defined as \(s\) and \(d\), which are normalized as \(s/D\) and \(d/D\), respectively. The foundation separation distance ratios (\(s/D\)) were taken as 0.75, 1.0, 1.25, 1.5, 2.0, 2.5, 3.0 and 3.5, while the foundation embedment depth ratios (\(d/D\)) were taken as 0.25, 0.5, 0.75 and 1.0. The width of the tetrapod bucket foundations (\(B\)) is equal to \(\sqrt{2}s+D\). Finite-element results in this study show that the undrained capacities of the tetrapod bucket foundations are dependent on the dimensionless quantities \(s/D\) and \(d/D\), regardless of \(D\).
# Characteristic Analysis and Experiment of Adaptive Fiber Optic Current Sensor Technology
## 4 Effect of Magnetic Field Changes on a Straight-Through Magneto-Optical Glass Current Sensor
### Comsol Simulation Models
#### 4.1.2 Material Selection  
The material, relative dielectric constant \(\varepsilon_{r}\), relative magnetic permeability \(\mu_{r}\), and refractive index \(n\) in each simulation region are shown in Table 3.  
The current was set to 2 kA, and the magnetic field change \(\Delta H\) was set to 0 A/m, -300 A/m, 300 A/m. The relationship between the rotation angle \(\theta\) and the transmission distance \(L\) for different magnetic field changes is shown in Figure 6, and the maximum rotation angle \(\theta_{max}\) and the corresponding angle error \(\Delta\theta\) are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Region** & **Material** & \(\varepsilon_{r}\) & \(\mu_{r}\) & \(n\) \\ \hline Wire & Copper & 1 & 0.9999 & – \\ Air region & Air & 1 & 1 & 1 \\ Magneto-optic glass & ZF-7 Glass & 4.10 & 1 & 1.44698 \\ \hline \end{tabular}
\end{table}
Table 3: Material parameters.  
Figure 5: Geometric model of magneto-optical glass units.  
Figure 6: Effects of magnetic field changes on the relationship between \(\theta\) and \(L\).  
Figure 6 and Table 4 show that the rotation angle increases linearly with transmission distance. A greater change in magnetic field and longer distance lead to larger deviations in rotation angle. \(\Delta\theta=-0.0106\) rad \(\approx V\Delta H\)L when \(\Delta H=-300\) A/m, and \(\Delta\theta=0.0110\) rad \(\approx V\Delta H\)L when \(\Delta H=300\) A/m. It can be seen that the magneto-optical output signal is superimposed with an offset linearly related to the distance when the magnetic field changes, which is consistent with Equation (14).  
The above analysis shows that the optical fiber output signal is sensitive to temperature drift, while magneto-optical glass is sensitive to magnetic field changes. Therefore, an adaptive technology of fiber optic current sensor is required in order to achieve stable output from the sensor under temperature fluctuations.
# Research on Equalization Strategy of Lithium Battery Pack Based on Multi-Layer Circuit
## 3 Multi-Layer Equilibrium System Model
### Equalizing Currents  
In the m-th layer equalization circuit, the i\({}_{\text{m}}\) equalizer can complete the bidirectional transfer of the current of its left and right adjacent battery modules (B\({}_{\text{m,i,m,l}}\) and B\({}_{\text{m,i,m,r}}\)), and the equalized current on the battery is I\({}_{\text{m,i,m,l}}\) and I\({}_{\text{m,i,m,r}}\). As shown in Figure 2, to transfer energy from a high SOC battery to a low SOC battery it is necessary to control the equalization current through the equalizer to complete battery equalization. Before equalization, the current direction can be determined by the battery's SOC, if the B\({}_{\text{m,i,m,l}}\)/B\({}_{\text{m,i,m,r}}\) is greater than the SOC of B\({}_{\text{m,i,m,r}}\)/B\({}_{\text{m,i,m,l}}\), a new variable can be used I\({}_{\text{m,i,m}}\) to replace I\({}_{\text{m,i,m,l}}\) and I\({}_{\text{m,i,m,r}}\). \[ \begin{array}{l}\text{I}_{\text{m,i,m,l}}=\text{k}\text{I}_{\text{m,i,m}}\\ \text{I}_{\text{m,i,m,r}}=\text{k}\text{I}_{\text{m,i,m}}\end{array}  \tag{1}\]  
\[ \begin{array}{l}\text{k}=\left\{\begin{array}{cc}\text{k}_{\text{m,i,m}}- \text{k}^{\prime}_{\text{m,i,m}}\text{j}_{\text{m,i,m}}&\text{B}_{\text{m,i,m,l }}\\ -\text{k}_{\text{m,i,m,j}}\text{j}_{\text{m,i,m}}+\text{k}^{\prime}_{\text{m,i,m }}&\text{B}_{\text{m,i,m,r}}\end{array}\right.\\ \left\{\begin{array}{cc}\text{k}_{\text{m,i,m}}=-1,\text{k}^{\prime}_{\text{ m,i,m}}=0,\text{if SOC}_{\text{m,i,m}1}>\text{SOC}_{\text{m,i,m,r}}\\ \text{k}_{\text{m,i,m}}=0,\text{k}^{\prime}_{\text{m,i,m}}=-1,\text{if SOC}_{ \text{m,i,m,l}}<\text{SOC}_{\text{m,i,m,r}}\end{array}\right.\end{array} \]  
where I\({}_{\text{m,i,m}}\) represents the equalization current flowing through the i\({}_{\text{m}}\) equalizer on the m-th layer; k represents the magnitude and direction of the equilibrium current; j\({}_{\text{m,i,m}}\) represents the efficiency of the equalizer when transferring current; SOC\({}_{\text{m,i,m}1}\) represents the total SOC of B\({}_{\text{m,i,m,i}}\); and SOC\({}_{\text{m,i,m,r}}\) represents the total SOC of B\({}_{\text{m,i,m,r}}\), in Equation (1),  
Figure 1: Schematic diagram of equalization system composed of 2\({}^{\text{m}}\) single cells. Figure 2: Buck–Boost equalization circuit. \(0<\mathrm{j_{m,i_{m}}}<1\). To represent the current direction, when the SOC of \(\mathrm{B_{m,i_{m},i}}\) is greater than the SOC of \(\mathrm{B_{m,i_{m},r}}\), the balanced current \(\mathrm{I_{m,i_{m},l}}\) flowing through \(\mathrm{B_{m,i_{m},l}}\) is less than 0, and the balanced current \(\mathrm{I_{m,i_{m},r}}\) flowing through \(\mathrm{B_{m,i_{m},r}}\). Combining the circuit shown in Figure 1 and the above analysis, in a battery module directly connected to the equalizer, the equalizing current values are equal, so the equalizing current flowing through each battery is:  
\[
## 2 Materials and Methods
### Quantitative Evaluation of the Acquired Image  
#### 2.4.1 Segmentation Performance Evaluation  
We calculated the accuracy, F1-score, and intersection over union (IoU) values to quantitatively evaluate the performance of the region-growing-based segmentation algorithm.  
Figure 1: Acquired images for (**a**) standard image (ground truth) and (**b**) noisy image.  
For all three factors, a value closer to one indicates a better segmentation performance. Accuracy is the most intuitive metric for evaluating segmentation performance, representing the degree to which it predicts true and false values. The formula for calculating accuracy is as follows [19]:  
\[Accuracy=\frac{TP+TN}{TP+FN+FP+TN} \tag{3}\]  
where, _TP_ is a true-positive value that predicts a true value as true, and _TN_ is a true-negative value that predicts a false value as false. _FP_ is a false-positive value, predicting a false value as true, and _FN_ is a false negative, predicting a true value as false.  
F1-score is the harmonic average value of precision and recall. Precision is the ratio of the _TP_ area to the sum of the _TP_ and _FP_ areas, and recall is the ratio of the _TP_ area to the sum of the _FN_ and _TP_ areas. The formula for calculating the F1-score is shown in Equation (4) [20]:  
\[F1-score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{4}\]  
IoU refers to the intersection of the original and comparison images divided by the union. The formula for calculating IoU is shown in Equation (5) [21].  
\[IoU=\frac{Overlapping\ region}{Combined\ region} \tag{5}\]
# Highly Enriched Uranium-Free Medical Radioisotope Production Methods: An Integrative Review
###### Abstract  
The ever-growing need for radiopharmaceuticals, i.e., compounds containing pharmaceutical drugs and radioisotopes used for medical diagnostic imaging (SPECT/PET scan) and treating neoplasms, is significantly leading to an increased demand for such substances in hospitals and clinics worldwide. Currently, most large-scale productions of radioisotopes required for radiopharmaceuticals are carried out in research reactors, via the fission of highly enriched uranium. However, because large amounts of radioactive waste are produced as byproducts in this process, new greener methods are needed for radioisotope production. This work presents an integrative literature review and summarizes enriched uranium-free methods for radioisotope production, accomplished through the adoption of new reaction routes, distinct acceleration technologies, or by using other physical processes. This review considered forty-eight studies published from 2010 to 2021 on three established virtual databases. Among these selected works, a cyclotron is the most adopted HEU-free method for radioisotope production, and \({}^{44}\)Sc, \({}^{68}\)Ga, and \({}^{99}\)Trc are the medical radioisotopes most often reported as produced by using the investigated HEU-free production methods.  
radioisotope production; nuclear medicine; radiopharmaceuticals; radionuclide
# On-Board Ship Detection in Micro-Nano Satellite Based on Deep Learning and COTS Component
## 4 The Composition of Ground Demonstration and Verification System  
In order to verify the feasibility and effectiveness of the proposed scheme, we set up a ground demonstration and verification system. The operation process is shown in Figure 6.  
The ground users first determine the observation area according to the requirements, and then send an imaging instruction to the image simulator to simulate the function of payload imaging. After the image simulator selects the image of the relevant area, the image is transmitted to the on-board intelligent processing unit. When the imaging is completed, the ground users send a processing instruction to the on-board processing unit. Then, the on-board processing unit uses the proposed method to detect ship target from the image. The target slices and information are extracted from the large-scale image in one minute. After the on-board processing, the ground users send a data transmission instruction to the on-board intelligent processing unit to transmit the target patches and information to the ground handheld terminal. Finally, the users can check the target patches and information through intelligent APP. The following is the introduction of each device.  
Figure 6: The operation process of the ground demonstration and verification system.
## 2 Preliminaries
### Sliding Window Join in Data Stream Management Systems  
A full evaluation of a join between two unbounded streams of tuples is not practical because unbounded memory is required. In data stream management systems, a join of two streams \(R\) and \(S\) are often modeled as a _sliding window join_[16, 17, 18]. In a sliding window join of \(R\) and \(S\), a newly arriving tuple of \(R\) could produce join results only with the tuples of \(S\) that belong to a window of \(S\). The window could be either _time-based_ or _tuple-based_[19]. A time-based sliding window join of \(R\) and \(S\) works as follows. For a tuple \(r\) of \(R\) arriving at time instant \(t\), let \(\mathsf{w}_{S}(t,\,h_{S})\) denote the window of \(S\) for \(r\) where \(h_{S}\) is the window size. \(\mathsf{w}_{S}(t,\,h_{S})\) is defined to contain those tuples of \(S\) that have arrived in the time interval [\(t\)-\(h_{S},\,t\)]. A sliding window of \(R\) is defined similarly, and a newly arriving tuple of \(S\) is joined only with the sliding window of \(R\). In a tuple-based sliding window join, the window of \(R\) (\(S\)) is defined to contain the last \(n\) tuples of \(R\) (\(S\)), where \(n\) is a positive integer associated with the window.
## 3 Materials and Method
### Baselines  
To comprehensively evaluate the effectiveness of the proposed method, several state-of-the-art privacy protection techniques were selected as baselines, including FHE [15], DP [38], SMC [36], and FL [42]. The core concept of Fully Homomorphic Encryption is to perform computations directly on ciphertext without decryption, ensuring high security. Differential privacy protects privacy by adding noise to data or query results, which enhances the training and inference performance of the framework. Secure Multi-party Computation distributes data to different participants and jointly completes computations. After dividing the computation task into sub-tasks, the results are aggregated to perform the overall operation. For example, the distributed implementation of an addition task is \(z=z_{1}+z_{2}\), where \(z_{1}=x_{1}+y_{1}\) and \(z_{2}=x_{2}+y_{2}\). Federated learning updates a global model by training models locally and aggregating gradients from all participants, as defined by  
\[\theta_{t}=\frac{1}{K}\sum_{k=1}^{K}\theta_{t}^{k}, \tag{32}\]  
where \(\theta_{t}^{k}\) denotes the parameter updates for the \(k\)-th node, and although it effectively avoids privacy risks associated with data centralization, it still faces challenges in model aggregation and complex scenarios. These methods address data privacy protection from various perspectives.