esa-sceva/satcom-chunk-collection huggingface dataset:
# Coupling Denoising to Detection for SAR Imagery
## 2 Materials and Methods
### SAR Remote Sensing Dataset  
#### 2.1.1 Description  
We constructed our datasets with 60 TerraSAR-X images from German Aerospace Center [34] and 55 COSMO-SkyMed images from Italian Space Agency [35], which is mainly covering harbor- and airport- peripheral areas. For TerraSAR-X satellite, the images have resolutions from 0.6 m to 1 m, and is of the size in the range from about 6 k \(\times\) 2 k to 11 k \(\times\) 6 k pixels (sorted by their area). For COSMO-SkyMed satellite, the images have a resolution of 1m, and is of the size in the range from about 13 k \(\times\) 14 k to 20 k \(\times\) 14 k pixels (sorted by their area). Each remote sensing image is labeled by experts in aerial image interpretation with multiple categories such as airplane (A), etcetera (E) and ship (S). The ship/airplane classes contain a variety of civil and military ships/airplanes while the etcetera class includes support vehicles, air defense weapons and air defense vehicles. Some example ship/airplane objects are shown in Figures 2 and 3 for TerraSAR-X and COSMO-SkyMed imagery, respectively.  
Our labeled objects include a total of 15.7 k instances of 3 categories; 3.7 k instances for A class, 0.2 k instances for E class, and 11.8 k instances for S class, which implies that our datasets are quite imbalanced between the categories and relatively skewed towards S class. The class distribution by type of satellite imagery is plotted in Figure 4. Furthermore, target objects in our dataset exist at a variety of scales due to our multiresolution images and the variety of shapes, especially for ships objects. We measure the bounding box size of objects with \(w_{bbox}\times h_{bbox}\) and present the frequency of boxes by size as a histogram in Figure 5, where \(w_{bbox}\) and \(h_{bbox}\) is the width and height of the bounding box, respectively.  
Figure 4: Number of annotated instances per category for TerraSAR-X and COSMO-SkyMed imagery.  
Figure 3: Example airplane (top) and ship (bottom) objects in COSMO-SkyMed image. The groundtruth bounding boxes labeled as corresponding class are plotted in red color.
## 4 The SMA-LRBs
### The Hysteresis Model of SMA Wires
#### 4.2.1 SMA-LRB with Double-Cross Wires  
In double-cross arrangement, each of the two continuous SMA wires is passed through eight steel hooks welded to the top and bottom supporting plates of the LRB, as can be seen in Figure 6. [PERSON] and [PERSON] proposed and utilized this configuration of wires to increase the length of the SMA wire with the purpose of reducing the wire's strain. For an LRB with specific dimensions, the required SMA wire to wrap around the isolator is the longest for the double-cross arrangement. As a result, the effective strain in the SMA wires generated due to the shear deformation in the LRB decreases because wires have a longer initial length.  
The strain in the SMA wire is calculated by Equation (7). Where \(l_{0,SMA}\) is the initial length of the SMA wire and \(l_{SMA}\) is the length of the SMA wire after the isolator undergoes a deflection of \(\Delta\)X in the X-direction. For the double-cross arrangement of wires, these parameters are determined from Equations (8) and (9). In which l, w, and h are the distances between hooks in X, Y, and Z directions, respectively. Having calculated the strain, the axial stress in SMA wires can be determined from the idealized stress-strain relationship of shape memory alloy based on [PERSON]'s super-elasticity model [54]. The idealized super-elastic behavior of SMA is shown in Figure 7. After that, the axial force in the wire, F, can be found from Equation (10). The resultant force exerted to the LRB in the X-direction, and the consequent hysteresis of the SMA wires is comprehensively explained in [24] and is not repeated here. In order to achieve the hysteresis curves of the SMA wire, a MATLAB function is developed by the authors based on the presented algorithm by [PERSON] and [PERSON] and was verified in a previous study [23]. This algorithm calculates the force applied by the SMA wire to the LRB at each time step for a specific shear displacement.  
\[\varepsilon_{SMA}=\frac{l_{SMA}-l_{0,SMA}}{l_{0,SMA}} \tag{7}\]  
\[l_{0,SMA}=4(\sqrt{\frac{l}{2}^{2}+h^{2}}+\sqrt{\frac{(\frac{w}{2})^{2}+h^{2}}}) \tag{8}\]  
\[l_{SMA}=2\sqrt{\left(\frac{l}{2}+\Delta x\right)^{2}+h^{2}}+2\sqrt{\left( \frac{l}{2}-\Delta x\right)^{2}+h^{2}}+4\sqrt{\Delta x^{2}+\left(\frac{w}{2} \right)^{2}+h^{2}} \tag{9}\]  
\[F=\sigma_{SMA}\pi\,\tau_{SMA}{}^{2} \tag{10}\]
# Optimal Dispatch Strategy of Virtual Power Plant for Day-Ahead Market Framework
## 4 Beetle Antenna Search Algorithm (BAS Algorithm)  
The BAS algorithm is a meta-heuristic search algorithm where the beetle can be compared with a triangle and the antennas of the beetle can be considered as two long sides of the triangle. The line between the triangle is denoted as the movement of the beetles and smell concentration is denoted with blue round shapes. The schematic diagram of the BAS algorithm is shown in Figure 4.  
The beetle will sense the odor by its two antennas. When the distance between the beetle and the food location changes, the concentration of the smell also changes [47]. By continuously moving and searching for the odor concentration, the beetle can finally arrive at the point of the source. The mathematical modeling for the beetle antenna search (BAS) algorithm can be followed as:  
1. The fitness function is considered here as \(f(y^{t})\), the \(y^{t}\) can be denoted as: \[y^{t}=[y^{1},y^{2},\ldots,y^{n}]\] (19) where \(t\) denotes the no. of iterations.
2. The beetle orientation is randomized as: \[\overset{\rightarrow}{a}=rand(m,1)/\left\|rand(m,1)\right\|\] (20) where \(rand(m,1)\) denotes the random direction of the beetle, which is the matrix of the (\(m\)x\(1\)) dimension. \(\overset{\rightarrow}{a}\) is the unit vector.
3. The co-ordinates of the two antennas after each iteration are: \[y_{c}=y^{t-1}+l^{t-1}*\overset{\rightarrow}{a}\] (21) \[y_{d}=y^{t-1}-l^{t-1}*\overset{\rightarrow}{a}\] (22)  
Figure 4: Schematic diagram of the BAS algorithm.  
where \(y_{c},\ y_{d}=\) co-ordinates of the right and left antennas, respectively; \(L=\) sensing length of the antenna that is decided with step \(\beta\). \[\beta^{t}=\gamma\ \beta^{t-1}\] (23) where \(\gamma\ =\) constant which indicates the decay speed of the search step. 4. The resultant formula can be achieved by solving the above co-ordinates as \[y^{t}=y^{t-1}-\partial\beta\overset{\rightarrow}{a}sign(f(y_{c})-f(y_{d}))\] (24) where \(y^{t}\) is the updated step of the previous step \(y^{t-1}\). Where \(\partial\) represents the step size of searching. Where _sign(.)_ represents a sign function. \(f(y_{c})\), \(f(y_{d})\) represent the fitness values of the left and right sides of \(y\), respectively.  
The algorithm consists of the main parts of the local fast search algorithm which can improve the path planning of the beetle. The pseudo code of the algorithm is implemented as follows:  
1. An objective function is developed as \(f(y^{t})\), where variable \(y^{t}=[y_{1},y_{2},\ldots,y_{n}]^{T}\)
2. Initialize the parameters \(y^{0}\),\(l^{0}\)
3. Output \(=[y_{bat},f_{bat}]\)
4. while (t \(<T_{\max}\))
5. Generate the direction vector unit \(\overset{\rightarrow}{a}\) according to Equation (20)
6. Search the variable with two antennas according to Equations (21) and (22)
7. Update the variable according to Equation (24)
8. If \(f(y^{t})<f_{bat}\), then \(f_{bat}=f(y^{t}),y_{bat}=y^{t}\)
9. Return \(y^{t}_{bat},f^{t}_{bat}\)
# Institutional Adoption and Implementation of Blended Learning in the Era of Intelligent Education
## 4 Results and Discussion
### Transformation of Institutional Courses
#### 4.2.2 Course Application  
Starting in 2015, the administration office provided a variety of convenience for BL courses, such as specifically scheduling online learning hours according to instructors' designs and arrangements. Courses in S university transformed gradually based on two principles: firstly, they followed the routine of \"occasional \(\rightarrow\) frequent \(\rightarrow\) normalized\" in terms of implementation frequency; secondly, the followed the routine of \"partial \(\rightarrow\) majority \(\rightarrow\) whole\" in terms of course transformation. Such course application policy worked quite well in the past 7 years. Actual implementation details are shown in Table 4.  
As is shown in Table 4, the online class hours showed a rapid growth trend due to the fact that an increasing number of courses participated in the BL education reform year by year.  
In fact, domestic experts and peers have widely recognized the teaching concept, design, and effect of these BL courses, represented by the fact that a total of 17 BL/online courses of S university have won provincial first-class course certification (ranking first among universities in Guangdong Province), and 14 courses have won national first-class course certification (ranking first among independent medical universities in China) since 2019.  
Apart from BL courses' recognition, S university's systematic BL reform practice and overall teaching practice has won the second and grand prizes of Teaching Achievement Award in Guangdong Province in 2021.  
This result is in correspondence with [PERSON]'s finding that teacher development opportunities could bring about curriculum innovation [19].  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline  & **2015** & **2016** & **2017** & **2018** & **2019** & **2020** & **2021** & **Total** \\ \hline Online 1 & 1168 & 2996 & 5756 & 6170 & 8783 & 23,361 & 22,913 & 71,147 \\ Total 2 & 115,034 & 115,604 & 115,967 & 122,733 & 136,844 & 149,577 & 148,159 & 903,918 \\ OL/Total & 1.02\% & 2.59\% & 4.96\% & 5.03\% & 6.42\% & 15.62\% & 15.47\% & 7.87\% \\ \hline \hline \end{tabular}  
* This table only counts the schedule recorded in the administration office. In fact, some BL courses arrange online learning using extra schooling time, which was not recorded here. 2 Total schooling hours include hours of online learning, FZF of BL, PBL, lecture, hands-on workshop, experiment, discussion, PE, exam, internship, social practice, etc.  
\end{table}
Table 4: Teaching hours of BL course in S university.  
Figure 2: (**a**) Iteration of the teaching videos; (**b**) iteration of BL activities. Note that course activities mainly include watching videos, reading documents, participating in course discussions, completing tests and assignments, participating in self- and peer assessment, etc.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 2 Model
### CHP Model  
In the greenhouse, biogas not only can be used as primary energy for electricity and heat supply, but also can produce carbon dioxide by burning. Carbon dioxide can fertilize plants, which is an effective utilization of the biomass energy. CHP adopts the mode of electricity determined by heat, which means the absorption heat pump is used to meet the heat demand first. If the demand cannot be met, the ASHP is used to heat the system. CHP is composed of gas microturbine (MT), absorption heat pump and biogas digester. The organic biomass used to produce biogas is relatively easy to obtain because of the biogas digester in the greenhouse, thus we do not need to consider the consumption of methane in CHP model.  
The flue gas waste heat generated by MT can provide part of the heat load through waste heat boiler and AHP. The thermal power \(H_{CHP}(t)\) provided by CHP can be calculated by the following equation [21]:  
\[H_{CHP}(t)=\frac{P_{CHP}(t)[1-\eta_{CHP}(t)-\eta_{l}]}{\eta_{CHP}(t)}\eta_{AP} \tag{1}\]  
where \(P_{CHP}(t)\) is the current output power, \(\eta_{CHP}(t)\) is the power generation efficiency, \(\eta_{l}\) is the heat dissipation coefficient of MT, and \(\eta_{AP}\) is the recovery efficiency of absorption heat pump.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**EH Equipment** & **Input Energy Form** & **Output Energy Form** \\ \hline Microturbine & Gas & Gas, Heat \\ AHP & Heat & Heat \\ ASHP & Electricity & Heat \\ Transformer & Electricity & Electricity \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy conversion of components in energy hub.  
Figure 1: The basic on-line diagram of greenhouse micro grid.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 5 The \(D^{3}\)-Tree
### Node Joins and Departures
#### 5.2.2 Node Redistribution  
The redistribution guarantees that if there are \(z\) nodes in total in the \(y\) buckets of the subtree of \(v\), then after the redistribution each bucket maintains either \(\lfloor z/y\rfloor\) or \(\lfloor z/y\rfloor+1\) nodes. The redistribution cost is \(O(\log N)\), which is verified through experiments presented analytically in this work.  
The redistribution in the subtree of \(v\) is carried out as follows (Algorithm 5). We assume that the subtree of \(v\) at height \(h\) has \(K\) buckets. A traversal of all buckets is performed in order to determine the exact value of \(|v|\) (number of nodes in the buckets of the subtree of \(v\)). Then, the first \(k\) buckets, will contain \(\left\lfloor\frac{|v|}{2^{h}}\right\rfloor+1\) nodes after redistribution, where \(k=|v|\bmod 2^{h}\). The remaining \(K-k\) buckets will contain \(\left\lfloor\frac{|v|}{2^{h}}\right\rfloor\) nodes. The redistribution starts from the rightmost bucket \(b\) and it is performed in an in-order fashion so that elements in the nodes are not affected.  
We assume that \(b\) has \(q\) extra nodes, which must be transferred to other buckets. Bucket \(b\) maintains a link _dest_ to the next bucket \(b^{\prime}\) on the left, in which \(q\) extra nodes should be put. The \(q\) extra nodes are removed from \(b\) and are added to \(b^{\prime}\). The crucial step in this procedure is that during the transfer of nodes, internal nodes of PBT are also updated since the in-order traversal must remain untouched. More specifically, the representative \(z\) of \(b\) and its left in-order adjacent \(w\) are replaced by nodes of bucket \(b\), and then, \(z\) and \(w\) along with the remaining \(q-2\) nodes of \(b\) are added to the tail of bucket \(b^{\prime}\). Afterwards, the horizontal and vertical links for the replaced binary nodes are updated for nodes that point to them. Finally, bucket \(b\) informs \(b^{\prime}\) to take over, and the same procedure applies again with \(b^{\prime}\) as the source bucket.  
The case where \(q\) nodes must be transferred to bucket \(b\) from bucket \(b^{\prime}\) is completely symmetric. In general, \(q\) nodes are removed from the tail of bucket \(b^{\prime}\), two of them replace nodes \(w\) and \(z\) and the remaining nodes are added to the head of \(b\). The intriguing part comes when \(b^{\prime}\) contains less nodes than the \(q\) that \(b\) needs. In this case, \(b\) has to find the remaining nodes in the buckets on the left, so _dest_ travels towards the leftmost bucket of the subtree, until \(q\leq\sum_{i=1}^{s}|b_{i}|\), where \(|b_{i}|\) is the size of the \(i\)-th bucket on the left. Then, nodes of \(b_{s}\) move to \(b_{s-1}\), nodes from \(b_{s-1}\) are transferred to \(b_{s-2}\) and so on, until _dest_ goes backwards to \(b^{\prime}\) and \(q\) nodes are moved from \(b^{\prime}\) into \(b\).
## 2 Materials and Methods
### Material  
A commercially available BaTiO\({}_{3}\) powder was purchased from a local supplier. An X-ray diffraction pattern of the powder (Figure 1) confirmed barium titanate as the main phase. The powder was ball milled in a planetary mill Pulverisette 6 (Fritsch, Idar-Oberstein, Germany) for 2 h in the presence of 2 mm zirconia balls and ethanol to reduce the size of powder particles to <1 \(\upmu\)m. The powder was dried in a furnace at 70 \({}^{\circ}\)C for 3 h. The milled powder's particle size and morphology were characterized with scanning electron microscopy (SEM), using JSM-6390 LA (JEOL Ltd., Tokyo, Japan) and FEI Quattro S (Thermo Fisher Scientific, Waltham, MA, USA) electron microscopes. The results of SEM imaging were processed with image analysis techniques to refine the size of the powder particles. The image analysis was conducted with FEI Avizo (Thermo Fisher Scientific, OR, USA) software. The phase of BT was assessed in X-ray patterns recorded with a Bruker D8 Advance (Bruker, Billerica, MA, USA) diffractometer. Phase analysis was carried out with the use of Match! software (Crystal Impact, Bonn, Germany) and Crystallography Open Database (COD) [8]. SGAS software [9] was applied for fitting powder diffraction pattern profile with Le Bail method and for the refinement of unit cell parameters with Rietveld method.  
After the particle size study, the powder was mixed with 2 wt % Disperbyk 100 (BYK-Chemie GmbH, Wesel, Germany) dispersant and homogenized in Retsch PM400 (Retsch, Haan, Germany) planetary ball mill for 30 min in order to uniformly distribute the dispersant over powder particles and break up the clusters. The homogenization resulted in the preconditioned powder. Ceramic pastes were prepared by mixing the preconditioned powder with a commercial organic photopolymerizable composition, Yellow Dental Clear (HarzLabs, Moscow, Russia).
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 7 Experimental Study
### Performance Evaluation for \(D^{3}\)-Tree
#### 7.1.3 Cost of Element Search with/without Node Failures  
To measure the network performance for the operation of single queries, we conducted experiments in which, for each \(N\), we performed \(2M\) (\(M\) is the number of binary nodes) searches. The search cost is depicted in Figure 8a. An interesting observation here was that although the cost of search in \(D^{3}\)-Tree doesn't exceed \(2\cdot\log N\), it is higher than the cost of BATON, BATON\({}^{*}\), and P-Ring. This is because when the target node is a _Bucket_ node, the search algorithm, after locating the correct leaf, performs a serial search into its bucket to locate it.  
To measure the network performance for the operation of element search with node failures, we conducted experiments for different percentages of node failures: 10%, 20%, 30%, 50% and 75%. For each \(N\) and node failure percentage, we performed \(2M\) searches divided into four groups, each of \(M/2\) searches. To better estimate the search cost, we forced a different set of nodes to fail in each group.  
Figure 8b depicts the increase in search cost when massive failures of nodes take place in \(D^{3}\)-Tree, BATON, different fanouts of BATON\({}^{*}\) and P-Ring. We observe that \(D^{3}\)-Tree maintains low search cost, compared to the other structures, even for a failure percentage \(\geq 30\%\).  
Figure 7: Element update operations. (**a**) average messages, (**b**) load-balancing rate of winner \(D^{3}\)-Tree, (**c**) load-balancing height of winner \(D^{3}\)-Tree.  
Details about the behaviour of the enhanced search mechanism of \(D^{3}\)-Tree in case of node failures are depicted in Figure 8c,d, which show the average number of messages required and the success percentage, respectively.  
Experimenting, we observed that when the node failure percentage is small (10% to 15%), the majority of single queries that fail are those whose elements belong to failed nodes. When the number of failed nodes increases, single queries are not always successful since the search mechanism fails to find a path to the target node although the node is reachable. However, even for the significant node failure percentage of 30%, our search algorithm is 85% successful, confirming thus our claim that the proposed structure is highly fault-tolerant.
## 4 Discussion  
### Spectral Analysis  
Vis/NIR region contains meaningful information about the major X-H chemical bonds, i.e., C-H, N-H and O-H. All of the molecules including hydrogen have a measurable Vis/NIR spectrum, resulting in a large amount of organic materials to be suitable for Vis/NIR analysis [34]. In Figure 3, the reflective spectral signatures of the mixture samples provide typical chemical information about the OF because they include overtones of the fundamental vibrations of the organic molecules and combination bands caused by the stretching and bending of N-H, C-H, and C-O groups, which primarily occur in the Vis/NIR region [35; 36; 37]. In particular, the spectral absorption region at 655 nm shows lipid component vibration. The feature waveband within the ranges of 600 to 700 nm is mainly assigned to the functional lipid groups including C-H, CH\({}_{2}\), and CH\({}_{3}\) bonds [38]. To produce FW fertilizer, oil waste is extracted and removed from FW through oil-water separation. After this separation process, the FW could be converted into biogas, for combined heat and power generation, or for fuel after purification. The remaining solid digestate was converted into OF and compost [11]. Despite the oil waste extraction process, however, some lipid components remained and were detected in the OF spectral absorption peak at 655 nm.
## 5 Experiment Results
### Parameters Setting  
This study primarily considers two key parameters, i.e., the destruction length '\(d\)' and the temperature parameter for acceptance criterion '\(T\)'. Table 2 presents the various values applied to these two parameters. The design of experiment (DOE) Taguchi method [34; 35] was used to construct an orthogonal array \(L_{16}\) and the proposed IIG was executed independently 30 times for each parameter combination. The response variable was collected as the average fitness value. Table 3 presents the experimental
## Appendix A Extended Experiments
### Impacts of \(\beta\) on Adacomp  
As in the discussion about Equation (9), the range of \(\beta\) is \((0.5,5]\). To present a comprehensive investigation, we selected \(\beta\) from \([0.6,1]\) with step size 0.1 and from \([1,5]\) with step size 0.5. Furthermore, learning rate was set as \(0.01,0.1,1\), and other settings were the same as in Figure 2. Detailed results are shown in Figure 13, from which two conclusions are obtained. For simplicity, \(lr\) is an abbreviation for learning rate and \(c\) in the parentheses denotes clipping gradient with bound 10.  
_First_, Adacomp is more sensitive to \(\beta\) when the learning rate is small. When \(lr=1\), Adacomp fails at two settings \(\beta=4,5\), and the number of failing settings decreases with \(lr\). When \(lr=0.01\), the effective \(\beta\) almost lies in the range \([0.6,1]\). This shows that Adacomp is insensitive to learning rate when \(\beta\leq 1\). _Second_, clipping gradient can significantly improve the robustness of Adacomp. Based on the training process, we found that the divergence happened at the early few iterations where the gradient norm tended to infinity. To overcome this, we clipped gradients with bound 10 (larger or less has no impact). Results when \(lr=0.01(c),0.1(c),1(c)\) show that Adacomp is robust to both \(\beta\) and learning rate when additionally clipping the gradient.