esa-sceva/satcom-chunk-collection huggingface dataset:
# Stability of MSW Landfill Slopes Reinforced with Geogrids
## 4 Conclusions  
In this study, stability analyses of MSW landfill slopes in unreinforced and geogrid-reinforced conditions were carried out. Analyses were carried out using FEM and LEM. Based on the analysis results, the following main conclusions can be drawn:  
* According to the results obtained from the unreinforced and reinforced analyses, the FS of the MSW landfill slope changes depending on the internal friction angle, cohesion, unit weight, and material model parameters of the solid waste. Considering that the range given for these parameters in the literature is quite wide, choosing these parameters correctly is very important in the stability calculations of the slope.
* By using geogrid reinforcements, the FS of MSW landfill slopes can be improved significantly. Reinforced slopes behave similar to a composite material due to the interlocking and frictional resistances that occur between the reinforcements and the waste material. With the use of geogrid reinforcements, the stability of the slopes increases, while the FS increases significantly.
* According to the results obtained from the reinforced analyses, it has been seen that the FS can be increased by up to 1.86 times by placing four geogrid reinforcements in the 1V/2H MSW landfill slope. In other words, if geogrid reinforcements are placed inside the MSW landfill slope, more solid waste can be stored compared to the traditional 1V/3H slope.
* The optimum number of geogrids and the vertical spacing required for different heights, slope angles, and different indices and shear strength parameters of the solid waste material can be obtained by numerical modeling of MSW landfill slopes.
* Since the limit equilibrium and finite element methods use different approximation principles in slope stability analysis, there are differences between the FS values obtained by the two methods.
* While the boundaries of the slip surface are determined by the user in LEM analyses, the slip surface is automatically created by the program in FEM analyses. In addition, the failure surface mechanism can be revealed by plotting shear strain increases in FEM analyses.
* Considering the mechanical creep behavior in the analysis of MSW landfill slopes, more realistic solutions can be obtained. In the preliminary analyses carried out using the SSCM within the scope of this study, there is a slight difference in FS values obtained using the MCM. Although the MCM is frequently used in studies on the subject, advanced material models that can take into account mechanical creep and time-dependent behavior should be used.  
Conceptualization, project administration, supervision, writing--review & editing, and writing--original draft: M.S.K.; methodology, data curation, software, validation, and formal analysis: S.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Data Analysis Algorithm
### Neural Network Analysis  
Neural networks that mimic the information processing functions of the brain nerves of organisms can be classified into supervised learning and unsupervised learning networks, according to the learning methods.  
Figure 8 shows the supervised learning process of neural networks, which use training data consisting of input values and target values to adjust the weight, which is the strength of the formation, according to certain rules, to minimize the error between the neural network output value and target output value. Therefore, the weight is set such that the correspondence between the input and output from the given input and output data is implemented well [22].  
In unsupervised learning, on the other hand, no target values corresponding to the input values are provided. In this case, the weights are adjusted for similar input patterns so that each node produces a similar output.  
In this study, the multilayer perceptron (MLP) was used, which is the most popular model for supervised learning [23]. The MLP consists of units with many interconnected nodes. Each node function usually consists of a weighted sum and differential nonlinear activity function. Figure 9 shows the most useful activation functions of MLP.  
In artificial neural networks, the activity function of a node defines the output of that node, given an input or set of inputs. A standard integrated circuit can be expressed as a digital network of activity functions, which can be \"ON\" (1) or \"OFF\" (0), depending on the input.  
If three activation functions are to be described as equations, they can be expressed as the following equations:  
\[\text{Logistic function}: f(x)=\frac{1}{1+e^{-x}}, \tag{7}\] \[\text{Hyperbolic }\tan\text{gent function}: f(x)=tanh\left(\frac{x}{2}\right)=\frac{1-e^{-x}}{1+e^{-x}},\] (8) \[\text{Identity function}: f(x)=x, \tag{9}\]  
Figure 8: Structure of neural network.
# Vision-Guided Hierarchical Control and Autonomous Positioning for Aerial Manipulator
## 4 Autonomous Positioning and Grasping
### SLAM-Based Autonomous Positioning and Navigation System
#### 4.1.2 LiDAR Odometry  
First, the raw data collected by the LiDAR sensor is processed to handle point clouds. During the UAV flight, laser beams may hit screens, sky, or other non-reflective surfaces, causing laser degradation and refraction, resulting in ineffective reflection information. Such laser points must be removed using a point cloud library tool. Subsequently, a breadth-first traversal method is employed to downsample the point cloud, reducing the number of laser points and eliminating uncertain objects in the environment. Equation (2) computes the curvature of laser points [36]:  
\[c=\frac{1}{|S|\|P^{L}_{(\mathbf{q};\mathbf{\mu})}\|}\left\|\sum_{j\in S_{j}\
eq i }\left(P^{L}_{(q,j)}-P^{L}_{(q,j)}\right)\right\| \tag{2}\]  
where \(P^{L}_{\mathbf{q}}\) represents points in the current frame under the coordinate system of the LiDAR, \(S\) denotes the set, and \(c\) represents the curvature of the point. The laser points are arranged in descending order based on their curvature values. Larger curvatures indicate lower smoothness and correspond to corner points, while smaller curvatures indicate higher smoothness and correspond to line points. Here, two curvature thresholds, \(c_{1}\) and \(c_{2}\), are set in descending order. Neutral points are eliminated based on Equation (3), resulting in the current frame's point cloud \(P_{\mathbf{q}}\), as follows:  
\[c>c_{1},\text{cornerpoint}\] \[c_{1}>c>c_{2},\text{neutralpoint} \tag{3}\] \[c_{2}>c,\text{linepoint}\]  
Second, we perform inter-frame matching--a crucial step for constraining the pose relationship between frames. We use a point-line registration approach [37] to improve computational efficiency and ensure real-time performance. Corner points and line points extracted from the current frame point cloud \(P_{\mathbf{q}}\) are respectively labeled as \(P^{e}_{q}\) and \(P^{h}_{q}\). The corner points and line points extracted from the previous frame are denoted as \(P^{e}_{q-1}\) and \(P^{h}_{q-1}\), respectively. Since the UAV is in motion from times \(q-1\) to \(q\), the associated pose of each point is adjusted to the corresponding time. The laser points from each frame are  
Figure 5: Framework of the SLAM-based positioning method.  
projected onto the initial time, denoted as \(\widetilde{P}_{q}^{e}\) and \(\widetilde{P}_{q}^{h}\). Corner point matching involves selecting a point in \(\widetilde{P}_{q}^{e}\) and identifying the two closest points in \(P_{q-1}^{e}\) to form a distance constraint between a point and line. The line point matching involves selecting a point in \(\widetilde{P}_{q}^{h}\) and identifying the three closest points in \(P_{q-1}^{h}\) to form a distance constraint between a point and plane. We use the Levenberg-Marquardt optimization method to identify the rotation and translation matrices that minimize these two distance constraints. This yields the pose transformation \(\Delta T_{L}\) obtained through inter-frame matching.
# Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures
## 4 Conclusions  
The ever-increasing demand for new wireless services, together with the evolution of cellular networks, has led to the 5G era. The new network generation introduces major changes and, as a consequence, significant challenges in terms of radio planning. This work focused on providing a generic analysis for this problem, emphasizing the need for dense standalone small-cell networks in the mmWave band of 28 GHz, in order to achieve the capacity goals of 5G. The complex nature of the problem led us to employ a properly modified multi-objective optimization algorithm in order to provide solutions in the context of the multiple constraints and specifics of a 5G mmWave network. The three main constraints of coverage, capacity and cost were studied for a range of cases that reflected scenarios from dense to ultra-dense network deployments in order to achieve high capacity.  
The produced results showed that the denser the microcell deployment, the higher the area throughput and also that sectored microcells double the throughput for ultra-dense networks compared to dense networks since better interference control is achieved through the operation of one, instead of two or three, sector antennas. In terms of how dense 5G networks with standalone small-cell architectures can actually get, the analysis showed cell radii below 400 m that reached 120 m for the ultra-dense sectored network. Finally, the spectral efficiency reached 9.5 bps/Hz/Km\({}^{2}\) with no MIMO/beamforming, which leads to the conclusion that the 5G spectral efficiency goal can be easily exceeded if these techniques are also exploited.  
Conceptualization, G.E.A., D.A.Z., G.V.T., P.K.G. and D.I.K.; methodology, G.E.A., D.A.Z. and P.F.; validation, G.E.A., P.F. and D.A.Z.; formal analysis, P.F.; investigation, G.E.A., D.A.Z. and G.V.T.; writing--original draft preparation, G.E.A, D.A.Z. and G.V.T.; writing--review and editing, G.E.A, D.A.Z., G.V.T., P.F., P.K.G. and D.I.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 5 Proposed Work Overview
### Asset-Specific Bridge Selection (ABS)
#### 5.2.6 Final Selection  
The ABS uses this comprehensive scoring system to select a bridge that offers the best balance between these factors, ensuring the bridge is highly suitable for asset transfer. The bridge score \(B_{\text{Score}}\) is normatively scaled between 0 and 1, indicating the suitability of the bridge for the specific transfer requirements. Algorithm 1 explains the miner and bridge selection process (MBS). Only miners from the approved set of DeFi mining pools, denoted as BMi, are eligible for selection as miners (BMiX or BMiY) if they are part of this miner set. By tailoring unique miners and bridges according to the specific characteristics and requirements of various asset types, CrossDeFi can provide more efficient, highly secure, maximum speed, and user-friendly cross-chain asset transfers.  
```
1:Intention: Selects most suitable miner and bridges for asset transfer.
2:procedureMBS
3: Initiate the BMS.
4: Select \(BMiX\) and \(BMiY\) based on the consensus and Fscore.
5:\(BMi_{\text{Score}}=w_{1}R_{i}+w_{2}TF_{i}+w_{3}Lq_{i}+w_{4}5L_{i}\);
6:for each miner \(i=1\) to \(k\)do
7:if\(BMi_{\text{Score}}\geq BMiTH\)then
8: Miner selected.
9:else
10: Repeat the BMS.
11:endif
12:endfor
13:endprocedure
14:procedureABS
15: Initiate the bridge selection process.
16: Estimate the \(B_{\text{Score}}\) using Equation (6).
17: Apply Equations (7)-(11) in Equation (6).
18:for each bridge \(i=1\) to \(N\)do
19:if\(B_{\text{Score}}=1\)then
20: Bridge is highly suitable.
21: Select the bridge for asset transfer.
22:else
23: Bridge is not suitable.
24: Repeat the ABS process until a suitable bridge is selected.
25:endif
26:endfor
27:endprocedure
28: The miner transfers the assets through the selected bridge.
```  
**Algorithm 1** Miner and bridge selection process
# An NUTSF at Sub-Region for Suppression of Torque Ripple in Switched Reluctance Motors
## 3 Conventional TSF Control Strategy
### Region Allocated and Operation Principle of Conventional TSF  
Based on the characteristic of the switching stator windings in an SRM, the rotor will rotate ahead of a rotor pole pitch angle after all the phase windings of the SRM are conducted once in turn. Taking the exponential TSF as an example, Figure 5 shows the region allocated at a rotor pole pitch angle based on the characteristics of inductance and the torque reference of each phase in the conventional TSF control strategy. The outgoing phase and the incoming phase start demagnetizing and exciting at the same position, respectively, so the turn-off angle of the outgoing phase is equal to the turn-on angle of the incoming phase; that is, \(\theta_{off}^{k-1}=\theta_{on}^{k}\). \(L_{a}\), \(L_{b}\) and \(L_{c}\) are the inductances of phases A, B and C in Figure 5, respectively; \(T_{a}\), \(T_{b}\) and \(T_{c}\) are the torque references of phases A, B and C.  
The whole conduction region of a phase winding can be divided into the TpE and SpC. Taking phase B as an example in Figure 5, in the TpE, only the inductance of phase C \(L_{c}\) decreases; both \(L_{a}\) and \(L_{b}\) increase. Phase C has been cut off and phases A and B have the current to provide positive torque in the TpE, where phase A is the outgoing phase whose TSF \(q_{down}(\theta)\) is presented in Formula (6) and phase B is the incoming phase whose TSF \(q_{up}(\theta)\) is presented in Formula (6). In the SpC region, only phase B has the current to provide positive torque whose TSF is 1 in Formula (6). Similarly, the other phases also work in the TpE and SpC regions according to their respective torque sharing functions.  
Figure 4: Four conventional TSF waveforms in TpE: (**a**) cosine; (**b**) linearity; (**c**) exponent; (**d**) cube.  
Figure 5: Region allocated diagram of conventional TSF control.  
It can be seen from the region's allocated and operation principle the TSF of the incoming phase or outgoing phase in the conventional TSF control strategy is unified in the whole TpE and the shape is fixed.
## 3 Data and Methods
### Deriving Tree Density and DBH Maps from Calibrated Satellite Data  
The three VIs calculated from each of the four images prior to the fire (Table 3), were regressed against tree density and mean DBH from field survey to obtain an empirical relationship between the two. Because of the mismatch between the spatial resolutions of the satellite and field data, VIs were averaged over a window of 3 \(\times\) 3 (3 m) pixels. The image that provided the best coefficient of determination (i.e., highest R\({}^{2}\)) was used to calibrate the three VIs and derive stand density (trees ha\({}^{-1}\)) and DBH (cm tree\({}^{-1}\)) maps for the WUI area.  
Six tree density maps and six DBH maps (i.e., from 3 VIs \(\times\) 2 satellites) were created using the best empirical relationships. These maps were then used to derive maps of aboveground woody biomass (AGB) loss (Section 3.6) and environmental economic loss (Section 3.9).
## 3 Discussion  
In addition to the important characteristics of time-varying, ocean tides have different variations in the offshore and in the deep sea. Since the tide difference is affected by tidal forces, topography and other conditions, it varies by time and location and it is larger in the offshore than in the deep sea. Due to the non-linearity of tidal power in the offshore, additional tidal constituents such as overtides or compound tides are formed. These tidal constituents have complexities of nonlinearities, small amplitudes and short wavelength. These tidal constituents interact with other principal tidal constituents (such as the most active M4 tidal constituent interacting with the M2 tidal constituent)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Tidal** & **Correction** & **X (m)** & **Y (m)** & **Z (m)** & **B (\({}^{\circ}\))** & **L (\({}^{\circ}\))** \\  & **Height (m)** & **Distance (m)** & & & & & \\ \hline
**mean** & \(2.829\times 10^{-1}\) & \(3.054\times 10^{-1}\) & \(1.371\times 10^{-1}\) & \(7.802\times 10^{-2}\) & \(2.197\times 10^{-1}\) & \(5.939\times 10^{-7}\) & \(1.036\times 10^{-6}\) \\ \hline
**std** & \(1.639\times 10^{-1}\) & \(1.747\times 10^{-1}\) & \(8.317\times 10^{-2}\) & \(5.127\times 10^{-2}\) & \(1.467\times 10^{-1}\) & \(5.423\times 10^{-7}\) & \(1.119\times 10^{-6}\) \\ \hline \end{tabular}
\end{table}
Table 1: Tidal height modulo and the corresponding tidal positioning correction by the OTCP method.  
Figure 4: Tidal height modulos and the corresponding tidal positioning corrections of the tracks with the fitting of the two.  
Figure 3: Tidal height modulos and the corresponding tidal positioning corrections of the tracks.  
with amplitudes reaching a significant range (the M4 tidal constituent reaches 1 cm in some areas of the Atlantic [44]), which has a negligible effect on tidal height and its changes. In addition, as the depth of the offshore water becomes shallower than in the deep sea, the friction between the tide wave and the bottom of the seabed changes the propagation process [24], and the propagation of the tide wave is more complicated than in the deep sea. Due to the diversity of offshore straits and bay forms (long straits, semi-enclosed wide bays, narrow long semi-closed bays, etc.), tide waves and tide difference in offshore vary in variety and characteristics, and they are more complex than those in deep seas [45]. Therefore, the influence of the offshore tidal height variation characteristics on the positioning of specular reflection points needs to be taken seriously. The TPX08 ocean tidal model assimilates the altimeter data and the model is enhanced in accuracy and resolution in the offshore, it can calculate the global offshore tide amplitude distribution of high quality. In this section, the offshore part of the specular reflection point track is separated from the deep sea part, and then their tidal height variation and the different influences on the tidal correction are compared.
## 3 Deep Learning-Based Target Recognition Techniques
### Deep Learning-Based Target Segmentation Techniques  
#### 3.2.1 Semantic Segmentation Techniques Based on Deep Learning  
The deep-learning-based semantic segmentation technique classifies each pixel of the image by a deep learning technique, which can identify the spatial information and basic shape of the target. The traditional CNN network used for segmentation tasks is problematic for its large computation, low computational efficiency, and unsatisfactory segmentation effect. To address such problems, [PERSON] et al. [74] proposed a Full Convolutional Network (FCN) compatible with images of arbitrary size. To input images with arbitrary sizes, the FCN uses the skip layer method to combine the feature maps of each convolutional layer and then employs bilinear interpolation to achieve upsampling to obtain a more delicate segmentation effect. [PERSON] et al. [75] used the FCN algorithm to segment the images of guava and estimated fruit pose using the center position of fruit and
## 5 Copper Nanoparticles and Their Antibacterial Role
### Mechanism of Action  
Nanoparticles can reduce or stop the evolution of resistant bacteria because nanoparticles (NPs) target multiple biomolecules at once. Bacteria can be divided in two categories based on cell wall structure: Gram-positive and Gram-negative. Gram-negative bacteria have an additional outer membrane along a thin peptidoglycan layer in the cell wall, while the Gram-positive bacteria present only a thicker peptidoglycan layer in the cell wall.  
Literature shows that Gram-positive bacteria have a higher resistance to the nanoparticle's mechanism of action. The assumption is that the difference in cell walls affects the bacteria's resistance. The Gram-positive bacteria present a ticker wall which acts as a protective layer which makes the interaction between nanoparticles and cell wall more negligible.  
The additional outer layer of Gram-negative bacteria is coated in lipopolysaccharideaccharide, which has a negative charge. The ions released by nanoparticles have a positive change, giving them a high affinity for the negative molecules present in the cell wall (Figure 2). This attraction leads to a buildup and intake of ions which lead to intracellular damage [64].  
Both types of bacteria have negatively-charged walls which influences the interaction between nanoparticles and the ions released. Studies with Salmonella typhimurium, a Gram-negative bacteria, show that the bacterium's cell wall is populated with a mosaic of anionic surfaces. This facilitates forming of areas with high concentration of nanoparticles binding to the cell wall which leads to an increased toxicity [64].  
Besides the interaction of copper nanoparticles with bacteria's cells, copper ions can interact with DNA, intercalate with nucleic acid strands and can disrupt biochemical processes [65]. Ions of copper can also produce hydroxyl radicals which damage essential proteins [66].  
It is worth remembering that bacteria accustomed to exposure to heavy metals show a higher resistance to metal nanoparticles such as TiO\({}_{2}\), Al\({}_{2}\)O\({}_{3}\) and carbon nanotubes. As an example, _E. coli_ and Cupriavidus metallidurans, which are Gran-negative bacteria, _E. coli_ was killed by all prior mentioned nanoparticles, while Cupriavidus metallidurans was resistant [64].