esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of the 3-30-300 Green City Concept: Warsaw Case Study
## 3 Results
### Visibility of 3 Trees  
Figure 4 shows the results of the analysis of the visibility of three trees from different sides of buildings in the different districts of Warsaw. This graph shows three values. The first one (red) tells what percentage of buildings have no flats from which at least three trees can be seen. The second value (yellow) tells how many buildings have a proportion of flats from which three trees can be seen. The last value (green) represents the percentage of buildings in which at least three trees can be seen from each potential flat, i.e., the entire building meets the adopted condition.  
Figure 4: Visibility of 3 trees from buildings by districts (red (0%)—percentage of buildings where 3 trees cannot be seen from any flats, yellow (partly)—percentage of buildings where 3 trees can be seen from only some flats (partly), green (100%)—percentage of buildings where 3 trees can be seen from all flats).  
Analyzing the spatial distribution of the results obtained, one may see that districts such as Wawer, Wesola, and Rembertow have the highest percentage of buildings with a minimum of three trees visible. This condition is met by more than 80% of the buildings in each of these districts, suggesting that in these districts, trees are mostly scattered between buildings. This is probably due to the peculiarities of the districts, as these are areas with a predominance of detached houses that have gardens with vegetation, among which there are also many trees.  
The Ursus district has the highest number of houses with zero tree visibility (over 30%). This may be due to the large number of new buildings being built on brownfield sites. They make maximum use of the land for housing but do not secure spaces for green areas, even as small as individual trees.  
The results of the tree visibility analysis also show that the highest percentage of buildings, from which only a part of three trees can be seen, are located in the districts of Praga Pohoc, Srodmiescie, Wola, and Zoliborz (60% and more). These are rather old districts with fairly dense buildings. There are quite a lot of squares and trees in these parts of Warsaw, but probably the dense housing development makes them visible only from some parts of the buildings.  
The visibility of three trees for the whole city is presented in Figure 5. It shows that 57.82% of the houses in Warsaw offer a view of a minimum of three trees from any part of their building, while 36.74% only from some parts of the building. Only 5.44% of residential buildings do not offer a view of three trees from any side of the building, which means that the number of buildings in Warsaw without any trees around them at all is very small.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps Generated Using GIS-Based Methods
#### 3.1.3. Global One-Degree Maps of Forest Area, Carbon Stocks, and Biomass, 1950-2010  
Based on a compilation of forest area and growing stock data reported in international assessments performed by the FAO, MCPFE (now Forest Europe) and the United Nations Economic Commission for Europe, [PERSON] et al. [53] provide global forest biomass at 1\({}^{\circ}\) spatial resolution with five-year intervals from 1950 to 2010. They initially used a ratio between forest area (from a forest raster map) and the interpolated forest area value (calculated from the international assessments performed by FAO, MCPFE, and UNECE) to scale the raster values in the forest map. Then, the growing stock gridded map was derived from interpolated forest growing stock data divided by the adjusted forest area, and multiplied with calculated forest area maps. Finally, forest growing stock were converted to biomass and carbon using the IPCC default biomass conversion and expansion factors [50]. Uncertainty in this dataset was subjectively defined as the sum of basic data uncertainty, methodological developments over time, and interpolation and mismatch between the forest map and statistical data, but it was not formally evaluated.
# Mutual-Information-Based Incremental Relay Communications for Wireless Biomedical Implant Systems
## 4 The Proposed Protocol
### Transmission Route Selection
#### 4.2.2 Cooperative Technique  
When \(d_{SR_{i}}\leq d_{R_{i}D}\), the relay-based cooperative technique is selected for the data transmission. The communication flow is shown as follows: implant sensor \(\rightarrow\) body relay \(\rightarrow\) coordinator. However, it is clear that if there are numerous relay nodes in the network, the implanted sensor may have several available communication links to deliver sensed data. To balance energy consumption among all implanted sensors, the proposed protocol chooses a new relay node in each round. The relay selection algorithm is given as [29]  
\[C(i)=\frac{d(i)}{E(i)} \tag{12}\]  
where \(i\in\{S_{1},S_{2}\ldots S_{n}\}\), \(d(i)\) denotes the transmission distance between the in-body sensor node \(i\) and the potential relay node, and \(E(i)\) represents the residual energy of the implanted sensor \(i\). The relay node with the minimal cost function value is selected for data transmission.
applied sciences Article  
Dissolution of Magneisia in Silicate Melts and Diffusivity Determination from CLSM Studies  
[PERSON]\({}^{\ast}\)\({}^{\copyright}\) and [PERSON]  
Chair of Ceramics, Montanuniversitaet Leoben, Peter-Turner Strasse 5, 8700 Leoben, Austria  
\({}^{\ast}\) Correspondence: [EMAIL_ADDRESS]; Tel.: +43-384-2402-3217; Fax: +43-384-2402-3202  
Magneisia is one of the vital and extensively used refractory components. In this study, the dissolution of magneisia is investigated at 1450, 1500, and 1550 \({}^{\circ}\)C in three silicate slags in the CaO-Al\({}_{2}\)O\({}_{3}\)-SiO\({}_{2}\)-MgO system using high-temperature confocal laser scanning microscopy to determine its effective binary diffusivity. The pore-free fragments of single-crystal fused magnesia particles were used, and the effects of experimental parameters and slag properties on the dissolution of magnesia were assessed. The ranking of dissolution times in the three slags at the three temperatures did not agree with the trend expected from the CaO/SiO\({}_{2}\) ratio of each slag. Instead, several quotients serving as reference numbers were tested. The effective binary diffusivities were calculated considering all the impacting phenomena and parameters. The diffusivities of magnesia at 1500 \({}^{\circ}\)C in the slags with CaO/SiO\({}_{2}\) weight ratios of 0.65, 0.93, and 1.17 are 2.67 \(\times\) 10\({}^{-10}\), 1.81 \(\times\) 10\({}^{-10}\), and 3.20 \(\times\) 10\({}^{-10}\) m\({}^{2}\)/s, respectively. The diffusivity of magnesia in one of the three slags was compared with rotating finger test results, which showed good agreement. The plausibility of diffusivity was checked using an Arrhenius plot.  
magnesia; corrosion resistance; diffusivity; dissolution Article
# EMI Threat Assessment of UAV Data Link Based on Multi-Task CNN
## 5 Hyperparameter Optimization and Model Training
### Training Setting  
On the basis of the experimental data in Section 3, 1320 groups of VPP, STFT spectrogram, and NDC of UAV's data link interfered by EMI are obtained, which are, respectively, input into the channels of the network. The samples of three EMI categories are divided randomly into a training set, validation set, and test set according to the ratio of 6:2:2. The training set optimizes weights by updating model parameters. The validation set is used to optimize the hyperparameters of the network to prevent overfitting and underfitting, so as to obtain a better model. Finally, the network evaluates the final model on the test set.  
The MIMT-CNN is built and trained based on the neural network toolbox of MATLAB2020b. The computer platform is a Win 10 system with the GPU of NVIDIA GTX1060. In order to stabilize the training process and prevent the gradient from disappearing or exploding, the Adam optimizer with an adaptive learning rate is used for training. The initial learning rate of \(6.1884\times 10^{-6}\) is obtained by Bayesian optimization, which then decreases to 50% of the previous value after one epoch. The Adam optimizer uses gradient estimation to directly calculate the adaptive learning rate of different parameters, which has the advantages of high computational efficiency and small memory requirement [33].
###### Abstract  
Allebotny has been applied to control nuisance algae in aquatic systems, but the effects of allelochemicals on the broad spectrum of algae are not well understood. We investigate algicidal effects of the allelochemical juglone on the bloom-forming, harmful algae _Microystis aeruginosa_ and _Stephanodiscus hantzskii_, and on several non-target algal species including cyanobacteria (_Anabena fos-aquae_, _Oscillatoria curciveps_, and _Phormidium subfuscum_), diatoms (_Asterionella formosa_, _Fragiralia crotonensis_, and _Synedra acus_), and green algae (_Chlorella vulgaris_, _Scenedesmus corris_, and _Scenedesmus quadricunda_), in laboratory and field enclosure bioassays. Under three treatment concentrations (0.01, 0.1, and 1 mg L\({}^{-1}\)) of juglone, _Microystis_ cell density is significantly reduced by 35-93%. Concentrations of 0.1 and 1 mg L\({}^{-1}\) inhibits _Stephanodiscus_ growth almost equally (66% and 75%, respectively). To contrast, juglone produces a stimulatory allebotntic effect on three green algae, and other tested diatoms showed hormesis. Overall, the cyanobacteria are more sensitive to juglone than the green algae and diatoms. These results indicate that the allebotntic effects of juglone on microalgae vary depending on their characteristic cellular morphology and anatomy.  
alleopathy; juglone; algicidal potential; differential effects; harmful algae; hormesis Article
## 5 Conclusions  
This article studies the influence of structural dimensions on the vibration of coupled systems through a combination of response surface methodology and multi-objective optimization. The following conclusions are drawn:  
1. According to the simulation analysis results and experimental test results, it can be concluded that the shaft frequency excitation of the fan causes significant resonance in the structure, exhibiting severe vibration. The axial resonance vibration modes are caused by the undulation modes of the motor plate, and the vibration modes will appear in the third-order or fourth-order modes when the structure of the frame changes;
2. The effects of the motor plate thickness P6, connecting tube outer diameter P3, mounting plate thickness P1, and connecting tube inner diameter P2 on the axial vibration characteristics weakened in order. Under the interaction, the outer diameter of the connecting plate P3 and the thickness of the motor plate P6 have the greatest impact on the axial vibration characteristics;
3. The multi-parameter experimental design confirmed that the external excitation frequency must be more than 7 Hz away from the structural modal frequency to avoid resonance. After structural optimization, the maximum axial amplitude of the frame is reduced from 5.38 mm/s to 2.15 mm/s, and the corresponding axial modal frequencies increased from 42.2 Hz to 52.3 Hz (an increase of 24%). The frequency ratio is greater than 1.2, which can produce a vibration isolation effect, avoid resonance at maximum speed, and achieve optimized design goals.  
Conceptualization, L.Z. (Longfei Zhou) and K.L.; methodology, D.Z. and T.H.; software, C.W.; validation, L.Z. (Liangyu Zheng) and K.L.; formal analysis, D.Z., L.G., and C.W.; investigation, D.Z. and L.Z. ([PERSON]); resources, M.L. and L.G.; data curation, L.G. and C.W.; writing--original draft preparation, L.Z. ([PERSON]); writing--review and editing, L.Z. ([PERSON]) and K.L.; visualization, L.Z. ([PERSON]) and T.H.; supervision, K.L., M.L., and C.W.; project administration, K.L. All authors have read and agreed to the published version of this manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52376005, the National Natural Science Foundation of China, grant number 52378094, the Henan Province Science and Technology Development Plan Project, grant number 222102220033, the Henan Provincial Science and Technology Research Project (Grant number 242102220004), and the Luoyang Science and Technology Development Plan Project, grant number 2302035A.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company Zhejiang Yilida Ventilator Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
## 2 Materials and Methods
### Mass Change, Water Absorption and Internal Damage  
Before freeze-thaw testing, the specimens were dried in a drying oven at 60 \({}^{\circ}\)C for 24 h. This relatively low temperature was selected to avoid potentially destroying the pore structure at a high temperature [31] and to decrease the influence of the drying process on the water absorption of the specimens. It was followed by measuring an oven-dried mass and \(E_{dym}\). The specimens were immersed in sulfate solution (or water) for a week [32] as a presaturation stage; then the saturated surface dry mass was recorded. These natural saturated specimens were then subjected to 60, 90, 120, 150 and 180 FT cycles, during which they were exposed to water or sulfate solution. Sodium sulfate solution at 5% (by mass) was used in the present study as sulfate solution. After FT testing, the saturated mass, oven-dried mass and \(E_{dym}\) were analyzed again to determine the frost damage.  
The freeze-thaw induced mass loss (percent) of concrete specimens can be calculated by  
\[\Delta m_{N}=\left(m_{1}-m_{2}\right)/m_{1}\times 100\% \tag{1}\]  
where \(m_{1}\) (g) and \(m_{2}\) (g) are the mass before and after \(N\) freeze-thaw cycles, respectively.  
As mentioned before, micro-cracks were developed with FT cycles and they provided additional paths for solution penetrating into the internal pores. Consequently, saturated water absorption provides an indirect way to quantify the development of micro-cracks [33; 34],  
Figure 3: Internal temperature of notched beam in freeze–thaw cycles.  
Figure 2: Schematic of the notched beam (**A**) and experimental apparatus of the notched beam (**B**) under quasi-static three-point bending test.  
which was also used in the present work to evaluate FT damage. The volume of solution-filled pores and voids (\(V\), mm\({}^{-3}\)) was calculated from the saturated mass (\(m_{s}\), g) and oven-dried mass (\(m_{d}\), g) using Equation (2):  
\[V=(m_{s}-m_{d})/\rho \tag{2}\]  
where \(\rho\)(g \(\cdot\) mm\({}^{-3}\)) is the density of water.  
The extent of internal damage caused by freeze-thaw cycles can be regularly evaluated by a relative dynamic elastic modulus (RDEM). Here, a damage parameter (1-RDEM) was used to assess the frost damage, which can be expressed by Equation (3):  
\[D=1-E_{dyn,FT}/E_{dyn,0} \tag{3}\]  
where \(E_{dyn,0}\) (GPa) and \(E_{dyn,FT}\) (GPa) are the dynamic elastic modulus before and after FT cycles.
## 5 IoT Communication Protocols
### IPv6 over Low-Power Wireless Personal Area Networks Protocol (6 LoWPAN)  
As the core hardware of the Internet Protocol (IP) network, it is the 6 LowPAN network standard, rather than the IoT protocols such as Bluetooth and ZigBee [86], which specifies embedding protocols and header compression. Because multiple Zigbee-certified devices can be connected, Zigbee is an appropriate protocol for the IoT. As more Zigbee devices are connected, the number of communication pathways connecting them grows, reducing the likelihood of a single-point signal failure. Zigbee is interoperable and standardises network and application layers, allowing devices from various manufacturers to communicate with one another. Furthermore, Zigbee is based on the 2.4 GHz frequency band, which can be used without a licence anywhere in the world. Solution providers can sell the same product wherever they are on earth as a result of this. In addition, Zigbee has a data transfer rate of 250 kbit/s across 16 channels. Zigbee is a mesh network, which is a communication system made up of radio nodes that are connected in a mesh topology. The mesh network is distinguished by its high degree of interconnectedness. Each mesh network device functions as a node, connecting to the signal and passing it on to the next device. Zigbee can support up to 65,000 devices in a network while preserving signal strength and data transmission capabilities. Certain networks like Wi-Fi, 802.15.4, and sub-1 GHz ISM will also include the frequency range, physical layer, and communication. Internet protocol version 6 (IPv6) has been a major aspect of the IoT in recent years.
## 4 Simulation and Result Discussion of the MOO Problem  
### Non-Dominated Sorting Genetic Algorithm (NSGA-II)  
For the MOO problem, it is difficult to directly judge the merits and demerits of the individual. It needs a comprehensive index to evaluate all objectives. NSGA-II is a kind of multi-objective evolutionary algorithm based on Pareto-optimal solutions to solve the MOO problem, which has conflicting objectives. NSGA-II can build solution sets with fast non-dominated sorting and the crowd distance computing method, which is an adaptive global optimization algorithm and has better performance such as a faster convergence speed, better convergence effect, better robustness, and adaptive ability. It assures a good convergence towards the Pareto-optimal front without losing the solution diversity [29]. A detailed explanation and steps of NSGA-II can be found in [30]. In this study, the population size of the algorithm was set to be 200, and the number of generations was set to be 100. The flowchart for implementing the algorithm in solving the MOO problem is shown in Figure 7.  
In the present work, according to the characteristics of optimization variables, binary cross mutations are simulated by real number encoding. The algorithm mutation can be adjusted adaptively to improve the global search ability in the early stage and local search ability in the later stage. Meanwhile, in order to increase the convergence speed and ensure the global optimality of the Pareto solution set, the process of simulating the binary cross mutation of the algorithm is as follows:  
\[\left\{\begin{array}{c}x_{i}^{(k+1)}=c_{i}(\lambda)x_{i}^{(k)}+(1-c_{i}( \lambda))x_{i+1}^{(k)}\\ x_{i+1}^{(k+1)}=c_{i}(\lambda)x_{i+1}^{(k)}+(1-c_{i}(\lambda))x_{i}^{(k)}\\ x_{j}^{(k+1)}=(1+c_{j}(\lambda))x_{j}^{(k)}\end{array}\right. \tag{18}\]  
where \(x_{i}^{(k+1)}\) and \(x_{i+1}^{(k+1)}\) are the individuals before crossing; \(x_{i}^{(k)}\) and \(x_{i+1}^{(k)}\) are the individuals after crossing; \(k\) is the evolutionary algebra; \(c_{i}(\lambda)\) is the cross coefficient, using the dynamic state adjustment mechanism; \(x_{j}^{(k)}\) is the individual to be mutated; and \(c_{j}(\lambda)\) is the coefficient of variation.  
It is necessary to make corresponding changes in \(c_{i}(\lambda)\) and \(c_{j}(\lambda)\) to maximize the performance of the algorithm according to the change of the solution set. The algorithm is further improved so that \(c_{i}(\lambda)\) and \(c_{j}(\lambda)\) can adapt to the changes of the solution set. The adaptive change model is expressed as:  
\[\left\{\begin{array}{c}\lambda=N_{p}/N_{a}\\ c_{i}(\lambda)=\frac{1}{2}e^{\lambda}\\ c_{j}(\lambda)=e^{\lambda}\end{array}\right. \tag{19}\]  
where \(\lambda\) is the adaptive coefficient of variation; \(N\) is the number of populations; and \(N_{p}\) is the optimal number of solutions.  
Figure 7: The optimization algorithm processes based on the non-dominated sorting genetic algorithm (NSGA-II).
## 3 Computed Tomography and Mitral Valve
### Other Potential Use of CT in Transcatheter Mitral Valve Interventions  
Choosing the optimal approach for TVMI is also essential in procedural planning, and CT plays a crucial role in this context. Thus, in patients undergoing a transapical procedure, CT can be used to understand which intercostal space is close to the apex of the left ventricle and could allow the device to be deployed perpendicular to the mitral annulus, thus avoiding obstruction, perivalvular leaks, and canting [80]. Although most mitral procedures require intraprocedural transesophageal echocardiography guidance, CT can determine the optimal site for the septal puncture in those with the transseptal approach. Although there are no specific guidelines, it is preferred to puncture the interoposterior section to obtain good coaxiality to the mitral annulus [95]. Moreover, it is very useful to calculate the distance from the puncture site to the mitral valve.  
CT is also helpful in determining the optimal fluoroscopic viewing angle of the mitral annulus for device deployment, which should be perpendicular to the mitral annulus (coplanar angle) [80]. Knowledge of this angle is fundamental for interventionists for accurate coaxial device positioning. Indeed, any change from this fluoroscopic angle can translate into inaccurate deployment, potentially increasing procedural complications.  
In patients undergoing mitral valve-in-valve (VIV), CT can identify patients at higher risk of complications, providing information on the pre-existing prosthesis and thus predicting the risk of LVOT obstruction [90].  
Finally, CT has a role in the follow-up of patients undergoing TMVI, especially in assessing the presence of any subclinical thrombosis. Although the clinical significance of the latter is not yet known, CT can diagnose this complication by highlighting it as a thickening of the hypodense flap with the decreased movement of the prosthetic flaps [96]. CT also has a role in predicting the risk of mitral stenosis after the MitraClip procedure. In a study by [PERSON] et al., annulus diameters (both anteroposterior and medial-lateral), annulus area, and mitral valve orifice area were inversely associated with transvalvular gradients [97].
## 4 Numerical Simulation  
The three-dimensional numerical models are established using the finite element method (FEM) with Plaxis 3D software (version 2024.1.0.1060). The geometry of the numerical model aligns with the model test (Figure 9). The numerical models have dimensions of 3 m in length, 1 m in width and 1.5 m in depth. Over 38,000 elements are utilized, ensuring that the model responses are mesh-independent. The soil consists of 10-node units, and the modified cam-clay model is used as constitutive model. The unit weight of soil is set at 18.5 kN/m\({}^{3}\). The parameters \(M\), \(\lambda\) and \(\kappa\) are assigned to the values of 1.385, 0.052 and 0.022, respectively, according to the consolidated undrained triaxial shear testing data. The sheet piles are modeled by the equivalent stiffness method as a rectangular plate employing a 2D plate unit. The pipe piles and the anchor rod are modeled by a 1D embedded beam unit and a 1D beam unit.