esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Existing methods for calculating terrain entropy in grid digital elevation models (DEMs) often face computational anomalies in specific topographies within small windows. To address this issue, an improved method was developed based on the Euclidean distance approach. This method was inspired by [PERSON]'s technique of weighting information entropy by the average distance between points with the same value and different values. Specifically, vectors were formed between grid points and categorized by value consistency and relative positions. Those formed between points of different values were classified by the value of the starting point as well as parallel and adjacent relationships. This comprehensive grouping strategy was integrated into distance calculations, becoming a new probability operator that accurately reflects terrain spatial characteristics. Experimental verification confirms that the method proposed aligns with the fundamental concept of entropy, yielding a regression equation of \(y=0.011\text{ln}\,x+0.463\) with a coefficient of determination of 94.73%, a reliability of 44.015, and a measurement ability of 0.757. For the mixed iterative images with gradually increasing spatial disorder, their entropy values should follow a logarithmic trend. Therefore, a logarithmic function is used for fitting. A determination coefficient greater than 50% indicates that the method adheres to the original definition of entropy and is effective in capturing the increasing spatial disorder of the grid DEM. A lower reliability value suggests smoother data computation between the two iterations. A lower measurement ability value indicates slower convergence for grid DEMs with gradually increasing spatial disorder. The improved method was also tested on simulated and real DEMs, and the results showed a strong correlation between calculated terrain entropy values and terrain complexity. By effectively capturing spatial information changes, this approach overcomes the shortcoming of computational anomalies and demonstrates high reliability in terrain entropy calculation in grid DEMs.  
digital terrain analysis; grid digital elevation model; terrain entropy 2020
## 4 Discussion
### Scientometric Analysis Insight: Role of Co-Citation Reference and Keyword Co-Occurrence Analysis
#### 4.2.1 Impact of Climate Change on Forest WUE and Gas-Exchange Characteristics  
Climate change significantly affects gas exchange in forests, mostly through variations in photosynthesis and respiration rates [82]. Elevated atmospheric CO\({}_{2}\) can enhance photosynthesis; this phenomenon is known as CO\({}_{2}\) fertilization. This enhanced photosynthesis can amplify carbon uptake by trees, which potentially improves productivity [39]. However, rising temperatures can lead to a surge in respiration rates, which may harm the photosynthetic mechanism, which eventually reduces photosynthetic capacity [82; 83]. Under extreme conditions such as drought, trees switch to a survival mode to conserve water and use internal carbon instead of facing carbon starvation. One common example of a survival mode mechanism is the partial closure of stomata to reduce water loss by evaporation [84]. Under climate change, these complex interactions further complicate the overall understanding of gas-exchange dynamics within forest ecosystems.  
Different tree species in forests react differently to climate change, especially in terms of WUE and gas-exchange capabilities [85]. Tree species that demonstrate superior resilience are those that can acclimatize physiologically to elevated temperatures and varying moisture levels, resulting in enhanced forest productivity [86]. Meanwhile, variations in WUE and gas exchange caused by climate change may have a significant impact on forest ecosystem functions. These changes potentially influence carbon-sequestration and -storage abilities and modify the composition of species. These factors eventually lead to a decrease in biodiversity because various species may flourish while others cease under such conditions [87]. In mixed forests, water-related interactions among woody plants are intricate due to both stand structure and species composition being significant factors [88]. Mixtures have the potential to be more productive than monocultures and may therefore use more water, leading to higher vulnerability to droughs [89]. Physiological adjustments and increases in biomass, leaf area, sapwood area, and root stratification are responsible for the higher water-use efficiency (WUE) in mixed species forests compared to monocultures [90]. Therefore, it is important to comprehend the variations in water use and drought stress of woody plants in mixed forests due to compositional, structural, and environmental factors in order to effectively manage and design appropriate practices for their functioning and dynamics [91].  
Thus, the intricate interaction among climate change, WUE, and gas exchange is a complex matter, affected by various environmental factors and adaptability of tree species. In the face of ongoing climate change, effective forest-management techniques and understanding species resilience are crucial factors for sustaining the health of forests and ecosystems.
## 2 Experimental Setup
### SHPB System  
The impact experiments were conducted using the SHPB system at China University of Mining and Technology, Beijing, and the schematic and physical map of the experimental setup are shown in Figure 1. The SHPB bar system consists of a striker, an incident bar, a transmitted bar and an absorbing bar. The striker and bars are made of high-strength Al alloys with an elastic wave velocity of 5090 m/s, which share a maximum diameter of 50 mm. The striker with a length of 400 mm launched by the gas gun impacts the incident bar to generate a compressive wave, which propagates through the specimen and afterwards to the transmitted bar. The strains induced by the wave on the bars are measured by two sets of strain gauges attached on the incident and transmitted bars; the voltage signals from strain gauges are then amplified by a strain amplifier.  
The principle of SHPB is based on the theory of one-dimensional stress wave propagation and stress uniformity assumptions. The velocities at the incident bar end (\(v_{1}\)) and the transmitted bar end (\(v_{2}\)) are [30]:  
\[v_{1}(t)=C_{0}[\varepsilon_{i}(t)-\varepsilon_{r}(t)] \tag{1}\]  
\[v_{2}(t)=C_{0}\varepsilon_{t}(t) \tag{2}\]  
Figure 1: Schematic diagram of the SHPB experimental system.  
where \(C_{0}\) is the 1D longitudinal stress wave speed of the bar; \(\varepsilon_{t}(t)\), \(\varepsilon_{r}(t)\) and \(\varepsilon_{t}(t)\) are the incident, reflected and transmitted strain signals, respectively; \(t\) is the time.  
\[\dot{\varepsilon}(t)=\frac{v_{1}(t)-v_{2}(t)}{L_{S}}=\frac{C_{0}}{L_{S}}[ \varepsilon_{i}(t)-\varepsilon_{r}(t)-\varepsilon_{t}(t)] \tag{3}\]  
\[\varepsilon(t)=\frac{C_{0}}{L_{S}}\int_{0}^{t}[\varepsilon_{i}(t)-\varepsilon _{r}(t)-\varepsilon_{t}(t)]dt \tag{4}\]  
where \(L_{s}\) is the length of the specimen.  
When the pressures on both ends of two bars reach an equilibrium then:  
\[\varepsilon_{i}(t)+\varepsilon_{r}(t)=\varepsilon_{t}(t) \tag{5}\]  
Equations (3) and (4) can, thus, be simplified as follows:  
\[\dot{\varepsilon}(t)=-\frac{2C_{0}}{L_{s}}\varepsilon_{r}(t) \tag{6}\]  
\[\varepsilon(t)=-\frac{2C_{0}}{L_{S}}\int_{0}^{t}\varepsilon_{r}(t)dt \tag{7}\]  
According to [PERSON]'s third law, the stresses at the incident (\(\sigma_{1}(t)\)) and transmitted ends (\(\sigma_{2}(t)\)) of the specimen are same and satisfy the following equations:  
\[A_{S}\sigma_{1}(t)=A_{0}E_{0}[\varepsilon_{i}(t)+\varepsilon_{r}(t)] \tag{8}\]  
\[A_{S}\sigma_{2}(t)=A_{0}E_{0}\varepsilon_{t}(t) \tag{9}\]  
where \(E_{0}\) is the Young's modulus of the bars; \(A_{0}\) and \(A_{s}\) are the cross-sectional area of the bar and specimen, respectively.  
Therefore, the average stress of the specimen is:  
\[\sigma(t)=\frac{A_{0}E_{0}}{A_{S}}\varepsilon_{t}(t) \tag{10}\]  
where \(\sigma(t)\) is the stress of the specimen.
# Carbon Nanotubes-Based Hydrogels for Bacterial Eradiation and Wound-Healing Applications
## 5 CNT-Based Nanocomposites for Wound Healing  
CNTs enhance the biological activity of wound-healing materials due to their unique interaction pattern with biomolecules, cells, and nearby tissues [129; 130]. As discussed above, wound healing is a natural physiological process that responds to any skin injury. This complicated mechanism involves sophisticated interactions between cell types, coagulation factors, connective tissue, cytokines, growth factors, and the vascular system [2; 131]. The schematic wound-healing process is described in Figure 5a, where four stages of wound healing are demonstrated. These stages are hemostasis, inflammatory, proliferative, and maturation phase. The hemostasis phase is a narrowing of the blood vessels to stop bleeding immediately after an injury to the skin tissue [132]. The blood-clotting system is activated at this stage, and a barrier is formed to prevent excessive bleeding from the injury site. In this process, platelets are attached with collagen, which results in the activation of aggregates. Inflammation is the next stage in the wound-healing process, which is initiated by releasing inflammatory mediators (prostaglandins and histamine) to prepare the wound site for growing new tissue [2; 132; 133]. In this stage, neutrophils (white blood cells) are introduced into the wound to kill the bacteria and remove the inflammatory mediators. Usually, one to two days are required for complete localization of these cells in the injury, which results in continuous removal of residues. The movement of protein and other factors development by these cells results in immune cells' attention toward the wound for facilitating the repair of tissue. The next stage is the proliferative phase, where the wound site constructs a new connective tissue with extracellular matrix (ECM) and collagen [133; 134]. This construction follows the contraction of wound edges, which significantly promotes wound healing. Furthermore, the granulation process produces a new wound matrix, in which the ground substance provides a scaffold-like structure for the development of fresh blood capillaries and connective tissues. Re-epithelial is the last stage of proliferation, where the epithelial cells are transferred around the wound. This process is fast in the presence of a moist wound environment [135]. The maturation or remodeling phase is the final phase to the healing of the wound; it can last somewhere between 21 days and 2 years; the maturation period depends on the wound type and condition. In this phase, newly
## 4 Experiments and Analysis
### Ablation Study  
To answer RQ2, ablation studies were performed, focusing on two points: semantic learning of basic blocks and GMN with enhanced attention. The efficacy of our semantic learning for basic blocks was assessed by testing various versions of our model:  
* (w/o) Type: The model excludes instruction type information.
* (w/o) Type & ITP: The model excludes instruction type information and omits the ITP task during pre-training.  
These models were evaluated using three datasets constructed with functions from Salite3, Glqk and Xml software. The results are shown in Figure 9 where the vertical axis  
Figure 8: Comparison of time overhead for different methods.  
Figure 7: Comparative results of homologous function search using various methods.  
represents the average detection precision. GMN+, (w/o) Type, (w/o) ITP, and (w/o) Type & ITP achieved average precision values of 0.9367, 0.8780, 0.8687, and 0.8317, respectively, across different datasets. GMN+ consistently achieved the best performance in different datasets, demonstrating the significance of integrating instruction type and executing ITP during pre-training.  
We further evaluated two versions of our model, BERT-GNN and BERT-GMN, which substitute the GMN with enhanced attention with standard GNN and GMN, respectively. As illustrated in Figure 10, where the vertical axis represents average detection precision, BERT-GMN outperformed BERT-GNN. This improvement is attributed to the graph matching mechanism employed by GMN, which builds upon the foundational GNN structure to effectively perceive differences between nodes, enhancing similarity detection. Meanwhile, GMN+ outperformed the BERT-GMN model because GMN+ incorporates an additional graph attention mechanism to focus on nodes containing significant semantic information within the CFG.
## 1 Introduction
By the definition of projected range-to-go, the dynamic model of the vehicle is transformed from the time domain to the flight range domain. Then the dynamic model and constraints are convexification and discretization, and the final sequential convex optimization expression is proposed;
2. According to the landing points under the different constant control laws, the initial trajectory generation problem of the vehicle in any spatial state is transformed into a similar initial state trajectory generation problem by rotation transformation. And the initial trajectory that can basically realize practical guidance and meet the dynamic model is obtained by interpolation;
3. According to the concept of the nonlinear illegal degree of iteration trajectory and the distribution of the nonlinear illegal degree, the grid points probability density function is proposed, and the grid points adjustment law is proposed by the probability density function, which realizes efficient and fast grid points adjustment. The section of this paper is arranged as follows: Section 1 describes the research status of convex trajectory generation of re-entry glide vehicles and the innovation of this paper; in the Section 2, the dynamic model and constraints of the trajectory generation in the flight range domain are described; in the Section 3, the convexity and discretization process of the trajectory programming in the flight range domain are described; in the Section 4, the initial trajectory generation method and grid point adjustment strategy are proposed; in the Section 5, operation of the simulation is used to verify the research content; the Section 6 summarizes the conclusion of this paper.
## 5 Results and Discussions
### Discrepancies in Collected Seismicity Data
The individual spatial analysis of magnitude and depth of seismic activity, as presented in Figure 6, suggests active seafloor spreading in the Red Sea. The majority of seismic events occur at shallow depths, which correlates with the genesis of new oceanic crust. The aggregation of earthquakes along specific latitudes and longitudes is likely indicative of the structural characteristics of the Red Sea rift, including segmented spreading centers and transform faults. Such analysis enhances the comprehension of tectonic dynamics within the Red Sea and contributes to the refinement of models that describe plate interactions in this region.
## 4 Simulation and Numerical Results
### Radar Performance  
In this subsection, we verify the radar performance of different radar signal processing methods. To ensure the validity of the comparison results, we set the spectral efficiency of both the MIMO-OFDM system and the MIMO-OFDM-IM system to 4 bps/Hz by adjusting the relevant parameters.
# Formal Modeling of IoT and Drone-Based Forest Fire Detection and Counteraction System
## 2 Related Work
The next algorithm is focused upon the principle of Dumpster-Shafer and suggests that humidity and temperature sensors are used by the nodes. A modern, effective forest fire detection method has been proposed [33], using networks with wireless sensors (WSNs). The detection accuracy of the proposed device is improved by applying multi-criteria detection, in which an alert judgment relies on several characteristics of a forest fire. An updated edition of the algorithm aimed, in general, at minimizing the wrong alarm number is proposed in [34]. The revised version of the algorithm called SFIDE (Satellite Fire Detection) brings into consideration several approaches earlier produced for the detection of fire. This article [35] introduces an innovative approach for the identification of forest fires by the use of a new color map, the Forest Fire Detection Index (FFDI), created by the authors.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell  
### Design and Operation of the EWS-16T Cell  
Figure 1 illustrates the circuit schematic of the proposed EWS-16T SRAM cell, and Figure 2 presents its corresponding layout. The EWS-16T cell is designed with a dual-access architecture to enhance write performance while maintaining high radiation tolerance. It comprises two storage nodes (Q and QB) and two internal nodes (S1 and S0). Transistors P7 and P8 are controlled by the write word line (WWL) and connect the internal nodes S1 and S0 to the bit lines BL and BLB, respectively. Meanwhile, transistors N7 and N8 are controlled by the word line (WL) and connect the storage nodes Q and QB to the bit lines BL and BLB. When the stored data in the cell represents logic '1', nodes Q and QB hold  
logic values of '1' and '0', respectively, and internal nodes S0 and S1 store logic values of '0' and '1'. This complementary storage structure allows robust data retention and controlled feedback operation within the cell.  
Figure 3 presents the transient waveforms of the EWS-16T cell under hold, read, and write operations, confirming stable functionality under typical conditions. The detailed behaviors of each operation mode are explained below.  
Figure 1: Schematic of the proposed EWS-16T SRAM cell.  
Figure 2: Layout of the proposed EWS-16T SRAM cell.
# Evaluation and Characterization of Alginate Extracted from Brown Seaweed Collected in the Red Sea
## 2 Materials and Methods
### Characterization of Sodium Alginate--FTIR Analysis  
To identify the functional groups of the isolated sodium alginate, the Fourier Transform Infra-Red Spectroscopy (FTIR) method was used according to [PERSON] [45]. The dried sodium alginate was grounded into a fine powder (<1 mm) and mixed with dried KBr powder to make a homogeneous compressed thin tablet, and was then placed in a sample pan with the subsequent recording of the infrared spectrum using a Bruker Alpha FTIR spectrometer. All of the spectra were the average of two independent measurements from 4000 to 400 cm\({}^{-1}\) with 128 scans, each at a resolution of 2 cm\({}^{-1}\).
## 1 Introduction
### Research Method  
This review centers on research papers pertaining to implantable sensors over the past five years and incorporates publicly accessible data of certain commercially available sensors. Through systematic sorting and integration of relevant materials in recent years, it conducts a profound analysis of the advantages and limitations manifested in the selection of different sensing mechanisms, communication methods, and biodegradable materials during the design and manufacturing processes of implantable sensors. In the realm of sensor design, distinct sensing mechanisms each possess unique applicable ranges and physical quantities amenable to detection; different communication methods correspond to specific requirements regarding implantation distance and communication rate; and the selection of diverse biodegradable materials necessitates a comprehensive consideration of factors such as the implantation environment and the anticipated implantation duration. This paper predominantly employs a research approach integrating qualitative and quantitative analyses to elaborate meticulously on the optimal applicable environments of various modes and presents some crucial data. All data are sourced from references, and in-depth interpretations and comparative analyses of data under different modes are also executed. The objective is to aid readers in designing sensors that meet their specific needs based on the data summarized herein, thereby enabling the functions and structures of sensors to more readily attain the design objectives.
## 4 Results and Discussion
### Effect of Water Temperature  
To examine the effect of water temperature on the ionic current signals, experiments with the water temperature ranging from 23 \({}^{\circ}\)C to 60 \({}^{\circ}\)C were conducted. It was found that the peak values of the ionic current pulses are water temperature dependent. As shown in Figure 5 (experimental data can be seen in Table A4 of Appendix A), the magnitude of the ionic current signals generated by contacting the platinum sheet with the copper working electrode in DI water are linearly proportional to the water temperature from about 23 \({}^{\circ}\)C to 60 \({}^{\circ}\)C. Nevertheless, this experiment was conducted in DI water, and there are few mobile ions in DI water. The effect of water temperature on the ionic current has a limited relationship with ion migration. Such a result can be attributed to CPD generation. It is well known that the work function represents the minimum thermodynamic work  
Figure 4: Effect of ionic concentration on the ionic current signals.  
needed to remove an electron from a solid to a point in the vacuum. Although the work function itself does not change with temperature, the electrons inside the material will gain more energy due to the increasing temperature. This can cause the energy of some electrons to exceed the energy threshold of the work function, thereby increasing the probability of more electrons transferring from the copper working electrode to the gold sheet. As a result, a larger electric current pulse will be detected with higher water temperature.
## 2 Study Area and Datasets  
The Longquan to Qingyuan highway, as one section of the G25 national highway, is the main study object in the present study. This highway is about 55 km in total, from Longquan City, via Chatian, Xiaomei, Huangtian and Zhukou, to Qingyuan County. It is in the southernmost end of Zhejiang Province, adjacent to Fujian Province, as illustrated in Figure 1a. The study area is identified by the 1 km buffer of the Longqing highway, as shown in Figure 1b. The area is hilly and mountainous landforms, with an elevation range of 120 m\(\sim\)1900 m. The highest mountain of East China is seated northeast of the highway, namely Huangmao Jian (1929 m). As the large elevation difference in the area, its climate difference is also very significant in space. Commonly, the area below 800 m has a sub-tropical humid monsoon climate, while the area above 800 m is close to temperate and monsoonal climate. The average annual temperature is 17.4 \({}^{\circ}\)C, and total annual precipitation is 1760 mm. The rainy season is mainly from March to June, and it is influenced by typhoon activities.  
To investigate the slope ground deformation and identify slope hazards along the highway, 30 scenes of ascending Sentinel-1A (S1A) satellite images were obtained from the European Space Agency in this study. The time span is from 8 August 2020 to 5 April 2021 with a time interval of 12 days. The images are acquired in C-band (wavelength of 5.64 cm) and generated into Level-1 Single-Look Complex (SLC) products. The imaging mode is the Interference Wide (IW) Mode, the width is 250 km, and the image spatial resolution is 20 m \(\times\) 5 m (azimuth \(\times\) range). Observation parameters for the images are as follows: path numbers, 142; average incidence angle, 43.1\({}^{\circ}\); polarization mode, VV.  
In this study, the 30 m resolution SRTM 1 Sec and 90 m resolution STRM 3 Sec DEM data are selected as the external DEM data for master-slave image coregistration and terrain phase removal. Optical remote sensing images were obtained from National Platform for Common Geospatial Information Services ([[https://www.tianditu.gov.cn/](https://www.tianditu.gov.cn/)]([https://www.tianditu.gov.cn/](https://www.tianditu.gov.cn/)), accessed on 22 August 2021). The geological map at 1:200,000 scale for the study area was accessed from National Geological Archives of China ([[http://www.ngac.org.cn/](http://www.ngac.org.cn/)]([http://www.ngac.org.cn/](http://www.ngac.org.cn/)), accessed on 22 August 2021).