esa-sceva/satcom-chunk-collection huggingface dataset:
# Power-Efficient Resource Allocation for Active STAR-RIS-Aided SWIPT Communication Systems
## 4 Simulation Analysis
Among them, the AP transmission power demanded by systems with assistance of active STAR-RIS/RIS is generally smaller than that demanded by systems with assistance of passive STAR-RIS/RIS, and the AP transmission power demanded by systems with assistance of active STAR-RIS is the smallest. This is because active STAR-RIS and active RIS have a certain amplification effect on the signal, thereby reducing signal  
Figure 4: Relationship between AP transmission power and quantity of transmission antennas. Figure 5: Relationship between AP transmission power and the quantity of active STAR-RIS units \(M\). attenuation, while passive STAR-RIS and passive RIS can only offset path loss by increasing AP transmission power, thereby meeting the minimum energy requirements received by the energy receiver. Compared to active RIS, active STAR-RIS not only reflects but also transmits signals, increasing the coverage range of signals sent by base stations. Even if the energy receiver is in a relatively poor channel environment and cannot receive the reflected signals, it can still receive signals transmitted from active STAR-RIS, improving the utilization of base station transmission signals and optimizing the efficiency of system operation. As shown in Figure 7, the variation of AP transmission power with SINR value at the information receiver is shown, where \(N=2\), and \(M=10\). Similarly, we compare active/passive STAR-RIS and active/passive RIS. In Figure 7, as the SINR value increases, the AP transmission power of four models gradually increases. This is because the larger the SINR value at the IRs, the higher the required communication quality, thus requiring higher AP transmission power. From Figure 7, it can also be seen that the active STAR-RIS model proposed in this paper requires the smallest AP transmission power. This is because active STAR-RIS can reflect and amplify signals as well as transmit and amplify them, unlike passive RIS, thus making more use of frequency spectrum resources and reducing path loss. Although active RIS can amplify signals, it cannot transmit them. Although passive STAR-RIS can reflect and transmit signals, it cannot amplify them. Therefore, under the same conditions, the required AP transmission power is higher than active STAR-RIS. Figure 6: Relationship between AP transmission power and power received by minimum energy receiver. Figure 7: Relationship between AP transmission power and SINR value at the information receiver.
# Comparative Study of Blockchain Hashing Algorithms with a Proposal for HashLEA
## 5 Security and Performance Analysis
### Statistical Analysis of HashLEA  
It is a fundamental property of hash functions that they exhibit both confusion and diffusion, which is critical for resisting statistical attacks. Diffusion ensures that any alteration to the input message results in significant, pseudorandom changes throughout the hash output, thereby maximizing the unpredictability of the changes. As defined by [PERSON], confusion serves to mask the relationship between the input message and the resulting hash, thereby enhancing resistance to cryptographic analysis. In order for a hash function to be considered secure, it should be the case that a single bit change in the input will result in approximately 50% of the output bits being altered.  
The evaluation of this behavior typically conducted four key metrics in statistical analysis. Let \(N\) represent the total number of tests, \(H_{i}\) denote the number of changed bits in the hash output for the \(i\)-th test, and \(n\) signify the length of the hash output in bits. These metrics collectively assess the hash function's capacity to achieve an optimal balance between confusion and diffusion.  
The number of average bit changes:  
\[B_{\text{c}}=\frac{1}{N}\sum_{i=1}^{N}H_{i} \tag{1}\]  
Percentage of bit change:  
\[R_{\text{c}}=\frac{B_{\text{c}}}{n}\times 100\% \tag{2}\]  
The standard deviation of bit change:  
\[\Delta B_{\text{c}}=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(H_{i}-B_{\text{c}})^{2}} \tag{3}\]  
The standard deviation of \(R_{\text{c}}\):  
\[\Delta R_{\text{c}}=\sqrt{\frac{1}{N-1}\sum_{i=1}^{N}(\frac{H_{i}}{n}-R_{\text {c}})^{2}}\times 100\% \tag{4}\]  
In the evaluation process, the Mersenne Twister (MT19937) pseudorandom number generator is employed to generate 1000 random input data. Corresponding hash values are then computed for these messages. Subsequently, a single bit in an arbitrary position of each original input data is altered, and the resulting hash values are generated forcomparison. Table 1 presents the alterations between the hash values of the original and modified messages, alongside data from other hash functions for comparison purposes. The proposed hash function consistently achieves the ideal 50% average bit change in both the number and percentage of altered bits across all tested versions. Furthermore, the standard deviation of these changes is notably low, indicating stable and predictable diffusion characteristics. The proposed function has shown strong consistency in comparison with other hash functions, reflected in the lower standard deviation values of the average number of bit changes (\(B_{c}\)) and the percentage of bit changes (\(R_{c}\)), providing further evidence of its robustness and reliability.
## 2 Related Research  
### LEACH Protocol for WSN  
The LEACH protocol [6] was introduced to promote balanced energy utilization and the efficiency of WSNs by dividing the network into multiple clusters. LEACH organizes nodes into clusters and employs a probabilistic threshold to elect a CH within each cluster. LEACH is recognized as the first and most popular hierarchical routing protocol to utilize clustering, typically adopting a Time Division Multiple Access (TDMA)-based MAC protocol. It organizes sensor nodes into clusters, where each cluster is managed by a CH responsible for aggregating data from its members and forwarding it to the sink node. The LEACH protocol operates in cyclical phases, with each cycle comprising two primary phases: the setup phase and a steady-state phase (or the data transmission phase). During the setup phase, CHs are elected, clusters are formed, and a TDMA schedule is established, as illustrated in Figure 1.  
The LEACH protocol adopts a TDMA scheme for to its energy efficiency and simplicity, making it better suited than Frequency Division Multiple Access (FDMA) for resource-constrained WSNs. TDMA prevents collisions by assigning unique time slots to each node, reducing idle listening and ensuring predictable, synchronized communication, which aligns well with LEACH's cyclical operation phases. In contrast, FDMA requires more complex hardware for fine frequency tuning and consumes more energy for frequency stabilization, making it less practical for WSNs. Thus, the TDMA-based LEACH protocol not only conserves energy but also ensures simple, collision-free communication as the optimal choice for LEACH's clustering-based design.
# Facile Synthesis of Highly Conductive Vanadium-Doped NiO Film for Transparent Conductive Oxide
###### Abstract  
Metal-oxide-based electrodes play a crucial role in various transparent conductive oxide (TCO) applications. Among the p-type materials, nickel oxide is a promising electrically conductive material due to its good stability, large bandgap, and deep valence band. Here, we display pristine and 3 at.%V-doped NiO synthesized by the solvothermal decomposition method. The properties of both the pristine and 3 at.%V:NiO nanoparticles were characterized by field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), X-ray diffractometry (XRD), Raman spectroscopy, ultraviolet-visible spectroscopy (UV-vis), and X-ray photoelectron spectroscopy (XPS). The film properties were characterized by atomic force microscopy (AFM) and a source meter. Our results suggest that incorporation of vanadium into the NiO lattice significantly improves both electrical conductivity and hole extraction. Also, 3 at.%V:NiO exhibits a lower crystalline size when compared to pristine nickel oxide, which maintains the reduction of surface roughness. These results indicate that vanadium is an excellent dopant for NiO.  
vanadium doping; size tuning; bandgap; transparent conductive oxide; electrical conductivity +
Footnote †: journal: Applied Physics Letters
# Antimicrobial Peptides--Mechanisms of Action, Antimicrobial Effects and Clinical Applications
## 3 Antimicrobial Effects of AMPs
### Immunomodulatory Activity of AMPs
#### 3.4.1 Defenses  
Since human \(\alpha\)-defensins 1-4, most commonly expressed by neutrophils, are called neutrophil peptides 1-4 (HNP) [97], together with lysozyme, proteases, and other proteins, they participate in the destruction of bacterial pathogens. This gives them exceptional importance in the immune system [98]. Human \(\alpha\)-defensins 5 and 6 are produced and secreted mainly by Paneth cells located at the base of Lieberkuhn's crypts in the small intestine and by epithelial cells of the male and female genital organs [99]. In addition, some human AMPs (\(\beta\)-defensins, LL-37) can attract immune cells such as white blood cells, dendritic cells, and mast cells [100]. Beta defensins originate from the epithelial cells of the skin and mucosa of mammals [101]. Thus far, six types of human \(\beta\)-defensins have been isolated and designated as hBD 1-6 [102].  
Regarding antimicrobial activity and expression level, hBD-1, hBD-2, and hBD-3 defensins were characterized [101]. Epithelial cells constitutively express hBD-1, hBD-2, and hBD-3, but after stimulation by microorganisms and pro-inflammatory cytokines [103]. The role of \(\beta\)-defensin in connecting adaptive and innate immunity is significant [104]. More specifically, hBD-3 has the ability to rapidly enter TLR4-stimulated macrophages and dampen the expression of pro-inflammatory genes [105]. The role of \(\beta\)-defensin peptides is multifunctional, so in addition to the defensive, antimicrobial function, they also have a clearly expressed immunomodulatory function [106]. Since they are ubiquitous across mucosal surfaces, they are considered essential factors in homeostasis and health [107].  
The administration of human peptides has been shown to have multiple protective effects in an in vivo model of infection [19]. Furthermore, some AMPs can act as regulatory molecules, as evidenced by in vitro research showing that defensins can attract phagocytes and lymphocytes to the site of infection, induce fibroblast proliferation, and modify ion flow in epithelial cells [108]. In addition, AMPs can induce bacterial lysis, promote macrophage phagocytosis, prevent the spread of infection, induce mitosis of fibroblasts and epithelial cells, and promote fibroblast growth to improve wound healing [1]. Finally, based on the ability of AMPs to stimulate complement activation and the production of cytokines and antibodies, the influence of AMPs on humoral immunity is currently being further evaluated [98].
# Integrated Precise Orbit Determination of Multi-Gnss and Large LEO Constellations
## 4 Result Analysis and Discussion
### Integrated POD with Full LEO Constellation
#### 4.1.2 Integrated POD with Different LEO Orbital Type
The average number of the used sun-synchronous-orbiting LEO satellite is 21.5, which is slightly larger than polar-orbiting LEO number, i.e., 20.6. In terms of ODOP, the ODOP values of the sun-synchronous-orbiting constellation present a stronger fluctuation with a larger amplitude than those of polar-orbiting constellation. For BDS GEO satellites, the changeless tracking geometry is the key limitation of their orbit determination. The main contribution of LEO satellites is to bring a fast variation to the tracking geometry for BDS GEO. The faster fluctuation of ODOP indicates that the sun-synchronous-orbiting constellation can bring more variations to the GEO tracking geometry, which can yield more pronounced orbit accuracy improvements for GEO satellites. For the 96-LEO solution, more available LEO satellites are introduced compared with the two 60-LEO solutions. Though the 96-LEO solution presents smaller ODOP values, a more moderate variation can be observed in GEO ODOP values for the 96-LEO solution compared with the 60 sun-synchronous-orbiting solution. The slow variation of ODOP values for the 96-LEO scheme may be the reason why the 96-LEO solution achieves a relatively worse orbit quality of GEO than the 60 sun-synchronous-orbiting solution. In the case of satellite G13, the differences between the ODOP variation of the two 60-LEO solutions is small, which leads to a relatively small discrepancy in the corresponding orbit accuracy, i.e., 1 mm. Figure 8: Time series of used LEO satellites number and the ODOP values for BDS GEO C02 (left) and GPS G13 (right).
# Analysis of the Interaction Effects of Shield Structure Oblique Passing under an Existing Tunnel
## 1 Introduction
[PERSON] [22] investigated the effect of construction sequence on the interaction of crossing tunnels by conducting three-dimensional centrifuge tests. To study the general principles of the new Austrian tunnelling method (NATM), [PERSON] [23] focused on the tunneling practices used in the Bolu Tunnel, assessed the conditions under which the NATM could be effectively implemented, and finally identified the types of ground to which the NATM principles applied and suggested relevant updates. [PERSON] [24] studied the effect of tunnel shape on the internal forces in the lining of tunnels using the Hyperstatic Reaction Method (HRM) and compared the internal forces derived from the HRM model with those derived from the numerical model Plaxis2D. Numerous domestic and foreign research findings have highlighted the problem of the disturbance caused by the diagonal underpassing of old and new tunnels being more complex than vertical underpassing. We have only a rough knowledge of the torsional deformation of existing tunnels, and existing studies have failed to fully simulate the actual construction process used in shield tunnel construction (e.g., the hardening process of the grouting layer and the jacking force of the shield lining rings are not considered), and these factors will have an impact on calculation accuracy. To reveal the mechanical response of an existing tunnel to being diagonally underpassed by a shield tunnel in greater depth, many scholars have used the finite element method to investigate the mechanism of interaction of the new tunnel with the surrounding rock and the existing tunnel. In view of the special geological environment of Guiyang City, Guizhou Province, subway construction in this area must be carried out in rocky strata. The applicability of shield construction to the Guiyang area is the focus of the current study, as this technique is being used here for the first time. There have been few studies on the mechanical response of existing horseshoe tunnels to new tunnels under rocky strata, and an in-depth analysis of the torsional deformation of horseshoe tunnels is another current research gap. Therefore, based on this engineering background, this paper investigates the effects of the mechanical response (e.g., internal forces and deformation) and ground properties (e.g., settlement) of an existing horseshoe tunnel underpassed by a two-lane shield tunnel using a three-dimensional numerical model that considers the characteristics of shield construction.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 4 Experiment and Analysis
### Experimental Conclusion  
This chapter mainly constructs the IQGMCL network through ablation experiments and verifies its progressiveness in the field of signal modulation recognition. In the process of building the IQGMCL network, when the input data need to be further processed after features are extracted by the convolution layer, a series of models are established for the number of LSTM layers in the network, the number of fully connected layers, whether to replace LSTM as Bi-LSTM, and whether to introduce multiplicative Gaussian noise, etc. Then, the network is compared with other existing networks on three open-source modulated signal datasets. Various signals' recognition accuracy contrasting curves are drawn. Meanwhile, the train_accuracy, train_loss, val_accuracy, and val_loss of IQGMCL and MCLDNN are compared, and the highest recognition accuracy of various models in the SNRs range and the average recognition rates when the SNR is greater than 0 dB are also compared. Finally, some confusion matrices are selected to observe the recognition effect of the networks more directly. Specifically, on the RML2016.10a dataset, the highest recognition accuracy of the IQGMCL network and the average recognition accuracy of the network when the SNR is greater than 0 dB are 93.52% and 92.3%, respectively, which are 1.31% and 1.2% higher than the MCLDNN network. The IQGMCL network has a faster convergence speed than the MCLDNN network. On the RML2016.10b dataset, the highest recognition accuracy of the IQGMCL network and the average recognition accuracy of the network when the SNR is greater than 0 dB are 93.72% and 93.3%, respectively, which are improved by 0.11% and 0.3% compared with the MCLDNN network. On the RML2016.04c dataset, the highest recognition accuracy of the IQGMCL network and the average recognition accuracy of the network when the SNR is greater than 0 dB are 99.42% and 97.5%, respectively, which are improved by 0.97% and 1% compared with MCLDNN network. Compared with other existing advanced networks, the IQGMCL network also has  
Figure 14: Partial confusion matrices on the RML2016.04c dataset about two networks. (**a**) IQGMCL-6 dB; (**b**) IQGMCL-8 dB; (**c**) IQGMCL-14 dB; (**d**) MCLDNN-6 dB; (**e**) MCLDNN-8 dB; (**f**) MCLDNN-14 dB.  
a better recognition effect on three datasets. It proves the progressiveness of the IQGMCL network in the current field of modulation recognition.
# An Application of an Unequal-Area Facilities Layout Problem with Fixed-Shape Facilities
## 3 A Genetic Algorithm (GA) for the UA-Flp  
A GA is developed for the UA-FLP presented in previous sections. The proposed GA consists of a BSH, LP model, and a dual simplex method. The GA consists of the following five stages. See Figure 2 for a flow diagram of the stages of the proposed GA:  
1. Randomly generate a set (or population) of solutions (\(P_{0}\)).
2. The BSH presented in [PERSON] and Hakobyan (2008) is used to construct the layout and determine the total weighed distance (TWD) for each solution in \(P_{0}\).
3. Based on BSH layout plans (obtained in stage 2), set binary variables _left\({}_{ij}\)_, _below\({}_{ij}\)_, and \(o_{i}\) in proposed MILP, and improve layout for chromosomes in \(P_{0}\) or \(P_{g}\) using the resulting LP model and the dual simplex method.
4. Perform crossover and mutation operations on best set of solutions from stage (3) to produce a new population of solutions (\(P_{g}\)).
5. Repeat stages (2) through (4) on \(P_{g}\) until a stopping criterion is met.  
The main components of the proposed GA are the solution representation, BSH, and the crossover and mutation operations. Each of these components are discussed below.
# Towards Automatic Landslide-Quake Identification Using a Random Forest Classifier
## 3 Results and Discussion
### Influence of Signal Attributes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{Classifier} \\ \cline{3-6}  & & Landslide & Earthquake & Noise & Sensitivity \\ \hline \multirow{3}{*}{Manual} & Landslide & 186 & 25 & 3 & 86.9\% \\ \cline{2-6}  & Earthquake & 19 & 190 & 5 & 88.8\% \\ \cline{2-6}  & Noise & 4 & 0 & 210 & 98.1\% \\ \hline \multirow{3}{*}{Manual} & Precision & 89.0\% & 88.4\% & 96.3\% & 91.3\% \\ \cline{2-6}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Confusion matrix for the classifier created using the time domain and the frequency domain features. Figure 8: Normalized values of each attribute calculated from 642 training signals. Figure 9: Performance of 24 attributes in classification. (**a**) Accuracy for 24-attribute, 23-attribute, and single-attribute classification; (**b**) accuracy for cumulated number of attributes. 2. F10 Attribute (LF1 classifier)  
Table 4 shows that the accuracy of the LF1 single-attribute classifier was 53.9%, and its sensitivity for noise events was over 70%. In Figure 6, it can be observed that the noise case had lower energy in the frequency range of 0.02-0.05 Hz than did the earthquake or landslide cases, so the LF1 attribute efficiently separates noise events from landslide and earthquake events. In order to prove that every attribute improves the accuracy of classification, all attributes were sorted by the accuracy of their single-attribute classifiers in descending order and accumulated to build new classifiers to calculate their accuracy. The accuracy increased with cumulative numbers of attributes, as shown as Figure 9b. We observed that when the attributes F23, F21, F18, F17, and F11 were introduced into the training set, the accuracy decreased slightly (<0.2%). It is noted that the slight decrease in the accuracy in the curve of cumulative attributes was likely due to the grouping of the training dataset. The accuracy of the classifier was expected to increase as the number of attributes increases. However, it is found that inclusion of several attributes (e.g., F16, F11) would lower the accuracy very slightly (Figure 9b due to the grouping effect [16]. This effect resulted in a slight decrease in accuracy when cumulating the number of attributes. The small drop in the value of accuracy caused by the grouping effect can be ignored. This means that after some attributes enter the training, their positive effect on the classifier is very small, but it will not have a negative effect.
## Materials and Methods
### Experimental Data Collection  
#### 2.2.1 Materials and Experimental Set-Up  
Commercial anode-supported NiYSZ/8 YSZ/LSCF button cells are used (outer diameter: 30 mm, active area: 3.14 cm\({}^{2}\), thickness anode/electrolyte/cathode: 240/8/50 \(\upmu\)m). The sealing used is Aremco Ceramabond(r) 552. The test rig and related equipment are fully described in Reference [36]. The cell is connected to an electronic load, simulating the user. Raw data are sampled at 1 Hz and processed with an average filter to remove measurement noise (on a 120-sample basis).
# Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
## 2 Materials and Methods
### Image Acquisition and Multispectral Models  
A cloudless orbital image of the site was acquired from the Google Earth Engine platform. The image is already corrected at the top of the atmosphere, where the conversion from digital numbers to sensor radiation was applied to the linear transformation conversion, the solar elevation and the Earth-Sun distance [15] for 2015/2016. The image used was from the Landsat-8 satellite with the OLI sensor (USGS Landsat 8 Collection 1 Tier 1 and Real-Time data TOA Reflectance) available in the LANDSAT/LC08/C01/T1_RT_TOA catalog for 18 January 2016, on path/row 222/074. The spectral bands used in this study were B1 (0.43-0.45 \(\upmu\)m), B2 (0.45-0.51 \(\upmu\)m), B3 (0.53-0.59 \(\upmu\)m), B4 (0.64-0.67 \(\upmu\)m), B5 (0.85-0.88 \(\upmu\)m), B6 (1.57-1.65 \(\upmu\)m), B7 (2.11-2.29 \(\upmu\)m), and B9 (1.36-1.38 \(\upmu\)m), all with a spatial resolution of 30 m. Besides the isolated spectral bands, a total of 13 VIs were calculated, as described by Table 1.  
For data acquisition from the 100 random repetitions (pixel by pixel) per cultivar on the orbital image, the Google Colab platform was used in Python language through the packages ee, os, and geemap [16].