esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Characterization of CoPcS and AC-CoPcS Catalysts  
The UV\(-\)VIS and FT-IR were used to confirm the structure of the purchased CoPcS catalyst. The metal phthalocyanines and their derivatives have special absorption spectra in the ultraviolet and visible regions. That is the Q band of 600\(-\)800 nm and B band or Soct band of 300-400 nm [31]. The UV\(-\)VIS spectra result of CoPcS catalyst is shown in Figure 2. The CoPcS have obvious absorption spectra in the B band and Q band with the maximum wavelengths located at 326 nm and 664 nm, which can be confirmed as the characteristic absorption peak of the CoPcS [32].  
FT-IR result of CoPcS is shown in Figure 3, the peaks of 1716, 1542, 1510, and 1400 cm\({}^{-1}\) corresponded to C=N stretching vibration, C-H bending vibration, C=C skeleton vibration in the benzene ring, and C-C stretching, respectively. We also found the peaks of 1120, 1053, 914, 732, and 717 cm\({}^{-1}\) of CoPc reported by [PERSON] et al. [33], and two characteristic peaks at 623 and 484 cm\({}^{-1}\) owing to the phthalocyanine skeletal and cobalt ligand vibrations reported by [PERSON] et al. [34] confirming the structure of the CoPcS.  
XPS was used to analyze the surface element and its chemical states of the AC-CoPcS catalyst, and the results are shown in Figure 4. The surface element of the AC-CoPcS is mainly carbon (C), oxygen (O), nitrogen (N), cobalt (Co,) and sulfur (S). This result indicates the fact that CoPcS was successfully combined into the AC through a chemical linkage of ethylenediamine because the Co and S appear in the XPS pattern of the new material. The CoPcS is very easy to dissolve in water, while the AC-CoPcS catalyst is well washed by deionized water after being prepared, so CoPcS appearing in the new materials cannot be physically adsorbed by the AC, but is chemically connected with it.  
Figure 2: UV–VIS spectra of the free CoPcS catalyst (test sample of CoPcS DMF solution with the concentration of 1.0 \(\times\) 10\({}^{-6}\) mol/L).  
The surface area results of the AC and AC-supported catalysts are listed in Table 1, the BET specific surface area of AC support, commercial, and newly prepared AC-CoPcS catalysts are 704.8 m\({}^{2}\)/g, 265.4 m\({}^{2}\)/g, and 301.3 m\({}^{2}\)/g. Compared with the commercial AC-CoPcS, the new AC-CoPcS catalyst shows a slightly higher BET specific surface area, larger total pore volume, and smaller average pore size, which may benefit the adsorption of the reagents to accelerate the catalytic reaction, but this should be proved by the catalytic performance of the two catalysts.  
Figure 4: The XPS spectra of the AC-CoPcS (**a**), C1s (**b**), Co2p (**c**), and S2p (**d**).  
Figure 3: The FT-IR spectra of the free CoPcS catalyst (KBr tabletting treatment).
###### Abstract  
To reduce the trial and error in a real clinical scenario, the finite element analysis (FEA) can be effectively used to simulate various effective pad designs and a material selection to reduce and redistribute peak plantar pressure in a diabetic foot with neuropathy. The aim of this study was to investigate the effect of pad design and material stiffness on the reduction in plantar pressure in a diabetic foot with neuropathy using FEA. Three-dimensional foot models with a customized insole (CMI) were created to study the peak contact pressure. Ethylene vinyl acetate, Nora(r) Lunalastike, and thermoplastic polyurethane were assigned to the top, middle, and base layers of the CMI, respectively. Two types of pads were proposed: a heel pad and a heel-forefoot pad. Four different materials with different stiffness were assigned as pad materials including a void pad. The FEA revealed that pads with soft materials reduced peak plantar pressure more effectively than stiffer pads. The use of a softer heel-forefoot pad reduced the peak plantar pressure at the midfoot and forefoot compared with other pads. The findings suggest that the material and design selection for the fabrication of CMIs with pads are important factors in reducing plantar pressure and may be useful in the management of a neuropathic diabetic foot.  
custom-made insole; finite element analysis; material stiffness; pad design; plantar pressure +
Footnote †: journal: Applied Sciences
## 4 Beam Steerability and Beam Forming  
### Dielectric Resonator with Beemsteerability  
A microstrip patch antenna is presented in this research work. The front and back side of the dielectric slab is arranged periodically to properly adjust the VCRM position. A good radiation pattern is attained with good matching of numerical and experimental results. A high value of 11.9 dB gain is achieved in [62]. A dielectric resonator antenna with parasitic elements is used to achieve high-order mode steerability. The beam steering was successfully achieved by switching the termination capacitor on the parasitic element and a narrow aperture in the ground plane to achieve a wider bandwidth, which can be potentially applied for device-device (D2D) communication in 5G Internet of Things (IOTs), which can interconnect, as shown in Figure 16. For a significant performance achievement, the size of the resonator might be less than the device antenna [63], and 36.78% impedance bandwidth is achieved. The entire design mechanism is shown in Figure 16.  
Figure 15: Antenna geometry: (**a**, **left**) side view (**b**,**c**, **left**) top and bottom view, and (**a**,**b**) (**right**) impact of MS on S\({}_{11}\) and axial ratio [61].  
A 193.16% in [57; 64] is achieved and four CSRRs in the ground plane are used. A total of 5.71% impedance bandwidth while the simple patch Antenna without the use of CSRRs offer an impedance bandwidth of 4.28% at 28 GHz.
## 5 Results and Discussion
### Evaluation of Different Algorithms Based on Computed Indexes
If \(x_{ij}\) is considered the value of the index in Table 4 for each method, a multicriteria decision model can predicate the ranks for different methods based on the following steps: (1) The consideration and definition of the suitable indexes for evaluating different methods; (2) the allocation of weight to the index, where the vulnerability, reliability, RMSE, and MAE based on the same importance have the same weight for this study; and (3) the prioritization of different methods based on different indexes. The dry year as a critical condition was selected, and compromise programming (CP) was used as a multidecision model for four reservoirs to select the best model based on the mentioned indexes in Table 4 [9]:  
\[L_{p}(S_{i})=\left[\sum_{j=1}^{n}\left(\frac{x_{j}^{+}-x_{ij}}{x_{j}^{+}-x_{j} ^{-}}\right)^{p}\right]^{\frac{1}{p}}, \tag{20}\]  
where \(L_{p}(S_{i})\) is the distance of a solution from the ideal solution; and \(x_{j}^{+}\) and \(x_{j}^{-}\) are, respectively, the ideal solution (the most value for maximizing criteria or lowest value for minimizing criteria) and the negative ideal (the most value for minimizing criteria or lowest value for maximizing criteria). Three values for \(p\) were selected for this model to evaluate distances (\(p=1\), \(p=2\), and \(p=\infty\)) as black distance, Euclidean distance, and Tchebycheff distance [26]. The multicriteria decision gave ranks to the method for different reservoirs based on the mentioned values of indexes. The results indicated that the CA method based on RMSE, MAE, the reliability index, and the vulnerability index received the first rank, and MBF, PSO, and GA received other ranks. The ranks showed that the CA based on all indexes had the best rank, and the MBF acted based on the next alternative.
## 4 Conclusions  
A study was conducted on the simultaneous crossing of two existing tunnels by a new tunnel, using transparent soft rock modeling tests. By varying the advanced footage of the new tunnel and the thickness of the soil layer, the main findings of this study can be summarized as follows:  
1. Through research on different aggregates and cementing materials, the most suitable artificial synthetic transparent rock-like materials for simulating the excavation of new tunnels were determined. Moreover, using incandescent lamps and colored spot surfaces, the influence caused by low transparency was compensated to a certain extent.
2. Along the length of the existing tunnel, the vertical displacement of the rock surrounding the tunnels consistently decreased from the center of the tunnel's axis toward both sides. The deformation pattern follows a Gaussian curve.
3. When the new tunnel simultaneously passes through the two existing tunnels, the deformation of the rock around the existing tunnels increases as the excavation footage of the new tunnel increases. The largest deformation of the existing tunnels occurs when the new tunnel crosses above or below them. Meanwhile, the deformation was most significant when the newly built tunnel advanced to 2.2D. Then it tends to stabilize.
4. With constant excavation footage, as the thickness of the soil interlayer between the new tunnel and existing tunnel a gradually decreases, the settlement of the rock around tunnel a increases. Similarly, the maximum deformation of the rock mass around existing tunnel b also increases as the thickness of the soil interlayer decreases.
5. The next step of this work will consider finding more reasonable ways and methods to enhance the material strength on the premise of ensuring transparency, so as to simulate more types of rock mass.  
Software, X.R.; Validation, M.L.; Writing--original draft, M.L.; Writing--review & editing, Q.O., X.R. and X.D.; Funding acquisition, Q.O. and X.D. All authors have read and agreed to the published version of the manuscript.  
This research was funding by the National Natural Science Foundation of China, grand number: U2268213 & 52178312 & 52108299, the Chongqing Outstanding Youth Science Fund, grand number: CSTB2022 NSCQ-JQX0020, the Chongqing technology innovation and application development special key project, grand number: STB2022 TIAD-KPX0144, and Chongqing construction technology project, grand number: City Kezi 2023 number 1-1.  
Not applicable.  
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.  
Author [PERSON] was employed by the company Chongqing City Construct Investment Group 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.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 7 Security Analysis
### Security Attributes
#### 7.1.6 Usability  
The PVPBC voting system supports voter usability while preserving privacy and verifiability. Specifically, PVPBC preserves voters' privacy and verifiability without requiring any complex actions to be performed by the voter during the pre-election, election, and post election phases. During the pre-election phase, a voter receives a VID from the TTP to be submitted to the PVPBC's frontend. Once the valid VID is submitted to the frontend, the voter is allowed to access the candidates list and cast her vote. All the voter's cryptographic primitives are managed by the front end (e.g. voter-key generation, and vote encryption, etc). During the post-election phase, the voter will receive the \(\alpha\) term which helps in reconstructing the tracking number. The tracking-number reconstruction is performed by the front end, meaning the voter does not perform any complex operation to locate the tracking number associated with her vote. To conclude, voter usability is fulfilled across all election phases without affecting the verifiability and privacy features.
# A Study on Cyber Target Importance Quantification and Ranking Algorithm
## 2 Related Works
### Definition of Cyber Target and Cyber-Targeting Process
#### 2.2.2 Definition of Cyber-Targeting Process  
Targeting is used to systematically analyze and prioritize targets to achieve the commander's objectives in consideration of operational requirements, capabilities, and evaluation results, and to apply appropriate killing and non-killing measures to targets. Target development refers to the process of identifying, evaluating, and documenting potential targets that contribute to the achievement of the commander's objectives as part of the second phase of the target-processing cycle [4].  
In military operations, the targeting process is divided into a deliberate targeting process of reviewing and analyzing targets over a period that is sufficient to carry out a normal targeting process and a dynamic targeting process of quickly processing unexpected targets.  
The deliberate targeting process is as follows. The target information maintains a close relationship with the operation and has continuity, as shown in Figure 2, for each cycle [4].  
In this study, various studies and manuals are referenced to define the cyber-targeting process based on the deliberate targeting process in Figure 2.  
The US Chairman of the Joint Chiefs of Staff guidelines CJCSI 3370.01B (2016) [5] describes more detailed content in the field of target development in JP 3-60 (2020) [4]. In addition, it contains the procedures and contents of each stage of target development, the output, electronic target folders (ETF) used in the development stage, and the contents of the target DB. It also defines data on joint targets, information and intelligence relationships, target development roles and responsibilities, and targeting process cycles, as well as presenting data items and examples for basic, intermediate, and detailed target development in the target development stage. [PERSON] [6], the current head of operations at the U.S. Cyber Command, said that the integration of cyberspace operational effectiveness is essential in the joint forces' operational planning and implementation in the cyber domain. It has also been suggested that the targeting process in a cyberspace operation be implemented in accordance with the targeting process of the joint unit commander. This also explains the application of the six-step procedure of the targeting process, major operational documents such as the United States CYber COMmand Joint Integrated Prioritized Target List (USCYCOM JIPTL) and Master Cyber Attack Plan (MCAP), and cooperation matters.  
Figure 1: Three Cyberspace layers and Cyber Target.  
Figure 2: Deliberate Targeting Process.  
Combining these studies and manuals [4; 5; 6], the cyber-targeting process applies the same steps as in Figure 2. It is executed in conjunction with the targeting process of joint operations. In addition, the contents of each stage are adjusted and executed in accordance with CO. Accordingly, in this study, the CO targeting process and main activities are summarized as shown in Figure 3.
# Diversity of Collagen Proteins and Their Biomedical Applications in Drug Delivery
## 3 Cell Receptor and Function of Collagens
### Integrin Receptor  
Integrins, as well-known cell surface heterodimeric receptors, are broadly distributed to almost all cells in tissues and organs such as the skin, muscles, kidneys, lungs, eyes, heart, gut, and blood vessels. [70]. Up until now, a total of 4 types of collagen-binding integrins, including \(\alpha\)1\(\beta\)1, \(\alpha\)2\(\beta\)1, \(\alpha\)10\(\beta\)1, and \(\alpha\)11\(\beta\)1, have been identified from 24 different types of integrins in human kinds, all of which play fundamental roles in regulating cell survival, migration, signal, and differentiation. These integrins transfer the signals through cells in a bidirectional way [71]. The \(\alpha\)1\(\beta\)1 receptor was first discovered and is commonly expressed in many activated organs, such as lymphocytes, the liver, and the heart. Collagen binding to the integrin \(\alpha\)1\(\beta\)1 receptor delivered the ability to regulate the proliferation of cells, MMP expression, and collagen synthesis [72]. The \(\alpha\)2\(\beta\)1 integrin, also termed CD49b, is an extracellular matrix receptor and is mainly distributed in bone, skin, and other  
Figure 3: The schematic diagram of the main collagen receptors and related signaling pathways. These signaling pathways include FAK, PLCy, SYK, JAK, and PI3K.  
internal organs. Integrin \(\alpha\)2\(\beta\)1 has a preferential affinity to fibrillar collagens I-III, V, and XI. The collagen motif GFOCER was responsible for the interaction between collagen and the \(\alpha\)2\(\beta\)1 integrin [73]. Integrin \(\alpha\)10\(\beta\)1 is a primary receptor of collagen type II, which is mostly located in the tissue of cartilage [74]. Integrin \(\alpha\)11\(\beta\)1 binds with fibrillar collagen such as collagen I and XIII and is expressed in tissues including embryos, muscle, and bone [75]. Except for the four integrins mentioned above, integrin \(\alpha\)6\(\beta\)4 and \(\alpha\)5\(\beta\)3 have been identified to bind with collagen XVII and XIX [76]. As the receptor for collagen, the integrin signaling pathways are involved in cell proliferation, survival, and migration. In detail, the extracellular integrin/collagen conjugator could trigger the cellular signal transduction pathways, including the Akt/PI3K, protein kinase C (PKC) cascades, and MAP kinase pathways through adapter proteins such as Src, focal adhesion kinase (FAK), and integrin-linked kinase (ILK) [77].
###### Abstract  
This study compares carbon sequestration rates along two independent tidal mangrove creeks near Naples Bay in Southwest Florida, USA. One tidal creek is hydrologically disturbed due to upstream land use changes; the other is an undisturbed reference creek. Soil cores were collected in basin, fringe, and riverine hydrogeomorphic settings along each of the two tidal creeks and analyzed for bulk density, total organic carbon profiles, and sediment accretion. Radionuclides \({}^{137}\)Cs and \({}^{210}\)Pb were used to estimate recent sediment accretion and carbon sequestration rates. Carbon sequestration rates (mean \(\pm\) standard error) for seven sites in the two tidal creeks on the Naples Bay (98 \(\pm\) 12 g-C m\({}^{-2}\): year\({}^{-1}\) (\(n\) = 18)) are lower than published global means for mangrove wetlands, but consistent with other estimates from the same region. Mean carbon sequestration rates in the reference riverine setting were highest (162 \(\pm\) 5 g-C m\({}^{-2}\): year\({}^{-1}\)), followed by rates in the reference fringe and disturbed riverine settings (127 \(\pm\) 6 and 125 \(\pm\) 5 g-C m\({}^{-2}\): year\({}^{-1}\), respectively). The disturbed fringe sequestered 73 \(\pm\) 10 g-C m\({}^{-2}\): year\({}^{-1}\), while rates within the basin settings were 50 \(\pm\) 4 g-C m\({}^{-2}\): year\({}^{-1}\) and 47 \(\pm\) 4 g-C m\({}^{-2}\): year\({}^{-1}\) for the reference and disturbed creeks, respectively. These data support our hypothesis that mangroves along a hydrologically disturbed tidal creek sequestered less carbon than did mangroves along an adjacent undisturbed reference creek.  
carbon sequestration; sediment accretion; mangrove wetlands; coastal geomorphology; blue carbon; \({}^{137}\)Cs; \({}^{210}\)Pb; _Rhizophora mangle_; sea level rise +
Footnote †: journal: Physics Letters B
Recent studies have presented methods to approximate the passband and stopband characteristics of traditional filter responses with fractional-order attenuations using fractional-order transfer functions. This approach requires appropriate selection of the transfer function coefficients to achieve the desired responses. To date, this method has been applied to realize lowpass Butterworth [18], Chebyshev [19], Inverse Chebyshev [5] and elliptic [20] fractional-order filter responses. While the coefficients of a \((1+\alpha)\) fractional-order transfer function to approximate the passband and stopband ripple characteristics of a second-order elliptic lowpass filter are presented in [20], this work expandson those results to: (i) explore how the selected bandwidth in the least squares coefficient selection impacts the coefficients and resulting magnitude response; and (ii) validate the elliptic responses using circuit simulations and experimental measurements from topologies realized with approximated fractional-order capacitors. The least-squares fitting process is applied in this work to multiple frequency ranges to evaluate the impact of the selected frequency band on the coefficients and resulting transfer function magnitude characteristics. MATLAB simulations of the magnitude responses of \((1+\alpha)\) order lowpass filters with fractional steps from \(\alpha=0.1\) to \(\alpha=0.9\) using the determined coefficients are presented to highlight the fractional-step compared to the second-order elliptic response. Further, SPICE simulations and experimental measurements of \((1+\alpha)=1.2\) and \(1.8\) order filters implemented with operational amplifier topologies using coefficients from the fitting process are given to validate the magnitude characteristics and stability of the proposed circuits.
## 3 AI Transforms the Development of Computational Materials Science
### The Model Form That Combines Materials Science and AI  
With the rapid development of the Materials Genome Initiative (MGI) and computational materials science, the field of materials science has accumulated a large amount of high-quality data, providing a solid foundation for data-driven materials research. At the same time, the advancement of ML and NNs has enabled material modeling to gradually evolve from early statistical methods to more advanced deep learning methods, greatly improving the ability to describe atomic interactions and electronic effects, and enhancing the generalization ability of the model in different systems.  
Based on methods and application scenarios, these models are categorized as follows. Data-driven Machine Learning Prediction Model: By analyzing experimental and computational data, it predicts material performance and characteristics, enabling rapid screening of candidate materials. Machine Learning Potentials: These models use machine learning to construct potential functions for interatomic interactions, enabling high-precision molecular dynamics simulations at a low computational cost. Graph Neural Network-Based Material Modeling: This approach represents atomic and bonding relationships through graph structures, making it suitable for performance prediction and design optimization of complex material systems. Each model has unique characteristics and functions (see Table 6). They provide diverse solutions for materials science research, driving innovation from basic research to industrial applications.
## 4 Performance Evaluation and Comparison
### Evaluation Metrics
#### 4.1.5 Area under the Receiver Operating Characteristic Curve (AUC-ROC)  
The AUC-ROC metric serves as a valuable performance indicator to evaluate the model's ability to distinguish between positive and negative instances. The ROC curve portrays the true positive rate (recall) against the false positive rate (1--specificity) at diverse classification thresholds. The AUC-ROC encapsulates the area beneath this curve and offers a singular value that quantifies the model's discriminative power. Ideally, a perfect classifier demonstrates an AUC-ROC value of 1, while a random or ineffective classifier exhibits an AUC-ROC value of 0.5 [28; 29].