esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Monocortical Anchorage vs. Bicortical Anchorage  
This FEA study indicated that cortical bone stress was higher in monocortical miniscrew placements compared to bicortical miniscrew placements. Compared with bicortical miniscrews, monocortical miniscrews provided inferior anchorage resistance and greater cortical bone stress.  
The stress differences between the monocortical variants were clearly significant, but the extent of the differences was much lower than between bicortical and monocorticalanchorage. With monocortical anchorage relatively high stresses were induced in the region of the cervical peri-implant bone. These were somewhat higher than in monocortical deepening anchorage, but significantly higher than in both bicortical anchorage placements. Cervical bone stress was clearly lower in the two bicortical anchorage placements compared to the two monocortical anchorage placements.  
Since miniscrews are not osseointegrated, their anchorage potential usually depends on the quantity of bone into which they are placed [27]. Clinically relevant dimensions of bone available for palatal miniscrew implant anchorage include both the outer cortical layers of the oral hard palate and the nasal floor. Bicortical anchorage (oral and nasal cortical layers) is a major determinant of the success of a miniscrew implant, and if the expander is too distant from the palatal mucosa, miniscrews may not reach the nasal cortical bone. Moreover, the application of force at too far a distance from the implant-bone interface increases the potential for miniscrew deformation and fracture.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## 2 Theoretical Background  
Before the advent of Bitcoin in 2008, numerous academic publications proposed various system concepts that are fundamental to blockchain technology. These concepts, including public key infrastructure (PKI) and cryptographic hashing, were utilized in different contexts. In their study, [PERSON] and [PERSON] [36] introduced a method employing cryptographic hashing to validate document timestamps, which laid the groundwork for the immutable ledger concept central to blockchain technology. Although these predecessor systems do not incorporate all the components that characterize modern blockchain technology, they establish an essential foundation for distributed ledger systems and play a crucial role in enabling blockchain functionality.  
A blockchain is a distributed database or ledger of interconnected data blocks safeguarded against alteration using cryptographic techniques [2]. Blockchain is administered through the network's user agreement rather than a central authority. Each block owns a copy of the ledger in a blockchain network or relies on network participants for the blockchain data [37]. The data cannot be erased once it has been added to the blockchain unless all or most of the network participants agree, which is known as immutability. The amount of data continues to grow as additional blocks are deployed. Each block in the blockchain includes the preceding block's information, which safeguards the data alteration [38]  
Smart contracts are advances based on blockchain's core technology concept, allowing customized functionality to be built on blockchain systems and the precise execution of automated agreements when the network terms are met [39]. One of the most progressive ideas for using smart contracts is to develop the nexuses of intelligent contracts to create decentralized groups and organizations that run autonomously (DAOs) [40]. Smart contracts are utilized in the blockchain to deliver various services to corporations, institutions, associations, and the general public, such as financial, notary, voting, and other services [41]. Following the initial popularity of blockchains with the introduction of cryptocurrencies, an attempt was made to continually utilize the technology for new applications [42].
# Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
## 5 Other Magnetic-Photocatalytic Composites
### Spinel Ferrite Composites
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{1}{c}{**Photocatalyst**} & \multicolumn{1}{c}{**Pollutants**} & \multicolumn{1}{c}{**Experimental**} & \multicolumn{1}{c}{**Findings**} & \multicolumn{1}{c}{**Ref. **} \\ \hline \multirow{3}{*}{Ag\({}_{3}\)PO\({}_{4}\)/MnFe\({}_{2}\)O\({}_{4}\)} & \multirow{3}{*}{MB; RhB} & \begin{tabular}{c} Sunlight (Iran, \\ \(\sim\)185 mW\(\cdot\)cm\({}^{-2}\)); \\ 2 g\(\cdot\)L\({}^{-1}\); 15 ppm; 80 min \\ \end{tabular}  & \begin{tabular}{c} 58\% MB deg. (MnFe\({}_{2}\)O\({}_{4}\)); \\ 80\% MB deg. (Ag\({}_{3}\)PO\({}_{4}\)); \\ 96\% MB deg. \\ (Ag\({}_{3}\)PO\({}_{4}\)/MnFe\({}_{2}\)O\({}_{4}\)) \\ \end{tabular}  & [115] \\ \hline \multirow{3}{*}{Ni\({}_{0.65}\)Zn\({}_{0.35}\)Fe\({}_{2}\)O\({}_{4}\)@rGO} & \multirow{3}{*}{MB} & \begin{tabular}{c} 300 W Xe lamp \\ (vis \(\lambda\) \(\lambda\) 420 nm); \\ 1 g\(\cdot\)L\({}^{-1}\); 5 ppm; 48 min \\ \end{tabular}  & \begin{tabular}{c} 100\% dec.; \\ 5 cycles—10\% activity loss \\ \end{tabular}  & [116] \\ \hline \multirow{3}{*}{BiFeO\({}_{3}\)-ZnFe\({}_{2}\)O\({}_{4}\)} & \multirow{3}{*}{MB} & \begin{tabular}{c} 100 W Xe lamp with cut-off \\ filter (\(\lambda\) \(\sim\) 420 nm); pH 2; \\ 1 g\(\cdot\)L\({}^{-1}\); 15 ppm; 120 min \\ \end{tabular}  & \begin{tabular}{c} \(\sim\)97\% deg. (BiFeO\({}_{3}\)-25 wt\% \\ ZnFe\({}_{2}\)O\({}_{4}\)) \\ \end{tabular}  & [119] \\ \hline \multirow{3}{*}{LaZF@rGO} & \multirow{3}{*}{RhB} & \begin{tabular}{c} UV lamp (\(\Delta_{\text{max}}\) = 254 nm; \\ 0.6 mW\(\cdot\)cm\({}^{-2}\)); 1 mM PDS; \\ pH 4.5; 0.25 g\(\cdot\)L\({}^{-1}\); 30 ppm; \\ 80 min \\ \end{tabular}  & \begin{tabular}{c} 100\% dec. (1:1 LaZF@rGO) \\ 80 min \\ \end{tabular}  & [117] \\ \hline \multirow{3}{*}{ZnS-WO\({}_{3}\)-CoFe\({}_{2}\)O\({}_{4}\)} & \multirow{3}{*}{MB} & \begin{tabular}{c} Tungsten-halogen lamp \\ (400–800 nm); 0.05 g; 50 ppm; \\ 180 min \\ \end{tabular}  & \begin{tabular}{c} 96\% deg.; \\ 4 cycles no activity loss \\ \end{tabular}  & [114] \\ \hline \multirow{3}{*}{PANI/BiOBr/ZnFe\({}_{2}\)O\({}_{4}\)} & \multirow{3}{*}{RhB; NB} & \ \begin{tabular}{c} HPHgL with cut-off filter \\ (\(\lambda\) \(>\) 420 nm); 1 g\(\cdot\)L\({}^{-1}\); 20 ppm \\ (RhB) and 5 ppm (NB); 30 min \\ \end{tabular}  & \begin{tabular}{c} 99\% RhB deg. \& 87\% NB red. \\ (7\% PANI/BiOBr/ZnFe\({}_{2}\)O\({}_{4}\)) \\ \end{tabular}  & [120] \\ \hline \multirow{3}{*}{dec.—decolorization; HPHgL—high pressure mercury lamp; LaZF—lanthanum-substituted zinc spinel ferrite; MB—Methylene Blue; NB—nitrobenzene; PANI—polyaniline; PDS—peroxydisulfate; red.—reduction; rGO—reduced graphene oxide; RhB—Rhodamine B.} \\ \cline{1-1} \cline{
## 4 Results of Experiment and Simulation  
Experiments and simulations were carried out to validate the performance of the proposed BNNN-based NFTSM control method in this section. The experimental system is shown in Figure 5. Comparisons are made between the results of the conventional PID method, the TSM in reference [19], the NTSM in reference [15], the NFTSM in this paper, the RBF neural network-based NFTSM. The parameters of the MSA control system are listed in Table 1.  
In this system, the BSNN has two inputs and one output. Each of the inputs corresponds to three B-spline activation functions which have \(n+1=7\) control points separately. The internal knot vector of B-Spline curve is \(\left[\begin{array}{cccccccc}1&1&1&2&3&4&5&6&6&6\\ \end{array}\right]\) and the degree of B-spline curve is 2. Moreover, the learning rate \(\eta=0.6\), the inertial coefficient \(\alpha=0.05\) and \(c=5\). \(l_{d}=7\) is adopted for NFTSM control method.
## 1 Introduction
In the field of plasma-based NF, the presence of water (as an electrode or as part of the gas stream) in the plasma zone has also attracted significant attention since water is considered a green hydrogen source. These discharges can generate a diversity of reactive oxygen species (ROS) and reactive nitrogen species (RNS). Using droplets in a corona discharge yielded an energy efficiency of 3.9 g/kWh for NO\({}_{\rm x}\)[25]. Moreover, adding water as a liquid electrode into discharge reactors also proved to be a good approach to generate aqueous nitrites and nitrates [26]. A corona discharge applied over a water surface was found to show an increase of more than 90% of NO\({}_{\rm x}\) transformation in HNO\({}_{2}\)/HNO\({}_{3}\) compared to cases without the presence of water [27; 28], thus dramatically reducing the need for downstream aqueous scrubbers to absorb the plasma-produced gaseous NO\({}_{\rm x}\). In contrast, another recent study which used a transient spark plasma demonstrated that the presence of water could induce an adverse effect by reducing the NF efficiency by approximately 20% compared to the dry air condition [29]. The divergence in the reported data between various works can be explained by an indirect comparison of the plasmas with and without water, poor plasma characterization, specifically plasma power and discharge regime, focusing only on the products generated in the gas or liquid phases. Moreover, an extensive number of studies mainly focused on miniaturized configurations and lab-scale plasma sources, and the upscaling impact is often overlooked. As such, from an industrial point of view, there is still a long way to go for plasma-assisted NF before it can be fully exploited and used as an auxiliary technology to H-B. To bridge this gap, it is important to obtain detailed insights into plasma-assisted NF in the presence and absence of water. The present work aims to contribute to the fundamental understanding of the impact of a plasma/liquid interface and the upscaling of the system on the energy efficiency of a plasma-assisted nitrogen oxidation process at atmospheric pressure in air. We studied plasmas generated in both multi-pin metal-metal and metal-liquid electrode configurations. First, we performed plasma diagnostics to gain insights into their power consumptions, discharge regimes, gas temperatures, and the presence of chemically reactive species, which determine the formation of stable nitrogen-containing products. Afterwards, we investigated the production of N-containing species in both the gas and liquid phases, while varying the gas flow rate and plasma power. The outcome of this fundamental study facilitates the understanding of the effect of a plasma/liquid interface in a multi-pin plasma reactor on the energy cost of the process.
# Overview and Perspective of Integrated Photovoltaics with a Focus on the European Union
## 3 Integrated Photovoltaics Options
### Infrastructure-Integrated Photovoltaics
#### 3.4.1 Closed Landfill Sites  
Landfills are brownfields and need supervision or even maintenance. Their use of PV plants will not affect sensitive ecosystems or protected areas but they offer an opportunity to offset the maintenance cost by income from selling PV electricity [37]. As far as closed landfills are concerned, the potential for the EU can reach 13 GWp. This EU estimate is a pretty conservative one as it is based on Corine Land Cover (CLC) characterization. Two case studies used in a 2017 analysis contained 15% (by area in UK) and 3% (by number in Hungary) of the available national characterization of landfill sites. Site identification by using national cadasters would result in multiple time higher potentials of landfill PV. Second, closed landfills are often connected to the electricity grid and in the case of landfill gas use, the PV system can improve the load factor of the plant.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## Appendix B Model Record Data Management
### Recent Advances and Emerging Clusters  
#### 4.4.1 Sustainability-Oriented Supply Chains  
Between 2022 and 2024, research on blockchain technology in supply chain management has increasingly concentrated on promoting sustainability efforts, with a particular emphasis on environmentally friendly supply chains, circular economies, and reverse logistics. [PERSON] et al. [78] suggested that blockchain technology can improve transparency in green supply chains, enabling companies to monitor materials and manufacturing processes to comply with environmental regulations and decrease their carbon emissions. Reverse supply chains can also benefit from blockchain technology. A reverse supply chain refers to the return of products from consumers to manufacturers for recycling, refurbishment, or disposal [79]. In the view of [PERSON] et al. [80], blockchain facilitates precise tracking and verification of returned goods for recycling or refurbishing, aligning with corporate sustainability objectives. In the context of a circular economy, [PERSON] et al. [81] contend blockchain aids in sustainable resource utilization by monitoring the entire lifecycle of products, from production to disposal, thus streamlining reclamation efforts and waste reduction.  
Nevertheless, research suggests that several challenges continue to impede the effective implementation of blockchain technology in sustainability-focused supply chains. These challenges include substantial setup expenses, opposition from the involved parties, and insufficient support from top management, all of which collectively hinder adoption. [PERSON] et al. [79] note that for blockchain to be successfully integrated into sustainable supply chains, its advantages for a business must significantly outweigh these barriers.
## 3 Experimental Results and Analysis
In summary, the proposed algorithm in this paper has better performance when improving the quality of hazy images, with improvements seen in contrast and sharpness. The details are more abundant. Moreover, the colors of the image enhanced by this algorithm are closer to the reality, and the color fidelity is higher. Finally, when faced with images of different fog densities, the method presented in this article exhibits strong stability both in terms of supervisor vision and objective evaluation indicators.
# Semi-Supervised Training for (Pre-Stack) Seismic Data Analysis
## 2 Methodology
### Long Short-Term Memory  
The Long Short-Term Memory (LSTM) architecture, which is a recurrent neural network model tailored for sequential data, relies on a crucial internal component called the cell state \(c^{t}\), acting as a memory. This architecture features three sigmoidal gates--the input gate \(i^{t}\), the output gate \(o^{t}\), and the forget gate \(f^{t}\)--which control the reading or modification of the cell state. At each timestep \(t\), the sigmoidal gates, cell state, and output \(h^{t}\) undergo updates when receiving input \(x^{t}\) and the output \(h^{t-1}\), as follows:  
\[i^{t}=\sigma(W^{xi}x^{t}+W^{hi}h^{t-1}+W^{ci}c^{t-1}+b^{i}), \tag{1}\]  
\[f^{t}=\sigma(W^{xf}x^{t}+W^{hf}h^{t-1}+W^{cf}c^{t-1}+b^{f}), \tag{2}\]  
\[c^{t}=f^{t}c^{t-1}+i^{t}\tanh(W^{xc}x^{t}+W^{hc}h^{t-1}), \tag{3}\]  
\[o^{t}=\sigma(W^{xc}x^{t}+W^{ho}h^{t-1}+W^{co}c^{t}+b^{o}), \tag{4}\]  
\[h^{t}=o^{t}\tanh(c^{t}), \tag{5}\]  
\(h^{t-1}\) and \(c^{t-1}\) represent the output and cell state from the previous timestep. \(W\)s and \(b\)s denote the weights and biases, respectively [65; 67; 68]. The output \(h^{t}\) can be fed into an output layer or dense layer (feedforward network) to calculate the network's final output  
\[y^{t}=\phi(Wh^{t}+b), \tag{6}\]  
in this context, \(W\) and \(b\) represent the weights and bias of the dense layer, respectively. The activation function \(\phi\) computes the output \(y\) at time step \(t\). The LSTM architecture in this study was trained using both supervised and unsupervised learning approaches.
# Pre-Flight SAOCOM-1A SAR Performance Assessment by Outdoor Campaign
## 3 SAOCOM Outdoor Experiment Setup
Figure 3: (**a**) Radome hosting the SAOCOM antenna tile; (**b**) Radome hosting the moving target. In addition to the moving target, a sampling equipment was placed to measure the transmitted chirp and to check the PRF.
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 5 Experiment and Analysis
### Experimental Environment
#### 5.1.2 Evaluation Metrics  
1. Data response time: this metric indicates how the cache placement algorithm affects the user's data retrieval time; that is, a shorter the data response time represents a more effective algorithm.
2. Cache service rate: this index reflects the quality of the cache placement algorithm. A higher cache service rate indicates a more successful algorithm. The cache service rate calculation is shown in Equation (29).  
Figure 3: The configuration of the experimental environment.  
\[\gamma=\frac{N_{cache}}{N_{access}} \tag{29}\]  
In Equation (29), \(\gamma\) denotes the cache service rate, \(N_{cache}\) represents the sum of the data being accessed in the cache, and \(N_{access}\) indicates the total amount of accessed data.
3. The replacement count: this indicator reflects the efficiency of the cache placement strategy in terms of the quantity of data blocks cached in the server. The formula is given in Equation (30). \[\lambda=\frac{\sum\limits_{i=1}^{n}\frac{e_{i}}{c_{i}}}{m}\] (30)  
In Equation (30), \(\mathrm{e}_{i}\) is the number of data blocks evicted from the server cache, \(c_{i}\) is the server's cache capacity, and \(m\) is the overall count of servers.
## Abbreviations  
ABCs age-associated B cells ADCC antibody-dependent cellular cytotoxicity ANA antinuclear antibody APCs antigen-presenting cells ASCs antibody-secreting cells BCR B cell receptor Bregs regulatory B cells CARCAM carcinoembryonic antigen-related cell adhesion molecule CDC complement-dependent cytotoxicity CRC colored carcinoma CT computed tomography CTLA-4 cytotoxic T-lymphocyte protein 4 DC dendritic cells HCC hepatocellular carcinoma HD healthy donor HL Hodgkin's lymphoma ICI immune checkpoint inhibitor irAEs immune-related adverse events iRECIST immune-related response evaluation criteria in solid tumors LAG-3 lymphocyte activation gene-3 mAb monoclonal antibody MHC major histocompatibility complex NSCLC non-small-cell lung cancer NK natural killer cells NR nonresponders NR-tp nonresponders timepoint OS overall survival PBMCs peripheral blood mononuclear cells PFS progression-free survival R rescponded rencal cell carcinoma R-tp responders timepoint SCCCHN squamous cell carcinoma of head and neck SCLC metastatic small cells lung cancer TILs tumor-infiltrating lymphocytes TIM-3 T cell immunoglobulin-3 TME tumor microenvironment Tregg regulatory T cells UC urothelial carcinoma
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 3 Experimental Results
### Bending Moment of Piles  
The bending moment \(M\) of micro piles during the elastic range can be back-calculated from the measured strain under the assumption that the plane section remains plane. It is calculated by the following formula.  
\[M=\frac{EI(\varepsilon_{l}-\varepsilon_{r})}{D} \tag{4}\]  
where \(E\) is the elastic modulus of concrete (kN/m\({}^{2}\)), \(I\) is the moment of inertia for section (m\({}^{4}\)), \(D\) is the diameter of pile (m), and \(\varepsilon_{l}\) and \(\varepsilon_{r}\) are the measured tensile and compressive strains at the outmost edges of the section.
# The Influence of Bit Edge Shape Parameters on Bone Drilling Force Based on Finite Element Analysis
## 5 Discussion  
The previous mostly related studies are presented in Table 9. It can be seen from Table 9 that there are mainly two types of research contents; one is about drill bit parameters, and the other is about with or without coolant. For the former case, in addition to the drilling speed and feed rate, literature [9; 10; 14] studied one parameter of the drill bit, i.e., the point angle, and literature [28] studied two parameters (point angle and helix angle); and for the latter, literature [40; 41; 42; 43] focused on the effect of the coolant on drilling temperature, thermal damage or drilling quality. As for this work, there are three novelties: (1) more  
Figure 9: Influence of bit edge shape parameters on drilling force. (**a**) Influence of point angle and helix angle on drilling force; (**b**) Influence of point angle and edge radius on drilling force; (**c**) Influence of edge radius and helix angle on drilling force. parameters (including point angle, helix angle and edge radius) of the drill bit edge shape are investigated on the premise of the predetermined rotation speed and feed rate. (2) The order of the influence of drill bit shape parameters on the drilling force is obtained: the edge radius is the largest, the point angle is the second, and the helix angle is the smallest. (3) The optimum combination of bit structure is obtained by using particle swarm optimization algorithm. Therefore, this work demonstrates that the number of main bit edge shape parameters affecting the drilling force are more than one or two, and their influence order on drilling force are different. Furthermore, the optimized matching of bit edge shape parameters using optimization algorithm is obtained. Hence, this paper provides a theoretical basis for selecting the appropriate drill bit shape in the design of drill and contributes to the further study of the bone drilling.
## 3 Results
### Random Forest (RF) Classification Using Individual Sets of Attributes  
The RF classification performance was first assessed using individually eight-band reflectance values, four fraction images from SMA, five selected vegetation indices, and eight GLCM texture metrics to distinguish among the five classes of pasture degradation (NDP, LID, MID, SAD, and SBD). The attributes were calculated from the image obtained on DOY 153 (2 June 2022).  
Among the attributes, band reflectance of SuperDove yielded the highest F1-scores, as depicted in Figure 11. This outcome suggests that reflectance values exhibit robust predictive power, enabling the classification model to effectively differentiate between distinct classes. Utilizing reflectance data in RF, an overall classification accuracy (OA) of 0.66 was achieved, surpassing the values recorded with SMA (0.53) and vegetation indices (0.50). The GLCM texture metrics yielded the lowest values of OA (0.34). Among the five classes tested for classification, the highest F1-scores were observed for NDP and SBD, representing non-degraded and severely degraded areas, respectively.  
Figure 10: Variations in Gray Level Co-occurrence Matrix (GLCM) texture metrics, specifically **(a)** texture mean and **(b)** texture variance, calculated from the Near-Infrared (NIR) band 8 of SuperDove for the five classes of pasture degradation.
# Edge Server Deployment Strategy Based on Queueing Search Meta-Heuristic Algorithm
## 5 Experimental Assessment
### Analysis of Simulation Results
#### 5.2.1 Overall Performance Comparison  
Each set of experiments was conducted seven times, and the results are presented using line graphs. Figure 4 illustrates the fitness function trends for each algorithm. It can be observed that QSA's fitness function curve declines rapidly, reaching the optimal value in fewer iterations. This results in a lower average number of hops compared to other methods. Specifically, QSA achieves an average hop count reduction of 0.1 to 0.6 times, demonstrating its superior performance in global optimization.  
Next, we compare the computational time required by each algorithm with the results presented in Figure 5. The experiments were conducted on a system equipped with Windows 11, a 12 th Gen Intel(r) Core(tm) i7-12700H CPU, and 16 GB of RAM. As shown in Figure 5, QSA requires 1.2 to 6 times more computational time than other algorithms. Most of this time is spent in the search process, where QSA's \"employees\" explore optimal solutions through three distinct operations. However, this additional time investment can be worthwhile in certain scenarios. For example, when service providers optimize edge computing infrastructure, achieving better deployment solutions is often more valuable than reducing computation time. In such cases, user feedback plays a crucial role, and providers may prefer investing more time to ensure improved service quality.