esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Damage Assessment  
Referring to the classification method of thermal damage prediction in the tunnel investigation and design stage proposed by [PERSON] et al. [28], the proposed grading method for predicting thermal damage during the tunnel investigation and design stage, the thermal damage of tunnels is classified into four grades according to temperature (Table 4).  
According to the thermal damage criteria in Table 4, the thermal damage along the tunnel is categorized, and it can be seen that the length of the tunnel is 74 km; the predicted length of the tunnel with thermal damage class I is 45.9 km, the predicted length of the tunnel with thermal damage class II is 18.4 km, the predicted length of the tunnel with thermal damage class III is 8.0 km, and the predicted length of the tunnel with thermal damage class IV is 0.18 km. The detailed distribution of thermal hazards along the tunnel alignment is shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**(Military) Rank**} & \multicolumn{1}{c}{**Ground Temperature (\({}^{\circ}\)C)**} & \multicolumn{1}{c}{**Degree of Heat Damage**} & \multicolumn{1}{c}{**Impact Description**} \\ \hline I & T \(\leq\) 28 & heatless & No heat damage, construction \\ II & 28 \(<\) T \(\leq\) 37 & minor heat damage & Mild heat damage, local cooling \\  & & & measures needed. \\ III & 37 \(<\) T \(\leq\) 50 & moderate heat damage & Moderate heat damage, enhanced \\  & & & ventilation and cooling \\  & & & measures required. \\ IV & 50 \(<\) T \(\leq\) 60 & more severe heat damage & Severe heat damage, special \\  & & & protective measures required \\  & & & during construction. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Tunnel thermal damage classification criteria.  
Figure 11: Diagram of the temperature impact of different schemes.
###### Abstract  
TiOx films were prepared by gas impulse magnetron sputtering under oxygen-deficient (ODC) and oxygen-rich conditions (ORC) and annealing at 100-800 \({}^{\circ}\)C was used. The O\({}_{2}\) content had an effect on their transparency level (T\({}_{\lambda}\)). The films from the ORC mode had ca. T\({}_{\lambda}\) = 60%, which decreased slightly in the VIS range after annealing. The film from the ODC mode had lower transmission (ca. <10%), which increased in the NIR range after annealing by up to ca. 60%. Differences in optical band gap (E\({}_{\text{g}}^{\text{opt}}\)) and Urbach energy (E\({}_{\text{u}}\)) were also observed. The deposition parameters had an influence on the microstructure of TiOx coatings. The ORC and ODC modes resulted in columnar and grainy structures, respectively. Directly after deposition, both coatings were amorphous according to the GIXRD results. In the case of TiOxORC films, this state was retained even after annealing, while for TiOxODC, the crystalline forms of Ti and TiO\({}_{2}\)-anatase were revealed with increasing temperature. Sensor studies have shown that the response to H\({}_{2}\) in the coating deposited under oxygen-rich conditions was characteristic of n-type conductivity, while oxygen-deficient conditions led to a p-type response. The highest sensor responses were achieved for TiOxODC annealed at 300 \({}^{\circ}\)C and 400 \({}^{\circ}\)C.  
gacs: sensing; n or p type of sensor response; amorphous film; TiOx coating; gas impulse magnetron sputtering; annealing
# Innovative Application of Blockchain Technology for Digital Recipe Copyright Protection
## 5 Improvement of Consensus Mechanisms and Quality Testing of Recipes  
### DPOS (Delegated Proof of Stake)  
The DPOS (proof of share authorization) [40] consensus mechanism is a bookkeeping method that borrows from real-life shareholder voting. In this network, the nodes are mainly categorized into verification nodes and ordinary nodes. The coin-holding nodes in the network elect 101 validation nodes through voting, and these validation nodes take turns exercising power with equal rights, including a series of operations in the network, such as transaction validation, packing to blocks, etc., which are completed by these validation nodes. When some nodes fail to complete their tasks on time or carry out malicious behaviors that may affect the whole blockchain network, the other ordinary nodes withdraw their votes for that node [41].  
Compared to POS [42] and POW [43], DPOS no longer uses competition to elect bookkeeping nodes and reduces the network's energy consumption by reducing the number of nodes. In addition, the DPOS mechanism improves the confirmation speed, with each block being generated in about 10 seconds. However, DPOS also poses some challenges:  
1. The enthusiasm of node voting is insufficient;
2. Malicious behavior cannot be stopped in time;
3. There is a lot of room for human manipulation in the voting process, so it is widely questioned as being too centralized.  
Therefore, according to the production environment, we need to make appropriate adjustments and optimizations to the DPOS consensus mechanism.
## 3 Results and Discussion
### Case Study on a Spatial Truss Design Problem  
Spatial truss design is representative in optimization design, which involves several design variables and is widely used to verify the prediction accuracy and robustness of different algorithms. In the following, the MSAMT proposed in this paper was applied to a spatial truss design problem and compared with existing algorithms. The design problem aimed to search for the best structural parameters under the maximum allowable horizontal displacement \(V_{max}\), including the length of the horizontal, longitudinal, and vertical bars (denoted as \(L_{1}\), \(L_{2}\), and \(L_{3}\)), the Young's modulus (denoted as \(E_{1}-E_{6}\)), and cross-sectional area (denoted as \(A_{1}-A_{6}\)) of the vertical, horizontal/longitudinal, and layer diagonal bars from the first to the fourth floor. Figure 7 shows the finite element model of the spatial truss structure comprising 72 bar elements under horizontal loads (\(F_{1}\ \sim F_{8}\)). The horizontal loads were fixed values set as \(F_{1}=F_{2}=3.0\times\mathrm{N}\), \(F_{3}=F_{4}=4.0\times\mathrm{N}\), and \(F_{5}=F_{6}=5.0\times\mathrm{N}\). The initial values and ranges of the parameters to be optimized are shown in Table 3. The objective function to be optimized is given by the following:  
\[\underset{\boldsymbol{x}\in\Omega^{\prime}}{\text{min}}|V(\boldsymbol{x})-V_{ max}|\ \ subject\ to:\ \boldsymbol{x}_{lb}\leq\boldsymbol{x}\leq\boldsymbol{x}_{ub} \tag{17}\]  
where \(V\) represents the maximum horizontal displacement, \(\boldsymbol{x}\) is a vector containing the 15 parameters to be optimized, \(\boldsymbol{x}_{lb}\) and \(\boldsymbol{x}_{ub}\) are the upper and lower bounds of the parameters, respectively, and \(d\) denotes the number of structural parameters (\(d=15\)).  
The HSAOA, DSP-SAEA, BiS-SAHA, and FSAPSO were chosen as the competing algorithms. The relevant parameters remained constant with the configurations from the previous sections. The optimal structural parameters and statistical results of the MSAMT  
Figure 6: The average run times of the proposed algorithm and the competing algorithms on each test problem (From (**a**) to (**e**) are the average run times of 10D, 20D, 30D, 50D, and 100D test problems, respectively).
## 3 Effects of F-T Cycles on the Mechanical Properties and Microscopic Parameters
### Effect of F-T Cycles on Particle Arrangement  
#### 3.3.1 Effect of F-T Cycles on Particle Orientation Probability Entropy  
Particle orientation represents the distribution probability of the orientation angle of particles within [0,2\(\pi\)], which can be used to delineate the orientation strength in various directions. The orientation angle of soil particles is symmetrically distributed within [0,2\(\pi\)], so only calculating the distribution within [0,\(\pi\)] is allowed. Therefore, we divided [0,\(\pi\)] into 18 azimuth zones with an angular density of \(\Delta\Theta=10^{\circ}\). The orientation intensity of the particles in each azimuth zone was separately counted and reflected by the orientation probability entropy (H\({}_{\rm m}\)) [40]. The formula is as follows:  
\[\rm H_{m}=-\sum_{i=1}^{n}P_{i}\log_{n}P_{i} \tag{6}\]  
where H\({}_{\rm m}\) is the probability entropy of the particle arrangement, n is the number of azimuth zones, and P\({}_{i}\) is the probability of the particle being in a certain azimuth zone.  
H\({}_{\rm m}\) denotes that the particles are arranged in the same direction or not when the value tends to zero or one, respectively. The diversifications of H\({}_{\rm m}\) under F-T cycles are shown in Figure 10.  
Figure 8: Schematic diagram of the D\({}_{\rm pr}\) algorithm.  
Figure 9: The curve of D\({}_{\rm pr}\) versus F-T cycles.  
From Figure 10, H\({}_{\text{m}}\) of S0 and S2 were both between 0.90 and 0.99, showing that the order of particles in each azimuth zone was poor. The H\({}_{\text{m}}\) of S0 and S2 was relatively large after the first F-T cycle, indicating that particle arrangement was the most chaotic and the soil state was the most unstable, while macro-performance was that cohesion and shear strength sharply decreased after the first F-T cycle.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 6 Performance Analysis and Computational Complexity
### Computational Complexity  
The prediction process is composed of large-scale channel state prediction and small-scale channel state prediction. In large-scale channel state prediction, \(N_{sch}\) large-scale channel state should be predicted, and, for each prediction, \(k\)-nearest neighbor prediction algorithm should act on \(N_{rl}\) reserved large-scale channel states. So, the computational complexity of the large-scale channel state prediction is \(O(N_{sch}N_{rl})\). In small-scale channel state prediction, the number of slots in a large-scale epoch is \(N_{large}\), and \(N_{sch}N_{large}\) small-scale channel state should be predicted; so, the computational complexity of the small-scale channel state prediction is \(O(N_{sch}N_{large})\). In general, \(N_{large}\ll N_{rl}\); so, the total complexity of the prediction process is \(CC_{1}=O(N_{sch}N_{rl})\). This also explains the content in Section 4.1.2, that the amount of storage can be reduced, and the prediction can be speed up, with the decrease of the length of stored historical channel state series.  
In terms of the improved genetic algorithm for modulation coding method selection, \(N_{c}\) is the number of evolution cycles, and, in each evolution cycle, the fitness of \(G\) chromosomes should be calculated. Because each chromosome represents modulation and coding modes of \(N_{sch}\) large-scale epochs, the performance of \(N_{sch}\) large-scale epochs should be summed. So, the computational complexity of the improved genetic algorithm for modulation coding method selection is \(CC_{2}=O(N_{c}GN_{sch})\). As described in Section 3.3, in a large-scale epoch, the average channel change should be less than a certain threshold, so that the same modulation and coding mode can be allocated for the slots in this large-scale epoch. In this way, the modulation and coding mode only needs to be scheduled once for each large-scale epoch; so, the number of scheduled parameters will decrease, and complexity of scheduling is reduced. Hence, the total complexity of prediction and scheduling schemes is \(O(CC_{1}+CC_{2})\).
# Prospects and Technical Challenges in Hydrogen Production through Dry Reforming of Methane
## 4 Large-Scale Applications of DRM
The DRYREF(tm) technology is an application of CSDRM, in which the use of CO\({}_{2}\) is designed to reduce the energy requirements of the full SRM process, due to the energy-demanding steam production step. In the process developed by Linde, a lower steam-to-carbon ratio  
Figure 8: Simplified block diagram of the SPARG(tm) process for syngas production [106]. (S/C \(<\) 1.8) is combined with CO\({}_{2}\) capture and recycling in order to reduce the overall carbon footprint of the process and the high steam requirements of SRM (S/C \(\geq\) 2.5). The result is an overall 5% and 3% reduction in OPEX and CAPEX, respectively, and a variable syngas H\({}_{2}\)/CO ratio (dependent on the initial S/C ratio) in the range of 1.0-3.0, which can be suitable for several downstream processes, such as aldehydes, methanol, acetic and formic acid synthesis [163]. The project started in 2010 and is currently on commercial demonstration scale since 2019, after the commissioning of the Linde Pilot Reformer, in Pullach, Germany [170]. The highly endothermic aspect of DRM requires high operating temperatures for high syngas yields, which promotes undesired parallel reactions and deactivation of conventional catalysts by sintering mechanisms. In addition, the limited stability of conventional  
Figure 10: Simplified block diagram of the DRYREFTM process for CO and H\({}_{2}\) production [162]. Figure 9: Simplified block diagram of the MIDREYM process for metallic iron production [159]. reactor materials at the required high temperatures further hinders the application of this process in industrial scale. Technological advancements are therefore required to obtain both reactors and catalysts that can withstand reaction temperatures above 1000 \({}^{\circ}\)C [171]. None of the processes presented above target the production of H\({}_{2}\) as the final product. Hence, large-scale data on DRM aiming at a high H\({}_{2}\)/CO ratio, as well as detailed information on sizing and overall energy efficiency of the process and specific facilities, are not available in the open literature. Besides catalytic development and process optimization at laboratory scale, life-cycle assessment and techno-economic analysis of this process are also required to evaluate the real environmental advantages of producing H\({}_{2}\) through this pathway and to assess its economic viability.
# Application of Machine Learning Models for Baseball Outcome Prediction
## 3 Result
### Feature Importance Analysis
Figure 5a,b provide insights into the interaction effects between offensive and pitching metrics using SHAP dependence plots. Specifically, these visualizations illustrate how key offensive indicators (wRAA and wRC+) interact with pitching-related contextual features (PLOB% and WHIP, respectively) to influence the model's prediction of game outcomes. Figure 4: The SHAP summary plot: the feature importance and impact on the model output. Point colors represent feature values (red: high, blue: low), while the \(x\)-axis shows SHAP values indicating the direction and magnitude of each feature’s effect. The spread of points reflects the variability of the feature influence across samples. Figure 5(a) plots the SHAP values of wRAA (Weighted Runs Above Average) against its original values, with color encoding the PLO8% (Percentage of Left On Base). A strong negative association is observed between the wRAA and its SHAP value, suggesting diminishing marginal returns of offensive production on the predicted win probability. Notably, data points with a higher PLO8% (depicted in red) tend to have less negative SHAP values for wRAA, indicating that effective pitching (i.e., the ability to strand baserunners) can moderate or amplify the impact of offensive contributions. This interaction highlights that the offensive value alone is insufficient without the corresponding pitching performance. Figure 5(b) similarly depicts the relationship between wRC+ (Weighted Runs Created Plus) and its SHAP values, with WHIP (Walks plus Hits per Inning Pitched) as the interaction feature. A comparable negative trend is present, the where increased wRC+ correlates with lower marginal model contributions after a certain threshold. However, observations with lower WHIP values (indicated by blue) are associated with higher SHAP values for wRC+, reinforcing the idea that offensive efficiency is more predictive of winning outcomes when supported by disciplined and effective pitching. Taken together, these results emphasize the interdependent nature of batting and pitching metrics in driving model predictions. The SHAP interaction analysis reveals that the explanatory power of offensive metrics is contextually modulated by the strength of the pitching staff, thus supporting a more holistic interpretation of baseball performance analytics within explainable machine learning frameworks.
# Robust Adaptive Control for an Aircraft Landing
###### Abstract  
A landing gear of an aircraft is required to function at touchdown in different landing scenarios with parametric uncertainties. A typical passive damper in a landing gear has limited performance in differing landing scenarios, which can be overcome with magnetorheological (MR) dampers. An MR damper is a semi-active system that can adjust damping force by changing the amount of electric current applied to it. This paper proposes a new robust controller based on model reference sliding mode control and adaptive hybrid control to improve the efficiency of absorbing landing impact energy, not only considering the variables of aircraft weight and sink speed but also managing uncertainties, such as ambient temperature and passive damping coefficient. To verify the effectiveness of the proposed controller, comparative numerical simulations were performed with a passive damper, a skyhook controller, and the proposed controller under various landing scenarios. The simulation results show that the proposed controller improves the total energy absorber efficiency by up to 10% higher than that of the skyhook controller. In addition, the proposed controller is demonstrated to have better adaptability and robustness than the other control algorithms in the differing landing scenarios and parametric uncertainties.  
m +
Footnote †: journal: Applied Sciences  
_Article_  
Robust Adaptive Control for an Aircraft Landing  
Gear Equipped with a Magnetorheological Damper
###### Abstract  
Bimetal CuFe (copper-iron) nanoparticles, which are based on the earth-abundant and inexpensive metals, have generated a great deal of interest in recent years. The possible modification of the chemical and physical properties of these nanoparticles by changing their size, structure, and composition has contributed to the development of material science. At the same time, the strong tendency of these elements to oxidize under atmospheric conditions makes the synthesis of pure bimetallic CuFe nanoparticles still a great challenge. This review reports on different synthetic approaches to bimetallic CuFe nanoparticles and bimetallic CuFe nanoparticles supported on various materials (active carbide, carbide nanotubes, silica, graphite, cellulose, mesoporous carbide), their structure, physical, and chemical properties, as well as their utility as catalysts, including electrocatalysis and photocatalysis.  
bimetal nanoparticles; copper-iron; magnetic properties; catalytic properties; synthesis; core@shell; Janus structures; battery; photocatalysis; water treatment 202  
small size and magnetic properties, they easily aggregate [14; 15; 16; 17]. Between the variety of transition metal macrocycle complex catalysts with different central metal atoms [14; 15], iron-based materials have been identified to exhibit the best activity for ORR under various metal loadings [16; 17; 18; 19; 20; 21; 22; 23].  
On account of their properties, nanomaterials based on copper and iron can effectively replace rare and expensive noble-metal catalysts commonly employed in commercial chemical processes. However, the synthesis and use of both copper and iron nanoparticles is still a challenge due to the high tendency of these materials to oxidize under atmospheric conditions. Therefore, complex nanoparticles (e.g., core@shell, alloys) have been recently adapted to overcome the instability of copper and iron nanoparticles in the presence of oxygen, water, and several chemicals.  
Since the fusion of the unique properties of copper and iron in one single entity promises multifunctionality and potential applications, a lot of effort is put into preparing such nanoparticles containing Cu and Fe. This coupling may result in the formation of nanocomposites with an extraordinary catalytic activity and ferromagnetic properties, which would allow for convenient separation of the nanocomposite catalyst from the reaction system in the applied magnetic field (Figure 1).
## 5 Enhancing QoS and QoE in ICN  
In this section, we will summarize and categorize the existing literature on QoS and QoE management in ICN. Depending on the strategy utilized to optimize QoS or QoE metrics, we have divided the research works into the following categories: (i) in-network caching, (ii) name resolution and routing, (iii) software-defined networking (SDN)-based ICN solutions, (iv) transmission and flow control, and (v) media-streaming strategies.
# Prognostic Value of Lymph Node Density in Lingual Squamous Cell Carcinoma
## 4 Discussion
### Recommendations and Guidelines for Future Research  
In consideration of the disagreement shown in the literature, future research in this field should focus on establishing a consensus regarding the LND cut-off point in LSCC and outlining the requirements for establishing whether LND is representative or not. In this context, the estimation of LND is highly dependent on the total number of dissected lymph nodes or lymph node output. Several studies have demonstrated a high prognostic value of this variable in terms of long-term survival, even in the absence of lymph node metastases [17; 21]. The AJCC establishes the threshold for selective cervical dissection as at least 10 lymph nodes and, in the case of radical dissection, at least 15 nodes [1]. However, several publications suggest that this threshold may be too low and some authors recommend dissections >18 nodes [17; 21; 22; 23], making it necessary to review the quality recommendations for cervical lymph node dissections, especially regarding the estimated LND. In this context, a meta-analysis of current publications reviewing LND could provide further evidence on the optimal lymph node output for LND-based lymph node staging.  
The main role described for LND is the selection of patients who would benefit from the application of adjuvant treatment, which entails the addition of chemotherapy to radiotherapy. [PERSON] [4] defined the currently accepted indications for adjuvant treatment intensification as positive surgical margins and extracapsular extension. Due to the significant morbidity associated with this treatment approach, future multicenter studies including larger cohorts may provide more accurate results to support prospective analyses incorporating LND in the decision on the adjuvant treatment.  
Furthermore, it is necessary to further investigate the relevance of other pathological characteristics of the tumor that may predict the need for more aggressive adjuvant treatment, such as the size and volume of the affected lymph nodes, the location of lymph node involvement, the presence of hidden micrometastases discovered by molecular analysis or the determination of ENE, which may affect the results reported [12].