esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Studies' Eligibility  
The methodology described in the preceding section was applied, resulting in an initial collection of 904 articles (refer to Figure 1). Subsequently, 480 duplicate references were eradicated using the Mendeley software 1.19.8. Another 399 references were excluded during the screening phase, adhering to the predetermined inclusion and exclusion criteria. This left a total of 81 articles for further consideration. A team of reviewers undertook the task of manually reviewing these 81 references. During this process, 42 articles were excluded due to insufficient methodologies and inadequately presented procedures. This elimination was based on the manual screening, during which the authors concentrated on the abstract, methodology, and results sections. The remaining 39 papers underwent an evaluation of both their overall quality and the process of data extraction.
###### Abstract  
In vibration control of compound structures, inter-substructure damper (ISSD) systems exploit the out-of-phase response of different substructures to dissipate the kinetic vibrational energy by means of inter-substructure damping links. For seismic protection of multistory buildings, distributed sets of interstory fluid viscous dampers (FVDs) are ISSD systems of particular interest. The connections between distributed FVD systems and decentralized static output-feedback control allow using advanced controller-design methodologies to obtain passive ISSD systems with high-performance characteristics. A major issue of that approach is the computational difficulties associated to the numerical solution of optimization problems with structured bilinear matrix inequality constraints. In this work, we present a novel iterative linear matrix inequality procedure that can be applied to obtain enhanced suboptimal solutions for that kind of optimization problems. To demonstrate the effectiveness of the proposed methodology, we design a system of supplementary interstory FVDs for the seismic protection of a five-story building by synthesizing a decentralized static velocity-feedback \(H_{\infty}\) controller. In the performance assessment, we compare the frequency-domain and time-domain responses of the designed FVD system with the behavior of the optimal static state-feedback \(H_{\infty}\) controller. The obtained results indicate that the proposed approach allows designing passive ISSD systems that are capable to match the level of performance attained by optimal state-feedback active controllers.  
Keywords:passive vibration control; fluid viscous dampers; static output-feedback control; decentralized control; iterative linear matrix inequalities; interstory damping systems; seismic protection +
Footnote †: journal: Applied Sciences
# A Virtual Prototype for Fast Design and Visualization of Gerotor Pumps
###### Abstract  
In the context of generation of lubrication flows, gear pumps are widely used, with gerotor-type pumps being specially popular, given their low cost, high compactness, and reliability. The design process of gerotor pumps requires the simulation of the fluid dynamics phenomena that characterize the fluid displacement by the pump. Designers and researchers mainly rely on these methods: (i) computational fluid dynamics (CFD) and (ii) lumped parameter models. CFD methods are accurate in predicting the behavior of the pump, at the expense of large computing resources and time. On the other hand, Lumped Parameter models are fast and they do not require CFD software, at the expense of diminished accuracy. Usually, Lumped Parameter fluid simulation is mounted on specialized black-box visual programming platforms. The resulting pressures and flow rates are then fed to the design software. In response to the current status, this manuscript reports a virtual prototype to be used in the context of a Digital Twin tool. Our approach: (1) integrates pump design, fast approximate simulation, and result visualization processes, (2) does not require an external numerical solver platforms for the approximate model, (3) allows for the fast simulation of gerotor performance using sensor data to feed the simulation model, and (4) compares simulated data vs. imported gerotor operational data. Our results show good agreement between our prediction and CFD-based simulations of the actual pump. Future work is required in predicting rotor micro-movements and cavitation effects, as well as further integration of the physical pump with the software tool.  
digital-twin; gerotor pump; hydraulic-systems; simulation; computer-aided design +
Footnote †: journal: Applied Sciences
# Advancements in Flame-Retardant Systems for Rigid Polyurethane Foam
## 2 Reactive-Type Flame Retardants  
The process of forming RPUF involves various reactions, including urethane formation, crosslinking reactions, and foaming reactions facilitated using a chemical blowing agent. The formation of the urethane linkage is illustrated in Figure 1a, while Figure 1b shows how the urethane group reacts with an isocyanate group to create allophanate, which results in chemical crosslinking. Reactive flame retardants demonstrate favorable interfacial compatibility with the matrix due to their chemical bonding interactions, resulting in minimal impact on the mechanical properties of RPUF. Meanwhile, reactive flame retardants containing multiple hydroxyl, amino, or epoxy groups can serve as polyols in the curing process of RPUF, providing flame retardant properties through the presence of phosphorus, nitrogen, or sulfur elements in their structure [8; 41; 46; 47; 48]. Additionally, reactive flame retardants with chemical bonding interactions are highly durable in industrial applications, as they prevent migration from the RPUF matrix. This section focuses on recent advancements in reactive flame retardants.  
Figure 1: Basic reaction scheme for (**a**) urethane and (**b**) allophanate formation.
## 4 Discussion
Collaborative research at the village level in Telangana, India, typically entails forming partnerships among academic institutions, government agencies, and local communities. This approach aligns with the emphasis on partnerships to achieve sustainable development as outlined in SDG 17. Although not the central objective, including crop yield assessment within the climate change framework, can make a valuable contribution to Sustainable Development Goal 13 by addressing climate adaptation and mitigation. Past studies reporting on the assimilation of remote sensing data in the SIMRIW crop model for rice yield estimation, where COSMO-SkyMed satellite data was used to derive LAI, concluded that SIMRIW-RS has the potential to estimate rice yield accurately (0.80) when LAI of rice is assessed with high accuracy from satellite data [54]. An RMSE of 11.75% was observed between simulated and actual rice yield while integrating remote sensing data in the DSSAT crop model using Taiwan's Particle Swarm Optimization [55]. Moreover, assimilating remotely sensed products with the DSSAT crop model and spectral indices-based regression analysis achieved an R\({}^{2}\) of 0.80, NRMSE < 10%, and an agreement of 90%, indicating that these two methods generated superior results to the semi-physical approach [56].
## 6 Future Work, Challenges, and Limitations
### Time Efficiency  
The training time and response time of the ML model are also indispensable factors that influence IPS performance [107]. The training time means the time used for the ML algorithm to train the algorithm with an offline dataset and build the model; in the meantime, the response time means the time used for the model to predict the output for the given new testing data. In particular, for fast-moving objects, this could pose a challenge for the proposed methods due to the ML requiring a specific duration to process the NLoS signal. Additionally, the NLoS classification process may also require more processing time in dynamic scenarios. Therefore, it is crucial to test the proposed methods in such conditions to evaluate their performance and identify potential limitations. Future work needs to consider conducting experiments in dynamic scenarios to assess the effectiveness of the ML algorithms. The authors in [108] focused on proposing a dynamic video coding approach that utilizes dynamic video recording resolution adjustment on wearable cameras and Lyapunov-based video preprocessing on smartphones. The results show that the approach achieves up to 50% reduction in power consumption on smartphones and up to 60% reduction in average delay.
## 2 Numerical Simulations of Vibratory Sheet Pile Driving
### Numerical Simulation Results
on wave travel velocities, the relationships between the collected ToAs and wave travel distance are illustrated in Figure 5. Specifically, Figure 5a shows the ToA's variation at the ground surface points (\(Z=0\)) along with the plan distance from the pile shaft, Figure 5b shows the ToA's variation of underground points within the depth of the pile shaft (\(-10\) m \(<Z<-3\) m) along with the plan distance from pile shaft, and Figure 5c shows the ToA's variation of underground points below the pile toe (\(-30\) m \(<Z<-10\) m) along with the radial distance \(r\) from pile toe. The wave velocities of R wave (\(v_{r}=111\) m/s), S wave (\(v_{s}=120\) m/s), and P wave (\(v_{p}=293\) m/s), which were computed based on elastic parameters of soil, are also illustrated in Figure 5. The comparison in Figure 5a indicates that the vibrations experienced by ground surface points (\(Z=0\)) represent the R wave, and the comparison in Figure 5b indicates that the vibrations experienced by underground points within the depth of the pile shaft (\(-10\) m \(<Z<-3\) m) represent the S wave from the pile shaft, whereas the comparison in Figure 5c reveals that the vibrations experienced by underground points below the pile toe (\(-30\) m \(<Z<-10\) m) represent two wave types from the pile toe: S wave and P wave. Figure 6 displays the particle motions of soil nodes at different plan distances (\(d=5\), \(20\), and \(40\) m) and depths (\(Z=0\), \(-7\), \(-25\) m) on the \(X\)-\(Z\) plane. It can be observed that the particle motions of the three ground surface points (\(Z=0\) m) display retrograde elliptical shapes and the vertical magnitude of motion is greater than the horizontal one, which is a typical characteristic of R wave. The particle motions of the three  
Figure 5: Distances vs. ToAs when pile toe is pre-embedded \(10\) m below the ground surface. (**a**) \(d\) vs. ToA of ground surface points on \(X\)-\(Y\) plane (\(Z=0\)); (**b**) \(d\) vs. ToA of buried points on \(X\)-\(Z\) plane (\(-10\) m \(<Z<-3\) m); (**c**) \(r\) vs. ToA of buried points on \(X\)-\(Z\) plane (\(-30\) m \(<Z<-10\) m). underground points (\(Z=-7\) m) take place predominantly in the vertical direction, which presents the feature of cylindrical S wave from the pile shaft. The particle motions of the three underground points (\(Z=-25\) m) display two nearly orthogonal directions, wherein particle motions induced by P and S waves from pile toe are parallel and perpendicular to the direction of wave propagation, respectively.
# Blockchain Applications in Agriculture: A Scoping Review
## 4 Discussion
### Summary of Evidence
#### 4.1.4 Variety of Agricultural Products and Countries  
The use of blockchain does not focus primarily on a specific product (RQ3), as we found in our research. Instead, there are general terms, such as crops, organic food, and water, that are mentioned in most studies, but beyond that, there is a dispersion of 31 different products. It is interesting to notice that we have more references to farming products than to animal products. Although the difference is about three times smaller, 39%, e.g., [78; 97; 98] vs. 14%, e.g., [73; 92], a significant percentage (38%) of the solutions do not indicate the industry to be used. A small percentage (9%) refers to goods needed in the agricultural sector, such as water, energy, and proper waste management. This shows us that researchers can focus on specific products that would be in line with blockchain logic but also that there is a need for agnostic solutions in the supply chain.  
The systems created for specific countries (RQ4) try to solve problems in the essential products of each country, but no specific association of an individual product with each country is shown. Even in China, for which more solutions have been created, mainly agricultural products are mentioned. Finally, it is worth noting that most current solutions are proposed for Asian countries.
###### Abstract
The objective of our paper was to predict changes in the cultivation area and productivity of forage crops in Korea in response to climate change in the Korean Peninsula. Therefore, we performed to create an electromagnetic climate map for identifying suitable cultivation areas by collecting production data for forage crops, establishing correlations with climate data, analyzing vulnerabilities to specific climate factors, tracking changes in the boundaries of suitable cultivation areas, and developing production prediction models for each forage crop based on climate factors. Although research has been performed on the production of many crops, including rice, wheat, barley, cabbage, and radish crops, not much research has been conducted on feed crops in Korea [5]. Recently, in order to improve the productivity of livestock farms, research on the productivity of feed crops is necessary, so the National Institute of Animal Science conducted a survey on production data for each feed crop and studied the experimental results. A major contribution of this study is that we collected production data through various sources, collected climate data according to the region and year of the production data, and established a database related to feed crops. The existing approach was to experiment with soil, fertilizer, etc. [3], but in this study, data were analyzed to determine the relationship between forage crop production and climate impact, and an electronic climate map of changes in cultivation areas that could be provided to livestock farms was constructed. The rest of this paper is organized as follows: In Section 2, the basic data collection and preprocessing are described and the research methodology is presented. The results of the analysis of the climate impact vulnerability of forage crops and the construction of a prediction model for forage crop production are described in Section 3. In addition, experiments to improve the coefficient of determination of the models are presented. Section 4 presents the electromagnetic climate map for the changes in the suitable cultivation area. Finally, conclusions and future works are described in Section 5.
# Satellite Network Task Deployment Method Based on SDN and ICN
## 3 System Model and Problem Formulation
### Task Model  
#### 3.2.1 Computing Task Decomposition  
Due to the limited computing resources, a single satellite is unable to independently complete the whole task. Therefore, we consider decomposing the computing task and deploying the decomposed subtasks to multiple LEO satellite nodes for collaborative processing.  
Task decomposition is represented by subtask tree and subtask association. The relationship of subtasks is represented by a directed single-in-single-out acyclic graph (DAG). The entry of the DAG graph represents the initial input of the program, and the input data of each subtask are searched independently after the task of each subtask is generated. The exit of the graph represents the output of the whole program. There are parallel and serial relationships between the subtasks. If the relationship between subtasks is parallel, the subtasks execute the corresponding tasks independently. If it is a serial relationship, the execution of subtasks can only be performed after the precursor task is completed.  
The whole task is divided into different layers. Each layer task is divided into several parallel subtasks according to the parallel relationship. After the partition, the resource consumption of a single subroutine is evaluated. If the resource consumption exceeds the system load, the parallel analysis will lead to a large number of parallel tasks in each layer, which reduces the response time of tasks. If the program cannot decompose the parallel relation, it is decomposed into serial subroutines according to the amount of computation. After task decomposition, the task initiation node \(O=\{task_{1},task_{2},\ldots,task_{n},G\}\) is used to represent the decomposed subtasks and the relationship between subtasks.
## 4 Discussion
### Moment-Rotation Envelope Curves
#### 4.2.1 Effect of the Steel Tube Wall Thickness on Joint Performance  
Figure 14a shows that the initial stiffness of specimen SJ-3 was 18.4% higher than that of specimen SJ-1. However, the initial stiffness of specimen SJ-4 was approximately equal to that of specimen SJ-2, indicating that a thicker steel tube only increased the stiffness to a limited extent; particularly for joints with thick end-plates, this effect was negligible. The flexural resistance of specimen SJ-3 approximately equal to that of specimen SJ-1, and the flexural resistance of specimen SJ-4 was only 14.1% higher than  
Figure 14: Comparison of the moment (M)-rotation (\(\theta\)) envelope curves of the specimens; (**a**) Effect of steel tube thickness and end-plate thickness; (**b**) Effect of the local strengthening connection method and anchorage method; (**c**) Effect of the steel beam section, and (**d**) Effect of the stiffeners.  
that of specimen SJ-2. These results indicate that an increase in the steel tube thickness also only had a slight improvement effect on the flexural resistance; particularly for joints with thin end-plates, this effect was negligible. Both the horizontal and vertical spacings of the bolts in the extended end-plate joints were relatively large, and the critical tensile capacity of the anchored blind bolts was higher at anchorage failure. Thus, a greater steel tube thickness did not provide the expected improvement in flexural resistance.
## 2 Related Works
### AMC Methods Based on Deep Learning  
In [36], an eye diagram of the original signal is used as an input to Lenet-5, thus linking the AMC problem to the field of image recognition. Convolutional networks for radio recognition are proposed in [37]. Simulations on the DeepSig dataset called RML2016.10A [37] show that recognition accuracy is higher than that of FB-AMC. The RML2016.10A dataset is generated based on the GNU Radio environment. The dataset includes eleven modulation signals, each of which consists of twenty signal-to-noise ratios (SNRs). Each SNR has 1000 samples. In [38], an LSTM-based AMC method is shown to outperform CNN models for small- or medium-sized received signals. In [39], long symbol rate signals are studied, and it is found that a stacked autoencoder can achieve better performance by increasing the simulation time. [PERSON] et al. [27] design two CNNs to recognize different modulation schemes. The first CNN is trained with IQ-sampled signals. This network can distinguish QAMs from other modulation schemes; i.e., this network cannot distinguish between 16 QAM and 64 QAM. The second CNN is trained with a constellation image and can distinguish between 16 QAM and 64 QAM. [PERSON] et al. [40] propose a novel CNN-based AMC method. They use a pruning technique to reduce the convolution parameters and the floating point operations per second (FLOPs). Experiments show that the convolution layer of this CNN, compared to the original CNN, reduces the convolution parameters without significant losses in recognition accuracy.
## 3 Improved LEACH Protocol  
### Network Model  
In this paper, a 2D static underwater acoustic sensor network is investigated, whose structure is shown in Figure 1. The network consists of a base station, several cluster head nodes, and many sensor nodes. All nodes are anchored to the seabed and autonomously form a cluster-based network. Cluster head nodes are selected using a clustering algorithm. The data collected by each sensor node are converged to the cluster head node and then relayed to the surface base station through vertical links. It is assumed that the network satisfies the following conditions:  
1. All nodes in the network are stationary and uniformly distributed.
2. All nodes are designed with a homogeneous architecture, allowing each node to function interchangeably as either a sensor node or a cluster head node.
3. All nodes know the positions and initial energy of other nodes before communication.
4. The network is partly connected, but all nodes in a cluster can communicate with each other in a half-duplex mode.
5. All node clocks in the network are synchronized.
6. During idle periods when there is no communication task, nodes in the network periodically synchronize their residual energy and other information.  
Figure 1: The two-dimensional static network.
## 2 Methods to Determine the Synergistic Effect of Sweetener Mixtures
### Sensory Evaluation Method I  
The first method is the most common and simple approach that has been popularly used [11; 17]. In this method, which is called the simple additive model, the perceived sweetness of the mixture is directly compared to the sum of the sweetness of the unmixed components. If the sweetness of the mixture is equal to, greater or less than the summed sweetness of its components, then it is regarded as having additive, synergistic and suppressive effects, respectively. However, this method has been questioned by many researchers because it does not consider the effect of sweetener concentrations on the results; thus, contradictory findings in the research on sweeteners' synergism could often arise when different doses of the components were added in the sweetener mixtures [18]. Furthermore, it should be noted that in this method, the concentrations of used sweeteners were usually expressed as a % (weight of sweetener/weight of solvent) rather than a molarity (moles of sweetener/volume of solvent), which cannot address the fact that sweeteners have different molecular weights and thus are not scientifically rational for studying the mechanical interactions between the sweeteners as well as the sweeteners and receptors [17].