esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 General Design and Features of the Instrument v2021
### Ongoing Instrument Variant: Cellular Communication  
A derivate of the satellite-based buoy is being developed to communicate through the cellular network instead. Using the same data processing and setup means that the buoy takes advantage of the previous validation. The purpose of a buoy operating on the cellular network is (1) lower cost (by a factor of 2 to 3 for the hardware, and up to 50 for the communication), (2) higher data-rates: time-series of continuous measurements can be transmitted if necessary.  
The buoy will operate under the limitation that it cannot stray far from the coast and the target areas reflect this: near-shore breaking wave measurements and drift and wave measurements inside fjords in Norway. The buoy will be designed to store as much data as possible locally if network service is temporarily unavailable. If the buoy runs out of memory in the mean time, it will drop uncritical data and store only processed data. In addition to the internal memory, about 900 kB of additional memory is available through the cellular modem for caching data, and other memory devices may be added at small extra cost and power usage if necessary.
# From Lean Production to Lean 4.0: A Systematic Literature Review with a Historical Perspective
## 3 Results
### Answering RQ3. Historical Evolution of the Term \"Lean\"
#### 3.2.2 Lean Manufacturing (1993)  
In 1993, [PERSON] introduced lean manufacturing (LM) in a similar sense to lean production: _Lean Manufacturing Organization, 21 st Century_[51].  
In 2003, [52], [PERSON] et al. also used this term with the same meaning as lean production and focused the topic on factory management. In 2013, [PERSON] et al. [5] presented the evolution of LM definitions.  
The most recent main reviews of lean manufacturing that confirm this equivalence with lean production were published between 2019 and 2020 [3; 4; 53; 5]. These reviews are perhaps targeted more to the fields of industry and, specifically, factory management.  
In 2016 [54], the first linkage with Industry 4.0 was made. Until 2020, the relationships between LM and Industry 4.0 were explored. In the last one, published in 2020 [55], [PERSON] et al. offered a holistic view toward integrating both concepts.  
As a conclusion, lean manufacturing can be considered synonymous with lean production, although targeted more at factory operations. After 2000, is the preferred term in the academic literature when referring to lean in the industrial field (Figure 6). That is probably to distinguish the production of goods, since the term \"production\" is used more and more in the service industries.
# Long Time Simulation Analysis of Geometry Dynamics Model under Iteration
## 1 Introduction
[PERSON] et al. [23] established the Hamel field variational product molecule of geometrically accurate beams. [PERSON] et al. [24] carried out path planning for a manipulator based on a geometric mechanics method. The above analysis shows that the discrete Lie group variational integrator and the Hamel method both change an ordinary differential equation into nonlinear equations for a numerical solution. Lie algebra works in the vector space which is mapped from a Lie group. The three methods solve the dynamics equations from different perspectives. The Hamel and Lie algebra methods reduce the equations' complexity. Based on the above explorations, some problems need to be analyzed. Firstly, the numerical simulations in the above explorations were performed with their own modeling methods, so the relation of different geometry expressions should be analyzed. Secondly, the extension of complexity of different geometry models and their numerical solutions should be analyzed. Thirdly, the numerical accuracy and stability of different geometry models need to be compared. The above analysis can help the engineer chose the best method to solve their own problem. Thus, in this paper, the geometric models for planar and 3D pendulums are established from three perspectives as reduced attitude, Lie group and Lie algebra, respectively. Based on the three models, the dynamics equations were transformed into nonlinear equations, and the Jacobi matrices of different equations were obtained. The numerical solutions were carried out by Newton iteration. Three methods were compared and analyzed based on the numerical results. This study provides a basis for the study of geometric dynamics of multi-body systems. The structure of this research is as follows. In the Sections 2 and 3, the concepts of a Lie group, Lie algebra and Newton iteration are introduced based on basic mathematical expressions. In the Section 4, three kinds of geometric dynamics model of planer pendulum are built. In the Section 5, four types of geometry dynamics model of 3D pendulums are built. In the Section 6, the simulation results of different geometric dynamics models are presented. The conclusions are summarized in the Section 7. The concrete derivation of each model is presented step by step, which should help the readers to understand the methods easily.
# Practical 3-D Beam Pattern Based Channel Modeling for Multi-Polarized Massive MIMO Systems +
## 3 A Practical Non-Stationary of 3-D Massive MIMO Channel Model
### Generation of the Cluster/Channel in System Level
#### 3.1.2 Toward the Receive Antenna Elements
The generation of the cluster for each antenna element of the non-stationary process is based on the array-time evolution of clusters and can be divided into generation of cluster for each antenna according to Equation (22), and the second part is an updated model of geometrical relationships with respect to the movements of the receiver and clusters determines all parameters for each cluster. In the first part, the contribution of the models not only the appearance and disappearance of the cluster on antenna arrays, but also non-stationary behaviors of the clusters on the time axis. The array-time cluster evolution is describing based on the algorithm where assumed per meter as the number of \(clusters_{n}\) and the initial cluster sets of the first transmit and receive antennas based on the Section 2, at the initial time \(t\), then, the cluster sets for \(\left(Qt(t+\Delta t)\bigcap Qr(t+\Delta t)\right)\) for both TX and RX recursively generate the cluster sets of the rest of antenna elements at the initial time instant \(t\) and \(\Delta\). Then, the cluster sets indices are reassigned from \(1\) to \(cluster_{n}\). \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline \((\theta,\phi)\),\((\theta,\varphi)\) & elevation and azimuth angles of the departure and arrival, respectively \\ \hline \(\mathbf{D}^{BS}_{n,i_{1},lk}(t+\Delta t)\) & distance vector between \(n_{th}\) cluster and \(lk_{th}\) transmit antenna element \\  & via \(i_{1 th}\) path \\ \hline \(\mathbf{D}^{BS}_{n}(t)\) & distance vector between \(n_{th}\) cluster and transmit antenna \\ \hline \(\mathbf{D}^{UE}_{n,j,l^{\prime}k^{\prime}}(t+\Delta t)\) & distance vector between \(n_{th}\) cluster and \(l^{\prime}k^{\prime}_{th}\) receive antenna element \\  & via \(i_{2 th}\) path \\ \hline \(\mathbf{D}^{UE}_{n}(t)\) & distance vector between \(n_{th}\) cluster and receive antenna \\ \hline \(\mathbf{D}^{BS}_{LOS,i_{1},lk}(t+\Delta t)\) & distance vector between \(LOS_{th}\) path and \(lk_{th}\) transmit antenna element \\ \hline \(\mathbf{D}^{UE}_{LOS,i_{1},l^{\prime}k^{\prime}}(t+\Delta t)\) & distance vector between \(LOS_{th}\) path and \(l^{\prime}k^{\prime}_{th}\) receive antenna element \\ \hline \(\vec{a}(\varphi,\theta)_{lk},\vec{a}(\varphi,\vec{\sigma})_{l^{\prime}k^{\prime}}\) & array response of the TX and RX, respectively \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of key parameter definitions.
# An Efficient Model-Based Clustering via Joint Multiple Sink Placement for WSNs
###### Abstract  
Wireless sensor networks consist of many restrictive sensor nodes with limited abilities, including limited power, low bandwidth and battery, small storage space, and limited computational capacity. Sensor nodes produce massive amounts of data that are then collected and transferred to the sink via single or multihop pathways. Since the nodes' abilities are limited, ineffective data transmission across the nodes makes the network unstable due to the rising data transmission delay and the high consumption of energy. Furthermore, sink location and sensor-to-sink routing significantly impact network performance. Although there are suggested solutions for this challenge, they suffer from low-lifetime networks, high energy consumption, and data transmission delay. Based on these constrained capacities, clustering is a promising technique for reducing the energy use of wireless sensor networks, thus improving their performance. This paper models the problem of multiple sink deployment and sensor-to-sink routing using the clustering technique to extend the lifetime of wireless sensor networks. The proposed model determines the sink placements and the most effective way to transmit data from sensor nodes to the sink. First, we propose an improved ant clustering algorithm to group nodes, and we select the cluster head based on the chance of picking factor. Second, we assign nodes to sinks that are designated as data collectors. Third, we provide optimal paths for nodes to relay the data to the sink by maximizing the network's lifetime and improving data flow. The results of simulation on a real network dataset demonstrate that our proposal outperforms the existing state-of-the-art approaches in terms of energy consumption, network lifetime, data transmission delay, and scalability.  
wireless sensor networks; sink; multiple sink deployment; sink placement; sensor-to-sink routing; ant-clustering algorithm; data transmission; data transmission delay +
Footnote †: journal:  
0000-0001-9500-0001-9500][PERSON]  
1
## 4 Application of a Multi-Adaptive Genetic Algorithm to the Problem of Human-Robot Cooperation
### Results and Discussion
The total working time is 3000 s. The process is divided into 100 cells that represent 30 s time slots. The color of each cell indicates the corresponding task; white represents the worker standing still, green indicates going to the pallet to provide sockets, and red represents going to the conveyor belt to provide bulbs. Figure 9 illustrates this in detail. Cells 1, 2, 3, and 4, in white, represent the first 120 s (30 s \(\times\) 4) during which the person stands still in the Home position and does not move. Cell 5, in red, represents the worker moving to the conveyor to supply additional bulb caps at second 121 and then returning to the Home position. Cells 6, 7, and 8, in white, represent the next 90 s (from second 151 to second 240), during which the worker stands still in the Home position. Cell 9 (from second 241 to 270), in green, represents the worker moving to the pallet to supply socket materials to the system. An integrated speaker system follows the signal of each time slot to notify the person whether to stand still or move and in which direction to move. The worker activities are optimized to minimize movement. The time between movement activities is consistent and does not overlap to ensure that the robot has sufficient materials to operate. Before using the multi-adaptive genetic algorithm, four people assemble 100 light bulbs in 50 rounds based on their observation ability, and a camera is used to record information. The average results of all four participants in the experiment are: the average number of times the cap is supplied is 25.85 times; the average number of times the tube is supplied is 21.03 times; the total number of movements is 46.87 times. After 50 runs of the MATLAB software program using the MGA algorithm, the average number of cap supplies is 21.52. The number of tube supplies is 15.02, and the total number of movements is 36.54. After applying the multi-adaptive genetic algorithm, the number of movements of the person decreases by about 22%.
# Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
###### Abstract  
Frost heave action is a major issue in permafrost regions that can give rise to various geotechnical engineering problems. To analyze and predict this phenomenon at a specimen scale, this study conducted a fully coupled thermal-hydro-mechanical analysis and evaluated the frost heave behavior of frozen soil considering geotechnical parameters. Furthermore, a parametric study was performed to quantitatively analyze the effects of major geotechnical properties on frost heave behavior. According to the results of the parametric study, the amount of heave tended to decrease as the particle thermal conductivity increased, whereas the frost heave ratio tended to increase as the initial hydraulic conductivity increased. After evaluating the sensitivity of each parameter to frost heave behavior through statistical analyses, an artificial neural network model was developed to practically predict frost heave behavior. According to the verification results of the neural network model, the trained network model demonstrated a reliable accuracy (R\({}^{2}\) = 0.893) in predicting frost heave ratio, even when the model used test datasets that were not part of the training datasets.  
finite element method; thermal-hydro-mechanical model; particle thermal conductivity; hydraulic conductivity; frost heave +
Footnote †: journal: Applied Sciences  
article  
Artificial Neural Network-Based Model for Prediction of Frost Heave Behavior of Silty Soil Specimen
# Life Cycle Assessment for the Production Phase of Nano-Silica-Modified Asphalt Mixtures
## 4 Results and Discussion
### CASE 2A: Impact Assessment of Unmodified (Conventional) Asphalt Mixture Analysis  
The analysis of unmodified (conventional) asphalt materials was needed to better understand the environmental implication of modifying conventional asphalt with nanomaterials. The results of the analysis are shown in Table 5.  
Any increase in the production of raw materials or a change in the FU will result in an increase in the impact scores in each category and vice versa.  
The modification of asphalt materials with nanomaterials results in an increase in environmental impacts, which is clear when comparing the results in Table 5 with that in Table 4. Across all impact categories, there is an increase in the impact scores. This fact is reinforced by Ref. [4] when the authors found that using Ethylene-Vinyl-Acetate (EVA) polymer as a modifier agent leads to a deterioration of the life cycle profile of the pavement compared to unmodified asphalt binder. However, the deterioration of the life cycle environmental profile with nano-modified asphalt materials is insignificant. Specifically, there was only a 0.4% increase in global warming, 0.8% increase in respiratory effects, 0.009% increase in ozone depletion, 0.98% increase in eutrophication, 1.0% increase in human health carcinogenic, 0.7% increase in photochemical oxidation, 0.96% increase in human health non-carcinogenic, 0.72% increase in ecotoxicity, and 0.85% increase in acidification. This means the modification of asphalt materials with nanomaterials (nano-silica) causes more impacts in human health carcinogenic than other impact categories. Apart from ozone depletion, the modification of asphalt materials with nano-silica contributes fewer impacts in global warming per 3% by weight of asphalt binder production of nano-silica.
# A Survey on MLLMs in Education: Application and Future Directions
## 3 Applications of MLLMs in Education
### Adaptive Learning Platforms
#### 3.1.3 Examples of Applications  
Several educational platforms and research initiatives have begun integrating MLLMs into adaptive learning systems. _Cognii_[40] is an AI-based virtual learning assistant that uses NLP and MLLMs to provide personalized tutoring and open-response assessments in natural language. _Carnegie Learning's MATHia_[41] incorporates AI to adapt mathematics instruction, potentially enhanced by MLLMs to interpret handwritten mathematical expressions and provide step-by-step feedback. _Knewton_[42] is an adaptive learning technology that could utilize MLLMs to process multimodal learner data, such as interaction patterns and facial expressions, to adjust instructional strategies.  
_Duolingo_[43, 44], a language learning app, employs AI for personalized lesson plans. The integration of MLLMs allows for speech recognition and dialogue practice, enhancing language acquisition through multimodal interactions. _Smart Sparrow_[45] is an adaptive e-learning platform that enables the creation of interactive and adaptive courseware, potentially leveraging MLLMs for richer content delivery and learner analytics.
## 3 SnBi-Based Solders
### Reliability  
The reliability of SnBi solder joint is sensitive to the high-density current, which is much more severe in the increasingly finer pitch of advanced electronical devices [68]. Compared with other Sn-based solders, the Cu\({}_{6}\)Sn\({}_{5}\) layer at the SnBi/Cu interface grows faster and the probability of cracks formation increases under high-density current. The current also causes the migration and segregation of Sn and Bi atoms in the solder, which results in the formation of Sn-rich layer and brittle Bi-rich layer on the anode side and cathode side, respectively [69; 70]. Numerous studies have shown that adding nanoparticles can significantly improve the electromigration resistance of SnBi solders.  
[PERSON] et al. [71] reported that nano-Ag particles could react with Sn to form fine and dispersed Ag\({}_{3}\)Sn particles, which refined the microstructure of the composite solder and hindered the migration of Bi atoms. After adding 2 wt.% Ag nanoparticles, Bi atomic flux of Sn58 Bi solder decreased from \(7.43\times 10^{12}\) atoms/cm\({}^{3}\) to \(4.77\times 10^{12}\) atoms/cm\({}^{3}\) under the current density of \(5\times 10^{3}\) A/cm\({}^{2}\) at 303 K, which meant SnBi-Ag solder joints have stronger EM resistance. Nano-Al\({}_{2}\)O\({}_{3}\) particles tended to gather at the surface of interfacial IMCs layer and inhibited the atomic migration in the solder joints under the electro-thermal treatment, and thus, the EM resistance of the composite solder is significantly improved [72].  
The addition of nanoparticles also has an important influence of the corrosion resistance. The corrosion rate can be calculated as:  
\[R=\frac{8.76\times 10^{7}\times(M-M_{t})}{ST\rho} \tag{2}\]  
Figure 10: The effect of Y\({}_{2}\)O\({}_{3}\) content on the spread area of SnBi–nano Y\({}_{2}\)O\({}_{3}\) composite solder on the Cu substrate [66].  
where \(M\) and \(M_{t}\) represent the mass of sample before and after corrosion, \(S\) is the surface area of sample, \(T\) is the corrosion time, and \(\rho\) is the density of sample [73]. Figure 11[59] illustrates that 0.05 wt.% and 0.1 wt.% Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles obviously suppress the growth of corrosion rate, especially in the first 10 days. The grain-refining effect of Cu\({}_{6}\)Sn\({}_{5}\) nanoparticles results in more boundaries, which can serve as corrosion barriers, thus improving the corrosion resistance of the composite solder. A similar phenomenon was detected in the corrosion process of the SnBi-GNSs composite solder [63]. After corrosion of 400 h, Bi dendrites formed and less ditches existed at the gapping space of surface microstructure with the addition of 0.05 wt.% GNSs. Besides, the corrosion rate of the SnBi-0.05 GNSs solder almost remained stable at 0.45 mm/y.
## 2 Materials and Methods
### Study Site and Experimental Design  
The experiment was conducted in the West Pomeranian region of Poland at the Agricultural Experimental Station of the West Pomeranian University of Technology in Szczecin. The study site was located in Lipnik near Stargard (N 53 deg20'35.8\", E 14 deg98'10.8\"). The soil under experiment was a light rust-brown sandy soil of poor rye complex. The average sum of long-term precipitation in the study location is 536 mm per year with an average air temperature of 8.2 \({}^{\circ}\)C. During the growing season (April-October), the average long-term precipitation is 359 mm with an average temperature of 13.3 \({}^{\circ}\)C. The year 2018 was drier with precipitation of only 253 mm between April and October compared to 615 mm in 2017 during the same period. The average temperature in the growing season of 2017 was 15 \({}^{\circ}\)C, whereas in 2018 the temperature was found to be higher and amounted to 16.8 \({}^{\circ}\)C.  
The experiment was two-factorial: factor A--two phenotypes of _Sida hermaphrodita_ (Sida1 and Sida2) and one phenotype of _Silphium perfoliatum_; and factor B--a method of establishing a plantation using seeds (seed) and seedlings (planting). Moreover, in the case of Silphium, the additional factor of the number of harvests in a season was introduced: one or two. The experiment was set-up at the turn of May 2016 in a split-plot design in four replications. The whole experiment had an area of 1250 m\({}^{2}\), and each plot was 12.6 m\({}^{2}\); there were 8 plots for Sida1, 8 for Sida2, and 16 for Silphium. Sida phenotypes were obtained from Germany and were selected from the two outermost habitats in terms of geographical location (the distance was approximately 620 km on the north-south axis) and varied original climatic conditions. Seedlings and seeds of Sida1 were obtained from Baden-Wurttemberg (MK Jungpflanzen GmbH, Biberach District) and of Sida2 from Lower Saxony (farm enterprise of [PERSON] in the vicinity of Osnabruck). Seedlings and seeds of Silphium were obtained also from Germany (N.L. Chrestensen Erfurter Samen und Pflanzenzucht GmbH (Thuringen)). During the experiment, there were no clear differences in the morphological traits of both phenotypes of Sida,particularly in reference to the shape and size of leaves, colour, and shape of stalks, and the structure and colour of flowers.  
The fore crop on the experiment site was spring barley harvested for grain. After harvest of the fore crop, a traditional method of mechanical cultivation of soil (post-harvest cultivation and pre-winter ploughing) was used. In the spring of 2016, a rotary tiller was used before sowing and planting. Pre-sowing fertilization was applied in the amount of N-100, P-35, and K-110 kg ha\({}^{-1}\). The same amount of mineral fertilization was used every year after the start of vegetation.
# Current Context and Research Trends in Linear DC-DC Converters
## 4 Dominant Characteristics of LDOs
### Low Noise  
LDOs are capable of filtering noise and ripples while operating as voltage regulators due to their high-power supply rejection ratio (PSRR). Therefore, LDOs can generate a clean DC voltage without noise components, which prevents the resetting of sensitive devices and minimises distortions in signals due to noise. Usually, standard LDOs have an integral noise above 50 uV\({}_{\text{RMS}}\). In addition, there are specific low-noise LDOs where the noise level is less than 30 uV\({}_{\text{RMS}}\), such as TPS7A8101 [18], TPS74401 [19], and TPS7H1210-SEP [20].