esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Optimization of a Toothed-Plate Single-Roller Crushing Device for Waste Plastic Film
## 4 Materials and Methods of the Crushing Test
### Assessment Indicators and Test Scheme  
In line with the recycling demands of plastic film processing companies, a three-factor, three-level orthogonal test was conducted with the size qualification rate of crushed film and the impurity content as optimization indexes. Referring to previous research on cleaning and separation equipment [22; 23; 25], film and impurities are separated better when the length of the crushed film is in the range of [50; 500] mm. Thus, a length in the range of [50; 500] mm was taken as the qualified size of the crushed film, which is defined as follows:  
\[Y_{1}=\frac{M_{\mathrm{Q}}}{M_{A}}\times 100\% \tag{8}\]  
where \(Y_{1}\) is the size qualification rate of the crushed film, %; \(M_{\mathrm{Q}}\) is the mass of the crushed film with qualified length, g; and \(M_{A}\) is the net film mass of crushed materials, g.  
The impurities in the crushed materials included cotton stalks, soil impurities, and film debris. The impurity content of the crushed film was calculated as follows:  
\[Y_{2}=\frac{M_{\mathrm{C}}}{M_{T}}\times 100\% \tag{9}\]  
where \(Y_{2}\) is the impurity content of the crushed film, %; \(M_{\mathrm{C}}\) is the mass of impurities in the crushed film, g; and \(M_{T}\) is the total mass of crushed materials, g.  
Before the test, it is important to prepare adequate test materials. The sampling process should pay attention to minimize the loss of impurities in the materials. The mass of each sample was 2.0 \(\pm\) 0.2 kg, and the material was evenly spread on the conveyor belt, as shown in Figure 9. After that, the crushing device was started, and the conveyor belt was started after the crushing roller ran steadily. When the discharge hopper had no material coming out, the main switch was turned off and samples of the crushed mixture were taken. Each sample group had a mass of 300 g \(\pm\) 10 g. Finally, the crushed mixture was separated into film and impurities, and the size qualification rate and impurity content of the crushed film were calculated. Each test group was repeated 3 times, and the data for each group was the average of 3 repeated tests.  
Based on the motion and mechanical analysis of the crushing process, it is known that the feeding speed, \(V_{0}\); the number of toothed plates, \(Z\); and the crushing roller rotation speed, \(\omega\), have a great impact on the indicators. In accordance with preliminary experi  
Figure 9: Crushing procedure.  
ments, the feeding speed, \(V_{0}\), was determined to be [0.25, 0.40] m/s; the numbers of toothed plates, \(Z\), were 6, 12, and 18; and the crushing roller rotation speed, \(\omega\), was [600, 900] r/min. The factors and levels for the Box-Behnken orthogonal test are shown in Table 2.
# Analysis of the Impact of Small Group Behavior on Cruise Ship Emergency Evacuation
## 4 Cruise Ship Evacuation Simulation Model Construction
### Comparison of Congestion Duration with and without Groups  
According to Figure 11, it can be observed that regions I-a, I-b, I-c, and II-b increased by 37.41%, 44.53%, 32.15%, and 6.69%, respectively, compared to the non-group situation. However, region II-a decreased by 8.55%. This indicates that group effects have different impacts on different evacuation methods.  
Figure 10: Variation in evacuation parameters with and without groups effect.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Congestion Area** & **Continuous Congestion Time/s** & **Percentage of Total Time/\%** & **Maximum Congestion Density Person/m2** \\ \hline I-a & 393 & 38.48 & 5.2 \\ I-b & 290 & 28.40 & 4.9 \\ I-c & 559 & 31.63 & 7.6 \\ II-a & 246 & 32.13 & 4.4 \\ II-b & 335 & 43.76 & 9.1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Continuous congestion time in the area with group.  
From Table 10, we can see that the time of starting congestion in area II-a without group is 372 s and lasts 269 s. The time of starting congestion with group action is 510 s and lasts 246 s. The data show that the group effect slows down the congestion formation in area I-b and also reduces the duration of congestion, which has a positive effect on evacuation. This is because members of the group tend to leave in pairs, but the response time of each passenger is different, and passengers who respond quickly need to wait for other group members, causing the congestion start time in this area to be delayed and the congestion duration to be shortened.  
From Table 10, we can see that the start of congestion in the area II-c without group is 482 s and lasts 189 s. The start of congestion with group effect is 508 s and lasts 290 s. These data indicate that the group effect slows down the formation of congestion in I-b, while also increasing the duration of congestion. This is due to the response time of group is the maximum response time among group members, this waiting behavior hinders the movement of other passengers, thus causing congestion situation increases, which has a negative effect on evacuation.  
In summary, considering the differences in congestion formation and duration with and without groups in different regions, it can be concluded that the effect of groups on congestion formation and duration is not singular.
## 3 Results
### Results for a Real-World Problem
The route given in Figure 30 clearly demonstrates that the algorithm proposed in this study provides a nice solution to an important real-world problem by diverting the truck to low-traffic roads to make deliveries. Finally, it is considered how the newly calculated route and delivery time will be affected if a drone is used in addition to the truck while delivering to the delivery points. In order to examine the changes in this situation, a drone that accompanies the truck has a range of 5 km and can move twice as fast as the truck used in the new situation. The resulting route is shown in Figure 31, where the black lines represent the truck's route, and the red lines represent the drone's route. It should be noted that no trucks are deployed on the road between nodes 1 and 2 with heavy traffic. As it is not affected by traffic on this road, even though it is among the options to assign a drone and obtain a route similar to that in Figure 30, the algorithm has obtained a different route, which it has found to be the shortest as a result of the calculations it has made. The calculated cost of this route in Figure 31 is 10,638.16. In other words, it has been observed that using drones reduces the cost compared to previous experiments. As a result, in this paper, an effective and new approach based on a dynamic genetic algorithm is proposed for the single-truck multi-drone problem. The results obtained in this article can also be used for the multi-truck multi-drone problem. For this, an effective solution will be obtained by taking into account the necessary input parameters for multiple trucks and by assigning them to the single-truck multi-drone sub-problems that will work independently and verified in this paper, thanks to an assignment algorithm that will be configured according to the carrying and other capacity values of the relevant trucks. Although no analysis application has been made for this in this paper, it is planned to develop this application within the scope of future studies and obtain a comparative analysis of the solution obtained. A schematic representation of a solution for the planned multi-truck multi-drone problem is given in Figure 32.  
Figure 30: A solution where a drone is not used and the traffic situation is taken into account.
###### Abstract  
With the explosive growth of data at the edge in the Industrial Internet of Things (IIoT), edge devices are increasingly performing more data processing tasks to alleviate the load on cloud servers. To achieve this goal, Programmable Logic Controllers (PLCs) are gradually transitioning into edge PLCs. However, efficiently executing a large number of computational tasks in memory-limited edge PLCs is a significant challenge. Therefore, there is a need to design an efficient memory allocation strategy for edge PLCs. This paper proposes a dynamic memory allocation strategy for edge PLCs. It adopts an approach of organizing memory into small blocks to handle memory requests from real-time tasks and utilizes a well-performing dynamic programming method for resource allocation problems to handle memory requests from non-real-time tasks. This approach ensures real-time performance while improving the efficiency of non-real-time task processing. In the simulation experiments, the algorithm implemented based on this allocation strategy is compared with the default method and several open-source memory allocators. The experimental results demonstrate that the proposed algorithm, on average, improves the speed of real-time task processing by 13.7% and achieves a maximum speed improvement of 17.0% for non-real-time task processing. The experimental results show that the allocation strategy effectively improves memory allocation efficiency in memory-limited environments.  
IIoT; edge PLCs; dynamic memory allocation; dynamic programming +
Footnote †: journal: Applied
## 3 Results and Discussion
### Moisture Susceptibility Test  
Figure 4 illustrates the TSR values of the five mixes. Generally, the TSR values of all RAP mixes were above the R0 (0.89), and the standard value (0.80) with the highest TSR value was recorded for the R100 mix (0.97), which was approximately 10% higher than R0. Interestingly, the TSR value increased as the RAP content increased, except for the R50 mix (0.93) where the TSR value was only 1.8% less than that of the R30 mix. Thus, the addition of RAP was very significant towards the sample's resistance capacity to moisture damage as represented by the TSR value. Furthermore, the TSR value is commonly associated with higher stiffness in RAP mixes. The moisture susceptibility is mainly characterised by the adhesive resistance between the aggregate and binder and the cohesive resistance of the binder particles. Since RAP aggregates were already covered with the aged binder, RAP mixes were generally less susceptible to damage compared to the virgin mix. The utilisation of rejuvenator in this study was successful in restoring the properties of the aged binder [30], although it could reduce the moisture resistance of the asphalt mix [23] while the use of 6% CRM effectively increased the rheological properties of the binder to reduce the impact of WEO [7; 23; 59]. Moreover, the rheological properties of the aged binder were also significantly higher (Table 2). Therefore, the combined use of WEO and CRM was effective in applying a high RAP content in the HMA mix design. It can be concluded that the use of RAP mixes for pavement construction prevents the likelihood of pavement suffering from unfavourable moisture damage.  
Figure 3: \(\mathrm{M_{R}}\) for the five mixes.
## 8 Current Challenges and Future Prospects
### Vertical Farming and Hydroponics  
In addition to adopting advanced technologies, new agricultural practices are also critical to overcoming geographical and resource constraints. While arable land is decreasing, it is assumed that 3 million people worldwide have shifted to urban areas, putting more stress on existing limited city resources [192]. Contemplating this quick migration, it is projected that before 2030, 60% of the world population will rely on urban areas, and these figures are expected to increase to 68% before 2050 [3; 193]. Considering these two issues, food production may be disastrous as soon as current agricultural practices are concerned. VF solves these problems because it can meet the challenges of shortage of water and land management, and at the same time, it seems very appropriate for adoption near urban areas or cities. At least in some parts of the world, VF is depicted as the solution to food shortages and shrinking arable land. Besides, hydroponics can play a very important role, as it greatly reduces the need for space and water. The rapid growth of technologies has promoted water and nutrient efficient farming, such as VF, making it more attractive to farmers. Alongside hydroponics and VF, the latest and improved solutions are needed to boost the arable land without disturbing forests and natural animal habitats. To accomplish this, it is necessary to focus on the desert, because it covers a third of the Earth's land surface. With Norwegian and Chinese companies/experts working in the deserts of Dubai, Qatar, Jordan, and China, solutions have begun to be developed [194; 195; 196]. The existence of advanced technologies, such as the IoT, is critical to achieving this target.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 3 Analysis of the Current Situation and Limitations of Top-Down Monitoring Methods  
The top-down method is a research approach that takes the atmosphere as its object of study. It is based on observations of GHG concentrations and meteorological field data, including ground observations, aerial surveys, and satellite data. It employs a combination of atmospheric chemical transport models and diffusion models to indirectly simulate carbon emission fluxes at varying scales. The top-down method relies, in part, on the emissions data obtained through the bottom-up method. However, it also incorporates information on atmospheric CO\({}_{2}\) concentrations derived from ground, airborne, or satellite observations. The method can be used to invert carbon emissions from biomass burning at the point, regional, and global scales. This allows for a more accurate estimation of carbon emissions than traditional survey and statistical methods while also reducing the uncertainty introduced by artificial factors such as parameter settings.
# Vibration Energy Harvesting from Raindrops Impacts: Experimental Tests and Interpretative Models
## 1 Introduction
The latter two effects cannot be explained by means of a linear model excited by an impulsive force and are not related to the presence of higher-order modes of the cantilever harvester, as discussed in [20]. To the best of authors' knowledge, there are no models explaining the increase in generated voltage and the measured waveform. Therefore, this paper focuses on the development of an interpretative model of the interaction between the falling drop and the water surface. A systematic experimental analysis was carried out in a laboratory environment [21] in order to analyze the effect of water depth on the resonance frequency, generated voltage, and damping ratio. The results confirmed the findings reported in [20] and showed a saturation phenomenon in the trend of generated voltage. Voltage increased with water depth but reached a maximum value for a certain depth since larger quantities of water in the spoon led to large spills after the impact. It is worth noting that the tests reported in [21] were carried out with artificial rain and no test with actual rain was carried out. This paper is organized as follows. The next section deals with the harvester and the equipment used for experimental tests. In Section 3 novel experimental results obtained with actual rain are compared with the ones obtained with simulated rain, and the effect of impact position is discussed. In Section 4 a video analysis is presented and correlated with the measured voltage. The mathematical model of the harvester is developed in Section 5, including a harvester model and an impact model. The impact model is novel, since the complex fluid motion generated by the raindrop impact is represented by the one-dimensional motion of two concentric water cylinders. The sizes of the oscillating cylinders are derived from the recorded motion of water inside the spoon. A good agreement between the experimental and numerical values of the generated voltage is found. Finally, after a discussion (Section 6), conclusions are drawn in Section 7.
## 3 Results and Discussion  
### Particle Packing Density and Excess Water Ratio  
The packing densities of the particles, which is defined as the ratio of the volume of solid particles to the bulk volume occupied by the particles [27], are given in the first column of Table 4 and plotted against the volume fraction of sand in Figure 6. As expected, the packing density, \(\phi_{max}\), increases with increasing sand volume fraction. This is due to the occupying effect which follows when the volumetric fraction of coarser particles is less than the optimum value [27; 28]. When the larger particles (sand grains) are added to smaller particles (cement and MNP), the larger particles occupy the space within the bulk and porous volume, thereby increasing the packing density. The minimum void ratio (\(V_{min}\)) and excess water ratio (\(W_{e}\)) were also determined and are given in Table 4, and \(W_{e}\) is plotted against the volume fraction of sand in Figure 6.  
Figure 5: Test setup to study pressure-driven flow of cementitious paste [9].  
In a state of maximum packing density, the coarser particles are tightly packed together, with just enough fine particles available to fill the voids between the coarser particles. Since there are no excess fine particles separating the coarse particles from each other, the coarser particles would slide against each other, resulting in large interparticle interactions and interlocking. Conversely, in the state of maximum mass flow rate, the coarse particles are separated from each other by the excess fines since there are more than enough fines necessary to fill the voids. The fine particles act like ball bearings, lowering the particle interlocking action and the smooth movement of coarse particles within the fluid [17]. Extending this theory to cementitious pastes, the excess amount of water in the paste is responsible for dispersing the particles and lubricating the cement paste [27]. Increasing the sand volume in the mixture from 0 to 40% therefore moves the mixtures from a state of mass flow rate towards maximum packing density, which is evident by the increasing \(\varphi_{max}\) and decreasing \(W_{e}\). This could arguably also lead to a reduction in mobility of the MNPs to form clusters due to the obstruction caused by the increasing presence of larger solid particles in the mixture as illustrated in Figure 7. The magnetorheological response of the mixtures was therefore investigated by measuring the storage modulus of the mixtures with and without magnetic field application and is discussed in the following section.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & \(\varphi_{max}\) & \(V_{min}\) & \(W_{e}\) \\ \hline M0 & 0.47 & 1.13 & 0.59 \\ M1 & 0.54 & 0.85 & 0.46 \\ M2 & 0.59 & 0.69 & 0.33 \\ M3 & 0.64 & 0.57 & 0.22 \\ M4 & 0.67 & 0.49 & 0.12 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Particle packing and excess water ratio.  
Figure 6: Packing density and excess water ratio as a function of sand volume fraction.
## 10 Spatial Predictions, Prediction Uncertainty, and Their Visualization  
### Spatial Predictions  
In spatial predictive modeling, the goal is not only to develop the most accurate predictive model, but more importantly to generate spatial predictions. The spatial predictions are usually produced using the most accurate predictive model developed according to the above procedures. To make predictions, in addition to the spatial predictive model, we need relevant information of each model predictive variable to be available at each grid cell at a desired resolution. When all this information is prepared, the spatial predictions can then be generated. The predictions contain three columns, i.e., longitude, latitude, and predictions. Sometimes, uncertainty of the predictions can be produced.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Model Classification for Blockchain-Based Federated Learning
#### 4.1.5 Peer-to-Peer Networking  
P2P network is different from the centralized management of the traditional C/S framework; it breaks the centralized design mode, all participants are treated equally, and the transaction data can be spread throughout the network. In the blockchain-based federated learning system, the use of P2P network technology has many advantages for FL data dissemination. P2P networks can provide a shared storage system for FL. Therefore, the participant information can be distributed in BFL to realize the distributed management of FL. For example, [PERSON] et al. [53] proposed distributed storage of medical information, which is obtained from multiple medical service providers by relying on blockchain technology in peer-to-peer coverage networks. Further, P2P networks can use peer relationships to protect the privacy of clients in federated learning. For example, [PERSON] et al. [73] used blockchain and crypto-primitives to achieve privacy protection FL between coordinated peer-to-peer clients, and proposed Bisocti: a fully decentralized peer-to-peer (P2P) multi-party ML approach to address centralized infrastructure for central services without compromising customer privacy.  
Chain FL [84] is a decentralized federated learning method that utilizes blockchain to delegate the responsibility of storing models to nodes on the network instead of centralized servers aggregating models so as to achieve federated learning with high security due to its embedded blockchain. Furthermore, ChainFL is compatible with both edge computing and blockchain technology, which can better support the embedding of security-centric offloading algorithms.  
In summary, a combination of blockchain and federated learning can achieve a robust decentralized learning model for federated learning training, and the parameters of the trained learning model can be securely stored on the blockchain and protected against unauthorized access and malicious behavior to ensure the privacy of user data [85]. With the support of audit mechanisms, data sources and provenance can be managed transparently. In addition, the framework makes various constraints on the participants, realizes their honest participation in training, and provides reasonable rewards or punishments to the participants.