esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Experiment Results
During the testing of both the original benchmark and the benchmark after PDEC, all tests guarantee that the reasoning model maintains consistent hyperparameters to ensure a fair comparison. The relevant results are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**PDEC**} & \multicolumn{4}{c}{**FB15K-237**} & \multicolumn{4}{c}{**YAGO3-10**} & \multicolumn{4}{c}{**NELL-995**} \\ \cline{3-11}  & & **MRR** & **His01** & **His03** & **His010** & **MRR** & **His01** & **His03** & **His010** & **MRR** & **His01** & **His03** & **His010** \\ \hline \multirow{2}{*}{CompIEX} & ✗ & 0.238 & 15.2 & 26.5 & 41.0 & 0.106 & 2.96 & 11.4 & 26.6 & 0.222 & 8.9 & 32.1 & 40.6 \\  & ✓ & **0.280** & **18.9** & **31.5** & **46.0** & **0.196** & **9.35** & **22.8** & **41.4** & **0.248** & **9.9** & **37.9** & **45.0** \\ \hline \multirow{2}{*}{TuckER} & ✗ & 0.323 & 0.238 & 0.355 & 0.494 & 0.332 & 26.8 & 34.3 & 47.9 & 0.293 & 21.6 & 32.5 & 41.7 \\  & ✓ & **0.327** & **25.5** & **34.8** & **46.8** & **0.344** & 27.5 & 35.1 & **49.0** & **0.298** & **23.1** & **33.9** & **41.9** \\ \hline \multirow{2}{*}{CompGCN} & ✗ & 0.355 & 26.4 & 39.0 & 53.5 & 0.411 & 37.9 & 48.3 & 57.4 & 0.461 & 38.0 & 49.1 & 58.9 \\  & ✓ & **0.363** & **26.9** & **40.3** & **51.6** & **0.428** & **38.3** & **49.9** & **58.8** & **0.481** & **39.2** & **51.1** & **60.1** \\ \hline \multirow{2}{*}{ReIEs-DSC} & ✗ & 0.368 & 27.4 & 40.5 & 56.0 & 342 & 27.9 & 36.1 & 49.1 & 0.548 & 48.2 & 59.0 & 66.7 \\  & ✓ & **0.372** & **28.6** & **43.1** & **56.9** & **0.349** & **27.8** & **35.7** & **49.6** & **0.562** & **50.0** & **61.2** & **67.5** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
Figure 4: Performance improvement brought by PDEC to baseline on FB15K-237. Figure 5: Performance improvement brought by PDEC to baseline on YAGO3-10
# Design of a Generic Wearable-Robot Monitoring System (GWRMS) for Patients with Lower Limb Paralysis
## 4 Experimental Results  
This section covers overhead and network latency when running the resource monitoring system of the GWRMS on a relatively low-end embedded system, Raspberry PI 4. Table 5 shows the specifications of the robot client system, Raspberry Pi 4, and the server system.  
Figure 13 shows the results for the case where local and remote network communication are performed 1000 times with regard to network delay, when the server requests data from the robot system with a wired connection.  
As shown in Figure 13, after measuring the network communication delay between the server and the robot client 1000 times, general network delays (average of 31.984 and 35.099 ms in local and remote network communications, respectively) were observed regardless of the type of network [15].  
Table 6 shows the results regarding GWRMS daemon overhead in the robot system. Average memory usage of the GWRMS daemon was 3398 KB, and average CPU usage was 0.7% per second when each module was operated according to the TCP/IP message request received from the server.  
Figure 12: Schematic diagram of the cloud server [14].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Raspberry Pi 4**} & \multicolumn{3}{c}{**Razer Blade Base 15 (2019)**} \\ \hline CPU & RAM & Storage & CPU & RAM & Storage \\ \hline BCM2711 & 4 GB & Micro SD & i7-9750H & 32 GB & Samsung 970 Pro 512 GB \\ \hline \hline \end{tabular}
\end{table}
Table 5: System specifications.  
Figure 13: Network delay between robot and server.  
Even in systems such as Overseer that have a small amount of overhead, there is a CPU usage gap of more than 2% between idle and running states. Therefore, the GWRMS daemon, which has a CPU usage of 0.7% while running, has a very low overhead regardless of whether it is in an idle state or not. In addition, Overseer uses 30 MB of memory even when monitoring is not being performed. In contrast, the GWRMS uses an average of 3 MB of memory, which means that it has a lower system overhead even with regard to memory usage.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 5 System Implementation
### Microservices  
We selected NestJS, a Node.js framework with built-in support for microservice architecture, to develop the back-end microservices, including Registry, Admin, Energy Token (ETK), and PoolMarket. A microservice is fundamentally a NestJS application that uses a transport layer different from HTTP. Nest supports several built-in transport layer implementations, called transporters, e.g., TCP, Redis, MQTT, RabbitMQ, and Kafka, responsible for transmitting messages between different microservice instances. For simplicity, we choose the default transporter TCP as the communication protocol. Most transporters support two types of message patterns: request-response and event-based message styles. The request-response method is ideal for exchanging messages between services. In contrast, the event-based message pattern is more suitable when a service just wants to publish events without waiting for a response.  
To balance the complexity of management and the granularity of the service, we implemented several related functionalities in one microservice.  
* Admin Service aggregates operations that require the contract deployer's permission. These include getSuppliers(), getConsumers(), approve(approveRequest), requestToken(tokenRequest), calculateSMP(), and calculatePoolPrice().
* Registry Service provides services related to user registration, including isRegisteredSupplier(account), isRegisteredConsumer(account), getSupplier(account), getConsumer(account), registerSupplier(supplierRegistryRequest), and registerConsumer(consumerRegistryRequest).
* PoolMarket Service provides all query services related to a market, including getspm(), getProjectedPoolPrice(), getMyBids(bidData), getDispatchedOffers(), getSystemMarginalMinutes(), getMarginalOffer(timestamp), getTotalDemand(timestamp), and getMinMaxPrices().
* ETK Service provides services concerning energy token operations, including getETCOwnerAddress(), getBalance(account), and allowance(allowanceRequest).
* Gateway Service acts as a client proxy, which aggregates all microservices functionality and provides a unified REST API portal to the front end and developers.  
These microservices enhance the reliability of the BPET system in two ways. First, the loose coupling structure assigns each service a specific task, allowing them to work relatively independently. If one service fails, its impact on others is limited, thus preventing catastrophic system failure. Second, container orchestration tools like Docker Swarm and Kubernetes provide high availability through a failover mechanism. They deploy multiple services as a cluster for an application and automatically switch to the backup service when the leader fails.
## 5 Calculation Results and Analysis  
### Analytic Solutions of the Submerged Speed  
For the state of direct sailing variable acceleration motion, analytical solutions of the submerged speed at four different rotation speeds were derived from Equation (15), as shown in Figure 9. As an initial condition, the variation of the submerged speed at different rotation speeds was inputted to the numerical simulation of other strongly nonlinear dynamics parameters, such as variable acceleration, via the UDF method. During the acceleration process of the simulation, the time step was set to 0.025 s. Consistent with the analytical solution of the dynamic model, the acceleration time was defined as 15 s.  
The direct sailing submerged speed during the acceleration motion process can be derived via hyperbolic tangent using Equation (15), as shown in Figure 9. During the variable acceleration process, the speed increases rapidly with time at the four different rotation speeds, and then the increase rate of the speed gradually decreases to zero. The maximum submerged speed is related with the thrust determined by the rotation speed of the pump. The motion of the propulsion is a variable acceleration process with a gradually reduction of the acceleration. The smaller the rotation speed, the smaller the thrust provided by the propulsion pump, and the longer it takes for the submersible to accelerate at a constant submerged speed.  
This can be expressed in another form as Equation (20).  
\[\frac{V_{s}}{V_{s\max}}=1-\frac{2}{e^{\frac{t}{\zeta}}+1}, \tag{20}\]  
Based on the first-order step transient response of the automatic control principle, a time constant \(\zeta\) was defined to be the time at which the speed is 0.632 times the maximum submerged speed [32]. The relationship between the submerged speed and time of the submersible is obtained, shown in Figure 10. It can be expressed approximately as Equation (21).  
\[\zeta=\frac{4575}{n_{i}}, \tag{21}\]  
Figure 9: Submerged speed with respect to time.
###### Abstract  
Structural deformations are one of the most significant factors that affects machine tool (MT) positioning accuracy. These induced errors are complex for accurate representation by a model, nevertheless they need to be evaluated and predicted in order to increase the machining performance. This paper presents a novel approach to calibrate a machine tool in real-time, analyzing the thermo-mechanical errors through fiber Bragg grating (FBG) sensors embedded in the MT frame. The proposed configuration consists of an adaptronic structure of passive materials, Carbon Fiber Reinforced Polymers (CFRP), equipped with FBG sensors that are able to measure in real-time the deformed conditions of the frame. By using a proper thermo-mechanical kinematic model, the displacement of the end effector may be predicted and corrected when it is subjected to external undesired factors. By starting from a set of Finite Element (FE) simulations to develop a model able to describe the MT structure stresses, a prototype has been fabricated and tested. The aim of this study was to compare the numerical model with the experimental tests using FBG sensors. The experimental campaign has been performed by varying the structure temperature over time and measuring the tool tip point (TTP) positions. The obtained results showed a substantial matching between the real and the predicted position of the TTP, thereby confirming the effectiveness of the proposed system.  
Keywords:kinematic model; fiber Bragg grating; deformations; error compensation; predicted model; multiple regression analysis; finite element analysis +
Footnote †: journal: Applied Sciences
# Novel Scoring for Energy-Efficient Routing in Multi-Sensored Networks
## 1 Introduction
In some proposed mechanisms [13], it is assumed that the nodes cooperate with each other while conducting a common routing protocol. However, in ad hoc and IoT networks, the smartness of nodes is very common. Therefore, this aspect must be properly addressed in such types of networks while designing an energy-efficient scheme. Many schemes focus on the individual contribution of each node towards energy efficiency by adapting a routing protocol [13], sleep behavior [11], coordination mechanism, data aggregation procedure [7], hop division [9], and cluster divisions [14], and so forth. The nodes may intelligently coordinate with each other considering each node's status. The nature of the deployment of the nodes also has a significant impact on the performance and lifetime of the networks. The energy efficiency techniques should adequately utilize the density or redundancy of the nodes in such types of networks [15]. There should be sufficient space in the mechanism to consider as many parameters as possible for designing an energy-efficient routing in a WSN-based IoT network. The parameters can be the selfishness of the node, neighborhood, connectivity through hop levels, density of the nodes, redundancy of nodes, energies, distances, and surrogate values such as points, score, or credit values for nodes, and so forth. In this work, we propose the node status and score-based route optimization protocol (NSSROP), where each node keeps some additional data to balance the routing load among all the nodes. In an IoT setup, a sensor node may have the shortest available route towards a sink. However, it should wisely choose a route that balances the load and elongates the life of the entire network. Some nodes may be placed at a location where they may get a higher rate of relaying requests compared to other nodes. This situation can highly degrade the network performance by unbalancing the load among the nodes. For this purpose, each node calculates some values for itself that are referred to as scores. Unlike other typical routing protocols, the proposed mechanism addresses various parameters associated with the routing and energy optimization in the network. These parameters are used for the calculation of the scores. During the selection of a route by the source node towards the central control, each forwarding node builds the route by considering these scores of the relay nodes. Modified route request (RREQ) and route reply (RREP) packets are used to exchange the variations in these scores. The remainder of this paper is ordered as follows: In Section 2, related works are described. In Section 3, the preliminary formations are described and the whole mechanism is explained. In Section 4, the simulation results are discussed. Lastly, Section 5 includes the conclusion and future work.
## 3 Results
### Mangrove Extent and Age Estimation  
#### 3.3.1 Mangrove Extent  
Figures 10 and 11 illustrate the development of the mangrove extent from 2009 to 2019. Figure 11 presents the result of the mangrove classification based on L7 (2009 Dry season-2012 Wet season), L8 (August 2013-August 2015), and the combination of S1-S2 (November 2015-November 2019). It can be observed that the mangrove belt expansion has strongly developed in the Porong River mouth. The mangroves on LUSI Island are clearly visible and have continuously expanded after the construction in 2011 (Figure 11). On the northern delta lobe, mangroves appeared in 2016 followed by the southern delta lobe in 2018. The land conversion to fish ponds in the hinterland is apparent as well in the figures.  
Figure 10 provides an illustrative visualisation of the mangrove dynamics. Starting in 2006 the mangrove forests tended to expand seawards. The mangroves in northern and southern delta lobes contributed significantly to this. Considering the time series map, it is likely that the southern delta mangrove will be attached to the LUSI Island within the next few years.  
The time series of the mangrove extent has been extracted for both on the region of interest (ROI) (Figure 12a) as well as for the LUSI Island-delta lobes (Figure 12b). Both areas exhibit a similar positive trend of development. Generally, it varies with season. The mangroves area recedes during the transition from dry to wet season and the mangroves regrow during the wet to dry season. However, the delta lobes' mangroves have a slightly different seasonal pattern. As in Figure 12b, the mangrove's seasonal variation is less pronounced. It is likely because it is more isolated and is affected less by human activities.
## 3 Results
### The Impact of High-Hydrostatic-Pressure Treatment on the Polyphenol Content of Marselan Red Wine
#### 3.1.3 PCA Analysis  
PCA was performed using the R software (version 4.3.1) to differentiate variations in polyphenols among the different HHP treatment groups. The analysis (Figure 3) showed that the first principal component (PC1) and second principal component (PC2) accounted for 42.20% and 20.04% of the total variance, respectively, totaling 62.24%.  
Figure 2: The effects of different HHP treatments on the content of polyphenic substances. Changes in polyphenolic compounds in wine as the processing time varies with constant pressure (100 MPa–600 MPa).  
In the PCA model, the positive axes of PC1 and PC2 primarily reflected the presence of compounds such as p-coumaric acid, quercetin, myricetin, and ethylparaben. The negative axis of PC1 and the positive axis of PC2 show information related to 3-hydroxycinnamic acid, coumaric acid, quercetin, and vanillic acid. The positive axis of PC1 and the negative axis of PC2 were strongly associated with compounds such as epicatechin, caffeic acid, protocetechnic acid, resveratrol, and ferulic acid. Conversely, the negative axes of both PC1 and PC2 reflected less information about polyphenic substances. Specifically, wine samples L-300-20, L-CK, and L-600-10 were primarily located on the positive axis of PC1 and the negative axis of PC2, with the L-300-20 treatment group showing the highest score on PC2, indicating a strong correlation with epicatechin, protocetechnic acid, resveratrol, and ferulic acid. This suggests that treatment under 300 MPa pressure for 20 min can enhance the content of polyphenols such as resveratrol and ferulic acid. On the positive axes of PC1 and PC2, the treatment groups L-600-20, L-300-10, and L-500-10 had similar scores, indicating weak differentiation and a primary association with p-coumaric acid and quercetin. Treatment groups L-100-20, L-500-30, and L-600-30 were positioned far from the other groups, with L-100-20 scoring high on PC1, implying a significant increase in polyphenols such as 3-hydroxycinnamic acid and coumaric acid.
## 1 Introduction
The PISA project created an improved version of the p-y approach, where additional resistance is added from the side and base friction [10; 17; 22]. For instance, ref. [20] conducted nine centrifuge tests on monopiles installed in kaolin clay, which were subjected to monotonic and cyclic loading. The results demonstrated that the API p-y curves of [15] underestimated the stiffness properties of the monopiles, and the displacement and rotation under cyclic loading depended on the number of load cycles and cyclic load amplitude, which is not accounted for in the current API p-y approach. Ref. [22] conducted field tests on large-diameter open-ended driven piles in soft clay of offshore China. Their tests involved driving a monopile of 2.2 m diameter into soft clay, and the piles were subjected to both monotonic and low-frequency cyclic lateral loading. Their results showed that the API p-y curves underestimated both initial stiffness and ultimate capacity, possibly due to reconsolidation from pile driving, which supports the results of [20; 24]. They proposed a hyperbolic function for the p-y curves, which provided an excellent match to the field-test results. Additionally, their p-y curves included a degradation factor, t, as a function of depth and cyclic-load level. Other methods to obtain the lateral ultimate capacity and deflection at the mudline of monopiles include physical testing, FE modelling or [PERSON]'s approach (e.g., [6; 8; 14; 23; 25; 26; 27; 28; 29; 30; 31; 32; 33]). While the lateral behaviour of monopiles has been investigated widely, there is currently no easily available way to deduce the pile capacity. Hence, using a new normalization approach for monopiles for OWTs, the authors are hopeful the new approach can be beneficial and insightful for OWT monopile design. The results obtained from this study can also verify the results from Beam on Nonlinear Winkler Foundation (BNWF) analyses, and benchmark new FEM results helping decision making for future developments of sustainable energy.
# Swarm Intelligence-Based Multi-Robotics: A Comprehensive Review
## 3 Applications of Swarm Intelligence in Multi-Robotics
### Search and Rescue  
SI has found significant applications in multi-robot systems [32], particularly in the field of search and rescue (SAR) operations [51, 52]. SAR missions often involve navigating complex and dynamic environments to locate and assist distressed individuals [53, 54]. The decentralized and self-organizing nature of SI makes it well-suited for these tasks. Various SI algorithms, such as ACO, PSO, and BA, have been effectively applied in this context.  
* Decentralized Coordination [52]: In SAR operations, multiple robots must explore large, often unknown, environments to find victims. Traditional centralized control approaches can be inefficient and prone to single points of failure. SI leverages decentralized coordination, where each robot operates based on local information and simple rules. This enhances robustness and scalability.
* Path Planning and Exploration [54]: SI algorithms excel in path planning and exploration, critical aspects of SAR missions. For instance, (i) ACO can simulate the behavior of ants foraging for food. Robots lay down virtual pheromone trails to mark paths that lead to successful discoveries (e.g., locating a victim). Other robots follow and reinforce these trails, optimizing the search paths over time; (ii) PSO can model the collective movement of birds or fish. Each robot adjusts its trajectory based on its own experience and the successes of its neighbors. This collaborative behavior helps cover more areas efficiently and quickly identify victims.
## 4 Results and Discussion  
For the analysis of the TYM model, we completed a comparison between the TYM model and two reference models, Okumura and COST 231 HATA, according to the measured data. The expected output results and the measured data in watt was converted into dBm values; the path loss value is given as it is in Equation 8:  
\[PL=\text{Transmitted power + Transmitting gain + Receiving gain - Received power} \tag{8}\]  
Figure 10 shows the output result from the three reference models: Okumura, COST 231 HATA, and TYM, compared to the measured data. The graph clearly shows that the TYM model has a higher accuracy than the other models, due to its flexibility between high and low frequencies. Meanwhile, the Okumara model only works only with low frequencies. The Okumara model is better than the COST-Hata model, however, because the COST-Hata works exclusively with limited frequencies. Both the Okumara and COST-Hata only focus on outside environments.  
Figure 10: Comparison between the TYM model and the other models with a frequency of 38 GHz.  
Figure 9: Standardized coefficients (96.7).  
Figure 11 shows the output result from the three reference models including the real measurement. The Okumura, COST 231 HATA, and TYM models are compared to the measured data. Figures 10 and 11 clearly shows that the TYM model has a higher accuracy than the other models, due to its flexibility between high and low frequencies. Meanwhile, the Okumara model only works only with low frequencies. The Okumara model is better than the COST-Hata model, however, because the COST-Hata works exclusively with limited frequencies. Both the Okumara and COST-Hata models only focus on outside environments, while the TYM model works with outdoor environmental factors such as humidity and temperature in addition to indoor environmental factors such as window size. Additionally, the TYM model worked with 5G, while the Okumara and COST-Hata models did not.