esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Specific Energy
It was fundamentally impossible to explain the complicated and complex behavior of rock during the cutting and chipping process with a single equation. Moreover, in the numerical simulation, the rock model consisted of particles of a certain size, and the size affected both the energy required to cut the certain volume of the rock and the failure pattern of rock. The numerical simulation of rock chipping involved breaking all of the particles between adjacent pick cutters, however, in a real cutting process, only the cutting energy is required to propagate cracks in the local region to cut the rock; rock chips are generated by the connection of the cracks made by adjacent cutters. Thus, the cutting energy required in the numerical simulations is fundamentally different from that required for the chipping process in actual rock cutting. Figure 17: The comparison of the specific energy between the LCM and the numerical results. Moreover, the differences in test settings between the LCM test and numerical simulation might affect the results. In the LCM test, the sides of rock specimen are usually restrained mechanically to prevent the splitting failure of the specimen, while the rock model in numerical simulation is perfectly fixed by means of boundary conditions without the confining stress. The confining pressure might affect the fragmentation patterns during rock cutting, as well as corresponding cutter forces and cutting efficiency [39; 40; 41]. The stiffness of the LCM may affect the testing results. The previous study [9] reported that movement and vibration of the cutting tool is caused by the finite stiffness of the LCM frame, and the lack of stiffness caused excessive brittle failure of the specimen due to a burst of strain energy accumulated in the frame and subsequent failure by vibration of the cutting tool. Numerical simulation cannot consider the effects because it did not allow displacement of the pick rock cutting. The position and movement of the pick are perfectly controlled by means of boundary conditions.
# A Discrete Particle Swarm Optimization Algorithm for Dynamic Scheduling of Transmission Tasks
## 4 Experiments
### Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Periods** & **22** & **23** & **24** & **25** & **26** & **27** & **28** & **29** & **30** \\ \hline GR & 0.885542 & 0.885767 & 0.885219 & 0.87728 & 0.880179 & 0.87955 & 0.852032 & 0.883815 & 0.863596 \\ IGA & 0.910287 & 0.912008 & 0.910358 & 0.904637 & 0.912072 & 0.902095 & 0.884231 & 0.909096 & 0.888886 \\ DPSO & 0.911239 & 0.913564 & 0.911396 & 0.906042 & 0.912925 & 0.90334 & 0.885226 & 0.910536 & 0.889882 \\ \hline
**Periods** & **31** & **32** & **33** & **34** & **35** & **36** & **37** & **38** & **39** \\ \hline GR & 0.890629 & 0.89359 & 0.857689 & 0.866087 & 0.873645 & 0.883004 & 0.867741 & 0.88413 & 0.867158 \\ IGA & 0.915545 & 0.91956 & 0.885552 & 0.889054 & 0.899454 & 0.910442 & 0.890941 & 0.914558 & 0.897373 \\ DPSO & 0.916702 & 0.920654 & 0.886297 & 0.89005 & 0.900186 & 0.911575 & 0.891782 & 0.915296 & 0.89685 \\ \hline
**Periods** & **40** & **41** & **42** & **43** & **44** & **45** & **46** & **47** & **48** \\ \hline GR & 0.862168 & 0.867621 & 0.86952 & 0.848084 & 0.876947 & 0.863869 & 0.875981 & 0.905513 & 0.858482 \\ IGA & 0.884312 & 0.896044 & 0.895931 & 0.872597 & 0.907236 & 0.887478 & 0.899854 & 0.930317 & 0.889568 \\ DPSO & 0.884925 & 0.896671 & 0.897014 & 0.873367 & 0.908403 & 0.888566 & 0.900728 & 0.931161 & 0.890556 \\ \hline \end{tabular}
\end{table} 
Table 11: _Cont._  
Figure 4: Multi-algorithm evaluation value comparison chart. According to Figures 4-6, the following statistics can be obtained:  
1. In the simulation experiment of multiple time periods, the evaluation values of the transmission effect for all time periods are averaged. The DPSO improved the evaluation value by 3.012295% compared to the GR, and the DPSO improved the evaluation value by 0.1111146% compared to the IGA. 2. In the simulation experiment with a maximum period of 48, the results of the DPSO were better than that of the IGA. In almost 60% of the 100 experiments, the IGA achieved the same results as the DPSO while other results were lower than that of the DPSO. The DPSO improved the evaluation value by 0.11115% compared to the IGA. 3. In the simulation experiment with a maximum period of 48, in 100 experiments the average execution time of the DPSO was 3.195 s, and the average execution time of the IGA was 10.388 s. The DPSO improved the execution efficiency by 69.246%. According to the simulation experiment, it can be concluded that the intelligent algorithm and mathematical model proposed can solve the DSTT problem of radio- and television-transmission stations. The algorithm is stable in many simulation experiments, and the DPSO has the highest accuracy and the shortest execution time. Figure 5: Two-algorithm evaluation-value comparison chart of 100 tests.
# Cooperation-Assisted Spectrum Handover Mechanism in Vehicular Ad Hoc Networks
## 5 Conclusions  
The spectrum handover is an important issue in the vehicular network. Therefore, this paper proposes a cooperation-assisted spectrum handover (CASH) scheme, which includes V2V cooperation, spectrum pre-scanning, fast partial scanning, and a dynamic scanning period. Then we use the network simulator to evaluate our method. According to the simulation results, the CASH mechanism can indeed lessen the scanning time, reduce the total number of handovers, and also greatly improve the overall spectrum utilization.  
In the future, we will consider the following two aspects. First, we will consider the carrier aggregation (CA) technology, so that the idle available spectrum can provide greater bandwidth to achieve higher transmission efficiency. The second is that we hope to introduce a machine learning or deep learning scheme to make the spectrum selection mechanism more intelligent, while considering the problem of spectrum interference.  
This research was funded by the Ministry of Science and Technology, R.O.C., under grants MOST 107-2221-E-163-001-MY3.  
Data are available upon request.  
The authors declare no conflict of interest.
## 4 Distributed Hotspot Network Architecture
### Distributed and Decentralized Hotspot Network Layer
#### 4.2.4 Dew Server as Hotspots  
Ordinary hotspot gateways can function in two ways: (i) as a light-weight hotspot miner, and (ii) as a data-only hotspot. It may not be advisable to configure an ordinary hotspot gateway as a full-fledged hotspot miner, due to the following problem: It is well known that as a blockchain grows after its inception, the full nodes experience a huge burden of processing and storing distributed ledgers on the local machine. Gradually, such full nodes tend to be out of sync with the main blockchain. Due to the very high computational requirements, one can think about using moderately resilient miner nodes in the presented architecture. Instead of using ordinary hotspot gateways as full miners, we can consider them as light-weight hotspot miners or data-only hotspots. It is possible to turn a hotspot device into a lightweight miner with a hotspot provisioning tool or configure it as a data-only hotspot (that is without mining functionality). In our architecture, each person can deploy their dew server to act as either a light-weight hotspot miner or a data-only hotspot broker. Dew servers can solve the synchronization problem that usually occurs with full miners. Thus, a dew server can solve this problem and opens a new way to mitigate the synchronization problems that are normally encountered with traditional network computers.
# Decentralized Public Transport Management System Based on Blockchain Technology +
## 3 System Design and Implementation
### Token-Based Vehicle Monitoring Model
Let the reduction indicators be denoted as R (Reduce), ranging from \(R_{1}\) to \(R_{n}\), with their values specified separately for each type of transport due to variations in maintenance cycles and requisite indicators between a bus and, for instance, a transport boat. In this context, the token increase indicators, I (Increase), take on values from \(I_{1}\) to \(I_{m}\), with their values also contingent on the specific type of transportation. Let:  
\(Q(T)_{t}\)--the value of the quality token;  
\(R_{i}\)--the reduction metric;  
\(I_{j}\)--the increase metric;  
\(a_{t_{1}}\)--the reduction ratio;  
\(b_{t_{2}}\)--the increase ratio;  
The formula for calculating the current state of a vehicle's quality, used to determine the required maintenance, is as follows:  
\[Q(T)_{t}=\left(\sum_{i=1,t_{1}=0}^{i=m_{1},t_{1}=m_{1}}R_{i}a_{t_{1}}\right)+ \left(\sum_{j=1,t_{2}=0}^{j=n_{2},t_{2}=m_{2}}I_{j}b_{t_{2}}\right) \tag{1}\]  
Based on the results of several interviews with representatives of transport companies, the basic quantitative values for buses were obtained. They may vary depending on the vehicle model and the operation of a particular route. Still, this accuracy is sufficient for primary modeling. In the basic scenario provided for by maintenance regulations, checkups and repairs should be performed every 15,000 km. This is the most common interval for buses. From interviews with representatives of the transport company, for 15,000 km, the average bus undergoes the following:  
* It will stay operational for 40 days;
* It will be capable of transporting 20,000 people;
* It will have 80 driver shifts;
* It will have an operational time of 600 h;
* It will make, at minimum, 1600 stops. In this scenario, the vehicle consumes 300,000 mini tokens from one maintenance to the next maintenance. Therefore, it must replenish the macro token during maintenance. At the same time, accidents, precipitation, temperature, and changes in proportions to increase any of the parameters (e.g., mileage), unlike the normal operational scenario, bring the vehicle closer to routine maintenance. Then, it is performed earlier, considering the actual operation of the bus. A private blockchain with information covering all transactions made per vehicle is one approach to implementing such a system. This works because, as a rule, transport is managed by one company in one geographical location. The automated recording of all transactions in the blockchain allows one to use the most transparent and complete data to manage repairs and maintain analytics for future periods based on already occurring transactions, as shown in Figure 1.
Similarly, [PERSON] and [PERSON] [4] proposed a digraph that illustrates the interrelationship of 24 SC resilience capabilities based on literature-based interpretive structural modeling. While abovementioned studies propose conceptual clarity in SC resilience definition, essential elements, and managerial practices but are limited in guiding practitioners to strategic decision with respect to supplier management in a complex network context. Moreover, given the broad scope and extensive coverage of these SC capabilities, practitioners are in need of refined lists that are more relevant to their specific SC design and management decisions. Second, existing multicriteria decision making (MCDM) studies in risk management focus on providing optimal supplier selection or resource allocation issues rather than assisting practitioners understand the state of the art in identifying vulnerable suppliers in a complex supplier network. Whilst there is an ongoing research in establishing comprehensive yet exhaustive criteria list from economic, environmental, and social perspectives [29; 30; 31; 32], only few studies mention and incorporate the nature of supply network design and its characteristics. Despite the gained attention of network or geographical complexity of modern supply chain design, there exists a limited guideline for the incorporation of network criteria such as geographical location or structural design [6; 33; 34]. Aligned with [PERSON] et al. 's [28] recent acknowledgement that there lacks an investigation of MCDM methods in the context of SC resilience, this study illustrates a useful tool for evaluating SC vulnerability and means to achieve resilient performance simultaneously. This study aims to contribute to the risk management field by (i) identifying main SC attributes that a supply manager should account for prior to designing and planning a resilient supply network, and (ii) proposing a MCDM approach to delineate vulnerable supplier and propose prioritization approach to supply network management. To this end, a bicriterion network resilience (BNR) model is developed, based on a ranking approach that considers superiority and inferiority intensities of supplier based on both SC capabilities in a centralized network structure. The investigated network structure depicts a real-world case of three tiers of 17 suppliers and distributors of an Air Conditioning and Energy product that are found in a global market. The remainder of this paper is organized as follows: Section 2 presents this study's literature background. This study develops our bicriterion resilience model in Section 3, and Section 4 proposes the prioritization approach and its application. Section 5 discusses the results of the case evaluation of global manufacturing company. Finally, Section 5 concludes our findings and addresses theoretical and practical implications, as well as its limitations and opportunities for future research.
## 2 Related Works
### Evolutionary Computation Approaches  
Some approaches have adopted various evolutionary computation approaches to perform sparse synthesis tasks. For example, the authors in [4] used three evolutionary optimization algorithms, quantum particle swarm optimization (QPSO), teaching-learning-based optimization (TLBO), and symbiotic organism search (SOS), to thin the CCAAs. Moreover, [PERSON] et al. [16] proposed an improved version of QPSO to minimize the SLLs of CCAAs, in which the array elements have been considered to be uniformly excited with unit excitation amplitude. In [7], the authors presented an accurate approach based on moth flame optimization (MFO) to solve the CCAA synthesis problem for achieving lower SLL. In addition, [PERSON] et al. [17] proposed an optimization method based on a comprehensive learning particle swarm optimizer (CLPSO) to reduce the SLL of CCAAs, in which the excitation current weights are considered. In [18], a method based on a combination of ant lion optimizer and sequential quadratic programming was proposed for concentric circular antenna array (CCAA) synthesis, in which excitation amplitudes of array elements were optimized for CCAAs with low maximum sidelobe level (MSL), narrow first null beamwidth (FNBW), and low dynamic range ratio (DRR). [PERSON] et al. [19] presented an optimized CCAA of antennas based on biogeography-based optimization (BBO). In [20], the authors used a political optimizer (PO), which is a novel evolutionary algorithm, to reduce the SLL of the CCAA. However, most of these methods only consider reducing the sidelobes or decreasing the number of the activated antennas as much as possible and do not consider turning off the specific number of array elements of CCAAs while decreasing the sidelobes.  
Different from these works, we consider turning off a specific number of antennas while reducing the sidelobe of CCAAs via a novel evolutionary algorithm in this work. The details of our model and problem are presented as follows.
## 2 Materials and Methods  
### Sample and Variables  
The study was reviewed and approved by the Ethical Committee of the University of Murcia. The sample contained 668 serves and 600 returns of serves from 14 professional matches (7 male and 7 female) of the 2019 Finals World Padel Tour. These tournaments gathered the best pairs in the world, ensuring the highest competitive level in all the matches. A total of 32 players, 16 men (mean (SD) age: 31.18 (7.27) years; height: 181.3 (4.1) cm) and 16 women (mean (SD) age: 28.66 (6.7) years; height: 168.4 (3.7) cm) performed the matches. Variables pertaining to serve and return statistics and point outcome were included in the analysis, following the methodology adopted in other similar studies [25]:  
* Serve shots were analyzed regarding the serve number (1 st and 2 nd serve), court side (right and left), serve direction (side wall, middle and T) and effectiveness indicator (winner, error and continuity) (Figure 1).  
* Return statistics were analyzed regarding the court side (right and left), stroke direction (down the line, middle and cross court), stroke height (straight and lob), stroke type (forehand and backhand) and effectiveness indicator (winner, error and continuity) (Figure 1).
* Point outcome: strokes were classified according to the winning or losing pair of the point (serving players win and returning players win).
* Total shots: number of total shots in the points were counted.
# Authenticated Multicast in Tiny Networks via an Extremely Low-Bandwidth Medium
## 1 Introduction
### Tradeeff between Channel Security and Payload Capacity  
It has become standard practice to protect communication channels with cryptographic mechanisms; encryption guarantees confidentiality of communication, while message authentication codes and digital signatures protect against message manipulation as well as unintentional changes. In many applications, the confidentiality and integrity of communication are fundamental requirements, necessitating the implementation of the above security measures.  
This is the case for the problematic networks mentioned above that suffer from limited communication capability. For example, consider the case of underwater sensors monitoring the environment; it is likely that parties illegally dumping waste into the ocean would be interested in manipulating communications between the sensor nodes in order to falsify reports and hide the source of pollution. Likewise, it is critical to protect underwater networks used for defense and anti-terrorist purposes, such as underwater telecommunication cables, power lines connecting off-shore power plants with the power grid, etc.  
In high-capacity communication networks, the major problem is to match the speed of cryptographic computation with the high speed of the communication channel. Decades of research and initiatives, such as NIST competitions for fundamental cryptographic primitives, have resulted in tailored solutions for most applications. (Nonetheless, the rules of the game can change in the post-quantum world, where the primitives of today will be replaced by post-quantum algorithms with much higher space and communication costs). In this case, the communication volume overhead resulting from security mechanisms is negligible; what really counts is processing speed. For reference, Table 2 presents the length of cryptographic data for a few message authentication methods. Note that if the communication speed is 300 kbps and three nodes transmit within 1 s, then adding ECDSA signatures to their messages means an overhead of only about 5 ms! However, if three sensors in shallow water wish to use the 1 s acoustic communication slot for their three messages, then the three ECDSA signatures would consume nearly the entire slot, leaving almost no room for the payload data.  
Asymmetric mechanisms have proven to be extremely useful for protecting information channels thanks to their scalability and the separation of public and private keys.  
However, there is a lower bound on the length of key material, this bound faces a rising trend due to the progress of cryptanalytic techniques. This makes asymmetric cryptography infeasible for securing networks with extremely low bandwidth; thus, only symmetric cryptography can be used.
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 4 Experimental Evaluation
### Comparison Methods  
Regarding the problem of App prediction about the LiveLab dataset, there are not many related experiments. [PERSON] et al. [35] mainly use data to study user application usage patterns, such as frequency of application usage, without focusing on App prediction. The main research point of the article [4] is the order about application startup. [PERSON] et al. [36] use a tree to describe the relationship between App and time and space, while AHNEAP uses an attributed multiplex heterogeneous network. Previous work generated nine candidate applications when predicting, which is not necessary for the purpose of loading the application in advance [37]. Therefore, we choose some basic baselines based on some methods [6; 10; 12; 17]. [PERSON] et al. [12] and [PERSON] et al. [17] adopt MFU and MRU, and [PERSON] et al. [6]adopt the Bayes network at the same time. In order to illustrate the advantages of AHNEAP, we will compare with the following commonly used baselines:  
* Most Frequently Used (MFU): The MFU method counts the users' history of mobile App usage and selects the most frequently used ones.
* Most Recently Used (MRU): The MRU method counts the users' history of mobile App usage and selects the most recently used ones.
* Bayes Network Model [6]: In the traditional Bayes model, the input feature is a tuple \((f_{l},f_{h},f_{d},f_{p},f_{u})\) or \((f_{l},f_{i},f_{d},f_{p},f_{u})f_{i}\): the location when using Apps, \(f_{h}\): the hour of day when using Apps, \(f_{i}\): the interval of day when using Apps, \(f_{d}\): the day of day when using Apps, \(f_{p}\): the latest used App, \(f_{u}\): a flag whether the battery is being charged when using Apps as user profile extracted from the historical records. This model is improved based on the traditional Bayes model with LivaLab dataset.
* Graph Embedding (GE): We'll predict the next application through only one of the time, location, and the previous application, purely using the GATNE-I model. In this method, we calculate the similarity of three types of relationships according to Adjusted Cosine Similarity separately and calculate the accuracy. Then, we choose the maximum as the final rate of each application.
# Integration of Multi-Branch GCNs Enhancing Aspect Sentiment Triplet Extraction
## 2 Related Works
### Application of GCN in ASTE  
In (ABSA) Aspect-Based Sentiment Analysis tasks, the syntax dependency tree plays an important role in catching the key feature from the review sentence [41; 42; 43]. Furthermore, it is well known that the regular method GCN is popular in handling dependency graphs in previous works. Regarding ASTE, GCNs are also used widely to fuse different sources of information. As mentioned above, [PERSON] et al. [21] employed a GCN to enhance the interaction between syntactic and semantic features. To fully exploit the potential information implied in syntactic and semantic features, the work [18] also integrated semantic and syntactic representations through a GCN module, which preserved the sequential information and enhanced the linguistic representation, simultaneously. Moreover, to overcome the problem of many aspect terms to one opinion term or one aspect term to many opinion terms, [PERSON] et al. [44] combined a GCN with a base encoder to build the span representations, which included both aspect terms and opinion terms. In [45], a GCN was also employed to model the graph based on the concatenated representations of aspects terms and opinion terms. Thus, it is quite clear that GCNs are extremely important in enhancing the feature representations in the ASTE task. Motivated by their impressive ability, we also process the work of feature fusion under the guidance of the GCN in this paper.  
Conclusively, as aforementioned, ASTE is a difficult and challenging subtask in ABSA, which attracts a lot of researchers' attentions. In this paper, inspired by the existing works which apply BERT and GCNs to NLP tasks, we propose a novel model MBGCN to process semantic feature, syntactic dependency type, part-of-speech combination, tree-based distance, and relative position distance, simultaneously.