esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Sandstone Cement-Stabilized Macadam
## 2 Materials and Testing Methods
### Materials
#### 2.1.2 Aggregate  
Four different coarse aggregates, including three types of sandstone marked A, B, C and one type of limestone marked D, were selected for the experiment. These sandstones were randomly obtained from three different production areas in northern Shaanxi, China. The main physical characteristics of the coarse aggregates are recapitulated in Table 2. The bulk density, porosity, water absorption and compressive strength values before and after the ruggedness testing were measured according to JTG E41-2005 [21]. The crushing value was determined following JTG E42-2005 [22].  
As shown in Table 2, the porosity, water absorption and crushing values of the sandstones (A, B, and C) are significantly higher than those of the limestone. Among them, the porosity and water absorption values of the sandstones are approximately 10 times those of the limestone, while the crushing values are approximately 2 times the limestone value. In contrast, the sandstones have lower compressive strength values compared with that of limestone. Moreover, after the robustness tests, the compressive strengths of the sandstones were significantly reduced to approximately half of the values before the tests, while the limestone showed little change.
# A Survey on Malleability Solutions for High-Performance Distributed Computing
## 5 Charm++ Malleability
### Charm++ Integration into an RMS  
CHARM++ leverages Torque/Maui as an RMS to manage and reallocate resources. This integration implied modification in both tools in order to enable malleability.  
The Torque/Maui RMS is composed of a set of daemons that are mapped in the different nodes of the cluster. The communication among them allows the correct scheduling of parallel jobs [47]. The main features of scheduling are described below:  
* The front-end includes the pbs_server daemon and Maui scheduler, whereas the compute nodes execute pbs_mom daemon.
* Clients submit jobs using command qsub in which the required resources are specified.
* Jobs are incorporated into the corresponding queue of the server.
* Maui chooses the \"most suitable\" job in the queue to be executed, according to the established scheduling policies.
* A node assigned to the parallel is referred to as _mother superior_, which receives the list of reserved nodes.
* All nodes perform a join operation before parallel job execution starts.  
Figure 6 shows the order in which these steps are taken.  
The integration of CHARM++ in the Torque/Maui RMS to enable malleability required new features to be incorporated [47,48]:  
* qsub is extended with an extra option (-L) to specify the upper bound rescaling.
* The communication between runtime and pbs_mom daemons had to be defined: -A Converse Client-Server (CCS) interface is designed in CHARM++ to control malleability actions via TCP/IP communication. -A CCS client is integrated into each pbs_mom daemon, while a CCS server will run for each job in the pbs_server. -The _mother superior_ assigns a unique port to communicate the CCS server and a specific parallel job.
* In the scheduler, Maui instructions allow to expand/shrink the resources on the server, according to this algorithm: -The scheduler notifies the pbs_server that a resource reconfiguration is required.  
Figure 6: Charm++ and RMS—workflow of the Torque/Maui batch system [47]. Based on: [[http://charm.cs.illinois.edu/newPapers/15-12/paper.pdf](http://charm.cs.illinois.edu/newPapers/15-12/paper.pdf)]([http://charm.cs.illinois.edu/newPapers/15-12/paper.pdf](http://charm.cs.illinois.edu/newPapers/15-12/paper.pdf)) (accessed on 7 May 2022). Adapted with permission from [47]. Copyright 2014, [PERSON].  
The pbs_server incorporates the modifications and forwards the information to _mother-superior_ node.
* The pbs_mom daemon of the _mother-superior_ reallocates the resource according to the received information.
* When the reconfiguration is completed, a _CCSExpand/CCSShrink_ message is sent to the CCS server. Notice that _CCSExpand_ can be immediately sent, but _CCShrink_ has to be sent only when the data from removed nodes are fully retrieved.  
Figures 7 and 8 depict how a job expansion and shrinkage are performed, respectively.  
The authors complete their study with a performance evaluation of malleable and evolving jobs analysis in a workload using standard benchmark and synthetic applications.
## 4 Computational Experiments
### Comparison between HGA and Multi-Objective Optimization Model
From the results in Table 2, we note that the Pareto frontier from the HGA dominates the MOOP for all 15 sample cases. Despite the superiority of the HGA, its computation time is significantly longer than the MOOP, averaging approximately 50 min for 1000 generations, irrespective of the problem size. By contrast, the MOOP exhibits faster computation times, which increase with larger problem sizes.
# Detection Method of Partial Discharge on Transformer and Gas-Insulated Switchgear: A Review
## 2 Method of PD Detection Based on Different Types of Signals Emitted during PD Activities
### Acoustic Method
Computer: The final stage involves recording the processed AE signals on a computer for storage and further analysis. The computer may have specialized software to handle data logging, signal visualization, and in-depth analysis of the recorded AE signals. The purpose of this recording system is to continuously monitor the power transformer for any signs of PD events, which can be indicative of insulation degradation or other potential failures or issues. By detecting and analyzing AE signals, maintenance personnel can take proactive measures to address developing problems before they escalate into major faults, thereby ensuring the reliability and longevity of the power transformer.
## 1 Introduction
Multiple previous studies have tried to overcome these challenges by applying various methods and approaches. [PERSON] et al. [20] applied a high-resolution distributed hydrological model (HYDROTEL), which comprises several novel methods and data sets and a new optimization algorithm to simulate the multiyear mean annual hydrograph and 2011 flood of the Richelieu river basin (U.S.-Canada transboundary basin). [PERSON] et al. [21] used satellite-observed rainfall and numerical weather prediction (NWP) for hydrological forecasting in the transboundary sparsely gauged Chenab River basin in Pakistan and India to reproduce the exceptionally high flood in 2014. [PERSON] et al. [22] presented the most comprehensive global georeferenced database of dams (GOODD), containing more than 38,000 dams as well as their associated catchments, which may be useful in machine learning approaches to dam identification. [PERSON] et al. [9] presented methods of monitoring river basin development and water variability for the transboundary river in North and South Korea using satellite imaginary. The advantages and efficiency of using of satellite data in transboundary regions were also affirmed by [PERSON] and [PERSON] [23] in the study of a modeling framework to predict the current and future reservoir operating rules in the lower Nile basin using satellite observations and a macroscale hydrologic model (VIC). Such studies propose useful methods to characterize the transboundary basins only in the case that the reference data, such as in-situ or satellite data, were fully provided. In the case of inaccessible or cloud-cover regions, where the methods used become insufficient and unreliable, a flexible and customizable hydrological model could be an optimal solution to overcome the challenges, especially if integrated with geographic information system (GIS) techniques and applied along with a number of science-based assumptions based on real conditions of the regions. In general, evaluating the impact of upstream dams in transboundary regions is one of the required steps to develop any flood mitigation, adaptation, and prevention plan in downstream basins, but this is still a large challenge due to various specific reasons. Thus, the objective of this study is to evaluate the impacts of upstream hydraulic structures on downstream discharge in the binational transboundaryregions in two cases: (1) Normal conditions of dam operation and (2) Extreme conditions based on dam failure scenarios, with a case study of the Imjin river basin, South Korea. Using estimations of dam parameters from GIS techniques, the Sejong University Rainfall Runoff (SURR) open-source model integrating a dam operation module developed in this study shows an ability to simulate the sudden change in downstream discharge and water level due to the impact of the upstream dams. This provides useful information that can be applied in disaster prevention and river management in binational transboundary regions.
# A Dynamic Multi-Layer Steganography Approach Based on Arabic Letters' Diacritics and Image Layers
## 3 The Proposed Approach
### The Metrics of Evaluating Steganography
#### 4.2.3 Robustness  
In the context of steganography, the term \"robustness\" refers to the text's ability to maintain the confidentiality of hidden information even when subjected to modifications such as compression [52]. While this is a crucial aspect of traditional text-based steganography methods, it is of lesser significance in the context of image steganography. In situations where transmission or reception distortions occur--whether intentionally or not--the text may become distorted rather than detected, rendering robustness moot. However, the proposed concept addresses this issue by Unicode encoding for the text and its diacritics. Any changes in the text's diacritics occur at the Unicode level, which ensures that any compression or conversion processes applied to the text cover and the hidden text will not affect its robustness, resulting in a high level of robustness that is immune to distortion.  
In the context of image evaluation, it is widely acknowledged that employing techniques such as image modification or compression can exert a substantial influence on the evaluation criterion, particularly when the LSB method is utilized. Consequently, to mitigate the potential negative effects of these techniques, it is recommended to utilize frequency domain-based methods as an alternative to spatial methods. Such methods offer the potential to enhance the accuracy and reliability of the evaluation process, thereby enabling more robust and effective image analysis.
# The Use of Machine Learning Methods in Road Safety Research in Poland
## 2 Materials and Methods
### K-Nearest Neighbors (KNN) Algorithm  
K-Nearest Neighbors (KNN) is a supervised machine learning algorithm widely applied in regression and classification problems across datasets of varying sizes, ranges, and contexts [57]. KNN is one of the simplest ML algorithms, characterized by its adaptive and easy-to-understand structure. This is a method that relies heavily on similarity analysis between observations in the feature space. The KNN algorithm is able to classify datasets using a training model similar to the test query, considering the \(k\)-nearest training data points (neighbors) closest to the query. The final classification is determined using a plurality vote of its neighbors [58]. The parameter \(k\) (the number of neighbors) significantly influences the model's performance. While the Euclidean distance is most commonly used for the calculations, other metrics, such as Manhattan distance, can also be applied [59].
# A Learning Game-Based Approach to Task-Dependent Edge Resource Allocation
## 3 System Model
### Edge Computation  
When a dependent task is offloaded to a proxy node, in order to fully utilize the resources purchased by the user, the proxy node uploads the sub-tasks of the dependent task to other ESs to achieve a collaborative execution of the task. When taking, as an example, a dependent task in ES \(j\) with the allocated sub-task \(\mathbf{v}_{n}\), as well as when a target server is assigned to ES \(m\), the execution of this task can be divided into three stages: the transmission phase, the execution phase, and the feedback phase of the result. When recording the completion time of sending as \(FT_{up}^{n}\), the transmission time is \(T_{up}^{n}\); then, we have  
\[FT_{up}^{n}=max\{AT_{up^{\prime}}^{n},\underset{i\in pre(\mathbf{v}_{n})\wedge a_{ i}=m}{\max}\{FT_{down}^{i}\}\}+T_{up}^{n}, \tag{3}\]  
\[T_{up}^{n}=\frac{d_{n}}{r_{j}}, \tag{4}\]  
where \(AT_{up}^{n}=max\{AT_{up}^{n-1},FT_{up}^{n-1}\}\) is the earliest available time of the uplink, \(i\in pre(\mathbf{v}_{n})\wedge a_{i}\
eq m\) means that \(\mathbf{v}_{i}\) is a direct predecessor of \(\mathbf{v}_{n}\) and \(\mathbf{v}_{n}\) when they are not executing on ES \(m\), \(FT_{down}^{n}\) is the result of the backhaul time of the parent task of \(\mathbf{v}_{n}\), and \(r_{j}\) is the size of the communication resources allocated for \(\mathbf{v}_{n}\).  
In the execution phase, we let \(FT_{m}^{n}\), \(T_{m}^{n}\), and \(f_{m}^{n}\) represent the completion time of \(\mathbf{v}_{n}\) on ES \(m\), the computation time, the computational resources allocated by ES \(m\) for \(\mathbf{v}_{n}\), and the arithmetic power required by ES \(m\) to process the data per unit number of bits, respectively. Then, the computation completion time of the task is written as follows:  
\[FT_{m}^{n}=max\{AT_{m^{\prime}}^{n},\underset{i\in pre(\mathbf{v}_{n})\wedge a_{ i}=m}{\max}\{FT_{m}^{i}\}\}+T_{m}^{n}, \tag{5}\]  
\[T_{m}^{n}=\frac{c_{n,m}}{f_{m}^{n}}, \tag{6}\]where \(AT_{m}^{n}=max\{AT_{m}^{n-1},FT_{m}^{n-1}\}\) is the earliest available time of the ES \(m\) processor, \(i\in pre(\mathbf{v_{n}})\wedge a_{i}=m\) denotes that \(\mathbf{v_{i}}\) is a direct precursor of \(\mathbf{v_{n}}\) and executes on ES, \(m\), \(c_{n,m}=d_{n}\eta_{m}\) is the number of CPU cycles required by ES \(m\) to process \(\mathbf{v_{n}}\), and \(\eta_{m}\) is the arithmetic power required by ES \(m\) to process one unit of bit data.  
We let \(T_{down}^{n}\) represent the return time of the result after \(\mathbf{v_{n}}\) computes the result on ES \(m\). Then, the result return completion time \(FT_{down}^{n}\) can be written as follows:  
\[FT_{down}^{n}=max\{AT_{down}^{n},FT_{m}^{n}\}+T_{down}^{n}, \tag{7}\]  
\[T_{down}^{n}=\frac{r_{n}}{r_{m}^{n}}, \tag{8}\]  
where \(AT_{down}^{n}=max\{AT_{down}^{n},FT_{down}^{n}\}\) is the earliest available time for the downlink and \(r_{m}^{n}\) is the communication resource allocated to \(\mathbf{v_{n}}\) by ES m for the return result.
###### Abstract  
The glass transition temperature (\(T_{\mathrm{g}}\)) is undoubtedly one of the most important characteristics of polymers, and investigating its dependence on their structure and composition is crucial from both fundamental and application points of view. This study deals with the unexpected relationship between \(T_{\mathrm{g}}\) and the average molecular weight between crosslinking points (\(M_{\mathrm{c}}\)) in nanophase-separated polystyrene-\(l\)-poly(dimethylsiloxane) (PSt-\(l\)-PDMS) and polystyrene-\(l\)-poly(dimethylsiloxane)/divinylbenzene (PSt-\(l\)-PDMS/DVB) polymer conetworks. In order to reveal the correlation between the \(T_{\mathrm{g}}\) and \(M_{\mathrm{c}}\), a library of PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB conetworks was synthesized, and their compositions and \(T_{\mathrm{g}}\)s were determined. Instead of the expected increase of \(T_{\mathrm{g}}\) with decreasing \(M_{\mathrm{c}}\), a reverse correlation was found. Namely, the \(T_{\mathrm{g}}\) decreases with decreasing \(M_{\mathrm{c}}\) in these conetworks. Correlation analyses showed that the \(T_{\mathrm{g}}\) linearly depends on \(1/M_{\mathrm{c}}\), similar to the Fox-Flory relationship for homopolymers with their \(M_{\mathrm{n}}\), that is, \(T_{\mathrm{g}}=T_{\mathrm{g},\alpha}~{}-K/M_{\mathrm{c}}\) for the investigated conetworks, independent of the absence or presence of relatively low amounts of DVB as an additional small molecular weight crosslinker. This means that the PDMS macrocrosslinker acts like scissors by interrupting the mobility of the crosslinked PSt chains in the conetworks, and the \(T_{\mathrm{g}}\) of the PSt segments will be close to that of PSt homopolymers with the same \(M_{\mathrm{n}}\) as \(M_{\mathrm{c}}\), as found by comparison. Consistent with previous findings with other conetworks, the presence of the _scissors effect_ of the macromolecular crosslinker in the PSt-\(l\)-PDMS and PSt-\(l\)-PDMS/DVB conetworks indicates that the scissors effect is a general phenomenon for polymer conetworks formed by crosslinking with a macromolecular crosslinker. The observed unusual \(T_{\mathrm{g}}\) versus \(M_{\mathrm{c}}\) relationship in the conetworks can be utilized in designing such novel materials with predetermined \(T_{\mathrm{g}}\)s required for targeted applications.  
polymer conetworks; polystyrene; poly(dimethylsiloxane); divinylbenzene; glass transition temperature; \(M_{\mathrm{c}}\); Fox-Flory equation; scissors effect +
Footnote †: journal:  
_Article_
# A Green Routing Protocol with Wireless Power Transfer for Internet of Things
## 5 Proposed OF for RPL
Summarizing, at the intermediate node \(j\), over a time interval \(T_{p}\), assuming the received power at the RF-EH circuit is \(P_{rx}\), the amount of harvested energy can be represented as ref. [38]  
\[EH_{j}=\eta_{j}T_{p}(P_{\mathrm{rx}}-P_{\mathrm{th}})^{+} \tag{5}\]  
where \(0\leq\eta_{j}\leq 1\) is the RF-EH process efficiency at node \(j\), \((z)^{+}\!\!=\max(z,0)\), \(P_{th}\) is the RF-EH sensitivity level, and \(P_{rx}\) can be expressed as function of Equation (1)  
\[P_{rx}=\frac{\eta_{j}h_{ij}\xi_{i}h_{i}PT_{r}}{T_{p}} \tag{6}\]  
and \(h_{ij}\) is the channel power gain between the node \(i\) and the node \(j\). According to the existing studies [38], the RF-EH circuit can only harvest energy when its receive signal power, \(P_{\mathrm{rx}}\), is greater than the RF-EH sensitivity level, \(P\)th, and the harvested energy is proportional to \((P_{\mathrm{rx}}-P_{\mathrm{th}})^{+}\). This additive metric is used for optimal route selection. Therefore, the following OFs are considered to investigate the performance of RPL in terms of the network lifetime:  
* default OF for RPL, i.e., OF0 considered for comparison in performance evaluation;
* energy-based OF, called OF1;
* energy-harvested OF, i.e., based on battery level and recharging capabilities aware, called OF2. For OF1, the RE routing metric is calculated as follows  
\[RE\doteq\frac{E_{r}}{E_{0}} \tag{7}\]  
where \(E_{0}\) is the node's initial energy and \(E_{r}\) is the node's remaining energy directly proportional to the battery level. It is worth pointing out that in deriving OF2 we refer to (4) and (6) for modeling both the battery level and the recharging capabilities awareness.
## 2 Related Work
### Related Work of Task Offloading  
Many scholars proposed some related research on the efficient computation and communication in other wireless networks (such as vehicle network, wireless network, wireless sensor network, etc.), based on methods of cloud computing, edge computing, collaborative computing offload, etc. Sensors are used in various fields to obtain data. When the data that are sensed are processed through Mobile Cloud Computing (MCC) or another computing method, they can be further turned into useful information [19]. For the communication problem in computation offloading, [PERSON] et al. proposed a task offloading scheme based on mathematical graph modelling. In this scheme, by considering the K-LARAC and M-LARAC heuristic algorithms, a new heuristic algorithm is introduced to find the optimal path that can evaluate the delay with the minimum service cost [20]. In the Cloud environment, due to the long distance between the Cloud and Mobile Devices (MDs), there is high latency and power consumption in resource allocation and task scheduling. In the application of the Internet of Things (IOT), Mobile Fog Computing (MFC) is introduced to improve speed and reduce energy consumption [21; 22]. Aiming at solving the problem of computing delay and load balancing in edge computing, [PERSON] et al. transformed the problem into a Markov decision process and designed a task offloading algorithm based on a depth deterministic policy gradient [23].  
Because the Cloud Computing (CC) based on centralized remote infrastructure results in significant communication delays, it cannot meet the requirements of delay-sensitive applications. To solve this problem, the concept of Edge Computing (EC) is proposed; that is, the ability of CC is relocated to the terminal equipment closer to the edge of the network, in which the optimization methods of mathematics, artificial intelligence, and control theory can better process the computational task offloading [24]. For the distributed task offloading in Mobile Edge Computing (MEC), [PERSON] et al. proposed a distributed task offloading mechanism based on the Stackelberg game theory in [25].
## 5 Discussion
### Limitations and Future Work  
Despite the contributions of this study, several limitations should be acknowledged. Firstly, the study recruited 30 young student participants who may have been generally more familiar with and accepting of robots compared with older populations. Consequently, they exhibited higher perceived safety towards the experimental robots. However, in real road environments, vulnerable groups such as the elderly, children, and individuals with disabilities are also important pedestrians. Their walking speeds, heights, and cognitive abilities differ from those of healthy young adults, and they may have different opinions about SADRs. Future experiments should expand the range of participants to include comparisons between young and elderly groups or focus on the opinions of vulnerable groups regarding SADRs.  
Secondly, this study used LED light strips and custom-made enclosures to simulate the experimental robots, with the robots' movements entirely controlled by the researchers. This setup did not fully replicate the behavior of real SADRs in uncontrolled environments. As [PERSON] et al. [52] noted, perceived safety is influenced by the presence of human assistants during HRI. Participants may perceive controlled, non-realistic robots as safer. Thus, future research should utilize real SADRs and explore more experimental parameters while minimizing the researchers' presence to obtain more comprehensive results.  
Thirdly, in this study, the robot generated noise from its wheels whilst moving, which became more pronounced as it approached the participants. This suggests that the sound signaling the robot's approach to the participants may have also been a factor influencing perceived safety. In future research, the impact of robot noise on perceived safety is an important topic worth exploring.  
Fourthly, the cultural background can influence color perception. The participants in this study included both Koreans and Chinese, who may have different color preferences. For instance, white is more favored by Koreans, whereas Chinese participants may prefer red [34]. Future research could benefit from conducting cross-cultural comparisons to extend the findings of this study.