esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The present study explores the effect of activating solution concentration (4, 6 and 8 M NaOH) on mechanically and thermally pre-treated carbonate-high illite clay (LCR). Pastes were prepared with an alkaline solution/clay (S/B) ratio of 0.55, which were cured at room temperature and relative humidity \(>\) 90% in a climatic chamber. At two and 28 days, compressive mechanical strength was determined, and the reaction products were characterised by X-ray Powder Diffraction analysis (XRPD), Fourier-transform infrared spectroscopy (FTIR) and Scanning Electron Microscopy - Energy Dispersive X-ray spectroscopy (SEM/EDX). Results obtained showed that the presence of reactive calcium in the starting clay induces co-precipitation of a mix of gels: An aluminium-enriched C-S-H gel (C-A-S-H) and a N-A-S-H gel, in which sodium is partially replaced by calcium (N,C)-A-S-H. Pastes prepared with higher (6 or 8 M) activator concentrations exhibit a more compact matrix than the ones prepared with 4 M NaOH. The findings show that the use of a 6 M NaOH solution yields a binder with two days compressive strength \(>\)20 MPa and 28 days strength of over 30 MPa.  
carbonate-high illite clay; alkali activation; NaOH; activator concentration +
Footnote †: journal: Applied
# Calculation of the Dangerous Failure Rate of the Safety Function
## 1 Introduction
Modified versions of these methods (dynamic fault trees analysis (DTFA) in the paper [8] and dynamic reliability block diagram (DRBD) in the paper [9]) largely eliminate these deficiencies. Like support, these methods use Markov chains, Petri nets, Bays nets, or Monte Carlo simulations. When evaluating RandF-SI, it is appropriate to consider three states of ESRCS, resp. SF (up state; down and safe state; down and dangerous state). The most used state-oriented methods include the continuous-time Markov chains (CTMC) method, either alone the paper [10] or in combination with discrete-time Markov chains (DTMC) in the paper [11]. The effect of random failures on RandF-SI of SF can be modelled using CTMC. By the DTMC, it is possible to model the influence of the online diagnostic mechanism, which works discretely or to model the impact of periodic maintenance. In the paper [11], the basic idea of how to solve a problem related to the evaluation of the differently operating  
Figure 1: Factors that affect the SI of an ESRCS. fault detection mechanisms to the RandF-SI of the SF is mentioned. A CTMC/DTMC combination can be used to solve this problem. A major disadvantage of SF analysis by the Markov chain (MC) is the state space explosion. The paper [12] deals with the mathematical procedure of the creation of the model of E-SRS, which consists of several subsystems. The proposed procedure consists of the suitable merging of models of subsystems into one model of ESRCS. Tensor constructions (the tensor sum and the tensor product) have been used for the merging of CTMCs of subsystems. The state explosion problem, which occurred during the merging of CTMCs of subsystems. The individual methods for analysis can be suitably combined. For example, the paper [13] deals with the procedure description and the definition of the mathematical relations for creating the model of the complex ESRCS. The proposed procedure consists of the decomposition of ESRCS into the mutually independent subsystems, the calculation of the required parameters of each subsystem by MC, and the creation of the resulting model of the whole ESRCS by the FTA. Subsequently, the dangerous failure rate of the whole ESRCS and the proof test interval of ESRCSs are determined by the FTA and the parameters of the subsystem. Quite often, a combination of FMEA and RBD methods in the paper [5] or a combination of FMEA and FTA methods in the paper [14] can be encountered.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Channel Model
#### 2.2.1 SINR Definitions  
We can define the downlink signal-to-interference-and-noise-ratio (SINR) at \(i\)-th user from all cells as  
\[\gamma(i)=\frac{p(k_{s})g_{t}(l_{s})h(k_{s},i)}{n(i)+\sum_{k\
eq k_{s}}^{K} \sum^{L}p(k)g_{t}(I)h(k,i)+\sum_{l\
eq l_{s}}^{L}p(k_{s})g_{t}(I)h(k_{s},i)}, \forall k\in\mathcal{K},\forall l\in\mathcal{L},\forall i\in\mathcal{I}, \tag{8}\]  
where \(k_{s}\) and \(l_{s}\) are the indices of the serving cell and the serving-beam for the \(i\)-th user, respectively, and \(g_{t}(\cdot)\) is the linear scale value of \(G_{t}(\cdot)\). \(p(k)\) is the Tx power of the cell. Furthermore, \(h(k,i)\) is a distance-based channel gain between the \(k\)-th cell and \(i\)-th user. We consider pathloss, shadow fading, small-scale fading, and the user received antenna gain for calculating \(h(k,i)\).
# Malware Detection Issues, Challenges, and Future Directions: A Survey
## 5 The Taxonomy of Feature Extraction and Representation Methods
### Feature Extraction Methods
#### 5.1.3 Graph-Based Extractor  
To extract only the most important features, raw data such as API calls and opcodes are formed into graphs that represent the appearance of each feature in that sample as well as its relationships with others. Therefore, the frequent sub-graphs among all the samples of a specific class or family are considered significant features that have to be extracted. Those sub-graphs consist of nodes (API calls, opcodes) and their relationships with other features (dependency or control flow). Some studies extract the features using algorithms that construct graphs. Those graphs consist of groups of nodes as basic blocks in the program. These basic blocks are connected by edges to represent the path of control flow between the nodes as well as the blocks [95]. Part of the previous studies [10; 80; 96], have used graph-based extraction methods to extract the optimal feature set. CFGs were created based on opcodes or API calls. Some studies used matching algorithms immediately to distinguish between malicious and benign constructed graphs, while others represented the constructed graphs as binary vectors, weight vectors, or weighted dependency graphs. However, it is difficult to build a unique graph for each individual malware and, considering the common graph-based behaviors, might lead to an increase in the detection time.
## 3 Methodology and Theoretical Background
### Fault Assessment in Selected LSHT Machines
#### 3.2.2 Broken Rotor Bars in SCIMs  
In this work, the analysis of broken bars is conducted by changing the electrical conductivity of the material associated with each rotor bar in the equivalent circuit of the 2D FE model. Three levels of fault severity are considered, in concordance with related studies for the detection of broken bars in commercial IMs [27; 29; 62; 63]:  
* **Level 1:** The electrical conductivity of the first rotor bar is modified to \(\sigma_{1}=0.5\sigma_{0}\), representing an initial broken bar scenario, which is a precursor to full bar breakage.
* **Level 2:** The electrical conductivity of the first rotor bar is modified to \(\sigma_{1}=0\), representing an incipient broken bar scenario.
* **Level 3:** The electrical conductivity of the first three rotor bars is modified to \(\sigma_{1}=\sigma_{2}=\sigma_{3}=0\), which represents a severe broken bar scenario.  
It should be noted that the analysis is conducted with the assumption of maximum asymmetry in the broken bars, to assess the worst-case scenarios in terms of performance degradation.
# Synthesis of Silicon Carbide Powders from Methyl-Modified Silica Aerogels
## 4 Conclusions  
\(\beta\)-SiC powders were synthesized by the carbothermal reduction of methyl-bearing surface-modified silica aerogel/carbon mixtures. Based on the XRD analysis, single-phase \(\beta\)-SiC was synthesized at 1525 \({}^{\circ}\)C from the silica aerogel/carbon mixture with a C/SiO\({}_{2}\) ratio of 3. The surface methyl groups that are covalently bonded to the silica aerogel serve as a carbon source for the carbothermal reduction reaction; therefore, the formation temperature for SiC is lower. The direct bond between silicon and carbon atoms in the surface methyl group results in the enhanced kinetics of the carbothermal reduction reaction. The results show that the C/SiO\({}_{2}\) ratio plays an important role in both the reduction of the formation temperature for SiC and the control of the morphology of the synthesized SiC particles. At a C/SiO\({}_{2}\) ratio of 0.5, SiC maintains the original spherical morphology of the silica aerogels. By contrast, agglomerates with fine SiC particles were synthesized from the SiO\({}_{2}\)-C mixture at a C/SiO\({}_{2}\) ratio of 3. This phenomenon can be attributed to different formation mechanisms for SiC, which depend on the silica-carbon ratio of the starting silica/carbon mixture. Almost pure SiC powder (99.95%) was obtained when the silica aerogel/carbon mixture at a C/SiO\({}_{2}\) ratio of 3 was reduced at 1525 \({}^{\circ}\)C for 5 h.  
Conceptualization, K.-J.L. and H.H.; methodology, Y.K., Y.H.K. and S.W.B.; analysis, K.-J.L., Y.K. and Y.H.K.; writing--original draft preparation, K.-J.L. writing--review and editing, H.H.; supervision, H.H.; project administration, H.H. and S.W.B. All authors have read and agreed to the published version of the manuscript. This work was supported by LG Chem. This research was supported by the Ministry of Trade, Industry & Energy (MOTIE), Korea Institute for Advancement of Technology (KIAT), through the Encouragement Program for The Industries of Economic Cooperation Region (P0002149). The authors declare no conflict of interest.
# UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring
## 8 Simulation
### Simulation Results
#### 8.3.1 Number of Dead Nodes vs. Simulation Time  
In this simulation run, we wanted to check how energy is utilized and how fast the sensor nodes' energy becomes completely exhausted. We simulate each case (DDC, NADC, URP) separately with same parameters such as number of nodes, simulation time, initial node energy, node/UAV communication ranges, data collection height, etc.  
We can conclude that using our developed system, the network lifetime can be improved by 20% or more on the expense of UAV energy, adding more intelligent sensor nodes and dual frequency support in the UAV and nodes (as Figure 9(a)).
# Economies of Scale and Efficiency in the Wastewater Treatment Sector: A Decision Tree Approach
## 4 Results
### Individual Efficiency Analysis
The results of the EAT analysis for each of the subgroups are shown in Figure 2. In the case of the first block (Groups 1.A; 1.B; 1.C), the presence of economies of scale cannot be confirmed for those facilities that treat under 10,000 P.E. Group 1.A (<3000 P.E.) obtains a score of 0.12. Group 1.B (3000-6000 P.E.) achieves, on average, a slightly higher score of 0.30. Finally, Group 1.C (6000-10,000 P.E.) records a lower score of 0.22. The results show that there is no clear trend regarding the use of lower inputs in the process and higher quantities of pollutants being removed. These findings are corroborated by the Kruskal-Wallis test, showing that the differences between the scores are significant. Continuing with the analysis of the smaller WWTPs, as shown in Figure 2, the following efficiencies are obtained: Group 1.A.1 (<1000 P.E.) obtains a score of 0.09; Group 1.A.2 (1000-2000 P.E.) records a score of 0.22, and, finally, Group 1.A.3 (2000-3000 P.E.) obtains a score of 0.24. However, the Kruskal-Wallis test corroborates that, with a significance of 0.479, the null hypothesis is accepted. This implies that there are no significant differences between Groups 1.A.1, 1.A.2, and 1.A.3: that is, for smaller WWTPs, the pattern of economies of scale is not met and, therefore, there is no clear relationship between the size of the WWTP and its efficiency. The results reveal that the optimal size at which economies of scale are obtained in the wastewater treatment sector, taking into account the different types of pollutants removed, is at 10,000 P.E. In addition to obtaining lower efficiencies, smaller-scale facilities (according to P.E.) do not demonstrate any improvements in efficiency related to the size of the facility. In contrast, the largest sample analyzed (from 10,000 P.E.) shows a clear positive trend in terms of the efficiency obtained on average in each of the main groups (Groups 1, 2, 3, and 4). These findings suggest that, with regard to the design of and investment in WWTPs, small towns or dispersed settlements may consider centralized wastewater treatment services. Nevertheless, the economic and environmental impact of  
Figure 2: Average efficiency results for each subdivided group. the centralization of these services should be assessed to determine the feasibility and the efficiency of the system. In this sense, the following section (Section 4.3) quantifies the potential savings for municipalities that might centralize these services.
## 2 Basic Concepts of DQ0-Based Models
### Basic Definitions
#### 2.1.2 Power in Terms of DQ0 Quantities  
Assume a general three-phase unit, as described in Figure 3.  
The instantaneous power flowing from the unit into the network at time \(t\) is  
\[P_{3\phi}=v_{a}i_{a}+v_{b}i_{b}+v_{c}i_{c}. \tag{16}\]  
Rewrite this equation as  
\[\begin{split} P_{3\phi}&=\begin{bmatrix}v_{a}&v_{b}&v_{c} \end{bmatrix}\begin{bmatrix}i_{a}\\ i_{b}\\ i_{c}\end{bmatrix}=\begin{pmatrix}T_{\theta}^{-1}&\begin{bmatrix}v_{d}\\ v_{q}\\ v_{0}\end{bmatrix}\end{pmatrix}^{\mathsf{T}}_{\theta}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}\\ =\begin{bmatrix}v_{d}&v_{q}&v_{0}\end{bmatrix}\begin{pmatrix}T_{\theta}^{-1} \end{pmatrix}^{\mathsf{T}}T_{\theta}^{-1}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}.\end{split} \tag{17}\]  
Figure 3: Three-phase unit connected to the network.  
By direct computations, it can be verified that  
\[\left(T_{\theta}^{-1}\right)^{\mathsf{T}}T_{\theta}^{-1}=\frac{3}{2}\begin{bmatrix} 1&0&0\\ 0&1&0\\ 0&0&2\end{bmatrix}, \tag{18}\]  
and therefore  
\[P_{3\varphi}=\frac{3}{2}\begin{bmatrix}v_{d}&v_{q}&v_{0}\end{bmatrix}\begin{bmatrix} 1&0&0\\ 0&1&0\\ 0&0&2\end{bmatrix}\begin{bmatrix}i_{d}\\ i_{q}\\ i_{0}\end{bmatrix}=\frac{3}{2}\left(v_{d}i_{d}+v_{q}i_{q}+2v_{0}i_{0}\right). \tag{19}\]  
This expression defines the total instantaneous power in terms of \(dq0\) quantities.
# A Mobility-Aware Adaptive Duty Cycling Mechanism for Tracking Objects during Tunnel Excavation
###### Contents  
* 1 Introduction
\t* 2.1 Overview of the main results
\t\t* 2.2.1 Overview of the main results
\t\t* 2.2.2 The main results
\t\t* 2.2.3 The main results
\t\t* 2.3.1 Overview of the main results
\t\t* 2.3.2 The main results
\t\t* 2.3.3 The main results
\t\t* 2.3.4 The main results
\t\t* 2.3.5 The main results
\t\t* 2.3.6 The main results
\t\t* 2.3.7 The main results *  
area could be checked and confirmed by the tracking system. Note that the sensors are attached to moving objects including the workers, so an electric battery would be an appropriate power source for the sensors. Tunnel construction time varies depending on the tunnel length, but typically takes several years. Hence, to minimize battery replacement while track moving objects over the entire construction time, an adaptive duty cycling mechanism is required for the devices making up the object tracking system.  
In this paper, we propose a novel mobility aware adaptive duty cycling mechanism for tracking objects at a tunnel construction site. Unlike existing approaches that focused on adaptation strategies of synchronization frequency, frame length, slot time, and handover to efficiently address the problem of mobility, we concentrate on adaptive duty cycling according to the node mobility, making it suitable for tracking objects with a given energy constraint. Our adaptation strategy aims to enhance network lifetime while reducing response time when objects are mobile. It tunes the duty cycle ratio by adjusting the sleep time depending on the change of Received Signal Strength Indication (RSSI) value. We investigated the energy consumption and latency of this adaptive duty cycling mechanism for various mobility conditions. Our approach was to meet the Quality of Service (QoS) requirements of the energy consumption and latency by considering the mobility conditions in tunnels. Note that GPS signal is typically utilized when developing an object tracking mechanism, but the GPS signal loss frequently occurs in construction sites. The extreme case of this environment is the tunnel construction sites because of its underground environment. Hence, the object tracking mechanism using GPS is not considered in this study.  
The rest of this paper is organized as follows: Section 2 is an introduction to the background and related information about duty cycling in WSNs. Section 3 presents the mobility aware adaptive duty cycling mechanism for tracking objects in tunnels. Section 4 is an evaluation of the energy consumption and latency of both adaptive and periodic duty cycling mechanisms. Finally, Section 5 summarizes the work.
## 3 Results and Discussion
### Impact Characteristics under Cryogenic and High Temperatures
#### 3.2.2 Damage Morphology
We presume that, at cryogenic temperatures, a greater incident velocity of the projectile correlates with greater damage and a larger damage area of the composite target plate, which differs from the result for metal blade-like projectiles (cf. Section 3.1.3 and previous studies [32]). For tests at high temperature, the exit surface consists of a rough fiber fracture surface with extensive yarn pull-out. The main failure forms are fiber tensile fracture, matrix cracking, and delamination failure, which is consistent with the results obtained at cryogenic temperature and room temperature.
## 5 Conclusions  
In conclusion, a high deviation between the planned and real positions of the mini-implants was found. This might partially be explained by the high number and proximity of implants per cadaver, as well as the high amounts of tooth abrasions. Therefore, it is likely that lower deviations would be found in clinical practice, especially after the introduction of vertical stops.  
Future studies are needed in order to improve skeletonized guides for patients with high degrees of tooth abrasion. Indeed, the stability issues encountered with the skeletonized insertion guides in cases with flat or abraded occlusal planes highlight the importance of a correct patient evaluation and of optimized design of the guides. Furthermore, well-designed clinical studies are needed in order to assess the accuracy of different guided insertion protocols within in vivo settings. However, in order to avoid overexposing the patients to radiation, and if the reliability of the latter can be verified, CBCTs might be replaced by intraoral scans.  
Conceptualization, B.W., D.D. and K.B.; methodology, B.W. and K.B.; software, A.P. and K.B.; validation, A.P., G.B. and K.B.; formal analysis, A.P. and L.S.; investigation, A.P. and K.B.; resources, B.W., D.D. and K.B.; data curation, A.P. and K.B.; writing--original draft preparation, A.P., G.B. and L.S.; writing--review and editing, all; supervision, K.B.; project administration, K.B.; funding acquisition, B.W. and K.B. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The BENEfit(r) implants were provided free of cost by PSM Medical (Gunningen, Germany). The full-arch guides were provided by Easy Driver (Uniontech, Parma, Italy) free of charge.  
**Institutional Review Board Statement:** Ethics approval was obtained from the Ethics Committee of the Heinrich Heine University of Dusseldorf, Germany (Prot. number: 2018-130_2).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data will be provided upon reasonable request.  
**Acknowledgments:** We acknowledge the kind help of [PERSON] (Department of Orthodontics, University Hospital Dusseldorf) for the design of the skeletonized insertion guides.  
**Conflicts of Interest:** B.W. and D.D.D. are shareholders of the Tadman GmbH company (Gunningen, Germany).
# Forecasting the Number of Road Accidents in Polish Provinces Using Trend Models
## 1 Introduction  
A traffic accident is an event that causes damage to property, as well as injury or death to traffic participants. According to the World Health Organization (WHO), about 1.3 million people die each year in road accidents. Those accidents account for about 3% of GDP in most countries around the world. Automobile accidents are the leading cause of death for minors and young people between the ages of 5 and 29 [1]. The United Nations General Assembly has set an ambitious goal of halving the number of traffic accident victims by 2030 [2, 3].  
Road accident severity is an attribute used to determine the severity of traffic collisions. Predicting accident severity is important for relevant authorities to develop road safety policies to prevent accidents and reduce injuries, fatalities and property damage [4, 5, 6, 7, 8]. Identifying key factors affecting accident severity is a prerequisite for taking measures to eliminate and reduce accident severity [9]. [PERSON] et al. [10] proposed a DNN (deep neutral network) multicarbon structure for predicting various degrees of injury, death and severity of property loss. This allows for a comprehensive and accurate analysis of the severity of traffic accidents.  
There are several sources of accident data. They are usually collected and evaluated by government agencies through responsible leadership agencies. Data are collected from police reports, insurance databases or hospital records. In some cases, traffic accident information is processed on a large scale for the transportation sector [11].  
Intelligent transportation systems are currently the most important source of data for analyzing and predicting traffic accidents. These data can be processed using the vehicle's GPS device [12]. In addition, vehicle information can be detected using a roadside vehicle detection system, which can continuously record vehicle information (speed, traffic volume,vehicle type, etc.) [13]. License plate recognition systems can also collect large amounts of traffic data during surveillance [14]. Another source of traffic and accident information is social media, but its accuracy can be insufficient due to the incompetence of reporters [15].  
In this article, we chose a trend model to predict the number of traffic accidents by state. Exponential smoothing and neural networks for predicting the number of traffic accidents have been used by authors in other studies [16; 17].
# Collective Communication Performance Evaluation for Distributed Deep Learning Training
## 5 Experiments in the Linux Shell Environment
### Parameter Server Subroutine
#### 5.1.2 Gather  
In Figures 15 and 16, we can observe the latency results of the gather (allgather) operation within the parameter server method. Notably, the bare metal setup consistently exhibits outstanding performance, with CUDA-aware, OpenMPI, and MPICH libraries performing well except when using a single GPU. For instance, in the case of using four GPUs, MPICH showed a latency of 2.56 s, OpenMPI of 2.38 s, and CUDA-aware MPI of 2.22 s, indicating a mere 7% difference between each library. Additionally, when compared to other environments, MPI's cross-docker configuration demonstrated lower latency, or less than 0.5 s difference. However, NCCL exhibited a noticeable divergence. The difference between single docker and cross-docker setups exceeded 46%, and it is evident that latency increases significantly in the cross-container environment for both the broadcast and allgather operations performed in the parameter server method.