esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Relationship Between the Perceptual Evaluation Results and the Acoustic Features  
GLMMs were fitted to investigate whether listeners' responses could be predicted by the acoustic features of the tone production. The GLMMs revealed significant main effects of _tonal distance_ [x\({}^{2}\)(1) = 23.507, \(p\) < 0.001], _DCT1_ [x\({}^{2}\)(1) = 5.819, \(p\) = 0.016], and _DCT3_ [x\({}^{2}\)(1) = 5.940, \(p\) = 0.015] on the responses, suggesting that, in general, the tonal distances, the mean F0, and the F0 curvature were taken into account when the native-speaking listeners were making their judgements. Separate GLMMs were then fitted for each tone; the results of pairwise comparisons for each tone and variable are listed in Table 2. The main effect of _tonal distance_ was found for T4 (_p_ < 0.001) and T6 (_p_ = 0.019), and there was also a marginal effect of _tonal distance_ for T1, T2 and T5 (_p_s < 0.1). Furthermore, the effect of _DCT1_ was significant for T1, T4 and T6 (_p_s < 0.01), the effect of _DCT2_ was significant for T2, T4 and T5 (_p_s < 0.05), and the effect of _DCT3_ was significant for T1 and T4 (_p_s < 0.001). These statistics confirmed the relationship between the native listeners' judgements and the acoustic features in L2 Cantonese tone production.  
Figure 2: Production matrix of the six Cantonese tones produced by the native speakers (**A**) and the immigrants (**B**). The grey cells represent the production accuracy for each tone and the pink cells highlight the incorrectly perceived tones with a percentage higher than 10%.
# Sensor Network Environments: A Review of the Attacks and Trust Management Models for Securing Them
## 6 Trust Model and Calculation Techniques
### Trust Thresholds  
A trust threshold is the limit of the trust value according to which the node is considered trustworthy or not. One attribute that appears to be lacking in many models is a dynamic threshold. Most models employ a static threshold. Some offer a user-defined threshold [19, 20, 32]. Adding a dynamic threshold to a model creates a far more flexible model that can be used in more than one specific network case. For example, if two identical smart city network topologies are being used in two different cites, a trust model with a fixed threshold may not give the same results on both, due to differences in the network traffic, recorded data, etc. A dynamic threshold for trust could solve this issue. The authors of [3] use a dynamic threshold based on the average value of trust in the network.
## 3 Methodology
### The Biome-BGC Model  
Biome-BGC is a process-based biogeochemical model used to simulate the carbon, nitrogen, and water cycles within the terrestrial ecosystems from a single plot to regional and global scales [11]. It uses a daily time-step and was developed from the Forest-BGC model [45]. Previous studies have successfully applied this model to a variety of biomes such as forests and grasslands [17; 18; 20; 46]. There are several critical processes in the Biome-BGC model, including photosynthesis, respiration, evapotranspiration, decomposition, the allocation of photosynthetic assimilates, and mortality [11]. To model the photosynthesis process, the Biome-BGC model converts leaf C into an equivalent leaf area by multiplying the user-defined specific leaf area (SLA) parameters. SLA is a measure of the thickness of a leaf and is defined as the leaf area per unit mass. The Biome-BGC model further partitions leaf C and leaf area into sunlit and shaded fractions using a two-leaf photosynthesis model [47; 48] and calculates each photosynthetic process separately. The carbon flux simulated by the Biome-BGC model mainly includes GPP, Net Primary Productivity (NPP), Net Ecosystem Productivity (NEP), and NEE. Among them, GPP is the total carbon fixed by photosynthesis, NPP is the difference between GPP and autotrophic respiration, NEP is the difference between NPP and heterotrophic respiration, and NEE is the difference between NEP and the carbon loss caused by fire. In this study, we used the newly released Biome-BGC v4.2 to simulate the GPP of alpine grasslands on the TP (available at [[http://www.ntsg.umt.edu/project/biome-bgc.php](http://www.ntsg.umt.edu/project/biome-bgc.php)]([http://www.ntsg.umt.edu/project/biome-bgc.php](http://www.ntsg.umt.edu/project/biome-bgc.php))).
## 2 Cluster Head Selection  
The first step in cluster-based protocols is the selection of CHs. CH as the local coordinator or sink of the cluster handles numerous tasks of coordination of the work between node members, collection of information within the cluster, data fusion processing and transmission of the aggregated data towards the global sink. In addition, even division of the nodes into clusters is dependent on the number and location of the CHs. Therefore, CH selection plays a significant role in the subsequent procedures of a clustering algorithm and thus performance, lifetime and energy-efficiency of the network. Due to this importance, many researchers have focused on optimizing the CH selection process. Based on the different strategies implemented in CH selection processes, we classify them as self-organized schemes, assisted schemes and multi-factor evaluation schemes. Figure 2 shows the taxonomy of CH selection.
# An Analysis of MPTCP Congestion Control
## 7 MPTCP Congestion Control
### Alternative Approaches
Subflows with marked ACKs in the same time span indicate a shared bottleneck and are grouped in a same set (coupled _cwnd_). The algorithm uses similar concept to that in DCTCP and uses the subflow congestion degree to dynamically adjust _cwnd_ and smooth any fluctuations in window size. The authors experimental results showed that SB-CC, DWC, SBD, BALIA and OLIA achieved fair throughput over a shared bottleneck. As for disjoint paths, SB-CC, DWC and SBD aggregated throughput was much higher than BALIA and OLIA. Specifically, SB-CC and SBD throughput ratio to TCP was close to one. Deployment of such a scheme requires both the end-hosts and intermediary devices to properly react to ECN notifications. Further, [PERSON] et al. [45] argue that the existing coupled CC are grouped in CA state while most TCP sessions in the Internet are short flows and are unlikely to leave the SS state. MPTCP subflows in SS are uncoupled which results in the compound overall MPTCP _cwnd_ being larger than that of a TCP flow. The authors proposed a new CC called Linked Slow-start Algorithm (LISA) which is applied to the subflows if and only if they are in SS. In LISA, the minimum initial window is set to 3 packets to ensure TCP-friendliness and a maximum of 10 packets to allow full bandwidth achievement in disjoint paths. When a new subflow joins the connection, LISA finds the subflow with the largest sending rate within the last RTT period, and depending on the subflow _cwnd_ size, [3,10] packets are taken away by the new subflow. LISA also hinders the existing subflow _cwnd_ from increasing for every ACK. However, LISA could not provide any gains for small file transfers (smaller than 200 KB) and added more weight to the CA phase and reduced overall performance for large file sizes (larger than 500 KB) at shared bottlenecks. [PERSON] et al. showed that the MPTCP CC algorithms do not affect the overall performance of mice flows in high-speed paths. Mice flows operate in SS phase for the entire transmission and they tend to only last for a fraction of a second [46]. The discussed works have not been deployed in the real-network (e.g., Internet). The current simulation/experimental results showed that using a bottleneck detection system at the transport layer could bring some advantages to the current MPTCP coupled CC in some cases. However, none of the proposed solutions fully address the conservative behaviour of coupled CC in various conditions (e.g., different RTTs and bandwidth).
# A DDPG-Based USV Path-Planning Algorithm
## 3 Simulation
### Design of Environment Settings and Reward Functions  
In our simulation, a grid of 320 by 320 units will be employed, where each unit on the map signifies 5 m. And randomly designed obstacles on the map and indicated them with black squares on the map.The unmanned boat will traverse 5 units--or 25 m--in the direction of its current heading during each movement. This setup aims to curtail the amount of motion searches and expedite the algorithm's validation. The training will be performed on the map 2000 times, with each iteration confined to 500 steps. If the final destination is not reached within those 500 steps, the attempt will be categorized as a failure.  
The criteria for comparison include the mean number of steps taken, average rewards earned, success rate, overall path length, and the frequency of substantial-angle turns. To streamline the simulation process, obstacles have been shaped into rectangular forms. Moreover, we will establish a control group by constructing a conventional, fully connected neural network using the traditional Actor-Critic algorithm. To bolster the stability and efficiency of training, we will implement a Q target network to augment the convergence of iterations.  
The visual representation of the simulation environment is as follows: obstacles are depicted as black rectangles, the starting point is marked by a red triangle, and the target point is symbolized by a green pentagram. After 2000 times of training, the path-planning trajectories for two algorithms are obtained. The red path illustrates the DDPG algorithm tailored for USV, while the blue path represents the Actor-Critic algorithm.
# Smart Waste Collection System with Low Consumption LoRaWAN Nodes and Route Optimization
## 3 System Proposed
### Optimization Route Engine  
The following pages focus on system section, related to the gathering, storing, processing and later use of sensors' data. In reference to Figure 2 which shows information subsystems, Figure 6 indicates which of them are part of the route optimization for collecting waste.  
The diagram located on the upper side presents the whole optimization process. It has four stages:  
1. Node selection: nodes visited by the waste collection fleet are selected. The criteria selection of nodes depends on the sensor variables at that moment--the weight and volume of each container. A threshold will be established to select the nodes that must be collected. This will depend on variables, such as current regulations of the town where the system is deployed [85] as well as waste intended to be collected. This is the reason why the criteria will change depending on the specific case study. When specifying the threshold, the following factors should be considered: filling frequency, data availability in the system and whether a criterion should be established or not, in accordance with the last waste report from the town. The criteria applied in this case study will be further explained later.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Hosting** & **Open Source** & **Price Plan** \\ \hline LoraServer.io & Self-hosted & yes & free \\ The Thing Network & & Self-hosted/3 rd party & yes & free/paid \\ (TTN) & & & \\ Actility & 3 rd party & no & free (limited)/paid \\ Loriot & 3 rd party & no & free (limited)/paid \\ Senet & 3 rd party & no & free (limited)/paid \\ \hline \hline \end{tabular}
\end{table}
Table 3: Several LoRaWAN network server software options.  
2) CVRP data: once the nodes and the depot location have been obtained, the geographic information of every node is loaded from the geographical information subsystem to get the matrix of costs that will be employed for the CVRP resolution. The number of available vehicles must also be defined, as well as an ending criterion related to the time intended to be spent seeking the best solution. The system was implemented using the Graphhopper [86] framework and data from OpenStreetMap.
3) CVRP solver: this is executed with previously indicated data in the route optimization subsystem. The heuristic construction of the solution and the algorithm of the local search are applied until the stop criteria is reached. A benchmark is employed to select the fastest optimization algorithm that can be used to obtain a feasible solution during the indicated time. Different construction heuristics are used for different local searches.
4) Best solution found: once the best or most feasible solution is found (during the time specified under the stop criteria), it is published in the MQTT broker. The data persistence subsystem receives the information and stores the solution for that day's collection plan. User applications (both mobile and web) will be notified through MQTT with the route that must be followed.
## 2 Experimental Details
### Synthesis of CuCo\({}_{2}\)S\({}_{4}\)@Ni  
A simple one-step solvothermal strategy was adopted to grow CuCo\({}_{2}\)S\({}_{4}\) on the Ni-foam. Before the actual experiment, ultrasonication treatment was employed on the Ni-foam in acetone, deionized water (DI), and ethanol for 15 min each and dried in an oven for 2 h. This process will help clean up the current collector's surface impurities. Followed by the cleaning of the Ni-foam; two different 60 mL solvents were prepared, with one containing DI water (50 mL) and ethylene glycol (10 mL) while the other contained DI water (50 mL) and glycerol (10 mL). A mixture of both solvents was stirred for 15 min using a magnetic stirrer. AR regents of copper nitrate pentahydrate (2 mmol), cobalt nitrate hexahydrate (4 mmol), and thiourea (10 mmol) were poured in the above solvents with continuous stirring for 30 min. Here, the purchased chemicals were utilized without further treatment or purification process. The mixed solution was then transferred to the 125 mL Teflon vessel, and cleaned Ni-foam (2-cm \(\times\) 5-cm) was aligned vertically to the walls of the Teflon vessel. The closed Teflon vessel was further placed in a stainless steel autoclave assembly, and the autoclave was placed in an electric oven. The electric oven temperature was raised to 120 \({}^{\circ}\)C and maintained for 8 h. After finishing the reaction time, CuCo\({}_{2}\)S\({}_{4}\)-loaded Ni-foams were carefully fetched from the autoclave assembly. The loaded Ni-foams were washed with DI water and ethanol and dried overnight at 80 \({}^{\circ}\)C. For simplification, the coated electrodes are referred to as CCS-EG and CCS-glycerol.
## 5 Influence of IN  
### Influence on the Entries of the Covariance Matrix  
The first big difference between the _C-model_ and the _R-model_ under IN appears when observing the three-dimensional (3D) representation of the matrices **Y** and **R**. Since no signal processing is assumed in the _C-model_, IN samples appear added to the thermal-noise-only version of **Y** (Figure 6, left) and are clearly noticed as pronounced peaks in the sample covariance matrix **R**, plotted in the right-hand side of Figure 6. On the other hand, one can notice from Figure 7 that IN peaks were practically eliminated when the _R-model_ is considered. This shows the intrinsic ability of the implementation-oriented model (_R-model_) to combat IN, _i.e._, IN has been reduced by the inherent signal-processing tasks performed by each CR in the _R-model_, particularly by the low-pass receiver filtering, the hard limitation at the ADC and the whitening filtering. It is worth mentioning that no countermeasure specifically designed to combat IN was added to the _R-model_ at this point. Figures 6 and 7 were obtained considering the system parameters listed in Table 2.  
Figure 5: ROC curves for MED (or RLRT) and ED under parameter variations.  
Figure 6: 3D plots of matrices \(\mathbf{Y}\) (left) and \(\mathbf{R}\) (right) under the _C-model_.  
\begin{table}
\begin{tabular}{l r r r} \hline \multicolumn{3}{c}{_C-model_ and _R-model_} \\ \hline \multicolumn{3}{c}{**Matrices plots**} & \multicolumn{3}{c}{_ROC curves_} \\ \cline{2-4}  & \multicolumn{2}{c}{**Moderate IN**} & \multicolumn{2}{c}{**Strong IN**} \\ \hline Signal-to-noise ratio (SNR) in dB & \(-10\) & \(-10\) & \(-10\) \\ Number of primary transmitters (\(p\)) & \(1\) & \(1\) & \(1\) \\ Number of CRs (\(m\)) & \(50\) & \(6\) & \(6\) \\ Samples collected by each CR (\(n\)) & \(50\) & \(50\) & \(50\) \\ Impulsive to thermal noise power ratio (\(K\)) & \(2\) & \(1\) & \(10\) \\ Probability of impulsive noise (\(p_{\text{IN}}\)) & \(1\) & \(1\) & \(0.2\) \\ Fraction of CRs hit by impulsive noise (\(p_{\text{CR}}\)) & \(0.1\) & \(0.5\) & \(0.5\) \\ Samples affected by impulsive noise (\(N_{\text{s}}\)) & \(3\) & \(10\) & \(10\) \\ Number of impulsive noise bursts (\(N_{\text{b}}\)) & \(1\) & \(1\) & \(1\) \\ \hline \end{tabular}
\end{table}
Table 2: System Parameters for IN Analysis.  
Figure 7: 3D plots of matrices \(\mathbf{Y}\) (left) and \(\mathbf{R}\) (right) under the _R-model_.
###### Abstract  
The aim of this paper is to assess the size and geometry effects on the mode I notch fracture toughness of polymeric samples containing rounded-tip V-shaped (RV) notches (V-notch with a finite radius at the notch tip). First, using a large number of fracture tests on an RV-notched Brazilian disk and semi-circular bending polymeric samples with four different sizes, the size-dependent values of the notch fracture toughness are obtained. Then, the mean stress criterion is modified for characterizing the size-dependency of notch fracture toughness in polymeric samples. The resulting modified mean stress criterion considers higher order terms of the stress field when calculating the fracture process zone length around the tip of the defect. Additionally, the critical distance \(r_{\mathrm{c}}\) is assumed to be associated with the specimen size and a formula containing fitting parameters is utilized for considering this trend of \(r_{\mathrm{c}}\). The comparison between the values of notch fracture toughness obtained from experiments and those predicted by the modified mean stress criterion shows that the suggested approach can provide accurate estimations of size-dependent values of notch fracture toughness in polymeric specimens containing RV notches.  
polymeric notched specimens; size effect; modified mean stress; notch fracture toughness
# Evolution towards Smart and Software-Defined Internet of Things
### Motivation  
IoT architectures have seen a rising trend of transformation and updates since their inception. The IoT ecosystem has been evolving to meet the model's requirements and expectations, as well as the growing demands of users. IoT has been molding continuously to address the challenges at first to meet the fundamental requirements for IoT, such as security and privacy [13, 14]. IoT alone will not be able to meet the demands of today's world. Security is no longer a reactive phenomenon to be addressed when the need arises, but rather a proactive and by-design requirement in today's world [15].  
Security is critical at both the network's edge and its core. In today's world, IoT must be dynamic, flexible, and distributed enough to ensure availability in the event of a disaster [16, 17]. Furthermore, with the increasing amount of data generated by IoT devices, efficient and intelligent channel allocation methods and a multi-tiered control layer framework are required to avoid communication bottlenecks and ensure smooth data transfer [18]. IoT does not have the functions or capability to address these issues or meet the demands of today's world on its own. As a result, technologies such as SDN and NFV form the foundation of IoT architectures to create the stable, secure, efficient, smart, flexible, and intelligent systems that are expected and overcome traditional IoT architectural limitations such as weak security, data distribution inefficiency, provenance, and traceability of sources, excessive human intervention, lack of interoperability, and service-awareness in device configuration [19].  
A lot of research has been carried out in these domains of IoT, SDN, and NFV, resulting in the evolved form of smart and efficient IoT frameworks we have the privilege of using today. However, in terms of the evolution of IoT architectures, from primitive and traditional frameworks to highly evolved software-defined and virtualized platforms, there is a missing link. This research gap has necessitated the creation of this comprehensive survey, which captures the various stages of IoT structures and their advancements in an effort to better estimate future requirements and current weaknesses by studying the evolution cycle. Therefore, we cover nearly every aspect of IoT development in this paper, including IoT classification and applications, smart and cognitive IoT architecture, smart software-defined IoT, IoT architecture evolution, and state-of-the-art IoT simulators. The current state of IoT challenges and future directions are also discussed in this paper.  
Figure 1 shows the taxonomy of the rest of the paper, which is organized as follows. In Section 2, IoT classification and applications are discussed. Section 3 explains the smart or cognitive IoT architecture, followed by Section 4, which describes smart software-defined IoT. The evolution of IoT architecture is given in Section 5. State-of-the-art IoT simulators are described in Section 6, followed by IoT challenges and future directions in Section 7, and finally, the paper is concluded in Section 8.
# A Review of Blast Loading in the Urban Environment
## 3 Numerical Methods
### BeamBlast  
[PERSON] [6] describes the development of a new module to be used in blast simulations named BeamBlast. The module aims to find a middle ground between semi-empirical methods and CFD analyses to balance simulation speed and accuracy. It is specifically for use in \"Military Operations in Urban Terrain\" (MOUT) scenarios, where such attributes of the module are desirable. The module runs within a wider simulation software suite known as \"Target Vulnerability Assessment Code\" (TARVAC), which is generally used to determine the lethality of ammunition required for destroying a given target. BeamBlast also aims to provide a fast-running path-based blast propagation model, which requires limited user input.  
The module implements path-finding algorithms that identify paths from a blast origin to a specified location of interest, taking surface and edge interactions into account. Afterwards, the pressure and time data are calculated for a given set of paths and explosive masses, based on the Kingery and Bulmash relationships [59]. Initial validations were conducted using simple rectangular models that represented street buildings. The numerical data were compared to experimental data [106], and simulations were run at three different levels of complexity. The simplest simulation measured pressure and impulse on  
Figure 8: BlastFoam simulated peak-specific impulse data for different predetermined object location biases at multiple numbers of test runs, adapted from [PERSON] et al. [41]a building wall directly opposite the blast. The intermediate simulation measured pressure and impulse on the side face of the same building. The complex simulation measured the pressure and impulse on the ground at the rear of the building.  
In the simplest scenario, BeamBlast demonstrated relatively good agreement with experimental pressure data throughout the test duration. The impulse data were seen to drift slightly, although this was attributed to problems with an offset correction. The results from the intermediate and complex scenarios also exhibited relatively good agreement with the experimental data, but deviated later on, after approximately 10 ms. The main concluding remark regarding BeamBlast was that _\"it is capable of reproducing the propagation of relatively weak blast waves around rectangular obstacles up to a few blast time lengths\"_. The deviations in the complex scenario data set may suggest that BeamBlast struggles to replicate blast behaviour in the later stages after detonation accurately--potentially due to the cumulative errors from multiple blast wave interactions. Additional updates were planned for the software in 2015, and further review is required to analyse the sophistication of BeamBlast in the present day.