esa-sceva/satcom-chunk-collection huggingface dataset:
# Network Slicing for mMTC and URLLC Using Software-Defined Networking with P4 Switches
## 3 The Proposed SDNPS
### Pseudocode of SDNPS  
We assume that all switches in the data plane of SDN would support P4. Prior to describing the SDNPS pseudocode, we first define the symbols used by the SDNPS. As summarized in Table 2, Ts denotes the sampling period for INT data. \(Hd\), \(Hq\), and \(Hu\) are the thresholds of delay, queue occupancy, and link utilization, respectively, at any switch that triggers a path change in the same slice because a bottleneck at any switch in a flow path leads to severe performance degradation for the path. \(Di\), \(Qi\), and \(Li\) denote the processing delay, queue occupancy, and link utilization of switch \(i\), respectively.  
As listed in Table 3, when each URLLC or mMTC packet arrives at the ingress switch, it is forwarded through the assigned flow path in its corresponding slice. The protocol field in Flow_h is set to 0 \(\times\) 01 every 0.5 s, and each switch along a flow path identified by the RouteID field adds its INT data following Flow_h before forwarding the packet. The INT data comprise the queue occupancy, link throughput, and processing delay for each switch \(i\) along the flow path. When a packet carrying INT data arrives at the egress switch, the INT data are duplicated and sent to the application plane to collect the status of each switch on the flow path. The network slice controller is responsible for changing paths in each slice and informing the reroute module of any path reconfigurations. mMTC packets are rerouted to the other paths in the mMTC slice if the queue occupancy or link utilization of at least one switch is larger than \(Hq\) or \(Hu\), respectively. URLLC packets will be rerouted to the other paths in the URLLC slice if the processing delay, queue occupancy, or link utilization of at least one switch is larger than \(Hd\), \(Hq\), or \(Hu\), respectively, because URLLC services are time critical. All P4 switches on a flow path are activated to update the forwarding rules when necessary.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Symbols** & **Denotations** \\ \hline Ts & Sampling period for INT data \\ \(Hd\) & Threshold of delay for each switch \\ \(Hq\) & Threshold of queue occupancy for each switch \\ \(Hu\) & Threshold of link utilization for each switch \\ \(Di\) & Hop Delay of switch \(i\) \\ \(Qi\) & Queue occupancy of switch \(i\) \\ \(Li\) & Link utilization of switch \(i\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols and denotations.  
Figure 8: Flow_h fields.  
Figure 7: SDNPS data packet format.  
Figure 9: INT_h fields.
## 4 Analysis of Damage Characteristics
### Damage Process  
It is mentioned in Section 3.1 that the failure process of layered sandstone goes through the elastic deformation stage, yield stage, brittle damage stage, and residual failure stage. In this section, the acoustic emission curve and stress cloud map are combined to further study this process. It should be noted that, according to the previous results, we found that although the data on the strength and peak stress of sandstone with different bedding strengths and different bedding angles were different, the damage process was similar. Therefore, this section studies weakly layered sandstone, and the results can be applied to other sandstone.  
In Figure 5, each stage of the damage process is analyzed:  
1. Elastic deformation stage: Stress increases linearly with strain, and the slope of the curve remains constant. With the continuous loading of the displacement, the strain energy gradually accumulates, but the number of AE events does not change significantly, and no cracks are generated at this time.
2. Yield stage: This is also the stage of crack emergence, expansion, and penetration. When the stress exceeds 50% of the peak value, the slope of the curve decreases somewhat, but it is not obvious; there is a trace of AE events, but no obvious fluctuations; at this time, the tiny cracks sprout. When the stress exceeds 80% of the peak value, the number of AE events fluctuates slightly, but there is no obvious increase, and the micro-cracks begin to expand and penetrate to form larger cracks. A small amount of plastic deformation occurs in the rock at this stage, but it is not significant.
3. Brittle damage stage: After the stress exceeds the peak value, the curve drops suddenly, the accumulated strain energy is released instantly, and the number of AE events shows a steep increase in the number of single peaks; the cracks generated in the yield stage extend rapidly, and the specimen undergoes brittle damage.
4. Residual damage stage: The brittle damage occurs in an instant, followed by the residual damage stage, where the AE signal suddenly disappears. The stress level is extremely low in this stage, and the specimen is biased. In the whole damage process, the elastic stage is the longest, followed by the yield stage. The brittle damage stage is instantaneous, so the residual damage stage, when the rock body has destroyed the end, strictly speaking, cannot be attributed to the main process of damage. The figure shows a schematic diagram of the stress bar in the y-directional stress field damage cloud, in which the negative sign represents the compressive stress and the opposite is the tensile stress. It can be seen that the y-directional stress field damage cloud varies with the stress-strain curve as follows: the stress state before the peak stage is compressive.  
Figure 5: _Cont._
# A Context-Aware Conversational Agent in the Rehabilitation Domain
## 4 Data Collection  
### Monitoring Sensor Types  
To ascertain that all data collection and monitoring objectives are met, diverse types of sensors, both ambient and wearable, are being integrated to the platform; commercial sensors have been enhanced with IoT functionalities, while other sensors may be built from scratch to operate according to desired specifications. Therefore, the system receives as input varied types of data, some of which can be directly explored (i.e, temperature measurements) while others require further manipulation to extract meaningful conclusions (i.e., sleep sensor data). Notably, the following sensors and systems will be used:  
* Blood pressure sensor: A traditional off-the-shelf blood-pressure system will be enhanced with wireless connectivity, converting it to a smart IoT device.
* Blood sugar sensor: A wireless module will be integrated to a common blood sugar sensor to imbue WiFi capabilities and to enable cloud communication.
* Sleep quality sensor: To assess a user's sleep quality, data from a sensor-integrated sheet that is placed beneath his/her bed is used. The embedded sensors include a Wi-Fi module, which, for the duration of the sleep, uploads data to the REA platform for further analysis. Depending on parameters like body movement, posture, and interruptions, a proactive system intervention could be triggered.
* Vision system: It consists of a depth camera, a thermal camera, and an RGB camera, sending data via a USB connection to a local server, which analyses the relevant footage using computer vision models for crowd detection, fall detection, and activity recognition [36].
* Bed management system: Dual gyroscopic sensors and IoT functionality will be added to patient beds that will allow for remote control of both the head and leg section of each bed using voice commands. Moreover, the same applies to each section's angle, since it is possible for the user, either a patient or a caregiver, to tilt the respective section to the desired position.
* Wearable sensor: Physiological measures like the patient's temperature, heart rate, and blood oxygen levels are monitored by the REA wearable sensor, which takes the form of a bracelet. The integrated accelerometer and gyroscopic sensors give the potential of an early warning in fall emergencies. Through a Bluetooth Low Energy (BLE) gateway, all readings are forwarded to AWS via Wi-Fi.  
As already mentioned in Section 3.1, the AWS platform accommodates REA's data collection infrastructure, of which the IoT service is responsible for secure sensor communication via an MQTTbroker; a user-controlled and -parameterised public key infrastructure is available, while AWS Lambda functions tackle sensor MQTT messages before being processed by a task engine, of which the role is to save data in the database or to push notifications to related services. A REST API handles access to the stored database data.
# Enforcing Optimal ACL Policies Using K-Partite Graph in Hybrid SDN
###### Abstract  
Software Defined Networking (SDN) as an innovative network paradigm that separates the management and control planes from the data plane of forwarding devices by implementing both the management and control planes at a logically centralized entity, called controller. Therefore, it ensures simple network management and control. However, due to several reasons (e.g., deployment cost, fear of downtime) organizations are very reluctant to adopt SDN in practice. Therefore, a viable solution is to replace the legacy devices by SDN devices incrementally. This results in a new network architecture called hybrid SDN. In hybrid SDN, both SDN and legacy devices operate in such a way to achieve the maximum benefit of SDN. The legacy devices are running a traditional protocol and SDN devices are operating using Open-flow protocols. Network policies play an essential role to secure the entire network from several types of attacks like unauthorized access and port/protocol control. In a hybrid SDN, policy implementation is a tedious task that requires extreme care and attention due to the hybrid nature of network traffic. Network policies may be implemented at various positions in hybrid SDN, e.g., near the destination or source node, and at the egress or ingress ports of a router. Each of these schemes has some trade-offs. For example, if policies are implemented near the source nodes then each packet generated from the source must pass through the filter and, thus, requires more processing power, time, resources, etc. Similarly, if policies are installed near the destination nodes, then a lot of unwanted traffic generated causing network congestion. This is an NP-hard problem. To address these challenges, we propose a systematic design approach to implement network policies optimally by using decision tree and K-partite graph. By traversing all the policies, we built up the decision tree that identifies which source nodes can communicate with which destination. Then, we traverse the decision tree and constructs K-partite graph to find possible places (interfaces of the routers) where ACL policies are to be implemented based on the different criteria (i.e., the minimum number of ACL rules and the minimum number of transmissions for unwanted traffic). The edge weight represents the cost per criteria. Then, we traverse the K-partite graph to find the optimal place for ACL rules implementation according to the given criteria. The simulation results indicate that the proposed technique outperforms existing approaches in terms of computation time, traffic optimization and successful packet delivery, etc. The results also indicate that the proposed method improves network performance and efficiency by decreasing network congestion and providing ease of policy implementation.  
Keywords:Hybrid SDN; Policy Implementation; Graph Computation; Traffic Optimization +
Footnote †: journal:
## 3 The Proposed Scheme
### Complexity Analysis  
With regard to the complexity of the proposed scheme, the \(\mathcal{S}1\) and \(\mathcal{S}2\) are dominant in the receiver. However, the computational complexity of the training stage is really hard to evaluate. We know that the DL network is always trained in offline ways. However, as long as the model is well-trained, it can be deployed with the optimized \(\hat{\mathbf{\Theta}}\) and the forward propagation is employed for signal calculation. Thus, only some additions and multipliers are needed in the testing stage. For simplicity, only the forward propagation is considered when quantifying the computational complexity. Considering that all of the hidden layers employ \(D\) neurons, the complexity of \(\mathcal{S}1\) can be approximately expressed by \(\mathcal{O}\big{(}\mathcal{M}D+(\mathcal{L}1-2)D^{2}\big{)}\). Similarly, the complexity of \(\mathcal{S}2\) is denoted as \(\mathcal{O}\big{(}\mathcal{P}D+(\mathcal{L}2-2)D^{2}\big{)}\).
## 1 Introduction
In order to quantify coke phases that are difficult to segment automatically, as well as certain sinter phases such as primary and secondary hematite discussed above, the traditional approach employs manual point counting by a trained petroographer. The problem with this approach, apart from it being labor intensive and thus expensive, is that it can be very subjective. It is even possible for an individual petroographer to report different results for the same sample if re-analyzed after a significant time. Automated optical image analysis reduces the subjectivity and makes the characterization more consistent. The approach adopted during OIA to segment phases with similar reflectivity would be analogous to what petroographers employ during manual point counting--i.e., segmentation by structure/texture. CSIRO's Carbon Steel Futures group developed the optical image analysis software Mineral4/Recognition4 for optical image analysis of major ironmaking feedstock materials such as ores (iron ore in particular) including lump and fine ores, sinters, pellets and briquettes, coal, coke etc. [9; 10]. It can comprehensively characterize phase abundances, porosity, liberation/association, texture, and other sample characteristics. The first and the most important step during characterization is the correct identification of phases (see [14]). Even if a multi-thresholding [20] approach is used, it will not necessarily allow for the acceptable segmentation of phases when their reflectivities overlap. To achieve this, a textural identification module was developed for the software, allowing the segmentation of phases which have the same or significantly overlapping reflectivity, but different morphology. To characterize a particular material, an \"analysis profile\" is developed, which records individual parameter settings and adjustments made during different stages of image analysis. The textural identification unit is a subset of the mineral/phase identification stage. It is engaged when necessary and can perform differently for different materials/phases according to the profile settings. Figure 1: Thresholding of hematite in sinter during image analysis: (**a**) original image; (**b**) partial identification of hematite (segmentation of the brightest part); and (**c**) full identification. Reflectivity histograms show the different red channel low and high threshold values and represent screenshots from Zeiss AxioVision software. This article demonstrates algorithms developed for textural segmentation of different ironmaking feedstock materials performed by the textural identification module in Mineral4/Recognition4. These algorithms are based on well-established image analysis procedures such as binary Erosion, Dilation, size-based noise reduction (Scrapping) etc. [21]. Similar approaches can also be used for image analysis of any other materials within a very wide range of possible OIA applications.
## 2 Materials and Methods
### Deep Learning Model for EPVS Rating Estimation
#### 2.4.1 Preprocessing  
During the preprocessing step, first the signal intensities of MRI were normalized in the range 0-1 by dividing the maximum value for each MRI scan. Then, in three selected MRI slices of the left and right brain, basal ganglia regions were cropped. Cropping the basal ganglia regions was necessary because EPVS ratings were estimated solely based on the basal ganglia region. Overall, a total of 576 images (6 images \(\times\) 96 subject) were obtained and used as a dataset. Finally, the cropped images were enhanced using a Haar-transform-based approach [32]. The application of Haar-transform to the cropped images resulted in emphasized images with clear visibility of EPVS. For illustration purposes, the cropped image before and after applying Haar-transform to the selected subject is shown in Figure 2. In Figure 2 (circled with red color), we can clearly observe that the EPVS of the basal ganglia region has better visibility. Finally, the pre-processed images were resized to 112 \(\times\) 86 before feeding to the deep neural network.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**EPVS Rating** & **0** & **1** & **2** & **3** & **4** \\ \hline Number of EPVS & 0 & 1–10 & 11–20 & 21–40 & \textgreater{}40 \\ Number of images & 120 & 150 & 198 & 90 & 18 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rating score of EPVS and manual ratings of the dataset (576 images in total) in basal ganglia region (0 (no EPVS found); 1–10 (mild); 11–20 (moderate); 21–40 (frequent); \textgreater{}40 (severe)).  
Figure 1: Proposed framework for direct rating estimation of EPVS. Each highlighted box represents the image pre-processing to enhance the EPVS region and the EPVS rating estimation model.
###### Abstract  
The application of deep neural networks to address automatic modulation recognition (AMR) challenges has gained increasing popularity. Despite the outstanding capability of deep learning in automatic feature extraction, predictions based on low-data regimes with imbalanced classes of modulation signals generally result in low accuracy due to an insufficient number of training examples, which hinders the wide adoption of deep learning methods in practical applications of AMR. The identification of the minority class of samples can be crucial, as they tend to be of higher value. However, in AMR tasks, there is a lack of attention and effective solutions to the problem of Imbalanced-class in a low-data regime. In this work, we present a practical automatic data augmentation method for radio signals, called SigAugment, which incorporates eight individual transformations and effectively improves the performance of AMR tasks without additional searches. It surpasses existing data augmentation methods and mainstream methods for solving low-data and imbalanced-class problems on multiple datasets. By simply embedding SigAugment into the training pipeline of an existing model, it can achieve state-of-the-art performance on benchmark datasets and dramatically improve the classification accuracy of minority classes in the low-data imbalanced-class regime. SigAugment can be trained for uniform use on different types of models and datasets and works right out of the box.  
automatic modulation recognition (AMR); deep learning; data augmentation; low-data regime; imbalanced-class
## 4 Technologies for Smart Grid
### ICT Infrastructure for Advance Protection System  
The SG protection is expected to respond not only grid infrastructure faults such as the failure in equipment and natural catastrophe, but also unplanned cyber-attacks. A layer-based ICT infrastructure is required to incorporate into SG system in order to avoid illegal access, and allows the system resiliency during a cyber-attack [64].  
Three coordinated layers, such as predictive, inherent adaptive and corrective protections, based ICT infrastructure has been proposed in reference [65]. The ICT infrastructure must have the ability to address the malicious attacks on transmitted information in the SG. The consequences of the malicious attacks include delay in information transmission corrupt data, or make the network inaccessible to exchange information. In the data integrity attacks, attempts are made to purposely modify or corrupt data in the SG. Whereas, in the information privacy attacks attempts are made to spy on communications to obtain the targeted data such as customer's account information [30; 65]. Smart protections along with layer-based ICT infrastructure have been proposed by several researchers in order to address malicious attack [66; 67]. ICT infrastructure of smart grid is represented in Figure 8.  
Figure 7: Power and telecommunication network in smart grid. It works as the backbone of SCADA system. It contains wireless radio connection, dial-up lines, ethernet, and IP protocols. Fiber-optic and power line communications are expected to integrate into SG system [63].
At present, methods for the removal of heavy-metal ions mainly include redox coprecipitation, ion exchange and membrane separation, reverse osmosis and adsorption, etc. Among these methods, adsorption is an energy-saving, highly efficient, and environmentally friendly approach [4], and is, therefore, widely used for the removal of heavy metals. The heavy-metal removal efficiency is determined by the adsorbent used. Current adsorbents include activated carbon, graphene, carbon nanotubes, etc., owing to their porous structure and unique surface characteristics, which are all advantageous for the removal of heavy metals. However, these materials are not cheap, and their large-scale use results in high costs [5]. In addition, nanomaterials may potentially be toxic to the environment and human body [6]. Therefore, finding an economical, environmentally friendly material with good adsorbent properties is a key focus of current research in this area [7]. Biochar is a carbon-enriched solid product obtained from the pyrolysis of biomass in the absence of air or under insufficient oxygen supply. Biochar has a nanoscale porous structure and interconnected pores that heavy-metal ions can easily enter. The surface of biochar contains functional groups rich in N, S, P, etc., which are able to form stable complexes with heavy-metal ions. At the same time, biochar is extremely stable in the environment, not easily mineralised or decomposed by microorganisms, and has a long retention time. More importantly, waste straw, litter, weed, animal manure, and even sludge can be used as raw materials for the production of biochar, thus being an important example of agricultural recycling [8]. However, the heavy-metal removal efficiency of biochar varies due to differences in the starting material, preparation temperature, environmental conditions (such as pH), modification of the biochar's surface, and the amount of adsorbent used [9]. We selected starting materials that are common biomasses easily obtainable in large amounts, i.e., rice husk and cotton straw. Pyrolysis temperatures of 300, 400, 500, 600, and 700 \({}^{\circ}\)C were used to prepare biochar, and to study its adsorption characteristics towards Cd\({}^{2+}\) ions and other influencing factors. Various characterisation methods were used to examine the surface characteristics and adsorption mechanisms of various biochar samples in order to provide a theoretical basis for biochar remediation of Cd\({}^{2+}\) pollution, and the comprehensive utilisation of biomass.