esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Organized Fast Routing Protocol for Radial Underwater Networks
## 2 Related Work
### Opportunistic Protocols  
The Depth Based Routing protocol (DBR) [26] is based on opportunistic routing. Every node ranks the quality of the route and the next hop is based on the number of hops. There is no mechanism of recovery in case that there is no forwarding node in the direction of the sink node. If the depth of the neighbor receiving the packet is less than the depth of the sender, it will forward the packet. Nodes closer to the sink forward the packet first. However, communication void problems may occur if the nodes are very sparse. The Weighting Depth and Forwarding Area Division DBR (WDFAD-DBR) [27] protocol considers the depths of expected forwarding nodes to improve the delivery ratio, using a location prediction mechanism. The forwarding region adaptively changes according to the density of nodes and the channel conditions, to reduce the number of duplicate packets. Segmented Data Reliable HydroCast [13] is similar to DBR, but it addresses the problem of voids and local maxima. Each local maximum node maintains a recovery route towards a neighbor deeper than itself. It has a higher energy consumption compared to DBR.  
The Multi-Sink Opportunistic Routing Protocol (MSORP) [28] uses a two tier topology. The bottom tier includes the sensor nodes, the mesh nodes and the underwater (UW) sinks and the top tier contains the surface buoy and the monitoring center. Sensor nodes send their data to mesh nodes, which aggregate the data and forward it to the sink. Void-Aware Pressure Routing (VAPR) [29] uses depth information, hop count and sequence numbers to find the next hop and the directional path. Sink nodes start the localization process by transmitting beacon packets. In order to elude the voids in the network, the next hop selection is based on the data forwarding direction and the next-hop's data forwarding direction. Efficient Opportunistic Routing Technology (EFFORT) [30] uses opportunistic end-to-end cost (OEC) to reduce the number of forwarding nodes, and thus increase the network lifetime. The OEC cost is determined on the basis of a forwarding node's residual energy and link reliability.  
Opportunistic Void Avoidance Routing (OVAR) [31] allows nodes which are farther away from the sink than the sender to take part in packet forwarding. However, the nodes closer to the sink node will have priority. This helps to elude the voids or local maxima problem.
## 2 Materials and Methods
### Model for a Dual Standby Communication System  
The system consists of three communication devices with parallel connections, which operate independently.  
The failure of one device was not considered to affect other devices. The system is divided into eight simple states based on Markov analysis: (0, 0, 0), (1, 0, 0), (0, 1, 0), (0, 0, 1), (1, 1, 0), (1, 0, 1), and (0, 1, 1) being operational states, and (1, 1, 1) a failure state. All possible states between them also transition, with changes shown in Figure 8.  
The \"normal\" mode of operating is when all devices are operating without interruption; one can fail, and when one fails, the second device can fail. Two devices cannot be in a failure state simultaneously; if the first one fails, and the second one fails, all three will fail. The repair parameters are the same as the failure parameters.  
Description of probability is shown in Figure 8:  
\(\mathrm{P_{t}}[0,0,0]\) is the probability of system operation at time (t).  
\(\mathrm{P_{t}}[1,0,0]\) is the probability of the first device failing and operation of the second and third devices at time (t).  
\(\mathrm{P_{t}}[0,1,0]\) is the probability of the first and third devices being operational and the probability of the second device failing at time (t).  
\(\mathrm{P_{t}}[0,0,1]\) is the probability of the first and second devices being operational and the third device failing at time (t).  
\(\mathrm{P_{t}}[1,1,0]\) is the probability of the first and second devices failing and the third device being operational at time (t).  
\(\mathrm{P_{t}}[1,0,1]\) is the probability of the first and third devices failing and the second device being operational at time (t).  
\(\mathrm{P_{t}}[0,1,1]\) is the probability of the first device being operational and the second and third devices failing at time (t).  
\(\mathrm{P_{t}}[1,1,1]\) is the probability of the system not operating at time (t).  
\(\lambda_{1}[t]\) is the failure function when the operational status shifts from all three devices being in a normal state to the second device failing.  
\(\lambda_{2}[t]\) is the transition failure function when the operational status shifts from the first and third devices being in a normal state and the second device failing to the first device being in a normal state and the second and third devices failing.  
Figure 8: Reliability model of a communication system with dual standby devices.  
The availability function can be expressed as:  
\[\text{A(t)}=\text{P1}+\text{P2}+\text{P3}+\text{P4}+\text{P5}+\text{P6}+\text{P7}\]  
Reliability operation probability can be expressed as the formula:  
\[\text{A}=\underset{\text{t}\rightarrow\infty}{\text{lim}}\text{A(t)} \tag{16}\]  
We used the case of Weibull distribution in this paper:  
\[\lambda_{\text{i}}(\text{t}) = \beta\lambda_{\text{i}}\text{t}^{\beta-1},\text{ i }=\overline{1,12} \tag{17}\] \[\mu_{\text{i}}(\text{t}) = \beta\mu_{\text{i}}\text{t}^{\beta-1},\text{ i }=\overline{1,12} \tag{18}\]
## 6 Implementation and Case Study
### Adversarial Conditions
#### 6.4.2 RIC Access Attacks  
Here, adversaries target RIC components to overload the network or infer sensitive models.  
**Threat Model:** Let \(\lambda\) denote the legitimate request rate and \(\lambda^{\prime}\) the adversarial flooding rate. When \(\lambda^{\prime}>\lambda_{\text{thresh}}\), the xApp is classified as rogue:  
\[\lambda_{\text{thresh}}=\mu+3\sigma \tag{19}\]  
where \(\mu\) and \(\sigma\) are the mean and standard deviation of request rates in benign scenarios.  
In false KPM (Key Performance Measurement) reporting,  
\[\text{Reported KPM}=\text{True KPM}+\Delta_{\text{fake}} \tag{20}\]  
where \(\Delta_{\text{fake}}\) exceeds a statistically acceptable bound (\(\pm 3\sigma\)).  
**Defense Mechanisms:**  
* **Continuous Authentication:** Short-lived tokens (50 ms refresh cycle) ensure frequent verification.
* **Micro-Segmentation:** Each xApp's access is confined to its minimal network slice, limiting blast radius.
* **Model Integrity Checking:** Model inversion attempts are detected by comparing weight updates \(\Delta W\) with trusted distributions, applying \(\ell_{2}\) bounds.  
Our Zero Trust Architecture (ZTA) framework reduces unauthorized access attempts by 89% compared to a flat access control baseline.
## 2 Mathematical Modeling of DER System
### Linear Load Model Considerations  
From Figure 1, the linear load model considers two fundamental factors that serve to understand the equations of modeling the DER system with a connected load.  
Figure 1: Distributed energy system circuit.  
1. Considers the \(Y_{load}\) admittance as a time-varying parameter, where only the admittance values are known considering the two generic DER operating regimes: empty operation (minimum admittance) and nominal (maximum admittance).
2. Considers current disturbances such as: sags, swell, notch or peak currents among other disturbances, and are modeled by the disturbance current \(i_{d}\)[18]. The \(i_{d}\) current is also an input to the DER system, but it is not controllable and its value will depend on the load, unlike the \(V_{DER}\) signal. In this way, the \(i_{d}\) current is considered as a disturbance to the system and its presence is seen as a source of loss of system performance.  
The load admittance value is in the range of \([0.0001;0.2\,S]\), that is:  
\[Y_{\text{load}}=\frac{1}{Z_{\text{load}}},Y_{\text{load}}\in\Delta:=\{Y_{ \text{load}}:0.0001\,S\leq Y_{\text{load}}\leq 0.2\,S\} \tag{1}\]
## 1 Introduction
The characteristics of the jet breakup were compared from subcritical to supercritical conditions. They hypothesized that the high pressure would accelerate the jet breakup processes. A continuous phase transition became dominant and no droplet formation was observed. However, all the above studies are concerned only with the phenomenological study of supercritical jet injection. There is still a lack of relevant research on the instability of the supercritical jet shear layer using molecular dynamics. Depending on the conditions of the initial fluid and the environment, there are typically four kinds of supercritical injection processes [14]: I. The supercritical jet is injected into the supercritical environment; II. The subcritical jet is injected into the supercritical environment; III. The supercritical jet is injected into the subcritical environment; IV. The subcritical jet is injected into the supercritical environment and then into the subcritical environment. Injections of type II, type III and type IV are generally considered to be transcritical because they pass through the critical point during the injection process. More importantly, type II is becoming more and more important because this type of condition occurs most often in liquid rocket engines. Therefore, we focus on type I and type II in our study. In the present study, an accurate simulation method of the supercritical spray using molecular dynamics was performed and the spatial instability of the jet mixing layer was studied. Firstly, the molecular dynamics simulation of the thermodynamic property of nitrogen was carried out and compared with the Soave-Redlich-Kwong (SRK) equation of state. Then the molecular dynamics simulation of the supercritical nitrogen jet was performed at different conditions. The working conditions of the supercritical and the subcritical nitrogen jets ejected into a supercritical environment were calculated. Through the analysis of the results, the distribution of jet velocity, density, temperature and other jet characteristics were obtained. The interface was determined and the disturbance growth rate of the transcritical shear layer under different conditions was obtained. Finally, the growth rate obtained by molecular simulation was compared to the results of the linear stability analysis.
# Enterprise Security for the Internet of Things (IoT): Lightweight Bootstrapping with EAP-NOOB
###### Abstract  
The emergence of radio technologies, such as Zigbee, Z-Wave, and Bluetooth Mesh, has transformed simple physical devices into smart objects that can understand and react to their environment. Devices, such as light bulbs, door locks, and window blinds, can now be connected to, and remotely controlled from, the Internet. Given the resource-constrained nature of many of these devices, they have typically relied on the use of universal global shared secrets for the initial bootstrapping and commissioning phase. Such a scheme has obvious security weaknesses and it also creates undesirable walled-gardens where devices of one ecosystem do not inter-operate with the other. In this paper, we investigate whether the standard Extensible Authentication Protocol (EAP) framework can be used for secure bootstrapping of resource-constrained devices. EAP naturally provides the benefits of per-device individual credentials, straightforward revocation, and isolation of devices. In particular, we look at the Nimble out-of-band authentication for EAP (EAP-NOOB) as a candidate EAP authentication method. EAP-NOOB greatly simplifies deployment of such devices as it does not require them to be pre-provisioned with credentials of any sort. Based on our implementation experience on off-the-shelf hardware, we demonstrate that lightweight EAP-NOOB is indeed a way forward to securely bootstrap such devices.  
EAP-NOOB; contiki; IoT; bootstrapping; security +
Footnote †: journal: Sensors
## On the Routing Protocol Influence on the Resilience of Wireless Sensor Networks to Jamming Attacks  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and**  
**[PERSON] 1**  
1 Department of Electrical and Computer Engineering, Tecnologico de Monterrey, Ave. Eugenio Garza Sada #2501 Sur, Monterrey, NL 64849, Mexico; E-Mails: [EMAIL_ADDRESS] (C.M.-P.); [EMAIL_ADDRESS] (J.A.N.-F.)  
2 School of Science and Engineering, Tecnologico de Monterrey, Campus Estado de Mexico, Carretera al lago de Guadalupe Km 3.5, Col. Margarita M. de Juarez, Atizapan 52926, Mexico; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +52-81-8404620.  
Academic Editors: [PERSON] and [PERSON]  
_Received: 30 November 2014 / Accepted: 20 March 2015 / Published: 27 March 2015_  
**Abstract:** In this work, we compare a recently proposed routing protocol, the multi-parent hierarchical (MPH) protocol, with two well-known protocols, the _ad hoc_ on-demand distance vector (AODV) and dynamic source routing (DSR). For this purpose, we have developed a simulator, which faithfully reifies the workings of a given protocol, considering a fixed, reconfigurable _ad hoc_ network given by the number and location of participants, and general network conditions. We consider a scenario that can be found in a large number of wireless sensor network applications, a single sink node that collects all of the information generated by the sensors. The metrics used to compare the protocols were the number of packet retransmissions, carrier sense multiple access (CSMA) inner loop retries, the number of nodes answering the queries from the coordinator (sink) node and the energy consumption. We tested the network under ordinary (without attacks) conditions (and combinations thereof) and when it is subject to different types of jamming attacks (in particular, random and reactive jamming attacks), considering several positions for the jammer. Our results report that MPH has a greater ability to tolerate such attacks than DSR and AODV, since it minimizes and encapsulates the network segment under attack. The self-configuring capabilities of MPH derived from a combination of a proactive routesupdate, on a periodic-time basis, and a reactive behavior provide higher resilience while offering a better performance (overhead and energy consumption) than AODV and DSR, as shown in our simulation results.  
keywords: wireless sensor networks; jamming; network resilience; routing protocols; network performance +
Footnote †: journal: Journal of Network Science
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 4 Trends in Literature for Different Types of ML Interpretation Techniques  
### Analysis of Trends in Literature for Different Types of ML Interpretation Techniques  
Figure 2 plots the numbers of publications on various types of interpretation technique each year. In analyzing the trend lines, it becomes evident that each category of interpretation techniques experienced varying degrees of interest and utilization over time. Notably, the hybrid and model-based categories exhibit an initial period of stronger growth, followed by a period of much more gradual growth, before a final spurt of interest around 2020. The post hoc and representation-based categories demonstrate fairly constant exponential growth, starting from a lower base. The growth of the representation-based category is strongest, though currently it still achieves the lowest number of publications annually.  
The trends in the logarithm of Google Scholar references from 1986 to 2022 were notably influenced by pivotal developments in the respective techniques. For instance, the introduction of the mind-the-gap model (MGM) in 2015 and counterfactual explanations (CEs) in 2022 within the hybrid category contributed to its sustained upward trajectory. Similarly, the inclusion of decision trees (DTs) in 1986 and random forests in 2001 within the model-based category contributed to its consistent yet slightly slower growth. Within the post hoc category, the combined introduction of Bayesian rule lists (BRLs) and Shapley additive explanations (SHAPs) in 2017, permutation feature importance (PFI) in 2010, local interpretable model-agnostic explanations (LIMEs) in 2016, and deep learning important features (DeepLIFT) and gradient-weighted class activation mapping (Grad-CAM) in 2019 accounts for the observed fluctuations, denoting varying levels of interest over time. Moreover, the incorporation of saliency maps (SMs) in 1995, activation maximization (AM) in 2009, and attention mechanisms (AtMes) in 2018 within the representation-based category contributes to its modest yet gradually increasing trend, reflecting sustained but relatively moderate scholarly attention to these interpretation techniques over the years. In summary, the trends in the logarithm of Google Scholar references from 1986 to 2022 were significantly shaped by pivotal developments within each technique category, demonstrating the nuanced evolution of ML interpretation techniques. The introduction of specific techniques not only influenced their individual trajectories but also led to spill-over effects, reshaping the broader landscape of scholarly interest and research focus in the field. These insights underscore the intricate interplay between technological advancements and scholarly attention, emphasizing the dynamic nature of the ML interpretation landscape and the continued evolution of research priorities within the scholarly community. These findings provide valuable insights into the dynamic landscape of interpretation techniques and the evolving focus within the scholarly community.
# Intrinsic Interference Cancellation Scheme for FBMC-OQAM Systems Based on Power Multiplexing
## 3 Description of the Proposed Scheme
### Power Penalty for Intrinsic Interference Cancellation  
The total power of \(\mathbf{s}_{CAP}\) transmitted in the CAP method is calculated by  
\[\begin{split}\varepsilon_{CAP}&=tr\Big{\{}\mathbb{ E}\Big{\{}\mathbf{s}_{CAP}\mathbf{s}_{CAP}^{H}\Big{\}}\Big{\}}\\ &=tr\Big{\{}\mathbb{E}\Big{\{}\mathbf{C}\mathbf{C}^{H}\Big{\}}+ \mathbb{E}\Big{\{}\delta_{CAP}\mathbf{\delta}_{CAP}^{H}\Big{\}}\Big{\}},\\ &=(N-1)+\mathbf{\delta}_{CAP}^{H}\mathbf{\delta}_{CAP}\\ &=(N-1)+\big{(}\mathbf{\theta}^{\prime}_{CAP}\big{)}^{2}\end{split} \tag{22}\]  
where \(\mathbb{E}\big{\{}\mathbf{a}_{CAP}^{H}\mathbf{a}_{CAP}\big{\}}=N-1\) is the power of a transmitted vector \(\mathbf{a}_{CAP}=[a_{1}\ a_{2}\ \cdots\ a_{N-1}]^{T}\) that contains \(N-1\) data symbols and \(\left(\mathbf{\theta}^{\prime}_{CAP}\right)^{2}\) described in (6) is the power penalty for intrinsic interference cancellation.  
Similarly, the total power of \(\mathbf{s}\) in (12), which is transmitted in the proposed method, is calculated by  
\[\begin{split}\varepsilon_{proposed}&=tr\Big{\{}\mathbb{ E}\Big{\{}\mathbf{s}\mathbf{s}^{H}\Big{\}}\Big{\}}\\ &=tr\Big{\{}\mathbb{E}\Big{\{}\mathbf{Q}\mathbf{G}(\mathbf{Q} \mathbf{G})^{H}\Big{\}}+\mathbb{E}\Big{\{}\delta\mathbf{\delta}^{H}\Big{\}} \Big{\}},\\ &=N+\mathbf{\delta}^{H}\mathbf{\delta}\\ &=N+\big{(}\mathbf{\theta}^{\prime}\big{)}^{2}\end{split} \tag{23}\]  
where \(\mathbb{E}\big{\{}\mathbf{a}^{H}\mathbf{a}\big{\}}=N\) is the power of the transmitted vector \(\mathbf{a}=[a_{1}\ a_{2}\ \cdots\ a_{N}]^{T}\) that contains \(N\) data symbols, and \(\left(\mathbf{\theta}^{\prime}\right)^{2}\) described in (16) is the power penalty for intrinsic interference cancellation. It is obvious that \(\mathbf{\theta}^{\prime}\) and \(\mathbf{\theta}^{\prime}_{CAP}\) have very close values; hence, the proposedmethod and the CAP method have similar power penalties for intrinsic interference cancellation.
###### Abstract  
The rapid growth in the number of vehicles has led to traffic congestion, pollution, and delays in logistic transportation in metropolitan areas. IoT has been an emerging innovation, moving the universe towards automated processes and intelligent management systems. This is a critical contribution to automation and smart civilizations. Effective and reliable congestion management and traffic control help save many precious resources. An IoT-based ITM system set of sensors is embedded in automatic vehicles and intelligent devices to recognize, obtain, and transmit data. Machine learning (ML) is another technique to improve the transport system. The existing transport-management solutions encounter several challenges resulting in traffic congestion, delay, and a high fatality rate. This research work presents the design and implementation of an Adaptive Traffic-management system (ATM) based on ML and IoT. The design of the proposed system is based on three essential entities: vehicle, infrastructure, and events. The design utilizes various scenarios to cover all the possible issues of the transport system. The proposed ATM system also utilizes the machine-learning-based DBSCAN clustering method to detect any accidental anomaly. The proposed ATM model constantly updates traffic signal schedules depending on traffic volume and estimated movements from nearby crossings. It significantly lowers traveling time by gradually moving automobiles across green signals and decreases traffic congestion by generating a better transition. The experiment outcomes reveal that the proposed ATM system significantly outperformed the conventional traffic-management strategy and will be a frontrunner for transportation planning in smart-city-based transport systems. The proposed ATM solution minimizes vehicle waiting times and congestion, reduces road accidents, and improves the overall journey experience.  
adaptive traffic management system; internet of things; machine learning; DBSCAN method; intelligent traffic management; smart road; intelligent transport system +
Footnote †: journal: _Sensors_**2022**, 22, 2908. [[https://doi.org/10.3390/s22082908](https://doi.org/10.3390/s22082908)]([https://doi.org/10.3390/s22082908](https://doi.org/10.3390/s22082908))
# Void Hole Avoidance for Reliable Data Delivery in IoT Enabled Underwater Wireless Sensor Networks
## 2 Related Work and Problem Statement
Thus, B-DBR looks for a forwarder in all possible directions within its transmission range to find an alternate path to deliver data at the destination; while C-DBR and CA-DBR minimize end-to-end delay along with collision by making clusters in the network. The related work is summarized in Table 1.