esa-sceva/satcom-chunk-collection huggingface dataset:
# Performance of the Transport Layer Security Handshake Over 6 TiSCH
## 8 Conclusions  
In this article, we investigate the network performance of the TLSv1.2 and DTLSv1.2 handshake when messages are transferred over a 6 TiSCH network. Although DTLS has been the defacto security protocol for the IoT in the past years, we show that some caution is advised when using it to protect application traffic in low-bandwidth, multihop networks. The unreliability of the underlying transport layer forced the DTLS designers to reimplement several TCP-like features in the DTLS handshake protocol. However, these mechanisms are less flexible than their TCP counterpart. On the one hand, DTLS' handshake outperforms TLS' handshake when it has an ideal communication link between both peers. It uses between 500 B and 800 B less, and completes roughly 0.6 s faster. On the other hand, the performance of the DTLS handshake degrades rapidly when the link quality deteriorates, the performance of the TLS handshake remains more stable in the same conditions. When transport layer packets have a 5% probability of getting lost, the DTLS handshake duration more than doubles, and it uses between \(\pm\)37% and \(\pm\)45% more bytes. The TLS handshake duration grows between \(\pm\)10% and \(\pm\)15%, and the number of exchanged bytes grows between \(\pm\)2% and \(\pm\)15%. The different TCP options allow a developer to tailor the TCP stack and the TLS handshake to the characteristics of the constrained network.  
Besides the handshake comparison, we also propose an improvement for the TLS handshake based on a TCP PEP. The PEP aims to resolve the issues that arise when 6 LoWPAN fragmentation adds network latency, hidden to the TCP retransmission mechanism. Experimental results show that the PEP accelerates the handshake and removes any spurious retransmissions.  
Our current work focuses on analyzing the performance of TLSv1.3 and DTLSv1.3 in constrained networks.  
Conceptualization, T.C., F.R.; Methodology, T.C.; Software, T.C.; Writing--original draft, T.C., M.V., and T.W.; Writing--review & editing, M.V., T.W., F.R. and B.T.; Supervision, B.T. and F.R.; Project administration, B.T. All authors have read and agreed to the published version of the manuscript.  
This work is partly funded by the European Commission through H2020 SPARTA, grant agreement No 830892, www.sparta.eu.  
Not applicable.  
The source code used for the experiments is hosted at [[https://github.com/openwsn-berkeley/openwsn-fw](https://github.com/openwsn-berkeley/openwsn-fw)]([https://github.com/openwsn-berkeley/openwsn-fw](https://github.com/openwsn-berkeley/openwsn-fw)) and [[https://github.com/ARMmbed/mbedtls](https://github.com/ARMmbed/mbedtls)]([https://github.com/ARMmbed/mbedtls](https://github.com/ARMmbed/mbedtls)) (accessed on 1 February 2021).  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Model Validation
#### 3.2.1 E. coli Isolates in Co-Cultures  
First, within the variability evaluation, the \(RMSE\) values [38] between milk growth and milk with 1% NaCl were evaluated for each _E. coli_ isolate. Although the PS2 isolate showed the highest difference in milk numbers between 1% and 0% (\(RMSE\) = 0.67 log CFU/mL), the \(RMSE\) for the BR isolate was only 0.37 log CFU/mL, indicating that this isolate was less sensitive to NaCl addition. In the evaluation of the differences between isolates BR and PS2 in the same medium, they were lower in milk without salt addition (\(RMSE\) = 0.75 log CFU/mL) than in milk with 1% NaCl (\(RMSE\) = 1.11 log CFU/mL). Therefore, these values are more about the different behaviour between _E. coli_ isolates, competitiveness, and sensitivity to NaCl than about variability. The lowest calculated \(RMSE\) values for LAB in all combinations ranged between 0.22 and 0.30 log CFU/mL. For _G. candidum_ sensitive to NaCl, higher \(RMSE\) values of 0.49-0.69 log CFU/mL were found between co-culture growth in milk without NaCl and milk with the addition of 1% NaCl.  
The prediction capability of the H-GD model was also evaluated through the bias (\(B_{\mathrm{f}}\)) and accuracy (\(A_{\mathrm{f}}\)) factors [37] for each microorganism in the _E. coli_ co-cultures. For _E. coli_, the HG-D model data of isolate BR were validated with the experimental data of isolate PS2. The calculated \(B_{\mathrm{f}}\) values for the growth of _E. coli_ isolates were within 0.993-1.387 and the \(A_{\mathrm{f}}\) values ranged from 1.283 to 1.704. With high probability, the prediction of _E. coli_ growth was affected by the growth ability and sensitivity of the PS2 isolate to the addition of NaCl.  
LAB growth was accurately predicted for all co-culture cases and the calculated \(B_{\mathrm{f}}\) values were between 0.996 to 1.003, while the \(A_{\mathrm{f}}\) values ranged from 1.026 to 1.035 showing that, on average, the predicted value was 2.6 to 3.5% different (either smaller or greater) from the observed values. Accurate prediction of LAB growth also confirmed the fact that their growth was minimally affected by other co-culture populations such as _G. candidum_ or _E. coli_ as the values of the interaction coefficient \(I_{\mathrm{LE}}\cong 1.0\) indicated before (Table 2).
###### Abstract  
In the rapidly evolving landscape of next-generation 6G systems, the integration of AI functions to orchestrate network resources and meet stringent user requirements is a key focus. Distributed Learning (DL), a promising set of techniques that shape the future of 6G communication systems, plays a pivotal role. Vehicular applications, representing various services, are likely to benefit significantly from the advances of 6G technologies, enabling dynamic management infused with inherent intelligence. However, the deployment of various DL methods in traditional vehicular settings with specific demands and resource constraints poses challenges. The emergence of distributed computing and communication resources, such as the edge-cloud continuum and integrated terrestrial and non-terrestrial networks (T/NTN), provides a solution. Efficiently harnessing these resources and simultaneously implementing diverse DL methods becomes crucial, and Network Slicing (NS) emerges as a valuable tool. This study delves into the analysis of DL methods suitable for vehicular environments alongside NS. Subsequently, we present a framework to facilitate DL-as-a-Service (DLaaS) on a distributed networking platform, empowering the proactive deployment of DL algorithms. This approach allows for the effective management of heterogeneous services with varying requirements. The proposed framework is exemplified through a detailed case study in a vehicular integrated T/NTN with diverse service demands from specific regions. Performance analysis highlights the advantages of the DLaaS approach, focusing on flexibility, performance enhancement, added intelligence, and increased user satisfaction in the considered T/NTN vehicular scenario.  
distributed learning; vehicular networks; network slicing; edge intelligence; integrated terrestrial non-terrestrial networks +
Footnote †: journal: Sensor and Actuator Networks  
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Footnote 0: Copyright** © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Elite Multi-Criteria Decision Making--Pareto Front Optimization in Multi-Objective Optimization
## 3 Methodology
### Algorithm
#### 3.1.2 Topsis  
Once the weights of the objectives are calculated, the next step in the optimizer is to use the TOPSIS method to rank the individual policies except for the extreme individuals as shown in Figure 2. The TOPSIS method is as follows.  
* Normalize the actual fitness values using Equation (5). \[F_{ij}=\frac{f_{ij}}{\sqrt{\sum_{j=1}^{m}f_{ij}^{2}}}\] (5) where \(i\) is the individual policy, \(j\) is the objective number, \(f\) is the actual fitness value, and \(F\) is the normalized fitness value.
* Next, find the fitness weights by taking the product of the weight of each objective with fitness values using the equation as shown below: \[W_{ij}=w_{j}*F_{ij}\] (6) where \(w_{j}\) is the weight of each objective, and \(F_{ij}\) is the normalized fitness value calculated in Equation (5) for the individual policy \(i\) and objective \(j\).
* Depending on the \(W_{ij}\) value, the best and worst-performing individuals of each objective are selected and flagged as the best or worst individual policy.
* The Euclidean distances between all of the individuals to both the best and worst individuals are calculated and assigned as \(S_{i}^{-},S_{i}^{+}\), where \(S_{i}^{+}\) is assigned as the distance to the best individual and \(S_{i}^{-}\) is assigned as the distance to the worst individual.
* The last step is to find the degree of approximation \(D_{i}\) using Equation (7), \[D_{i}=\frac{S_{i}^{-}}{S_{i}^{-
# Visible Spectrum and Infra-Red Image Matching: A New Method
## 3 Proposed Method
### Regression Modeling Using Corresponding Descriptors
LSS is based on local self-similarity between a small region and a larger one around the feature points and computed with sum of square differences approach. MFD uses directional Log-Gabor filters. These filters are more computationally expensive than simple directional filters used in MN-SIFT. HoDM uses Sobel filters in four different directions to compute image gradients. Then absolute values of the gradients are calculated and the weak gradients are suppressed with a hypothesis. The four image gradient values at each pixel location are compared and binarized. The absolute and binary gradients are then read with a spatial pooling scheme to compute HoDM descriptors. The HoDM process is also computationally expensive than MN-SIFT.
# A Novel Method for Detecting Advanced Persistent Threat Attack Based on Belief Rule Base
## 3 The BRB-Based APT Detection Model
### APT Detection Method Based on BRB
#### 3.2.3 Construction of Rules in BRB-Based APT Detection Model  
The original belief rule base expert system based on the above four features can be established by expert knowledge. Depending on the features, the results can be divided into normal traffic, normal attacks, and APT attacks. APT is the long-duration and multi-stage attack. APT utilizes small transferred bytes, the low traffic rate, and long attack time to avoid the detection. Therefore, the sample in which the area path is abnormal, time interval is higher than the normal, the rate is lower than the normal, and the transferred bytes is low has a high probability of being an APT attack. The abnormal path means an unknown abnormal connection has appeared. It is likely that an attack has occurred. The higher time interval means abnormal connections are infrequent, which helps to hide them; however,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attributes** & \multicolumn{3}{c}{**Reference Values**} \\ \hline Traced Path & Area-1 & Area-2 & \\ Traffic Rate & Low & Medium & High \\ Average Time Interval & Low & Medium & High \\ Average Transferred Bytes & Low & Medium & High \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reference values.  
normal connections are very frequent. The lower traffic rate and transferred bytes means anomalous connections make up only a small fraction of normal traffic when transferred, which reduces the chances of an intrusion detection system detecting it. Because of the large amount of traffic transferred, strict detection of each traffic by the intrusion detection system generates a large number of alarms, causing the normal transmission to not work properly. Then, the rules can be constructed such as follows:  
\[\begin{array}{l}\text{R}_{1}:\text{ IF }(\text{\emph{areapath} is \emph{Abnormal}})\ \land\ (\text{\emph{trafficrate} is \emph{Low}})\\ \land(\text{\emph{timeinterval} is \emph{High}})\ \land\ (\text{\emph{transmithytes} is \emph{Low}})\\ \text{Then Attack is}\{(\emph{No\_Attack},\ 0),(\emph{Normal\_Attack},0),(\emph{APT},1)\}\\ \text{with}\ \theta_{1}=1,\delta_{1}=1,\delta_{2}=1,\delta_{3}=1,\delta_{4}=1 \end{array} \tag{5}\]  
Those with normal attributes are normal samples, while those between normal and APT belong to normal attacks.  
The overall process is shown as Figure 3. Expert knowledge is significant, but not precise enough as a numerical parameter. The optimization algorithm discussed in Section 3.1 is used to optimize the parameters set by the expert to improve the accuracy of detection. The final trained model is used to detect APT attacks and gives interpretability results to trace back APT attacks.  
Figure 3: Process architecture.  
Figure 2: Features extraction.
# A Randomized Algorithm for Optimal PID Controllers
###### Abstract  
A randomized algorithm is suggested for the syntheses of optimal PID controllers for MIMO coupled systems, where the optimality is with respect to the \(H_{\infty}\)-norm, the \(H_{2}\)-norm and the LQR functional, with possible system-performance specifications defined by regional pole-placement. Other notions of optimality (e.g., mixed \(H_{2}/H_{\infty}\) design, controller norm or controller sparsity) can be handled similarly with the suggested algorithm. The suggested method is direct and thus can be applied to continuous-time systems as well as to discrete-time systems with the obvious minor changes. The presented algorithm is a randomized algorithm, which has a proof of convergence (in probability) to a global optimum.  
continuous-time linear systems; discrete-time linear systems; state-space representation; proportional-integral-differential controllers; static output feedbacks; optimal controllers; randomized algorithms; convergence in probability
## 5 Technical Challenges and Ethical Issues
### Ethical Issues  
The application of digital twin technology in personalized medicine carries the potential for groundbreaking advancements in healthcare. However, it also raises concerns about equitable access and ethical implications. For one, the technology may not be universally accessible to every individual or community, thereby exacerbating existing inequalities and creating a new form of 'digital divide' [47]. In regions where healthcare resources are already limited, the introduction of sophisticated digital twin systems could further marginalize those who cannot afford or access this level of personalized care [73].  
Moreover, while digital twins can aggregate and analyze data across a broad population to identify medical patterns, this capability comes with ethical complexities [47]. The technology might inadvertently lead to segmentation that could be deemed discriminatory. For instance, if patterns in the data suggest higher susceptibility to certain conditions among specific ethnic or socio-economic groups, there is a risk that these groups could face stigmatization or unfair treatment. This could manifest in various forms, such as higher insurance premiums or even restricted access to certain types of medical care [73].  
Therefore, while digital twin technology in personalized medicine holds immense promise, it also necessitates careful consideration of ethical and socio-economic factors to ensure that its benefits can be universally realized without introducing new forms of inequality or discrimination [74].
# A Photonic Label-Free Biosensor to Detect _Salmonella_ spp. in Fresh Vegetables and Meat
## 2 Materials and Methods
### Fresh Vegetable Samples  
The biosensor response was assessed using numerous replicates of fresh red Batavia samples, as well as various batches of fresh salad samples. These samples were sourced from a vegetable-processing facility in the region of Valencia (Spain). They were gathered over one year. Prior to their inclusion in the study, these samples had already undergone comprehensive evaluation through traditional plate counting methods, confirming the presence of _Salmonella enterica_.
## 4 Classification of Auto-Configuration Protocols
### Stateful Protocols
#### 4.1.7 D2 hcp  
The Distributed Dynamic Host Configuration Protocol (D2 HCP) [18] is an auto-configuration protocol for mobile _ad hoc_ networks which derives from improvements made to the [PERSON]'s [13] protocol which guarantee the uniqueness of IP addresses used in the network. The protocol makes the nodes of a MANET work together to manage the unique and correct IP address assignment in a distributed manner. All network nodes have the same role; there is no special node type that centralizes the management.  
The protocol is based on the OLSR routing protocol [19] to perform synchronization, and thus detect changes in the network. This synchronization procedure produces a null overhead in network traffic compared to that generated by the OLSR protocol. In this scheme, when a node enters the network, as well as assigning a valid IP address, a range of consecutive addresses are given. From that point on, the new node is responsible for managing the range of addresses, and can allocate part of its addresses to other nodes requesting entry into the network.  
To request a new IP address, the requesting node, referred to as client, sends a _Server_Discovery_ message to its neighbours. The neighbour nodes that are part of the network will answer this request by sending a _Server_Offer_ message. The client node will choose one of the received responses and will send a _Server_Poll_ message to the chosen neighbour node requesting an address. The node which will assign the new address will receive the server name. When the client node receives the _Server_Poll_ message, it will divide its address range into two halves, and give the second half of the client. If the addresses provided by the server node were not their own, but a third node of the network, the server sends an _IP_Range_Request_ message formally requesting these addresses to the node that owns the address range. The third network node sends an _IP_Range_Return_ message authorizing the node that sent the _IP_Range_Request_ message to assign the address block to the client node. After receiving the _Server_Poll_ message, if the provided addresses were from the server node, or if _IP_Range_Return_ message was necessary in the case of having had to ask for address to a third node, the node server sends an _IP_assigned_ message to the client with assigned range.  
Design Considerations:  
* Each node has as associated information, a single block of contiguous free IP addresses for auto-configuration, which include the own IP address of the node itself.  
* The responsibility of recover the IP addresses that a node makes available when it leaves the network is one that can join this free block to the right of its own. This is not possible when the block to be collected contains the lowest address of the network. In that case, the node that collects the block is one that can add to it on the left.
* By dividing the free addresses in two blocks to deliver one of them to a new node entering the network, the node that makes the client server delivers sub-block does not contain its own IP address.
## 4 Blockchain Technologies: Layer 1 and Layer 2  
Blockchain technologies aim to revolutionize digital asset interaction through peer-to-peer decentralized networks. Layer 1 is the term that is used to describe the underlying main blockchain architecture, which includes the creation of blocks, consensus mechanism, and database partitioning, while Layer 2 is an overlaying network that lies on top of the underlying blockchain. It aims to improve scalability and reduce transaction costs significantly by aggregating transactions, processing in parallel, and handling transactions off-chain. These two layers work together to create a more effective, adaptable, and user-friendly digital platform [46].  
Bitcoin and Ethereum networks are the most dominant ones for their distinct features and significant contributions to the blockchain System. Bitcoin is the foundational Layer 1 network, primarily intended for enabling direct transactions between peers using its native coin BTC. It uses a Proof-of-Work (PoW) consensus algorithm, with miners verifying transactions and ensuring network security. However, its simplicity and limited functionality make it a strong platform for digital currency. Ethereum, on the other hand, utilizes the Ethereum Virtual Machine (EVM) to facilitate smart contracts, expanding the range of possible blockchain applications beyond simple financial transactions. Ethereum's Layer 1 initially employed a PoW method but has now transitioned to Proof-of-Stake (PoS) to address scalability concerns and reduce its ecological impact.  
Decentralized networks offer potential benefits, but scalability remains a barrier for many blockchain projects. Increased network congestion can lead to higher transaction costs and reduced throughput, negatively impacting the user experience. In particular, both Bitcoin and EVM-based Layer 1 networks face challenges in terms of scalability and high transaction costs. Bitcoin faces limitations due to its restricted transaction capacity and slow block times, while Ethereum's adaptability and smart contract functionalities have led to increased demand, worsening its scalability concerns. Both networks are exploring Layer 2 alternatives, such as the Lightning Network for Bitcoin and optimistic and ZK rollups for Ethereum, to improve transaction processing capabilities while maintaining security and decentralization [47].
# Vibration Reduction on Circular Disks with Vibroacoustic Metamaterials
### Vibrations of Rotating Disks
#### 1.1.2 Non-Rotating Observer  
When measuring vibrations of circular disks in a stationary reference system, the gyroscopic effect leads to a splitting of the natural frequencies [26; 27]. The resulting two waves can be conceptually interpreted as traveling waves moving with or against the direction of rotation of the disk [25]. The wave propagating in the direction of rotation can be attributed to the increasing eigenfrequency \(f_{1}\), while the wave moving against the rotation direction can be associated with the decreasing eigenfrequency \(f_{2}\). Both natural frequencies of the waves traveling with and against the direction of rotation can be calculated using Equation (3) [25].  
\[f_{1,2}=f_{\mathrm{e}\omega}\pm k\cdot f_{\mathrm{n}} \tag{3}\]
# Capacity of Heterogeneous Mobile Wireless Networks with \(D\)-Delay Transmission Strategy
## 3 Network Model
### Mobility Model  
Two types of models are considered in this paper.  
1. _Two-dimensional i.i.d. mobility model_: Our two-dimensional mobility model is defined as follows: 1. There are \(n\) mobile _ad hoc_ nodes and \(m\) stable helping nodes distributed in a square of unit are. In each time slot, the \(n\)_ad hoc_ nodes are uniformly, randomly positioned in the square. 2. The node positions are independent of each other, and independent from time slot to time slot, so the nodes are totally reshuffled in each time slot. 3. There are \(n\) source-destination pairs in the network. Each node is both a source node and a destination node.
2. _Three-dimensional i.i.d. mobility model_: Our three-dimensional mobility model is defined as follows: 1. There are \(n\) mobile _ad hoc_ nodes and \(m\) stable helping nodes distributed in a cube of unit volume. In each time slot, the \(n\)_ad hoc_ nodes are uniformly and randomly positioned in the unit cube. 2. The rest is the same as the two-dimensional i.i.d. mobility model.  
In this paper, we consider two time scales of mobility: fast mobility and slow mobility. They are described as follows:  
1. _Fast mobility:_ The mobility of nodes is at the same time scale of packet transmission, hence in one time slot, only one-hop transmission is allowed. In this case, the transmission rate (bandwidth) is independent of the number of nodes.
2. _Slow mobility:_ The mobility of nodes is much slower than the packet transmission, hence \(W=\Omega(n)\). In this case, the packet size should be \(W/h\left(n\right)\) to guarantee that \(h(n)\)-hop transmissions are feasible in one time slot.