esa-sceva/satcom-chunk-collection huggingface dataset:
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
## 3 Framework Design
### Framework Features  
The following characteristics and features are considered as fundamental properties of the system by default:  
* The user does not always have to provide credentials to be continuously authenticated beyond the point-of-entry, unless a specific situation requires it.
* Users are not required to set up any security configuration, but are required to provide some related information and preferences that will be enhanced with contextual information collected by the system itself. The homeowner configures the user and group policies, which can be easily applied.
* Any undesired offensive event, such as using another user's credentials, will not permit access to services with high-security levels.
* The devices need to be protected in two scenarios:
* From local users with access to the wireless network who are not permitted to access the home devices
* From remote access by attackers who try to access home devices.
* Users are provided access privileges that expire without the need for manual revocation in the case of not manually terminating the access session. For example, re-checking the utilized contextual information, such as calendars and locations, should be performed at particular time intervals. When there is some variation from predefined policies, the access session will be automatically revoked.
* The re-check interval is set to one minute as an initial value, and this time is then updated based on the average of the previous access sessions of the user. For example, if the average of the previous access sessions is 10 minutes, then the frequency of re-checking the context information would be one-third of this time. When any of the authentication requirements do not meet predefined roles (the predefined confidence levels, as shown in Table 6, are the minimum threshold for accessing the required device), the access session will be automatically revoked. Access to some devices is restricted to home (local area) boundaries, using short-range wireless communication methods to prevent access from outside the home environment via the used wireless network. Another attribute used for ensuring the correct connected device is by comparing some of the device sensor readings, such as temperature, with locally established sensor readings.
* Once the authentication process is complete, the device that can be accessed will appear in the user's access page devices list. Some devices can be accessed anonymously or without additional authentication, while others will be greyed-out or hidden entirely if blocked.
* The access control policies will be assigned based on the resulting authentication average weight. All authentication logs are registered in the database in order to be used for future logging decisions and analysis.
## 4 Wireless Body Area Networks (WBANs)
### New Sensors Generations for BASNs
#### 4.4.4 Bio-Inspired Nodes  
As the end point of the evolution trends, we envision bio-inspired units (nodes), both from the architecture and technology viewpoint. The physical scale of these devices approaches a few cubic microns (or less), and the interaction between the target sensor and the sensor itself disappears. Molecular engineering and nanotechnology will make these devices a reality in the near future. Some research efforts showed that some of the features required in the sensor node can be realized by molecular-scale devices, which are often engineered with the use of bio-molecules. These devices operate autonomously, powered by chemical reactions inspired by biological systems. The process of construction and architecture of these devices will also resemble natural processes in biology: bottom-up self-assembly, self-reproduction and self-repair will be necessary in addition to the safety and biocompatibility.
## 5 Contiki
### Communication Protocol Support  
Contiki supports a rich set of communication protocols. In Contiki, an application can use both versions of IP _i.e._, IPv4 and IPv6. Contiki provides an implementation of _u_IP, a TCP/IP protocol stack for small 8 bit micro-controllers. _u_IP does not require its peers to have a complete protocol stack, but it can communicate with peers running a similar lightweight stack. The _u_IP implementation has the minimum set of features needed for a full TCP/IP stack. _u_IP is written in C, it can only support one network interface, and it supports TCP, UDP, ICMP, and IP protocols.  
Contiki provides another lightweight layered protocol stack, called Rime, for network-based communication. Rime provides single hop unicast, single hop broadcast, and multi-hop communication support. Rime supports both best effort and reliable transmission. In multi-hop communication, Rime allows applications to run their own routing protocols. Applications are allowed to implement protocols that are not present in the Rime stack.  
Contiki does not support multicast. Therefore Contiki does not provide any implementation of group management protocols such as the Internet Group Management Protocol (ICMP), or Multicast Listener Discovery (MLD) protocol.  
Since memory is a scare resource in embedded devises, _u_IP uses memory efficiently by using memory management mechanisms. The _u_IP stack does not use explicit dynamic memory allocation. It uses a global buffer to hold the incoming data packets. Whenever a packet is received, Contiki places it in the global buffer and notifies the TCP/IP stack. If it is a data packet, TCP/IP notifies the appropriate application. The application needs to copy the data in the secondary buffer or it can immediately process the data. Once the application is done with the received data, Contiki overwrites the global buffer with new incoming data. If an application delays data processing, then data can be overwritten by new incoming data packets.  
Contiki provides an implementation of RPL (IPv6 routing protocol for low power and lossy networks) [22] by the name ContikiRPL [23]. ContikiRPL operates over low power wireless links and lossy power line links.
## 3 Required Data Rate in V2V for Safe Overtaking
### Derivation of the Required Data Rate
From Figure 11, the required sensor data rate becomes high from \(d_{\text{be}}=15\) m to \(d_{\text{be}}=10\) m, but it becomes low from \(d_{\text{be}}=10\) m to \(d_{\text{be}}=5\) m. This result tells us that sensing with no cooperative perception on a two-lane road heavily depends on many factors such as the size and the location of vehicles, which will make the requirements complicated. On the other hand, sensing with cooperative perception simply depends on \(d_{\text{be}}\). Figure 10: The required sensor data rate with the options of cooperative perception and the driving path when the distance between the ego vehicle and the blocking vehicle is 5 m.  
Figure 12 shows the required sensor data rate using two different LiDAR sensors. One is a linear spacing LiDAR sensor, and the other is a non-linear spacing LiDAR sensor. In the case of linear spacing, the LiDAR sensor has an equally spaced elevation angle resolution such as Velodyne VLP-16. On the other hand, a non-linear spacing LiDAR sensor such as Velodyne VLP-32 has a dense and sparse spacing part. In this analysis, we fixed the number of lasers between the two LiDAR sensors. The details of non-linear spacing are shown in \[\mathbf{p}=\mathbf{O}+t\mathbf{d} \tag{7}\]0 From the figure, we can see that a non-linear spacing LiDAR sensor has a better ability to recognize a far object. However, notice that non-linear spacing provides sparse information about a near object. \[\mathbf{p}=\mathbf{O}+t\mathbf{d} \tag{7}\]1 The required sensor data rate with the options of cooperative perception and 5 s overtaking when the distance between the ego vehicle and the blocking vehicle is 5, 10, and 15 m.  
\[\mathbf{p}=\mathbf{O}+t\mathbf{d} \tag{7}\]2 The required sensor data rate with the options of a linear and a non-linear spacing LiDAR sensor at \(d_{\text{be}}=10\) m.
# Formal Analysis of DTLS-SRTP Combined Protocol Based on Logic of Events
## 5 Formal Analysis of DTLS-SRTP Combined Protocol
### SRTP Protocol Confidentiality Certification Process
 \tag{12}\]  
In the protocol's sending events, if a subject encrypts and sends a ciphertext \(S\) or a random number \(n\), then based on the above formula, it is possible to determine the subject who performed that sending event. Suppose there is any \(thr\) as an instance on \(R_{1}\), let \(e_{3}<e_{4}<e_{5}<e_{6}<e_{7}\) be the actions on \(thr\), and the addresses of \(e_{0},e_{1},...,e_{7}\) are all \(B^{\prime}\). For the atoms \(s_{3},n,k_{s},n^{\prime},k^{\prime}_{s},s^{\prime}_{3}\), we have  
\[ \begin{split}& Recv(e_{3})=s^{\prime}{}_{3}\wedge Decrypt(e_{4}) =(<B^{\prime},n,k_{s}>,k_{a},s^{\prime}{}_{3})\wedge\\ & Encrypt(e_{5})=(msg,k_{b},s_{4})\wedge Send(e_{6})=s^{\prime}{}_{4 }\end{split} \]  
For \(e_{4}\in E(Decrypt)\), \(Decrypt(e_{4})=(<A^{\prime},n,k_{s}>,k_{b},s_{4})\) according to the decryption axiom \(AxiomD\):  
\[ \begin{split}& DEMatch(e^{\prime}{}_{4},e_{4})\equiv\begin{pmatrix} plaintext(e^{\prime}{}_{4})=plaintext{text}(e_{4})\\ \wedge ciphertext(e^{\prime}{}_{4})=ciphertext{text}(e_{4})\\ \wedge MatchingKeys(key(e^{\prime}{}_{4});key(e_{4})\end{pmatrix}\end{split}  \tag{13}\]  
Therefore, we have \(Has(A^{\prime},n)\), where \(A^{\prime}=loc(e^{\prime}{}_{4})\), considering the events before the decryption event \(e_{7}\):  
\[ \begin{pmatrix}\forall B^{\prime}:Id.\forall e\in E(Send).\forall e^{\prime }\in E.\forall M.\\ Send(e)=M\wedge SafeMsg(M,s,k)\\ \wedge e<e^{\prime}<e_{7}\wedge loc(e^{\prime})=B^{\prime}\\ \wedge Has(A^{\prime},n)\end{pmatrix} \Rightarrow \begin{pmatrix}\exists k\in \mathcal{K}.Has(A^{\prime},k)\\ \forall\exists e^{\prime\prime}\in E(New).\\ New(e^{\prime\prime})=n\wedge loc(e^{\prime\prime})=A^{\prime}\end{pmatrix}  \tag{14}\]  
Therefore, we have \(Has(A^{\prime},k_{a})\lor Has(A^{\prime},k_{b})\), and by the key axiom \(AxiomK\), we can deduce \(A^{\prime}=A\lor A^{\prime}=B\). Further, by the following formula:  
\[ \begin{split}&\begin{pmatrix}\mathit{Safethr}(X,s)\wedge Has(X, M)\\ \wedge\mathit{SafeMsg}(M,s,k)\end{pmatrix}\Rightarrow\begin{pmatrix}\exists k \in K.Has(X,k)\vee\\ \exists e\in E(New).New(e)=n\\ \wedge loc(e)=X\end{pmatrix}\\ \Rightarrow(\forall X:Id.Has(X,n)\Rightarrow(X=A\lor X=B))\end{split}  \tag{15}\]  
In summary, the SRTP protocol satisfies confidentiality.
## 3 Auction-Based Cooperative Rescue Plan  
Auction is an efficient way to allocate resources, for its low information cost and high efficiency. It often takes place in economic activities. Bidders make bids on the item according to their evaluation of the item. Auctioneers allocate the item based on bidders' bids.  
The task allocation algorithm based on distributed auction mechanism will be adopted to allocate rescuing tasks between rescue teams within the scope of cooperation. The notations related to the auction-based algorithm are shown in Table 1. All teams are assumed to be equal in the rescue capability. When a number of teams work together as a coalition, the coalition's speed of rescue would be the summation of \(s_{nuble}\) of all the teams in this coalition. There exist two types of role in the allocation, that is, task publisher \(A_{i}\) and task responder \(B_{j}\), corresponding to the auctioneers and bidders in auction, respectively. The purpose of the auction-based algorithm is maximization of overall performance. In what follows, we detail how the rescue teams act to reach cooperation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(N=\{1,\ 2,\ \ldots,\ n\}\) & The set of buried site, where \(i\in N\) denotes a buried site \\ \(M=\{1,\ 2,\ \ldots,\ m\}\) & The set of rescue teams, where \(j\in M\) denotes a rescue team \\ \(T_{i}\) & The task of rescuing buried site \(i\), where \(i\in N\) \\ \(A_{i}\) & The auctioner who publishes task \(T_{i}\), where \(i\in N\) \\ \(B_{j}\) & The team \(j\) who received auction message, i.e., the bidder, where \(j\in M\) \\ \(u_{i}\) & The utility for a bidder who completes task \(T_{i}\), where \(i\in N\) \\ \(g_{ij}\) & The net utility for bidder \(B_{j}\) who completes task \(T_{i}\), which equals \(u_{i}-c_{ij}\) \\ \(c_{ij}\) & The cost of \(B_{j}\) participating in task \(T_{i}\), where \(i\in N\) and \(j\in M\) \\ \(c_{ij}^{\prime}\) & The opportunity cost of \(B_{j}\) participating in task \(T_{i}\), where \(i\in N\) and \(j\in M\) \\ \(t_{nend}\) & The time limit for completing rescue operations in each buried site \\ \(n_{i}\) & The number of teams required to complete task \(T_{i}\), which is related to \(t_{nend}\) \\ \(d_{ij}\) & The distance between task \(T_{i}\) and \(B_{j}\), where \(i\in N\) and \(j\in M\) \\ \(t_{ij}\) & The time \(B_{j}\) spent in rescue operation of task \(T_{i}\), where \(i\in N\) and \(j\in M\) \\ \(y_{j}\) & The number of buried sites within the scope of cooperation \\ \(l_{j}\) & The number of available rescue teams within the scope of cooperation \\ \(\lambda\) & The coefficient of bid price, \(\lambda\in(0,\ 1)\) \\ \(p_{ij}\) & The bid on task \(T_{i}\), which is made by \(B_{j}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main notations related to the auction-based algorithm.  
Figure 3: Searching plan of rescue teams.
## 4 Results and Discussion
It seems that the performance of oleophobic material should be much better in terms of self-cleaning of the filter on the upstream side of the filter where the oil film is created, as shown schematically in Figure 14. However, if the capillary forces acting on the oil so that they confine it to entering the liquid into porous material are too high, it can lead to a massive accumulation of the oil on the inlet and filter blockage. This effect was observed for highly oleophobic modifications obtained by coating with the commercial products EC5028 and EC5029 (Figure 10). The measurements of the contact angle with oil (Figure 4) confirm this hypothesis. For these coatings, the increase in pressure drop was very fast, so that it exceeded the range of the dP sensor before the steady-state value was reached. To conclude, the best results obtained for the fiberglass media dip-coated using 0.2% PDMS solution in hexane provide a good balance between minimizing the so-called \"jump dP\" related to the passage of air through the film and blocking the material pores by the liquid at the filter inlet and the reduction of capillary-driven reverse flow when the filtration system is down (Figure 11). As shown in Figure 4, it is characterized by a moderate wettability with the oil (i.e., a reduced oleophilicity compared to the native material). It was also confirmed that the PDMS coating layer on the material surface is robust at the conditions used in the research--it was not washed out in contact with the oil nor mechanically damaged due to shear stresses during the operation.
## 3 Results
### Cross-Section Imaging  
To better characterize the modifications of the top-most few micrometers below the surface after implantation, FIB is applied to the nanocrystalline W film surface and the cross-sectional morphology is observed by SEM. A protective layer of Pt is deposited onto the investigated surface in situ prior to the cross-sectioning. One selected blister is cut by FIB, and the cross-section image of the blister with a diameter of about 2 \(\upmu\)m is shown in Figure 5, which clearly shows the formation of cracks below the surface. As shown in Figure 5, cracks are observed, extending deeply into the bulk. From the cross-sectional imaging, we can find that cracks extend down to 3 \(\upmu\)m (Figure 5). As can be seen from Figure 5, the deepest crack depth is 3 \(\upmu\)m while our film thickness is 6 \(\upmu\)m. Therefore, the formation of cracks does not occur at the boundary between the deposit and the substrate nor within the substrate, but in the deposited layer. Similar cracks/voids were observed by [PERSON] et al. [10] and [PERSON] et al. [11].  
Figure 4: SEM images of the surfaces of W bulk (**a**,**c**,**e**) and nanocrystalline W films (**b**,**d**,**f**) exposed to 100 eV deuterium plasma under different fluences at 500 K: (**a**,**b**) irradiation for 2 h (fluence: 1.30 \(\times\) 10\({}^{25}\) D/m\({}^{2}\)); (**c**,**d**) irradiation for 4 h (fluence: 2.59 \(\times\) 10\({}^{25}\) D/m\({}^{2}\)); (**e**,**f**) irradiation for 8 h (fluence: 5.18 \(\times\) 10\({}^{25}\) D/m\({}^{2}\)).
## 3 Results and Discussion
### Effect of Carbon Source  
Sugars are essential for biomass accumulation, serving as a vital energy source due to typically diminished photosynthetic activity in in vitro growing tissues. They also contribute to supply of carbon for biosynthetic processes and cell wall synthesis [75]. Moreover, sugars function as signal molecules that can either repress or activate plant genes, as noted by [PERSON] et al. [76]. Callus induction in meristem and stipe was observed in PESI solid medium with different carbon sources (glucose, lactose, galactose, fructose, sucrose, or sorbitol) at 12 \({}^{\circ}\)C in dark (Figure 9). The selected calluses induced from meristem and stipe tissues are shown in Figure 10. Meristem tissue exhibited higher callus induction rates in PESI medium containing sucrose (51.42 \(\pm\) 5.05%) or glucose (43.60 \(\pm\) 7.88%) compared to standard PESI medium (40.31 \(\pm\) 17.41%). Stipe tissue displayed an overall higher rate of callus induction in PESI medium supplemented with 2% sucrose (58.48 \(\pm\) 5.66%) or glucose (53.10 \(\pm\) 7.15%). It could be due to the factor that the inclusion of organic carbon sources may alleviate stresses arising from heterotrophic conditions in algal growth [40]. Furthermore, sucrose proves to be a relatively economic choice compared to other carbon sources. Consequently, subsequent experiments were performed in PESI medium containing 2% sucrose at 12 \({}^{\circ}\)C in the dark.  
Figure 10: Callus induction in meristem (**a**) and stipe (**b**,**c**) on PESI solid medium supplemented with 2% sucrose.  
Figure 9: Effect of carbon sources on callus induction. Values are means \(\pm\) standard errors (S.E.) of 10 individual replicates per experiment (\(n\) = 10). *: Indicates significant differences using [PERSON]’s HSD test using a multiple-way ANOVA test at \(p\) < 0.05.
## 1 Introduction
However, since the computational accuracy of the numerical method depends on the refinement of the crack-tip element, it is difficult to obtain accurate computational results when the spacing of multiple cracks is small (i.e., it is difficult to refine the crack-tip element). In order to improve the calculation accuracy of the numerical methods, some semi-analytical methods have been investigated. [PERSON] et al. [18] proposed an integral equation method to calculate hydro-mechanical coupling SIFs based on the elementary solutions and superposition principle, which is suitable for multiple crack-hole problems. [PERSON] et al. [19] used [PERSON]'s method and the superposition method to derive the SIF formulas for multi-cracks and combined them with the numerical method to obtain hydro-mechanical coupling SIFs. Integral methods [20; 21; 22] are widely used to calculate stress-intensity factors, usually for problems involving free crack surfaces (i.e., the crack surface without force). [PERSON] and [PERSON] [23] developed a modified J-integral for internally loaded cracks in two-dimensional (2D) cases, and their work was extended by [PERSON] and [PERSON] [24] to three-dimensional (3D) cases to calculate hydro-mechanical coupling SIFs. However, with the above semi-analytical method, the greater the number of cracks, the more complicated the calculation process, and the stress field does not satisfy the loading condition of the hydraulic pressure on the crack surface. The modified semi-weight function method is a calculation method used for single cracks (a crack surface without force); it has the advantages of having a uniform expression and simple calculation process [25], and it can be extended to calculate multi-crack hydro-mechanical coupling SIFs (the crack surface with hydraulic pressure) while retaining those advantages. In this paper, an extended semi-weight function method is first proposed based on the new hydro-mechanical coupling stress function and the extended reciprocal theorem of the work integral formula. Then, two examples are used to verify the effectiveness of the extended semi-weight function method. Finally, the effects of integral paths and hydro mechanical coupling loading conditions are analyzed in order to provide a theoretical basis for conducting safety evaluations in geotechnical engineering.
# A Deep-Learning-Based Approach to the Classification of Fire Types
## 1 Introduction
Water-based suppression systems effectively extinguished Class A fires, particularly those involving combustible solids like wood or paper. Conversely, foam or carbon dioxide suppression systems proved more productive for Class B fires involving flammable liquids or gases. Additionally, the performance of dry chemical extinguishers was particularly notable in combating Class C fires involving energized electrical equipment. The ability to quickly and efficiently detect the presence of fire and determine the class or type of fire can significantly reduce possible loss and damages yielded from fires. This work presents a set of empirical investigations that detect the presence of fires and automatically classify the type of fire. It introduces a multiclass dataset based on the types of origin of fires. This information can help firefighters identify how to deal with a fire more efficiently, reducing risks and minimizing damage. Indeed, our methodology includes two different classification settings: a single-level classification structure and a two-level classification structure (pipeline). Firstly, this work experimented with a flat single-level classification structure in which all images were processed and classified into suitable classes. The classes were no fire and solid-material, chemical, electrical, or oil-based fires (five classes in total). Secondly, it experimented with two levels of nested classifications. It started with a binary classification for fire detection in which the classes were a fire or no fire. The images that were classified in the fire class entered another classification level to determine the type of fire. The second-level classification included four classes: solid-material, chemical, electrical, or oil-based fires. In addition, this work contributes a manually collected and annotated benchmark dataset of five classes that were carefully collected and released publicly to serve as a testbed for further research investigations.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 3 NAND Flash Memory Reliability
### Endurance
This is an unrecoverable error, resulting in the block being marked as _bad_ (overprovisioning ensures that failed blocks do not reduce the device capacity). When the number of bad blocks exceeds the device specifications, the entire chip fails. Several techniques of _wear leveling_[77] are adopted by current NAND suppliers to distribute the number of P/E cycles among the blocks and make degradation uniform. Typical endurance values for SLC cells are in the \(10^{5}\) P/E cycles range, which drops by an order of magnitude or even more when moving to MLC [62]. An even lower value (around 1000) can be expected for TLCs. For SSDs, this specification translates into the total amount of writable data in--say--five years for a given workload. Figure 3: Endurance characteristics of a NAND cell [63], © 2004 IEEE.