esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Analysis of Finite Element Calculation Results
#### 3.2.2 Prediction of Chloride Ion Penetration Depth in Concrete  
From Figure 11, it can be observed that when only considering the changes in chloride ion concentration, the depth of chloride ion penetration inside the concrete gradually increases over time. However, the rate of penetration decreases gradually with time. According to [PERSON]'s Second Law [27], during the ion transport process, the higher the chloride ion concentration outside the boundary, the more rapid the penetration speed of chloride ions in the solution, and the shorter the time required for the chloride ion concentration to reach the threshold value. Therefore, the simulation results are consistent with the inferences from [PERSON]'s Second Law.  
In this section, using the simulated values obtained from finite element calculations, a quadratic polynomial fitting was first applied to quantify the relationship among chloride ion penetration depth, chloride ion concentration, and corrosion time under the same voltage conditions. Figure 13 presents the fitted surfaces of chloride ion penetration depth versus chloride ion concentration and corrosion time under different voltages (it displays the fitted surface under the voltage of 150 mV).  
Figure 12: Comparison between simulated and experimental values of chloride ion corrosion depth.  
Figure 13: Surface fitting of corrosion depth of chloride ions over time at different chloride ion concentrations.  
Subsequently, a cubic polynomial fitting was performed between the fitting coefficients of chloride ion penetration depth, chloride ion concentration, and corrosion time under the same voltage conditions and the voltage itself. This resulted in a quantitative relationship between chloride ion penetration depth, voltage, chloride ion concentration, and corrosion time, as expressed in Equation (3):  
\[d_{t}=10^{-5}\cdot(\left[\begin{array}{cccc}-10^{6}&255450&-15453&397\\ -10^{6}&-318456&17157&1831\\ 3053.2&-610&75.9&6.1\\ -408215&94375&-4482&-570\\ -2.2&0.4&-0.04&-0.003\\ -1453.2&325&-23.3&1.3\end{array}\right]\left\{\begin{matrix}\psi^{3}\\ \psi^{2}\\ \psi\\ 1\end{matrix}\right\}^{T}\left\{\begin{matrix}1\\ C_{C\Gamma^{-}}\\ t\\ C_{C\Gamma^{-}}^{2}\\ C_{C\Gamma^{-}}^{2}\end{matrix}\right. \tag{3}\]  
where \(d_{t}\) is the chloride ion corrosion depth of reinforced concrete (m), \(\Psi\) is the voltage intensity of the stray current (V), \(C_{C\Gamma^{-}}\) is the chloride ion concentration in the service environment (mol/L), \(t\) is the corrosion time of reinforced concrete (days).
## 2 Related Work and Problem Description
### Time-Sensitive Network and Deterministic Network  
Time-Sensitive Networking (TSN) is an extended standard Ethernet technology that is backward compatible with standard Ethernet. It enables us to achieve low jitter, low latency, and robust communication channels through standard Ethernet. As an IEEE standard, TSN will be a vital component of future real-time Ethernet communication [24]. TSN transmission technology is a protocol suite consisting of multiple sub-protocols, each serving different functions [25].  
By providing determinism and high reliability through compatible collaboration for data transmission, TSN protocols offer low latency, low jitter, and extremely low data loss rates. Through techniques like clock synchronization, bandwidth reservation, gate operation mechanisms, flow filtering and policing, frame preemption, and cyclic queuing forwarding, TSN enhances the real-time capabilities of standard Ethernet, opening a new realm for real-time Ethernet communication.  
The CQF (Credit-Based Shaper with Queueing and Forwarding) gate control scheduling used on a single satellite in this paper is based on the IEEE 802.1 Qch protocol. This protocol is widely employed in Deterministic Networking (DetNet) transmissions, aiming to provide deterministic quality of service guarantees for network services [26]. As depicted in Figure 2, each TSN switch applied to satellites with CQF functionality features two queues for the CQF mechanism: an even queue and an odd queue. Within a fixed time slot (T), one queue continuously receives and buffers incoming data frames, while the other queue transmits frames received in the previous time slot. The tasks performed by the two queues in alternating time slots are opposite. Clearly, utilizing this technology on each satellite within the parallel subnetwork architecture is beneficial for reducing latency jitter and uncertainty in multi-hop networks.
## 3 The Proposed Protocol
### The Proposed Scheme
#### 3.3.2 The Registration Phase  
During the registration phase, both the UAV and the GCS need to be registered with the CA; the exact process of registration of the UAV is given below:  
\begin{table}
\begin{tabular}{c c} \hline
**Notation** & **Description** \\ \hline \(ID_{i}\) & The identity of the drone \\ \(PID_{i}\) & The Pseudo identity of the drone \\ \(S_{A}\) & The private key of A \\ \(P_{A}\) & The public key of A \\ \(CertA\) & Certificate of A \\ \(MK_{AB}\) & The master key shared between A and B \\ \(AK_{AB}\) & The authentication key shared between A and B \\ \(SK_{AB}\) & The Session Key shared between A and B \\ \(RK_{AB}\) & The Refresh key shared between A and B \\ HAMC & Hash-based Message Authentication Code \\ \(SC(i,j)\) & The sending counter value of node i that is shared with node j (SC(i,j) = RC(j,i)) \\ \(RC(i,j)\) & The receiving counter of node i that is shared with node j (RC(i,j) = SC(j,i)) \\ \(Date_{A}\) & Deadline for Certificate A \\ \(H(X)\) & Hash value of X \\ \(E(K,X)\) & Encrypt plaintext X with key K and symmetric algorithm \\ \(MAC(K,Z\oplus X||Y)\) & Encryption of messages using MAC algorithm and the key K \\ \(HMAC\) & Hash-based message authentication codes \\ \(t\) & Timestamp \\ \hline \end{tabular}
\end{table}
Table 2: Explanation of symbols.  
1. The drone \(U_{i}\) chooses its own identifier \(ID_{U_{i}}\), then generates a random number as the private key \(S_{U_{i}}\), calculates the message digest \(PID_{U_{i}}=H\big{(}S_{U_{i}}\big{|}\big{|}ID_{U_{i}}\big{)}\), and computes the public key \(P_{U_{i}}=G\times S_{U_{i}}\).
2. The drone \(U_{i}\) generates message \(\big{\{}ID_{U_{i}},PID_{U_{i}},P_{U_{i}}\big{\}}\) and calculates encrypted message \(E\big{(}P_{CA},\big{\{}ID_{U_{i}},PID_{U_{i}},P_{U_{i}}\big{\}}\big{)}\). The certificate private key is used to over-encrypt the message sent, and \(U_{i}\) sends a message to the CA requesting registration.
3. The CA decrypts the information after receiving it and calculates the certificate information \(C_{U_{i}}=\big{[}H\big{(}ID_{U_{i}},P_{U_{i}},Date_{U_{i}}\big{)}\big{]}S_{CA}\).
4. The CA loads out the drone certificate \(Cert_{U_{i}}=\big{\{}ID_{U_{i}},P_{U_{i}},Date_{U_{i}},\big{[}H\big{(}ID_{U_{i }},P_{U_{i}},Date_{U_{i}}\big{)}\big{]}S_{CA}\big{\}}\) for the drone \(U_{i}\)  
where \(H()\) is a hash function; \(Date_{A}\) is the time expiry date of the certificate; \(P_{A}\) is the public key of node A; and \(H\big{(}ID_{U_{i}},P_{U_{i}},Date_{U_{i}}\big{)}\) is a signature pair using the CA's private key \(S_{CA}\). The ground control station is similar to the UAV registration. Therefore, we will not go into details.
# Analysis of Elastic Nonlinearity Using Continuous Waves: Validation and Applications
## 4 Efficiency and Robustness of the Approach
### Dependence on Signal Distortions  
The proposed procedure is based on fitting experimental data with a sinusoidal function. The presence of nonlinearity induces distortions of the signals from the pure cosine wave behavior, e.g., due to the appearance of higher order harmonics. In general, being nonlinearity weak, distortions are negligible and the approach is expected to work well.  
However, for strong nonlinearities (or large amplitudes), the proposed procedure might no longer be correct. The problem was analysed and results are presented in Figure 9. Simulations were carried out varying the nonlinear parameter \(\delta_{S}\) (keeping fixed the nonlinear parameter on damping \(\delta_{\lambda}\)) and the relative error in the estimate of the nonlinear parameters of velocity \(\delta_{c}\) and attenuation \(\delta_{a}\) are calculated as a function of the distortions of the signal. The latter are estimated as the ratio of generated third harmonic amplitude over fundamental harmonic amplitude.  
As expected, increasing the nonlinear parameters corresponds to increasing distortions of the signal and the estimation obtained from our method becomes poorer and poorer. However, in particular for the velocity nonlinearity (Figure 9, left), the accuracy remains excellent up to a few per cent of distortion, which is high if compared to nonlinearity observed in the cases of interest.
###### Abstract
[27] proposed a new power load forecasting system by combining data preprocessing, hybrid optimized algorithm and certain individual conventional prediction methods, which conquers the shortcomings of individual conventional prediction model and obtains a single model optimization with higher prediction accuracy than traditional forecasting model. Another problem of power load forecasting is that the research direction is relatively single. Specifically, most of the previous analysis only focuses on the point prediction of load, and rarely considers the load interval prediction together to prediction modeling and analysis. This is not enough to meet the needs of engineering applications, or to ensure the reliability of the power system. Probability interval prediction can display more messages, and its results can help managers to implement appropriate policies. However, the research on interval modeling and prediction is still lacking. At present, the main research direction of uncertainty quantification is mainly statistical methods, including quantile regression [28], bootstrap method [29], kernel density estimation [30], etc. in addition, there are interval prediction methods based on artificial neural network, including lower upper bound estimation method (LUBE) and so on [31]. Table 1 summarizes the existing point prediction and interval prediction methods and models, and evaluates the advantages and disadvantages of these methods. For point forecasting, from the traditional statistical model to the artificial neural network model, and even for the recently developed hybrid model, the prediction accuracy has been continuously improved, but the models still have a lot of space for improvement. According to the nonlinear characteristics of load, this paper proposes a method of nonlinear combination of single prediction model, and uses swarm intelligence optimization algorithm to optimize the model parameters to further improving the prediction effect. For interval forecasting, no matter quantile regression method [32], bootstrap method [33], kernel density estimation method [34] or LUBE method, these methods have their own advantages and disadvantages that are difficult to overcome. In conclusion, the interval prediction method is not uniform and further research and investigation are needed according to the existing knowledge to obtain more effective results [35]. Therefore, based on the hypothesis of distribution, this study develops a new architecture of interval prediction, which is better than most single model interval prediction architectures.
# Relief Aircraft Dispatch Strategies Based on Different Levels of Information Sharing Systems
## 4 Dispatch Strategy Based on the Information Sharing System
### Level Two and Corresponding Transportation Method
#### 4.2.1 One-Destination Strategy  
Here, one flight has only one destination (one rescue shelter). It is a common dispatch method for disaster relief activities [1; 35]. According to the reported injury request, the dispatchment center assigns one aircraft to one rescue shelter. Upon arrival at the rescue center, the aircraft collects as many injured persons as possible and returns to the airport directly. This method is a traditional and conservative dispatchment method. It is easy and quick to assign and recycle aircraft.  
However, each aircraft has only one destination, and the aircraft capacity might not be utilized fully if the number of injuries in some rescue centers is less than the aircraft capacity. Therefore, a multiple-destination strategy is proposed.
## 6 Simulated Results
### Packets Drop Ratio  
Figure 3 shows the experimental results of the EGCIR protocol in the comparison of existing solutions. Based on the results, it is revealed that the EGCIR protocol reduces the fraction of packet drop ratio by an average of 34% and 70% than other solutions under varying number of nodes and attackers. The packet drop ratio is the fraction of lost packets during data transmission. In the existing solutions, the slow security convergence provides open access to malicious nodes to simply alter or drop data packets. Moreover, the frequent exchange of control messages for construction routing paths explicitly decreases the energy resources of the sensors and leads to an increased packet drop ratio. In addition, the EGCIR protocol excludes the energy inefficient sensors from data routing and significantly contributes to a decrease in the number of packets drop. Unlike other solutions that perform frequent re-election over the entire network boundaries, the EGCIR protocol uses energy threshold and analysis of time frame. Whenever any cluster head during data routing drops its energy level, the source cluster head reselects alternative cluster heads among its members for data routing, which particularly reduces the chances of link failure and packets retransmission.  
Figure 2: Impact of the nodes and network throughput.
# Automatic Addition of Fault-Tolerance in Presence of Unchangeable Environment Actions +
## 6 Experimental Results
### Addition of Fault-Tolerance
#### 6.2.1 Failsafe Fault-Tolerant Shepherding Problem  
For this variation, we change the two-dimensional version described in Section 6.1.2 as follows: there is no requirement of steering the sheep to the desired location. Instead, we require that the farmer must be always close to the sheep. Specifically, we require that the farmer and sheep can be far apart at most by one row or one cell. We model this requirement as a safety property with the set of bad transitions  
\[\delta_{b}=\Big{\{}(s_{0},s_{1})|\] \[\
eg\Big{(}\big{(}r_{s}(s_{1})=r_{f}(s_{1})\wedge c_{s}(s_{1})=c _{f}(s_{1})\big{)}\vee\] \[\big{(}|r_{s}(s_{1})-r_{f}(s_{1})|=1\wedge c_{s}(s_{1})=c_{f}(s_{1 })\big{)}\vee\] \[\big{(}r_{s}(s_{1})=r_{f}(s_{1})\wedge|c_{s}(s_{1})-c_{f}(s_{1})| =1\big{)}\Big{)}\Big{\}}.\]  
The invariant, the set of environment transitions \(\delta_{e}\) and the set of program restrictions \(\delta_{r}\) are the same as those for two-dimensional case explained in Section 6.1.2. For addition of fault-tolerance we have a set of faults as the input. We consider two sets of faults and try to repair the program with each of them. The first set captures faults that can change the state of the program arbitrary, that is, for any state the program may change to any other state. We denote this set by \(f_{arbitrary}\). The second set of fault transitions that we consider captures faults that cause the system transition to a state where both farmer and sheep co-locate in one of the cells except the desired cell. We model this transition as a set of fault transitions  
\[f_{same-location}=\big{\{}(s_{0},s_{1})|\] \[r_{s}(s_{0})=0\wedge c_{s}(s_{0})=n-1\wedge\] \[r_{s}(s_{1})=r_{f}(s_{1})\wedge c_{s}(s_{1})=c_{f}(s_{1})\wedge\] \[\
eg(r_{s}(s_{1})=0\wedge c_{s}(s_{1})=n-1)\big{\}}\]  
We modeled this problem using JavaScript for different sizes. The Algorithm 2 does not find any solution for the case of arbitrary faults, \(f_{arbitrary}\). From the completeness of Algorithm 2, we know that there is no solution for this case. On the other hand, the algorithm finds a solution for \(f_{same-location}\). Table 2 shows the repair time for different sizes.
# Design and Implementation of a Contextual-Based Continuous Authentication Framework for Smart Homes
###### Abstract  
There has been a rapid increase in the number of Internet of Things (IoT) devices in the last few years, providing a wide range of services such as camera feeds, light controls, and door locks for remote access. Access to IoT devices, whether within the same environment or remotely via the Internet, requires proper security mechanisms in order to avoid disclosing any secure information or access privileges. Authentication, on which other security classes are built, is the most important part of IoT security. Without ensuring that the authorized party is who it claims to be, other security factors would be useless. Additionally, with the increased mobility of IoT devices, traditional authentication mechanisms, such as a username and password, are less effective. Numerous security challenges in the IoT domain have resulted in the proposal of many different approaches to authentication. Many of these methods require either carrying an authentication token, such as a smartcard, or restricting access to a particular physical location. Considering that most IoT devices contain a wide array of sensors, a large amount of contextual information can be provided. Thus, real-time security mechanisms can protect user access by, for example, utilizing contextual information to validate requests. A variety of contextual information can be retrieved to strengthen the authentication process, both at the time of access request and throughout the entire access session, without requiring user interaction, which avoids the risk of being discovered by attackers of these features. In this paper, we introduce a continuous authentication framework that integrates contextual information for user authentication in smart homes. The implementation and evaluation show that the framework can protect smart devices against unauthorized access from both anonymous and known users, either, locally or remotely, in a flexible manner and without requiring additional user intervention.  
contextual information; quality of contextual information; continuous authentication; IoT; smart homes +
Footnote †: journal:
## 4 Discussion  
In this study, we have designed a vibrotactile vest to cue attitude information of aircraft for pilots and systematically investigated the effectiveness of vibrotactile coding methods with a combination of multiple vibration parameters.  
The experimental data shows that, in general, the participants were positive about the use of vibrotactile vest to provide flight attitude. The experimental results also indicate that the rhythm performed better than coding parameters of intensity and mode (see Table 3). The average reaction time for the preferred coding method (LRM) is about 0.9 s, which meets the requirements of reducing pilots' spatial disorientation with tactile command. In the current study, subjects received training for only 10 min, thus the presented vibrotactile vest can be further improved on the reaction time to which the pilots respond to the vibrotactile patterns.  
It should be noted that there may be more discrimination between the different vibrotactile patterns if participants were driving a real aircraft instead of being seated, due to the continuous change of direction in practice. Besides the design of the current vibrotactile vest, it is convenient to wear for people of different waist sizes.
## 2 Materials and Methods
### Testing Procedure  
A motorized vertical test stand, TVO-S (AstraLab, Mariano Comense, Italy), was used to perform a single-point vertical compression, applying a constant, preset load. Models were placed in a container (24 cm \(\times\) 12 cm \(\times\) 14 cm) serving as horizontal support to ensure that the force application occurred at the same location and remained perpendicular to the plane. This container was filled with distilled water at a constant temperature of 37 \({}^{\circ}\)C and positioned under the load. An Isw-100W immersion heater (INKBIRD, Shenzhen, China) was used to maintain the water temperature at 37 \({}^{\circ}\)C (Figure 4). Each sample underwent a 3 h stress-relaxation test. After placing the aligner correctly on the model, a speed of 1 mm/min was set, achieving the desired deflection within one minute of testing. A constant deflection force of 1N was maintained, with data collected every second to obtain a precise stress-relaxation curve for each material. An external force gauge holder, Sauter TVO-A01, was connected to a computer equipped with Sauter AFH Fast data transmission software (AstraLab, [PERSON], Italy), providing a relaxation curve (force [N]/time [s]) for each observation period. Each aligner type was tested at three points (gingival, middle, and incisal/occlusal) on elements 1.1 and 2.6. Three equivalent trials were conducted for each point using different aligners for each. To compare stress decay across samples, normalized stress was calculated using the following equation to determine stress decay percentage:  
\[\text{Stress Decay }\%=\sigma/\sigma_{\text{max}}\times 100\]  
where \(\sigma\) represents the initial stress of the material, and \(\sigma_{\text{max}}\) represents the maximum stress reached during observation.
## 1 Introduction
The ability to process data at the edges of the network and the automatic analysis of water consumption data at a local level allow providing direct feedback based on the actual consumption without the need to rely on data available on the cloud. Moreover, the operation of safety-critical systems, such as water pressure valves, is always operational and secure besides any potential and unexpected connectivity issues. **Improved privacy**: Detailed and continuous sensing of water consumption raises various privacy concerns since it is possible to identify certain actions of the users while analysing the data from a central point [36; 44; 45]. Following the fog computing paradigm, detailed data collected from the smart water metering devices are analysed at the nearest available edge-based processing point, and only aggregated information reaches the cloud servers. In this way, disaggregation techniques that rely on detailed, real-time data cannot be effectively used from a central point of view, thus significantly improving the privacy of the end-users. The resulting system is deployed in a city environment and evaluated in real-world conditions. The performance of the system is evaluated in terms of (a) network performance, (b) responsiveness and computational capabilities at the network's edges, (c) I/O operations on the edges of the network and (d) data encryption and decryption capabilities and resilience against threats faced.