esa-sceva/satcom-chunk-collection huggingface dataset:
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology
### Solution Architecture
#### 3.1.2 Response Phase  
Figure 2 shows the workflow architecture of the response phase. The methodology of this response architecture is based on historical data and includes four essential components: the Data collection module, Data analysis module, Response module, and Feedback module. The data collection module is designed to gather and preserve historical information from an attached database of network traffic and security events. The subsequent phase of the  
Figure 1: Workflow of Detection Phase.  
solution architecture involves a data analysis module that scrutinises the historical data gathered in the preceding phase. The data analysis module determines whether the source has been identified previously by comparing real-time data with historical data collected via the data collection module. The Response module adhered to the established flow of the Data analysis module. It incorporates a temporary rule and supervises the traffic of the rule, thereby significantly contributing to the overall effectiveness of the response mechanism. The final component of the response-phase architecture is the feedback module. The rules and policies were refined by providing feedback to the data collection module based on the performance of the response module. The module determines whether the rule inserted in the preceding section has been triggered. If the attacker repeatedly and frequently violates the rule, the module establishes a customised and unique permanent drop rule. Data analysis and feedback systems are pivotal elements of the response phase. This approach, which is based on the analysis of historical data, offers a more efficient response mechanism for timing side-channel attacks in SDNs by allowing for a tailored response to each attack.
###### Abstract  
In this work, we investigated the effect of natural phytoregulators (i.e., methyl jasmonate and abscisic acid) on quality physicochemical parameters, carotenoid and anthocyanin content and antioxidant activity of purple carrots in storage at 5 \({}^{\circ}\)C for 21 days. As a result, study of the natural evolution of fresh-untreated purple carrots in storage exhibited increase in carotenoids (from 1.41 to 3.79 mg EBC g\({}^{-1}\) DW) and stability of anthocyanins (2.18 vs. 2.23 mg EBCg\({}^{-1}\) DW) but significant loss of organoleptic quality. Treatment with methyl jasmonate and abscisic acid resulted in similar or even higher carotenoid content (1.61 and 2.15 mg EBC g\({}^{-1}\) DW for methyl jasmonate and abscisic acid, respectively) as compared with the value measured in fresh-untreated carrots before storage (1.41 mg EBC g\({}^{-1}\) DW). In contrast to carotenoids, anthocyanins and antioxidant activity mostly decreased with the treatments. However, physicochemical parameters indicating organoleptic quality improved considerably, which was meaningful considering their importance in terms of consumer acceptance. These results reflect the slowing-down effect of natural phytoregulators on spoilage of purple carrots over storage. Optimization of this approach is scheduled to minimize anthocyanin losses. It can therefore be an interesting approach to extending purple carrot shelf-life with no need for artificial preservatives.  
annocyanis; antioxidant activity; carotenoids; phytoregulators; purple carrot Article
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
###### Abstract
However, only a few studies have examined the feasibility of employing a DC distribution system to support the AC grid frequency, considering the detailed dynamics of both DC and AC grids [28]. Previous studies presented a microgrid model based on the data collected from field data of an existing microgrid; the model was verified to produce an accurate response compared to the existing microgrid and was used to study the response in the case of symmetrical fault [29]. Therefore, it is essential to examine the dynamic performance of the hybrid AC/DC system, considering both grids' detailed dynamics and the control methods required to mitigate any adverse impact that could affect the stability of the entire system. The paper's contributions to the field can be summarized as follows:  
1. This paper presents a comprehensive modelling of an AC-DC microgrid considering the interlinking voltage source converter equipped with an inertial support loop, the DC link dynamics, AC grid load modelling and synchronous generator dynamics (governor and the AVR dynamics). 2. A new virtual inertia controller based on a high-pass filter (HPF) compensator is proposed and its dynamic performance is compared with the low-pass filter (LPF)-based virtual methods used in the literature; the advantages of the proposed method are highlighted, showing the differences between the two controllers. 3. Using eigenvalue-based analysis, a stability analysis is provided to study the impact of the system parameters' uncertainty on the entire system stability to demonstrate the interaction dynamics that arise after adding the virtual inertia loop. 4. Finally, time domain simulations and performance check of the proposed method are presented, considering different operating scenarios. The paper is organized as follows; Section 2 introduces the utilized system's model. In Section 3, a stability study of the effect of varying system parameters is performed. In Section 4, simulation results are depicted; first, the linearized state space model is verified compared to the results from Matlab Simulink and the results of the proposed controller are presented. In Section 5, the paper is concluded.
## 4 Stray Node Detection Protocol and Method of Avoiding Message Collision
### Autonomous Response-Type Stray Node Detection Protocol
#### 4.1.2 Collision Avoidance Method  
It is difficult to achieve efficient communication with low-bandwidth ultrasonic communication because the communication speed is extremely low and because the scarcity of channels causes collisions to occur frequently. In the autonomous response-type protocol, the application layer controls the message transmission timing of each normal node so that multiple normal nodes will not send packets simultaneously. This prevents bursty traffic from arising. Figure 6 shows the sequence diagram for this collision avoidance. The leader node staggers the time when each normal node \(i\) sends a message. Timer T1(\(i\)), whose value is varied in advance from one normal node to another, is used for this purpose. Since messages are sent in multiple hops, a node that has received a message from another node forwards the REPLY message after an elapse of T2(\(i\)). T1(\(i\)) and T2(\(i\)) values are set as follows. _Self Time_ in the equation is an arbitrary time set by the user. Random Time takes a random number between two arbitrary numbers.  
T1(\(i\)) = ID \(\times\)_Self Time_ of node \(i\)  
T2(\(i\)) = Random Time  
Figure 5: Flowchart for the autonomous response-type stray node detection protocol.
## 3 Results
### Limitations and Future Work  
The limitations of this study are outlined below:  
* This study considered an isolated building model.
* All simulations were performed under isothermal conditions.
* The study considered a constant wind perpendicular to the inlet openings.
* No internal obstacles were considered within the building model.  
Future research will aim to conduct a detailed analysis of how varying wind directions and velocities, as well as non-isothermal conditions, affect the performance of natural ventilation. Additionally, in realistic design scenarios, the development of a more comprehensive configuration that accounts for the influence of interior furnishings, wall partitions, and physical obstructions is essential to enhance the practical applicability and reliability of natural cross-ventilation strategies.
## 5 Evaluation
### Performance Evaluation
#### 5.2.3 Scalability  
Finally, we analyze the scalability of the proposed system by measuring the overall throughput of all phases (not only of the encryption one) with respect to different data sizes, ranging from 10 kb to 3000 kb. Figure 4 shows the overall throughput, including data encryption, hashing, and storing the encrypted and hashed data on IPFS. As we can observe from the graph, initially, the throughput was around 3 kb/s for 10 kb of data. However, as data size increases to 3000 kb, the throughput impressively grows to over 307 kilobytes per second. This shows the robust ability of the proposed system to manage and process large data sets effectively, highlighting the proposed solution's scalability. Therefore, the proposed solution is suitable for adoption in data-intensive applications where scalable security is critical.  
Figure 4: Overall throughput in kb/s.  
Figure 3: Total time in ms.
## 2 Data and Methods
### Methods
Additionally, \(\theta_{i}\) refers to the direction in one fitting window and ranges from \(1^{\circ}\) to \(360^{\circ}\) with an interval of \(1^{\circ}\). The letter \(i\) represents the number of internal tides considered within the window. The first three largest internal tides (\(i=3\)) were extracted, considering the fourth \(M_{2}\) internal tides in the region to be insignificant. If readers are interested in this method, more details can be found in [PERSON] and [PERSON] [23], [PERSON] et al. [24]  
An example is shown in Figure 2, describing the relationship between the amplitudes of \(M_{2}\) internal tides and their propagation directions for the fitting window ranging from \(14.2^{\circ}\)N to \(15.8^{\circ}\)N and \(67.2^{\circ}\)E to \(68.8^{\circ}\)E. The first step was to divide the window into \(360^{\circ}\) degrees and apply Equation (3) over the whole window data for each degree. Thus, each angle corresponds to one fitting with an amplitude \(a_{i}\) and a phase \(\varphi_{i}\). Figure 2a outlines the relationship between angle and amplitude, which represents the three most significant internal tides. The most prominent signal in this window appears along \(225^{\circ}\) (Wave 1), which is regarded as the largest internal tide at (\(15^{\circ}\)N, \(68^{\circ}\)E). The second step is to extract the second largest internal tides (Wave 2) from Figure 2a. Note that the influence of Wave 1 is observed not only along \(225^{\circ}\), but also along other directions, so the contribution of Wave 1 must be removed. Equation (3) was used again to predict the SSH generated by Wave 1, considering the amplitude and phase of Wave 1 were known from Figure 2a. Then this prediction was subtracted from the original data, as shown in Figure 2b. The third step is to repeat the above process to extract the third significant wave, as shown in Figure 2c. After completing the calculation for one fitting window, these operations are extended to the entire domain with a step of \(0.25^{\circ}\). Finally, the energy flux was calculated following the works of [PERSON] [37], [PERSON] [38], and [PERSON] and [PERSON] [23]. The vertical structures of baroclinic pressure and horizontal velocity, \(\Pi(z)\), were first calculated using:  
\[\Pi(z)=\rho_{0}c^{2}\frac{d\Phi(z)}{dz}, \tag{4}\]  
where \(\rho_{0}\) represents the density of seawater. The vertically integrated energy flux was then obtained as [24]:  
\[ \begin{split} F&=\frac{1}{2}\int_{-H}^{0}u(z)p(z)dz, \\ &=\frac{1}{2}a^{2}[\frac{\rho_{0}g^{2}\omega k_{1}}{\omega^{2}-f^ {2}}\int_{-H}^{0}\Pi^{2}(z)dz],\\ &=\frac{1}{2}a^{2}F_{n},\end{split}  \tag{5}\]  
where \(u(z)\) denotes the horizontal velocity; \(p(z)\) denotes the pressure; \(F\) denotes the vertically integrated energy flux; and \(F_{n}\) denotes the integrated energy flux per unit amplitude.
# Continuous Mobile User Authentication Using Combined Biometric Traits
## 6 Evaluation and Results
### Potential Drawback of Accelerometer Data  
Although the presented configuration, using position data combined with values from the accelerometer sensor, provides good theoretical performance in regard to AUC and EER this may not happen in a real-world scenario.  
This may be because all the training data was captured solely in one sitting. This means that most probably every user who was asked to perform the recording of the touch input data, was capturing it in the same or similar position. For example, when a user was doing the recording while being a passenger of a car and was sitting all the time while doing so. Because of that, the classifier may know nothing about the behavior (regarding data captured by the accelerometer sensor), when the user would be walking or lying in a bed. Furthermore, when we were asking the participants to capture the data, we set no constraint on which posture they were allowed to take. This means that some of them were standing while others were sitting.  
It is important to note that this fact is crucial for interpreting the prediction performance of the continuous user authentication system. It may be the case that the classifier is learning when a user is lying in bed, rather than the behavior when performing touch gestures on the screen of the device. One possibility of tackling this aspect would be to normalize the accelerometer by subtracting the initial position of every data point. However, we would still need a more sophisticated test set, containing information about the user's position in the room.  
When we are comparing the performance of the classifier using single gestures versus grouping of multiple inputs, we can see that the AUC is only increasing slightly with a larger grouping factor. While the AUC rises from 93.9% up to 100%, the EER is falling more sharply from 12% when using single gestures to approximately 1.34% when combining 25 stroke inputs. Although grouping that many gestures together may raise problems, we can see in Figure 6 that using position and accelerometer data for our training process resulted in acceptable performance regarding the EER of the classifier.  
Figure 6: ROC multiple gestures for position and accelerometer data with gesture lengths bigger than 50—6 hidden states and 7 mixtures.
# A Perspective on Ethernet in Automotive Communications--Current Status and Future Trends
## 5 Car Architecture Evolution and Relevant Challenges  
Automotive Ethernet has the potential to be the main real-time high-bandwidth information highway interconnecting the major in-car subsystems to enable dynamic sharing of the full resource pool across a car. However, to achieve high performance, lower costs, and wiring harness reduction, the in-car network architecture needs to be revised [49,50].  
In fact, although the domain-based architecture discussed in Section 2 has a cleaner design and entails less interdependence than the \"flat\" one, it is still not optimized for wiring, and this has an impact on cost and space. For this reason, a new architecture, called zonal architecture, was devised. Zonal architectures make a shift from the logical distribution of the functionalities typical of the domain-based architecture to their physical distribution [51, 52, 53].  
An example of zonal architecture is shown in Figure 3. It can be seen that in zonal architecture, the wiring of the electronic components does not depend on their functions, but on their physical location in the car.  
Several zonal gateway controllers handle communication with the sensors and actuators of one physical area, combining the network protocols required for each of the devices. Zonal gateways are connected with each other and with the central CPU (or high-performance computer) through an Ethernet backbone.  
The benefits of e zonal architecture are manifold. First, it simplifies the layout of sensors, actuators, and cabling inside the car, with a significant weight reduction for the wiring harness. Second, as there are fewer ECUs, there is also a reduction in packaging space. Moreover, as the zonal controller is connected to the legacy in-car network, a large number of sensors and actuators can be flexibly added to the car without the need to be directly connected to the zonal controller. Finally, a reduction in labor cost for installing wires and maintenance is also expected. Zonal architectures pose new issues, however, as each zonal gateway needs to manage the multiple legacy network protocols used to interconnect sensors, actuators, and ECUs. To accomplish this, in each zone Ethernet gateways working at speeds of 10 Mbps, 10 Mbps, 1 Gbps, 5 Gbps, and up to 10 Gbps will be connected to traditional automotive networks [54] (such as LIN, CAN, CAN FD, and FlexRay) to run protocol conversion functions and fast control functions in the periphery of the car. The communications between zones will instead go through an ultra-high speed Ethernet backbone, operating at 10 Gbps to 25 Gbps.  
The introduction of Automotive Ethernet and the TSN standards in the car networking architecture introduces several challenges. Some of them are discussed below.
## 1 Introduction
[13] showed that ultrasonic treatment of alignates at different frequencies caused degradation, reordering, and changes in their properties, such as molecular weight and viscosity. They demonstrated that ultrasonic treatment of alginate could also increase its hydrophobic interaction and interfacial activity. In addition, [PERSON] and [PERSON] successfully employed ultrasonic  
Figure 1: Molecular structure of sodium alginate. treatment to reduce the molecular weight of two sodium alignates in saline solutions in 2020 [14]. The use of oxidized sodium alginate in tanning was first reported by [PERSON] et al. in 2018 [15]. To obtain oxidized sodium alginate (OSA), sodium alginate (SA) was selectively oxidized using NaIO4. This oxidation increased the thermal stability of leather powder. During the reaction, the C2-C3 bond between two adjacent hydroxyl groups in the glucuronic or mannuronic units is cleaved. The 1,2-diol group converts into a dialdehyde. As a result, OSA becomes an open-chain biopolymer containing aldehyde groups. See Figure 2.  
However, the industrial use of NaIO4 is limited by its relatively high cost. In addition, the industrial use of NaIO4 requires great caution because it can cause severe skin burns, eye injuries, and organ damage in case of prolonged exposure. It is highly toxic to aquatic organisms and can cause a fire or explosion. This substance is registered under REACH and cannot be used/imported in the European Economic Area in quantities greater than 1000 tons per year. This study introduces a new sustainable route for obtaining an oxidized alginate, developing a biodegradable SA-based retanning agent that is easy to adopt in the tanning industry without the need for changes in the current technology. The present study aims to develop a biodegradable, chromium-, and aldehyde-free leather using alginate derivatives as a retanning agent to significantly reduce potentially toxic substances, petroleum-based products, and the environmental impact of leather production. At the same time, it will meet the quality requirements of the high-end footwear and leather goods industries. The characteristics of the leather obtained with the new tanning system using alginate derivatives will be compared with those of three types of conventional leather, as well as alternative materials to genuine leather: leatherette, Pinatex(r) by Ananas Anam online store, and Desserto(r) by Desserto Mexico in Zapopan, Jalisco, Mexico. Leatherette is an artificial leather commonly used in furniture upholstery, clothing, and leather goods. Pinatex(r) and Desserto(r) are derived from the recovery of plant-origin fibers and structured with various polymers to attain the required resistances.
## 2 Materials and Methods
### Problem Formulation
\begin{array}{c}\theta=\theta_{in};\ \ \ \mathrm{x}=-L_{u},\ 0\leq r\leq 1\\ \frac{\partial\theta}{\partial x}=0;\ \ \ \mathrm{x}=-L_{u},\ 1\leq r\leq(1+t_{w}) \\ \frac{\partial^{2}\theta}{\partial x^{2}}=0;\ \ \ \mathrm{x}=L_{h}+L_{d},\ 0\leq r \leq(1+t_{w})\\ \frac{\partial\theta}{\partial r}=0;\ \ \ \mathrm{r}=0,-L_{u}\leq x\leq(L_{h}+L_{d}) \\ \frac{\partial\theta}{\partial r}=0;\ \ \ \mathrm{r}=(1+t_{w}),-L_{u}\leq x<0\ or\ L_{h}<x\leq(L_{h}+L_{d}) \\ \frac{\partial\theta}{\partial r}=-\frac{1}{(1+t_{w})}B_{io}(1+\theta);\ \ \ \mathrm{r}=(1+t_{w}),\ \ 0<x<L_{h} \end{array}  \tag{3}\]  
The temperature and heat flux are assumed to be continuous at the inner wall of the tube:  
\[\theta_{w}=\theta_{b}\ \mathrm{and}\ k_{wf}^{*}\bigg{(}\frac{\partial\theta}{ \partial r}\bigg{)}_{w}=k_{bf}^{*}\bigg{(}\frac{\partial\theta}{\partial r} \bigg{)}_{b};\ \ \ \mathrm{r}=1,-L_{u}\leq x\leq(L_{h}+L_{d})\]  
Thus, the mathematical formulation of the conjugate forced-convection problem consists of Equation (3), the dimensionless differential Equations (1) and (2), and the boundary conditions. The dimensionless parameters for the problem include the bulk fluid _Pe_; the Stefan number, _Ste_; the inlet dimensionless temperature, \(\theta_{in}\); the dimensionless particle diameter, \(d_{p}\); the volumetric fraction of PCM particles, \(c_{v}\); the property ratios for the PCM particles with respect to the suspending fluid, \(k_{wf^{\prime}}^{*}\), \(k_{pf^{\prime}}^{*}\), \(P_{wf^{\prime}}^{*}\), \(\rho_{pf^{\prime}}^{*}\), \(c_{p,wf}^{*}\), and \(c_{p,pf}^{*}\); and the dimensionless tube wall thickness, \(t_{w}\). The particle Biot number, \(B_{ip}\) and the property ratios of the bulk with respect to the fluid, \(k_{bf}^{*}\), \(\rho_{bf}^{*}\), and \(c_{p,bf}^{*}\), which appear in the foregoing formulation, are evaluated using the relationships given in [PERSON] et al. (2016).
## 4 Simulation and Experiment Results  
Acoustic SLAM presents a general framework, suitable for any application involving mobile acoustic sensors for scene mapping. This paper considers the application of robotic systems, typically equipped with a sound source array and odometer. Experiments were performed in two different cases. The first set of experiments was designed to make a room simulation to verify the feasibility and validity of the method which was based on the above robot motion model and observation model. The second set of experiments evaluated the method performance in realistic acoustic environments using a moving sound source array and a reference microphone integrated onto the robot.