esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 2 Background and Related Work  
### Introduction to Automotive Ethernet  
With the rapid advancement of automotive electronics, there has been a notable increase in the number of automotive electronic products. These products are becoming more functional and complex, surpassing the capacity and bandwidth limits of traditional vehicle bus networks. To address this challenge, in-vehicle Ethernet has emerged as a new LAN technology for connecting automotive electronic units. In comparison to traditional bus networks, in-vehicle Ethernet offers data transmission rates of up to 100 Mbit/s or even 1 Gbit/s over a single pair of unshielded twisted pair wires. It also meets the automotive industry's requirements for low power consumption, low electromagnetic radiation, affordability, high reliability, low latency, and synchronous real-time performance [11].  
Within Vehicular Ethernet, the frame structure has evolved over time, resulting in five distinct frame formats. Among these, the Ethernet frame format is primarily categorized into Ethernet II and IEEE 802.3. IEEE 802.3 Ethernet stands as one of the major standards [12]. The IEEE 802.3 Ethernet frame format is briefly described below, with the format depicted in Figure 2.  
An Ethernet frame consists of several key fields. The Preamble is a 7-byte sequence of alternating 0s and 1s that enables bit synchronization. The Start of Frame Delimiter is a 1-byte field set to 10101011, marking the start of the frame. The Destination Address and Source Address fields are each 6 bytes long, containing the MAC addresses of the recipient and sender, respectively. The Length field is 2 bytes, indicating the frame size, with a maximum value of 1500 bytes. The Data or Payload field holds the actual data, padded with 0s if less than 46 bytes. Finally, the Cyclic Redundancy Check (CRC) is a 4-byte field providing a 32-bit hash code for error checking.
## 3 Methodology  
### Dependability  
Dependability is a common term used to describe the availability and factors affecting reliability, maintainability, and the level of maintainability [44; 45]. The term availability is commonly used as a measure of operational safety [44; 46]. The availability is expressed in quantitative indicators and, as such, represents a measure of operational safety and thus a measure of quality in use [47]. The performance of availability has a decisive effect on operational safety and quality in use due to the well-known fact that the machine should first of all be available for work, in order to realize the other performances as well [44; 47]. Dependability is a complex function that depends on the following performances [48]:  
* performances of reliability;
* performances of maintainability;
* performances of logistic support for maintenance.  
Operational safety: \"A collective term used to describe the availability performance and factors that determine these performances: reliability performances, maintainability performances, and logistics support performances\" [37].  
The dependability of technical systems is conceptually stipulated by ISO-IEC standards [49; 50].
# An Improved Forwarding of Diverse Events with Mobile Sinks in Underwater Wireless Sensor Networks
## 5 ESDR: Event Segregation Based Delay Sensitive Routing
### Packets Forwarding Strategy  
In our proposed protocol, it is envisioned to formulate broadcasting mechanism for all types of nodes considering the delay sensitivity requirements of critical and very critical nodes. In our proposed protocol, we have planned the task dividing approach in which packets are forwarded either to the sink (floating on surface) or to CN based on their type and criticality requirements. Sojourn tour of CN can cause larger value of delay as data collected by CN is delivered to data center after its complete round trip. Hence it is ruled out to forward delay sensitive packets to CN. On the other hand, it is also inadequate to forward all the generated packets to surface sink(s) especially in deep underwater environments or in regular data gathering applications. That is why it is planned that delay sensitive nodes will forward the packets to surface sink using broadcasting and multi hop communication approach. Improved forwarding functions for critical and very critical packets are explained in upcoming sections. Forwarding function involves priority values \(p_{v}\) whose value is increased to distinguish the forwarding of very critical packets from the critical packets. Normal nodes try to forward or broadcast their respective packets to CN. Hence CN mobility is planned and influenced as per normal nodes distribution and data generation. Broadcasting of normal node's packets to CN can also involve one hop communication. If a normal node has packet to forward and it is not in the range of CN then it would check if any of its neighbours in the range of CN such that the packet is sent to that neighbor which relays the packet to the nearest CN. In order to accomplish this,normal nodes share their neighbor list with each other. Disjoint neighbors are updated by each node. Nodes with depth less than transmission range directly send their data to surface sink irrespective of their type.
## 2 Literature Review  
In recent years, with the development of Internet of Things (IoT) and communication technologies, many cities have implemented monitoring systems to record the number of vacant parking spaces [7; 8]. VPS prediction can be seen as a spatio-temporal forecasting task that utilizes historical data collected from devices to predict the future availability of parking spaces. Research on vacant parking space prediction can generally be divided into two categories. One type is based on statistics, and the other type is based on machine learning (ML) and deep learning (DL).
# Virtual Reality Training Application for the Condition-Based Maintenance of Induction Motors
## 6 Discussion
From the survey results (Figure 15), the users reported very high satisfaction with the experience (75% highest rating) and believed that this simulator can help them in the teaching of and training in induction motors (73% strongly agree). As highlighted, opinions were positive towards the interaction design. This is a complex and challenging process, but a special effort was made to achieve natural interactions as well as to mitigate any usability constraints of the controllers. The user only had to focus on the manipulation of objects to accelerate the learning curve. A single mechanism was used to pick up and to drop objects as well as to place them in position. Additionally, an attachment system was programmed to help the user to place the object in the desired position. Results that invite reflection are, for example, those related to the following questions: at each step did you know what to do, as a large group can be lost at certain times. Gaze and eye-position data were studied from the data that had been collected in order to reduce this problem. These data showed that some users have problems understanding what to do in some exercises until they seek help from the virtual assistant. Therefore, this virtual assistant was programmed to take the initiative much earlier. Small fonts used in some information panels were also corrected to improve comprehension. However, the information provided in the application was considered sufficiently clear and explanatory by these users. Results from users reported a high level of immersion in the experience, which can still be improved, as some users commented that they lost the sense of immersion when hearing the outside world due to the lack of ambient sound in the application. For these reasons, a sufficiently audible industrial ambient sound was programmed to help with user immersion. Finally, more than half of the users reported finding the visual aspects realistic. This aspect is important because the application achieves the four key objectives of a VR-tool: interaction, immersion, user involvement and photorealism [46]. Figure 15: Summary of some of the questions from the participants after VR training with the application.
## 2 Materials and Methods  
### Physical Models  
The schematic of the combined solar chimney (CSC) is shown in Figure 1. The structure was composed of a Trombe wall and an inclined-roof solar chimney, which consisted of an external glass cover and an internal storage wall both in the vertical and inclined part. An air channel was formed between the external glass cover and the internal storage wall. There were four operation modes for the combined solar chimney: a natural ventilation mode for the summer, an anti-overheating mode for the summer, a space heating mode for the winter, and a preheating mode for the winter. The changing of the operation modes could be realized by the switching of the air vents. The natural ventilation mode in the summer (S1) was to open the indoor air outlet, the roof air outlet, and the north air inlet; all other vents were closed, as shown in Figure 1. The anti-overheating mode in the summer (S2) was to open the outdoor air inlet and the roof air outlet; all other vents were closed. The baffle in the above both modes was open. The space heating mode in the winter (W1) was to open the indoor air inlet and the indoor air outlet; all other vents were closed. The baffle in the above both modes was closed.  
Figure 1: Schematic diagram of a combined solar chimney: (**a**) summer natural ventilation mode (S1); (**b**) summer anti-overheating mode (S2); (**c**) winter space heating mode (W1); and (**d**) winter preheating mode (W2).  
In order to simplify the expression, the CSC summer operation mode 1 (natural ventilation mode) and summer operation mode 2 (anti-overheating mode) were referred to as S1 CSC and S2 CSC, respectively. By this analogy, the operation modes of the Trombe wall in the summer and winter were written as S1 TW, S2 TW, W1 TW, and W2 TW for short. There was only a natural ventilation mode for the inclined-roof solar chimney (IRSC) so only the performance in the summer was considered. For the CSC, the top outlet of the inclined part was closed in the winter and its operations were the same as those of the Trombe wall. Therefore, only the performance of the space heating mode and the preheating mode of the Trombe wall in winter conditions were analyzed in this paper.  
The physical model of the CSC in different modes as well as the contrastive structures of the TW and IRSC are presented in Figure 2. The geometric parameters of the chimney were determined according to the real size of a rural house in China (with a floor height of over 3.0 m). The dimensions of the different models are listed in Table 1.
# A New Multivariate Approach for Real Time Detection of Routing Security Attacks in VANETs
## 6 Results and Discussion
### Analysis of the Network Traffic under Black Hole Attack  
In this part, we simulated a scenario of AODV routing protocol using the simulation settings defined in the previous section (Table 2). In this scenario, 100 vehicles are involved with 10 random CBR connections over a simulation duration of 100 s. This scenario is divided into two parts. In the first part, i.e., from the simulation start time until the sixtieth seconds, all vehicles behave normally with no malicious activities. During the second part of this scenario, one malicious node is activated at the sixtieth second which is concerned with the black hole attack. The results of the traffic monitoring in terms of throughput (TH), dropped packets ratio (DPR), and overhead traffic ratio (OTR) before and after initiating the black hole attack are given in Figures 4 and 5.  
the presence of the attacker node that tries to drop all the packets rather than delivering them to their intended destination. The variations in Figure 6 reveal a noticeable increase in the overhead traffic ratio after launching the attack in the sixtieth second compared to the normal situation. This finding can be justified by the fact that the number of successfully transmitted packets is lower than the number of routing control packets generated by the AODV routing protocol under black hole attack. As a result, the values of the overhead traffic ratio, which is defined as the ratio between the total number of control packets generated by the AODV routing protocol during route discovery and route maintenance to the total number of transmitted packets goes up. Conclusively, we can say that black hole attack severely affects the AODV performances and disturb the correct execution of the routing operations. These findings cope with our previous study [10], where it was confirmed that AODV performances are highly influenced by the introduction of routing attacks against AODV routing protocol.
## 5 Application to Platoon Protection
### Effect of Whisper Check With Platoons
In particular, it shows the distance lost by the last vehicle \(Z\) compared with the case in which vehicles employ the whisper checks, under various values of \(\beta\) and \(\gamma\) and \(\alpha=10\). We fix the value of \(\alpha\) because the platoon followers travel at the same speed as the platoon leader and had little effect on the velocity change of \(Z\). When all vehicles use the whisper check, \(Z\) travels 10 km at a velocity of 80 km/h without any distance loss, regardless of \(\beta\) and \(\gamma\) values, because the vehicles successfully filtered out forged messages. In contrast, in the absence of the whisper check, the average velocity of \(Z\) decreases as \(\beta\) and \(\gamma\) increase, and there is lost distance. For instance,  
Figure 11: Velocity changes of the non-platoon vehicles in the first vehicle group without forged message filtering. Figure 12: Velocity changes of platoons without forged message filtering. for \(\alpha=\beta=\gamma=10\), the average velocity of \(Z\) while moving 10 km dropped to about 52 km/h, far below 80 km/h with the whisper check in play. Although we set the inter-vehicle distance to sufficiently large values so that the roadside attack did not cause direct collisions, the loss of average speed and the potential discomfort due to repeated and wild fluctuations of the vehicle speed would be enough to annoy the vehicle riders. If the inter-vehicle distance were lower for higher road throughput or fuel-efficiency, it would increase the probability of more serious events.
###### Abstract  
Reference evapotranspiration (\(ET_{0}\)) is a key component of hydrologic cycle and it is important for water resources management. Analysis of \(ET_{0}\) changes is particularly critical for understanding the impacts of climatic change on hydrology in ecologically fragile regions. In this study, using the Penman-Monteith method and the Mann-Kendall test, the variation characteristics of \(ET_{0}\) on the Tibetan Plateau (TP) from 1970 to 2018 was analyzed, and the dominant climatic factors controlling the change of \(ET_{0}\) was also explored. The result shows that in TP region: (1) there was an abrupt change in the trend of \(ET_{0}\) around 1997, and the \(ET_{0}\) declined at a rate of \(-25.9\) mm\(/\)decade during 1970-1996 but increased by \(31.1\) mm\(/\)decade during 1997-2018; (2) \(ET_{0}\) is most sensitive to solar radiation, then relative humidity, wind speed and mean temperature; (3) the decrease of \(ET_{0}\) before 1997 was mainly due to the decline of wind speed and the increase of relative humidity, while the increase of \(ET_{0}\) after 1997 was mainly due to the decrease of relative humidity. The results of this study can provide data reference for the research of water balance on the TP.  
T +
Footnote †: journal: Applied Sciences
## 4 Discussion
### Inositol Biosynthesis  
The main metabolic pathway generating inositol involves the dephosphorylation of inositol-3P by inositol mono phosphatase (IMP) starting from G6P [17; 18; 19; 20]. Our IMP analysis revealed a marked change in inositol localization in the tomato fruits, especially during the mature period. In the control group, high levels of inositol were detected in the middle pericarp, but in the drought-stressed group, inositol levels were high in seeds.  
On the other hand, the transcript levels of IMP and inositol oxygenase (MOO) genes decreased under drought stress (Supplementary Table S1 and Figure S1). After being synthesized by IMP, inositol is metabolized by MOO to glucuronate and then to pectin [21]. Transcriptome analysis showed that the expression levels of genes encoding multiple enzymes involved in pectin biosynthesis were reduced by drought stress [9].  
These results suggest that inositol is expressed in the mesocarp under normal conditions and that inositol is involved in pectin synthesis. However, under drought stress, inositol biosynthesis via IMP is suppressed and pectin biosynthesis is also downregulated. On the other hand, inositol binds to 1,2-Diacyl-sn-glycerol, is metabolized to phospholipids as phosphatidylinositol, and accumulates in seeds [8]. Inositol might be used as a source of phospholipids to protect seeds from dehydration rather than for pectin biosynthesis in the mesocarp and outer pericarp.
## 4 Experiments Results and Discussion
### Results and Discussion
#### 4.3.1 Results of the Detonation Shock Tunnel Test
Since the physical parameter of the epoxy has large deviations due to the manufacturing processes, the \(pck\) of the epoxy in the present sensors is calibrated using a comparison to the theoretical heat values instead of using the data in Table 1. Different \(pck\) values of the epoxy are tested in Equation (14) to correct the results of the NTC-2.5 sensors; the \(pck\) value that achieves the best agreement between the corrected and theoretical values is used. Figure 17 shows the heat flux curves of C05 (NTC-2.5) with/without corrections. The corrected result agrees well with the experimental results and the results of the C02 (NTC-10) sensor. The \(pck\) values of epoxy for the five NTC-2.5 sensors are shown in Table 4; the relative standard deviation between the  
Figure 16: Comparison of the heat flux curves of C02 (NTC-10) and C05 (NTC-2.5). Figure 15: Heat flux curves of repeated experiments for C01 (NTC-10). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor No.**} & **Run 1** & **Run 2** & **Run 3** & **Average Value** & **Theoretical Value** & **Relative Error** \\ \cline{2-7}  & & & & **MW/m\({}^{2}\)** & & **\%** \\ \hline C01 & 0.190 & 0.190 & 0.197 & 0.192 & 0.196 & \(-\)2.02 \\ C02 & 0.163 & 0.162 & 0.170 & 0.165 & 0.170 & \(-\)2.79 \\ C03 & 0.141 & 0.140 & 0.148 & 0.143 & 0.152 & \(-\)5.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the experimental results of the NTC-10 sensors with the theoretical value. values is about 11%, which is attributed to an error in the production process. Although this deviation may be improved, a calibration of each sensor is needed if high accuracy heat transfer measurements are desired. This applies also to other sensors since the _pck_ values of Type-E thermocouples also require detailed calibration.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 3 Architecture Overview
### Medium Access Communication Protocol for LoRa Radio Technology  
In radio communications, a robust MAC protocol has to exist to coordinate the medium access in an effective way, especially in the case of multiple gateways, which is the case of this work. Thus, our platform employs the multi-gateway LoRa MAC protocol developed in [36], which follows an approach based on carrier sense multiple access with collision avoidance (CSMA/CA) with Request To Send (RTS)/Clear To Send (CTS) message exchange to control the medium access of the devices. Moreover, influences from protocols such as the multiple access with collision avoidance (MACA), multiple access with collision avoidance for wireless (MACAW), and the IEEE 802.11 MAC layer can be found.
# A Hybrid Architecture for Safe Human-Robot Industrial Tasks
## 5 Intention Estimation Module  
### Estimation of the Operator Velocities  
Another key aspect of the HRC problem is intention estimation (IE), which involves predicting human movements. In this work, a linear Kalman filter (LKF) was implemented to estimate the operator's velocity by combining OptiTrack sensor acquisitions with a motion model. It predicts the future position and velocity of each rigid body present in the collaborative workspace and updates these predictions with new sensor readings, improving accuracy. This real-time estimation, applied to Equation (1), allows the robot to anticipate the operators' movements, adjust its actions, and avoid potential collisions, ensuring safe and efficient interaction, as explained in Section 6. The advantage of using an LKF is its ability to smooth trajectory data and continuously refine velocity estimates, which enhances the robot's responsiveness and safety in dynamic, industrial environments.  
The implemented LKF solves the problem of estimating the state of a discrete-time process governed by the equations  
\[x_{k+1} = \begin{bmatrix}I_{3}&\Delta tI_{3}\\ O_{3}&I_{3}\end{bmatrix}x_{k}+w_{k}, \tag{2}\] \[y_{k} = \begin{bmatrix}I_{3}&O_{3}\end{bmatrix}x_{k}+n_{k} \tag{3}\]  
where \(\Delta t\) is the sampling time, \(I_{3}\) and \(O_{3}\) are the identity and zero matrices of size \(3\times 3\), respectively; \(w\) and \(n\) are the process and measurement noises with covariance matrices \(W\) and \(N\), respectively. Finally, \(x\) is the state vector of the system, i.e., the position and the velocity of the rigid body \(x=\begin{bmatrix}p_{RB_{i}}^{T}&p_{RB_{j}}^{T}\end{bmatrix}^{T}\), and the measured output \(y\) is a vector containing the coordinates of the point \(P_{RB_{i}}\) described in Section 4.4. The covariance matrix \(N\) is experimentally estimated, while the covariance matrix \(Q\) has been chosen as  
\[Q=\begin{bmatrix}I_{3}\Delta t^{2}&O_{3}\\ O_{3}&Q_{2}\end{bmatrix} \tag{4}\]  
where \(Q_{2}\) quantifies the uncertainty on the velocity dynamics (assumed constant) of the state equations. Note that the LKF equations used in this work are the standard ones and are therefore not included here for the sake of brevity.  
Figure 4: Four rigid bodies tracked by the OptiTrack system (**left**) and visualized in ROS2 (**right**).  
Figure 5 on the left shows the estimated velocities of the four rigid bodies appearing in Figure 4: two belonging to an operator holding a toolbox and the other two belonging to another operator carrying out an assembly operation. The developed LKF software computes and displays the three components of the estimated velocity (rgb arrows) and the resulting speed (yellow arrow). Their length is scaled according to their own module: \(v_{RB_{1}}\) and \(v_{RB_{2}}\) represent a human walking and they are significantly higher than \(v_{RB_{3}}\) and \(v_{RB_{4}}\) representing the speeds used for the assembly task.