esa-sceva/satcom-chunk-collection huggingface dataset:
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
###### Abstract  
Privacy disclosure has become a key challenge in vehicular ad hoc networks (VANETs). Although IEEE, ERSI, etc. suggest that a pseudonym-based scheme is a solution, how to support pseudonym management and vehicle authentication is still an open issue. In this paper, a secure VANETs authentication scheme (PAS) is proposed, where software-defined network (SDN) is integrated as a suitable infrastructure to support anonymous authentication and pseudonym management, while removing the requirement for pseudonym certification in the dynamic VANETs environment. The security and performance analysis indicate that PAS is able protect the privacy of vehicles and has a high efficiency.  
VANETs; SDN; anonymous authentication; pseudonym management +
Footnote †: journal: Applied  
0000-0001-9991-4699-3899-3/10/0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-000  
vehicles in VANETs is likely to be leaked. Consequently, it becomes impossible to rely on a single trusted authority to establish a trusting relationship among the entities. (ii) In the traditional authentication schemes, if a vehicle misbehaves, the informant needs to submit relevant evidence to the trusted authority to revoke the illegal vehicle. However, these schemes do not mention how to determine the integrity and authenticity of the evidence, which makes the evidence difficult to trust. (iii) When providing communication services for vehicles, RSUs have to store and maintain some necessary information for vehicles, which causes huge pressure to be applied to the RSU due to its limited computation and storage capacity. (iv) Authentication protocols in the proposed schemes are treated as one-model-fits-all. Once the protocols are determined, it becomes hard to upgrade and update them.  
As a result, in terms of the conflicting goals of security and efficiency, we propose a privacy-preserving authentication scheme based on SDN for VANETs (PAS). The contributions are summarized as follows:  
1. The proposed scheme integrates with software-defined network (SDN) to support vehicle identity management and anonymous authentication.
2. Integrity, confidentiality, non-repudiation, and the unforgeability of vehicle identities are guaranteed when using the secure-data-sharing approach.
3. A complete privacy-preserving authentication scheme is proposed, including a vehicle-to-infrastructure (V2I) authentication protocol, vehicle-to-vehicle (V2V) authentication protocol, and pseudonym revocation. Furthermore, extensive experiments are adopted to evaluate PAS compared with the typical ones.  
The organization of the paper is structured as follows: Section 2 describes the related works about anonymous authentication schemes in VANETs. The preliminaries such as SDN, bilinear mapping, GDH assumption, and BLS signature mechanism are sketched in Section 3. Section 4 formalizes the network architecture, trust model, threat model, and the details of PAS. Security analysis, privacy analysis, and performance analysis are discussed in Section 5. Finally, the conclusion and future work are presented in Section 6.
## 2 Materials and Methods
### Procedure  
To explore the influence of initial sensor orientation (sensor offset angle), a systematic approach to increasing, gradually, the initial sensor orientation offset was taken using a series of custom-made wedges to offset the sensor in the non-primary motion planes as described below (Figure 1).  
To simulate forward-bending movements (rotation about Y in Figure 2), the initial orientation was gradually increased from 0\({}^{\circ}\) to 20\({}^{\circ}\) of tilt (rotation about Z in Figure 2), from 0\({}^{\circ}\) to 20\({}^{\circ}\) of twist (rotation about X in Figure 2), and then via a combination of tilt and twist in 5\({}^{\circ}\) increments. These offsets were informed by preliminary testing where the initial sensor orientation offset could reach 20\({}^{\circ}\). To simulate lateral bending movements, the initial sensor orientation was gradually increased through 0\({}^{\circ}\) to 50\({}^{\circ}\) pitch (rotation about Y in Figure 2) in 10\({}^{\circ}\) increments, from 0\({}^{\circ}\) to 20\({}^{\circ}\) of twist (rotation about X) in 5\({}^{\circ}\) increments, and via a combination of pitch and twist. Larger pitch angles were explored due to the reported sacral pitch angles commonly reaching 50\({}^{\circ}\)[27].  
Once the initial orientation was established, the jig was moved through 40\({}^{\circ}\), 80\({}^{\circ}\), and 120\({}^{\circ}\) of simulated forward bending, or 40\({}^{\circ}\) and 80\({}^{\circ}\) of simulated lateral bending, with each movement repeated three times for each bending angle and each sensor orientation.  
To explore the effects of movement velocity on the resultant angle measurement, the jig was moved through a range of movement velocities guided by a metronome set at a slow velocity (0.5 Hz) and a fast velocity (1 Hz) to match known movement velocities [12; 24; 28; 29]. This was repeated for the series of increasing tilt and twist offsets described above.
# Free Vibration Analysis of Triclinic Nanobeams Based on the Differential Quadrature Method
## 4 Numerical Results
Finally, the last parametric investigation compares the response of the triclinic nanobeam under the assumption of constant, linear or quadratic variation in thickness. Table 8 summarizes the results in terms of the first-three non-dimensional natural frequencies, for different nonlocal and strain gradient parameters and boundary conditions. Based on the results in Table 8, a variation in the power-law index \(q\) yields a different vibration response. This is clearly affected by the combined values of power-law index and mode numbers. Moreover, it is worth noticing that the small-scale parameters do not affect significantly the response, for different power-law indexes, which is of great interest for design purposes. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(L/h\) & \(l\) & \(\mu\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & \(\omega_{4}\) \\ \hline
20 & 0 & 0 & 1.8721 & 4.6431 & 7.6597 & 10.5185 \\  & & 1 & 1.8724 & 4.6317 & 7.5959 & 10.3340 \\  & & 2 & 1.8727 & 4.6204 & 7.5345 & 10.1644 \\  & & 3 & 1.8730 & 4.6092 & 7.4756 & 10.0078 \\ \hline
1 & 0 & 1.8711 & 4.6479 & 7.7057 & 10.6684 \\  & & 1 & 1.8714 & 4.6368 & 7.6437 & 10.4891 \\  & & 2 & 1.8717 & 4.6257 & 7.5840 & 10.3237 \\  & & 3 & 1.8720 & 4.6148 & 7.5265 & 10.1705 \\ \hline
2 & 0 & 1.8692 & 4.6453 & 7.7270 & 10.7511 \\  & & 1 & 1.8695 & 4.6345 & 7.6678 & 10.5819 \\  & & 2 & 1.8703 & 4.6238 & 7.6107 & 10.4249 \\  & & 3 & 1.8701 & 4.6132 & 7.5556 & 10.2785 \\ \hline
100 & 0 & 0 & 1.8750 & 4.6920 & 7.8464 & 10.9740 \\  & & 1 & 1.8753 & 4.6805 & 7.7813 & 10.7822 \\  & & 2 & 1.8756 & 4.6692 & 7.7187 & 10.6059 \\  & & 3 & 1.8759 & 4.6579 & 7.6585 & 10.4431 \\ \hline
1 & 0 & 1.8729 & 4.6873 & 7.8654 & 11.0694 \\  & & 1 & 1.8731 & 4.6763 & 7.8035 & 10.8894 \\  & & 2 & 1.8735 & 4.6653 & 7.7438 & 10.7227 \\  & & 3 & 1.8738 & 4.6544 & 7.6864 & 10.5678 \\ \hline
2 & 0 & 1.8707 & 4.6813 & 7.8744 & 11.1201 \\  & & 1 & 1.8710 & 4.6706 & 7.8160 & 10.9550 \\  & & 2 & 1.9558 & 4.6645 & 7.7548 & 10.8016 \\  & & 3 & 1.8711 & 4.6497 & 7.7051 & 10.6554 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effect of the \(L/h\) and small-scale parameters on the natural frequencies for a clamped-free cantilever triclinic nanobeam. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \(L=\mu=1\) & & & \(\mu=2,l=1\) & & & \(\mu=1,l=2\) \\ \cline{2-9} \(q\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) & \(\omega_{1}\) & \(\omega_{2}\) & \(\omega_{3}\) \\ \hline \multicolumn{9}{c}{Simply supported} \\ \hline
0.2 & 3.14119 & 6.27982 & 9.41334 & 3.13556 & 6.23616 & 9.27387 & 3.14686 & 6.32382 & 9.55485 \\
0.5 & 3.14120 & 6.27985 & 9.41338 & 3.13559 & 6.23622 & 9.27394 & 3.14687 & 6.32385 & 9.55489 \\
1 & 3.14117 & 6.27977 & 9.41
# LoRaWAN Physical Layer-Based Attacks and Countermeasures, A Review
## 2 LoRaWAN
### Device Activation
#### 2.6.2 LoRaWAN v1.1  
The LoRaWAN standard v1.1 improves communication security by introducing an additional join-server and further session keys. Similar to the above, the first step in OTAA requires storage of the join identifier (**JoinEUI**), **DevEUI**, **AppKey** and an additional secret key, **NwkKey**, to an end device. The latter three are also stored in the join server. As an end device proceeds to start OTAA activation, it creates the join-request packet, including **DevEUI**, **JoinEUI** and **DevNonce**. The integrity of the join-request packet is protected by the MIC, which is calculated using **NwkKey**. Once the network server approves the join request, it locates the join server defined by **JoinEUI** identifier. Next, a **JoinReq** message is sent to the join server, which contains the original join request message and the device-relevant parameters. Subsequently, on the join server, network-session keys (**SNwkSIntKey**, **FNwkSIntKey**, **NwkSEncKey**) and an application session key (**AppSKey**) are generated, which are later utilized to verify the integrity and the confidentiality of the MAC commands and payload data. The session keys and the join-accept message are encrypted with **NwkKey** and incorporated into a **JoinAns** message, which is delivered back to the network server. Given that **JoinAns** is successfully verified by the network server, the join-accept message is forwarded to the end device, which derives the above session keys out of the message. After the session-key exchange, the application server receives the **AppSKey** from the network server upon the first device uplink message and thus, the end-to-end security for an LoRaWAN v1.1 application is established. The ABP activation in version v1.1 follows the lines of version v1.0 and, so, the session keys and the **DevAddr** are preconfigured on end devices as well as on application and network servers.
## 3 Link Cost Metric With Reliability Assurance
### Link's Stability Model  
The main purpose of this paper is to research the methods of improving the satellite network reliability. A usual method of this is replacing the link's metric of distance with its reliability as the link stability metric. This part discusses the satellite cluster's link cost metric with the links' reliability and satellite dynamic model.  
From Equation (8), each link's reliability can be represented as \(Y=r\tau T^{-1}\), and through the min-max normalization, the reliability is changed to:  
\[\begin{array}{rcl}\chi_{ij}&=&\left(Y_{ij}-Y_{\min}\right)\left(Y_{\max}-Y_{ \min}\right)^{-1}\\ &=&\left[\left(\tau T^{-1}\right)_{ij}-\left(\tau T^{-1}\right)_{\min}\right] \left[\left(\tau T^{-1}\right)_{\max}-\left(\tau T^{-1}\right)_{\min}\right]^ {-1}\end{array} \tag{16}\]  
The period time \(T\) can be calculated by Equation (7), and the effective working time \(\tau\) per period can also be calculated through the following equation:  
\[\tau=\left\{t|x^{2}\left(t\right)+y^{2}\left(t\right)+z^{2}\left(t\right)\leq R ^{2},\ 0\leq t\leq T\right\} \tag{17}\]  
Thus, this paper sets Equation (16) to be the link cost metric. Finally, through the Equations (6), (16) and (17), all of the possible links' cost matrices \(\left[\chi_{ij}\right]\) can be obtained with reliability.
## 6 Limitations and Future Work  
Although this study achieved significant results, there are some limitations: the dataset used was collected from a single hospital, and the sample size and diversity are limited. In the future, it is planned to evaluate the model on broader and more heterogeneous datasets obtained from different centers in order to increase its generalizability. Furthermore, since the model obtained is difficult for expert physicians to interpret in decision-making processes, explainable artificial intelligence techniques will be integrated to make the decision mechanisms more transparent.
# Progressive Poling of Large Area, High r\({}_{33}\) Electro-Optic Polymer SEO100c
## 6 Results
### Electro-Optic Stability over Time  
In addition to examining the relation between the r\({}_{33}\) and applied poling voltage, the stability of the r\({}_{33}\) over time has been examined. The stability of the r\({}_{33}\) over time is critical to ensure consistent results for experimental devices and is a significant hurdle for the use of polymers in commercial products. A device was poled at 800 V and the r\({}_{33}\) was measured over time. Over a period of 65 days no significant relaxation of the r\({}_{33}\) was observed with a device stored at room temperature. The device was tested at 40 Hz, 100 Hz, 500 Hz, and 1k Hz with the average of the measurements presented as each data point. The error was calculated by finding the standard deviation of the data set. The initial measurement taken immediately after poling showed an r\({}_{33}\) of 74 pm/V, all subsequent measurements taken were within the standard deviation (\(+\)/\(-\)2.98 pm/V) or 4% of this initial measurement. Therefore, the electro-optic coefficient of SEO100c is stable for the first 65 days after poling. Figure 8 shows the evolution of SEO100c's r\({}_{33}\) value over time. Previously the stability of SEO100c's EO coefficient has been observed over 30 days [20]. This experiment has extended the time over which SEO100c has been shown to have stable EO properties.  
Figure 7: An increased r\({}_{33}\) for SEO100c is seen as the maximum poling temperature is increased.
## 3 Results
### Shock Absorber Cooling  
The shock absorber's ability to dissipate vibration energy depends, among other issues, on its ability to dissipate heat. If it is too small, this leads to excessive heating of the shock absorber and increases the likelihood of damage (mainly to the seals, which lead to unsealing of the chambers). The ability to dissipate heat depends on the geometry and the type of materials used (including paintwork), but also on the purity of the surface. This can be defined by determining the cooling curve of the shock absorber (Figure 7).  
Figure 6: Temperature changes at the surface (along the shock absorber axis).  
Figure 7: Shock absorber cooling curve.  
The heat exchange can be determined by [PERSON]'s law of cooling. The temperature change is defined by relation (1) [25]:  
\[\mathrm{T(t)/dt=-k\cdot(T_{o}-T_{a})}, \tag{1}\]  
where:  
\(T_{a}\)--ambient temperature (\({}^{\circ}\)C),  
\(T_{o}\)--initial temperature (\({}^{\circ}\)C),  
t--time (s),  
\(\kappa\)--cooling constant (1/s).  
After the integration of the equation (1), we obtain an expression that can be written in the form (2):  
\[\mathrm{T(t)=T_{a}-(T_{o}-T_{a})\cdot exp(-\kappa\;t)} \tag{2}\]  
Using the results shown in Figure 7, the value of \(\kappa\) equals 0.00071 s\({}^{-1}\). The cooling constant \(\kappa\) may also be defined by the Equation (3):  
\[\kappa=(\lambda\cdot A)/(m\cdot c), \tag{3}\]  
where:  
\(\lambda\)--equivalent heat transfer coefficient (W/(m\({}^{2}\cdot\)K)),  
A--heat exchange surface area (m\({}^{2}\)),  
m--mass (kg),  
c--specific heat (J/(kg\(\cdot\)K)).  
Assuming that the quantities shown above do not change along with the temperature, it is possible to determine the steady-state operating temperature for other ambient temperatures. This is conducted by using the equilibrium conditions between external work and dissipated energy (heat flux) (4).  
\[\mathrm{Q=E_{d}/t_{c}=A\cdot\lambda\cdot(T-T_{a})}, \tag{4}\]  
where:  
Q--heat flux (W),  
E\({}_{d}\)--energy dissipated during one cycle (J),  
t\({}_{c}\)--duration of one operating cycle (s).  
The lack of a linear relationship between the energy dissipated during one cycle (at a constant value of amplitude and frequency of movement) and the temperature requires an iterative solution using the relationship shown in Figure 9.
## 3 Results and Discussion
### Hydrochemical Verification
Due to the lack of sufficient observation wells, the contaminant concentrations were the result of interpolation calculations and were not reliable within the northwest of the site. At the same time, combined with the flow direction of the groundwater, there could be a source of pollution or a leak from the acid tank itself upstream of the anomaly, i.e., in the northwest direction, which could result in the anomaly. Unfortunately, we were not able to carry out further verification. Overall, profile ML-III and profile ML-IV were highly resistant. Near the two profiles, a high concentration of trans-1,2-dichloroethylene was detected in the groundwater of well GW1. The maximum value of trans-1,2-dichloroethylene detected in GW1 was 703 \(\upmu\)g/L and the concentration decreased toward the northeast. This was close to the semi-outdoor unloading platform and could be caused by the leakage of raw materials during loading, unloading, and transportation. According to existing experimental results and the actual site application analysis, most organic pollution is often insoluble in water and has a high electrical resistivity [35]. Pollutants penetrate downward into the groundwater under the action of their own weight. Due to the adsorption of the soil, they often adhere to the soil surface and fill the soil pores. Then, they obstruct the passage of free water, which leads to an increase in soil resistivity. When the pollutants enter the groundwater, some are affected by the flow of water and diffuse downstream. The other part of the pollutants is adsorbed onto the medium, prompting the area to show an abnormally high resistivity. Meanwhile, trans-1,2-dichloroethylene is a liquid organic compound with high volatility. The high-resistivity region within the profiles could be a mixture of the volatile phase and the liquid phase of anti-1,2-dichloroylene. Combined with what has already been reported, volatile organics could lead to an increase in resistance. The overall concentration of 1,2,3-trichlorobenzene was relatively low, reaching a maximum of 316 ug/L in the groundwater, and the resistivity of the groundwater reached 3984.064 \(\Omega\)-m in GW4, which could be due to convective dispersion in the southeast direction. According to the map of the factory, we could not explain the source of pollution. We thought that it was caused by the trichlorobenzene production workshop on the west side of the plant. As a result of the abnormity of the ERT result, we found new pollution in the field and tried to identify its source.
## 2 Materials and Methods  
### Study Population  
This retrospective study was performed at the IRCCS Istituto Ortopedico Galeazzi in Milan, Italy. Local ethics committee approval was obtained (RETRORAD, approved by the Ethical Committee of San Raffaele Hospital), and patient authorization was obtained for the publication of anonymized data.  
Patients who underwent both a lumbar spine DXA and a chest/abdominal CT between January 2015 and July 2022 were evaluated. Inclusion criteria were as follows: (i) maximum time interval of six months between the two examinations; (ii) non-contrast CT scan, which included at least the first four lumbar vertebrae, to obtain valid QCT values; (iii) CT scan performed with the patient positioned supine with arms raised up in order to avoid artifacts related to arms that may interfere with QCT measurements; (iv) age \(\geq\) 18 years to ensure a study population consisting of skeletally mature individuals; and (v) adequate image quality for both DXA and CT, to ensure accurate assessment of bone density and morphology.  
Exclusion criteria were (i) history of lumbar spine surgery, as metallic implants alter BMD measurements; (ii) technical issues affecting image quality, including motion artifacts, excessive noise, or inadequate CT acquisition parameters (QCT software version 5.1 only accepts a specific field of view and tube voltage); and (iii) history or presence of oncologic diseases with spinal involvement. Of note, the presence of osteometabolic or systemic diseases was not considered an exclusion criterion. Such conditions do not introduce a systematic discrepancy between the two imaging modalities and therefore did not affect the study's primary objective. Furthermore, patients with non-aggressive bone alterations (such as vertebral compression fractures or hemangiomas) were included whenever alternative vertebrae could be analyzed or partial measurements could be reliably obtained.  
The final cohort consisted of 105 participants, 87 (82.9%) females and 18 (17.1%) males, with a mean age of 69 years (SD: 11; range 40-86). More in detail, for the female participants, the mean age was 68.5 years (SD: 11; range 42-86), while male participants had a mean age of 70 years (SD, range 40-82.5).
## 4 EMTARP: System Model and Protocol Description  
### System Assumption  
We assume that all nodes have the same transmission range and energy level initially. The maximum transmission range of a node is assumed to be \(r\), and the maximum transmit power of a node cannot exceed the range of \(r\) considering the limited battery capacity of the WBAN device. We define two types of nodes: a static node and mobile node. The static node represents a node which is attached to the human body with no movements, and the mobile node represents a node which is attached to arms and legs. We construct a network with \(N\) sensor nodes and one sink node. Each sensor node is responsible for forwarding the data packet collected by itself or received from neighbor nodes to the sink node.
# Synchronization of Heterogeneous Multi-Robotic Cell with Emphasis on Low Computing Power
## 4 Validation of the Proposed Solution
### Simulation Validation of Solution Functionality
#### 4.1.4 Experiment 1b--Master Speed Change, Advanced Algorithm  
The case with a 10% limit for the error of the difference between operation duration performed by master and slave manipulators and a 15% significance limit for the similarity of two successive deviations \(D_{ij}\) and \(D_{ij+1}\) was validated via simulation.  
Figure 8: Percentage proportion deviation without feedforward synchronization—Experiment 2a.  
Figure 7: Operation duration times of master and slave manipulators without feedforward synchronization—Experiment 2a.  
Figure 9: Brief summary of Experiment 2a results.  
In simulation Experiment 1b, equal parameters of the whole simulated system were used as in simulation Experiment 1a, with the addition of the feedforward synchronization feature to the synchronization algorithm.
## 4 Dataset Annotation and Filtering
### Annotating Tool  
In developing a custom Named Entity Recognition (NER) system for our dataset, choosing the best annotating tool is a crucial part and very important to facilitating the annotating process and increasing efficiency. So, we decided to conduct a comprehensive study of four annotation tools: Brat [20], NER Annotator [21], Tagtog [22], and Prodigy [23]. We will compare these tools, showing the advantages and disadvantages of each of them, focusing on choosing the best suitable tool for our task and explaining our choice. The following Table 1 describes the comprehensive comparison.  
We chose to use the Brat annotation tool in our work since it has the ideal specifications for our work environment. Brat has a user-friendly interface which makes the annotating process easier for the annotating team and facilitates their work. Also, one of the biggest advantages of using Brat is its freeware and open-source nature. Brat can be deployed on our own web server, which has highly improved our workflow. This gave the annotating team a controlled environment and guaranteed data protection. The collaborative annotation capabilities of Brat were quite helpful for our research. Brat made it possible for several team members to work simultaneously on annotating the same dataset, which enhanced the annotation procedure and promoted productive collaboration. We were also able to customize the tool to meet the unique needs of the Encyclopedia of A.S. Pushkin dataset because of the flexibility and adaptability of the Brat tool, which ensured accurate and pertinent annotations.