esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Performance Analysis
### The Performance Analysis of the DPM  
The response time and exactness about a self-diagnosis request in the DPM simulation was compared in this section. The DPM simulation is proceeded with 2 PCs (client PC and server PC), but not with a real vehicle according to the increasing number of vehicles. It is assumed that each vehicle has 20 sensors. The sensor data is sent from the client PC to the server PC, and the server PC diagnoses the condition of a vehicle.  
The 1 st experiment is processed as follows.  
Figure 14: The error rate comparison of diagnosis according to the number of Training Data Set.  
Figure 13: The comparison of the average response time according to the kinds of sensors.  
Figure 15 shows an average response time of a self-diagnosis request. It takes 1.305 ms for the DPM to reply to the request of about 20 vehicles, 2.175 ms of 30 vehicles, 4.65 ms of 50 vehicles, 14.78 ms of 800 vehicles, and 19.71 ms of roughly 1200 vehicles. It takes 0.48 ms for the existing Cloud system to reply to the request of about 20 vehicles, 1.14 ms of 30 vehicles, 3.21 ms of 50 vehicles, 17.61 ms of 800 vehicles, 24.26 ms of 1200 vehicles. Therefore, it shows that the more vehicles there are, the more efficient the DPM is over the existing Cloud system.  
The 2 nd simulation shows the exactness comparison about a self-diagnosis request and reply.  
Figure 16 shows the experimental result of error times about a self-diagnosis request and reply between the DPM and the existing Cloud system. In case of transferring a request message to 100 vehicles or fewer, both the existing Cloud system and the DPM had 0 response error or 1 response error. The existing Cloud system had 3 response errors in the case of transferring a request message to 300 vehicles, 5 response errors to 500 vehicles, 10 response errors to 800 vehicles, and 13 response errors to 1200 vehicles. Therefore, the response exactness of the existing Cloud system was analyzed as 98%.  
Figure 16: The comparison result of error times about a self-diagnosis request and reply.  
Figure 15: The comparison of an average response time about a self-diagnosis request.  
The DPM proposed in this paper had 2 response errors in the case of transferring a request message to 300 vehicles, 6 response errors to 500 vehicles, 9 response errors to 800 vehicles, and 12 response errors to 1200 vehicles. Therefore, the response exactness of the DPM was analyzed as 99% and the DPM shows that the larger number of vehicles there is, the higher the response speed.
## 2 Methods and Experiments
The interference signal intensity is:  
\[I=|E_{1}+E_{2}|^{2}=(E_{1}+E_{2})\cdot(E_{1}+E_{2})^{*}. \tag{4}\]  
After a bandpass filter, the beat signal of OFC heterodyne interferometry is:  
\[I_{\text{beat}}=\sum_{m}2A_{1}A_{2}\cos[2\pi f_{beat}(t-t_{2})+2\pi f_{m}(t_{1 }-t_{2})]. \tag{5}\]  
Equation (5) is equivalent to this form:  
\[I_{\text{beat}}=2a\cos[2\pi f_{\text{beat}}(t-t_{2})+\varphi_{\text{beat}}], \tag{6}\]  
where \(\varphi_{\text{beat}}\) is the phase measured by the lock-in amplifier, and:  
\[a\cos\varphi_{\text{beat}}=\sum_{m}A_{1}(f_{m})A_{2}(f_{m})\cos 2\pi f_{m}\tau, \tag{7}\]  
\[a\sin\varphi_{\text{beat}}=\sum_{m}A_{1}(f_{m})A_{2}(f_{m})\sin 2\pi f_{m}\tau, \tag{8}\]  
Figure 2: (**a**) Optical spectrum of two beams; (**b**) Product of electric amplitude of two arms. where \(\tau=t_{1}-t_{2}\) is the relative time delay of the two beams. According to Equations (7) and (8):  
Figure 1:0  
then \(\varphi_{\text{beat}}\) is a function of relative time delay \(\tau\):  
Figure 1:1  
For the continuous-wave laser heterodyne interferometer, \(\varphi(\tau)=2\pi f_{c}\tau\), thus, the effective center frequency for OFC heterodyne interferometry is:  
Figure 1:2  
The effective center wavelength can then be calculated using \(\lambda_{c}=c/f_{c}\), where \(c\) is the velocity of light in the vacuum. Figure 2b illustrates the product of the electric amplitude of the two beams, which is the function of the optical frequency. If the product is symmetrical for the frequency at \(f_{m0}\), Equation (9) can be simplified as:  
Figure 1:3  
therefore, \(\varphi_{\text{beat}}=2\pi f_{m0}\tau\), meaning that the effective center wavelength does not change with the relative time delay of two beams. In this study, however, the product was not symmetric, thus, the effective center wavelength was calculated by simulation. In this simulation, the parameters of the system were the same as the experimental setup, including the electric amplitude of the two beams and repetition frequency of OFC. The carrier-envelope-offset frequency was regarded as zero. The phase \(\varphi_{\text{beat}}\) of the heterodyne interference signal was calculated at different relative time delay according to Equation (9), and numerical differentiation methods from Equations (10) and (11) were used to calculate the effective center wavelength at different relative time delay.
###### Abstract  
In Internet of Vehicles (IoV), the secure data transmission between vehicles and transportation infrastructure effectively ensures the safety and fast driving of vehicles, while authentication and key agreement protocols between vehicles and transportation infrastructure (V2I) play an important role in ensuring data security and user privacy. However, how to design a provably secure and lightweight V2I authentication protocol for IoV is a challenge. Recently, [PERSON] et al. proposed a conditional privacy-preserving and desynchronization-resistant authentication protocol for IoV, but we find that [PERSON] et al.'s scheme is vulnerable to identity guessing attacks, impersonation attacks, and a lack of session key secrecy once the attacker obtains data stored in smart card through side-channel attacks. We also point out that [PERSON] et al.'s protocol is vulnerable to Roadside Unit (RSU) captured attacks and lacks perfect forward secrecy. Therefore, we propose an improved V2I authentication protocol for IoV, which uses the Physical Unclonable Function (PUF) to resist RSU captured attacks, and designed a three-factor secrecy strategy to resist side-channel attacks; a conditional privacy-preserving strategy was also adopted to achieve anonymity and malicious user tracking. Furthermore, the proposed protocol is provably secure under the random oracle model and has low computation and communication costs.  
IoV; V2I authentication; conditional privacy-preserving; PUF; three-factor secrecy Article  
Improvement of a Conditional Privacy-Preserving and Desynchronization-Resistant Authentication Protocol for IoV.
# Coin Recognition Approach in Social Environments Using Virtual Organizations of Agents
## 4 Coins Recognition Process
### Homography  
The main objective of this stage is dealing with the aspect of freedom for the camera position. The existing methods usually limit camera position to a plane parallel to the coins.  
Therefore, this is a significant stage in the proposed process since it permits the recognition of coins to occur in images taken from different angles, thus allowing easier recognition from mobile devices.  
The image processing technique used in this step is based on a mathematical concept known as homography [41; 42].  
In geometry, a homography is a projective transformation that determines a correspondence between two geometric plane figures, so that each of the points and the lines of one of them will correspond, respectively, to a point and a line on the other, see Figure 5. Homography can be used to rectify images under two assumptions: (a) when the terrain is flat, (b) when the plane to which it is projected is parallel to the surface.  
Each pixel in an image is a 2D point that can be represented as a pair of values:  
\[\widetilde{x}=(x,y)\in R^{2} \tag{1}\]  
Additionally, points can be converted from \(n\)-dim vectors to an \(n+1\)-dim space. In the case of an image pixel (or coordinate), it can be done by:  
\[(x,y)\Rightarrow\left[\begin{array}{c}x\\ y\\ 1\end{array}\right] \tag{2}\]  
Homographies are given by a homogeneous and arbitrary \(3\times 3\)\(H\) matrix that transforms the points from one plane to another:  
\[\widetilde{x}^{\prime}=H\widetilde{x} \tag{3}\]  
More specifically:  
\[\left(\begin{array}{c}x\prime_{1}\\ x\prime_{2}\\ x\prime_{3}\end{array}\right)=\left[\begin{array}{ccc}h_{11}&h_{12}&h_{13} \\ h_{21}&h_{22}&h_{23}\\ h_{31}&h_{32}&h_{33}\end{array}\right]\left(\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array}\right) \tag{4}\]  
Figure 4: Cardboard detection.  
The resulting homogeneous coordinate \(\vec{x}^{\prime}\) must be normalized to get an inhomogeneous result:  
\[x^{\prime}=\frac{h_{11}x+h_{12}y+h_{13}}{h_{31}+h_{32}y+h_{33}}y^{\prime}=\frac{h _{21}x+h_{22}y+h_{23}}{h_{31}+h_{32}y+h_{33}} \tag{5}\]  
At the formal level, homographic expression can be defined as follows:  
\[H=A\cdot R\cdot(I-w\cdot t\cdot n)A^{-1} \tag{6}\]  
where:  
\(\bullet\)\(A\) is the camera calibration matrix,  
\(\bullet\)\(t\) is the position of the second view of the scene taken in the reference system of the first.  
\(\bullet\)\(n\) is a normal vector to the scene plane in the reference system of the first view.  
\(\bullet\)\(w\) is the inverse of the distance from the first view to the point of the scene  
\(\bullet\)\(R\) is the rotation matrix.  
As this transformation is not always enough to correct the deformation, in subsequent stages, we will apply contour detection techniques to the image, so that it is possible to identify whether the circles are really contained in the cardboard.
## 3 Medicinal Value
### Anti-Diabetic Activity  
Various studies have confirmed the anti-diabetic activities of _A. bisporus_[30; 65; 73; 74; 75; 76; 97; 98; 99; 100]. _A. bisporus_ holds significant levels of antioxidants such as vitamin C, D, and B12, as well as polyphenols, folates, and dietary fibers, which have inhibitory effects against diabetes and cardiovascular disorders [75]. [PERSON] et al. [76] isolated a variety of bioactive compounds from _A. bisporus_ that have the potential to defend the body against type 2 diabetes. [PERSON] et al. [100] found that _A. bisporus_ extract significantly increased insulin production and G6 PD activity with a substantial decrease in glucose concentration in the rat's body. Similarly, the use of _A. bisporus_ also caused a significant increase in low-density lipoprotein (LDL) levels, cholesterol, and triglyceride. Moreover, a decrease in malondialdehydeehyde level was substantial while superoxide dismutase, catalase, and glutathione peroxidase were significantly increased [100].  
The high amount of dietary fibers (about 19%) and some other valuable carbohydrate constituents in _A. bisporus_ may result in its glucose-lowering effect. The effect of dietary fibers from _A. bisporus_ is similar as observed in other high-valued mushrooms [97; 101]. The lectin-like molecules in _A. bisporus_ are responsible for decreasing glucose and increasing insulin contents. _A. bisporus_ is contributing to lower blood glucose levels due to high fiber contents. High fiber contents in _A. bisporus_ act as a barrier against the action of digestive enzymes, which lowers the blood glucose level [73; 74].  
Propionate, an ester of propionic acid extracted from _A. bisporus_, contributes to its anti-diabetic properties [102; 103]. Propionate is associated with gluconeogenesis and regulation of serum lipid levels and is reported to lower blood glucose levels. [PERSON] et al. [104]investigated the possible mechanisms in lowering the body's blood glucose level using _A. bisporus_. It was found that fibers in _A. bisporus_ are involved in bacterial fermentation, which contains an ample amount of oligosaccharidesacharide and polysaccharide and ultimately results in increased production of short-chain fatty acids in the colon.  
[PERSON] et al. [65] demonstrated that the intake of \(\alpha\)-glucan lowered the lipopolysaccharide-charide production, which resulted in a substantial decrease in glucose concentration. [PERSON] et al. [100] reported that terpenoids and flavonoids are secondary compounds that increase SOD activity in rats suffering from diabetes. [PERSON] et al. [98] reported that flavonoids increased the expression levels of endogenous antioxidant genes and consequently increase SOD activity. Terpenoids are involved in the modulation of SOD activity [99]. The high contents of zinc (7.5-15 mg 100 g\({}^{-1}\) DW) in the basidiocarp of _A. bisporus_ significantly improve the SOD activity [30].
## 3 Results
### Numerical Simulation
Under different ablation power (60 W, 50 W, 40 W), the highest temperature in the deflated group was 115.1 \({}^{\circ}\)C, 101.4 \({}^{\circ}\)C, 87.8 \({}^{\circ}\)C, respectively; the highest temperature in the inflated group was 93.5 \({}^{\circ}\)C, 83.4 \({}^{\circ}\)C, 73.2 \({}^{\circ}\)C, respectively. In the initial stage of ablation, the heating rate of the deflated group was slightly higher than that of the inflated group, and the temperature of the deflated group was always higher than that of the inflated group during the whole ablation process. Table 12 shows the isoperimetric ratio of the ablation area under different ablation parameters in the simulation experiment. From the data in the table, it can be seen that the isoperimetric ratio of the inflated group is in the range of 0.75-0.8, while the isoperimetric ratio of the deflated group is about 0.82-0.86, and under the same ablation power and ablation time, the isoperimetric ratio of deflated group is higher than inflated group. The results showed that the ablation area of inflated lung was less circular than the deflated lung, and the shape of the ablation area tended to be oblate. In addition, the isoperimetric ratio of both experimental groups gradually increase with the extension of the ablation time. This is because the constant temperature boundary is set on the periphery of the microwave antenna, which simulates the microwave antenna in reality. The water-cooled circulation system keeps the temperature low during the ablation process, so the growth of the long diameter is slowed down in the middle and late stages of ablation, and the increase of the  
Figure 11: Temperature curve at the slot of microwave antenna. short diameter is more obvious at this time, making the isoperimetric ratio of the ablation zone gradually larger. It was also found that the isoperimetric ratio of the deflated group decreased with the increase of ablation power, while the circularity of the inflated group did not change significantly with the change of power. In the clinical application of microwave ablation, the treatment plan can be reasonably formulated according to this feature. When the tumor shape is close to a spherical shape, the ablation parameters can be selected with low power and long ablation time, in order to achieve the effect of conformal ablation.
# An Experimental Comparison of Basic Device Localization Systems in Wireless Sensor Networks
###### Abstract  
Localization plays a crucial role in wireless sensor networks (WSNs) and it has sparked significant research interest. GPSs provide quite accurate positioning estimations, but they are ineffective indoors and in environments like underwater. Power usage and cost are further disadvantages, and so many alternatives have been proposed. Many works in the literature still base localization on RSSI measurements and often rely on methods to mitigate the effects of fluctuations in values, so it is important to know real values of RSSIs measured using common devices. This work presents the main localization techniques and exploits a real testbed to collect and evaluate RSSI measurements. An accuracy evaluation and a comparison among several localization techniques are also provided.  
localization; WSN; RSSI accuracy +
Footnote †: journal:
## 3 Results and Discussion
### Relationship among Sediment Properties and Heavy Metals
This is consistent with the results of the Spearman correlation analysis, where good correlation among OM, TN, TP, and heavy metals (Cr, Cu, Ni, Pb) were observed (Table 2). Therefore, the VF1 can be interpreted as anthropogenic pollution sources. In contrast, the VF2 displays strong positive loadings on grain size composition (i.e., clay, silt, and sand) with 24.1% of the total variance, which can be interpreted as lithogenic sources (i.e., natural origin). \begin{table}
\begin{tabular}{l c c c c c c c c c c c} \hline  & **Clay \({}^{*}\)** & **Silt \({}^{*}\)** & **Sand \({}^{*}\)** & **OM \({}^{*}\)** & **TN \({}^{*}\)** & **TP \({}^{*}\)** & **As** & **Hg** & **Cr** & **Cu** & **Ni** & **Pb** \\ \hline Silt & **0.911 \({}^{**}\)** & & & & & & & & & & & \\ Sand & **-0.911 \({}^{**}\)** & **-1.00 \({}^{**}\)** & & & & & & & & & \\ OM & 0.422 & **0.534 \({}^{*}\)** & **-0.534 \({}^{*}\)** & & & & & & & & \\ TN & 0.477 & **0.697 \({}^{**}\)** & **-0.697 \({}^{**}\)** & **0.673 \({}^{**}\)** & & & & & & & \\ TP & 0.163 & 0.433 & -0.433 & **0.688 \({}^{**}\)** & **0.842 \({}^{**}\)** & & & & & & & \\ As & \(-\)0.258 & -0.363 & 0.363 & -0.051 & -0.407 & **-0.538 \({}^{*}\)** & & & & & & \\ Hg & 0.218 & 0.332 & -0.332 & 0.497 & **0.673 \({}^{**}\)** & 0.376 & -0.037 & & & & & \\ Cr & 0.343 & **0.534 \({}^{*}\)** & **0.534 \({}^{*}\)** & **0.622 \({}^{**}\)** & **0.815 \({}^{**}\)** & **0.749 \({}^{**}\)** & -0.495 & **0.651 \({}^{*}\)** & & & & \\ Cu & 0.165 & 0.385 & -0.385 & **0.648 \({}^{*}\)** & **0.529 \({}^{**}\)** & **0.807 \({}^{**}\)** & -0.402 & **0.682 \({}^{**}\)** & **0.842 \({}^{**}\)** & & & \\ Ni & \(-\)0.073 & 0.152 & -0.152 & **0.635 \({}^{*}\)** & 0.657 \({}^{*}\)** & **0.741 \({}^{**}\)** & -0.301 & 0.535 \({}^{*}\)** & **0.714 \({}^{**}\)** & **0.568 \({}^{**}\)** & & & \\ Pb & 0.134 & 0.297 & -0.297 & **0.556 \({}^{*}\)** & **0.771 \({}^{**}\)** & **0.714 \({}^{**}\)** & -0.393 & **0.680 \({}^{**}\)** & **0.754 \({}^{**}\)** & **0.938 \({}^{**}\)** & **0.820 \({}^{**}\)** \\ Zn & 0.244 & 0.371 & -0.371 & 0.314 & **0.631 \({}^{*}\)** & 0.486 \({}^{*}\) & -0.358 & **0.801 \({}^{**}\)** & **0.622 \({}^{**}\)** & **0.763 \({}^The difference of PCA/FA factors obtained from grain size and heavy metals indicated grain size appears to be not a major controlling factor for the heavy metals' spatiotemporal variations in the river sediments. This is consistent with the results of the Spearman correlation analysis. The VF3 displays strong positive loadings on As only with 13.6% of the total variance, probably indicating the source of As was different from the other metals (Hg, Cr, Cu, Ni, Pb, and Zn).
## 4 Numerical Simulation
### Establishment of FE Model  
1. Mesh generation  
Commercial FE software, Midas Civil 2020, was utilized to perform a simulation and analysis of the construction process for hoisting installation. Here, the steel box girders were modeled using 3D beam elements and meshed based on the positions of diaphragms and supports along the longitudinal direction of the bridge. The entire model comprised a total of 207 elements and 252 nodes. Certain construction details, such as diaphragms,lifting points, and temporary facilities, were simplified and treated as concentrated loads to assess their respective impacts. Layout of the model elements and standard cross-section are shown in Figures 3 and 4, respectively.  
(2) Boundary  
Figure 5a,b show the simulation of the temporary corbel connection and support of the bridge, respectively. The nodes, which were arranged at the support positions according to the design scheme, were rigidly connected to the corresponding nodes of the beam body using a rigid connection. Meanwhile, the corresponding translational displacement was restrained at the support. Based on the specialized construction and installation plan of the steel box girder, the temporary supports were arranged during the construction process and the time of the system conversion was determined. When the hoisting work of the steel box girder was done, the temporary corbel connection was set, connecting with the former girder.  
(3) Construction conditions  
This paper presents the simulation of the hoisting construction process of a four-span continuous steel box girder under six different construction conditions. The hoisting order of each span is shown in Table 2.  
Figure 4: Standard cross-section of the steel box girder: (**a**) geometry layout (unit mm); (**b**) FE model.  
Figure 5: Simulation of the temporary corbel connection and support. (**a**) Temporary corbel connection; (**b**) support.  
Figure 3: Finite element model (span arrangement: 4 \(\times\) 90 m).  
During the hoisting process from the second to the last span, each step was further divided into two stages, according to the construction sequence specified in the design.  
* The hoisting span was connected to the former span by temporary corbel connection.
* The rigid connection formed after welding.  
The typical construction conditions are shown in Figure 6.
# A Blockchain-Based Protocol for Fair Delivery for Receips
## 6 Smart Contract Specification
### Main Smart Contract Functions
#### 6.2.2 Accept Function  
Once the sender delivers the encrypted document to the recipient, the latter can choose to accept or reject the final delivery of the document. If the recipient does not wish to accept the delivery, they simply do not execute any function of the smart contract. However, if they choose to accept the delivery, they must call the _accept_ function (see Listing 4), providing all the information associated with the delivery identified by _id_: _A_'s blockchain address, the hash on the encrypted document, the timestamp of the delivery, the deadline for accepting the delivery, and the evidence provided by \(A\) (_A_'s signature on the off-chain message that informed \(B\) of the data associated with the delivery, as described in Step 1 (Off-chain message)).  
```
1 functionaccept(bytes32_id,address_addrA,bytes32_h,uint256_tm,uint256_td,bytesmemory_fc)
2 validStatus(_id,State.undefined)
3 deadline(_td)
4 public{
5 require(validID(_id,_addrA,msg.sender,addrSC,_h,_tm,_td),\"Invalididentifier\");
6
7 docDelivery[_id].addrA=_addrA;
8 docDelivery[_id].addrB=msg.sender;
9 docDelivery[_id].td=_td;
10 docDelivery[_id].status=State.accepted;
11
12
13 emitResult(msg.sender,_id,uint(State.accepted),_fc);
14}
```  
**Listing 4** Fair delivery of documents: _accept_ function.  
As shown in Listing 4, the smart contract verifies that there are no data associated with the identifier provided by the recipient (State.undefined) and that the deadline for accepting the delivery has not expired by using the functions \(validStatus\) and \(deadline(td)\), respectively. If these validations are successful, the smart contract generates the identifier value by following the steps defined in Step 1 (Off-chain message) of Section 5. To achieve this, it uses the information provided by the recipient as the parameters of the function call, the blockchain address of the smart contract, and the blockchain address of the entity that signed the function call. If the generated value matches the _id_ provided by the recipient, the smart contract stores the addresses of the sender and recipient and the deadline value \(td\) in the \(docDelivery\) structure and updates the delivery status to accepted.  
In the end, the function emits an event indicating that \(B\) has accepted the delivery and containing the evidence provided by \(A\) to \(B\) during Step 1 (Off-chain message).