esa-sceva/satcom-chunk-collection huggingface dataset:
# A Low-Overhead Message Authentication and Secure Message Dissemination Scheme for VANETs
## 3 Proposed Approach
### System Model  
The system model for our scheme is shown in Figure 1. It consists of Department of Motor Vehicles (DMV), Road Side Units (_RSUs_), On-Board Units (OBUs), and Group Leaders (GLs). We describe the functions of these entities next.  
* **DMV:** We assume that all vehicles are registered with a trusted authority (\(TA\)), such as the Department of Motor Vehicles (\(DMV\)), that administers the registration of the vehicles. The \(DMV\) is assumed to be trusted and cannot be compromised. The \(DMV\) generates its public and private keys (\(PU_{DMV}\), \(PR_{DMV}\)) and distributes a \(PU_{DMV}\) to all \(RSUs\) and vehicles securely. In addition, the \(DMV\) generates pseudo-IDs (\(PID_{v}\)) for each vehicle, certificates corresponding to each pseudo-ID of a vehicle (\(Cert_{v}\)) where \(Cert_{v}=E((PID_{v},PU_{v},ts),PR_{DMV})\), and certificates of \(RSUs\) (\(Cert_{RSU}\)) where \(Cert_{RSU}=E((ID_{RSU},PU_{RSU},ts),PR_{DMV})\).
* **Vehicle:** Each vehicle is assumed to be equipped with an On-Board Unit (\(OBU\)) for computation and communication with \(RSUs\) as well as with other vehicles. The \(OBU\) stores the vehicle's public and private key pair (\(PU_{v}\), \(PR_{v}\)), its pseudo-IDs (\(PID_{v}\)), its certificates corresponding to each pseudo-ID of the vehicle (\(Cert_{v}\) signed by the \(DMV\)), and the public key of the \(DMV\) (\(PU_{DMV}\)).
* **RSU:** The Road Side Units (\(RSUs\)) are fixed entities along the roadside which facilitate V2V and V2I communication. \(RSUs\) are connected to each other and to the \(DMV\), possibly through the Internet. In our scheme, a \(RSU\) collects the messages sent by the vehicles in its region, authenticates the messages, aggregates the messages, and forwards them to vehicles within its region, as well as to vehicles in other regions as needed.
* **Group Leader (\(GL\)):** Each \(RSU\) divides its region into sub-regions based on the density of vehicles in the region. Then, the \(RSU\) selects one vehicle in each sub-region as a \(GL\). The \(GL\) is responsible for collecting, authenticating, and aggregating messages sent by vehicles in its sub-region and for sending them to the \(RSU\). The \(GL\) is also responsible for receiving messages from the \(RSU\), authenticating them, and disseminating them to vehicles in its sub-region. We describe the proposed method in detail next.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Description** \\ \hline \(E(M,K)\) & Encryption of \(M\) with Key \(K\) \\ \hline \(RSU\) & Roadside unit \\ \hline \(GL\) & Group Leader \\ \hline \(DMV\) & Department of Motor Vehicles \\ \hline \(Cert_{v}\) & Certificate issued to vehicle \(v\) by the \(DMV\) \\ \hline \(Cert_{RSU}\) & Certificate issued to \(RSU\) by the \(DMV\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
# Fake Base Station Detection and Link Routing Defense +
## 7 Implementation and Experimental Results
### Implementation and Experimental Setup and Optimality for Threshold Selection  
**Legitimate vs. faulty vs. fake base stations**  
We implement the base station and the user equipment along with the connectivity setup between them described in Section 3.2, including RRC and NAS. We conduct three experimental scenarios where the base station is legitimate vs. faulty vs. fake. The legitimate base station operation is described in Section 3.2, while the faulty (accidental) and fake operations are described in Sections 4.2 and 4.3, respectively. The faulty but legitimate base station ends the connectivity setup of NAS via connection release; in contrast, the fake base station continues to hold on to the NAS communication without explicitly ending the RRC/NAS connectivity setup.  
**Our scheme variants using different observations**  
Our scheme uses both the time duration \(T\) (the detector threshold is \(\tau_{T}\)) and the number of registration requests \(N\) (the detector threshold is \(\tau_{N}\)) as described in Section 5.1. In this section, we compare our scheme with _our scheme variants_ of using \(T\) only and using \(N\) only to better showcase the use of both \(T\) and \(N\). Our scheme using both \(T\) and \(N\) for the detection and blacklisting outperforms the variants using \(T\) only or using \(N\) only.  
**Optimality for threshold selection.**  
We define _optimality_ with respect to the error performance. More specifically, we achieve optimal accuracy performance if the false-positive error is minimized and then the false-negative error is minimized. If we can achieve a zero false-positives rate, we then minimize the false-negative rate. The false-positive error is prioritized because we design blacklisting and prioritize avoiding blacklisting legitimate base stations, which can be faulty (accidentally cannot provide connectivity at the time). The threshold achieving optimality does not need to be unique, i.e., there can be multiple thresholds which achieve minimum error performances. In fact, in our experiment, we have multiple thresholds achieving zero errors (zero false positives and zero false negatives), and we choose the lowest threshold values to make the detection more sensitive.  
Figure 5: Hardware setup for our implementation and experiment where the distance between the base station and user equipment is 5 m. The backend core network coexists with the base station in the same computer.
## 5 Discussions of Wave Velocities, Dispersion and Attenuation Characteristics
### Attenuation Coefficient  
In the case of finite permeability, the velocity has an imaginary part, which illustrates that waves are attenuated in the propagation procedure.  
Similarly, the potential function in Equation (48) can be expressed as an equivalent function of wave number \(l\).  
\[\varphi_{j}=A_{j}\exp\bigl{(}-l_{ij}(\omega)x\bigr{)}\exp\bigl{[}\bigl{(}l_{rj}( \omega)x+\omega t\bigr{)}i\bigr{]} \tag{51}\]  
where the \(l_{r}\) and \(l_{i}\) are the real and imaginary parts of the wave numbers, respectively. The wave number is \(l_{j}(\omega)=l_{rj}(\omega)+l_{ij}(\omega)i\). The term of \(\exp\bigl{[}(l_{r}x+\omega t)i\bigr{]}\) represents the mode of wave in the propagation procedure, and the amplitude of \(A\exp(-l_{i}x)\) reflects that the amplitude of the wave attenuates in the exponential form.  
The attenuation of the velocity can be described by the attenuation coefficient \(\beta\), which is defined that the ratio of the reduction of the amplitude after a wavelength and the amplitude in a wavelength. The detailed formulation is shown as Equation (52)  
\[\beta_{j}=1-\exp\bigl{(}-l_{ij}(\omega)L\bigr{)}=1-\exp\left(-l_{ij}(\omega) \frac{2\pi}{l_{rj}(\omega)}\right) \tag{52}\]  
where \(L\) is the wavelength in a period. The amplitude attenuation coefficient is a value between zero and one (\(0<\beta<1\)). The calculation of wave number \(l\) refers to Equations (21) and (30).
# A Methodology to Design Quantized Deep Neural Networks for Automatic Modulation Recognition
###### Abstract  
Next-generation communication systems will face new challenges related to efficiently managing the available resources, such as the radio spectrum. DL is one of the optimization approaches to address and solve these challenges. However, there is a gap between research and industry. Most AI models that solve communication problems cannot be implemented in current communication devices due to their high computational capacity requirements. New approaches seek to reduce the size of DL models through quantization techniques, changing the traditional method of operations from a 32 (or 64) floating-point representation to a fixed point (usually small) one. However, there is no analytical method to determine the level of quantification that can be used to obtain the best trade-off between the reduction of computational costs and an acceptable accuracy in a specific problem. In this work, we propose an analysis methodology to determine the degree of quantization in a DNN model to solve the problem of AMR in a radio system. We use the Brevitas framework to build and analyze different quantized variants of the DL architecture VGG10 adapted to the AMR problem. The evaluation of the computational cost is performed with the FINN framework of Xilinx Research Labs to obtain the computational inference cost. The proposed design methodology allows us to obtain the combination of quantization bits per layer that provides an optimal trade-off between the model performance (i.e., accuracy) and the model complexity (i.e., size) according to a set of weights associated with each optimization objective. For example, using the proposed methodology, we found a model architecture that reduced 75.8% of the model size compared to the non-quantized baseline model, with a performance degradation of only 0.06%.  
automatic modulation recognition; cognitive radio; low computational cost; FPGA; quantized neural networks; 5G; 6G +
Footnote †: journal:  
Article
# Proxy-Based Adaptive Transmission of MP-QUIC in Internet-of-Things Environment
## 4 Performance Analysis by Experimentations  
### Testbed Configuration  
This section discusses the testbed configuration for performance analysis of the proposed scheme. All experiments were performed by using the _network simulator_ version 3 (_ns-3_) [18], the MP-QUIC library [11], and the _docker_[19]. The docker has been implemented by using the DOCKEMU [20; 21].  
For performance analysis, we compare the proposed proxy-based adaptive MP-QUIC transmission with the existing two QUIC schemes: (1) SP-QUIC using a single path [22] and (2) the conventional MP-QUIC [11] that uses a simple _round-robin_ policy to determine the MP-QUIC path among available network paths, in which all available paths are used for data transmission sequentially in rotation. This performance evaluation is mainly targeted to the multipath network between the proxy and the server. As for performance metrics, we employ the _response delays_ from the server and the _total transmission delays_ required for transmission of 128-1024 KB data files by using HTTP/3 over QUIC. Each packet contains the payload of 50 bytes. To analyze the performance impacts on a variety of network conditions, we performed experiments for different link delays and packet loss rates in the network.  
To configure the multipath network environment, we employ the three different network paths between proxy and server, as shown in Figure 1. The number of clients (\(n\)) is set to 10 clients, and extended up to 50 clients. Each client sends both priority types, and the network bandwidth for each network link is set to 100 Mbps, 75 Mbps, 50 Mbps for Path-1, Path-2, and Path-3, respectively. The default value of link delay is set to 10 ms for each path in the backbone network.
## 4 Results and Discussion
### ANNs Model
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Learning Method**} & \multirow{2}{*}{**Cluster**} & \multicolumn{6}{c}{**Train Samples**} & \multicolumn{6}{c}{**Test Samples**} \\ \cline{3-10}  & & **MSE** & **MAPE** & **R\({}^{2}\)** & **Best Net** & **MSE** & **MAPE** & **R\({}^{2}\)** & **Best Net** \\ \hline \multirow{4}{*}{**Bayesian**} & C1 & 88,430.5 & 0.11 & 0.96 & 23 & 1,319,749.4 & 0.28 & 0.64 & 28 \\  & C2 & 196,227.7 & 0.14 & 0.85 & 11 & 233,921.15 & 0.16 & 0.83 & 8 \\  & C3 & 184,357.5 & 0.12 & 0.90 & 6 & 555,881.86 & 0.22 & 0.67 & 14 \\  & C4 & 984,475.6 & 0.24 & 0.60 & 20 & 1,416,811.9 & 0.32 & 0.17 & 1 \\ \hline \multirow{4}{*}{**Levenberg-Marquardt**} & C1 & 703,564.2 & 0.24 & 0.85 & 10 & 4,350,022.4 & 0.59 & 0.40 & 10 \\  & C2 & 170,293.8 & 0.13 & 0.88 & 28 & 596,872.9 & 0.21 & 0.65 & 19 \\  & C3 & 292,271.3 & 0.15 & 0.86 & 11 & 1,986,277.28 & 0.28 & 0.51 & 30 \\  & C4 & 1,172,343.4 & 0.22 & 0.72 & 1 & 1,986,277.28 & 0.28 & 0.51 & 13 \\ \hline \multirow{4}{*}{**Scaled conjugate**} & C1 & 1,199,184.6 & 0.34 & 0.69 & 26 & 2,470,866.3 & 0.60 & 0.36 & 19 \\  & C2 & 313,568.3 & 0.18 & 0.78 & 29 & 501,071 & 0.20 & 0.65 & 22 \\  & C3 & 537,631.3 & 0.20 & 0.73 & 13 & 794,414.1 & 0.24 & 0.62 & 12 \\  & C4 & 691,076.5 & 0.20 & 0.64 & 7 & 1,569,684 & 0.24 & 0.19 & 10 \\ \hline \multirow{4}{*}{**Resilient**} & C1 & 1,747,183.6 & 0.40 & 0.79 & 20 & 3,801,363.3 & 0.58 & 0.46 & 23 \\  & C2 & 291,211.5 & 0.17 & 0.80 & 14 & 549,758.4 & 0.23 & 0.66 & 5 \\ \cline{1-1}  & C3 & 448,423.2 & 0.19 & 0.77 & 23 & 811,113.4 & 0.24 & 0.58 & 2 \\ \cline{1-1}  & C4 & 691,076.5 & 0.20 & 0.64 & 26 & 1,569,684 & 0.24 & 0.19 & 7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The performance of the ANN models for different training algorithms. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Hidden Layer**} & \multirow{2}{*}{**Cluster**} & \multicolumn{6}{c}{**Train Samples**} & \multicolumn{6}{c}{**Test Samples**} \\ \cline{3-10}  & & **MSE** & **MAPE** & **R\({}^{2}\)** & **Best Net** & **MSE** & **MAPE** & **R\({}^{2}\)** & **Best Net** \\ \hline \multirow{4}{*}{**15**} & C1 & 3,258,528.8 & 0.48 & 0.45 & 6 & 47,971.7 & 0.10 & 0.95 & 16 \\  & C2 & 190,424.7 & 0.14 & 0.85 & 28 & 252,405.6 & 0.17 & 0.81 & 14 \\  & C3 & 258,905.9 & 0.16 & 0.85 & 23 & 314,016.4 & 0.17 & 0.82 & 12 \\  & C4 & 71,599.5 & 0.05 & 0.94 & 27 & 1,708,986 & 0.38 & 0.12 & 21 \\ \hline \multirow{4}{*}{**20**} & C1 & 96,033.5 & 0.11 & 0.95 & 16 & 915,133.4 & 0.35 & 0.67 & 13 \\  & C2 & 193,978.1 & 0.14 & 0.85 & 8 & 238,427.3 & 0.16 & 0.82 & 21 \\  & C3 & 193,647.1 & 0.13 & 0.89 & 1 & 567,313 & 0.23 & 0.64 & 4 \\  & C4 & 115,0  
It can be seen that there is a small variation between real data and ANNs outputs in four clusters, especially if there are sufficient number of samples in the cluster such as in Figure 3b,c. The variance coefficient shows a smaller value for ANNs results, it was about 0.08 for cluster 1, 2 and 0.03 for cluster 4. Cluster3 has a bigger variance coefficient CV = 0.15.
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 1 Introduction  
### Research Background  
Studies conducted by [1; 2] explain that manufactured technologies of the IoT industry are prone to become ubiquitous in modern civilization. The new developments in electronics and communication technology (RF semiconductors, system-on-chip microcontrollers, computing processors miniaturization) enable flexible conceptualization and rapid implementation of numerous IoT applications. Systems such as autonomous vehicles, smart cities, domotics, health, industrial IoT, smart farming, e-agriculture, smart metering, advanced tracking and surveillance, and intelligent public transportation, to name a few, are nowadays omnipresent in all sectors.  
Ericsson's Mobility Reports [3; 4; 5] clearly illustrate the tremendous evolution of mobile technology and the increase in mobile handhelds, base stations, mobile subscriptions, and data traffic. For instance, they predicted that 2022 would witness 29 billion connected nodes, of which 18 billion would be in the IoT domain [3]. There has been massive development in cellular IoT connections in the 4G, LTE, NB-IoT, Cat-M, and 5G bands: 4.4 billion wide-area IoT, 4.1 billion cellular IoT, and 17.8 billion short-range IoT connections. Approximately 1.9 billion 5G subscriptions are expected by the end of 2024 [5].
# A Mathematical Study of COVID-19 Spread by Vaccination Status in Virginia
## 3 Results
### Modeling with Booster Shots  
The sensitivity analysis depicted in Figure 3 also suggests that the cumulative cases and effective reproduction number (\(R_{t}\)) are highly dependent on the reduction from baseline transmission rate parameters for the two vaccination classes (\(\alpha_{1}\) and \(\alpha_{2}\)). To further investigate the effect that a reduction in transmission due to vaccination may have on the spread of COVID-19, we extend from the two-stage to the three-stage vaccination model (2) where \(j=3\) corresponds to fully vaccinated individuals who have received the booster shot. We use the parameters from Table 2 for \(j\in\{0,1,2\}\) and set \(h_{3}=h_{2}\), \(\gamma_{3}=\gamma_{2}\), and \(\delta_{3}=\delta_{2}\). That is, we assume the effects of contracting COVID-19 are similar for somebody with the booster shot as somebody who is fully vaccinated. We estimate a reduction in baseline transmission rate resulting from a booster shot of \(\alpha_{3}\geq 0.95\)[39; 40]. We assume that the booster shot uptake curve is similar to the second vaccine dose, delayed 210 days, and that the proportional decrease in people obtaining the booster is similar to that of the first to second shot (i.e., \(a_{3}=a_{3}\), \(r_{3}=r_{2}\), \(\tau_{3}=\tau_{2}+210\), \(\frac{M_{3}-M_{2}}{M_{2}}=\frac{M_{3}-M_{1}}{M_{1}}\)). For the purpose of forecasting COVID-19 spread past September 18, we use the transmission value of the final interval (\(\beta(t)=\beta_{8}=0.453\)).  
In Figure 5, we show forecast simulations for the period 23 January through 31 December 2021 incorporating booster shots, a hypothetical booster uptake schedule, and the utilized parameter values. The booster shot uptake curve \(V_{3}(t)\) predicts that 40.9% of the population will receive booster shots by 31 December 2021. The model predicts a 19.7-34.3% reduction in new COVID-19 cases from 18 September to 31 December 2021 depending on the efficacy of the booster shot in reducing transmission from baseline (\(\alpha_{3}=0.95-1.00\)). It is notable that, over the same period, there is a 17.6-31.0% reduction in new cases in unvaccinated individuals and a 14.4-25.2% reduction in new cases for partially vaccinated individuals. This strongly supports the notion that the protection offered by the booster shot, or vaccination in general, is not restricted to the individual receiving the shot; rather, it is distributed across the population and even across individuals of different vaccination statuses.
## 4 Distribution Problems
### Transportation Problem with Supply from Primary Source  
Consider now a transportation network where, in addition to locations with warehouse stocks and customer requirements, there will also be a primary source, which can represent the location of the transported commodity or a global warehouse, and customers can be supplied both from local warehouses and directly from the primary source.  
Assume the constraints and denotations from the location problem and two types of transportation equipment, one with a larger capacity \(k_{1}\) from the primary source to local warehouses and a cost \(n_{1}\) per 1 km of travel, and the other with a smaller capacity \(k_{2}\) to customers and a cost \(n_{2}\) per 1 km. Denoting the distance from the primary source to the \(i\)th local storage by \(e_{i}\), and the distance from the primary source to the \(j\)th customer by \(g_{j}\), we add binary decision variables \(z_{j}\) to indicate whether the \(j\)th customer receives the desired quantity directly from the primary source (in the positive case \(z_{j}=1\), otherwise \(z_{j}=0\)), the model with primary source and transportation technique information has the following form:  
\[z=\sum_{j=1}^{n}\frac{b_{j}}{k_{2}}g_{j}n_{2}z_{j}+\sum_{i=1}^{m}\sum_{j=1}^{ n}\left(\frac{b_{j}}{k_{1}  
\[\sum_{j=1}^{n}x_{ij}\leq ny_{i},\ i=1,\,\ldots,\,m \tag{71}\]  
\[\sum_{j=1}^{n}b_{j}x_{ij}\leq a_{i},\ i=1,\,\ldots,\,m \tag{72}\]  
\[x_{ij}\in\{0,1\},\ i=1,\,\ldots,\,m,\ j=1,\,\ldots,\,n \tag{73}\]  
\[y_{i}\in\{0,1\},\ i=1,\,\ldots,\,m \tag{74}\]  
\[z_{j}\in\{0,1\},\ j=1,\,\ldots,\,n \tag{75}\]  
The fractions in the objective function according to (69) indicate how many times the distance must be traveled for the customer to receive the required quantity. Since the fractions may have non-integer values, they must be rounded up to integers, the corresponding capacity may not be fully used for the last trip. The expression with the first summation in the objective function corresponds to the total cost of moving material from the primary source directly to customers, and the expression with the double summation in the objective function corresponds to the total cost of moving material from the primary source to local warehouses and from there on to the customers.  
If, instead of the conditions of the location problem, the simpler conditions of the allocation problem were assumed (i.e., dropping the decision as to whether or not to use a location for storage), the previous model would be simplified, the conditions (71) and (74) would be dropped and \(y_{i}\) would be omitted in the last term of the objective function (i.e., the fixed costs of all locations would be included).
# Seismic Analysis of Steel Solid Web Girder-RC Tubular Column Hybrid Structure
###### Abstract  
This paper aims to investigate the seismic performance of a novel type of steel-concrete hybrid supporting structure consisting of reinforced concrete (RC) tubular columns, steel solid web girder platform, and A-shaped steel frames. It is typically used to house air-cooled condensers (ACC) in thermal power plants (TPPs). First, the finite-element (FE) model was implemented in ABAQUS and the simulation approaches were validated by pseudo-dynamic test results of a scaled steel-concrete hybrid supporting structure. Then, the elasto-plastic time-history analysis of the steel solid web girder-RC tubular column hybrid structure was conducted. The El Centro (NS) record was scaled to peak ground acceleration (PGA) of 0.07, 0.20, 0.40 and 0.62 g to reflect the frequent, basic, rare, and very rare earthquakes. The dynamic characteristics, base shear force, lateral deformation performance, stiffness deterioration, and damage evolution characteristics were analyzed. The numerical results showed that the first vibration mode of this hybrid structure is torsion, due to its small torsional stiffness and the nonuniform distribution characteristics of stiffness and mass in the vertical direction; the lateral deformation shape is shear mode; and the damage mainly occurred on the RC tubular columns, while the steel components did not yield under severe earthquakes. In general, the overall seismic performance of the steel solid web girder-RC tubular column hybrid structural system could meet the seismic design requirements with respect to the high-intensity earthquakes.  
steel-concrete hybrid structure; steel solid web girder; RC tubular column; seismic performance; finite-element numerical modelling