esa-sceva/satcom-chunk-collection huggingface dataset:
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 2 Related Work  
### Traditional Cross-Regional Authentication Network  
The traditional intelligent transportation system usually has a three-layer or four-layer structure [13]. As shown in Figure 1, the order from high to low is certificate authorities (CA), regional security manager (SM), RSU, and vehicle node. The RSU can also undertake the role of the SM.  
Among them, CA has the highest management authority, which is used to issue vehicle identity information and related certificates, verify identity, and manage vehicle pseudonyms. The area managed by the regional SM node is called the security domain and is mainly responsible for information encryption and resource scheduling. A single CA is responsible for multiple SMs, and information exchange between SMs under the same CA needs to be processed by the CA. Located on the third layer is the RSU, which itself acts as a relay node to provide vehicle information with the upper network node when communicating. At the same time, there are also RSU nodes directly under the jurisdiction of the CA as a regional SM node.  
However, the intelligent transportation system based on central management has some problems when the vehicle cross-regional identity authentication. On the one hand, due to the limitation of the communication range, the key information of the vehicle needs to be processed by the central management agency during the cross-regional conversion, causing a high delay. Especially in the SAGIN, the vehicle has the characteristics of fast movement, frequent access, and disconnection while driving, which requires continuous identity authentication, so the requirements for delay are more stringent. On the other hand, the CA performs the binding of the vehicle's identity and public key. The attacker can forge the identity and send false information to realize the witch attack. Once the SM node is attacked, it may cause serious security accidents, such as the collapse of the central server.  
In response to the problems of the traditional network in the cross-regional identity management of nodes, some scholars have proposed to use blockchain to establish a decentralized network system to avoid loopholes in vehicle certification to ensure the safety and normal operation of the VANETs.  
Figure 1: Traditional intelligent ground transportation system structure.
## 2 Results and Discussion
### Characterization of Microcapsules  
#### 2.3.1 Characteristics in Response to Temperature Stimulation  
The odor release from microcapsules subjected to temperature stimulation was investigated (Table 5). First, microcapsules produced at a constant flow rate were collected in a glass bottle and closed with a septum cap. Temperature stimulation was applied to the bottle and the headspace vapor was measured using an odor sensor. For comparison, samples at room temperature and those in which the aqueous, inner oil, and outer oil phases were simply mixed (with and without heating) were measured. Preliminary experiments confirmed that the higher the fragrance concentration, the higher the sensor intensity obtained with the liquid combination (mineral oil and benzaldehyde) used in this experiment. The odor intensity was faint when sample no. 1 was sniffed. This confirmed that the microcapsules had odor-releasing properties. Sensor measurements showed that the sensor intensity was lower for the microcapsule samples than for the samples in which the liquids were simply mixed. This suggests that the microcapsules suppressed the release of the fragrance. In addition, higher sensor intensities were obtained for samples subjected to temperature stimulation than at room temperature. This may be because temperature stimulation increased the fluidity of the agarose aqueous solution shell, making it easier for the core and outer oil phase to come into contact. This may contribute to the release of the core into the outer oil phase, resulting in an increase in fragrance concentration in the outer oil phase. Furthermore, statistical analysis revealed that the sensor intensity of the thermally stimulated microcapsule sample (No. 1) was significantly higher than that of its non-stimulated counterpart (No. 2) (\(p\) = 0.04, Student's \(t\)-test). This indicates that thermal stimulation had a measurable effect on odor release from the microcapsules.
###### Abstract  
When constructing a wireless communication network, the line of sight of radio waves is limited by the terrain features in a ground communication network. Also, satellite communication networks face capacity limitations and are vulnerable to jamming. Aviation communication networks can solve the above-mentioned problems. To construct seamless aviation communication networks, fast counterpart location estimation and efficient beam steering performance are essential. Among various techniques used for searching the counterpart's location, the monopulse technique has the advantage of quickly estimating the location through a simplified procedure. However, the nonlinear characteristics of the monopulse ratio curve, which are inevitably caused by the general antenna beam shape, both limit the location estimation range and reduce the estimated location accuracy. To overcome these limitations, a method that improves the estimation accuracy and extends the range by correcting the sum and difference patterns using the beamforming technique of active phased array antennas was proposed. An antenna system model suitable for aviation communication networks was presented, and the proposed model was experimentally proven to be effective. An average angle error of 0.021\({}^{\circ}\) was observed in the estimation of the accuracy of the antenna location.  
direction-of-arrival; beamforming; active phased array; antenna; monopulse +
Footnote †: journal: Electronics
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Latent Variables (L.V)**} & \multirow{2}{*}{\begin{tabular}{c} **Measurement** \\ **Variables (M.V)** \\ \end{tabular} } & \multicolumn{2}{c}{**Estimate**} & \multirow{2}{*}{\begin{tabular}{c} **S.E.** \\ **(Standardized Estimates)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **C.R.** \\ **(Critical Ratio)** \\ \end{tabular} } \\ \hline \multirow{6}{*}{\begin{tabular}{c} Regional economic \\ benefit (R) \\ \end{tabular} } & R1 & 1 & 0.76 & & \\ \cline{2-6}  & R2 & 0.964 & 0.697 & 0.06 & 16.084 * \\ \cline{2-6}  & R3 & 1.041 & 0.722 & 0.062 & 16.722 * \\ \cline{2-6}  & R4 & 1.106 & 0.721 & 0.066 & 16.711 * \\ \cline{2-6}  & R5 & 1.095 & 0.741 & 0.063 & 17.248 * \\ \cline{2-6}  & R6 & 1.153 & 0.795 & 0.062 & 18.696 * \\ \hline \multirow{6}{*}{\begin{tabular}{c} Effects related to \\ increased use in air \\ transportation (A) \\ \end{tabular} } & A1 & 1 & 0.754 & & \\ \cline{2-6}  & A2 & 0.888 & 0.701 & 0.054 & 16.374 * \\ \cline{2-6}  & A3 & 0.957 & 0.718 & 0.057 & 16.832 * \\ \cline{2-6}  & A4 & 1.009 & 0.741 & 0.058 & 17.439 * \\ \cline{2-6}  & A5 & 1.005 & 0.743 & 0.057 & 17.485 * \\ \hline \multirow{6}{*}{\begin{tabular}{c} Effects related to \\ increased exchange with \\ other regions (M) \\ \end{tabular} } & M1 & 1 & 0.8 & & \\ \cline{2-6}  & M2 & 0.876 & 0.704 & 0.051 & 17.335 * \\ \cline{2-6}  & M3 & 0.907 & 0.722 & 0.051 & 17.897 * \\ \cline{2-6}  & M4 & 0.904 & 0.687 & 0.054 & 16.806 * \\ \cline{2-6}  & M5 & 0.985 & 0.786 & 0.049 & 19.99 * \\ \hline \multirow{6}{*}{\
\begin{tabular}{c} Effects related \\ to regional \\ development (R) \\ \end{tabular} } & R1 & 1 & 0.745 & & \\ \cline{2-6}  & R2 & 1.037 & 0.694 & 0.065 & 15.938 * \\ \cline{2-6}  & R3 & 1.031 & 0.719 & 0.062 & 16.591 * \\ \cline{2-6}  & R4 & 1.097 & 0.761 & 0.062 & 17.662 * \\ \cline{2-6}  & R5 & 1.063 & 0.718 & 0.064 & 16.551 * \\ \cline{2-6}  & R6 & 1.087 & 0.781 & 0.06 & 18.179 * \\ \cline{2-6}  & R7 & 1.156 & 0.789 & 0.063 & 18.393 * \\ \hline \multirow{6}{*}{
\begin{tabular}{c} Dis-benefit to the \\ region (B) \\ \end{tabular} } & B1 & 1 & 0.707 & & \\ \cline{2-6}  & B2 & 0.985 & 0.647 & 0.073 & 13.567 * \\ \cline{2-6}  & B3 & 1.035 & 0.734 & 0.068 & 15.303 * \\ \cline{2-6}  & B4 & 1.008 & 0.648 & 0.074 & 13.584 * \\ \cline{2-6}  & B5 & 1.085 & 0.721 & 0.072 & 15.038 * \\ \cline{2-6}  & B6 & 1.174 & 0.781 & 0.072 & 16.203 * \\ \cline{2-6}  & B7 & 0.964 & 0.67 & 0.069 & 14.02 * \\ \cline{2-6}  & B8 & 1.04 & 0.678 & 0.073 & 14.175 * \\ \hline \hline \end{tabular}  
* \(p<0.001\) (two-tailed).  
\end{table} 
Table 3: Results of Confirmatory Factor Analysis.
## 5 Experimental Analysis and Discussion
### Performance Evaluation  
The coordinators of the coordinator with 10 different nodes are shown in Table 5.  
The simulation has been performed on the NS-2 platform with different parameters which have been shown in Table 6.  
With different performance parameters, different routing protocols have been implemented on the different number of nodes, along with each node through different routing protocols with different packets. The efficiency of the routing protocol is always affected by the increasing number of nodes because the number of packets to be transmitted will be more which causes congestion in the network and hence degradation in performance. This leads to considering different parameters in such a way that the balance should be maintained. Some of the parameters with optimal values are represented in Table 7.  
Figures in Section 5 depict the performance of various routing protocols with different mobility models on different QoS parameters. All the three routing protocols are evaluated with different mobility models by a varying number of nodes from 25 to 75.  
In discussion, the QoS of routing protocols through packet delivery rate (PDR), average end-to-end delay, and throughput is analyzed.
## 4 Experiments
### Experimental Setup  
**Datasets.** Relevant experiments were conducted on two standard classification datasets, namely CIFAR100 [12] and Tiny ImageNet [13]. CIFAR100 contains a total of 60,000 color images with a size of 32 \(\times\) 32. Additionally, this dataset contains 100 categories with 600 images in each category. We followed the default split using 50,000 images for training and 10,000 images for testing the accuracy of the model. During training, random cropping and horizon flipping are applied to the data augmentation. In contrast, during testing, we do not perform any operations on the images. The Tiny ImageNet dataset contains RGB images of size 64 \(\times\) 64 with 100,000 images in 200 classes for training and 5000 images each for validation and testing. During training, the images are randomly rotated for data augmentation, whereas no operations were performed on the images during testing. The details of the data set are shown in Table 1.  
**Optimization and hyper-parameters.** Following previous research [26], we set the hyper-parameter temperature \(\tau\) to 3 and \(\alpha\) to 0.3. Each experiment was trained for 240 epochs with a batch size of 128. We use a stochastic gradient descent optimizer with a momentum of 0.9 and the weight decay was set to \(5~{}\times~{}10^{-4}\). The initial learning rate was 0.1, which was then divided by 10 at the 150 th, 180 th, and 210 th epochs. All the experiments were performed in PyTorch on GPU (RTX2080s) devices.  
**Model architecture.** We selected widely used neural networks including residual network (ResNet) [1], visual geometry group (VGG) [3], and wide residual network (WRN) [39]. Additionally, we followed the most common structure of multi-exit networks constructed by dividing the above networks into four parts as the backbone network, i.e., by adding three early exit classifiers, as shown in Figures 2 and 3. We divide the network according to its own constituent blocks, for example, the ResNet family of networks adds early exits after the ResBlock. Additionally, the FC in the figure indicates the fully connected layer. To prevent the early exits from interfering with the performance of the final exit and ensure that the final exit can benefit from them, we added simplified attention modules [33] at each exit to enable each exit classifier to acquire the unique features of these modules. The attention module mainly consists of a convolutional layer and a deconvolutional layer. Additionally, mutually different exit classifiers ensure the effectiveness of ensemble learning.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Total** & **Category** & **Size** & **Data Augmentation** \\ \hline CIFAR100 & 60k & 100 & 32 \(\times\) 32 & random cropping and horizon flipping \\ Tiny ImageNet & 120k & 200 & 64 \(\times\) 64 & random rotation \\ \hline \hline \end{tabular}
\end{table}
Table 1: The details of the dataset.
## 3 Proposed Hybrid Wind-Diesel Contribution to Frequency Regulation
### VSWT Inertial and Proportional (I&P) Control Loops  
VSWT inertial and proportional control loops play an important role in releasing the kinetic energy available in the wind turbine so that \"hidden inertia or synthetic inertia (SI)\" is accessible to the grid. In [91], SI is defined as the response of a generating unit to frequency changes, particularly a power exchange that is proportional to the rate of change of frequency (RoCoF). Furthermore, the controlled contribution of electrical power from a unit that quickly corrects FD is called a fast frequency response (FFR) [28]. In this paper, the proposed VSWT inertial and proportional control loops used to provide the RoCoF and FFR are given in Equation (10). Here, \(\text{K}_{\text{pn}}\) constantly weighs the FD, while \(\text{K}_{\text{dn}}\) weighs the RoCoF. The output of this I&P control loop is a torque signal \(\Delta\text{T}_{\text{n}}\). Owing to the existence of a wind energy conversion system, the inertial and proportional control loops add a torque signal \(\Delta\text{T}_{\text{n}}\) to the power reference output to be tracked by the converter [28; 48].  
\[\Delta T_{n}=[K_{pn}+K_{dn}\frac{d}{d_{1}}](f_{ref}-f) \tag{10}\]  
Therefore, after the addition of the proposed VSWT I&P control loops, the torque supplied to the DFIG and converters will be the sum of both the proposed torque and mechanical torque (Figure 2) generated by the PI controller or speed controller, represented by Equation (11) and shown in Figure 14.  
\[T_{total}=T_{n}^{o}+\Delta T_{cm}+\Delta T_{n} \tag{11}\]
## 2 Proposed Segmentation Methodology
### Adaptive Cylindrical Neighborhood Definition
The distances are then used as factors when the weights, for the next iteration of the plane fitting procedure, are determined (see Equations (3) and (4)) according to Huber method [30]. \[ \begin{array}{l}p_{i}=1\;\;\text{for}\;\;nd_{i}\leq\sigma\\ P_{i}=\dfrac{\sigma}{nd_{i}}\;\;\text{for}\;\;nd_{i}>\sigma\end{array}  \tag{3}\]  
\[nd_{i}=\dfrac{|ax_{i}+by_{i}+cz_{i}-d|}{\sqrt{a^{2}+b^{2}+c^{2}}} \tag{4}\]  
where \(p_{i}\) is the weight of the \(i\)-th point, \(nd_{i}\) is the shortest Euclidean distance between the plane and the \(i\)-th point, \((x_{i},y_{i},z_{i})\) are coordinates of the \(i\)-th point, and \(\sigma\) is the accuracy of the laser data in the depth direction. This procedure is repeated until the plane parameters cease to change from one iteration to the next or until the maximum number of iterations (e.g., 10 iterations) is reached. Afterwards, a buffer is defined above and below the final fitted plane. The size of this buffer depends on the noise level expected in the laser datasets. More practically, two times the accuracy of the laser data in the depth direction (covering around 95% of the laser points on the plane) is set for the above and below parts of the final plane. Finally, the neighborhood is defined by the points located inside the established cylinder (see Figure 2b). The points whose distances (_i.e._, \(nd_{i}\) s) are less than the determined threshold are selected as the members of the neighborhood; otherwise they are rejected as shown in the following formulas:  
\[ \begin{array}{l}SN=\{s_{1},s_{2},\ldots,s_{n}\}\\ s_{i}\in CN\;\;if\;\;nd_{i}\leq threshold\\ s_{i}\
otin CN\;\;if\;\;nd_{i}>threshold\end{array}  \tag{5}\]  
where \(SN\) is the set of points belonging to the spherical neighborhood, \(s_{i}\) is the \(i\)-th point in the neighborhood, \(CN\) is the set of points belonging to the adaptive cylindrical neighborhood, and _threshold_ is \(2\sigma\) as above-mentioned. Figure 2: (**a**) The first fitted plane for a spherical neighborhood centered at a point in question; and (**b**) the final fitted plane derived through the iterative procedure using the weight function (side view). Moreover, the point of interest is not considered in the remaining procedures of this study if the iteration stops after the completion of a maximum number of iterations. One should note that the points that are already involved in the neighborhood for a certain target point are not excluded in the procedure of neighborhood definition for another target point.
## 6 SAR Payload Performance Analysis
### SAR Payload System Design Tool  
A system design tool was developed for the S-STEP SAR payload to aid the system design and evaluate its imaging performance based on a mathematical model [2; 3; 4]. The input parameters included antenna gain, transmitted power, noise figure, etc. In particular, the SAR payload system design tool could analyze the PRF timing diagram, NESZ, and the impulse response function for various SAR modes.  
The graphical user interface of the SAR payload system design tool for S-STEP is displayed in Figure 21 which depicts the system parameter settings, analysis plots (one major and two subplots), and the SAR payload operational parameters table. In addition, the collection geometry and access region of the S-STEP SAR satellite for performance evaluation are depicted in Figure 22a,b, respectively, where \(\eta\) denotes the incidence angle, \(h\) represents the satellite altitude, and \(\alpha\) indicates the off-nadir angle. The key performance parameters of the S-STEP SAR payload for the high-resolution and wide-swath modes are detailed in Table 3.  
Figure 21: System design tool of the S-STEP SAR payload.  
Figure 20: Antenna patterns of the S-STEP SAR payload in (**a**) azimuth and (**b**) elevation.
# A Universal Low-Complexity Demapping Algorithm for Non-Uniform Constellations
## 5 Performance Evaluation
### BER Simulation Results
(**a**) CR = 2/15; (**b**) CR = 6/15; (**c**) CR = 10/15; (**d**) CR = 13/15. Figure 7: BER Performance comparison of Max-Log-MAP and SCSR demapping algorithms over i.i.d Rayleigh channel. Results presented for 1D-1024 NUCs. (**a**) CR = 2/15; (**b**) CR = 6/15; (**c**) CR = 10/15; (**d**) CR = 13/15.
# Visibility Assessment of New Photovoltaic Power Plants in Areas with Special Landscape Value
## 1 Introduction
The assessment of the _GAPT_ variable in a geographical area makes it possible to obtain a set of GIS maps that help to visually identify the most suitable locations for the installation of new PV power plants in terms of their visibility. In summary, the main objective of this work is the development of a new methodology based on GIS to determine, in an objective way, the places where future PV facilities will have a greater or lesser visibility for a set of potential observers in movement, and its application to places or geographical areas with special landscape or cultural protection. The novel aspects covered in this work are:  
* Assessment of the degree of visibility (total or partial) of the new PV plants;
* Assessment of the possible observation time of visitors or tourists, taking into account the route they follow and their speed;
* The proposed visibility enhancement factor for PV plants that may be fully or partially visible with the sky in the background. The article is structured as follows: Section 2 presents the methodology used for the evaluation of the _GAPT_ variable for all the areas surrounding the paths followed by the observers; Section 3 presents a case study with the application of the proposed methodology for the selection of suitable sites for the installation of two-axis PV trackers with an installed power capacity of 10 kW (the area under study corresponds to the area crossed by the Way of St. James in the region of La Rioja, Spain, declared a World Heritage Site by UNESCO) [29]; the results of the case study are shown and discussed in Section 4; finally, Section 5 presents the conclusions.
## 2 Information Reconciliation in QKD Post-Processing  
Information reconciliation is a fundamental technique employed in key agreement protocols, involving the extraction of shared information through a public discussion between two correlated sources of random variables. In the context of QKD post-processing, the concepts of channel coding are leveraged to address the information reconciliation problem, based on the Slepian-Wolf coding scheme.
# Digital Cloud Environment: Present Challenges and Future Forecast
###### Abstract  
This article addresses the challenges of a digital cloud environment when it comes to global scalability with a large number of remote servers and an unsecure public cloud, such as Amazon. The goal of the study was to work out an approach for evaluating the reasonable system capacity under heavy workload. For that purpose, Zabbix monitoring solution is used and business metrics are applied in relation to existing system ones. A prediction data model is proposed to compute the future forecast of the user activity based on the collected historical statistics and to verify whether capacity adjustment is possible or not. The results of capacity planning are implemented at Genesys International Telecommunications Company. System analysis of the production environment indicates the possibility to downscale the capacity of certain virtual servers, which allowed savings to the annual operational costs of $3500 (50%) for each affected server.  
internet of things; cyber security; public cloud; digital environment; virtual machine; digital transformation; monitoring system; prediction data model +
Footnote †: journal: Future Internet