esa-sceva/satcom-chunk-collection huggingface dataset:
# A Cross-Chain-Based Access Control Framework for Cloud Environment
## 4 The Framework's Functionality
### Transaction Processing (Phase 3)  
The processing of the broadcast access transaction that originated from cloud user zones that integrate the IBC protocol can be resumed in the following steps (as shown in Figure 3):  
1. The cloud user generates his access transaction addressed to the IBC-activated cloud front-end zone (sending chain). In this step, he indicates the corresponding channel and port, a hash of his certificate, as well as his client attributes.
2. Before broadcasting, the cloud user generates a digital signature by signing the transaction using his private key from the supported key scheme of the sending zone (in this case, the secp256k1 scheme).
3. The sending chain verifies the legitimacy of the transaction and compares the account address and the certificate hash combination with the stored addresses and hashes. If there is no match found, the transaction drops.
4. The signed transaction is encrypted using an encryption key of a symmetric algorithm, such as chacha20 poly1305 or other algorithms used in the BC application layer (logic modules) of the sending chain. This layer also incorporates algorithms for encryption  
Figure 2: Sequence diagram of cloud users’ IBC interactions between cloud front-end/back-end blockchains.  
key exchanges (such as Diffie-Hellman) that share the encryption key between the sending chain and the receiving chain. This is due to the IBC protocol, which uses the cryptographic primitives of the BC and the consensus mechanisms.
5. The sending chain's IBC module encapsulates the received data payload from the logic module into an IBC packet, generates cryptographic commitments of it based on Merkle roots, and stores it in the chain's states.
6. The relayer listens, queries the packet and commitment proofs from the sending chain, and then relays it to the receiving chain.
7. The IBC module of the receiving chain verifies the validity of commitment proofs, and then delivers the packet to the logic module for further processing.
8. The receiving chain decrypts the received tx, disclosing the tx information.
9. The access control mechanism is triggered automatically to process the received attributes.
10. If the attributes do not match directly with the specified CSC policies, a semantic process using ontology is started to draw knowledge about them.
11. If the attributes match the deployed policies directly or semantically, the controlling node creates a signed URL to the requested cloud resources. In our case, the URL is signed with the CSC's private key.
12. The access decision and the signed URL are added to the acknowledgement (ACK) packet.
13. Similar to the previous steps, the receiving chain uses its IBC module to send an ACK to the sending chain along with proofs that the receiving chain has committed the ACK to complete the cross-chain transaction.
14. The cloud user accesses the cloud resource via the received signed URL.
## 4 Experimental Verification  
### Experiment Setup  
To validate the proposed control method, the comparative experiments were carried out on the flexure-based micro-motion manipulator as depicted in Figure 1, while the whole experimental system is established as depicted in Figure 3. The two actuators are controlled by a modular piezo-servo controller (XE500/E50, XMT Harbin, China). A dSPACE processor board DS1007 equipped with a 16-bit ADC card (DS2102) and a 6-channel high resolution incremental encoder interface card (DS3002) is utilized to output the excitation voltage of the piezo servo controller and capture the real-time data for the length gauges. Since the piezoelectric actuator is made up of multiple piezoelectric layers glued together, it is sensitive to the pulling force, which may bring damage to the actuator. Therefore, preloading forces were applied on the two piezoelectric actuators by tightening the bolts to guarantee their operational safety.  
To show the effectiveness of the proposed LODCARC sufficiently, three more controllers, namely the PI, ILC and DCARC controllers, were implemented for comparison. A traditional PI controller, as a typical kind of PID controller, consists of a proportional term and an integral term, which is simple and widely-used. On the other hand, an individual ILC controller uses the same learning function in the proposed learning-based optimal DCARC controller as discussed above in Section 3.2. It was noted that a second order low-pass with the expression of \(Q(s)=\frac{\omega_{n}{}^{2}}{s^{2}+2\zeta_{m}s+\omega_{n}{}^{2}}\) is used for the \(Q\)-filter in an ILC controller, where \(\zeta\) is damping ratio and \(\omega_{n}\) is the crossover frequency. In this case, \(\zeta\) = 0.7 and \(\omega_{n}\) = 5026.55 rad/s. The PD-type learning function of ILC controller, as mentioned in Equation (31), was chosen as \(K_{p}\) = 4.5 and \(K_{d}\) = 3.0. As for the DCARC controller, the boundaries for the variation in parameters in the \(X\)-axis were chosen as \(\theta_{x\text{min}}\) = [6.13 \(\times\) 10\({}^{-7}\), 5.90 \(\times\) 10\({}^{-3}\), 6.10, -2]T and \(\theta_{x\text{max}}\) = [6.38 \(\times\) 10\({}^{-7}\), 6.10 \(\times\) 10\({}^{-3}\), 6.55, 2]T, while the boundaries for the variation in parameters in the Y-axis are chosen as \(\theta_{\rm{ymin}}\) = [5.15 \(\times\) 10\({}^{-7}\), 5.90 \(\times\) 10\({}^{-3}\), 6.50, \(-\)6]T and \(\theta_{\rm{ymax}}\) = [5.29 \(\times\) 10\({}^{-7}\), 6.20 \(\times\) 10\({}^{-3}\), 6.80, 6]T. The diagonal positive definite matrix \(\Gamma\), which indicates the adaptation rate in Equation (5) is chosen as \(\Gamma\) = diag [1.50 \(\times\) 10\({}^{-16}\),5.00 \(\times\) 10\({}^{-9}\), 5.00 \(\times\) 10\({}^{-3}\), 1.75 \(\times\) 10\({}^{-1}\)]. It was noted that the DCARC term of the proposed learning-based optimal DCARC controller and the DCARC controller introduced above were the same. In addition, to make sure that the comparative experiments were accurate and fair, the proposed LODARC controller used the same control parameters as a DCARC controller and the same filter parameters as an individual ILC controller.
## 4 The Finite Element Model  
A three-dimensional model of the waveguide illustrated in Figure 2, with a rectangular cross-section, was developed in COMSOL Multiphysics. Material properties were described by the spatial function (1) with complex permittivities \(\varepsilon_{\text{L}}\) and \(\varepsilon_{\text{G}}\). The waveguide walls acted as perfect electric conductors. Rectangular ports at either end of the waveguide excite and absorb the propagating modes within, and were backed by perfectly matched layers. The waveguide was given a finite length sufficiently larger than \(z_{0}\), so that the ports were located far away from the layer, in the limit (2). The reflection (\(R_{Pm}\)) and transmission (\(T_{Pm}\)) power coefficients were calculated using the scattering parameters obtained from COMSOL at the reflection (\(S_{11}\)) and transmission (\(S_{21}\)) ports, which were then multiplied by the scale factor (17) to account for the ambient losses \(\Im\{\varepsilon_{\text{G}}\}\), where the number \(m\) describes the ratio of waveguide length (i.e., distance between the two ports) to thin layer length \(2z_{0}\).  
The simulations presented in this paper were run on a computer equipped with an Intel i7-10700K processor with 64 GB RAM, running on Windows 10. In the case of a waveguide length of 17 cm, the solution time was 1 s for \(f=1\) GHz and 16 s for \(f=3.5\) GHz.
## 5 Model Test of the Progressive Collapse of the Surrounding Rock of a Large-Span Cavern
### Results and Analysis of a 60 m Large-Span Cavern Model Test  
Figure 6 shows the progressive collapse process of the surrounding rock of a large-span cavern. The collapse of the surrounding rock of the large-span cavern starts from the middle of the vault and occurs three times successively.  
Figure 5: Visualization of large-span cavern geomechanical model test device.  
It can be seen from the figure that, when the model frame rotates slowly to 61\({}^{\circ}\), the surrounding rock at the top of the cavern collapses for the first time, and the surrounding rock does not continue to collapse after 30 min of rest, as shown in Figure 6a. Due to the large span of the cavern, with the increase in the load and the surrounding rock being under the action of gravity, the middle part of the cavern vault first undergoes tensile and shear damage, and the local collapse phenomenon occurs. The half width of the collapse body is 19.9 cm, and the height is 16.6 cm. The height of the first collapse body was 27.7% of the cavern span.  
Continue to rotate the model box slowly. When the model box rotates slowly to 74\({}^{\circ}\), the surrounding rock of the ceiling of the cavern vault collapses twice instantaneously. After leaving it to stand for 30 min, it can be observed that the model does not continue to deform, as shown in Figure 6b. With the increase in overburden, the remaining part of the surrounding rock at the arch foot cannot support the top pressure. Taking the maximum span of the cavern as the collapse width, it will extend again above the vault until a new collapse arch appears. The half width of the collapse body is 30 cm, and the height is 31.6 cm. The height of the first collapse body was 52.7% of the cavern span.  
Figure 6: Progressive collapse process diagram of large-span caverns, unit: cm. (**a**) The first collapse, where the angle of the model box is 61\({}^{\circ}\); (**b**) the second collapse, where the angle of the model box is 74\({}^{\circ}\); (**c**) the third collapse, where the angle of the model box is 85\({}^{\circ}\); and (**d**) the progressive collapse profile.  
When the model box rotates slowly to 85\({}^{\circ}\), the top of the cavern collapses for the third time. After standing still for 30 min, the model does not continue to deform. According to Figure 6c, it can be seen that the collapse range of the cavern continues to increase along the arch foot of the second collapse, forming the shape of the third collapse. The half-width of the collapse body is 30 cm, and the height is 45.1 cm. The height of the first collapse body was 75.2% of the cavern span.  
Continue to rotate the model box slowly to 90\({}^{\circ}\), and the surrounding rock of the model will be relatively stable from 85\({}^{\circ}\) without collapse. The outline of the gradual collapse process of the final large-span cavern is shown in Figure 6d.
###### Abstract  
Structural health monitoring (SHM) has been extensively used in the railway industry, with applications ranging from railway infrastructures to carbody shells. An SHM method that dominates monitoring procedures is Acoustic Emissions (AE). The utilisation of the AE method could use a significantly large amount of data, collected and forwarded to terminal computers using wireless communications. Nowadays, the use of 5G is taking over traditional wireless such as Wi-Fi and 4G telecommunications. However, errors in the transmission due to noisy channels may be experienced. The SHM system may result in the wrong detection of a potential defect in a railway infrastructure with dangerous consequences, such as derailment. Hence, methods for adequately dealing with these errors need to be established, such as Forward Error Correction (FEC) codes. In this paper, we suggest the use of the wireless FEC codes applied to a number of deployed AE devices, in order to perform correction at the transmissions. We investigate the new POLAR codes and compare this method with the Reed-Solomon (RS) codes. We present simulations that the POLAR codes are more efficient with trials using the AFF3 CT simulator.  
SHM; railway; wireless; 5G; FEC codes; noisy channel; POLAR; Reed-Solomon +
Footnote †: journal: _Infrastructures_**2022**, 7, 41. [[https://doi.org/10.3390/infrastructures/030041](https://doi.org/10.3390/infrastructures/030041)]([https://doi.org/10.3390/infrastructures/030041](https://doi.org/10.3390/infrastructures/030041))
## 2 Related Work
### Influence Maximization  
The IM problem is a crucial problem in information diffusion research due to its potential commercial value. Basically, IM focuses on finding a set of \(k\) seed users on a social network to maximize the number of influenced nodes. [PERSON] et al. [1] first proposed two information diffusion models, Linear Threshold (LT) and Independent Cascade (IC). On these models, they formulated IM problem as a combinatorial optimization problem and designed a natural greedy algorithm with approximation ratio is \(1-1/e\). IM has been received much attention from the following aspects: proposing efficiency algorithms [2; 3; 4; 5; 6; 7; 8; 9; 24] and studying its variants [7; 10; 25; 26; 27; 28].  
[PERSON] et al. [1] first purposed Greedy algorithm based on Monte-Carlo simulation with \((1-1/e-\epsilon)\) approximation guarantee. To improve the running time of Greedy algorithm, [PERSON] et al. [3] proposed the cost-effective lazy forward (CELF) algorithm, which is up to 700 times faster than Greedy algorithm. This algorithm is improved in [29]. Several works propose heuristic algorithms to find solutions in large networks [8; 9; 30; 31]. Although those heuristics are often faster in practice, they fail to retain the \((1-1/e-\epsilon)\) approximation guarantee and often give lower results than greedy algorithm. [PERSON] et al. [9] proposed a heuristic algorithm based on the maximum influence arborescence (MIA) structure. In the LT model, [PERSON] et al. [8] proposed using local directed acyclic graphs (LDAG) to approximate the influence of nodes. Recently, [PERSON] et al. [2] made a theoretical breakthrough for finding solution to IM by proposing Reverse Influence Sampling (RIS) algorithm. RIS algorithm returns a \((1-1/e-\epsilon)\) approximate solution with probability at least \(1-n^{-l}\). The main idea of the RIS algorithm is to generate Reachable Reverse (RR) sets to estimate the objective function and use greedy algorithm for a collection that includes a large enough RR set to the find solution. This motivates many state-of-the-art methods for IM including TIM/TIM++ [5], IMM [6], and SSA/D-SSA [4].  
Recently, IM's variants have also received much attention due to their potential commercial value [25; 27; 28]. [PERSON] et al. [28] studied \(k\)-Boosting problem, which aims at finding the set of \(k\) usersto boost so that the \"boosted\" influence spread is maximized. The authors of [25] investigated distance-aware influence maximization, which takes into account the effect of distance on influence process for IM problem. They showed that the objective function is monotone and submodular and proposed RIS-based and MIA-based algorithms. Influence Maximization with awareness of the topic are also studied in [27]. In this work, each user is associated with a profile that consists of the users preferences on different topics in their model and the problem asks to select seed set with budget and topic queries so that the competitive influence function is maximized.
###### Abstract  
Concerns about the expanding human population's adequate supply of food draw attention to the field of Food Security. Future-focused analysis and processing of agricultural data not only improve planning capabilities in this field but also enables the required precautions to be taken beforehand. However, given the breadth and number of these regions, field research would be an expensive and time-consuming endeavour. With the advent of remote sensing and optical sensors, it is now possible to acquire diverse data remotely, quickly, and inexpensively. This study investigated the limitations and capabilities of remote sensing data application in the field of planning Food Security. As a result, Sentinel 2 and Shuttle Radar Topography Mission (SRTM) data were used to estimate winter wheat yields with a high degree of accuracy (98.03%) using the Mamatkulov technique and the MEDALUS model, which was both free and widely available. This method can make it possible to make predictions about the productivity of newly created crop fields or for which we do not have information about the productivity of previous years, without the need to wait for building regression models or any field studies. Considering the outcome, wide-range and larger analyses on this topic can be carried through.  
SQI; yield; NDVI; DEM; slope; LAI; remote sensing; MEDALUS 2023
# Parameter Identification of Structures with Different Connections Using Static Responses
## 5 Parameter Identification for a Frame with Semi-Rigid Connections
### Nodal Displacement Error Analysis Caused by Nodal Rotational Stiffness Changes  
As shown in Figure 8, to compare the effect of different nodal rotational stiffnesses on the nodal displacement of the steel frame structure, steel frame structures with different nodal rotational stiffnesses are simulated, and the nodal displacements of the semi-rigid frame structure with the rigid frame are compared under the same applied forces. Assuming that the nodal rotational stiffness of the semi-rigid frame ranges from 2 \(\times\) 10\({}^{3}\) to 9.8 \(\times\) 10\({}^{4}\) kN\(\cdot\)m/rad and the nodal rotational stiffness of the rigid frame is infinite, the member stiffness matrix of the frame element with semi-rigid connections at the ends can be obtained by using Equation (8), and the member-global stiffness matrix of the frame can be obtained by using the member stiffness matrix. The nodal displacements of the semi-rigid frame and rigid frame were calculated using Equation (3).  
Figure 7: Frame element with semi-rigid connection.  
Consequently, the nodal displacements of the semi-rigid frame structure with different nodal rotational stiffnesses are compared with those of the rigid connection. Figure 9 shows the absolute value of the relative error for the nodal displacement with different nodal rotational stiffnesses. The smaller the rotational stiffness, the greater the nodal displacement error. Therefore, in the damage identification of rigid steel frame structures, if the joints are damaged, especially if the damage is serious, it is necessary to consider the impact of the damage on the nodal displacements.  
Figure 8: Three-member semi-rigid frame example.  
Figure 9: Nodal displacement error caused by different nodal rotational stiffnesses. (**a**) Degree of Freedom 1–3; (**b**) Degree of Freedom 4–6; (**c**) Degree of Freedom 7–9.
# IoT-Based Smart Surveillance System for High-Security Areas
## 1 Introduction
### Contributions of this Study
#### 1.2.3 Cost-Effectiveness  
A UWB radar is used to detect the motion of living things as well as life signs such as respiration or heartbeat, but it is very costly. In contrast, GMS is cost-effective, can enable the motion detection of an object, and is suitable for security purposes, specifically the detection of a suspected object behind an obstruction.
## 4 Experimental Evaluation
### Analyzed Techniques  
In addition to the negotiation techniques under study, presented in Section 3.3, we have included a comparison with three reference techniques:  
* Random reference: As a first baseline, in this technique, each AP chooses a channel randomly to use with their clients.
* Sequential channel search (SCS): This is a more realistic baseline and inspired by LCCS [11]. In this algorithm, APs are activated in sequence, and each of them chooses the channel where it finds the lowest interferences from other active APs and their clients. If there are several least congested channels, it chooses one of them randomly.
* Augmented Lagrangian harmony search optimization (ALHSO): In addition to our negotiators based on simulated annealing, we wanted to have, as a reference, a nonlinear optimizer using  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario** & \# APs & \# Sensors & \(\boldsymbol{\
u}\) & \(\bar{I}\) \\ \hline
1 & 50 & 350 & 237 & 22.53 \\
2 & 50 & 350 & 241 & 21.53 \\
3 & 50 & 350 & 240 & 21.81 \\
4 & 50 & 500 & 439 & 34.72 \\
5 & 50 & 500 & 414 & 39.26 \\
6 & 50 & 500 & 427 & 34.30 \\
7 & 100 & 500 & 490 & 47.62 \\
8 & 100 & 500 & 487 & 51.57 \\
9 & 100 & 500 & 527 & 48.63 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of scenarios.  
complete information. We have chosen harmony search, which is an evolutionary optimization algorithm inspired by musical composition [41]. More concretely, we have used a public implementation that uses augmented Lagrange multipliers to deal with restrictions [42].
# Dempster-Shafer Fusion Based on a Deep Boltzmann Machine for Blood Pressure Estimation
## 2 Signal Processing and Feature Selection from Oscillometric Wave Signals  
This study was confirmed by a research ethical committee of the institution, and every volunteer signed an informed consent rule prior to measurements according to the BP measurement protocol of the institutional research ethical board. The BP data were recorded from 85 subjects between the ages of 12 and 80 years that had no history of cardiovascular disease. Among them, 37 were females and 48 were males. Five sets of BP measurements per subject were collected using a measuring device according to the recommendations of the Sphygmomanometer Committee (SP10) standard [5; 19]. Measurements obtained by two nurses were averaged to provide single target SBP and single target DBP values. In this work, the feature data were extracted from the OMW signal and envelope as shown in the third box of Figure 1. First, the features were obtained from the OMW signals and oscillometric envelopes for use as input data to estimate the target SBP and DBP. The target BPs had to be converted into the BP classifier category. A summary of the feature vectors is provided in [12].
# Low-Complexity Online Calibration for Large-Scale Multi-Beam Antennas
## 3 Low-Complexity Online Calibration Method
### Complexity Discussion  
Conventional CDMA calibration schemes mainly consists of orthogonal methods and nonorthogonal methods. The orthogonal method requires \(K\) parallel matched-filters to achieve despreading. and the nonorthogonal method involves the inverse operation of the \(K\)-order matrix. For a large-scale antenna system, the computational complexity of matched filtering and matrix inversion is too high to be realized by hardware.  
The receiver of the proposed online calibration method can achieve despreading with only one matched-filter instead of \(K\) matched-filters and no matrix inversion operation is required. Meanwhile, the calibration accuracy performance can be maintained without degradation. Therefore, the proposed online calibration method is especially suitable for large-scale antenna system.