esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.  
indoor positioning system; machine learning; WLAN fingerprinting; higher education; learning environment +
Footnote †: journal: Sensors  
osajournal  
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## 3 The Proposed Architecture
### Advanced Service Layer
Since all the sensors information related to the authentication process are saved on the BC, a client request is sent from the sensor (physical layer) to the BC (data and cloud layer) via a communication protocol (for instance, LoRA in network/protocol layer) passing through a TCP/IP connection (in the BC tools and transport layer, which is responsible for adding a digital signature to the client request) to a web application running under  
Figure 4: Smart contract sample data structure for sensor access control. Figure 5: Smart contract code for authentication process. HTTP protocol (in the application layer) containing the sensor identification (sensorID), user identification (userID) and user password (userpwd). Once the application server received the client request, it will check the correctness of the received information with the BC data structure (i.e., the sent sensorID should match the sensorID in the blockchain, the userID needs to match a value in the AuthorizedPerson table and userpwd should match AuthenticationPwd). In the case where all sent information are correct, the application server will authorize the client access to the sensor. According to Figure 5, in the case where the sent password (i.e., userpwd) is different from the stored password in the BC (i.e., AuthenticationPwd), the smart contract algorithm will increment the variable Numberoftentativewithwrongpassword related to the sensorID and the application server will deny the access to the sensor. The latter variable will serve as a feature in the ML process to detect a brute force attack. Moreover, CommandLocation in the BC structure will serve as a feature in the ML process to detect a DDoS attack. By employing BCT components such as digital signature, Hash function and cryptography algorithms in the IIoT networks, various attacks such as eavesdropping, MitM, poisoning and evasion attacks can be de-activated by exploring inherited features provided by the BCT components such as the tractability and integrity. In addition, the Hash function is used to link between the BC nodes to ensure a high traceability when a new block is added at the level of the advanced service layer, so that injection attacks cannot be performed or may be reduced by intruders. Finally, a digital signature based on a message digest can provide authenticity, nonrepudiation and message integrity.
## 4 Simulation Experiment and Analysis of desensitization Optimization
### Analysis of Optimization Results
From the perspective of different systems, \(\Delta C7\) and \(\Delta C8\) of the A, B, and C systems are approximately equal; \(\Delta C5\) and \(\Delta C6\) of the B system have a decreasing trend compared with the A system and the linear variation of \(\Delta C5\) and \(\Delta C6\) with the field of view of the C system being minimal. It can be seen from Section 2 that the first-order coefficients of the sensitivity of the astigmatism terms are the astigmatism coefficients introduced by the first-order term of misalignment, namely \(\alpha_{1}\) and \(\alpha_{2}\). The constant term of the sensitivity of the astigmatism terms is the astigmatism coefficients introduced by the square term of misalignment, namely \(\alpha_{3}\). The sensitivity of the coma terms is the coma coefficients introduced by the first-order term of misalignment, namely \(\beta_{1}\) and \(\beta_{2}\). From the optimization results, it can be seen that the reduction of sensitivity mainly depends on the astigmatism coefficients introduced by the first-order term of misalignment. Finally, the performance of system A without desensitization and system C after final desensitization were compared by Monte-Carlo analysis method including 2000 samples, as shown in Figure 9. The abscissa is the MTF, the left ordinate is the number of samples, and the right ordinate is the cumulative distribution. The histogram of Monte-Carlo results shows that the performance distribution of the system before desensitization is relatively scattered, and the results with the best and worst performances appear in the system before desensitization. For 80% of the MTF, the system without desensitization is above 0.443, and the system after final desensitization is above 0.468. The results obtained before desensitization and after final desensitization are not much different. From the point of view of the cumulative integral curve, when the MTF is high, the slope of the curve of the system after final desensitization is relatively large, that is, the performance distribution of the system is more concentrated under the tilt tolerance. The standard deviation of the system without desensitization is 0.035. The standard deviation of the system after final desensitization is 0.020, which is 59% of the system without desensitization, indicating that the tilt sensitivity of the system after final desensitization is lower. A desensitized system can produce neither very good nor very poor tests. Therefore, for system performance control, desensitized systems are preferable to those optimized only for image quality. Figure 9: Monte-Carlo tolerance analysis results of the optical system A without desensitization and C after final desensitization.
# Query Rewriting for Incremental Continuous Query Evaluation in HIFUN
## 3 Preliminaries--The HIFUN Query Language
### Query Rewriting  
Independently of how a given query is evaluated, the formal model of HIFUN supports query rewriting. An incoming query or a set of queries can be rewritten at the conceptual level, in terms of other queries to reduce the evaluation cost.  
Common Grouping and Measuring Rewriting Rule (CGMRR): Let \(Q=\{(g,m,op_{1}),\ldots,\)\((g,m,op_{n})\}\) be a set of \(n\) queries with the same grouping function and the same measuring function, but possible different reduction operations. In this case the rewriting of \(Q\) is the following: \(Q^{\prime}=(g,m,\{op_{1},\ldots,op_{n}\})\), meaning that the grouping and the measuring steps are done only once and the \(n\) reduction operations are applied to the measuring results.  
Common Grouping Rewriting Rule (CGRR): Let \(Q=\{(g,m_{1},op_{1}),\ldots,(g,m_{n},op_{n})\}\) be a set of \(n\) queries with the same grouping function but possibly different measuring and reduction operations. In this case the rewriting of \(Q\) is the following: \(Q^{\prime}=\{g,(m_{1},op_{1}),\ldots,(m_{n},op_{n})\}\), meaning that the grouping is done only once and the \(n\) measuring and reduction steps are applied to the results of grouping.  
Common Measuring and Operation Rewriting Rule (CMORR): Let \(Q=\{(g_{1},m,op),\ldots,\)\((g_{n},m,op)\}\) be a set of \(n\) queries with the same measuring function and the same reduction operation, but possibly different grouping functions. In this case the rewriting of \(Q\) is the  
Figure 2: An analytic query (**a**) and its answer (**b**).  
following: \(Q=\{(g_{1}\wedge\ldots\wedge g_{n},m,op),(proj_{G_{1}},(g_{1}\wedge\ldots\wedge g _{n},m,op),op),\ldots,(proj_{G_{N^{\prime}}},(g_{1}\wedge\ldots\wedge g_{n},m,op), op)\}\), where \(G_{i}\) denotes the domain of definition of \(g_{i}\). This rewriting rule is derived from the following basic rewriting rule of the HIUN Language [30; 31]:  
Basic Rewriting Rule (BRR): Let \(Q=(g_{2}\circ g_{1},m,op)\) and \(Q^{\prime}=(g_{1},m,op)\). Then \(Q\) can be rewritten as follows: \(Q=(g_{2},ans_{Q^{\prime}},op)\) under the assumption that the operation \(op\) is distributive.  
A more concise expression of this rule is the following: \((g_{2}\circ g_{1},m,op)=(g_{2},(g_{1},m,op),op)\), which can be read as: evaluate first the \"inner\" query \(Q^{\prime}=(g_{1},m,op)\); then use its answer as the measuring function to evaluate the \"outer\" query \(Q=(g_{2},ans_{Q^{\prime}},op)\).
# Random Frequency Division Multiplexing
## 1 Introduction
Through training, optimal parameters of the neural networks are learned and thus near ML detection performance is obtained in both fixed and varying channel models under quadrature phase shift keying (QPSK) modulation than classical detectors [8]. A DL-aided Logarithmic Likelihood Ratio (LLR) correction method is proposed for improving the performance of MIMO receivers [9], where it is typical to adopt reduced-complexity algorithms for avoiding the excessive complexity of optimal full-search algorithms. A DNN is trained for detecting and correcting both over-confident and under-confident LLRs with a relatively low complexity compared to the popular reduced-complexity receiver detector techniques. This paper investigates a RFDM algorithm for faster transmission efficiency. It compresses all symbols by a rectangular matrix. This matrix is the measurement matrix of CS, which can compress and measure signals simultaneously. Of course, the measurement matrix needs to satisfy certain conditions, and there are many matrices that meet these conditions; the Gaussian random matrix is one of the most commonly used measurement matrices which can ensure the reconstruction of the original high-dimensional signal with high probability. Meanwhile, since the signal is not a sparse signal, which is the premise of CS, the reconstruction algorithm cannot detect the signal from a small number of measured values. A DNN is leveraged to learn the implicit mapping function between the received compressed signals and original transmitted signal. The major contributions of this paper can be summarized as follows: (1) The investigated RFDM scheme compresses the original high-dimensional signal through a Gaussian random matrix, which can transmit more data, or the same amount of data, using fewer subcarriers or antennas. (2) A DNN detector is proposed to recover the original high-dimensional signal directly from the low-dimensionaldata by using the pre-trained DNN network. This novel signal multiplexing and detection method for MIMO communication systems is useful when the number of antennas or subcarriers is limited. That is to say, the main advantage is that the amount of data to be transmitted is reduced, or that the same subcarrier or transceiver antenna can transmit more data. The compression rate of our experimental setting is the fixed 0.75 which is related to the properties of measurement matrix and the accuracy of the reconstruction algorithm. The remainder of this paper is organized as follows. The basic concept of OFDM and the origin of our idea are introduced in Section 2; this will help us to understand the feasibility of our solution. Section 3 describes the DNN-based RFDM algorithm which consists of two parts: the compression of the transmitted signal and the reconstruction of the signal based on DNN model. The schemes extending to multiple antennas are described in Section 4. The simulation results are analyzed in Section 5. Finally, the paper is concluded in Section 6.
# Experimental Research on Mechanical Performance of SSRC Columns under Eccentric Compression
## 5 Crack Width Analysis
### Revision of Force Characteristic Coefficient  
Considering the aforementioned results, it is not appropriate to use directly these two codes to calculate the maximum crack width of SSRC columns under the long-term load for the following reasons: First, the low elastic modulus and high strength of stainless steel reinforcements increase the ductility and lateral displacement SSRC columns. The large displacement results in a large maximum crack width in the SSRC column. In addition, the bonding properties of stainless-steel reinforcements and concrete differ from those of traditional reinforced concrete and have an effect on the crack width.  
Considering the good correlation between the calculation results of GB 50010-2010 and the test values, GB 50010-2010 is still used to calculate the crack width of SSRC columns under long-term loads, and only \(\alpha_{\text{cr}}\) needs to be adjusted. Based on the existing test results, it is recommended to set \(\alpha_{\text{cr}}\) to 2.7. As depicted in Figure 14, the revised theoretical calculation value, \(w^{c}_{\text{max}}\), of the long-term maximum crack width is more consistent with the measured values, \(w^{\text{exp}}_{\text{max}}\), of SSRC columns. The average value of \(w^{\text{exp}}_{\text{max}}/w^{c}_{\text{max}}\) is 1.034, and the variation coefficient is \(\delta=0.0733\).  
Figure 13: Comparison of calculated results with test results: (**a**) maximum crack widths for short-term load, (**b**) mean crack spacing, and (**c**) maximum crack widths for long-term load.
# Defense against Adversarial Attacks in Image Recognition Based on Multilayer Filters
## 2 Background
### Adversarial Attack  
Based on the different ways of generating adversarial samples, we consider two types of adversarial attacks: iterative attacks and optimization-based attacks. FGSM (fast gradient sign method) is currently one of the most popular iterative adversarial attack methods [11]. The core idea of this method is to add some adversarial vectors to the original image. The direction of this vector matches the gradient of the loss function. The FGSM vector can be expressed as Formula (2), as follows:\[x^{adv}=x+\alpha\times sign(\bigtriangledown_{x}Loss(x,y)) \tag{2}\]  
\(Loss(x,y)\) is the loss function used to train the classifier, where \(\bigtriangledown_{x}\) is the gradient in the image space, \(\alpha\) is the iteration step (set \(\alpha=\epsilon\)), \(x\) is the input vector (image), \(y\) is the true vector class, and \(x^{adv}\) is an adversary example.  
The basic iterative method (BIM) [42] is an iterative attack method that performs multiple iterations with small steps to determine the direction of disturbance based on the decision boundary of the classifier during each iteration. Projection gradient descent (PGD) [43] is a stronger iterative attack method that extends the fast gradient sign method by finding the gradient direction with the highest loss and projecting it onto the sample. VNIFGSM [12] is a method called variance adjustment to enhance the category of iterative gradient-based attack methods and improve their attack transferability. Specifically, in each iteration of gradient calculation, we do not directly use the current gradient for momentum accumulation, but further consider the gradient variance of the previous iteration to adjust the current gradient, in order to stabilize the update direction and remove poor local optima.  
Intuitively, the disturbance generated by the iterative method can effectively fool the network, but it is also easy to overfit specific network parameters, so the transferability is poor. The single-step method is weak in attack. Therefore, we conduct comprehensive experiments to test the effectiveness of proposed defense methods against three iterative attack methods under different attack scenarios.  
In addition, optimization-based methods directly optimize the distance between the real and adversarial examples. Instead of leveraging the training loss, [PERSON] and [PERSON] (C&W) [44] designed an L2-regularized loss function based on the logit layer representation for generating adversarial examples. AutoAttack [45] integrates multiple advanced attack algorithms, including FGSM, PGD, etc., to achieve a comprehensive and effective combination of attack strategies. AutoAttack uses a combination of these algorithms to form an automated and gradually enhanced attack strategy. This means that it can adapt and optimize attacks through continuous experimentation to maximize the exposure of the model's vulnerability.
## 3 Results and Discussion
### Extreme Load Distribution of the Blade  
Though the extreme loads at the root can be used to evaluate whether the blade is overloading for the wind turbine, as well as the safety of the blade bolts, it is not enough for the preliminary blade preliminary. The load distribution along the blade span is also important. Luckily, this model can obtain the extreme load at any section. To further test it, the extreme load distributions for the 1.5 MW blade and 2.0 MW blade were obtained and compared with FOCUS results respectively in Figures 10-13. The extreme loads were close at most sections for flapwise bending moments, though there was little difference in the middle of the blade in Figures 10 and 12. From Figures 11 and 13, we can see that the edgewise bending moment was also close at the blade tip, while there was obvious differences near the root. This method needs further improvements for edgewise load prediction. Overall, the PSO-ELPM model can accurately predict extreme bending moments in flapwise, which  
\begin{table}
\begin{tabular}{c l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Bending Moment (kNm)**} & \(\boldsymbol{V_{1}\) (m/s)} & \(\boldsymbol{\Omega}\) (rad/s) & \(\boldsymbol{\beta_{2}}\) (\({}^{\circ}\)) & \(\boldsymbol{\gamma}\) (\({}^{\circ}\)) & \(\boldsymbol{\psi}\) (\({}^{\circ}\)) \\ \hline
1.5 & CMF\_ROOT & 8328.10 & 38.26 & 2.84 & 3.50 & \(-\)8.00 & 25.90 \\ MW & CME\_ROOT & 2801.07 & 38.26 & 2.84 & 37.53 & \(-\)8.00 & 52.73 \\ \hline
2.0 & CMF\_ROOT & 17877.90 & 38.87 & 2.45 & 7.20 & \(-\)8.00 & 16.34 \\ MW & CME\_ROOT & 4352.48 & 38.87 & 2.45 & 90.00 & \(-\)8.00 & 339.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Extreme bending moments and their corresponding status parameters without considering the constraint conditions about the \(\beta_{m}\).  
Figure 9: Iterative processes of variables: (**a**) Extreme CMF_ROOT calculation; (**b**) Extreme CMF_ROOT calculation for the 2.0 MW wind turbine blade.  
has the main influence on blade design. Maybe these loads can be used for evaluating the maximum blade deflection, which is a very important constraint for blade design. Moreover, from Table 2, we can see the extreme bending moments in edgewise happen at the maximum rotor speed. However, it is not easily determined due to \(\Delta\Omega\). Maybe this is the reason why these loads cannot be accurately evaluated.
# Seismic Damage Analysis of Box Metro Tunnels Accounting for Aspect Ratio and Shear Failure
## 4 Results and Discussion  
### Failure Mechanism of Turnels with B/H = 1.0  
In this section, we investigated the failure mechanisms of box tunnels due to flexure and shear yielding. For single box tunnels, a shear plastic hinge was shown not to develop, even after flexural plastic hinges were formed at all four corners. It was primarily due to the support from the surrounding soil medium. For double and triple box tunnels, both flexural and shear plastic hinges were observed. Figure 5 depicts the plastic hinge formation sequences of double box tunnels with \(V_{s}=50\) m/s and \(V_{s}=400\) m/s for soil. The dots in pink indicate locations of flexural plastic hinges and orange dots represent shear failure (loss of the shear capacity). It can be observed that shear plastic hinges started occurring in the double box tunnels at approximately 3% free-field shear strain with respect to \(V_{s}=50\) m/s (i.e., soft soil) and at about 0.7% shear strain with \(V_{s}=400\) m/s (i.e., stiff soil). It should be noted that the shear plastic hinges only form in the interior column after flexural plastic hinges form at all four corners of the outer frame. Moreover, the shear plastic hinges form throughout the interior column element almost simultaneously. Since the concrete member loses its axial load bearing capacity when it is severely damaged in shear, a structural collapse of the box tunnel was shown to occur. This collapse mechanism of reinforced concrete box structure is in line with those observed in previous studies [45; 46; 47; 48].  
Figure 6 shows the formation and development of plastic hinges for triple box tunnels with \(V_{s}=50\) m/s and \(V_{s}=400\) m/s for soil. Again, it was observed that the shear plastic hinges in the interior columns form after flexural plastic hinges form at all four corners of the outer frame. The shear failure occurred in the inner column of the triple box tunnel at about 4% free-field shear strain for \(V_{s}=50\) m/s and 0.75% for \(V_{s}=400\) m/s. Even after the shear plastic hinges formed in the interior columns, however, a complete collapse state was not observed for the triple box tunnel. This might be due to the presence of two interior columns, which enhance the load bearing capacity and structural redundancy. The internal forces were redistributed right after the occurrence of interior columns yield in shear. As the lateral load increased, flexural plastic hinges at the corners continued to spread, while shear hinges at the interior columns maintained their shear strengths. It is disparate to what is observed in the double box tunnel. It was also reported in [49] that an increase in the number of  
Figure 4: Comparison of racking ratio and flexibility ratio relationships.  
interior columns (number of bays) can produce additional structural redundancy of the reinforced concrete frame structures.