esa-sceva/satcom-chunk-collection huggingface dataset:
# Biomass Fly Ash Self-Hardened Adsorbent Monoliths for Methylene Blue Removal from Aqueous Solutions
###### Abstract  
The use of methylene blue (MB) by several industries generates contaminated industrial wastewaters that must be purified before discharge into the environment. Its removal can be achieved by adsorption, and low-cost and easily available materials should be used as adsorbents. Biomass fly ash (BFA) generated from biomass combustion, for heat and power generation, is increasing worldwide since the process is considered CO\({}_{2}\) neutral. However, most of the ash is still landfilled. This study aims to evaluate the valorisation of BFA as a low-cost porous bulk adsorbent for MB removal from wastewaters. The monoliths were obtained after 14 days of curing just after adding water and a porogenic agent (aluminium powder) to the BFA, using the self-hardening ability of this waste. The BFA was characterised for chemical (XRF) and mineralogical (XRD) composition, particle size distribution (laser diffraction-COULTER) and morphology (SEM). The monolith sample cured for 14 days was characterised for density, porosity (total and open), microstructure, compressive strength, and MB removal ability (batch tests). The results showed that the addition of aluminium powder (0.09 wt.%) promoted an increase in interconnected porosity and the MB removal efficiency reached 80% for the most porous samples. The equilibrium data for the adsorption process were well characterised by a type 2 Langmuir isotherm equation with a monolayer adsorption capacity (qmax) that ranged from 0.22 to 0.66 mg/g.  
biomass fly ash; self-hardening; green processing; bulk porous adsorbents; dye removal; micropollutants; sorption +
Footnote †: journal: Applied Sciences
# Authentication Based on Non-Interactive Zero-Knowledge Proofs for the Internet of Things
## Security Proofs
### Man in the Middle Attack  
An MitM attack happens when an attacker secretly relays and possibly alters the communication between two parties who believe they are directly communicating with each other. This is one of the most used attack schemes in wireless networks.  
In the case of the proposal, an MitM attack cannot be carried out because there is no way to gain information during the transaction. In particular, the scheme uses a single beacon to send some information from the sender to the receiver. Therefore, there is no communication between them, so that other users cannot intercept communication and, hence, cannot impersonate communications.  
If the scheme is used to establish a secret key through the Diffie-Hellman algorithm, thanks to the fact that the protocol uses strong mutual authentication with secret keys, it is robust against an MitM attack. Mutual authentication refers to two parties authenticating each other at the same time. Consequently, an MitM attack against the proposed scheme would not succeed. The attacker could intercept beacons, but without access to secret parameters of the scheme, it cannot get any confidential information. The configuration parameters of the proposed scheme are only accessible for the legitimate users of the network and are provided by a trusted third party during the initialization of the scheme.
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AOR & Accelerated over-relaxation \\ AWGN & Additive white Gaussian noise \\ BER & Bit error rate \\ BS & Base station \\ CAOR & Chebyshev-accelerated over-relaxation \\ CMAs & Complex multiplications and additions \\ CP & Cyclic prefix \\ CSI & Channel state information \\ i.i.d. & Independent and identically distributed \\ IoT & Internet of Things \\ ISI & Inter-symbol interference \\ LS & Least square \\ M-MIMO & Massive multiple-input multiple-output \\ MIMO & Multiple-input multiple-output \\ MMSE & Minimum mean-squared error \\ MU & Multiuser \\ MUD & Multiuser detector \\ OFDM & Orthogonal frequency-division multiplexing \\ OFDMA & Orthogonal frequency-division multiple access \\ QAM & Quadrature amplitude modulation \\ SOR & Successive over-relaxation \\ SSOR & Symmetric successive over-relaxation \\ ZF & Zero-forcing \\ \end{tabular}
# DFT-Low-Latency Bit-Slice Design of Discrete Fourier Transform
## 4 Functional Blocks of the Proposed System
### The Left-Shift Block  
The aim of this block is multiplication by a power of two. It is implemented by a left shift. Therefore, the multiplication for the term \(2^{l}\) can be performed by a shift to the left by \(l\) positions. It is evident that \((b-1)\) different blocks are necessary because \((b-1)\) are the multiplications to be carried out, as given by expression (7).
###### Abstract  
The present paper deals with an effort to model impact damage in 3D-FE simulation. In this work, we studied the scattering behavior of an incident A\({}_{0}\) guided wave mode propagating towards an impacted damaged zone created within a quasi-isotropic composite plate. Besides, barely visible impact damage of the desired energy was created and imaged using ultrasonic bulk waves in order to measure the size of the damage. The 3D-FE frequency domain model is then used to simulate the scattering of an incident guided wave at a frequency below an A\({}_{1}\) cut-off with a wavelength comparable to the size of the damaged zone. The damage inside the plate is modeled as a conical-shaped geometry with decayed elastic stiffness properties. The model was first validated by comparing the directivity of the scattered fields for the A\({}_{0}\) Lamb mode predicted numerically with the experimental measurements. The modeling of the impact zone with conical-shape geometry showed that the scattering directivity of the displacement field depends significantly on the size (depth and width) of the conical damage created during the point-impact of the composite with potential applications allowing the determination of the geometric characteristics of the impacted areas.  
guided waves; CFRC composite plate; impact point damage; 3D-FE modeling +
Footnote †: journal: Applied Sciences
# JARUA: Joint Embedding of Attributes and Relations for User Alignment across Social Networks
## 5 Experiments
### Experimental Results  
#### 5.3.1 Comparative Performances  
The methods in our experiment can be divided into three categories. The first group is purely based on network structure, including IONE, ABNE, PALE, and JARUA-s. The second group is based on user attributes. Here, we have only JARUA-a. The third group integrates attributes and network structure, including REGAL, TADW, CENALP, Grad-Align, Grad-Align+, JARUA-v, JARUA-t, and JARUA. Table 3 summarizes the overall experimental performances.  
All four methods in the first group perform better on DBLP than on WD. The reason for this is that the WD structure information is highly noisy and incomplete, and thus cannot provide effective information for recognizing users. For example, INOE enjoys higher Hit-Precision (k = 3) by 33.4% and ABNE by 33.8%. The variant model JARUA-s beat the other three models by at least 11.2% on DBLP and 8.4% on WD, demonstrating the superiority of JARUA in capturing the characteristics of the network structure.  
JARUA-a in the second group achieves Hit-Precision scores of 0.401 and 0.723 on WD and DBLP, respectively. The latter is significantly higher than the former. This result is because the latter's attributes are much richer and cleaner, while the attributes on WD, especially users' blogs and posts, fluctuate greatly in the content, number of posts, and publishing frequency. Besides, it can be observed that JARUA-a performs significantly better than the structure-based models. Its score beats other baselines in the first group by at least 29.8% and 27.2% on WD and DBLP, respectively. This is mainly the result of the plentiful attribute information of the data sets.  
In the third group, the methods can incorporate both attribute and structure information for UA. JARUA-v performs considerably better than the other five comparison methods, e.g., 10.2% higher than Grad-Align on WD and 33.1% higher than REGAL on DBLP. This is attributed to the differentiation and utilization of multi-type user attributes. Although the others can make use of attributes, they do not distinguish different attribute types. Username plays the strongest role among all attributes on WD, while other attributes (location and posts) are fairly diverse and noisy. Thus, subword-level attributes dominate the model performance.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 2 Related Work
The obtained clean samples and the defense distillation algorithm were used to train the residual network to obtain the spectrum sensing model. However, the robustness and generalization of this model to attack perturbations are poor and algorithm performance relies on a large number of labeled samples. To improve the robustness of the defense model for the attack perturbations and reduce the dependence of the model training on labeled samples, we propose the unsupervised spectrum sensing algorithm with defending adversarial attacks (USDAA).The USDAA algorithm uses less labeled samples, and the algorithm has a better defense effect against PGD and FGSM white box \(PUAA\) than the DAED algorithm [12]. In addition, the robustness of the USDAA algorithm is better than that of the DAED algorithm.
# A Game Theory-Based Model for the Dissemination of Privacy Information in Online Social Networks
## 3 A Game-Based Model for Privacy Information Dissemination
### Validation of the Model
The following conclusions can be drawn after comparison to the real data: the higher the intimacy between the initial communication node and the privacy subject, the more likely the receiver will protect the privacy and block the spread of privacy information. In contrast, the lower the intimacy, the easier it is to spread the privacy information. Users tend to disseminate the privacy information of users with high popularity. The scope of privacy information dissemination of high popularity users is much larger than that of low popularity users. The higher the popularity, the faster the information dissemination, and it can be spread throughout the whole only after a few rounds of forwarding net. The smaller the Nash equilibrium probability that the receiver likes, the greater the probability that the receiver likes it, the greater the possibility that the sender chooses to send, and the wider the coverage of information.
## 2 System and Channel Model
### Combined Model for Turbulence and Pointing Errors  
In view of the above, the combined PDF for the normalized irradiance, \(I\), is estimated through the following integral:  
\[f_{I}(I)=\int f_{I|I_{a}}(I\big{|}I_{a})f_{I_{a}}(I_{a})\,dI_{a}\,, \tag{11}\]  
where \(f_{I|I_{a}}(I\big{|}I_{a})\) stands for the PDF of \(I\) conditioned on \(I_{a}\). This is straightforwardly obtained from Equation (9) via a procedure discussed by [PERSON] et al. in [17], which yields:  
\[f_{I|I_{a}}(I|I_{a})=\frac{1}{I_{a}}f_{I_{p}}\left(\frac{I}{I_{p}}\right)= \frac{q^{2}}{\left(A_{0}\,g\right)^{q^{2}}I_{a}}\left(\frac{I}{I_{a}}\right)^ {q^{2}-1},\,0\leq I\leq A_{0}\,g\,I_{a} \tag{12}\]Substituting then Equation (3) and Equation (12) into Equation (11), we obtain:  
\[f_{I}(I)=\frac{q^{2}A_{(\aleph\,\text{or}\,\Re)}}{\left(A_{0}g\right)^{q^{2}}}\,I^ {q-1}\sum\limits_{(\aleph\,\text{or}\,\Re)}a_{k(\aleph\,\text{or}\,\Re)}\int \limits_{1/A_{0}g}^{\infty}I_{a-k}^{\frac{q+k-2q^{2}-2}{2}}K_{a-k}(2\sqrt{B_{( \aleph\,\text{or}\,\Re)}I_{a}})\,dI_{a}. \tag{13}\]  
Next, by replacing the Bessel function by its Meijer-Gequivalent, which wasprovided by [PERSON] and [PERSON] in [PERSON] and Marichev (2010), and solving the integral in Equation (11) (using equations (07.34.21.0085.01) and (07.34.16.0001.01) of Wolfram Function site in [PERSON] and Marichev (2010)), we conclude that:  
\[f_{I}(I)=\frac{q^{2}A_{(\aleph\,\text{or}\,\Re)}B_{(\aleph\,\text{or}\,\Re)}}{2 A_{0}g}\sum\limits_{(\aleph\,\text{or}\,\Re)}a_{k(\aleph\,\text{or}\,\Re)}B_{( \aleph\,\text{or}\,\Re)}^{-\frac{q+k}{2}}G_{1,3}^{3,0}\left(\begin{array}{c }\frac{B_{(\aleph\,\text{or}\,\Re)}}{A_{0}g}\,I\,\left|\begin{array}{c}q^{2 }\\ q^{2}-1,\,a-1,\,k-1\end{array}\right.\right), \tag{14}\]  
where \(G_{p,q}^{m,n}(\cdot)\) symbolizes the Meijer-Gfunction (Equation (07.34.02.0001.01) of Wolfram Function site in Adamchik and Marichev (2010)).
# Optimization of Electric Field Assisted Mining Process Applied to Rare Earths in Soils
## 3 Results and Discussion
### Real Soil Electromining
#### 3.4.2 Y3+ Migrational Profile in the Reactor Cathodic Chamber  
Figure 7 shows the concentration profile of Y3+ in the reactor CC (distance from anode = 22.5 cm), in which an advance in the Y3+ migrational front through time can be observed. However, similarly to the EM-03 behavior, seen in Figure 3c, the EM-RS also presented a tendency to reduce the Y3+ concentration in the CC. This profile happens due to an increase in the species concentration gradient in the CC, favoring Y3+ diffusion from CC to the reactor bed. After the addition of fresh electrolyte (t = 140 h), a change in the Y3+ concentration profile in the CC was observed favoring the increase in species extraction.  
The migrational behavior of the Y3+ ion can be attribute to variation in electrochemical potential (\(\overline{\mu}_{i}\)), which is given by the species chemical potential (\(\mu_{i}\)) and electric potential (\(\mu^{*}\)) [40; 41], according to:  
\[\overline{\mu}_{i}=\mu_{i}+\mu^{*} \tag{8}\]  
being,  
\[\mu_{i}=\mu_{i}^{0}+\text{RT ln}C_{i} \tag{9}\]  
and  
\[\mu^{*}=z_{i}\Im\psi \tag{10}\]  
where, \(\mu_{i}^{0}\) is the standard chemical potential of the species i, R (8.314 J mol\({}^{-1}\) K\({}^{-1}\)) is the universal constant of gases, T is the temperature (K), and C\({}_{i}\) is the concentration of the i-th species (mol L\({}^{-1}\)). The i species charge is represented by z\({}_{i}\) (3+), \(\Im\) is the Faraday constant (96,485 C), and \(\psi\) denotes the applied electric potential (V) [41].  
Considering that the EM-RS was conducted by the potentiostatic method and admitting that the electric potential (Equation (10)) is constant during the experiment, the electrolyte replacement altered only the chemical potential of Y3+. The reduction of this variable corresponds to the obtention of a more diluted solution, favoring the mass transfer of the species from the soil to the electrolyte solution.  
Another consequence of the addition of fresh electrolyte was the reduction of the medium pH (Figure 8), increasing the acidity and contributing to the desorption of the species from the soil [36; 42]. Thus, after the desorption step, the ion is transferred to the electrolyte solution, which presents a higher conductivity and is more susceptible to the electric field action, and following, migrates towards the CC, promoting the extraction of Y3+ [43; 44].
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Placing Densely Distributed Acoustic Sensors on the Structure  
Bi [95] proposed a method that combines the analysis passive time reversal method (APTRM) with the equivalent sound source method (ESM) for locating sound sources in arbitrarily shaped shells. An experiment was conducted in a rectangular wooden cabinet using a spherical array of 48 microphones. This method can only locate sound sources with frequencies above 1000 Hz.
## 3 Datasets and Methods
### Methodology
#### 3.2.1 Feature Extraction
Keypoints describe regions of interest of an image where each keypoint is calculated by taking a neighborhood of pixels into consideration with specific intensities. For the last feature, binary robust invariant scalable keypoint (BRISK) [24] is chosen which returns these keypoints as a vector of size 64, representing the bins of an image histogram. When dealing with sign images, however, BRISK may compute keypoints catering to objects in the background of the sign user if the images have not been taken in a clutter-free environment. So, to retain the keypoints of only the hand region, BRISK is applied on the edge image as shown in Figure 5. Each of the concentric circles in Figure 5 represents a keypoint, and the radius of the circle indicates the span of pixels it considers. One thing to note while using these keypoints is the fact that their count can vary across images. Applying the CNN described in Table 4 on the BRISK image keeps the number of features for each image consistent. BRISK, similar to other keypoint algorithms, is invariant to scale and rotation, i.e., any kind of transformation that preserves lines and features across diverse geometric variations of the same image, and it is also computationally less expensive. Figure 4: Original image (**left**); masked image (**middle**); edge image (**right**). Figure 3: Similarity between sign images: ‘a’ (**left**) and ‘e’ (**right**).
## 5 Proposal Architecture and Its Deployment  
Our choice of MQTT for this study is based on its suitability for IoT applications requiring efficient, lightweight communication. MQTT's publish-subscribe model aligns well with many IoT communication patterns, and its low overhead is beneficial in resource-constrained environments often encountered in IoT deployments. However, we recognize that the optimal protocol choice can vary depending on specific application requirements and hardware constraints. In the context of this study, we focus on evaluating MQTT's performance specifically in Dark Net communications for IoT applications where privacy and anonymity are crucial considerations.  
Figure 2 provides a high-level overview of our proposed architecture, integrating MQTT with the TOR network for secure IoT communications. This diagram illustrates the flow of data from IoT devices through MQTT clients, the TOR network, and finally to the MQTT broker configured as a TOR hidden service. The following sections will elaborate on each component of this architecture, detailing our objectives, design considerations, implementation process, and the challenges we addressed to create a secure, efficient, and scalable solution for IoT communications in privacy-sensitive environments.
## 4 Conclusions  
Despite the inherent advantages of the Kalman filter, it has rarely been used for the inverse estimation of incident waves from motion sensors in the open literature. In the present paper, both real-time wave spectrum and elevation were obtained through the adaptive Kalman-filter algorithms.  
A problem of amplified noise occurs in the zone associated with the Kalman filter when vessel's motion RAO is small in the high frequency region and there is sensor error. A new solution to the problem is presented, so that the small gain in the RAO increases by a certain amount by using the Winer filter. If the proposed modified TF is applied, the overestimation of real-time wave spectra in the high-frequency region can be suppressed. The real-time incident wave profile can also be inversely estimated.  
Figure 34: (a) Evolution of parameters, (b) R (adaptive) and Q (fixed) during sea state change.  
Figure 35: Evolution of P during sea state change using Modified TF and Adaptive R.  
The vessel's motion transfer functions and numerically generated motion-sensor signals were used to the present Kalman-filter processes. The Kalman filter has been tested for various sea states and different vessel types while using the modified TF through Wiener filter. The test results were promising.  
In the forthcoming papers, the development is to be extended to multi-directional waves and the case with forward speed. The full-scale test with a real vessel in the real sea is also planned to further assess the developed Kalman-filter algorithms.  
All authors have equally contributed to publish this article related to the design of the target model, validation of the numerical model, parametric study, analysis, and writing.  
This research was a part of the project titled 'Real-time Sea State Estimates and Applications' funded by the Daewoo Shipbuilding & Marine Engineering (DSME).  
The authors declare no conflict of interest.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 4 Physical Modelling
### Other Approaches
For this reason, the model is called a free-field HRTF model. This approximation provides partially accurate results at only large wavelengths with respect to the typical size of the head. The free-field model is applied, for instance, in [139, 140]. Spherical heads are also considered in constructing simplified models. In fact, an HRTF can be exactly computed for a hard sphere (see [141, 142] for an analytical derivation of the scattering of a plane wave from a rigid sphere). Exact solutions can be useful benchmarks for validating experimental procedures. Simplified head shapes also enable simple applications of ray tracing approaches to ITD computation. When applied to a spherical head with antipodal ears, these approaches lead to the well-known Woodworth formula. Extended versions of this formula, together with an explanation of the original model of Woodworth, can be found in [143], where the formula is adapted to describe situations where the ears are located in non-antipodal positions and the direction of the incoming wave is such that more than a single path contributes to the ITD. In [144], the Woodworth formula is also applied to elliptical heads. All these ray tracing-based methods foresee frequency-independent ITD and work properly at higher wavelengths, where diffractive contributions are less significant. Other approaches are based on the idea that simple geometrical shapes can model parts of an individual's body like the head, torso, and pinnae. In [145], for instance, regular spheres, oblate and prolate spheroids, and elliptical discs are used to model the head, torso, and ears. One example of these simplified shapes is given by the snowman model [146], where the head and torso are modelled as a couple of spheres or ellipsoids.