esa-sceva/satcom-chunk-collection huggingface dataset:
# Survey of Blockchain-Based Applications for IoT
## 3. Scope of This Survey  
This survey categorized blockchain applications in the IoT. As blockchain technology revolutionizes the way devices communicate, automate tasks, exchange messages, and store data, it touches a wide range of products and applications. To help control the subject, the applications covered in this survey are divided into eight areas covered in Sections 4-11 of this survey:  
Section 4--Identity and Access Control  
Section 5--Data Protection and Privacy  
Section 6--Data Integrity and Trust  
Section 7--Smart Contracts and Automation  
Section 8--Blockchain Scalability and Efficiency  
Section 9--Economic and Incentive Models  
Section 10--Decentralized AI and the IoT  
Section 11--Digital Assets and NFTs in the IoT  
Recent surveys on blockchain applications in the IoT have concentrated on specific aspects of the eight areas listed above. In contrast, this survey aimed to examine these areas thoroughly. Table 3 below compares this survey to similar surveys conducted over the past few years in a selected 12 subjects. In this comparison, we took into consideration the depth of discussion of the subject and the main components covered in these surveys.  
This survey paper is structured into 13 main sections, as illustrated in Figure 4. Section 1 introduces the topic, providing an overview of the paper's objectives. Section 2 presents background information on IoT and blockchain technologies, establishing a foundation for discussion. Section 3 defines the scope of this survey and compares it with existing surveys. Sections 5-11 explore various blockchain applications in the IoT, while Section 12 examines industry-specific use cases. Section 13 identifies key challenges in integrating blockchain technology with the IoT and proposes potential solutions. Finally, Section 14 concludes the paper, summarizing key findings and future research directions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **Permissionless Blockchain** & **Permissioned Blockchain** & **Consortium Blockchain** \\ \hline Access Control & Open to anyone & Restricted to specific & Restricted to a group of \\  & & participants & organizations \\ \hline Participants & Anonymous or & Known and authenticated & Known and authenticated \\ \hline Consensus & Public mechanisms like & Private mechanisms like & \\  & PoW or PoS & PBFT & Private mechanisms, agreed upon \\ \hline Transparency & Fully transparent & Limited to authorized & \\  & & users & Limited to consortium members \\ \hline Speed & Slower due to a large & High speed due to fewer & High speed due to controlled \\  & number of nodes & nodes & participation \\ \hline Scalability & Challenging to scale & Easier to scale & Easier to scale compared to \\  & & & permissionless \\ \hline Use Cases & Cryptocurrencies, open & & \\  & networks & Supply chain management & Banking consortia, trade finance \\ \hline \hline \end{tabular}
\end{table}
Table 2: Blockchain network types.
## 2 Algorithms in the Classification System
### Classification Algorithm of SVM  
The principle of support vector machine (SVM) to classify samples is to obtain a hyperplane that can separate different kinds of samples in a high-dimensional space where the input space has been mapped [41]. The expression of the hyperplane is  
\[w^{T}X+b=0, \tag{7}\]  
where \(X\) represents the training set including \(N\) samples \(x_{i}\)\((i=1,\ldots,N)\) and the corresponding class label \(y_{i}\in\{-1,1\}\) so \(X=\{(x_{i},y_{i})|i=1,\ldots,N\}\); \(b\) is the bias term; and \(w\) is a vector of tunable weights.  
The closest sample to the hyperplane on each side is called support vector (SV). The sum of the distance between the SVs on both sides and the classification hyperplane is the classification interval. To ensure the best classification performance, the classification interval must be the largest. The one-sided distance is approximately \(\frac{1}{\|w\|}\), then for linear separable samples, the optimization problem in SVM is expressed as follows [41]:  
\[\begin{split}\min_{w,b}\frac{1}{2}\|w\|^{2}\\ s.t.y_{i}\Big{(}w^{T}x_{i}+b\Big{)}-1\geq 0,\ i=1,\ldots,N \end{split} \tag{8}\]  
But for nonlinear separable samples, the above constraints cannot be met, which results in no feasible solution to the optimization problem. Therefore, a nonnegative relaxation variable \(\zeta_{i}\) is introduced, and the corresponding penalty term is added to the original objective function. Then, the optimization problem turns to be [42]  
\[\begin{split}\min_{w,b,\zeta}\frac{1}{2}\|w\|^{2}+C\sum\limits_{i =1}^{N}\zeta_{i}\\ s.t.y_{i}\Big{(}w^{T}x_{i}+b\Big{)}\geq 1-\zeta_{i},\ i=1,\ldots,N \\ \zeta_{i}\geq 0,\ i=1,\ldots,N\end{split} \tag{9}\]  
where \(C\) represents penalty variable.  
By introducing the high-dimensional space mapping kernel function \(K(x,y)\) and adopting Lagrange multiplier method, the optimization problem is turned into a convex quadratic programming problem to solve (\(\alpha\) is a set of Lagrange multipliers) [42]:  
\[\begin{split}\min_{\alpha_{i}}\Psi(\alpha_{i})&= \frac{1}{2}\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}y_{i}y_{j}K\Big{(}x_{i},x_ {j}\Big{)}\alpha_{i}\alpha_{j}-\sum\limits_{i=1}^{N}\alpha_{i}\\ s.t.0&\leq\alpha_{i}\leq C,\ i=1,\ldots,N\\ &\sum\limits_{i=1}^{N}y_{i}\alpha_{i}=0\end{split} \tag{10}\]  
Solve the problem above to obtain the optimal solution of \(w,b\) (shown as \(w^{*},b^{*}\)) and the optimal classification hyperplane \(w^{T}X+b^{*}=0\). Finally, the training of SVM is completed and the samples can be recognized through the model.
# Lightweight Deepfake Detection Based on Multi-Feature Fusion
###### Abstract  
Deepfake technology utilizes deep learning (DL)-based face manipulation techniques to seamlessly replace faces in videos, creating highly realistic but artificially generated content. Although this technology has beneficial applications in media and entertainment, misuse of its capabilities may lead to serious risks, including identity theft, cyberbullying, and false information. The integration of DL with visual cognition has resulted in important technological improvements, particularly in addressing privacy risks caused by artificially generated \"deepfake\" images on digital media platforms. In this study, we propose an efficient and lightweight method for detecting deepfake images and videos, making it suitable for devices with limited computational resources. In order to reduce the computational burden usually associated with DL models, our method integrates machine learning classifiers in combination with keyframing approaches and texture analysis. Moreover, the features extracted with a histogram of oriented gradients (HOG), local binary pattern (LBP), and KAZE bands were integrated to evaluate using random forest, extreme gradient boosting, extra trees, and support vector classifier algorithms. Our findings show a feature-level fusion of HOG, LBP, and KAZE features improves accuracy to 92% and 96% on FaceForensics++ and Celeb-DF(v2), respectively.  
deepfake detection; feature fusion; Histogram of Oriented Gradients (HOG); Local Binary Pattern (LBP); KAZE descriptors +
Footnote †: journal: Applied sciences
## 5 Case Study
### PM from Different Perspectives
#### 5.4.3 Business Process Comparison Considering Complaint Causes  
In terms of case duration, the median case duration of \"not in time\" cause CHS is 29 hours and the mean case duration is 56.6 hours; the median case duration of \"other cause\" CHS is 33.5 hours and the mean case duration is 70.9 hours. In general, both of the two types of CHS cases are similar to the overall level of case duration. The case length of \"not in time\" cause CHS (8 events) and \"other cause\" CHS (nine events) are similar to the overall level as well.  
In terms of case variants, of the 2276 \"not in time\" cause CHS cases, 1068 types of case variants are involved; of the 1710 \"other cause\" CHS cases, 958 types of case variants are involved. For \"not in time\" cause complaint, RC-1 \(\rightarrow\) FCHRCH \(\rightarrow\) FCHRCEB \(\rightarrow\) FCHRCEB \(\rightarrow\) TBC (20/cases) is the most frequent case variant; for \"other cause\" complaint, RC-1 \(\rightarrow\) IR (165 cases) is the most frequent case variant. From the perspective of most frequent case variants, the complaint causes of \"not in time\" and \"other cause\" are significantly different.  
Figure 11 presents the mining results of different complaint causes by Disco 2.2.1 [36]. For both two types of CHS cases, the most frequent activity is FCHRCEB. As far as the differences between the two are concerned, ACL-2 follows FCHRCEB in \"not in time\" cause CHS cases and ACL-2 follows FCHRCEB or FCHRCH in \"other cause\" CHS cases.  
The business process comparison results of \"not in time\" cause complaint and \"other cause\" complaint are as presented in Figure 12[39]. The control-flow difference of the \"not in time\" cause complaint and \"other cause\" complaint is 44.44%. The activity of FCHRCH in blue oval show that it is executed more frequently in \"not in time\" cause CHS (89.72%) (group A) than in \"other cause\" CHS (80.12%) (group B), and the activities of TBC and FCHRCEB share the similar characteristics. In terms of the handover of different activities, RC-1 \(\rightarrow\) FCHRCH, FCHRCH \(\rightarrow\) FCHRCEB and FCHRCEB \(\rightarrow\) TBC are more frequently in group A compared with group B. FCHRCEB \(\rightarrow\) FCHRCH and TBC \(\rightarrow\) FCHRCH are less frequently in group A compared with group B. In terms of the activity handover efficiency for TBC \(\rightarrow\) FCHRCH, the performance of group A is much poorer than group B.
## 3 Types of Supplementary Dampers and Their Characteristics  
### Viscous Dampers  
#### 3.1.1 Operating Principle  
Viscous dampers are extensively utilized as supplementary damping devices in civil engineering due to their effectiveness and dependability. These devices function as passive energy dissipation systems that leverage the principles of fluid viscosity to absorb seismic energy and mitigate structural vibrations [41,42]. Typically, they comprise a piston that moves within a cylinder filled with a viscous fluid. During seismic activity, the movement of the piston forces the fluid through orifices, thereby converting kinetic energy into thermal energy. This process significantly diminishes the dynamic response of the structure, resulting in reduced displacements, accelerations, and internal forces [43,44].  
A notable benefit of viscous dampers is their velocity-dependent characteristics, which enable them to deliver optimal damping forces at critical junctures during seismic events [45,46]. They are particularly adept at managing both low- and high-frequency vibrations, rendering them suitable for a variety of structural applications. Furthermore, viscous dampers can be seamlessly integrated into diverse structural systems, such as moment frames and braced frames, thus providing engineers with considerable flexibility in their implementation [47,48,49].  
Recent technological advancements in viscous dampers have facilitated the emergence of adaptive and semi-active systems. These cutting-edge dampers possess the capability to modify their properties in real-time, responding to structural behavior or external control signals, which enhances their performance across a broader spectrum of seismic conditions [50]. Moreover, ongoing research into high-performance fluids and optimized damper designs continues to enhance the energy dissipation capacity and reliability of viscous dampers, thereby broadening their applicability in seismic design.
## 3 Proposed 3-D Sound Image Reproduction Based on Spherical Harmonic Expansion  
In this section, we propose a novel 3-D sound reproduction method for 22.2 multichannel audio based on SH expansion [13]. The proposed method controls the direction and distance of a 3-D sound image by controlling sound directivity on the basis of mode matching [14]. Mode matching generates weighting factors that match the reproduced directivity pattern with the target directivity pattern. In addition, the mode strength used in the mode matching includes the distance of the 3-D sound image. Here, in the spatial sound field, the directivity pattern consists of various types of basic directivity, such as monopole and dipole. On the basis of this fact, SH expansion [15] analyzes the strength of each directivity pattern like a Fourier series expansion. Examples of Fourier series expansion and SH expansion are shown in Figure 4.  
The method proposed in [2] has achieved interactive directivity control outside the dodecahedron loudspeaker array using SH expansion, as shown in Figure 5a. The proposed method reproduces the 3-D sound image inside of the 22.2 loudspeakers, as shown in Figure 5b. The proposed method generates a directivity pattern including a peak at the target sound image position. Furthermore, it calculates weighting factors by mode matching in the SH domain. Hence, we can control the amplitude by transforming the obtained weighting factors with consideration of the distance of the 3-D sound image. Here, more than three loudspeakers except for the LFEs are used to reproduce the 3-D sound image. Sections 3.1 and 3.2 explain SH expansion and the proposed method based on it, respectively.
## 4 Proposed Methodology
### Solution Method  
The way of determining the optimal combination of AGC units and DR reserve power for maximizing the wind accommodation is presented as the following flowchart (see Figure 3). First, the wind power and load curve are forecasted and the output power of thermal units is scheduled using the direct current optimal power flow (DCOPF) method in Matpower [30]. Based on the wind model described in Section 2, the wind power fluctuation \(\Delta P_{w}\) is calculated. Considering the operating characteristics of smart appliances, including ACs, WHs, and EVs, it is obvious that their working status are the accumulated results of previous operation, and there is a load fluctuation \(\Delta P_{l}\) calculated in Equation (22). The wind and load fluctuations result in the system imbalance, and the windaccommodation is maximized with the combination function of the supply-side and demand-side considering the constraints in Section 4.1.  
When the AGC units regulating capability and DRP are not large enough to smooth the power imbalance, there would be wind energy curtailment with a negative fluctuation or load shedding with a positive fluctuation. In the condition of positive fluctuation, the comfort range is expanded to increase the DRP within the consumers' acceptable limits.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Stance Phase Sensor Activation**} & **TFA Groups** & **Healthy Groups** & **Effect Size** \\  & **n = 21** & **n = 24** & **\(p\)** & **Cohen’s d** \\ \hline
1 sensor activation \% & 80.7 \(\pm\) 10.7 & 40.2 \(\pm\) 22.2 & 0.001 \({}^{*}\) & 2.32 \\ \hline
2 sensor activation \% & 71.7 \(\pm\) 15.5 & 60 \(\pm\) 21.2 & 0.043 \({}^{*}\) & 0.63 \\ \hline
3 sensor activation \% & 80.8 \(\pm\) 10.2 & 54.2 \(\pm\) 25.5 & 0.001 \({}^{*}\) & 1.36 \\ \hline
4 sensor activation \% & 86 \(\pm\) 10 & 62.1 \(\pm\) 28.9 & 0.001 \({}^{*}\) & 1.105 \\ \hline
5 sensor activation \% & 77 \(\pm\) 25.3 & 69 \(\pm\) 22.9 & 0.273 & 0.33 \\ \hline
6 sensor activation \% & 61.4 \(\pm\) 16.7 & 41 \(\pm\) 23.9 & 0.002 \({}^{*}\) & 0.98 \\ \hline
7 sensor activation \% & 53 \(\pm\) 17.2 & 62.6 \(\pm\) 14.7 & 0.040 \({}^{*}\) & 0.6 \\ \hline
8 sensor activation \% & 47 \(\pm\) 16.7 & 67 \(\pm\) 10.8 & 0.001 \({}^{*}\) & 1.4 \\ \hline \hline \end{tabular} \({}^{*}\)\(p<\) 0.05, Sensor activation percentage significant differences between the TFA subjects and the HS.  
\end{table} 
Table 2: Demonstration of the sensor activation percentage of the eight sensors with the TFA sound limb and the healthy subjects in the stance phase gait cycle (mean \(\pm\) standard deviation). \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Age (Years Old)**} & **Body Height** & **Body Weight** & **BMI** \\  & **(cm)** & **(kg)** & **BMI** \\ \hline TFA (n = 21) & 29.7 \(\pm\) 5 & 169.5 \(\pm\) 6.6 & 73.8 \(\pm\) 7.5 & 25.6 \(\pm\) 1.2 \\ \hline Healthy subjects (n = 24) & 21.2 \(\pm\) 1.7 & 166.1 \(\pm\) 6.8 & 67.4 \(\pm\) 8.1 & 24.3 \(\pm\) 1.6 \\ \hline \hline \(p\) & 0.001 \({}^{*}\) & 0.09 & 0.009 \({}^{*}\) & 0.006 \({}^{*}\) \\ \hline BMI: body mass index = body mass/body height\({}^{2}\). \({}^{*}\)\(p<\) 0.05, significant differences between the TFA subjects and the HS. and 8. The maximum plantar pressure values were higher in the TFA group, especially in the anterior part of the foot. According to the evaluation of the underfoot pressure of the sound leg with the use of a TF prosthesis, the maximum pressure point shifted to the forefoot, and the forefoot and midfoot were more active during walking than in the control group. The rear foot, pressure distribution, and usage percentage were less than those of the control group. Because of the independent-sample \(t\)-test, a significant difference was found between the groups, with medium and large effect values.
## 1 Introduction  
The enhanced Loran system evolved from the Loran-C (long-range navigation) system. It is an internationally standardized positioning, navigation and timing (pnt) service system. It is the latest development achievement of low-frequency long-range radio navigation timing technology. It meets the needs of PNT services in aviation non-precision approach, port approach, land vehicle navigation and location service in terms of PNT service accuracy, reliability, effectiveness, perfection and continuity. Additionally, it can provide high-precision time and frequency signals in the order of hundreds of nanoseconds. The eloran transmitting antenna is an important part of the system; its electrical performance and coverage ability are very important [1; 2; 3].  
Loran transmitting antennas are a mature technology has that has benefitted from many years of research and development. Typically, they comprise single-tower umbrella antennas or four-tower inverted cone antennas [4; 5].  
China plans to build three enhanced Loran launchers in the western region. Its purpose is to cover most parts of China with Loran time service signal, which will be used for time service and navigation in the future. At present, the performance of the Loran-C system in all aspects of operation is relatively backward. Therefore, the three new transmitting stations will choose the Loran transmitting antenna with higher performance. In particular, the Tibet Naqu transmitting station, one of the three largest transmitting stations, will realize the construction of Loran transmitting antennas for the first time in regions above 4500 m above sea level in the world. Referring to the past design and construction experience of Loran transmitting antennas, it is difficult to meet the current needs. Therefore, the design and selection of the transmitting antenna will face new challenges. The antenna not only needs to cope with the complex geographical characteristics of Tibet, but also needs to meet the requirements of the transmitter and improve the signal radiation capacity as much as possible, which will put forward higher requirements for the structural and electrical performance design of the transmitting antenna. Its main purpose is to carry the signal transmitted by a 2500 kW high-power transmitter and radiate it efficiently. The radiation efficiency is more than 70%. It is planned to cover most of the land area in Western China. The main performance requirements are as follows: (1) the working center frequency is 100 kHz; (2) The bandwidth exceeds 3.5 kHz; (3) The effective height exceeds 180 m; (4) The characteristic impedance is 200-300 \(\Omega\)[6, 7].
# On the Study of Influences of Different Factors on the Rapid Topospheric Tomography
## 5 Tomography Experiments and Results
### Influences of Ground Meteorological Observation Data
The introduction of ground meteorological data has strong constraints at the station location, which therefore mainly improves estimates at the bottom layers.
# Design of a Noise Mitigation System Using Lightweight Graded Micro-Porous Material
## 3 Design, Fabrication, and Validation of Graded Micro-Porous Material System
### Prediction of Acoustic Responses in MATELYS AlphaCell  
The flow properties calculated and provided in Table 5 were used in MATELYS AlphaCell to make acoustic predictions for the two chosen combinations of graded \(\mu\)PHB composite material. As previously indicated, MATELYS AlphaCell is by default built with the transfer matrix method essential for making graded material predictions.  
* Absorption Coefficient Response  
Figure 14 shows the predictions of absorption coefficient versus frequency for the two selected graded \(\mu\)PHB composite material systems of a total thickness of 50 mm (typically used in aircraft acoustic liners). Both combinations show absorption coefficients \(\geq\)0.50 beyond a 600 Hz frequency. Also, the response predictions are shown for three different particle-size groups of \(\mu\)PHB composite material, and it is noticed that the response of the graded material systems lies between the two limits of the large and medium particle-size \(\mu\)PHB composite materials between 250 to 800 Hz; beyond 800 Hz, the two selected graded combinations show a higher sound absorption.  
Figure 14: Predicted absorption coefficient versus frequency for graded material design.  
Figure 13: Processed graded \(\mu\)PHB composite specimens of two combinations.  
* Transmission Loss Response  
The transmission loss versus frequency response was predicted using the same flow properties listed in Table 5 for the two different graded combinations of the material system, and the results are shown in Figure 15. From this result, we see that the graded uPHB composite material system responses lie between the two limits of medium and large particle-size uPHB composite materials.  
The two selected combinations, L50, M25, S25, and L37.5, M37.5, S25, demonstrated an absorption coefficient \(\geq\)0.50 and transmission loss \(\geq\)20 dB over the broad frequency range through predictions using MATLAB's AlphaCell.
## 3 Materials and Methods
### Data Description
#### 3.2.3 Force Loading  
AE data and DIC data represent two different modalities, and there are no common features between them. The two types of modal data used in this study were acquired under the same experimental conditions, which ensures a correlation between the data exists, namely, force loading. Force loading refers to the process of applying a force to a material or structure. With the variation of the force loading, the features of the AE data and DIC data also change. Therefore, in this study, force loading is used to align the features of the AE data and DIC data for the subsequent fusion analysis.