esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 4 Experimental Setup
### Benchmark Methods
#### 4.5.4 Omp  
OMP is a channel-estimation algorithm in UWA due to its effectiveness in taking advantage of channel sparsity. UWA channels tend to possess a small number of strong multipath components, and sparse recovery methods like OMP are thus most appropriate. This approach offers higher quality estimation with less pilot overhead compared to traditional approaches like LS and is an appropriate baseline against which sparse channel-estimation performance can be measured.  
Traditional methods like LS suffer from high noise sensitivity and poor performance at low SNR due to the inability to exploit channel statistics. In contrast, MMSE improves accuracy through channel and noise statistics but at the cost of high computational complexity and perfect channel knowledge, which is difficult to obtain in dynamically changing underwater environments. Recently, neural networks including fully connected models, have performed more effectively by learning nonlinear channel behavior and even end-to-end OFDM receivers combining estimation, equalization, and demodulation. However, underwater channels create model mismatch, requiring ongoing retraining that prevents practical deployment. In addition, OMP, relying on channel sparsity, fails due to the diffuse multipath and Doppler effects prevalent underwater and is also computationally intensive. Therefore, the proposed TL-based OFDM receiver is presented as a solution, which enables models to efficiently learn to accommodate changing channel conditions at a minimal level of retraining. This approach addresses the channel mismatch issue and achieves improved robustness under dynamic underwater environments.
# Prediction of Remaining Fatigue Life of In-Service Bridge Cranes
## 4 Fatigue Residual Life Prediction of Bridge Crane
### Residual Life Prediction Equation for Constant-Amplitude Loading  
When the bridge crane's main girder structure is subjected to the load of the transverse load, the initial length of the crack \(a_{0}\) and the critical length of the crack \(a_{c}\) are known, and the integral of Equation (5) is carried out to obtain the fatigue crack extension life [20] of the crack from the expansion of the initial length \(a_{0}\) to the critical length \(a_{c}\) (the number of load cycles experienced N\({}_{a}\)). After determining the critical crack size \(a_{c}\), the crack extension life N\({}_{a}\) under equal amplitude loading can be calculated from Equation (6).  
\[\text{N}_{a}=\frac{(1-\text{R})\text{K}_{\text{c}}}{\text{C}\left(f\Delta \sigma\sqrt{\pi}\right)^{\text{m}}(0.5\text{m}-1)}\left(\frac{1}{a_{0}^{0.5 \text{m}-1}}-\frac{1}{a_{\text{c}}^{0.5\text{m}-1}}\right)-\frac{1}{\text{C} \left(f\Delta\sigma\sqrt{\pi}\right)^{\text{m}-1}(0.5\text{m}-1.5)}\left( \frac{1}{a_{0}^{0.5\text{m}-1.5}}-\frac{1}{a_{\text{c}}^{0.5\text{m}-1.5}}\right) \tag{6}\]  
where \(f\) is the correction factor (\(f\)=\(f^{\prime}\)\(\times\) K\({}_{\text{t}}\), where K\({}_{\text{t}}\) is the stress concentration factor. For general weld size, the stress concentration factor is about 1.191-2.073 [26]; here, take the average concentration factor K\({}_{\text{t}}\) = 1.5. By checking the table of correction coefficients, It can be found that \(f^{\prime}\) = 0.8, thus the stress intensity factor correction factor f = 0.8 \(\times\) 1.5 = 1.2 can be obtained); when dealing with fatigue assessment stresses, \(\Delta\sigma\) is used as the nominal stress, which is calculated globally.
# Jet Noise in Airframe Integration and Shielding
## 3 Options for Computation of Jet Noise
### Active Flow Control for Jet Noise Reduction  
In passive control, a certain permeant change in the design is implemented. This is distinguished from \"active\" control, which can be switched on or off as the need arises. While there are several technologies for passive control, the discussion here is focused on active control.  
In active noise control (ANC), a small disturbance (input) is introduced somewhere in the flow field via an actuator to modify the flow field and its radiation pattern. Modifying the jet flow via ANC has been extensively studied both numerically and experimentally for various applications.  
Figure 19 shows the effect of exciting the jet at a pair of Strouhal numbers on the spreading rate of the jet. The figure shows that excitation has a dramatic effect on increasing the spreading rate, which has technological applications in low observables. Thus, one approach for reducing jet noise is through manipulation of the flow mixing and turbulence generation. Further details can be found in the works by [PERSON] et al. [63; 64; 65; 66], [PERSON] et al. [67], [PERSON] and [PERSON] [68], and [PERSON] and [PERSON] [69].  
Large-eddy simulations have been used to demonstrate the effect of open-looped excitation on supersonic jet noise [70]. In Figure 20 from [PERSON] et al. [71], a supersonic jet at Mach number was actuated with different types of signals. Four cases are shown in the figure. In the top two figures, a single frequency mode (\(St=0.4\)) was used but at different levels of energy levels (0.04, 0.001). In the bottom left figure, bi-modal excitation at two frequencies of fundamental and subharmonic (\(St=0.4,~{}0.2\)) were imposed at the jet exit. At the bottom right figure, disturbances at random  
Figure 19: (**a**) Development of the momentum thickness for an excitation and unexcited case (**a**) computation (Ref. [65]) and (**b**) experimental data (Ref [66]).  
Figure 18: Very large-eddy simulations. The pressure oscillation at various Strouhal numbers (Ref. [58; 62]).  
frequencies were used. These numerical experiments give some guidance in the control process. For single frequency excitation, the amplitude matters, as demonstrated in the top two figures. It is also clear from the bottom left figure that bi-modal fundamental subharmonic excitation has a pronounced effect on the control process. This is believed to be due to the vortex-pairing mechanism. In the studies by [PERSON] [63], a theoretical analysis was given, which shows that if a jet is excited at a single frequency, its subharmonic could be considerably amplified. This has been demonstrated experimentally by [PERSON] and [PERSON] [68]. In fact, [PERSON] and [PERSON] [72] have experimentally demonstrated for a subsonic jet that if the jet is excited at a subharmonic of the peak frequency, the radiated noise is reduced. While this was for subsonic flow, it provides some guidance and evidence for the supersonic case.
## 2 Stress State of Inclined Pillar  
### Load Calculation of Inclined Pillar  
Before ore-body mining, we assume that the ore-body is in the in-situ stress field in which the vertical stress is \(\sigma_{v}\), the horizontal stress is \(\sigma_{h}\), and the dip angle of the ore-body is \(\theta\). In the mining of the inclined layered ore-body, the effect of both beam bending and stress arching is not considered. The compressive stress \(\sigma_{\theta}\) and shear stress \(\tau_{\theta}\) on the pillar are calculated by the tributary area method, as shown in Figure 2, where \(\sigma_{v}=\gamma H\), \(\sigma_{h}=k\gamma H\), \(\gamma\) is the weight of the overlying strata rock mass, \(H\) is the average buried depth of the ore-body, and \(k\) is the ratio of average horizontal stress to vertical stress, named the in-situ stress ratio. Then, compressive stress \(\sigma_{\theta}\) and shear stress \(\tau_{\theta}\) can be obtained.  
\[\left.\begin{aligned} \sigma_{\theta}&=\frac{\gamma H \big{(}k\sin^{2}\theta+\cos^{2}\theta\big{)}}{1-\eta}\\ \tau_{\theta}&=\frac{(1-k)\gamma H\sin\theta\cos \theta}{1-\eta}\\ \end{aligned}\right\} \tag{1}\]  
where \(\eta\) is the area extraction ratio, defined by (area mined)/(total area of ore-body) [8; 34].  
\[\eta=\frac{[(L+w_{0})(w+w_{0})-Lw]}{(L+w_{0})(w+w_{0})} \tag{2}\]  
where \(L\) represents the length of the pillar, \(w\) stands for the width of the pillar, and \(w_{0}\) represents the width of the room.
# Anti-Multipath Performance Improvement of an M-ary Position Phase Shift Keying Modulation System
## 3 The Scheme of Multipath Delay Estimation and Normalized Symbol Joint Decision
### Multipath Delay Estimation  
Generalized cross-correlation (GCC) is one of the conventional methods for finding the time differences but it requires a priori statistics of the received signals in order to obtain accurate delay estimates [21]. In practical applications, these priori knowledge are often difficult to obtain or incomplete, which will affect the actual performance of this methods. On the other hand, due to the possible relative motion between the transmitter and the receiver, the time delay will change over time. These two problems will cause the GCC method to estimate the delay inaccuracy.  
Compared to the above method, the least mean square time delay estimation(LMSTDE) method does not rely on statistical prior knowledge of the input signal and noise, and it can constantly adjust its parameters during the iterative process. In addition, the adaptive filter in the LMSTDE method  
Figure 5: Channel Model.  
is simple in design, low in computational complexity, and easy to implement. The block diagram of LMSTDE is shown in Figure 6 with \(\hat{a}\) fixed at unity [22].  
Where \(x(d)\) and \(y(d)\) are the two signals received by the receiver. The basic idea of LMSTDE method is to estimate the delay through an adaptive noncausal FIR filter in the form of \(W(z)=\sum\limits_{i=-p}^{p}w_{i}z^{-i}\). The delay value found with the least mean square error compared with the reference signal is the time delay estimate. The filter weight vector coefficients that are iterated according to the LMS algorithm are given by  
\[w_{i}(d+1)=w_{i}(d)+\mu_{w}e(d)x(d-i) \tag{4}\]  
where the error \(e(d)=y(d)-\sum\limits_{i=-p}^{p}w_{i}(d)x(d-i)\), \(\mu_{w}\) is a positive scalar to control the convergence rate and stability of the iteration. When the adaptive filter converges, a time delay estimate can be obtained from the weight vector peak coordinates of the adaptive filter. The delay \(\hat{D}_{w}(d)\) is given by [23]  
\[\hat{D}_{w}(d)=arg\max_{t}\{\sum\limits_{i=-p}^{p}w_{i}(d)sinc(t-i)\} \tag{5}\]  
where \(sinc(\cdot)=\frac{sin\pi(\cdot)}{\pi(\cdot)}\).
# Fiber Bragg Grating-Based Optical Signal Processing: Review and Survey
## 2 Review of Design Methods for Fiber Bragg Gratings
### Synthesis of Fiber Bragg Gratings from a Targeted Reflection Specifications  
The design of FBG generally starts from the specifications of the target reflective spectral response \(H_{R}(f)\) and pursue the generation of the refractive index modulation (Equation (1)) required to produce such response. This problem is generally known as the inverse scattering or grating synthesis problem [34].
## 5 Case Study of Combined Positioning in Tunnels  
The positioning performance in a real tunnel environment was verified by field tests in the Yudushan Tunnel in Beijing. The tunnel has a total length of 9.2 km, with open sections at the entrances and exits that can receive GNSS signals; however, the interior of the tunnel is a closed environment where GNSS data cannot be received. Vehicles travel at a speed of approximately 60 km/h inside the tunnel. The sensors used for the tests include GNSS RTK, INS, odometer, a laser radar for lateral distance measurements inside the tunnel, and lane map data. GNSS RTK differential data were gotten according to the virtual reference station data provided by Qianxun Location FindCM service.  
The tests involved driving from Beijing towards Zhangjiakou (designated as the forward direction), then returning in the opposite direction, making a total of two round trips with four passages through the tunnel. Three methods were used in each test: 1 GNSS/INS combination; 2 GNSS/INS/OD combination; 3 GNSS/INS/OD combination with lateral distance measurement and lane constraints.
# Comparative Study of Harmony Search Algorithm and its Applications in China, Japan and Korea
## 2 Harmony Search Algorithm  
This section describes HS as it is reported in the literature along with its inspired idea and operating searches. Inspired by the excellent properties of a harmony, the HS algorithm was conceived by [PERSON] and his colleagues in 2001 [1]. Musicians strive for a beautiful, aesthetically determined harmony. Similarly, optimization methods search for an optimal situation (global optimum) determined by an objective function value.  
The aesthetic calculation of HS relies on sounds produced by a musical community. A series of dynamic variables assess the objective function. Continual repetition may generate more aesthetic vibrations, and regular simulations will generally boost the efficiency of the task. The following steps are the main phases of the regular HS:  
1. Generate random vectors (\(x^{1}\), \(x^{2}\), \(x^{3}\),..., \(x^{HMS}\)), as many as Harmony Memory Size (\(HMS\)), and save them in the Harmony Memory (\(HM\)) matrix: \[HM=\left[\begin{array}{ccc|c}x^{1}_{1}&\cdots&x^{1}_{n}\\ \vdots&\ddots&\vdots\\ x^{lms}_{1}&\cdots&x^{lms}_{n}\end{array}\right|\begin{array}{c}f(x^{1})\\ \vdots\\ f(x^{lms})\end{array}\right]\] (1) As can be seen in Equation (1), the last column in HM is fitness (cost) value.
2. Generate new harmonies \(x^{\prime}\). Then, for every component \(x^{\prime}\): * With a probability of HMCR (Harmony Memory Considering Rate; \(0\leq\) HMCR \(\leq\) 1), select a value from the HM: \(x^{\prime}{}_{i}\gets x^{\text{integer}}_{i}(rand[0.1]\times HMS)+1\). * With a probability of (1-HMCR), perform uniform random search between lower and upper bounds.
3. If the value in Step 2 was obtained from the HM, then: * With a probability of PAR (Pitch Adjusting Rate; \(0\leq\) PAR \(\leq\) 1), slightly modify \(x^{\prime}{}_{i}\): \(x^{\prime}{}_{i}\gets x^{\prime}{}_{i}+BW\times(2\times rand-1)\), where _rand_ denotes an evenly generated random value in interval span zero and one, and BW is the maximum variation in pitch adjusting phase. * With a probability of (1-PAR), do not change everything.
4. Repeat Steps 2 and 3 from 1 to n, then update the HM matrix.
5. Repeat Steps 2 to 4 until the stopping condition (e.g., maximum Number of Function Evaluations or Iterations, NFEs) is satisfied. Algorithm 1 shows the pseudo-code of standard HS.  
In the following sections, we categorize and compare the applications of various fields of study for each country.
## 4 Recent Advances and Future Directions
### Model Interpretability Techniques
IGs excel in providing gradient-based attributions for complex neural networks, anchors generate precise rule-based insights for localized explanations, counterfactual explanations bridge transparency with actionable recommendations, inherently interpretable models offer built-in transparency by design, and attention mechanisms enhance model trust by revealing input priorities. Each method caters to specific interpretability needs, ensuring that models remain transparent and trustworthy across diverse domains [313; 314; 315; 316]. Future research on model interpretability is likely to explore more sophisticated techniques that balance the trade-off between model complexity and transparency. Integrating interpretability directly into the model training process, rather than relying solely on post hoc explanations, holds great promise. As ML continues to be applied in critical areas such as healthcare, finance, and legal systems, the demand for interpretable and reliable models will drive further innovation and adoption in these fields [317; 318; 319].
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
###### Abstract  
This paper presents a systematic literature review based on the PRISMA model on machine learning-based Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks. The primary objective of the review is to compare research trends on deployment options, datasets, and machine learning techniques used in the domain between 2019 and 2024. The results highlight the dominance of certain datasets (BoT-IoT and TON_IoT) in combination with Decision Tree (DT) and Random Forest (RF) models, achieving high median accuracy rates (>99%). This paper discusses various datasets that are used to train and evaluate machine learning (ML) models for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) networks and how they impact model performance. Furthermore, the findings suggest that due to hardware limitations, there is a preference for lightweight ML solutions and preprocessed datasets. Current trends indicate that larger or industry-specific datasets will continue to gain popularity alongside more complex ML models, such as deep learning. This emphasizes the need for robust and scalable deployment options, with Software-Defined Networks (SDNs) offering flexibility, edge computing being extensively explored in cloud environments, and blockchain-integrated networks emerging as a promising approach for enhancing security.  
Keywords: intrusion detection system (IDS); internet of things (IoT) network; distributed denial of service (DDoS); machine learning (ML); systematic review +
Footnote †: journal: _Algorithms_
## 2 Materials and Methods
### Search Strategy  
Medical Subject Headings (MeSH) terms (www.nlm.nih.gov/mesh/meshhome.html, accessed on 21 June 2021), entry terms, free terms, and keywords related to the aim of this review were included in the search strategy. No restrictions on language or date were applied. The search strategy was developed using the Boolean operators AND/OR for PubMed and then adapted to each database according to their syntax rules. A manual search was carried out in the reference lists of the articles selected for the systematic review to detect relevant publications missed in the database searches. Documents from Google Scholar covered the first 200 matches, which were manually processed to verify whether possible eligible studies were missing from the primary database search. Records retrieved from more than one database were computed only once. Authors and co-authors of studies not retrieved in full text were contacted by email (up to five attempts), from October to December 2021. The search strategy is described in Table 2. \begin{table}
\begin{tabular}{l l} \hline \hline
**Database** & **Search Strategy** \\ \hline \multirow{7}{*}{**PUBMED**} & (Cornea[Mesh] OR Cornea*[tiab] OR cornea ex vivo[tiab]) AND (Culture Media[Mesh] OR HEPES[Mesh] OR \\  & Crypoprotective Agents[Mesh] OR Organ Preservation solutions[Mesh] OR Organ Preservation[Mesh] OR \\  & Cryppreservation[Mesh] OR glucose[Mesh] OR acids[Mesh] OR vitamins[Mesh] OR penicillins[Mesh] OR \\  & streptomycin[Mesh] OR Bicarbonates[Mesh] OR Adenosine Triphosphate[Mesh] OR Methylcellulose[Mesh] OR \\  & Culture Media[tiab] OR Cryopreservation[tiab] OR Adenosine Triphosphate[tiab] OR cornea max[tiab] OR cold \\  & storage medium[tiab] OR hypothermic storage[tiab]) AND (Chondroitin Sulfates[Mesh] OR Dextrans[Mesh] OR \\  & Gentamicins[Mesh] OR Complex Mixtures[Mesh] OR Optisol[tiab] OR Dextran[tiab] OR Gentamicin Sulfate[tiab] \\ \hline \multirow{7}{*}{**SCOPUS**} & INDEXTERMS(Cornea) OR TITLE-ABS-KEY(Cornea*) OR TITLE-ABS-KEY(”cornea ex vivo”) \\  & AND INDEXTERMS(“Culture Media”) OR INDEXTERMS(HEPES) OR INDEXTERMS(“Cryoprotective Agents”) \\ \cline{1-1}  & OR INDEXTERMS(“Organ Preservation solutions”) OR INDEXTERMS(“Organ Preservation”) OR \\ \cline{1-1}  & INDEXTERMS(Cryopreservation) OR INDEXTERMS(glucose) OR \\ \cline{1-1}  & INDEXTERMS(acids) OR INDEXTERMS(vitamins) OR INDEXTERMS(penicillins) OR \\ \cline{1-1}  & INDEXTERMS(streptomycin) OR INDEXTERMS(Bicarbonates) OR INDEXTERMS(“Adenosine Triphosphate”) \\ \cline{1-1}  & OR INDEXTERMS(Methylcellulose) OR TITLE-ABS-KEY(“Culture Media”) OR \\ \cline{1-1}  & TITLE-ABS-KEY(“Adenosine Triphosphate”) OR TITLE-ABS-KEY(“cornea max”) OR TITLE-ABS-KEY(“cold storage medium”) OR TITLE-ABS-KEY(“hypothermic storage”) AND INDEXTERMS(“Chondroitin Sulfates”) OR \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Search Strategy.
# Renewable Energy-Based Energy-Efficient Off-Grid Base Stations for Heterogeneous Network
## 2 System Architecture and Mathematical Modeling
### Energy-Sharing Model  
This section proposes an energy-sharing algorithm to ensure green, optimal, and uninterrupted power supply for an off-grid HetNet configuration. The basic features of the macro, micro, pico, and femto cells are presented in Table 2.  
To propose the algorithm, an energy sharing model is used, which is illustrated in Figure 4. In the core of HetNet, there are cells. A cell is formed with a macro base station (BS) placed at the centre and one or more micro BSs are connected to the macro BS in a star topology. There can be multiple micro BSs inside the cell of a macro BS. Because of the star connection arrangement, each of these smaller BSs is directly connected to the central macro BS. As such, a macro BS can share energy with all other micro BSs in the cell. A macro BS can also share energy with other adjacent macro BSs directly connected in the network. All the connections for energy sharing between the BSs has been intended to do through low resistive power lines, and in every case it is half-duplex. In the HetNet, different types of cells are required for various applications, with various qualities of service (QoS) requirements. The distance between the cells is small in the heterogeneous network. As a result, the low resistive power line method can be practicable for sharing energy [58]. This connection for energy sharing may effectively use BSs' renewable energy sources.  
Through the energy sharing policy, the excess electricity will be shared according to the flow diagram illustrated in Figure 5. Here, \(E_{ref}\) is the amount of excess electricity that is 10% greater than the load requirement. The resistive value of the proposed connection has been taken from the American Wire Gauge (AWG) standard conductor size table [59].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Cell Type** & **Cell Size** & **Power Consumption** \\ \hline Macro & 1–30 km & Tens of Watts \\ Micro & 0.4–2 km & 1–6.3 W \\ Pico & 4–200 m & 200 mW–2 W \\ Femto & 10 m & 20–200 mW \\ \hline \hline \end{tabular}
\end{table}
Table 2: Cell size and transmitted power for a heterogeneous network [57].  
Figure 4: Energy-sharing model among the adjacent BSs of HetNet.  
The total shared energy (\(E_{Share}\)) to the adjacent BSs can be calculated as [25]:  
\[E_{Share}=E_{Gen}+E_{batt}-E_{BS}-E_{l}-E_{Excess}, \tag{8}\]  
where \(E_{l}\) is the path loss because of the resistance of the conductor, which can be calculated as [25]:  
\[E_{l}=l^{2}R(l)\times t_{r}=\frac{P_{Share}^{2}R(l)}{V^{2}}\times t_{r} \tag{9}\]  
The percentage of energy savings because of the energy sharing policy can be calculated as [25]:  
\[E_{Save}(\%)=\frac{\sum_{i=1}^{N}E_{Share}(t)}{\sum_{l=1}^{N}E_{BS}(t)}\times 1 00\% \tag{10}\]
# Raster Angle Prediction of Additive Manufacturing Process Using Machine Learning Algorithm
## 4 Results and Discussion
### Hyperparameter Optmization and Cross Validation
Moreover, conducting systematic studies to investigate the impact of various factors, such as the printing speed, temperature, and layer thickness, on raster angle formation would provide valuable insights into the underlying mechanisms and facilitate the development of more sophisticated predictive models. Finally, fostering collaboration between researchers, industry stakeholders, and end-users can facilitate the translation of research findings into practical applications, ultimately driving innovation and advancements in raster angle prediction for AM processes.