esa-sceva/satcom-chunk-collection huggingface dataset:
# Efficient Low-Complexity Turbo-Hadamard Code in UAV Anti-Jamming Communications
## 3 Low-Complexity Turbo-Hadamard Codes Based on Tail-Biting
### The Tail-Biting Turbo-Hadamard Encoder  
As mentioned above, the component codes are constructed. To achieve higher code gains and better performance when combating jamming, the overall tail-biting turbo-Hadamard codes are made up of \(M\) component convolutional-Hadamard codes. As for each component code, the output bits of different interleavers are entered into the same tail-biting convolutional-Hadamard encoder; thus, the \(m\)th component tail-biting convolutional codewords can be given by \(\mathbf{C}^{(m)}=\{\mathbf{D}^{(m)},\mathbf{q}^{(m)},\mathbf{P}^{(m)}\},m=1,2,\cdots,M\). Moreover, the transmitter only sends the \(\{\mathbf{q}^{(m)},\mathbf{P}^{(m)}\}\) part of each component code to avoid reducing the transmission efficiency. Meanwhile, considering the complexity of the decoder, the value of \(M\) should not be too large. The overall codewords \(\mathbf{C}\) of the tail-biting turbo-Hadamard can be written in  
\[\mathbf{C}=\{\mathbf{D},\mathbf{q}^{(1)},\mathbf{P}^{(1)},\mathbf{q}^{(2)},\mathbf{P}^{(2)},...,\mathbf{q}^ {(M)},\mathbf{P}^{(M)}\}. \tag{10}\]  
As shown in Figure 4, the uncoded bits are interleaved via each interleaver \(\pi^{(m)}\), \(m=1,2,\cdots,M\). It is worth noting that the performance of the improved tail-biting turbo-Hadamard codes are related to the correlation of each interleaver.  
The code rate of the tail-biting turbo-Hadamard code is  
\[R_{\text{TB}}=\frac{r}{r+M(2^{r}-r)}, \tag{11}\]  
Figure 4: A tail-biting turbo-Hadamard encoder with \(M\) component convolutional-Hadamard encoders. \(\{\tau^{(m)},m=1,2,...,M\}\) are interleavers corresponding to each component code.  
Figure 3: Structure of component tail-biting convolutional-Hadamard codes.  
whereas that of the terminate-state-zeroturbo-Hadamard code is given by  
\[R_{\text{T0}}=\frac{r}{(1+\eta/K)(r+M(2^{r}-r))}. \tag{12}\]  
The efficiency loss in the terminate-state-zero turbo-Hadamard scheme is \(\eta/K\). When the information stream is very short, for instance, 100 bits, the efficiency loss is 10% (\(r=5,\eta=2\)). However, the tail-biting turbo-Hadamard code does not exhibit this efficiency loss at all.
## 4 Conclusions  
In this paper we addressed the detection of various intracranial hemorrhage types by employing double-branch deep feature extraction and machine learning classifiers. The results justify the idea of searching for relevant features in different representations of the data (normalized image in multiple intensity windows and 3D spatial context) and their concatenation for classification using random forest. The system offers the highest detection efficiency in intraventricular and intraparenchymal hemorrhage.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **Dataset Size (No. of Slices)** & **SDH** & **EDH** & **IPH** & **IVH** & **SAH** \\ \hline [PERSON] et al., 2018 [16] & 536,266 & \(\left[86.3\right]\) & 93.1 & — & 77.2 \\ [PERSON] et al., 2019 [12] & 76,621 & 84.0 & 72.0 & 93.0 & 87.0 & 78.0 \\ [PERSON] et al., 2020 [19] & 401 series \(\times\) 32–64 slices & 54.1 & 62.3 & 81.3 & 82.2 & 61.8 \\ [PERSON] et al., 2020 [22] & 870,301 slices & 71.8 & 48.9 & 83.8 & 88.4 & 73.2 \\ Our approach (DB-RF) & 372,556 & 89.1 & 72.7 & 93.1 & 96.6 & 89.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of classification F1 scores obtained using state-of-the-art methods [12; 16; 19; 22] and our double-branch random forest (DB-RF) model. All values in (%).  
**Author Contributions**: Conceptualization, A.S. and P.B.; methodology, A.S.; software, A.S.; validation, A.S. and P.B.; formal analysis, P.B.; investigation, A.S.; resources, A.S.; data curation, A.S.; writing--original draft preparation, A.S. and P.B.; writing--review and editing, P.B.; visualization, A.S.; supervision, P.B.; project administration, P.B.; funding acquisition, A.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by the Silesian University of Technology, Poland, grant number 12/FSW18/0003-03/2019.  
**Conflicts of Interest**: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Requirements Engineering for a Drone-Enabled Integrated Humanitarian Logistics Platform
## 3 AIRDROP Overview
### Control System  
The Control System (Figure 1) encompasses the Command Center Web Application (CCWA) and First Responder Mobile Application (FRMA). On the one hand, the function of CCWA is to facilitate mission planning, management, and supervision. It integrates multiple source data into an aggregated operational picture to be used by a mission commander with the authorization of making mission-relevant decisions. It is responsible for managing drone missions, first responder task assignments, and incident reports for the provision of actionable business insights. It provides real-time information on vehicles, first responders, and critical facilities (e.g., hospitals, bridges, roads).  
On the other hand, FRMA aids first-response units with situation visualization, task assignment, incident reporting, live video streaming, and drone mission requests. Its interface provides tailored information to the first responder team (e.g., firefighters, paramedics, police officers, volunteers, etc.) which uses it each time. First responders can interact via a chat function, ensuring effective communication with operational decision-makers. When a drone mission is requested via the FRMA, it needs to be approved by the CCWA-operator before it is executed by the drone.
# Narrowband Internet of Things (NB-IoT):
## 3 Narrowband-IoT Protocol Stack  
This section presents the NB-IoT protocol stack based on state of the art of the PHY and MAC layers to identify the knowledge gap and define future research directions. NB-IoT adopts the same protocol stack as the legacy LTE. However, some design changes in both PHY and MAC layers were introduced to support the massive long-range connections with up to additional 20 dB MCL than in legacy technologies such as LTE, GSM, and GPRS. Those changes are described in what follows.
## 3 Case Study
### Case 3  
The SJZA Expressway (2018) connecting through Jiangsu, Zhejiang, Anhui, and Shanghai, with a total length of 89 km was used as case study no. 3. Briefly, K25 + 100 (denoted as D1 section) and K25 + 135 (denoted as D2 section) were selected for the field test. The index properties of the soil layers in the tests are shown in Table 2. The width of the embankment was 35.0 m, the embankment height was 4.0 m, and the slope was 1:1.5. The embankment filling was mainly composed of crushed stone mixed with sub-clay. The internal friction angle was 35\({}^{\circ}\), and the average unit weight was 22 kN/m\({}^{3}\). All piles were arranged in a triangle, the pile diameter was 0.3 m for the D1 section and 0.4 m for the D2 section surface. The pile center-to-center spacing was 2.0 m for the D1 section surface and 2.5 m for the D2 section surface. The pile lengths were 14.0 m each. The pile caps were square with a side length of 0.9 m for section D1 and 1.0 m for section D2. Figure 11 illustrates the pile layout of the test section. A layer of high-strength steel-plastic geogrid was laid on the top surface of the pile support plate, with a maximum elongation of 6% and a tensile strength of 120 kN/m.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Type** & _h (m)_ & _w (\%)_ & \(e\) & \(\gamma\) **(kN/m\({}^{3}\))** & _E\({}_{8}\)_ **(MPa)** & _c\({}^{\prime}\)_ **(kPa)** & _\(\varphi\)_ **(\({}^{\circ}\))** \\ \hline Silty clay & 1.5 & 37.9 & 1.010 & 18.2 & 3.39 & 22.4 & 24.7 \\ Mud clay & 6.9 & 52.0 & 1.457 & 17.0 & 1.87 & 18.0 & 24.0 \\ Soft clay & 19.1 & 47.4 & 1.339 & 17.3 & 2.17 & 15.0 & 19.6 \\ Silty clay & & & & & & & \\ (Medium & 2.4 & 28.1 & 0.740 & 19.4 & 5.57 & 19.5 & 38.0 \\ density) & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Index properties of the subsoil.  
Figure 9: Embankment versus time curve and settlement versus time curve.
## 2 Materials and Methods
### DPPH Scavenging Assay  
DPPH method was used to evaluate the free radical scavenging activity of tested samples with the slight modification from the previous method [37]. A total of 0.2 M DPPH was dissolved with analytical grade ethanol for DPPH radical solution. In 96-well plate, 20 \(\mu\)L of extract and 180 \(\mu\)L of DPPH solution were added and then shaken vigorously in the dark for 30 min at 25 \({}^{\circ}\)C. Different concentrations of KKD-NT and KKD-WA (100, 200, 400, 600, 800, and 1000 \(\mu\)g/mL) were adopted to obtain IC50. The absorbance was assessed at 517 nm. The percentage inhibition of the samples was calculated by using the formula: (1-Absorbance of sample/Absorbance of control) \({}^{\ast}\) 100.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Samples**} & **Vitamin C** & **Gallic Acid** & **Ellagic Acid** & **Daidzin** & **Total Phenolics** & \multirow{2}{*}{**Total Flavonoids**} \\  & **(L-Ascorbic Acid)** & **(mg/g DW)** & **(mg/g DW)** & **(mg/g DW)** & **(mg GAE/g DW \({}^{\ast}\))** & **(mg QE/g DW \({}^{\ast}\))** \\ \hline KKD-NT & 88.66 \(\pm\) 2.92 & 3.67 \(\pm\) 0.16 & 0.11 \(\pm\) 0.02 & 0.05 \(\pm\) 0.01 & 147.2 \(\pm\) 0.70 & 1.30 \(\pm\) 0.01 \\ KKD-WA & 52.83 \(\pm\) 1.85 & 5.10 \(\pm\) 0.03 & 0.36 \(\pm\) 0.01 & 0.04 \(\pm\) 0.00 & 115.2 \(\pm\) 2.12 & 1.10 \(\pm\) 0.02 \\ Pomergnate & T & 0.13 \(\pm\) 0.00 & 0.01 \(\pm\) 0.00 & 0.01 \(\pm\) 0.00 & 16.66 \(\pm\) 1.32 & 0.32 \(\pm\) 0.02 \\ Orange & 0.03 \(\pm\) 0.00 & T & ND & ND & 27.01 \(\pm\) 1.14 & 0.94 \(\pm\) 0.03 \\ Lemon & 0.11 \(\pm\) 0.00 & T & T & ND & 19.52 \(\pm\) 0.84 & 0.66 \(\pm\) 0.01 \\ Blueberry & T & T & ND & T & 21.96 \(\pm\) 1.58 & 0.86 \(\pm\) 0.06 \\ Raspberry & 0.01 \(\pm\) 0.00 & T & 0.02 \(\pm\) 0.00 & T & 25.79 \(\pm\) 2.47 & 0.63 \(\pm\) 0.03 \\ \hline \hline \end{tabular}  
* mg GAE/g DW: mg gallic acid equivalents (mg GAE)/g dry weight (DW) of fruits; ** mg QE/g DW: mg quercetin equivalents (mg QE)/g dry weight (DW) of fruits.  
\end{table}
Table 2: Total amount of vitamin C, phenolics, total flavonoids of KKD-NT and KKD-WA and other fruits.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Standard**} & \multirow{2}{*}{**Solvent**} & **Regression** & **R2** & **LOD** & **LOQ** & **Recovery** & **RSD** \\  & & **Equations** & **(mg/mL)** & **(mg/mL)** & **(\%)** & **(\%)** & **(\%)** \\ \hline L-Ascorbic acid & 0.1\% (\(\
icefrac{{p}}{{v}}\)) 1 M HCL & y = 20,641x + 99 & 0.99998 & 2.01 & 6.71 & 99.13 & 0.24 \\ Gallic acid & & y = 14,948x + 113.86 & 0.99971 & 1.75 & 5.84 & 99.87 & 0.08 \\ Ellagic acid & Methanol & y = 43,616x \(-\) 34.482 & 0.99999 & 0.57 & 1.90 & 99.35 & 0.12 \\ Daidzin & & y = 86,955x \(-\) 72.593 & 0.99981 & 0.39 & 1.31 & 98.50 & 0.45 \\ \hline \hline \end{tabular}  
* LOD = Limit of Detection; LOQ = Limit of Quantitation; RSD = Relative Standard Deviation.  
\end{table}
Table 3: Parameter of calibration curves and recovery rates of the reference standard compounds.
# Constrained Dynamic Mean-Variance Portfolio Selection in Continuous-Time
## 3 Solution Scheme for Problem (\(\mathcal{P}_{\text{MV}}\))  
To solve problem (\(\mathcal{P}_{\text{MV}}\)), we first reformulate it as a special case of the LQ control problem (\(\mathcal{P}_{\text{LQ}}^{T}\)) investigated in [18] and use the solution provided in Section 3 of [18] to solve a constrained dynamic MV portfolio selection problem (\(\mathcal{P}_{\text{MV}}\)). We utilize the embedding technique introduced by [PERSON] et al. [2] to overcome the difficulties of the inseparability of variance term in the sense of dynamic programming. We consider the following auxiliary problem (\(\widehat{\mathcal{P}}_{\text{MV}}(\lambda)\)) by introducing the Lagrange multiplier \(\lambda\in\mathbb{R}\) for constraint \(\text{E}[x(T)]=d\):  
\[(\widehat{\mathcal{P}}_{\text{MV}}(\lambda)): \min_{\{u(t)\}|_{t=0}^{T}}\text{E}[(x(T)-\lambda)^{2}]+\int_{0}^{ T}\text{E}[u(t)^{\prime}R(t)u(t)]dt-(\lambda-d)^{2}\] \[(s.t.)\ \ \{x(t),u(t)\}\ \text{ satisfies (\ref{eq:eq:eq:eq:eq:eq:eq:eq:eq: eq:eq: eq: eq: eq: eq: eq: eq: eq: eq: eq: eq:  
**Lemma 1**.: _For any \(t\in[0,T]\), the following inequalities hold true:_  
\[\hat{G}^{mv}(t)\rho(t)^{2} \leq 1, \tag{7}\] \[\hat{G}^{mv}(t)\rho(t)^{2} < 1. \tag{8}\]  
Note that, in Lemma 1, the strict inequality only holds for (8). Such a result plays an important role in the following.  
The following result characterizes the solution of problem (\(\mathcal{P}_{\text{MV}}\)).  
**Theorem 1**.: _The associated optimal portfolio policy of problem \((\mathcal{P}_{\text{MV}})\) is given by_  
\[u^{*}(t)=\begin{cases}\hat{K}^{mv}(t)(x(t)-\lambda^{*}\rho(t))& \text{if }x(t)-\lambda^{*}\rho(t)\geq 0,\\ -\hat{K}^{mv}(t)(x(t)-\lambda^{*}\rho(t))&\text{if }x(t)-\lambda^{*}\rho(t)<0, \end{cases} \tag{9}\]  
_where_  
\[\hat{K}^{mv}(t) =\arg\min_{K:H(t)K\geq 0}\hat{f}^{mv}(t,K,\hat{G}^{mv}(t)), \tag{10}\] \[\hat{K}^{mv}(t) =\arg\min_{K:H(t)K\geq 0}\hat{f}^{mv}(t,K,\hat{G}^{mv}(t)). \tag{11}\]  
_Moreover, the optimal Lagrange multiplier \(\lambda^{*}\) is_  
\[\lambda^{*}=\frac{d-x_{0}\hat{G}^{mv}(0)\rho(0)}{1-\hat{G}^{mv}(0)\rho(0)^{2}}. \tag{12}\]  
Usually, the investor is interested in the MV efficient frontier, i.e., the Parato optimal set of expected terminal wealth \(d\) and the associated minimum variance \(\text{Var}[x(T)]\). Substituting \(\lambda^{*}\) back into \(v(\widehat{\mathcal{P}}_{\text{MV}}(\lambda))\) yields the semi-analytical expression of the MV efficient frontier as follows:  
\[\text{Var}[x^{*}(T)]= v(\widehat{\mathcal{P}}_{\text{MV}}(\lambda^{*})-\int_{0}^{T} \text{E}[(u^{*}(t))^{\prime}R(t)u^{*}(t)]dt\] \[= \frac{\hat{G}^{mv}_{0}\rho(0)^{2}}{1-\hat{G}^{mv}_{0}\rho(0)^{2} }\big{(}\text{E}[x^{*}(T)]-x_{0}\rho(0)\big{)}^{2}-\int_{0}^{T}\text{E}[(u^{* }(t))^{\prime}R(t)u^{*}(t)]dt, \tag{13}\]  
with \(\text{E}[x^{*}(T)]\geq x_{0}\rho(0)^{-1}\). Note that the second term can be evaluated by the Monte Carlo simulation method after computing all \(\hat{K}^{mv}(t)\) and \(\hat{K}^{mv}(t)\).
# Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
## 5 System Architecture and Design Scheme  
### System Architecture  
To address the inconsistency of user load data and privacy protection issues, this paper proposes a differential privacy clustering federated learning method based on the Louvain algorithm for heterogeneous data. The Louvain algorithm is a community detection method for complex networks. The system architecture, illustrated in Figure 2, is divided into three modules.  
Module One involves all clients conducting differential privacy federated learning until convergence. During this phase, the server calculates the similarity of the local update vectors from the clients for the current round. Module Two constructs a complex network and employs the Louvain algorithm for community clustering. Module Three focuses on personalized adaptive differential privacy clustering for federated learning load forecasting.
## 5 Conclusions  
In this paper, an effective rainfall-ponding prediction model is constructed based on LSTM for multi-step prediction of urban waterlogging. Relevant data from rainfall stations and ponding points in Nanjing from January 2015 to August 2018 were collected, and the models were trained with MSE, MAE and MSLE as loss functions and predicted the future 1 h ponding depths, and the following conclusions were drawn by calculating and comparing the prediction accuracy of each model:  
1. The LSTM fully exploits the nonlinear relationship between the rainfall data of each rainfall station and the ponding data of individual ponding survey points, and has a good multi-step prediction effect on the future ponding process.
2. With the increase of time step, the prediction accuracy of each model decreases to different degrees, and 0-40 min in the future is the time range to achieve better prediction effect.
3. The model trained with MSLE as the loss function has high prediction accuracy, but the prediction effect is not good in extreme or special conditions, while the model trained with MAE as the loss function can better predict the excessive ponding depth in special conditions.
4. The limitation of our study lies in the fact that the number of positive samples in the data set is relatively small. In future research, we will set about extending the scale of the data set to build a predictive model with better performance. Meanwhile, we will consider other deep learning models, such as convolutional neural networks (CNNs), to improve the prediction of urban flood waterlogging depth.  
Conceptualization, Y.L.; methodology, Y.L. and Y.Y.; validation, W.Z.; formal analysis, Y.X.; investigation, Y.Y.; resources, Y.L.; data curation, Y.Y. and Y.L.; writing--original draft preparation, Y.Y. and Y.L.; writing--review and editing, Z.L. and S.S.; supervision, W.Z.; funding acquisition, Y.L. and W.Z. All authors have read and agreed to the published version of the manuscript.  
The authors acknowledge the National Key Research and Development Program of China (Grant: 2019 YFC1510204), the National Natural Science Foundation of China (Grant: 42175177, U2240216), the Fundamental Research Funds for the Central Universities (Grant: 2019B10714) and the Special Basic Research Key Fund for Central Public Scientific Research Institutes (Y521002).  
Not applicable.  
Readers who are interested in our work and willing to use our model can contact the corresponding author. Data and codes used for this work are available from the authors upon reasonable request.  
The authors thank the anonymous reviewers for their very valuable comments. The authors declare no conflict of interest.
## 4 Fire Retardation of Styrenic Polymers with Phosphorous-Containing Compounds
### Additive Modifications of Styrenic Polymers
#### 4.1.3 Phosphorus-Containing Intumescent Formulations (P\({}^{+5}\))
Despite the significant improvement in the fire retardance of the modified  
Figure 9: Chemical structures of phosphorous compounds used for the modification of g–C\({}_{3}\)N\({}_{4}\)[70; 71]. styrenic composites, this system requires relatively high loading of the IFR (30 wt%), which negatively affected the mechanical properties of the protected polymers. Indeed, the tensile strength was reduced from 43.5 MPa for the unmodified PS to 30.5 MPa for the PS/APP/CA composite, whilst the values of flexural strength dropped from 71.2 MPa in PS to 55.5 MPa in the modified polymer. Meanwhile, there was no influence on the impact strength of the PS/APP/CA composite [75]. Another P-containing IFR system is based on a combination of zeolites, APP and PER, with the addition of a Lewis acid as a char promoter [76]. In this study, 4A-zeolite was treated with ammonium chloride (NH4 Cl), to obtain an NH4\({}^{+}\)-exchanged zeolite (4A-NH4\({}^{+}\)), which was then heated to 550 \({}^{\circ}\)C and 770 \({}^{\circ}\)C to obtain the Lewis acid (4A-L zeolite) and the Bronsted acid (4A-H\({}^{+}\) zeolite), respectively. It was found that the PS composite with 18 wt% of APP and 2 wt% of 4A-L zeolite had a higher efficiency in the char formation and better fire retardance in comparison to the composite containing 4A-H\({}^{+}\) zeolite. The Lewis acid based on 4A-L zeolite can catalyse the cross-linking reactions within the PS/APP/4A-L composite. The water generated during the esterification of APP and PER interacted with the Lewis acid on the 4A-L zeolite, producing different complex compounds. Then, these complexes continuously reacted with the unsaturated -C=C- bonds to form primary carbonium ions, which resulted in the acidic catalysis, Friedel-Crafts alkylation, and a char formation [76]. In another study, [PERSON] et al. (2014) advocated the use of ammonium salts of 1-hydroxy ethylidene-1,1-diphosphonic acid (HEDPA) as an acid source in the IFR formulations [77]. The performance of the composites containing HEDPA was compared with those containing the precursor, 1-hydroxy ethylidene-1,1-diphosphonic acid (HEDP) (Figure 11). It was demonstrated that the efficiency of the materials with the salt (HEDPA) moieties is far superior to that observed for the PS composites with the acid (HEDP), since the former have achieved the V-0 rating in the UL-94 testing and the LOI increased to from 17 vol% 28 vol% measured for the unmodified PS [77]. Furthermore, the new IFR system with HEDPA/PER/MEL exhibited an excellent synergistic effect and a good compatibility with the PS matrix [77]. Figure 11: A synthetic route for the preparation of ammonium salt of HEDPA [77]. Figure 10: A char-forming mechanism in the ammonium polyphosphate/carbonisation agent (APP/CA) system [75].
## 3 Results and Discussion  
In order to ensure direct comparability of Raman and MIR-ATR spectroscopy, both setups were simultaneously applied for real-time monitoring of glucose, ethanol, and biomass concentrations during four aerobic yeast fermentations. The recorded spectra were evaluated by MLR and PLS models. In contrast to the well-established PLS regression, which uses broad or even complete spectral ranges, the MLR models in this work are based on only two (2D) or four (4D) selected narrow spectral ranges of 20 cm\({}^{-1}\). MLR models are particularly well-suited for the calibration of compact multi-channel sensors [10], where the determination of analyte concentrations is based on the linear combination of signal intensities in a few transmission ranges of optical filters (channels). Therefore, in the present study, it was additionally investigated if MLR models can offer as good performance parameters (RMSEC, RMSEP) as the sophisticated PLS evaluation of spectra. This approach enables estimating if spectrometer setups can be replaced by miniaturized and low-priced multi-channel Raman or MIR-ATR sensors without loss of accuracy.
# A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes
This article reviews the existing AI approach in wind turbines as a sustainability measure. The scope of this study is limited to the performances of these systems and the optimization process, which has economic and environmental impacts. To the authors' knowledge, this review-type research is the first in the specialized literature exclusively focused on this issue. This paper is structured into seven sections. Section 2 presents the basic concepts of wind power, turbines, and ML techniques in the wind power context. In addition, classic methods in the field of wind energy and the latest technological advancements in turbine design optimization are outlined. The research methodology, including the selection process of scientific articles, data sources, search strategies, inclusion/exclusion criteria, and data analysis techniques, are described in Section 3. Section 4 focuses on ML models applied to wind power and the evaluation metrics employed by the ML models for the optimized wind power process. The analysis of influencing parameters in wind energy with ML is depicted in Section 5. Section 6 presents the discussion by underlining the analysis of results, comparative analysis of ML models, evaluation of impact and contribution, research gaps identification, study limitations, the impact of discoveries on the improvement of wind energy and future solutions, and recommendations for future research. Finally, the conclusions are displayed in Section 7.