esa-sceva/satcom-chunk-collection huggingface dataset:
# Ceramic Matrix Composites for Aero Engine Applications--A Review
## 8 The Future of CMCs
### Technology Considerations  
It has been found from the early 90s that lower-cost CMCs are worth developing for achieving better service temperatures, resulting in a multi-attribute utility better than that of superalloys for usage in exhaust ducts. The main reason was the need for improvement of the superalloys manufacturing techniques, achieving greater cost reduction results than other techniques, such as directional solidification and single-crystal casting [251]. Some of the first manufacturing techniques developed for the creation of more cost-effective materials were slurry infiltration and chemical vapor infiltration.  
According to the literature, a third of the aerospace technological contribution has been spent on turbine inlet temperature (TIT) material improvement and, more specifically, nickel-based superalloys created with directional solidification and single-crystal technology [251; 252]. The rest of the aerospace technological contribution was created for the introduction of internal cooling passage for turbine blades, vanes, and cooling technology advancement. In more recent years, research has shown that CMC materials exceed the  
Figure 22: CMC material performance against other composite matrices.  
thermal efficiency and power output required for turbine inlet temperature making them suitable to be applied in both rotary (nozzles, vanes, turbine blades) and stationary turbine engine parts [253]. Hence, aerospace companies have started using these types of manufacturing techniques for the creation of CMC components in both cold and hot sections of the gas turbine engine. Although it must be said that the commercialization of CMC materials requires more effort as some of the most common manufacturing procedures, cost a lot more than other types of superalloy composition techniques, such as chemical vapor infiltration (CVI) and polymer infiltration and pyrolysis process [254; 255].  
The commercialization of CMCs is in progress. The aerospace industry requires such materials, which will increase the life expectancy of the components used in turbine engines as well as other aero-engine parts, as well as lower the manufacturing cost and the environmental impact. For these steps to be accomplished, the literature suggests the utilization of R&D methods, which are needed for understanding the interactions of the composite constituents with one another and the application environments in which these composites are applied. To achieve that, iteration of composite material selection, manufacturing processes, testing, and post-test evaluation measures must take place to assess better material performance. The R&D methods also need to be linked
## 5 Potential Challenges
### Performance Analysis and Learning Mechanism  
The efficacy of DL-guided communication systems has been demonstrated for many different applications, including massive MIMO, channel estimation, coding, and decoding, among others. However, no rigorous mathematical proofs or dependable theorems are presented to verify the system's accuracy. A firm theoretical foundation is crucial for comprehending the communication model and providing a foundation for network adaptation and the utilization of highly successful communication frameworks; therefore, this is where the bulk of our attention must be placed. Although it is common practice to transform the original input signals into different information representations to improve performance in the DL domain, achieving state-of-the-art performance in DL-based NOMA systems while changing the information representation styles is not guaranteed. While DL-based communication models are being developed, their optimal results and learning algorithms remain unclear. In addition, additional study is required before offering answers concerning the selection of training instances based on these networks.
# Advancements in Machine Learning-Based Intrusion Detection in IoT: Research Trends and Challenges
## 4 Deployment of the IDS
### Blockchain-Integrated Networks (BINs)  
Blockchain technology introduces a decentralized ecosystem that enhances security and privacy in IoT networks. These networks can leverage features like immutable data records, distributed consensus, and smart contracts.  
The blockchain is a decentralized and immutable storage model that consists of all transaction details that have been initiated by the peer node in the network, stored in a decentralized distributed ledger. Any transaction processed is verified by the consent of the majority of network devices [45].  
Studies such as [46; 47; 48; 45] highlight how implementing blockchain-enabled solutions eliminates the need for a trusted central authority by distributing control across all network participants, which enhances fault tolerance and reduces the risk of single points of failure. It also guarantees data integrity and a verifiable trail, building on the extra security of cryptographic hashing and digital signatures. The automated contracts stored in the blockchain can enforce predefined rules and execute actions without intervention, enabling in-place decision-making and alerting or taking appropriate actions. Some proposed systems integrate blockchain into their established IoT network [46; 47], while others [48; 46] append it with a separate blockchain authentication managing network.  
Ref. [49] utilizes blockchain technology in a smart city/urban environment; however, it mentions that this approach introduces computational overhead, which can limit performance in resource-constrained environments. Furthermore, storage requirements increase as each node stores a complete copy of the blockchain ledger, which can be thousands of devices. These resource-heavy protocols also introduce latency, throughput bottlenecks, and higher energy consumption, which is not suitable for some use cases. Ref. [45] states that blockchain is verifiable and immutable, yet it is vulnerable to different, increasingly common attacks. DDoS attacks often caused by flooding of the mempool/memory pool in a blockchain network have severe consequences for legitimate users.
# A Survey of Machine Learning Methods for Time Series Prediction
## 5 Experimental Results and Discussion
### Analysis
Domain-Specific Findings: LightGBM produces more accurate results for top research terms in emerging topics, even though it generally has higher errors than NN [37];
4. Inference Time: One study reported inference times for their models. They compared an XGBoost model (0.001 s) with an LSTM model (0.311 s) and a Bi-LSTM model (1.45 s), finding XGBoost to be 311 times faster than Bi-LSTM and 1450 times faster than LSTM. This drastically faster inference time emphasizes its practicality in time-sensitive applications [74];
5. Simulated vs. Real-World Data: LightGBM matches the neural network performance on simulated data, but outperforms on real-world datasets [51];
6. Time-Series Image Data: CNN models excel in prediction tasks involving time-series image data [3; 23; 44].
## 2 Literature Review  
Various integrated safety and/or security analysis approaches have been proposed in the literature, focusing on different phases of the system life-cycle. Regardless, they can be classified into three categories based on the discussion in [4, 5, 11]:  
* Security-informed Safety approaches that leverage on security-related information (e.g., intentional causes) for enhancing safety analysis;
* Safety-informed Security approaches that utilize safety-related information (e.g., component failures) for enhancing security analysis;
* Combined S&S approaches that analyse Safety and Security either in series or parallel and their respective artefacts are mutually integrated into subsequent analysis steps/phases/stages.  
STPA is an emerging hazard analysis technique that has been integrated into many S&S analysis approaches proposed in recent years. The S&S analysis methodology proposed in this paper is also built on STPA. Therefore, this section reviews some existing approaches that adopt or extend STPA, and highlights their limitations which the proposed methodology intends to address.
## 3 Feasible Trajectory Planning Algorithm
### SVM-Improved Heuristics  
The SVM classifier employs a polynomial kernel to classify the eligibility of a trajectory segment stating the nonlinear relationships between three predictors:  
* The length of the candidate segment;
* The change in direction;
* The \(x\) value in the relevant \(\Pi\) vector.  
The SVM decision function is expressed as  
\[f(\chi)=\sum_{i=1}^{N}a_{i}v_{i}K(\chi_{i},\chi)+b \tag{27}\]  
Therein, we have the following:  
* \(\chi=\{\chi_{1},\dots\chi_{i}\}\) is the input feature vector;
* \(N\) is the number of support vectors;
* \(\alpha_{h}\) defines the Lagrange multipliers for each support vector;
* \(v_{g}\in\{0,1\}\) defines the class labels of the support vectors, determining whether the input is acceptable or not for a trajectory;
* \(K(\chi_{i},\chi)\) is the polynomial kernel function;
* \(b\) is the bias term.  
The sequential minimal optimization (SMO) algorithm is used to solve the convex optimization problem in SVMs due to the nonlinear assumption of the problem. The dual form of the soft-margin SVM is expressed as  
\[\max_{\alpha}\sum_{i=1}^{N}\alpha_{i}-\frac{1}{2}\sum_{i=1}^{N}\sum_{j=1}^{N} \alpha_{i}\alpha_{j}v_{i}v_{j}K(\mathbf{\Omega}_{i},\mathbf{\Omega}_{j}) \tag{28}\]  
It is subject to the following constraints:  
\[\sum_{i=1}^{N}\alpha_{i}v_{i}=0\quad\text{and}\quad 0\leq\alpha_{i}\leq C \tag{29}\]  
where \(\alpha_{i}\) defines the Lagrange multipliers associated with the support vectors, and \(K(\mathbf{\Omega}_{i},\mathbf{\Omega}_{j})\) is the second-order polynomial kernel. SMO iteratively solves two \(\alpha_{i}\) values at a time until meeting its convergence criterion. The misclassification cost is adopted here in the dual form by modifying the bounds of the corresponding Lagrange multipliers \((\alpha_{i})\) by class-specific penalty terms \((C_{i})\). By assigning different \((C_{i})\) values to different classes, the optimization process penalizes misclassifications of certain classes more than others. This adjustment indirectly modifies the influence of the support vectors and thus the decision boundary during optimization.
## 3 Methods
### Crop-Type Mapping Algorithms and Parameter Setting  
Random Forest (RF), introduced in 2001, combines the bagging ensemble learning theory with the random subset method, making it a powerful and stable classifier [55; 56]. RF has been widely applied in agricultural remote sensing for various crop classifications due to its strong generalization ability, resistance to overfitting, and capacity to handle high-dimensional, heterogeneous feature sets [57; 58]. In this study, preliminary comparative experiments indicated that Support Vector Machine and Decision Tree classifiers yielded lower overall classification accuracies and less spatial stability across years compared to RF. Given these findings, RF was selected as the final classification algorithm to ensure higher accuracy, stability, and operational efficiency for crop mapping on the GEE platform. The number of trees was set to 100, while all other hyperparameters were maintained at default values [59].
## 2 Theoretical Background  
The ultimate bearing capacity of a foundation is defined as the maximum magnitude of the applied load for which failure plane extends to a considerable volume of soils. [PERSON] [29] first proposed the bearing capacity equation, by assuming that the soil is weightless. [PERSON] [30] proposed the ultimate bearing capacity equation of shallow foundations on soils by extending the equation proposed by [PERSON] [29].  
For strip footing, the ultimate bearing capacity (\(\sigma_{b(ult)}\)) of cohesive-frictional soils is calculated, as follows [30]:  
\[\sigma_{b(ult)}=cN_{c}+\gamma D_{f}N_{q}+0.5\gamma BN_{\gamma} \tag{1}\]  
where \(c\) is cohesion of soil, \(\gamma\) is unit weight of soil, \(B\) is width of footing, \(D_{f}\) is depth of foundation, \(N_{c}\), \(N_{q}\) and \(N_{\gamma}\) are the bearing capacity factors, which are functions of the internal friction angle, \(\phi\). The bearing capacity factors proposed by [PERSON] [31] are expressed by the following equations:  
\[N_{q}=e^{\pi\tan\phi}\tan^{2}\left(45+\frac{\phi}{2}\right) \tag{2}\]  
\[N_{c}=(N_{q}-1)\cot\phi \tag{3}\]  
\[N_{7}=(N_{q}-1)\tan(1.4\phi) \tag{4}\]  
Common techniques for improving soil-bearing capacity are the soil replacement by high quality material [32; 33; 34; 35; 36] and geosynthetic reinforcement. For the soil replacement technique, [PERSON] and [PERSON] [11] suggested the thickness of sand soil over clayey soil of more than four times the footing dimension.  
The RSF technique has considerable potential as an alternative cost-effective method. Several investigators [1; 5; 8; 14; 37; 38; 15; 39] have experimentally and numerically studied the behaviors of the footing on the geosynthetic reinforced cohesionless and cohesive soils. All previous investigators indicated that the RSF technique can increase the ultimate bearing capacity and decrease the settlement of footing. Previous studies revealed that the width of the footing (B), embedment depth of top reinforcement layer (U), width of horizontal planar form of reinforcement (W), vertical spacing between reinforcement layers (H), and number of reinforcement layers (N) were dominant factors controlling the soil-bearing capacity. The optimum values of U/B, W/B and H/B have been recommended: U/B = 0.25-0.5, W/B = 6, H/B = 0.2-0.4, and N = 4 [39; 40].
# Squeezing Backbone Feature Distributions to the Max for Efficient Few-Shot Learning
## 4 Results and Discussions  
### Comparison with State-of-the-Art Methods  
Performance on standardized benchmarks: in the first experiment, we conduct our proposed method on different benchmarks and compare the performance with other state-of-the-art solutions. The results are presented in Tables 1 and 2, and we observe that our method reaches the state-of-the-art performance in both inductive and transductive settings on all the few-shot classification benchmarks. Particularly, the proposed \(\text{PEM}_{n}\text{E-BMS}^{\star}\) brings important gains in both 1-shot and 5-shot settings, and the prior-independent \(\text{PEM}_{n}\text{E-BMS}\) also obtains competitive results on 5-shot. Note that for tieredImageNet we implement our method based on a pre-trained DenseNet121 backbone following the procedure described in [35]. From these experiments, we conclude that the proposed method can bring an increase in accuracy with a variety of backbones and datasets, leading to a state-of-the-art performance. In terms of execution time, we measured an average of 0.004 s per run. These results confirm the ability of the proposed methodology to reach state-of-the-art performance using the standardized benchmarks of the field of few-shot learning. \begin{table}
\begin{tabular}{c l l l l} \hline \hline \multirow{2}{*}{**Setting**} & \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**miniImageNet**} \\  & & **Backbone** & **1-Shot** & **5-Shot** \\ \hline \multirow{8}{*}{Inductive} & Matching Networks [49] & WRN & \(64.03\pm 0.20\) & \(76.32\pm 0.16\)\% \\  & SimpleShot [35] & DenseNet121 & \(64.29\pm 0.20\) & \(81.50\pm 0.14\)\% \\  & S2M2\_R [2] & WRN & \(64.93\pm 0.18\) & \(83.18\pm 0.11\)\% \\  & PT + NCM [1] & WRN & \(65.35\pm 0.20\) & \(83.87\pm 0.13\)\% \\  & DeepEMD[29] & ResNet12 & \(65.91\pm 0.82\) & \(82.41\pm 0.56\)\% \\  & FEAT[28] & ResNet12 & \(66.78\pm 0.20\) & \(82.05\pm 0.14\)\% \\  & \(\text{PEM}_{b}\text{E-NCM}\) (ours) & WRN & \(\mathbf{68.43\pm 0.20}\) & \(\mathbf{84.67\pm 0.13}\)\% \\ \hline \multirow{8}{*}{Transductive} & BD-CSPN [50] & WRN & \(70.31\pm 0.93\) & \(81.89\pm 0.60\)\% \\  & LaplacianShot [51] & DenseNet121 & \(75.57\pm 0.19\) & \(87.72\pm 0.13\)\% \\  & Transfer + SGC [19] & WRN & \(76.47\pm 0.23\) & \(85.23\pm 0.13\)\% \\  & TAFSSL [20] & DenseNet121 & \(77.06\pm 0.26\) & \(84.99\pm 0.14\)\% \\ \cline{1-1}  & TIM-GD [52] & WRN & \(77.80\)\% & \(87.40\)\% \\ \cline{1-1}  & MCT [53] & ResNet12 & \(78.55\pm 0.86\) & \(86.03\pm 0.42\)\% \\ \cline{1-1}  & EPNet [54] & WRN & \(79.22\pm 0.92\) & \(88.05\pm 0.51\)\% \\ \cline{1-1}  & PT + MAP [1] & WRN & \(82.92\pm 0.26\) & \(88.82\pm 0.13\)\% \\ \cline{1-1}  & PEM\_n E-BMS (ours) & WRN & \(82.07\pm 0.25\) & \(89.51\pm 0.13\)\% \\ \cline{1-1}  & PEM\_n E-BMS\({}^{\star}\) (ours) & WRN & \(\mathbf{83.35\pm 0.25}\) & \(\mathbf{89.53\pm 0.13}\)\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The 1-shot and 5-shot accuracy of state-of-the-art methods in the literature on miniImageNet and tieredImageNet, compared with the proposed solution. Best results are in bold.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 2 State of the Art
### Communication Constellations Architectures  
The services offered by NTNs can be broadly categorized into delay-tolerant and delay-sensitive. Delay-tolerant services include applications like non-instantaneous messaging and specific IoT monitoring use cases. In contrast, delay-sensitive services encompass near-real-time applications like Internet access and voice services.  
The required level of connectivity for a satellite constellation or an integrated satellite-terrestrial system is crucial for defining the architecture and its subsequent operational management, which is the focus of this paper. Thus, two main types of architectures are identified in the literature, each corresponding directly to the type of service provided: delay-tolerant or delay-sensitive. This work identifies these architectures as sporadic connectivity networks and persistent connectivity networks, respectively.
# Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios
## 3 AUV Control System
### AUV Linear Parameter-Varying Model  
To develop an MPC to control the AUV nonlinear dynamics, a design model permitting the prediction of the future AUV dynamics over a given future horizon is required. In this work, the design model is obtained in a Linear Parameter-Varying (LPV) form. The LPV modelling approach involves approximating a nonlinear dynamic model in a time-varying linear model form given the value of a set of measured/estimated variables. The LPV model of the AUV system involved represents the nonlinear dynamics model of Equation (6) in the following discrete time-varying state-space form:  
Figure 2: AUV Control System Architecture.  
\[x_{AUV}(k+1)=A_{d}(k)x_{AUV}(k)+B_{d}(k)\tau_{V}(k)+d_{d}(k) \tag{4}\]  
\[y_{AUV}(k)=C_{d}(k)x_{AUV}(k) \tag{5}\]  
where the state vector at the \(k\)-th time instance is:  
\[x_{AUV}(k)=[x(k),y(k),z(k),\phi(k),\ \theta(k),\ \psi(k),u(k),v(k),w(k),p(k),q(k),r(k)]^ {T} \tag{6}\]  
The measurement vector at the \(k\)-th time instant is:  
\[\begin{array}{c}y_{AUV}(k)=\\ [x_{m}(k),y_{m}(k),z_{m}(k),\phi_{m}(k),\ \theta_{m}(k),\ \psi_{m}(k),u_{m}(k),v_{m}(k),w_{m}(k), \ \theta_{m}(k),\ \tau_{m}(k)]^{T}\end{array} \tag{7}\]  
The virtual control signal vector provided by the MPC for driving the AUV rigid-body dynamics at the kth time instant is:  
\[\tau_{V}(k)=[X_{V}(k),Y_{V}(k),Z_{V}(k),K_{V}(k),M_{V}(k),N_{V}(k)]^{T} \tag{8}\]  
considering \(X_{V}(k),Y_{V}(k)\) and \(Z_{V}(k)\) virtual forces and \(K_{V}(k),M_{V}(k)\) and \(N_{V}(k)\) virtual moments generated over the \(x\), \(y\) and \(z\)-axis of the body-fixed reference frame. The time-varying state-space variables of the LPV system are iteratively computed with respect to the nonlinear dynamics model of Equation (1), such that:  
\[A(t)=\left[\begin{array}{cc}0_{6x6}&-J(\eta(k))M^{-1}(C(v(t))+D(v(t))\\ 0_{6x6}&-M^{-1}(C(v(t))+D(v(t))\end{array}\right]; \tag{9}\]  
\[B(t)=\left[\begin{array}{c}J(\eta(t))M^{-1}\\ M^{-1}\end{array}\right]; \tag{10}\]  
\[C(t)=[I_{12x12}],\ \ \ d(t)=\left[\begin{array}{c}-J(\eta(t))M^{-1}g(\eta(t))\\ -M^{-1}g(\eta(t))\end{array}\right] \tag{11}\]  
are the LPV continuous-time state-space matrices discretized with respect to the controller sampling-time _Ts_ as \(A_{d}(k)=A(v(t))\cdot\)_Ts_ + \(I\), \(B_{d}(k)=B(\eta(t))\cdot\)_Ts_, \(C_{d}(k)=C(t)\) and \(d_{d}(k)=d(\eta(t))\cdot\)_Ts_. Considering the LPV model of the vehicle, the vector of time-varying parameters is defined as \(\rho(t)=[\eta(t),v(t)]^{T}\) where \(A_{d}(k)=A_{d}\), \(B_{d}(k)=B_{d}(\rho(k))\), \(C_{d}(k)=C_{d}(\rho(k))\) and \(d_{d}(k)=d_{d}(\rho(k))\).
# Performance Analysis of a Stand-Alone PV/WT/Biomass/Bat System in Alrashda Village in Egypt
## 6 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & \multicolumn{2}{c}{**PV**} & \multicolumn{2}{c}{**WT**} & \multicolumn{2}{c}{**Biomass**} & \multicolumn{2}{c}{**Battery**} & \multicolumn{2}{c}{**Inverver**} \\ \hline  & \multicolumn{2}{c}{**Cap (B)**} & \multicolumn{2}{c}{**OAM (B)**} & \multicolumn{2}{c}{**Rep (B)**} & \multicolumn{2}{c}{**Cap (B)**} & \multicolumn{2}{c}{**OAM (B)**} & \multicolumn{2}{c}{**Fep (B)**} & \multicolumn{2}{c}{**OAM (B)**} & \multicolumn{2}{c}{**Fep (B)**} & \multicolumn{2}{c}{**OAM (B)**} & \multicolumn{2}{c}{**Fep (B)**} & \multicolumn{2}{c}{**Cor (B)**} \\ \hline  & \multicolumn{2}{c}{**Case 1**} & & & & & & & & & & & & \\ \hline HBO & 21,881,639 & 168,905 & 11,720,398 & 329,481 & 100 & 213,333 & 2259,588 & 19,385,299 & 36,346,583 & 212,239,648 & 2016,223 & 39,645,509 & 2367c \\ CFA & 21,382,826 & 164,758 & 11,472,860 & 329,480 & 100 & 213,333 & 2333,178 & 19,368,357 & 35,999,073 & 1250,132 & 200,536 & 39,645,510 & 3267c \\ GMO & 14,776,401 & 114,232 & 792,022 & 512,442 & 155,530 & 319,797 & 2242,913 & 19,967,278 & 37,473,031 & 13559,241 & 2242,106 & 39,458,510 & 3267c \\ STOA & 17,705,137 & 136,715 & 989,442 & 101,117.376 & 308,899 & 654,719 & 2348,039 & 19,290,416 & 36,167,936 & 12355,249 & 2008,305 & 39,645,510 & 3267c \\ \hline  & \multicolumn{2}{c}{**Case 2**} & & & & & & & & & & \\ \hline HBO & 21,426, & 165,530 & 11,491,98 & & & & & & & & & \\ CFA & 21,786,985 & 168,234 & 11,679,655 & & & & & & & & & \\ GMO & 21,295,604 & 163,330 & 1,343,733 & & & & & & & & & \\ \hline STOA & 26,505,080 & 205,052 & 14,258,760 & & & & & & & & & \\ \hline  & & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & & \\ \hline  & & & & & & & & & & & & & \\ \end{tabular}
\end{table} 
Table 11. The contribution of the yearly cost of the system regarding the type of cost which includes the yearly interest of capital cost, operating and maintenance costs, and annual replacement cost.